- (1). Pan—the horizontal shift (the hand sign symbol) allows movement around the map by clicking and holding the left mouse button. It is also possible to use the mouse wheel, both forwards and backwards, thus activating the modification of the scale, keeping the mouse cursor in the center of the map;
	- (2). Zoom window (red plus symbol), which allows a specific zoom by dragging the pointer in order to draw a rectangle, which then defines the area of interest to be scaled;
	- (3). Zoom to initial map extension's symbol allows zoom of the starting map;
	- (4). Zoom in—zoom out's symbol allows selection of the zoom level through a scale bar;
	- (5). Previous and next zoom: these two indicators allow one to scroll through the zoom history.
	- 1. Layer symbol activates or deactivates the layer management panel and its legend (D)
	- 2. Information allows obtaining information relating to the map description, set properties, contacts of the person responsible for the published data, and other features.
	- 3. Star tool allows selecting and filtering the geometries of a single layer into the map using various tools, and, subsequently, displays in the attributes table.
	- 4. Localization symbol activates or deactivates the localization tool (G).

**Figure 5.** Display of the platform with the opening of a popup relative to the grain size information about the shoreline sample of Capo Milazzo's PB.

**Figure 6.** Visualization on the WebGIS platform of the PB of Ramla Bay (Gozo).

Starting from these instruments WebGIS can be used to investigate the different layers of the project, crossing queries and comparing information. This platform offers the possibility of turning on and off layers of different or the same areas, as well as of different time intervals; this, in both cases, shows, in detail, the characteristics of the PBs and their evolution over time.

#### *3.2. Examples of Comparison*

Regarding the orthophotos, there is a possibility to view and overlap them in order, to visually notice the differences, in particular by playing on the opacity of one of the two to compare them more clearly. To make the comparison between orthophotos more effective, it is also possible to activate the layers relating to the respective shorelines (Figure 7). For example, certain geomorphological differences have also been digitized and shown within

the layers concerning the geomorphological parameters, which can be interrogated to view their characteristics.

**Figure 7.** The figure shows a comparison of the pocket beach of Rais Gerbi (Palermo) in two different time intervals through the management of opacity: (**a**) Spring-Summer Season; (**b**) overlap of Autumn-Winter season on Spring-Summer season. The respective shorelines are highlighted in orange.

Among the various combination that can be made by switching different layers on or off, it may be useful to combine lithology, samples and photo collections. Thanks to this overlap, it is possible to query the map in order to notice an affinities or differences at the mesoscale. The following example (Figure 8) shows how, by clicking on the photographic icon, the popups relating to both the latter and the underlying lithology layer will open simultaneously. In Figure 9, on the other hand, by clicking on the nearby shoreline sample, the popup will show its characteristics.

**Figure 8.** WebGIS platform that shows the overlap between samples, lithology, and photo collection of the pocket beach of Palma di Montechiaro (Agrigento). On the right, the popup between the last two obtained by clicking on the photo collection.

**Figure 9.** WebGIS platform that shows the overlap between samples, lithology, and photo collection of the pocket beach of Palma di Montechiaro (Agrigento). On the right, the pop-up of the shoreline sample obtained by clicking on the photo collection.

> It is evident, through these simple steps, how it is possible to notice the similarities between the lithological characteristics of the outcrop, in this case metamorphic, and the respective photographs of the study area and the nearby beach sediments.

> Another comparison can be made with regard to the sedimentological analysis, in order to learn the differences between the grain size and other statistical parameters (Figure 10). To compare samples related to the same vector layer, the selection tool should be used to click the geometries to study. By clicking the filter button, the attribute table of the selected geometries may be viewed.

> It may be useful to compare the surfaces of the beaches of successive surveys. Figure 11 shows how, by activating the respective layers and clicking on them in the map, the user can view both the visual and area differences by observing the popup.

> One of the layers present in the WebGIS consists of the mapping of *Posidonia oceanica* proximal to the PBs; through this platform, it is possible to observe its distribution according to the various bathymetric zones. In Figure 12, for example, it emerged that *Posidonia oceanica* develops mainly between 5 and 20 m deep.

**Figure 10.** Example of comparison between beach sediments for three different transepts taken along the PB of Cefalù (Palermo).

**Figure 11.** Example of comparison between beach surfaces of two successive surveys made in the PB of Tindari (Messina).

**Figure 12.** Distribution of *Posidonia oceanica* according to the various bathymetric zones on the PB of Capo Milazzo (Messina).

#### **4. Discussion**

While many of the beaches in the Maltese islands are subject to some form of management of varying degrees, (the most established being primarily based on management indicated in the achievement of the Blue Flag beach quality status), there is still a need for an overall holistic management policy and strategy. In Sicily, there is a lack of coastal planning, where coastal management has been focused on local or sectorial erosional rates, which had previously caused severe damage to the natural and archaeological cultural heritage [52]. A very large amount of public funds has been expended on coastal protection without prior consideration of coastal planning to better understand the needs of each individual beach and the interaction of the specific interventions with natural system processes.

The importance of developing (within the BESS project) a monitoring system supported by a surveillance network that specifically targets pocket beaches, is supported by a widespread concern that climate change may be disrupting the stability of otherwise natural systems [72]. Pocket beaches have been defined as ones that have, in their evolution, achieved a sediment balance or a state of equilibrium; they have also been referred to as 'sediment tight' systems. However, with an increasing influence of climate change on natural processes that influence either the arrival of sediments to a pocket beach (such as that via precipitation influenced watersheds) or the potential loss of sediments (such as the wave climate influencing a pocket beach embayment), it is paramount that such potential changes are clearly identified and monitored.

Considering the multiplicity of parameters involved in the study of PBs, and considering their small size, these micro-beaches can be considered as a sentinel system in which, changes in the shape or grain size of the deposit or in the composition of the fauna and flora, could give an indication of the global trend imposed by the effects of climate change. A geospatial database is a fundamental key for the creation of the GIS of the BESS project. All geospatial information acquired at different locations highlighted naturalistic and ecological details of particular interest. Then, they allowed modelling of the beach evolution and the definition of hazard levels in each PB. Such a work on a specific aspect of the much wider coast is clearly highly useful in contributing to a more holistic coastal zone management approach. The relevance of this work is also borne out by the acceptance by the E.U.'s Interreg Italia–Malta funding mechanism to support this initiative aimed at developing a WebGIS for mapping and visualization of coastal data.

In fact, the GIS plays an important and useful role in managing a large amount of parametric data describing PBs. It is, thus, necessary to create a comprehensive data

management system that allows cross-referencing of information related to different fields interacting in the driving of coastal dynamics processes. WebGIS is an online-distributed system that allows users to access geographic information data by processing services available through a web browser; it has a strong interactivity and dynamics. Compared with traditional GIS software (or online GIS mapping), WebGIS has a wider application range, more timely data updates, lower construction costs, and higher security. Many large internet companies, such as Google, Baidu, and Tencent, recently developed online maps that are based on WebGIS technology for public use [10]. The work described in this paper illustrates the strong potential of such a WebGIS in the manipulation and presentation of a wide set of territorial and marine datasets that may be utilized for more effective understanding and management purposes. Additionally, the WebGIS developed by the BESS project is a particularly useful tool to allow a wider user audience to better appreciate pocket beaches not just visually but also in terms of their functioning and overall complexity; the WebGIS system presents technical data in a manner that will, however, allow both expert and non-expert use, facilitating different levels of manipulation and understanding. The raising of such awareness in the general public has been shown to initiate a process that transforms their initial curiosity into a role of controller and stimulus/driver. On a wider scope, not limited to the PBs, the utility of such a system as WebGIS is much greater.

The lack of interest that is often demonstrated on public items may be countered by increasing interaction with such amenities. The BESS project describes 134 PBs as a territorial database, the evolution of which will hence forth become more visible through the developed website and, in particular, a remote surveillance system; this will allow public monitoring of the evolution of these systems, not only in the territorial sense, but also in terms of the actions taken by local politicians and stakeholders.

The results demonstrate that through the development of the BESS WebGIS, (dedicated only to PBs), it is possible to clearly describe all the geometric and morphological characteristics supported by geological, sedimentological, and bathymetric data. This simple and immediate online geo-database allows a variety of users to access the platform to view and draw as much information as possible. Unlike Desktop GIS, the use of which is limited to a personal computer that contains all the data of a specific project, a WebGIS is accessible remotely via a server, with no restrictions on the place or number of users who access it. WebGIS has the advantage of combining the potential of GIS with the usefulness of the Internet in showing interactive maps and spatial data, as well as any subsequent updates; this clearly makes WebGIS the cheapest and easiest way to distribute geospatial data. The results highlighted how simple and immediate it can be to view the results of related field campaigns once uploaded to a WebGIS platform.

The numerous examples listed demonstrate the effectiveness of WebGIS in comparing the various levels present, which helps to better understand its scientific value and to encourage research in this field. While the knowledge of dynamic evolution of a given layer describes the temporal trend, it is when different types of data are crossed that the potential of the WebGIS grows and multiplies the research ideas. While the BESS WebGIS is undoubtedly useful for those involved in research in this field, its usefulness can be extended to tourism, civil protection, and land-use purposes. Additionally, the BESS WebGIS portal allows:

Through the developed WebGIS it will be possible to create a management system for these specific territorial niches, based on an active monitoring plan, at low cost, and with a high technological component. This monitoring–management model is in line with the

scenarios of future management plans, which will provide for a detailed mapping of the environments concerned, so that protection systems can be created, not necessarily those that are structural, but rather systems based on good continuous management practice exploiting the intrinsic resilience of the system rather than weakening it. The challenge lies precisely in identifying the coastal management systems that make it possible to enhance the intrinsic characteristics of resilience of the beaches. Such systems will allow users to counter the effects of the changes that occur with the sea level rise, as well as with the worsening of marine weather conditions.

GIS has been improving rapidly over the years, but the use and technology development of the Internet still remain the key factors in the development of WebGIS. Only in this way, through a complete involvement of users, will it be possible to aim for an important information and awareness campaign linked to PBs. This will allow for the PBs, ecological pearls, to be monitored and preserved.

#### **5. Conclusions**

A WebGIS portal including various pocket-beach-related data and models in different temporal and spatial scales was created to present the results of the Interreg project, BESS, to a wider group of people. It is an effective way to share information about Sicilian and Maltese PBs, acquired by the project, and to compare coastal data with each other. The development process suggested that the ease of use, combined with a negligible realization cost, provides an opportunity for replicability and scalability in other geographical and administrative contexts, and also for different purposes. It also proposes that the barriers that limit end-user targets may be addressed though this mechanism, allowing a much wider audience to interact with geographic information data, requiring only an internet service for access; in this manner, not only experts but also those with less informatics skills and less high-performing computers, such as the general public, may interact with this database. The current BESS WebGIS was presented not as a point of arrival, but as a starting point: a platform that must be implemented for the overall management of all coastal data necessary for constant active monitoring. This project suggests that data management will be useful, above all, in predictive terms of the coastal morphological trend, as an effective tool that will support future planning, no longer local, but increasingly on a regional scale. To assist in addressing the project's development toward these directions, a video surveillance system was created, allowing a network of mayors and stakeholders involved with pocket beaches in their territories to become more directly involved with the project and its extensive data. Bringing together various public and private entities in the same direction increases the possibility of giving greater emphasis to coastal problems and identifying solutions. The portal and its further development at a regional level should improve beach-user choices by providing useful data, such as traffic conditions, services, and the quality of each location. The portal should also facilitate the process of initiating Integrated Coastal Zone Management at the local level. This is particularly so in Sicily, where the results of the project and the structure and methodology followed in the BESS project has already been integrated within the Regional Plan Against the Coastal Erosion, which is intended to become the official instrument to manage the coastal area in Sicily.

In addition, various entities will be given the opportunity to actively participate in a process of continuous study and monitoring. End users will have the possibility to view a familiar basemap namely OpenStreetMap, to help them explore the portal by following the specific guidelines. Local public administrations will be able to see the effects of changes in the coastline and, where necessary, plan interventions aimed at the conservation of the coast, which represents a heritage of great importance from a naturalistic and tourist point of view. Probably, defining GIS is a more complex challenge than naming it because the field itself is continually evolving and morphing. It is important to note that the results of the BESS project have been used for implementing the Regional Plan Against the Coastal Erosion (PRCEC with the Italian acronym) and they also contributed to scientific discussions about coastal area management as well.

**Author Contributions:** Conceptualization, G.R., A.M. (Anselme Muzirafuti) and S.L.; methodology, M.C., F.G., A.M. (Anselme Muzirafuti), and M.F.; software, M.C., F.G., A.M. (Anselme Muzirafuti) and M.F.; validation, A.M. (Anton Micallef), S.D., A.C., A.Z., O.S., F.I., A.T. and F.C.; formal analysis, A.M. (Anton Micallef), G.R., A.M. (Anselme Muzirafuti) and S.L.; investigation, M.C., F.G., E.C., A.M. (Anselme Muzirafuti) and M.F.; resources, F.G. and M.F.; data curation, M.C., F.G., A.M. (Anselme Muzirafuti) and M.F.; writing—original draft preparation, G.R., A.M. (Anselme Muzirafuti), F.G. and M.F.; writing—review and editing, G.R., A.M. (Anton Micallef) and A.M. (Anselme Muzirafuti); visualization, M.C., S.L. and F.P.C.; supervision, G.R.; project administration, A.Z.; funding acquisition, A.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** A.Z. is supported by the European Regional Development Fund (INTERREG Italia–Malta) for the Project BESS: Pocket Beach Management and Remote Surveillance System.

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** "BESS project website" at http://bess.pa.ingv.it/?lang=it, accessed on 5 August 2021.

**Acknowledgments:** This work was partially funded by the European Regional Development Fund (INTERREG Italia–Malta) under the Project BESS: Pocket Beach Management and Remote Surveillance System.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **Appendix A**

**Table A1.** Summary table of the layers/data present in the GIS.

#### **References**

## *Review* **IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges**

**Vu Khanh Quy 1, Nguyen Van Hau 1, Dang Van Anh 1, Nguyen Minh Quy 1, Nguyen Tien Ban 2, Stefania Lanza 3, Giovanni Randazzo 4 and Anselme Muzirafuti 4,\***

**Abstract:** The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector. To achieve this objective, we discuss the vision of IoT-enabled smart agriculture ecosystems by evaluating their architecture (IoT devices, communication technologies, big data storage, and processing), their applications, and research timeline. In addition, we discuss trends and opportunities of IoT applications for smart agriculture and also indicate the open issues and challenges of IoT application in smart agriculture. We hope that the findings of this study will constitute important guidelines in research and promotion of IoT solutions aiming to improve the productivity and quality of the agriculture sector as well as facilitating the transition towards a future sustainable environment with an agroecological approach.

**Keywords:** sustainable agriculture; food security; green technologies; Internet of Things; natural resources; sustainable environment; IoT ecosystem

#### **1. Introduction**

In order to meet the current global needs of humanity, new solutions and technologies are constantly being proposed and implemented. This has led to the advent of the Internet of Things (IoT) [1,2]. IoT is defined as the network of all objects that are embedded within devices, sensors, machines, software and people through the Internet environment to communicate, exchange information and interact in order to provide a comprehensive solution between the real world and the virtual world [3]. In recent years, IoT has been applied in a series of domains, such as smart homes [4,5], smart cities [6,7], smart energy [8,9], autonomous vehicles [10,11], smart agriculture [12–15], campus management [16,17], healthcare [18,19], and logistics [20,21]. Series of other IoT applications have been described by Shafique et al. [22]. An illustration of rich and diverse IoT applications for smart agriculture is provided in Figure 1.

**Citation:** Quy, V.K.; Hau, N.V.; Anh, D.V.; Quy, N.M.; Ban, N.T.; Lanza, S.; Randazzo, G.; Muzirafuti, A. IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. *Appl. Sci.* **2022**, *12*, 3396. https://doi.org/10.3390/ app12073396

Academic Editor: Manuel Armada

Received: 7 March 2022 Accepted: 25 March 2022 Published: 27 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** An illustration of IoT applications for smart agriculture.

According to the United Nations' (UN 2019) statistics, the world population is estimated to grow to 10 billion by 2050 [23]. As a consequence, the requirements of agricultural products are continually increasing. However, farmlands are declining, natural resources are increasingly depleted, and the rise of unpredictable nature challenges, such as global warming, salinization, and flooding, make food security the most concerning problem for all nations worldwide.

In recent years, with the aim of increasing agricultural production, new solutions and technologies have been introduced in the agriculture sector [24]. An emerging trend is the application of the IoT and big data. A significant number of studies have been focused on research, experiments, and applications [25,26]. According to the Cisco forecast, over 500 billion IoT devices will be connected to the Internet by 2030 [27]. The use of IoT and big data will enable smart agriculture and is expected to enhance efficiency and productivity [28].

Over the years, wireless sensor networks (WSN) have been strongly applied in the agricultural sector, building the foundation for developing smart agriculture [29]. The unique characteristics of WSN, such as the ability to self-organize, self-configure, self-establish, and self-recover, make it suitable for smart agriculture [30]. The sensor device consists of a radio frequency (RF) transceiver, sensor, microcontroller, and battery power [31]. The WSN focuses on applications such as environmental monitoring, machine control automation, and traceability [32–35].

Along with the development of science and technology, the urgent requirement for breakthrough solutions and technologies aiming at improving productivity and efficiency in the agriculture sector has led to adoption of the IoT. The primary motivation for their applications is the breakthrough progress of smart agriculture and its inevitable role as the future of smart and sustainable environment management. IoT integrates a series of existing solutions and technologies, such as WSN, cognitive radio, ad hoc networks, cloud computing, and end-user applications [36]. In the smart agricultural sector, automation solutions and technologies, mechanical machines, knowledge, decision-making tools, services, and software are integrated seamlessly to help farmers improve productivity, product quality, and profitability [37].

In this work, a comprehensive survey of IoT applications for smart agriculture is conducted. An analysis of 135 relevant works published between 2017 and 2022 was conducted. Firstly, relevant 550 papers published in the period of (2017–2022) were retrieved from major scientific databases, namely IEEE Xplore Digital Library, Science Direct, MDPI, and Springer, by using keywords such as IoT-enabled smart agriculture, smart agriculture, Internet of Things, aquaponics, monitoring forestry based on IoT, tracking and tracing, smart precision farming, greenhouse production, Sigfox, LoRa, Wi-Fi, LoRaWAN, and IoT ecosystems. In the next step, we excluded papers that were published in low-repute conferences and journals, and then we conducted the content analysis for the obtained paper. Finally, 135 papers were selected for the preparation of the present work.

In addition, we analyzed and discussed the benefits and challenges, open issues, trends, and opportunities of IoT in the smart agriculture sector. This work is organized as follows: Section 1 introduces our work, and in Section 2, we present an IoT ecosystem architecture for smart agriculture that consists of three main components: IoT devices, communication technology, and data storage and big data processes. Section 3 presents the IoT applications in agriculture, including (1) monitoring, (2) tracking and traceability, (3) precision agriculture, and (4) greenhouses. Section 4 introduces some open issues and future research challenges of IoT for smart agriculture. Issues are discussed for two main directions: business and technology. In Section 5, we present the main conclusions of this work.

#### **2. IoT Ecosystem Architecture for Smart Agriculture**

In this section, we present a common framework of an IoT ecosystem for smart agriculture based on three main components, including (1) IoT devices, (2) communication technologies, and (3) data process and storage solutions. An illustration of the IoT ecosystem for smart agriculture is presented in Figure 2.

**Figure 2.** An illustration of IoT ecosystems' architecture for smart agriculture.

#### *2.1. IoT Devices*

The common architecture of an IoT device consists of sensors to collect information from the environment, actuators based on wired or wireless connections, and an embedded system that has a processor, memory, communication modules, input–output interfaces,

and battery power [38,39]. The common architecture of a typical IoT device for smart agriculture is shown in Figure 3.

**Figure 3.** An illustration of the common architecture of an IoT device.

Embedded systems are programmable interactive modules, namely FPGAs (field programmable gate arrays). Sensor devices are specially designed to operate in open environments, in nature, in soil, water, and air to measure and collect environmental parameters that affect production, such as soil nutrients, humidity, temperature, etc. Smart farming solutions are agricultural operations that are often deployed on large farmlands, outdoors, so the devices that support solutions need some unique characteristics, such as the ability to withstand the effects of weather, humidity, and temperature instability throughout their service lifecycle. Some of their main features, as shown in Figure 4, make IoT devices suitable for smart agriculture solutions [40–42].

**Figure 4.** The main characteristics of IoT devices.

Depending on the required operation, there are several typical sensors applied in the smart agriculture sector. Sensors can be divided into several typical categories, such as (1) location sensor, (2) optical sensor, (3) mechanical sensor, (4) electrochemical sensor, and (5) air flow sensor. These sensors are used to collect information such as air temperature, soil temperature, air humidity, soil moisture, leaf moisture, precipitation, wind speed, wind direction, and solar radiation, and barometric pressure [21,24,36].

#### *2.2. Communication Technology*

The survey of communication technologies for IoT [43,44] indicated that to integrate IoT into the smart agriculture sector, communication technologies must progressively improve the evolution of IoT devices. They play an important role in the development of IoT systems. The existing communication solutions can be classified as: protocol, spectrum, and topology.

Protocols: many wireless communication protocols have been proposed for the smart agriculture sector. Based on these protocols, devices in a smart agricultural system can interact, exchange information, and make decisions to monitor and control farming conditions and improve yields and production efficiency. The typical, low-power communication protocol numbers commonly used in smart agriculture can be divided into short-range and long-range categories based on the communication range.

Table 1 presents some typical communication technologies for the smart agriculture sector. The values in Table 1 indicate that short-range communication technologies have a transmission distance of less than 20 (m), high energy efficiency, and low data rate. These protocols are often employed in sensor networks, while long-range communication technologies have transmission distances of up to several tens of kilometres, consume more energy, and are installed for backhaul device-to-device communications. A diverse survey of low-power communication technologies for IoT that presents solutions, challenges, and some open issues is described by Sundaram et al. [54].

Spectrum: Each radio device uses certain frequency bands for communication. The FCC (Federal Communications Commission) has defined unlicensed spectrum bands for unlicensed operations in scientific, industrial, and medical purposes [55]. These spectrum bands are often applied for low-power levels and short-range applications. Consequently, a series of common technologies for the smart agriculture sector, from wireless machine control and UAVs to communication technologies such as Wi-Fi and Bluetooth, use unlicensed

spectrum bands [56]. However, the use of unlicensed spectra faces several challenges, such as the quality of service guarantee, the cost of setting up the initial infrastructure, and the interference generated by the huge number of IoT devices [57,58].

A licensed spectrum usually is allocated to mobile networks. It provides more efficient network traffic, more reliability, enhances the quality of service (QoS), offers security, provides extensive coverage, and involves lower initialization infrastructure costs for users. However, the use of licensed spectrum bands has faced some limitations, such as high data transmission costs and the low energy efficiency of IoT devices [59].

Several recent studies have demonstrated the efficiency of unlicensed spectrum bands in the mm wave range. It uses extremely low power but provides large transmission distances and high data rates [60,61]. One limitation of the mm wave spectrum is that the data rate is strongly affected by weather conditions, especially rain [62].

Topology: The establishment of the communication spectrum band and operation protocol of IoT devices depends on the structure that deploys IoT devices for smart agriculture applications. Network structures for smart agriculture usually have two main types of nodes: sensor and backhaul nodes [63]. The common characteristics of IoT sensor nodes are short communication distance, low data rate, and high energy efficiency. In contrast, IoT backhaul nodes often require large transmission distances, high throughput, and data rates. Therefore, based on the role of each IoT network node, the sensor node or backhaul node selects and installs appropriate communication technologies [64]. Figure 5 presents a typical low-power network topology designed for measuring and monitoring factors in a smart farm. The system includes:

**Figure 5.** An illustration of the common IoT-based smart agriculture topology.

#### *2.3. Data Analytics and Storage Solutions*

In the smart agriculture domain, besides the main problems of sensing, collecting data, and controlling devices to respond to the real farming environment, data storage and processing are also important problems and face some challenges [26,28]. In reality, the number of collected data is huge, and traditional data storage, organization, and processing solutions are not feasible. Therefore, big data processing solutions need to be researched and applied for smart agriculture [65,66].

The complexity of data storage and processing is due to the unique characteristics of the smart agriculture field, including unstructured data and various formats, such as text, images, audio and video, economic figures, and market information. Recent solutions and technologies have introduced the use of cloud platforms for storage and data analytics, which are collected from farms [36,67]. In addition, cloud-assisted big data analytic solutions, such as edge computing [68] or fog computing [69], are also proposed to reduce latency and costs and support QoS.

The survey results demonstrate that, in recent years, many management information systems for smart agriculture have been proposed [70–72]. Nowadays, possible solutions have been developed and commercialized, providing solutions and services for farmers to manage farms and fields, aiming to increase productivity, reduce human labour, and enhance farming efficiency, as follows:

#### **3. Typical Applications of IoT in Smart Agriculture**

In recent years, a series of IoT applications for agriculture have been introduced. According to survey results, we divided these applications into categories based on their purpose, including monitoring, tracking and traceability, and greenhouse production. The detailed results are presented in the following subsection.

#### *3.1. Monitoring*

In the agriculture sector, factors affecting the farming and production process can be monitored and collected, such as soil moisture, air humidity, temperature, pH level, etc. These factors depend on the considered agricultural sector. Some smart agricultural sectors are applying the following monitoring solutions:

Crop Farming: In this sector, some vital factors that affect the farming process and production efficiency include air temperature, precipitation, air humidity, soil moisture, salinity, solar radiation, pest status, soil nutrient ingredients, etc. In [81], the authors designed an IoT device called FarmFox. This device allows real-time collection and analysis of the composition of the farming soil and transmits the information to farmers/owners via the Internet. The results demonstrate the health of the soil is monitored in real time to provide timely recommendations to farmers aiming to increase productivity and farming efficiency.

Furthermore, in [82], the authors proposed an IoT device to allow intelligent control of temperature and humidity factors, called a weather radar. This device will automatically turn on the warning mode using the light signal and send messages to the farmer when the temperature or humidity exceeds a pre-installed threshold. In [83], the authors introduced an IoT system based on Web GIS to monitor pest status and provide early warnings. In addition, this study also proposes a predictive model based on monitoring the habitat of pests and diseases. The efficiency of the proposed system was indicated, based on the predicted figures of the locust epidemic, to have a high accuracy rate (over 87%) in 2019 (China).

Monitoring information, such as soil condition, moisture, and temperature, and the prediction of natural factors, such as rainfall and weather, support the control of growing conditions of crops, helping farmers plan and make irrigation decisions to optimize production and reduce labour costs. In addition, the collected data, combined with big data processing technology, can provide recommendations for implementing preventive and remedial solutions against pests and diseases in farming.

Aquaponics: It is an integration of aquaculture and hydroponics. Aquaponics is a farming technique where fish waste becomes a source of nutrients needed by plants. One of the most important issues in such farms is constantly monitoring water quality, water level, temperature, salinity, pH, sunlight, etc. [84]. According to this research direction, in [85], the authors designed an IoT system to monitor the temperature and pH value of water for aquaponics farms. Moreover, this system is also equipped with a control system of water metrics to keep the fish habitat stable and an automatic fish feeding function to increase the productivity of the fish. The results show that the IoT system had stable operation and provided real-time monitoring parameters. The authors of [86] designed an aquaponics farm for households/urban areas based on IoT. This system recommends the proper ratio of fish and plants.

Consequently, the system decreases feed consumption as well as reduces carbon emissions into the environment. The primary purpose of this proposal aims to balance the self-sustaining ability of the aquaponics system. The experimental results demonstrate the number of fish decreases from 30 to 15, and the number of plants increases from 20 to 30, but the crop production will increase by more than 50%. A detailed and diverse survey of the IoT systems and devices for control and monitoring of aquaponics farms is introduced in [87]. Based on the obtained data, monitoring can improve the production of fish and plants through the control, supplementation, and regulation of nutritional ingredients in the water. The collected data were also used to automate the management of aquaponics farms to reduce labour costs.

Forestry: Humans depend on forests for survival. Moreover, forests play a vital role in the carbon cycle and provide a habitat for more than two-thirds of animal species in the world. Forests also have the effect of protecting watersheds, limiting floods, and mitigating climate change. The main factors that need to be monitored in a forest include soil ingredients, air temperature, humidity, and concentration of several different gases, such as oxygen, methane, ammonia, and hydrogen sulphide. A series of forest control systems and solutions are presented in [88,89] based on IoT and big data analytics.

In [90], the authors designed a peatland forest environmental monitoring system. This forest area plays a very vital role in the rainforest ecosystem of Brunei. However, the peatland forest type is very burnt. This work designed an IoT system to monitor environmental conditions, such as temperature, humidity, wind direction, barometric pressure, and manage possible disasters. For the purpose of enhancing feasibility, IoT devices use the solar-powered system and communicate with the monitoring centre based on the LoRa network. In [91], the authors proposed a solution to control forest changes and vitality by using high-resolution RapidEye satellite imagery. This solution has been deployed commercially in several states in Germany and has detected leaf diseases in a pine forest affected by pests. Survey results indicate that monitoring in forestry focuses on providing early warning systems against forest fires, pest control, or deforestation.

Livestock Farming: It is defined as the process of raising domesticated animals, such as cows, pigs, sheep, and goats, chickens, etc., in an agricultural environment to obtain traction, serve production, and obtain products such as meat, eggs, milk, fur, leather, etc. In this area, the factors to be monitored depend on the type and number of farming animals [92]. In [93], the authors designed a support system for the diagnosis, prevention, and treatment of diseases for livestock called VetLink. This system can provide recommendations for animal health for farmers in rural areas where it is difficult to access veterinary doctors immediately. In [94], the authors proposed a noncontact temperature measurement system and monitoring of animals to ensure early detection of diseases and animal health. This system can be used for remote monitoring of animal health and timely anomaly detection. In [95], the authors introduced a monitoring system for large-scale pig farms based on IoT. The specific solution is to attach an IC tag on each pig to monitor the behaviour of each pig, such as their period of feeding and resting and exercise. Data from sensors are collected and combined with data analytics solutions that can make recommendations for pig health.

The monitoring data of water, feed, and animal health for livestock in the farming process helps farmers set up livestock plans, reduce labour costs, and enhance production efficiency. While a series of solutions has been provided for monitoring large-scale farms, their application in small and medium-sized farms is very limited, especially in developing countries. This can be attributed to the high cost and the lack of knowledge needed to set up, manage, and operate IoT systems. Therefore, effective and low-cost solutions for agricultural IoT have much potential.

#### *3.2. Tracking and Tracing*

In order to meet the needs of consumers and increase profit value, in the future, farms need to demonstrate that products offered to the market are clean products and can be tracked and traced conveniently, thereby enhancing the trust of consumers in product safety and health-related issues. In order to solve this problem, a series of tracking- and tracing-based problems for the smart agricultural sector has been proposed, specifically as follows:

In [96], the authors designed an information system that allows tracking and tracing of agricultural products and foods such as dairy and vegetables, called SISTABENE. This system helps suppliers track the production process and errors arising in the supply chain, and helps end-users trace the origin of food. In [97], the authors proposed a food supply chain traceability system based on blockchain technology. It helps to track and trace agrifood supply chains' production process and trace the origin of agricultural products. This solution has been employed at Shanwei Lvfengyuan Modern Agricultural Development Co., Ltd. (Shanwei, China). Although there are still limitations, the results demonstrate that this solution has successfully supported the tracing of food and agricultural products through QR codes, improving product quality and ensuring the clear traceability of products. In [98,99], the authors proposed smart agricultural solutions to tracking and tracing agricultural products, thereby allowing consumers to know the product's entire history. These solutions enable tracking and tracing some of the data collected along the

supply chain, ensuring that consumers and other stakeholders can identify products' origin, location, and history.

#### *3.3. Smart Precision Farming*

The advent of the GPS (global positioning system) has created breakthrough advances in many fields of science and technology. The GPS provides the most important parameters for locating a device, such as location and time. GPS systems have been successfully deployed in many fields, such as smartphones, vehicles, and IoT ecosystems. However, GPS is only good support for outdoor systems and the sky. Meanwhile, the demand for the locating and navigating systems in the home and on the streets of smart cities is growing rapidly. Aiming to solve this problem, an advanced global navigation satellite system (GNSS) is being deployed [100]. Based on GPS and GNSS systems, suitable farming maps have been established for fields and farms. As a result, agricultural machinery and equipment can be operated autonomously [101]. Figure 6 presents an illustration of the typical cloud-assisted, IoT-based precision agriculture platform.

**Figure 6.** Cloud-assisted IoT-based precision agriculture platform.

In smart precision farming, one of the most important applications is the use of drones in monitoring and farming activities. Some common farming tasks using UAVs include spraying pesticides, fertilizing, sowing seeds, evaluating and mapping, and monitoring crop growth. In [102], the authors presented a detailed survey of drone applications for smart agriculture, including applications, control technology, and future trends of the UAV application for smart agriculture. In [103], the authors designed an automatic agricultural product classification system based on camera systems, image processing algorithms, and mechanical actuators. The experimental results for agriculture products such as oranges and tomatoes present a classification success rate of over 95%, and the sorting time for each product is less than 1(s). This solution can be adapted and applied to the classification of different agricultural products. In [104], the authors proposed a solution to estimate grape production. The proposed solution combines an RGB-D camera mounted on a mobile robot platform and size estimation algorithm for a bunch of grapes. The experimental results present an average error in the range of [2.8–3.5] (cm). The results demonstrate this solution is a feasible method for evaluating the productivity of large-scale grape farms.

The survey results show that smart precision agricultural equipment, such as irrigation systems, unmanned aerial vehicles (UAV), and smart agricultural equipment, etc., are configurable in an autonomous-control mode based on certain conditions or can be controlled remotely by the farmer via the Internet [105,106]. Smart precision farming helps to improve productivity and production efficiency and is suitable for large-scale farms. Nowadays, suppliers of precision agricultural equipment have IoT modules built into their machines, allowing machines to operate autonomously and remotely via the Internet [107].

#### *3.4. Greenhouse Production*

A greenhouse consists of walls and a roof, which are usually made from transparent materials, such as plastic or glass. In a greenhouse, plants are grown in a controlled environment, including controlling for moisture, nutrient ingredients of the soil, light, temperature, etc. Consequently, greenhouse technology makes it possible for humans to grow any plant, at any time, by providing suitable environmental conditions [108]. Figure 7 illustrates a smart agriculture IoT system for monitoring greenhouse farming factors based on IoT ecosystems.

**Figure 7.** An illustration of IoT application for monitoring farming conditions in a greenhouse.

In [109], the authors introduced an IoT-based greenhouse environmental monitoring system for multipoint monitoring in large greenhouses. Instead of using multiple sensors at different locations, this solution involves a drive system that allows the sensor system to move to different locations in the greenhouse. The experimental results show that the proposed system can effectively monitor multiple points in large greenhouses. In [110], the authors introduced an energy-saving temperature control technology for smart greenhouses. This study proposed two intelligent control methods: active disturbance rejection control and fuzzy active disturbance rejection control. The experimental results demonstrate that the proposed technology saves over 15% of the total energy consumption of the greenhouse. In [111], the authors designed an intelligent IoT system to monitor and control greenhouse temperature for energy efficiency and improve crop productivity. The experimental results for the Kingdom of Saudi Arabia, where daytime temperatures can be above 50 ◦C, demonstrate the efficiency of the proposed solution, including saving energy and predicting the rate of plant growth.

Recent studies indicated that solutions integrating IoT, big data processing, and artificial intelligence could be applied in greenhouses to reduce labour and energy efficiency. Moreover, it also provides direct connections between the greenhouse farms and the customer [112–115].

#### **4. Challenges and Open Research Directions**

The survey results indicate that IoT components for the smart agriculture sector, including hardware and software, have been focused on research and achieved many breakthrough results. Several IoT solutions have been deployed on large-scale farms/fields. However, the widespread deployment of IoT in the agricultural sector still presents some challenges. We have present two main problems: economic efficiency and technical problems. We consider these issues coupled with policies that will drive the integration of IoT technologies in agriculture.

#### *4.1. Economic Efficiency*

In agricultural economics, one of the most important characteristics is a low rate of profit of an investment project, which presents many risks from natural conditions. The benefit–cost of a new technology seeking deployment in agriculture should be carefully calculated to ensure a trade-off between the cost of technology implementation and the profit potential. Therefore, we discuss the economic aspects related to IoT implementation in smart agriculture.

There are several types of costs related to the implementation of IoT in agriculture. We divided them into categories, including (1) the system initialization cost and (2) the system operating cost. The system initialization cost includes hardware purchases (IoT devices, gateways, base station infrastructure). The system operating cost includes service registration cost and the cost of labour to manage IoT devices. Furthermore, additional operating costs include incurred costs from energy consumption, maintenance, data exchange among IoT devices, gateways and cloud servers. According to the opinion of *T*urgut and Boloni [116], the successful deployment of the IoT technologies will only happen if the customer benefits (customers need to know the benefits and potential) that IoT systems provide exceed their physical value and privacy costs. The businesses participating in the IoT domain will profit and achieve success. We can describe this process using these two conditions, as follows:

$$\text{Success of } \text{IoT Application} = \begin{cases} \ V\_{\text{service}} > \mathcal{C}\_{pri} + \mathcal{C}\_{h}^{user} + \mathcal{C}\_{pay}, \text{ Farmer Benefits} \tag{1} \\\ V\_{info} + \mathcal{R}\_{pay} > \mathcal{C}\_{h}^{busines}, \text{Businesses Bonefits} \tag{2} \end{cases} \tag{2}$$

where

*Vservice* is the expected value received by the IoT service users.

*Cpri* is the cost of the loss of privacy.

*Cuser h* is the equipment and hardware costs the user pays.

*Cpay* is the payment for the service fee.

*Vinf o* is the received information value.

*Rpay* is the received direct payment.

*Cbusiness h* is the share of the hardware and maintenance costs of the business.

According to the opinion of the service user (farmers or the owner of the farm), Equation (1) shows that the perceived value of the service for the user (*Vservice*) must be higher than the total of costs, including: the cost of the loss of privacy (*Cpri*), the equipment and hardware costs the user pays (*Cuser h* ), and the payments for the service fee (*Cpay*), while the opinion of the service provider, as shown in Equation (2), shows that the received information value (*Vinf o*) and the received direct payments (*Rpay*) must be higher than the share of the hardware and maintenance costs of the business (*Cbusiness h* ).

There is still a gap between service providers and service users (farmers or the owner of the farm), leading to the slow deployment of IoT applications in smart agriculture. In terms of the economic aspect, the analyzed results show that the need for a support policy from regulatory agencies and governments to allow service providers and service users to use IoT-based smart agriculture applications in their infancy can be met. As discussed in [117], to promote smart agriculture, the European Union has issued supportive economic policies, the so-called the European CAP (Common Agricultural Policy), whose annual budget amounts to approximately EUR 59 billion and is paid for by the nations of the EU.

In our view, to be able to apply IoT in the field of smart agriculture, service costs (*Cpay*) and the operating and system initialization cost of IoT (*Cuser h* ) needs to constantly be improved and optimized to reduce the cost of the IoT services for farmers. In addition, IoT businesses (service providers) also need to maximize the value of information obtained (*Vinf o*) to improve the profitability of the service providers.

In reality, service providers may commercially exploit the information received (*Vinf o*) in the period of providing services for farms, aiming to encourage the deployment of IoT applications in smart agriculture. Nowadays, several IoT platform providers allow free registration and use of services with some limitation conditions regarding services' functionality and ability processing; the number of connected IoT devices; and the number of data stored while premium functions and services charge users a fee.

In addition, one of the significant factors slowing down IoT adoption in agriculture is farmers' knowledge and ability to use IoT devices. In developed countries, this issue can be easily solved due to the accessibility of new technologies of farmers. Otherwise, in developing countries, where the majority of farmers in rural areas have very limited access to advanced technologies, this issue is a significant challenge [118,119].

#### *4.2. Technical Problems*

Interference: Deploying a huge number of IoT devices for smart agriculture can cause interference to different network systems, especially some IoT networks using short spectrum bands such as ZigBee, Wi-Fi, Sigfox, and LoRa (See Table 1). Interference can degrade system performance as well as reduce the reliability of IoT ecosystems. IoT networks that use cognitive technology to reuse unlicensed spectra increase the cost of the device. In our opinion, the advent of the 6G network [120] will allow a huge number of devices to connect to the Internet with an extremely high access speed and extremely large bandwidth. The full interference problem of IoT networks will be solved.

Security and Privacy: One of the most important problems of applying IoT in smart agriculture is the security problem, including the protection of data and systems from attacks on the Internet. In regard to system security, IoT devices' limited capacity and ability led to complex encryption algorithms that are impossible to implement on IoT devices. As a result, IoT systems can be attacked using the Internet to gain system control rights; IoT gateways are also attacked via denial of service [121–123]. In addition, cloud servers can be attacked by data spoofing to perform unauthorized tasks that affect the autonomous farming processes of farms. Cloud infrastructures can also be controlled by attackers [124,125]. Several issues of detailed IoT data privacy and security measures have been discussed in [126–128]. According to *Neshenko* et al., the IoT data security issue is one of the biggest problems slowing down IoT adoption in smart agriculture [129].

Regarding data security, the obtained information from IoT systems in farms is collected, processed, and commercially exploited by service providers to varying degrees. Therefore, one of the most important problems of policies regards the validity and legal status of farm data [130]. In reality, these data are of great value when aggregated and analyzed for large-scale agricultural activities. Consequently, without policies, the data privacy and security of farms can affect the competitive advantage of farmers/farm owners. In our opinion, using cryptography coupled with access keys is a possible solution to solve this problem. Keys could be made available based on a regional user group and to those who contributed to the database. For further complex cases, secure multiparty computation can be used, where the homomorphic encryption method [131,132], or this method combined with the blockchain [133], can be applied for the purpose of balancing privacy and data utility.

In our opinion, the security problems of IoT systems will be an exciting research topic and garner attention for both academia and industry research. An in-depth survey of threats and solutions to improve robustness, trust, and privacy for future IoT systems is presented in [134].

Reliability: Most IoT devices are expected to be deployed outdoors (in fields and farms). Harsh work environments lead to the rapid degradation of IoT devices' quality and can lead to unexpected manufacturer failures. The mechanical safety of IoT devices and systems must be ensured so they can withstand extremes of weather, such as temperature, humidity, rainstorms, and floods [135]. In our opinion, new materials and technologies need to continue to be studied to improve the durability of devices.

The open problems and challenges discussed in this section indicate that for IoT to be widely deployed in the smart agriculture sector, there are still many issues to be solved. Service providers need to reduce the service costs, more effectively exploiting the information collected from the farm. On the other hand, farmers need to improve their skills to be able to apply IoT solutions on their farm to enhance productivity and farming efficiency. Researchers need to continually study and propose optimal solutions and technologies to ensure IoT systems' privacy and security and improve the durability of IoT devices. These are really major challenges and exciting research topics in the future so IoT can be widely applied in the smart agriculture sector.

#### **5. Conclusions**

In this study, we presented an overview of IoT and big data for the smart agriculture sector. Several issues related to promoting IoT deployment in the agriculture sector have been discussed in detail. Survey results indicate that many studies have been performed to apply IoT for smart agriculture, aiming to enhance productivity, reduce human labour, and improve production efficiency. The benefits of applying IoT and big data in agriculture were discussed. In addition, we also pointed out the challenges we need to overcome to be able to accelerate the deployment of IoT in smart agriculture. However, there are still some challenges that need to be addressed for IoT solutions to be affordable for the majority of farmers, including small- and medium-scale farm owners. In addition, security technologies need to be continuously improved, but in our opinion, the application of IoT solutions for smart agriculture is inevitable and will enhance productivity, provide clean and green foods, support food traceability, reduce human labour, and improve production efficiency. On the other hand, this survey also points out some interesting research directions for security and communication technologies for IoT. We think that these will be very exciting research directions in the future.

**Author Contributions:** Conceptualization, V.K.Q., N.V.H., and A.M.; methodology, V.K.Q. and A.M.; validation, N.M.Q., D.V.A., A.M. and N.T.B.; resources, A.M.; writing—original draft preparation, V.K.Q., N.V.H., D.V.A., N.M.Q. and A.M.; writing—review and editing, V.K.Q., G.R., S.L. and A.M.; visualization, V.K.Q., N.V.H., D.V.A., N.M.Q., N.T.B., G.R., S.L. and A.M.; supervision, A.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.

#### **References**

## *Article* **Application of Remote Sensing Tools to Assess the Land Use and Land Cover Change in Coatzacoalcos, Veracruz, Mexico**

**Josept David Revuelta-Acosta 1, Edna Suhail Guerrero-Luis 1, Jose Eduardo Terrazas-Rodriguez 1, Cristian Gomez-Rodriguez 2 and Gerardo Alcalá Perea 3,\***

**Abstract:** Land use and land cover (LULC) change has become an important research topic for global environmental change and sustainable development. As an important part of worldwide land conservation, sustainable development and management of water resources, developing countries must ensure the use of innovative technology and tools that support their various decision making systems. This study provides the most recent LULC change analysis for the last six years (2015–2021) of Coatzacoalcos, Veracruz, Mexico, one of the most important petrochemical cities in the world and host of the ongoing Interoceanic Corridor project. The analysis was carried out using Landsat 8 Operational Land Imager (OLI) satellite images, ancillary data and ground-based surveys and the Normalized Difference Vegetation Index (NDVI) to identify and to ameliorate the discrimination between four main macro-classes and fourteen classes. The LULC classification was performed using the maximum likelihood classifier (MLC) to produce maps for each year, as it was found to be the best approach when compared to minimum distance (MDM) and spectral angle mapping (SAM) methods. The macro-classes were water, built-up, vegetation and bare soil, whereas the classes were an improved classification within those. Our study achieved both user accuracy (UA) and producer accuracy (PA) above 90% for the proposed macro-classes and classes. The average Kappa coefficient for macro-classes was 0.93, while for classes it was 0.96, both comparable to previous studies. The results from the LULC analysis show that residential, industry and commercial areas slowed down their growth throughout the study period. These changes were associated with socioeconomical drivers such as insecurity and lack of economic investments. Groves and trees presented steady behaviors, with small increments during the five-year period. Swamps, on the other hand, significantly degraded, being about 2% of the study area in 2015 and 0.93% in 2021. Dunes and medium and high vegetation densities (∼ 80%) transitioned mostly to low vegetation densities. This behavior is associated with rainfall below the annual reference and increments of surface runoff due to the loss of vegetation cover. Lastly, the present study seeks to highlight the importance of remote sensing for a better understanding of the dynamics between human–nature interactions and to provide information to assist planners and decision-makers for more sustainable land development.

**Keywords:** remote sensing; land use classification; GIS; Coatzacoalcos

#### **1. Introduction**

Land use and land cover (LULC) changes are among the research topics more recurrent in remote sensing [1–4]. Remote sensing provides comprehensive and extensive information to understand the interaction between terrestrial ecosystems and their responses to environmental factors [5–11]. This technology is considered a powerful source

**Citation:** Revuelta-Acosta, J.D.; Guerrero-Luis, E.S.; Terrazas-Rodriguez, J.E.; Gomez-Rodriguez, C.; Alcalá Perea, G. Application of Remote Sensing Tools to Assess the Land Use and Land Cover Change in Coatzacoalcos, Veracruz, Mexico. *Appl. Sci.* **2022**, *12*, 1882. https://doi.org/10.3390/ app12041882

Academic Editors: Dimitrios S. Paraforos and Anselme Muzirafuti

Received: 24 December 2021 Accepted: 4 February 2022 Published: 11 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

to obtain information from terrestrial surface characteristics at different temporal and spatial scales [12–16]. For more than half a century, scientists have used several types of remote sensing data. From those, the most commonly used source is the Landsat satellite based-data [17]. One of main uses of the Landsat-based satellite images is the identification or classification of LULC changes [18–22]. Some interesting conclusions have been driven through the use of remote sensing in LULC changes. It has been observed that changes in land uses are dynamic, non-linear interactions between humans and nature and are driven by complex stochastic processes [23]; worldwide changes in LULC for the last 300 years have shown gains in agriculture and losses in forest [24]; rapid population growth in Africa has been identified as the main driving force for the expansion of agricultural land, whereas in developing countries, urbanization dynamics are attributed to demographic factors [25] and transitions from cropland to urban and water bodies areas have been identified as an important trend in Europe [26].

Some of the more remarkable research found in literature is the work conducted by Yuan et al. [27]. Their research consists of the use of multi-temporal Landsat Thematic Mapper data to map and quantify the LULC changes in seven counties of the Twin Cities located in the metropolitan area of the state of Minnesota, United States. They used satellite images corresponding to the years 1986, 1991, 1998 and 2002. Yuan et al. demonstrated an urban zone increment from 23.7 to 32.8% in the study area. Surfaces such as rural areas, croplands, forests and wetlands decreased from 69.6 to 60.5%. Another unique example is the work carried out by Demissie et al. [28], where Landsat satellite data from 1973 to 2015 were classified to study land use change and its possible causes in Gonder, Ethiopia. The study showed that about 60.1% of the area experienced land use changes within the study period.

In Mexico, little research related to the quantification of LULC changes has been found within the literary review. One of the few works found was carried out by Colditz et al. [29]. Their research presents a methodology to develop a land use map in Mexico for the year 2005. The scheme was based on time series from a Moderate Resolution Imaging Spectroradiometer (MODIS) with a 250 m resolution and an extensive sampling of data for the different geographic zones of the Mexican surface. The results showed a map with an overall precision of 82.5% (Kappa statistic = 0.79). A further evaluation with 780 randomly generated samples within the classes with referenced field data indicated a precision equal to 83.4% (Kappa statistic = 0.80). Another study found in the literature includes the generation of a land use map of the Latin American and Caribbean zone for the year 2008. This project was developed within the framework of the project of the Latina American Network for Monitoring and Studying of Natural Resources (SERENA) [30]. Similar to Colditz et al. [29], time series and decision trees with MODIS data (250 m) were used for the classification of land use changes. The discrete SERENA model showed an overall accuracy of 84%. Other uses of remote sensing in Mexico include the detection of chinampas in the Xaltocan area in the Northern Basin of Mexico [31], the evolution of sea temperature on the west coast of Baja California [32] and/or hydropower assessment [33].

Due to the limitations observed in the use of remote sensing in Mexico, such as limited research using remote sensing and map course resolution, this study has integrated remote sensing, Geographic Information Systems (GIS) analysis, field sampling and image revision to assess the potential of the use of Landsat 8 satellite data to assess and monitor land use changes and coverage changes in the southeastern region of the state of Veracruz, Mexico, located in the southern coastal area of the Gulf of Mexico. Three widely used classification methods are explored to select the more appropriate algorithm for LULC classification under the same level of information previously extracted from field data, digital maps and ancillary data. This comparison allows for conclusions on the advantages and drawbacks of current classification algorithms. The analysis encompasses the definitions of macro-classes of land use and their derived classes. The Normalized Difference Vegetation Index, also known as NDVI, was used to identify and improve discrimination between macro-classes and classes within the study area. A supervised classification method, the Maximum

Likelihood Classification (MLC), was applied in this study due to its availability within the GIS applications in addition to it not requiring an extensive training process [34,35]. The most important advantage of the MLC as a parametric classifier is that it takes into account the variance within the selected classes and that, for normally distributed data, the MLC method performs a better qualification than other methods, namely, decision trees [34,36].

Additionally, this study proposes a methodology to obtain updated temporal information of the change in land use and land cover for the Coatzacoalcos area using Landsat 8 satellite imagery, which has a higher resolution with the available MODIS maps. The mapping of land use and its changes are critical and important factors for sustainable development as well as the monitoring of environmental impacts. The final product of this research will help government agencies to make decisions on urban development and preservation of available natural resources, since one of the emblematic projects in Mexico known as the Interoceanic Corridor is being executed in the present year. Lastly, this piece of research is going to serve as the baseline for additional contributions on LULC change prediction with more complex analyses of the drivers forcing these landscape modifications.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The study area is the southeastern region of the state of Veracruz, Mexico, in which one of the most important pretrochemical ports of the country is located at the city of Coatzacoalcos. This city is surrounded by the Coatzacoalcos and Tonalá rivers and extends between latitudes 18.06◦ and 18.21◦ and longitudes −94.22◦ and −94.64◦ (Figure 1). The area of study extends over an area about 220 km2. The port of Coatzacoalcos is dominated by the petrochemical sector [37]. Four of the petrochemical complexes located near the city make the city one of the most important oil areas in the world [38]. Coatzacoalcos has been a transportation hub for hundreds of years; it is connected by air, land, sea and railroad to the rest of the world. Currently, the Mexican government is working on a project called the Interoceanic Corridor of the Isthmus of Tehuantepec, which represents the modernization and installation of industrial parks inside the industrial areas of the ports of Coatzacoalcos and Salina Cruz, Mexico [39]. This project will represent economic and population growth within the city. As a result, land use assessments and projections have become priorities to help both state and federal government make decisions for the projected economical growth.

Physiographically, the study area has an average elevation of 10 meters above sea level. The soils found in the region are clayey lateritic Acrisol and Luvisol in high elevations and Gleysol, Cambisol, Vertisol and Nitosol in plains [40]. The vegetation consists of riparian vegetation, swamps and wetlands in floodable areas, mesophilic mountain pine oak forests in high areas and high evergreen forests in hills and acahual areas in abandoned and cultivated pastures. The climate of the region is a warm humid climate with abundant rains in summer. The average annual temperature varies between 24 and 25 °C, while the average annual precipitation varies between 1500 and 2500 mm [41].

**Figure 1.** Location of Coatzacoalcos city in Veracruz, Mexico.

#### *2.2. Data Acquisition and Processing*

The images used in this study were obtained from the Landsat 8 satellite, which captures multispectral images of 30 m resolution sampled with the OLI (Operational Land Imager) sensor. The Landsat 8 satellite also provides a 15 m panchromatic band, allowing for a greater spatial resolution. All images were geometrically corrected and acquired in level 1 (L1). The images can be directly downloaded from the EarthExplorer gateway of the United States Geological Survey (USGS) [17]. Table 1 displays the details of the images used in this study.

**Table 1.** Images of Landsat 8 OLI used in the present study.

The Landsat 8 OLI products used consist of band 2 (0.45–0.51 μm), band 3 (0.53–0.59 μm), band 4 (0.64–0.67 μm), band 5 (0.85–0.88 μm), band 6 (1.57–1.65 μm) and band 7 (2.11– 2.29 μm) [42]. All images were processed using the free software Quantum GIS (QGIS) version 3.14.16. Atmospheric correction was performed through the Semi-Automatic Classification Plugin (SCP) tool based on the Dark Object Subtraction (DOS1) algorithm [43]. The panchromatic band was used to provide higher spatial resolution. This is achieved using Gram–Schmidt Pan-Sharpening to produce a high-resolution color image that improves the training set and the classification process. Google Earth images were acquired according to acquisition dates (Table 1) and geoferenced using QGIS. The closest dates to the acquisition date were selected. Field data included collection of georeferenced points

and their corresponding land use for the last two years of the analyzed periods. Figure 2 shows the methodology used for the study.

**Figure 2.** Flow diagram of the LULC classification methodology implemented in the SCP tool.

The DOS1 atmospheric correction method was selected, as it is the most widely used method for LULC change detection [42]. This method uses the properties of the images. For instance, pixels conforming elements such as shadows, water or forests are considered dark objects when the magnitude of the reflectance is close to zero, namely, when the reflectance is less than or equal to 1%, and all the analyzed pixels are assumed dark elements. This assumption indicates that pixels receiving low values of solar radiation (∼100% shade) registered by the satellite correspond to atmospheric dispersion, which generally is caused by effects of the topography [44]. Once a dark object is found in the image, the minimum reflectance value of the total Digital Number (DN) histogram is assigned to the object. This minimum is then modified by the effects of atmospheric correction [45]. The surface reflectance is calculated using the following expression:

$$\rho\_s = \frac{\pi (L\_\lambda - L\_p) d^2}{ESUN\_\lambda \cos \theta\_s} \tag{1}$$

where *ρs* is the surface reflectance, *d* is the distance to the sun, *ESUNλ* is the mean solar exo-atmospheric irradiance, *Lλ* is the solar spectral radiance to the satellite, *θs* is the zenith angle and *λp* is the path radiance given by [46]

$$L\_p = L\_{\rm min} - L\_{\rm DC1\%} \tag{2}$$

where *Lmin* is the radiance corresponding to a digital count value for which the sum of all the pixels with digital counts lower than or equal to this value is equal to 0.01% of all the pixels from the image considered [46] and *LDO*1% is the radiance of Dark Object assumed to have reflectance value of 0.01.

#### *2.3. NDVI Classification*

Based on the interpretation of the Normalized Difference Vegetation Index (NDVI), the analysis of Google-Earth-extracted images, RGB (red, green, blue) images, panchromatic bands and ground-based georeferenced information, NDVI thresholds were proposed to classify the area of study into four macro-classes during the analysis period (2015–2021), as shown in Table 2.

**Table 2.** Suitable NDVI ranges identified for the LULC macro-classes.

The NDVI thresholds implemented in the proposed classification were selected after an extensive literature review of previous studies where the NDVI was used as the spectral index of diverse land covers [47–52]. The NDVI values were calculated by the equation proposed in [53,54]:

$$NDVI = \frac{\phi\_{nir} - \phi\_{red}}{\phi\_{nir} + \phi\_{red}} \tag{3}$$

where *φnir* is the near-infrared reflectance and *φred* is the red-band reflectance. Band 4 (0.64– 0.67 μm) and band 5 (0.85–0.88 μm) represent *φnir* and *φred*, respectively, in the Landsat products. The NDVI values vary from −1 to 1. Values close to 1 represent vegetation in optimal environmental conditions, whereas low values of NDVI indicate low vegetation density or a different land use. After the macro-class definition, a second classification was performed to refine the LULC characteristics through a more extensive discrimination of pixels. These new classes were defined according to the CORINE land cover inventory proposed by the European Environmental Agency [55]. This classification process required the use of the ancillary data and field data to assign new information to the training set. This assignation was repeated until a suitable spatial distribution was obtained. This new classification consisted of 14 classes, as shown in Table 3.

#### *2.4. Classifier Comparison*

In addition to its social and environmental objectives, this study seeks to evaluate the potential and the drawbacks of the minimum distance classifier [56], the spectral angle mapping classifier [57] and the maximum likelihood classifier [34,35,58] in deriving information on LULC. Although there are more classifications, such as artificial neural networks [34] and parallelepiped classification [56], the comparison was limited to the three selected methods because they are often used to classify LULC. However, limited information exists when the same training input and ancillary data are used to perform the classification process. Here, the comparison was set under the assumption that the same amount of information was available.

**Table 3.** Land use and land cover (LULC) classes and descriptions.

#### 2.4.1. Minimum Distance Classifier

The minimum distance method (MDM) calculates the Euclidean distance *d*(*x*, *y*) between the spectral signatures given in the training data set and the spectral signatures of the image pixels. The spectral distance is calculated through the following expression:

$$d(x, y) = \sqrt{\sum\_{i=1}^{n} (x\_i - y\_i)^2} \tag{4}$$

where *x* is the spectral signature vector of an image, *y* is the spectral signature vector of the training area and *n* is the number of bands of the image. Once the spectral distance is computed for every pixel, the class with the closest spectral signature to the training set is assigned according to the following discrimination function [56]:

$$\mathbf{x} \epsilon \mathbb{C}\_k \iff d(\mathbf{x}, y\_k) \prec d(\mathbf{x}, y\_j) \tag{5}$$

where *Ck* is the land cover macro-class or class, *yk* is the spectral signature of class *k* and *yj* is the spectral signature of class *j*. This equation is valid when *k* = *j*.

#### 2.4.2. Spectral Angle Mapping Classifier

The spectral angle mapping (SAM) algorithm computes the spectral angle between the spectral signatures of the image pixels and the training spectral signatures. The spectral angle *θ* is given by

$$\theta(x, y) = \arccos\left(\frac{\sum\_{i=1}^{n} x\_i y\_i}{\sqrt{\sum\_{i=1}^{n} x\_i^2} \sqrt{\sum\_{i=1}^{n} y\_i^2}}\right) \tag{6}$$

Thus, a pixel resides in the macro-class or class that has the lowest spectral angle, as provided by

$$\mathbf{x} \mathbf{x} \mathbf{C}\_k \Longleftrightarrow \theta(\mathbf{x}, y\_k) < \theta(\mathbf{x}, y\_j) \tag{7}$$

where *k* = *j*.

#### 2.4.3. Maximum Likelihood Classifier

The maximum likelihood classifier (MLC) is based on the probability that a pixel belongs to or is within macro-classes or a particular class [59,60].

The MLC algorithm calculates the weighted distance or probability *D* that an unknown value in the vector *Mp* belongs to one of the macro-classes or classes *Mc*. This likelihood is based on the Bayesian equation [61]:

$$D = \ln\left(a\_c\right) - \frac{\ln\left(|Cov\_c|\right)}{2} - \frac{(M\_p - M\_c)T(Cov\_c - 1)(M\_p - M\_c)}{2} \tag{8}$$

where *D* is the weighted distance or probability, *c* is a particular macro-class or class, *Mp* is the measurement vector of the candidate pixel, *Mc* is the mean vector of the sample of macro-class or class *c*, *ac* is the percentage probability of any pixel belonging to the macro-class or class *c*, *Covc* is the covariance matrix of the pixels in the sampled macro-class or class *c*, |*Covc*| is the determinant of the matrix *Covc*, *Covc*−1 is the inverse of *Covc* and *T* is the transposition function.

#### *2.5. Precision Assessment*

The analysis of the accuracy is an important step for the evaluation of the resulting classification because the users of the information, once the classification is performed, need to know how accurate the result is in order to use the data in their decision making. A minimum level of overall precision for a selected macro-class or class of at least 85%, according to the recommendations found in similar works [49,60,62,63], was proposed in this study. The selected ratio between a training set and validation set was 70/30. This means that 70% of the available information was used for training purposes, whereas 30% was used for validation. Acceptable precision measurements used in this work included producer precision (PA > 85%), user precision (UA > 85%), overall precision (OA > 85%) and the Kappa coefficient (*K*) [49,64,65]. The reference values of the Kappa coefficient proposed by Viera and Garrett [66] are shown in Table 4.

**Table 4.** Possible ranges of map comparison and level of agreement of Kappa coefficient (*K*).

For precision evaluation, a set of verification points are required. The sample should be designed to achieve low standard errors in precision estimates, and this is generally achieved by random selection of points. The number of samples should be calculated by

$$N = \left(\frac{\sum\_{i=0}^{N} (W\_i - S\_i)}{S\_o}\right)^2\tag{9}$$

where *N* is the sample size, *Wi* is the mapped area proportion of the class *i*, *Si* is the standard deviation of the stratum *i* and *So* is the expected standard deviation of overall accuracy, often valued at 0.01 [67–69].

#### *2.6. Validation Point Estimation*

The number of samples calculated with Equation (9) is shown in Table 5. The assumption of this expression claims that randomly generated validation points are proportional to the size of the selected class. In other words, the larger the class is, the more sample points are needed to verify the correct LULC assignation. For instance, as vegetation represented

the larger area of the city and its surroundings, more points were needed to validate the land use classification. This behavior can be seen in Table 5 for the macro-classes in question. Similarly, the number of samples required to validate the classes presents the same behavior; the classes representing mixed forest (vegetation) and bare surfaces (bare soil) needed 79 and 88 samples, respectively, as they are the largest land uses in the study area.

**Table 5.** Average number of samples for macro-class and class validation. Units in pixels.

It is important to mention that pixels representing the sea area were not counted, as they did not change significantly during this period of time and we found no factor for any small increment of decrease that suggested any environmental effect on ties. However, it was taken into consideration to illustrate the applicability of the classification algorithm. On the other hand, rivers and water bodies might be subjected to changes due to water availability or variations in hydrologic processes.

#### **3. Results**

This section is divided into four main subsections: First, the analysis of the land use and its spatial distribution at macro-class level is presented. Second, an analysis at class level is performed, similar to the one conducted in the first section. Third, an assessment of the land use change from 2015 to 2021 is carried out as a study case, and, lastly, a discussion of the possible drivers forcing land use change is established.

#### *3.1. LULC Classifier Assessment and Selection*

The three classification methods were run for the seven-year period proposed in this analysis. Results show a similar behavior for extracting LULC information using the same level of ancillary data. This condition allowed a fair comparison between the classification algorithms. Figure 3 shows the macro-class classification for the year 2017, which was the year which presented higher discrepancy among LULC classification methods. Graphically, one can observe that algorithms performed comparably. Significant variations were found within the clusters sharing bare soil and and built-up zones, whereas a more accurate definition was found for those composed of built-up vegetation and bare soil vegetation. These variations were attributed mostly to the spectral signature dispersion and departure within the pixels defining each class and the nearby areas. Bare soil and vegetation areas, being those mostly distributed over the west and east parts of the city, respectively, presented similar spatial distributions.

Numerically, all the classification methods presented good behavior for both overall precision and Kappa values, all being above 85% and 0.85, respectively (Table 6). The spectral angle mapping (SAM) was the method that showed the smallest overall precision and Kappa values, 89% and 0.86, respectively. The highest accuracy was obtained using the maximum likelihood classifier (MLC), which presented overall precision above 90% and Kappa coefficients above 0.90 for all the years in the study period. It was observed that the MLC required less field data to obtain accuracy compared to the other classification methods.

**Figure 3.** Maps of the macro-class-based classification for three classification methods for 2017: Top: minimum distance. Middle: spectral angle mapping. Bottom: maximum likelihood. Units: meters. Projection: WGS84 / UTM Zone 15N.

**Table 6.** Precision assessment of the macro-class-based classification for 2017 under three classification methods.

The three classification methods showed the advantages of a perfect decision boundary to distinguish the macro-classes and a consistent mathematical expression based on the decision boundary for further classifications. However, these methods might overtrain the decision tree, as the training set needs several examples to cover all the possible cases within a specific class. Lastly, training tends to require significant computational time to be effective. As the MLC presented the highest level of accuracy under the same training set, it was selected as the main classification method for this study.

#### *3.2. Macro-Class-Based LULC Classification*

The classification based on macro-classes represented a course scheme to generate four of the main land uses found in Coatzacoalcos. These macro-classes are water, built-up, vegetation and bare soil. The spatial distribution of them can be observed in Figure 4. Graphically, one can see that the terrestrial surface is proportionally divided into three land uses without considering water. Most of the urbanization or built-up areas are in the north and south of the city near the river, as they were sites of the first settlers since the foundation of the city. However, the south potential flood areas and their development

have been delayed, and small changes can be observed. It can also be seen that both north and south areas contain significant patches of vegetation, which, including the creation of green areas, is a good practice in urbanization development. However, urbanization in the west area of the city seemed to increment over the study period, but no vegetation buffers were observed in those zones. This indicates the absence of good practices in urbanization and government decisions in terms of territorial planning as well as the economic, social, cultural and environmental unconsciousness of new developers [70]. These zones may provoke a higher temperature sensation or heat islands, which might translate into health problems for the inhabitants [71]. Additionally, bare soil areas seemed to increase over time and were more prominent in the west area of the study. As seen in Figure 4, bare soil replaced areas of vegetation and also increased close to developed areas. These changes can be attributed to water availability during the growing season and afforestation during the development of urbanization.

**Figure 4.** Maps of the macro-class-based classification for the study period (2015-2021). Units: meters. Projection: WGS84 / UTM Zone 15N.

In terms of vegetation, the east part of the city seemed to remain unaltered, except for zones near Allende Village in the northeastern and southeastern parts of the city, where the industries and petrochemical complexes are located and trigger the soil degradation. Numerically, Table 7 supports the claim observed on the above maps. Pixels representing water show small variations attributed to possible changes in the surface water balance, but a more refined classification is needed to understand how these variations occurred. The maximum percentage of water land use was 28.58% in 2017, whereas its minimum was 28.43% in 2019. Urbanization or man-made construction increased from 19.88% in 2015 to 20.21% in 2021. This increment can be explained by the trend observed in Figure 5, which shows the last six 5-year censuses. Coatzacoalcos had a mean population growth rate from 1995 to 2015 equal to 2.68%. However, we can observe that a rate equal −0.54% was shown in 2020. This means that population growth decreased in the last census. This variation can be illustrated with the reported built-up information, where man-made construction showed similar increments on a year-to-year basis. Nonetheless, the growth from 2019 to 2020 was its minimum, and no change was seen in 2021. This decrease in population and the variation within estimated in this study have socio-economic drivers. The city of Coatzalcoalcos was the second most dangerous city in 2019 [72]. This fact

provoked serious economic and social issues due to extortion, racketeering and kidnapping. People moved to nearby cities to feel safe, and big companies closed their services; as a result, unemployment rate grew and investments in the city slowed down, which, in turn, decelerated urbanization and industry development.

**Table 7.** Percentage area estimated for the macro-class-based classification.

**Figure 5.** Last six five-year censuses in Coatzacoalcos, Veracruz. Source: National Institute of Statistics and Geography (INEGI) [73].

Bare soil also seems to increase visually in Figure 4 and is confirmed in Table 7. Bare soil variations are linked to a decrease in vegetation. These two variables are highly dependent on water availability, droughts and weather fluctuations. We can see that, in 2015, the area of study was 25.67% of bare soil, while, in 2021, it increased up to 26.37%. On the other hand, vegetation presented the inverse behavior, being 26% and 24.96% in 2015 and 2021, respectively. Analyzing the water availability, which is proportional to the precipitation, one can see in Figure 6 that rainfall tended to fluctuate from a minimum of 1107.6 mm in 2015 to a maximum of 1730.90 mm in 2017. This fluctuation, in general, tells us that, of the last three years of the period of analysis studied here, precipitation reduced drastically in 2019, which can explain the cause of the bare soil increment.

Additionally, Figure 7 shows that although minimum temperature tended to be steady from 2013 to 2021, mean temperature and maximum temperature increased during the study period. For instance, mean temperature was 26.60 ◦C in 2018, whereas the maximum temperature increased from 28.20 to 30.20 ◦C in 2018 and 2020, respectively. Increments in temperature increases soil evaporation and plant transpiration. If the latter is limited due to lack of water, water stress occurs in plants and they do not reach maturity, which might explain changes in pixel color and the move from vegetation to bare soil. It can be seen that the use of remote sensing for LULC evolution can be related and explained with inland information. This not only promotes the use of remote sensing but also validates its implementation. All the macro-class-based classification showed average user accuracy (UA) and producer accuracy (PA) higher than 90% (Table 8).

**Figure 6.** Annual precipitation in Coatzacoalcos, Veracruz. Source: Comision Nacional del Agua (CONAGUA) [74].

**Figure 7.** Annual precipitation in Coatzacoalcos, Veracruz. Source: Comision Nacional del Agua (CONAGUA) [74].

**Table 8.** Overall precision and Kappa coefficient (*K*) for macro-class-based classification.

This means that both producers and users of these maps can rely on this classification with at least 90% confidence. These values are comparable to the ones found in [47,48]. The mean overall precision for the study period is 94.81%. The Kappa coefficients for all the studied years are located in an excellent range, as they all are higher than 0.81, according to Viera and Garret [66]. Although this classification was appropriate for the proposed macro-classes, a more refined algorithm was implemented to understand more about the evolution of the LULC of Coatzacoalcos, as it is necessary for decision making purposes.

#### *3.3. Class-Based LULC Classification*

A class-based LULC classification was conducted to identify a more discrete evolution of the land use within the study period. This identification will help prioritize the location of areas that require ecological attention, the inclusion of best management practices (BMP) for water and soil conservation as well as a more appropriate location of the upcoming urbanization due to the Interoceanic Corridor project execution. At this level, we identified that pixels representing the sea did not change significantly. This behavior can be observed in Figure 8 and Table 9, in which the percentage area representing the sea is about 20.1% for the entire period. However, water courses and water bodies seemed to change over time. Although these changes might not be significant, these changes can be confusing. We observed in Figure 6 that the annual precipitation throughout the study period decreased. As a result, bare soil area increased due to lack of water. One could assume that water bodies and water courses cannot increase in area. Nonetheless, this claim might not be entirely true, as increments of bare soil decrease infiltration and enhance the overland flow of the areas draining to the closest water courses. For that reason, water courses and water bodies in western areas increased due to increment of overland runoff in areas classified into bare soil with different vegetation densities (i.e., bare surfaces, sparse vegetation or grassland) and some over-flooded wetlands (i.e., swamps).

Man-made construction was divided into industry, residential, commercial and roads classes. Industry shows a constant percentage from 2016 to 2021. The only change occurred in 2015. The industrial area called Etileno XXI, the largest petrochemical complex in Latin America, concluded 99.2% of its construction in 2015 [75]. This, once again, confirms the accuracy of the generated map. Residence land use increased from 11.85% in 2015 to 12.24% in 2019 and maintained a steady value in 2021 (Table 9). This, once again, matches the period of violence described above that eventually provoked a decrease in population, as seen in Figure 5, which means that residence land use remained as it was in the previous years. Commercial areas showed fluctuations due to the city's economical variation. It can be observed that, in 2018, commercial areas reached their maximum of 0.55%. However, the value decreased at the end of the study period (0.43%). A small increment was observed in 2021, when commercial areas increased to 0.50%. The city experienced transitions from high commercial zones to abandoned places due to the socio-economical problems previously described. These areas transformed either to low density vegetation areas or abandoned buildings. Lastly, in this class, roads were the most difficult to identify due to the Landsat satellite image resolution equaling 30 m. Roads close to the coast line and main avenues crossing from north to south and west to east were detected easily. However, narrow streets were not characterized as roads and they were either characterized as residence, commercial or industry classes. For that reason, although roads showed increments due to increase in population and urbanization development, no claim is expressed in order to avoid confusion in this class.

Vegetation plays an important role in our natural ecosystem and also holds up the biosphere in various ways. Vegetation helps to regulate the flow of numerous biogeochemical cycles, most importantly those of nitrogen, carbon and water. It also contributes to the local and global energy balances. In this study, the classes within vegetation included mixed forest, shrubland, wetland and dune. Figure 8 shows that mixed forest is the largest extension of the study area. One can see that the eastern and southwestern areas are dominated by this class, especially for those areas where no urbanization is present. Mixed forest occupied about 18% of city and its metropolitan area for the analyzed period, reaching its minimum in 2019 when the precipitation reached its lowest value (Figure 6). As previously mentioned, at this point, one can confirm that vegetation established in the urban areas were mostly mixed forest, having more pixels where the city was initially developed than where the more recent residential areas in the west of the city have been developed. Shrublands, in general, showed an increment over time. They reached their maximum percentage area, equal to 5.31%, in 2020 with some fluctuations in 2017, a year

preceded by annual precipitation below 1500 mm, which represents the driest year of the analyzed period.

**Figure 8.** Maps of the class-based classification for the study period (2015–2021). Units: meters. Projection: WGS84 / UTM Zone 15N.

**Table 9.** Percentage area estimated for the class-based classification.

One of the most representative classes in this classification is wetland (i.e, swamps) because it represents multiple biological, economic and social values. Swamps provide services to ecological well-being, such as groundwater recharge, water purification, microclimate regulation, food resources, biodiversity and carbon storage [76–78]. In the last decades, in Coatzacoalcos, urbanization has degraded swamps indiscriminately through industrial development or residential areas. These developments followed the erroneous idea that swamps were areas with dangerous species, such as snakes, alligators, mosquitoes, etc., which represented risks to the nearby areas [79]. This study evidences how wetland or swamp degradation continues. Visually, swamps located south and southeast of the city have mostly transitioned to some degree of vegetation density, either from water stress or landfill. In 2015, swamps represented about 2% of the study area, whereas, in 2021, this fraction reached 0.92%. This fact makes this study a good indicator to prevent and preserve swamps and wetlands within Coatzacoalcos and its metropolitan area due to the important ecological services that they represent.

Dunes, in Figure 8, are located along the coastline of the study area. In addition, there are some banks utilized by the industry (sandblasting) in the east area across the

Coatzacoalcos river. One can see that dunes were replaced mainly by residential areas in the northwest part of the city. In 2015, dunes occupied almost 3% of the area in question. However, this area declined to 1.96% in 2021. The most significant evolution of urbanization occurred from 2017 to 2019. Additionally, the western coastline presented a transition from dunes to bare surfaces because that area is naturally preserved and the absence of tourism has improved the growth of native vegetation. Lastly, bare surfaces are the dominant land use along with mixed forest and residential areas (Figure 8). This land use increased from 19.31 to 23.19% through the study period in Table 9. Visually, most of the transitions were from sparse vegetation and high grassland to bare surfaces. This might have occurred because of the decrease in infiltration and precipitation previously mentioned and explored in Figures 6 and 7. Sparse vegetation decreased from 2.79% to 0.79% and increased to 0.84% in 2021, whereas grasslands reduced from 1.51% to 0.42%.

Table A1 shows the UA, PA and *K* values from each class throughout the analyzed period. User accuracy and producer accuracy show remarkable performance, with values within 90–100% and 80.48–100%, respectively. These individual accuracies, PA and UA, represent how well referenced pixels of the ground cover class are classified and the probability that a pixel classified into a given category actually represents that category on the ground, respectively. As expected, water and its classes showed the best performance, as it is the macro-class that presented the least variation while the other classes presented more significant variability. The Kappa coefficient shown for all classes was located in the range of excellent, according to the suggested values by Viera and Garret [66]. One can expect from these results that all the classes provide at least 90% confidence to the users of this information. The mean UA and PA values are presented in Table 10. Both UA and PA also are greater than 90%, as shown in each of the selected classes. The overall precision of the maps is also above 90%, which indicates that more than 90% of the reference pixels were correctly classified, and the *K* values validate the quality of this study, as they are all above 0.95.

**Table 10.** Overall precision and Kappa coefficient (*K*) for class-based classification.

#### *3.4. Land Use and Land Cover Change through LULC Transition Matrix*

The last part of the analysis of these results includes the generation of the LULC transition matrix (Table A2), which indicates the transitions of each of the classes with respect to each other, for instance, how much area of the dunes converted to low vegetation density areas [69]. This matrix summarizes the changes already validated by the precision matrix but in terms of actual surface area. One disadvantage of this analysis is that it does not consider what happened throughout the two isolated years. However, it brings up an excellent tool for studies where LULC changes want to be predicted for future scenarios along with the possible drivers forcing those changes. The main diagonal contains the reference areas of the classes between the analyzed years. Columns named loss and gain represent the area lost or gained, respectively, for a particular class. Total gain or loss are calculated by adding up columns or rows of the reference area for each class. Particularly, this study selected the ends of the period, the years 2015 and 2021. The analysis of this matrix is straightforward. For instance, the water bodies gained 0.24 km2 but lost 0.0297 km2. Gains were associated with transitions of wetland (0.22 km2), shrubland (0.0063 km2), mixed forest (0.0009 km2) and bare surfaces (0.0081 km2) to water bodies. On the other hand, losses are due to a mixture of transitions, including sparse vegetation (0.009 km2), bare surfaces (0.009 km2) and wetlands (0.00117 km2). All these transitions occurred between the two selected classifications.

For the sake of simplicity, only the most significant changes are discussed here. Residential land use grew only 0.88 km2 during the last six years, which reflects a very small growth in comparison with that of industrial cities in Mexico, which has been characterized as about 5% per year [80], while Coatzacoalcos only grew 1% per year. Commercial areas reduced by 0.16 km2, resulting in abandoned areas due to the previous mentioned socio-economic issues. One of the prominent changes was in areas of mixed forest, which gained 0.52 km2 and lost 0.43 km2, indicating reforestation practices and more sustainable urbanization development in some new residential, industrial and commercial areas but some activities of deforestation. Swamps, on the other hand, lost 2.09 km2. This loss warns us of the possible future loss of ecological services provided by wetlands and their habitat, which are essential for biochemical processes and water purification because some of the non-point pollution in the city is contained by them. Lastly, vegetation densities fluctuated the most among them, namely, grassland and sparse vegetation density areas transitioned to bare surfaces. The latter gained 9.12 km2, whereas the former two lost 2.48 and 4.66 km2, respectively, which represents 80% of the bare surfaces gain.

#### **4. Discussion**

LULC changes measure the transitions of different land uses in complex interactions between humans and the physical environment [81,82]. Analysing LULC changes helps facilitate sustainable land use planning to protect and conserve the natural habitat and resources. The present study applied available remote sensing technology to classified course land uses, similarly to what has been presented in several studies [23,81,83–86]. The macro-classes identified were water, built-up, vegetation and bare soil. This first division allowed us to discriminate NDVI spectral indexes to improve a latter classification. Fractions of the area of study divided into macro-classes help us to observe the evolution of the city and its metropolitan area development and to explore the possible drivers forcing the changes in this initial classification. Broadly, some socio-economic factors, climate and topography were proposed as possible drivers. These drivers have also been identified as important factors for land use evolution [23,87–89]. This paper identified that security, precipitation, climate and topography were the main drivers causing LULC changes. Measurements such as producer accuracy (PA) and user accuracy (UA) showed high confidence, since they were higher than 90% for most of the macro-classes. These values are comparable and even superior to previous studies found in literature [49,84,89,90]. One can observe that the maximum likelihood classifier (MLC) is a suitable classification method to obtain acceptable results, as cited by several scientific contributions [34,35,58]. After the analysis of the macro-classes, 14 classes were exhaustively found to analyze and identify the land use in detail. Among those classes, some discussions are driven. Industry has experienced no change since the last petrochemical complex opened in 2015. Commercial areas have declined and residential zones present a slow increment due to the abovementioned socio-economic drivers, which served to confirm the proposed hypothesis. Mixed forest and bare surfaces with low vegetation density tended to increase in surface area because reforestation programs and vegetation implemented in the coastline by the government have been a priority, as wind erosion has provoked much damage to the city's infrastructure [91]. Swamps (i.e., wetlands) also showed an important decrease in spatial contribution, losing about 2 km2. This, in turn, might become an important environmental issue, since swamps are seen as important regulators of water pollution, carbon storage and habitat of species [92–95]. For that reason, actions need to be considered to avoid the continuous degradation of local wetlands and swamps. Additionally, it was observed that about 80% of the bare surface areas came from sparse vegetation and grassland areas. Associated changes in precipitation and high temperatures throughout the assessment period were responsible for these changes, as shown in results section. As a result of increments of bare surfaces, infiltration decreases and overland flow is exacerbated because soil properties such density, porosity and hydraulic conductivity depend on the level of established vegetation [96,97]. Lastly, a LULC transition matrix reflected the changes in

surface area for each of the classes identified in this study. This is the baseline for future research that involves local problems in the city of Coatzacoalcos. A subsequent prediction of the land use using more extended spatial information of the drivers forcing the changes observed here will be proposed as an extension of this work. Additionally, the city is facing an issue with the relocation of solid residues, which naturally correlated with the actual land use. As a result, this study and methodology using higher-resolution imagery (i.e., sentinel satellite) help to study possible and suitable landfill sites.

#### **5. Conclusions**

This paper determined land use and land cover (LULC) over the period 2015–2021 for the city of Coatzacoalcos and its metropolitan area, located in the state of Veracruz, Mexico. Based on images from Landsat 8, MLC, Geographic Information Systems (GIS), NVDI spectral index and field data, the annual land use variability was produced up-todate as reliable information for decision making and ecosystem preservation during the execution of the ongoing project called the Interoceanic Corridor. This project represents Mexico's narrowest stretch between the Pacific and Atlantic oceans and the expansion and modernization of Coatzacoalcos and Salina Cruz ports.

The objective of this study was to provide the most recent information on land use spatial distribution that Coatzacoalcos experienced in the last six years and to improve the current available course resolution maps. The satisfactory results can be summarized in four main aspects: (1) built-up areas, including industry, residential and commercial areas, have slowed down their growth due to socio-economical drivers such as security and null monetary investments; (2) vegetation such as mixed forest and low density vegetation (bare surfaces, sparse vegetation and grassland) has been sustained and increased over time due to reforestation or migration from other classes; (3) swamps experienced considerable degradation over the past five years and (4) high and medium vegetation densities have transformed mostly to low vegetation densities due to climate drives such as low precipitation and possible high soil evaporation, which might also increase the overland flow for those areas.

Lastly, this study demonstrated the use of free available Landsat data and their processing by open source tools. It provided an accurate approach to mapping and assessing LULC changes over time. This methodology can be applied similarly for longer periods of time and other satellite products and contributes to improving the number of applications of remote sensing and research in Mexico and other developing countries.

**Author Contributions:** Conceptualization, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; methodology, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; validation, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; formal analysis, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; investigation, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; resources, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; data curation, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P.; writing—original draft preparation, J.D.R.-A., E.S.G.-L., J.E.T.-R., C.G.-R. and G.A.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

**Acknowledgments:** The authors would like to thank the anonymous reviewers for their valuable comments and feedback on this article.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Appendix A**

**Gain**

0.0189

 0.5238

 0.2439

 0.0486

 0.882

 0.1584

 0.3159

 0.522

 0.6948

 0.0306

 0.0198

 9.1179

 0.396

 0.1062

 218.1617

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## *Article* **Three-Dimensional Convolutional Neural Network on Multi-Temporal Synthetic Aperture Radar Images for Urban Flood Potential Mapping in Jakarta**

**Indra Riyanto 1, Mia Rizkinia 1, Rahmat Arief 2 and Dodi Sudiana 1,\***

**Abstract:** Flooding in urban areas is counted as a significant disaster that must be correctly mitigated due to the huge amount of affected people, material losses, hampered economic activity, and floodrelated diseases. One of the technologies available for disaster mitigation and prevention is satellites providing image data on previously flooded areas. In most cases, floods occur in conjunction with heavy rain. Thus, from a satellite's optical sensor, the flood area is mostly covered with clouds which indicates ineffective observation. One solution to this problem is to use Synthetic Aperture Radar (SAR) sensors by observing backscatter differences before and after flood events. This research proposes mapping the flood-prone areas using machine learning to classify the areas using the 3D CNN method. The method was applied on a combination of co-polarized and cross-polarized SAR multi-temporal image datasets covering Jakarta City and the coastal area of Bekasi Regency. Testing with multiple combinations of training/testing data proportion split and a different number of epochs gave the optimum performance at an 80/20 split with 150 epochs achieving an overall accuracy of 0.71 after training in 283 min.

**Keywords:** urban flood; Sentinel-1a; Synthetic Aperture Radar (SAR); 3D Convolutional Neural Network; multi-temporal data

#### **1. Introduction**

Flooding is one of the most detrimental disasters, especially in cities such as Jakarta, because it affects a large number of residents in ways such as material losses resulting from damaged properties due to flood inundation and diseases caused by degraded sanitation in the flooded area. A major flood in Jakarta results in 8.7 trillion IDR or 625 million USD of losses and recovery efforts [1]. At present, most of the flood mapping in Indonesia has not fully utilized satellite spatial data because it still relies on data reported by the local government in the form of numerical data [2]. The visualization of the flood map is based on tabulated data in the area map that does not represent the actual conditions, resulting in a discrepancy between the reported flood area and the actual area. This difference will affect the handling of floods, such as calculating the impact of damage, the number of residents affected by the flood, and the inefficient distribution of aid. Problems that arise due to limited spatial information regarding floods can be solved by using multi-sensor remote sensing satellite data. Many technologies have been developed to predict, prevent and mitigate flood disasters more accurately, including remote sensing technology using images obtained from airborne and spaceborne platforms [3–5]. The earlier and most common form of remote sensing is optical photography, with overhead images providing information on the affected area.

**Citation:** Riyanto, I.; Rizkinia, M.; Arief, R.; Sudiana, D. Three-Dimensional Convolutional Neural Network on Multi-Temporal Synthetic Aperture Radar Images for Urban Flood Potential Mapping in Jakarta. *Appl. Sci.* **2022**, *12*, 1679. https:// doi.org/10.3390/app12031679

Academic Editors: Anselme Muzirafuti and Dimitrios S. Paraforos

Received: 20 December 2021 Accepted: 31 January 2022 Published: 6 February 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

When a disaster occurs, urban floods usually coincide with rain, so when observed using optical sensors on remote sensing satellites, the area is covered with clouds. With this occlusion, satellite optical sensors are not effectively used for flood observation. One solution to observing floods in cloud-covered areas is to use Synthetic Aperture Radar (SAR) sensors such as Sentinel-1, ALOS PALSAR, TerraSAR-X, and other radar sensors. The image produced by SAR is a monochromatic image containing reflectance information from the observation area by observing the difference in the backscatter before and after the incident to identify the flooded area [6].

Wide-scale Earth monitoring satellites begin with the Landsat program (Land Satellite) to monitor the Earth's surface. At present, many optical satellite systems operate at high resolution. The most widely used images are from QuickBird, SPOT-5, and Worldview Series. Despite having the ability to detect very sharp objects, optical satellite systems have the potential to be unable to detect objects on the Earth's surface due to cloud cover. Until recently, SAR data processing was mostly used for rural areas rather than in urban areas. For urban areas, SAR data has its problems, namely speckle noise, because, in urban areas, many buildings cause radar waves to experience much scattering, and the reading of the reflected waves is disturbed by multipath interference [7,8]. The double-bounce characteristic of radar signals caused by buildings is a challenge related to its contribution to SAR image speckle noise. However, it can be used to detect the presence of buildings and distinguish them from other surfaces such as soil, vegetation, and water [9–11]. The solution to detect the presence of floods through SAR images is multi-temporal filtering, which is filtering based on changes in the backscattering characteristics of SAR images taken at different times. The method used to identify and distinguish the occurrence of flood from SAR data is generally divided into two groups, namely polarization and interference. The polarization method detects the presence of water, based on changes in backscatter polarization caused by specular reflections from the water surface [10,12–14], while the interference method detects water based on changes of coherence due to changes in the spatial distribution pattern of the object that produces the backscatter [10,15–17].

Several studies related to floods in settlements used SAR and Light Detection and Ranging (LiDAR) data for small towns located on one side of a river and areas with homogeneous slopes [18–20]. Another study used the Decision Tree Classifier (DTC) to differentiate surface types based on light reflection [21,22], a condition that is difficult to meet because flooding occurs with cloudy skies. A seasonal disaster mapping system based on field observations is practical for generating initial data [23,24] but impractical for periodic events. Research on radar satellites for disaster management to date still has elevation resolution in the range of2m[6,7,25,26]. For Jakarta, especially in the city center to the northern coast, which often experiences floods, this is not appropriate because the flood depth is less than2m[27].

To map floods, previous methods were initially dominated by thresholding [6,11,13,14,28], Probability Density Function (PDF) [9,10], and more recently, Logistic Regression [29] and the Storm Water Management Model (SWMM) [30]. More specifically, in remote sensing image segmentation applications, some researchers use the Normalized Difference Vegetation Index (NDVI) to classify vegetation, classify water levels with the Normalized Difference Water Index (NDWI), and the classification of floods using Normalized Difference Flood Index (NDFI) [31,32]. Other researchers detected changes in land surface with radar images using the principle of interferometry to find coherence between images [15,16,33]. Research in the last three years has led to the use of Machine Learning (ML) in order to segment and classify increasingly complex datasets, such as the Adaptive Neuro-fuzzy Inference System (ANFIS) [34], Support Vector Machine (SVM) [35,36], Convolutional Neural Network (CNN) [36–38], and more recently with various Swarm Intelligence (SI) variants such as Particle Swarm Optimization (PSO) [39,40]. Sameen and Pradhan used a Residual Neural Network to detect the potential for landslides, where this method is intended to detect changes in soil texture as the initiation of landslides [38]. The CNN model with this residual block is used to process LiDAR data. The neurons on CNN are activated using the Rectified

Linear Unit (ReLU). This study proposes three-dimensional CNN in mapping flood areas to filter and weigh neurons and map the potential flood areas in urban areas with better accuracy and fewer number of images.

Yu Li et al. proposed an Active Self Learning method on CNN to detect floods in urban areas from the SAR image ensemble [37]. The dataset used is four TerraSAR-X images of HH polarization with the composition of one pre-event image, one co-event image, and two post-event images. Linyi Li et al. proposed a high-resolution urban flood mapping method (Super-Resolution Mapping of Urban Flood, SMUF) with the fusion of the Support Vector Machine and General Regression Neural Network (FSVM-GRNN) [35]. Because the urban flood area in the observation area is not very dense, the accuracy of this FSVM-GRNN is 80.2%.

Shen et al. proposed a machine learning process to make corrections to the mapping of flood inundation areas in near-real-time (NRT) using SAR, where the observation area is an open area without many obstacles on the surface [41]. At the time of segmentation, there are difficulties in classifying areas that are flooded with areas that have surface reflection properties similar to those of the water surface. ML is performed to correct speckle noise and another scattering, which can interfere with data reading and classification. The filtering method is used in most SAR image processing but its effect is to reduce the effective resolution and change the signal statistics and cannot completely remove noise. To overcome this, Shen et al. used the Logistic Binary Classifier (LBC) in a correction step to practice detecting the presence of water in the pixels contained in the water bodies and the surrounding buffer areas.

The objective of this work is to investigate the mapping of flood potential in Jakarta and nearby coastal areas using three-dimensional CNN on co-polarized (VV) and crosspolarized (VH) Sentinel-1a SAR images. A three-dimensional classification combines the two-dimensional image and one-dimension multi-temporal processes into a single convolution. The images are then pre-processed into grayscale images to be converted into a vector data format. The 2 January 2020 images were also sampled as flood and non-flood target sub-images, along with the corresponding locations from other images, to form the multi-temporal value changes of the flooded locations along with the consistency of the non-flooded locations. The CNN training is performed with training/test percentage values of 70/30; 80/20; and 90/10 with varying epochs between 100 and 160 iterations to obtain the best combination with the highest accuracy and the shortest processing time.

#### **2. Materials and Methods**

#### *2.1. Location and Data*

A radar image is generated from the reflection of active microwaves emitted from the radar vehicle (airplane or satellite). The transmitter in a radar system is an antenna array consisting of a waveguide and emitting a very narrow beam of microwave waves. The radar sensor moves along a trajectory, and the area highlighted by the radar moves (known as the footprint) along the surface being swept to form an image. A digital radar image consists of many pixel dots representing the backscatter or backscatter of a point on the surface. Figure 1 shows an example of a SAR image from 2 January 2020 that is free from cloud cover with the bright dots showing high backscattering while the dark dots represent low backscattering, while Figure 2 is the optical image from the same date showing cloud cover.

The radar system generally has a wavelength undisturbed by interference from water particles and water vapor in the air (clouds and rain). Because they are not dependent on illumination (irradiation) from the sun or other sources, the radar system can function day and night and in all weather. Synthetic Aperture Radar (SAR) works by detecting the phase-change of reflected signal caused by the movement of the platform to obtain the surface image with good resolution (i.e., visually discernible). The SAR system is generally divided into two wavelengths, namely short (C-band and X-band) and long (L-band and P-band) waves. Early SAR satellite systems use a single platform such as Radarsat. Currently, the most commonly used are satellite constellation systems such as TerraSAR-X and TanDEM-X pair, four-satellites Cosmo-SkyMed in X-band, three-satellites Radarsat Constellation Mission, and Sentinel 1 satellite pair, which give shorter revisit time and higher temporal resolution [20,41–43]. Figure 3 shows the backscatter mechanism of shortwave radar (illustrated with black arrows) and long (illustrated arrows in blue) on various surfaces under normal conditions and during a flood. On the grass surface, there are surface reflections at both wavelengths due to the relative roughness of the surface. For short waves, the scattering is due to the thickness of the grass, while long waves can penetrate deeper [44].

**Figure 1.** SAR image of Jakarta on 2 January 2020.

**Figure 2.** An optical image from Landsat-8 of Jakarta on 2 January 2020.