**About the Editor**

### **Radu-Daniel Pintilii**

Radu-Daniel Pintilii (Associate Professor) is a geographer who achieved a Ph.D. in the field of Geography from the University of Bucharest in 2008. He is an employee of the Faculty of Geography (Department of Human and Economic Geography) and a Ph.D. advisor of two Doctoral Schools (The Faculty of Geography and the Interdisciplinary School of Doctoral Studies) in the same University. He is also the Scientific Manager of the Research Center for Integrated Analysis and Territorial Management (CAIMT), with main interests in Natural Resources, Forestry, GIS, Spatial Planning and Statistics. He has attended more than 50 International Scientific Conferences and published 45 ISI-indexed Articles, scientific reviewer of more prestigious journals and the H-index, according to WoS, is 13. He developed important collaborations with prestigious European higher education institutions, via the CIVIS Network (Austria, Belgium, France, Germany, Great Britain, Greece, Italy, Spain, Sweden and Switzerland).

## *Editorial* **Forest Recreation and Landscape Protection**

**Radu-Daniel Pintilii 1,2,3**

### **1. Introduction**

Forests, which are viewed as an important natural resource for humanity, have been constantly threatened with degradation or even extinction since the dawn of the modern period. Landscape protection, especially forest conservation, represents an important research topic of the last century. While forest recreation focuses on forests as the main destinations for public recreational activities, landscape protection refers to the conservation, development, and restoration of natural beauties. Urban and peri-urban forests have become places for recreational activities of populations living in big cities all over the world. The lack of green spaces is always felt by modern society within its current built environments. In recent years, demands for forest recreation activities have been increasing both in volume and in diversity. In some areas, predominantly rural ones, the forest acts as a site for the seasonal collection of berries and mushrooms (non-timber forest products, NTFPs), which are integrated into rural life as key social activities. Additionally, landscape protection measures have been imposed during the last two or three decades due to the immense, adverse human effects on all the landscape components. Landscape protection is imperatively required due to the development of human settlements over the last few decades, which may be viewed as a forceful and constant expansion. It is imposed not only for scientific and ecologic reasons but also for social and cultural ones, and this is why the driving mechanisms for managing forests in the future should be better understood.

### **2. The Methodology Used in the Papers of the Special Issue** *2.1. Study Areas*

The origins of the studies presented the papers of this Special Issue cover a large selection of countries (Figure 1), which encompass the three continents (Asia, Europe, and Oceania) and have different natural and socio-economic conditions, as reflected in their climates and microclimates, vegetation (a repartition of plant species), air quality (pollution), and different profiles of economic activities (agricultural, industrial, or services, including tourism and recreational activities). There are two papers that focus on large areas, encompassing the countries around the Mediterranean Sea [1,2]. The other papers cover smaller areas, such as islands (Sulawesi in Indonesia [3], New Zealand in Oceania [4]), countries (China, South Korea, and Taiwan [5,6]), or even smaller areas, such as mountains (Moravian Karst Plateau in the Czech Republic and Plitvice Lakes National Park in Croatia) [7,8], or cities (Warsaw in Poland and Xi'an in China) [9,10].

**Citation:** Pintilii, R.-D. Forest Recreation and Landscape Protection. *Forests* **2022**, *13*, 1440. https:// doi.org/10.3390/f13091440

Received: 5 September 2022 Accepted: 7 September 2022 Published: 8 September 2022

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**Copyright:** © 2022 by the author. 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.** Study areas of the papers in this Special Issue (left: paper number, according to the refer‐ ences; middle: study area; and right: continents). Data source: [1–10]. **Figure 1.** Study areas of the papers in this Special Issue (left: paper number, according to the references; middle: study area; and right: continents). Data source: [1–10].

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

Authors used data obtained from diverse sources, with the data providers including official authorities [3,9,10], satellite images or GIS [3], other platforms [1,6,8], and ques‐ tionnaires [5,4]. The methodologies applied are diverse and complex, from simple moni‐ toring and descriptive statistics [1,2,7] to advanced models (Shapiro–Wilk and Kappa tests [1,3], social media content, cluster analysis, natural language processing [8], and senti‐ ment analysis [8,6]). The processing of the data was performed using specialized software, including R Software [1,10], SPSS [4,5,9], KH Coder Software [8], and GIS [1–10]. Authors used data obtained from diverse sources, with the data providers including official authorities [3,9,10], satellite images or GIS [3], other platforms [1,6,8], and questionnaires [4,5]. The methodologies applied are diverse and complex, from simple monitoring and descriptive statistics [1,2,7] to advanced models (Shapiro–Wilk and Kappa tests [1,3], social media content, cluster analysis, natural language processing [8], and sentiment analysis [6,8]). The processing of the data was performed using specialized software, including R Software [1,10], SPSS [4,5,9], KH Coder Software [8], and GIS [1–10].

#### **3. Summary of the Papers in this Special Issue (Forest Recreation and Landscape Protection) 3. Summary of the Papers in This Special Issue (Forest Recreation andLandscape Protection)**

The papers published in this Special Issue, "Forest Recreation and Landscape Protec‐ tion", cover a wide range of topics that can be divided into two main categories: studies focusing on forest recreation and those focusing on landscape protection. The papers in‐ vestigating forest recreation refer to the tourism activities in forests, which are regarded as leisure activities (e.g., mountain biking). The others, focusing on landscape protection, concentrate on the impact of forest (in)stability on human health (air quality) and, espe‐ cially, the lack of forests and forest degradation as direct consequences of human eco‐ nomic activity. The papers published in this Special Issue, "Forest Recreation and Landscape Protection", cover a wide range of topics that can be divided into two main categories: studies focusing on forest recreation and those focusing on landscape protection. The papers investigating forest recreation refer to the tourism activities in forests, which are regarded as leisure activities (e.g., mountain biking). The others, focusing on landscape protection, concentrate on the impact of forest (in)stability on human health (air quality) and, especially, the lack of forests and forest degradation as direct consequences of human economic activity.

In the first paper, Ciobotaru et al. [1] present the environmental issues related to for‐ est loss in one of the important areas of the Earth, the Mediterranean region. The paper analyzes the tree cover loss and cumulative tree cover loss in each country, acting as an important index of the tendency towards tree cover loss in each country, over a total area In the first paper, Ciobotaru et al. [1] present the environmental issues related to forest loss in one of the important areas of the Earth, the Mediterranean region. The paper analyzes the tree cover loss and cumulative tree cover loss in each country, acting as an important index of the tendency towards tree cover loss in each country, over a total area of 581 Mha. The forest loss is determined by certain natural and anthropogenic activities, such as intense forest fires, overgrazing, agricultural expansion, and illegal logging.

In the second paper, Hr ˚uza et al. [7] underline the important role of "controlled recreation" by building and tracing certain bike routes in the mountainous area of the Moravian Karst in Poland. While cycling has become one of the popular recreational activities of today's world, it is also important for the distribution of groups of visitors in the area, especially the bikers, who use only the designed routes.

Kim et al. [5] offer an international comparative analysis of the perception of the natural environment in South Korea, Taiwan, and Indonesia. Using a specific methodology, the perceptions regarding environmental conservation and the use of urban forests were analyzed, and some significant differences between the three countries, which have different histories, cultures, and economic developmental levels, were identified. Among all the countries, Taiwan showed the highest positive results, while concerns regarding the "limits of growth" were significant in South Korea and those regarding the "ecological crisis" more pressing in Taiwan and Indonesia.

Jiang et al. [9] show that, as the urbanization and industrialization processes in China have accelerated, pollution has become one of the most dangerous issues in the cities. The urban green spaces, as "green infrastructure", are an important part of the urban ecosystems, with the purposes of air purification and refreshing. By considering the meteorological factors over time, the effects of green spaces with different vegetation structures and effects on air purification were determined. The results underline the positive effect of urban green spaces on air quality, and this could be used as an important theoretical basis for optimizing the structure of urban green spaces.

Another paper focusing on air quality, Hoppa et al. [10], studies the impact of air pollution on people's health, which presents as one of the world's leading environmental health threats. The authors identify children as the most vulnerable social group, who are most exposed to the harmful effects of air pollution. With greater exposure time, more negative effects are observed. The study investigates the role of trees in air purification during winter, as a leafless period, on children's school routes. The study was conducted in Warsaw, and the results obtained demonstrate the weak impact of the tree canopy in the winter season.

Based on habitat preferences, Kinho et al. [3] investigate the spatial distribution of the *Magnolia* genus in the northern part of Sulawesi using 786 waypoints. They confirmed that the *Magnolia* spp. distribution is influenced by factors such as the annual temperature range, precipitation seasonality, and elevation. Endemic and endangered species, such as *Magnolia sulawesiana* Brambach, Noot., and Culmsee, were identified in the central part of Sulawesi. This study could provide a scientific basis for efficient forest management and the development of conservation strategies and landscape protection measures.

Sergiacomi et al. [8] conducted a survey, using the social media TripAdvisor platform, of 15,673 visitor reviews of the Plitvice Lakes National Park in Croatia. Their aim was to detect the strengths and weaknesses of the National Park. The methods of analyses that the authors used could be applied in different countries with similar natural contexts and protected areas. The results obtained show that visitors were both interested in naturalistic and landscape aspects and the accessibility and management of routes and visits to the park.

Zeng et al. [6] used deep learning technology to select six case studies in China and to analyze geotagged photos of forest landscapes posted by forest recreationists in a specific application. The paper also used Deepsentibank to perform a sentiment analysis of forest landscape photos. This facilitates a better understanding of Chinese forest recreationists' forest landscape preferences. The authors concluded that people preferred flat views and had a strong preference for forest trail landscapes, which plays an important role in forest landscape recreation, and that the photos evoked both positive and negative emotions.

Sancho-Knapic et al. [2] also underline the impact of air quality on human health. The study was conducted in Mediterranean forests, examining the variations in air quality under the influence of different factors (temperature and leaf development). The six most

abundant monoterpenes were detected, and it was found that their abundance in the air increased after the drought period. The study results show that people who enjoy forestbased activities in Mediterranean conifer areas are more exposed to air monoterpenes when the temperature increases. This happens when the leafy canopy is well developed and after a drought period. under the influence of different factors (temperature and leaf development). The six most abundant monoterpenes were detected, and it was found that their abundance in the air increased after the drought period. The study results show that people who enjoy forest‐ based activities in Mediterranean conifer areas are more exposed to air monoterpenes when the temperature increases. This happens when the leafy canopy is well developed

Sancho‐Knapic et al. [2] also underline the impact of air quality on human health. The study was conducted in Mediterranean forests, examining the variations in air quality

*Forests* **2022**, *13*, x FOR PEER REVIEW 4 of 7

Bayne et al. [4] refer to recreational activities in the production forests of New Zealand. These kinds of forests offer many opportunities for mountain biking, particularly in smaller commercial peri-urban plantations and forest parks maintained as dedicated mountain bike parks. The paper explains the impacts of, and the responses of forest owners and managers to, increased mountain biking activities in commercial forest estates. This paper is useful for urban and peri-urban forest planners (managers) who accommodate recreational activities such as mountain biking in production forests. It suggests policies and best practices that can help to protect commercial interests and forest ecology. and after a drought period. Bayne et al. [4] refer to recreational activities in the production forests of New Zea‐ land. These kinds of forests offer many opportunities for mountain biking, particularly in smaller commercial peri‐urban plantations and forest parks maintained as dedicated mountain bike parks. The paper explains the impacts of, and the responses of forest own‐ ers and managers to, increased mountain biking activities in commercial forest estates. This paper is useful for urban and peri‐urban forest planners (managers) who accommo‐ date recreational activities such as mountain biking in production forests. It suggests pol‐ icies and best practices that can help to protect commercial interests and forest ecology.

#### **4. Conclusions 4. Conclusions**

This Special Issue, entitled "Forest Recreation and Landscape Protection", comprises 10 papers by 59 authors from 12 countries on three continents: Asia (Indonesia, China, South Korea, India, Japan), Europe (Spain, Italy, Poland, Czech Republic, Croatia, Romania), and Oceania (New Zealand). Their multiple affiliations permitted the authors to study and research a great expanse of topics and areas related to forests, as an important natural resource and forest ecosystem service provider, as well as other elements with direct or indirect impacts on the forests (Figure 2). This Special Issue, entitled "Forest Recreation and Landscape Protection", comprises 10 papers by 59 authors from 12 countries on three continents: Asia (Indonesia, China, South Korea, India, Japan), Europe (Spain, Italy, Poland, Czech Republic, Croatia, Roma‐ nia), and Oceania (New Zealand). Their multiple affiliations permitted the authors to study and research a great expanse of topics and areas related to forests, as an important natural resource and forest ecosystem service provider, as well as other elements with direct or indirect impacts on the forests (Figure 2).

**Figure 2.** Authors' affiliation(s) and country (or countries) (right) by paper, with references (left). Data source: [1–10].

Another riveting aspect is the diversity of the keywords used in the papers (Figure 3). All the papers are centered around the *forest* as the main keyword, appearing in diverse combinations: forest management [4,7], forest recreation [7,8], forest landscape [6], and forest air [2], plantation forest [4]. Others relate to the tree species (*Magnolia sulawesiana* [3], *Pinus pinaster* [2], *Pinus radiata* [4]). Other keywords relate to the activities linked to forests, such as recreational activities (visitor monitoring [7], nature experience [5], single-track bike trails [7], TripAdvisor reviews [8], geotagged photos [6], path analysis [2]) and economic activities (commercial forestry, trade-off [4]). Other keywords center on the landscape, the other key focus of this Special Issue, and associated terms such as environment [1], landscape protection, and conservation [3]. Another riveting aspect is the diversity of the keywords used in the papers (Figure 3). All the papers are centered around the *forest* as the main keyword, appearing in diverse combinations: forest management [7,4], forest recreation [7,8], forest landscape [6], and forest air [2], plantation forest [4]. Others relate to the tree species (*Magnolia Sulawesiana* [3], *Pinus pinaster* [2], *Pinus radiata* [4]). Other keywords relate to the activities linked to forests, such as recreational activities (visitor monitoring [7], nature experience [5], single‐ track bike trails [7], TripAdvisor reviews [8], geotagged photos [6], path analysis [2]) and economic activities (commercial forestry, trade‐off [4]). Other keywords center on the landscape*,* the other key focus of this Special Issue, and associated terms such as environ‐ ment [1], landscape protection, and conservation [3].

**Figure 2.** Authors' affiliation(s) and country (or countries) (right) by paper, with references (left).

*Forests* **2022**, *13*, x FOR PEER REVIEW 5 of 7

Data source: [1–10].

**Figure 3.** Keywords of the papers in this Special Issue. Data source: [1–10]. **Figure 3.** Keywords of the papers in this Special Issue. Data source: [1–10].

Figure 4 offers an illustration of this multitude of 58 keywords grouped into 11 fam‐ ilies, from all 10 papers (economic activity, environment, impact, index, landscape, man‐ agement, methodology, pollution, region, resources, and tourism). The family of the key words is placed in the middle of the Figure, the paper numbers are on the left side, and the keywords are on the right side of Figure 4. Figure 4 offers an illustration of this multitude of 58 keywords grouped into 11 families, from all 10 papers (economic activity, environment, impact, index, landscape, management, methodology, pollution, region, resources, and tourism). The family of the key words is placed in the middle of the Figure, the paper numbers are on the left side, and the keywords are on the right side of Figure 4.

**Figure 4.** Papers in the Special Issue/family of keywords/keywords. Data source: [1–10]. **Figure 4.** Papers in the Special Issue/family of keywords/keywords. Data source: [1–10].

The papers from this Special Issue adopt an interdisciplinary approach that considers the aspects affecting forest degradation, including tree cover loss and deforestation, on the one hand, and those that lead to landscape conservation and protection, on the other. All the papers use an adequate and modern variety of methodologies, providing a real source of inspiration for further research papers and Ph.D. theses. The papers from this Special Issue adopt an interdisciplinary approach that considers the aspects affecting forest degradation, including tree cover loss and deforestation, on the one hand, and those that lead to landscape conservation and protection, on the other. All the papers use an adequate and modern variety of methodologies, providing a real source of inspiration for further research papers and Ph.D. theses.

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

**Conflicts of Interest:** The author declares no conflict of interest. **Conflicts of Interest:** The author declares no conflict of interest.

#### **References References**

Media: The Case Study of Plitvice Lakes National Park. *Forests* **2022**, *13*, 717. https://doi.org/10.3390/f13050717.


*Article*

## **Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat**

**Jiale Jiang 1,2,3,4, Weidi Cai 1,2,3,4, Hengbiao Zheng 1,2,3,4, Tao Cheng 1,2,3,4, Yongchao Tian 1,2,3,4, Yan Zhu 1,2,3,4, Reza Ehsani 5, Yongqiang Hu 6, Qingsong Niu 6, Lijuan Gui 6 and Xia Yao 1,2,3,4,\***

Received: 29 September 2019; Accepted: 13 November 2019; Published: 14 November 2019

**Abstract:** Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly e ffective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under di fferent conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the di fferent VIs varied at di fferent stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under di fferent cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under di fferent conditions, which should assist the precision management of N fertilizers in agronomic practices.

**Keywords:** leaf nitrogen concentration; leaf nitrogen accumulation; unmanned aerial vehicle (UAV); digital camera; vegetation indices

## **1. Introduction**

Nitrogen (N) is a component of many important compounds in plants, and thus plays an important role in plant growth [1,2]. Plant growth dominantly depends on the N supply [3]. A deficiency in N would reduce crop photosynthesis, whereas higher rates of N fertilization do not necessarily improve crop yield and can lead to serious water pollution [4–6]. Furthermore, leaf N concentration (LNC) is related to the photosynthetic capacity of leaves, and thus allows N fertilizer applications and grain quality to be modeled [7,8]. Therefore, timely quantification of LNC is a prerequisite for fertilization guidance and environmental quality [9,10].

Unmanned aerial vehicle (UAV) platforms have become a promising approach in precision agricultural assessments because they enable the non-destructive measurement of crop growth status, with a very high spatio-temporal resolution [11,12]. Due to their advantages of low cost, light weight, convenient operation, and simple data processing, digital cameras have been commonly deployed on UAVs in crop phenotype research [13]. Compared to other sensors, digital cameras can operate successfully in a range of working environments. Given that adequate image exposure can be set based on the weather conditions, data can be collected under both sunny and cloudy conditions [13]. Therefore, color images can be instantly acquired for researchers and farmers to monitor crop growth status [14].

A consumer-grade RGB camera is an "o ff-the-shelf" device, with red, green, and blue channels. Because each pixel value in color images can be calculated from the digital number (DN) values of specific bands, color indices can be extracted to accentuate a particular vegetation greenness and identify the vegetation features [14,15]. Hunt et al., [16] used the normalized green–red di fference index (NGRDI) from RGB images to estimate the biomass of corn, alfalfa, and soybean, and found a linear correlation between the NGRDI and biomass. Kawashima and Nakatani [17] used a video camera to analyze the color of wheat leaves for estimating chlorophyll content. Woebbecke et al. [18] investigated the capability of several color indices to distinguish vegetation from the background, and found that the excess green vegetation index (ExG) could provide a near-binary intensity image outlining a plant region of interest. Moreover, color indices from RGB cameras containing a large amount of information regarding crop status can be used to estimate the vegetation fraction, plant height, biomass, and yield [19–21]. However, many vegetation indices (VIs) proposed for crop status monitoring contain the near infrared (NIR) bands [22–24]. Therefore, RGB cameras with a Bayer-pattern array of filters have been modified by replacing either the blue or red channel with a NIR channel to obtain color near-infrared (CIR) images [25]. Based on a newly-developed digital CIR camera system, Hunt et al., [25] found a strong correlation between the green normalized di fference vegetation index (GNDVI) and leaf area index (LAI) in winter wheat. This CIR camera system has also been used to assess winter crop biomass [26]. Four VIs, the normalized di fference vegetation index (NDVI), enhanced NDVI (ENDVI), GNDVI, and ExG, derived from UAV-based RGB and CIR images, have been shown to be reliable to assess experimental plots [27].

Previous studies have indicated that it is feasible to estimate crop growth status using RGB and CIR images, but few studies have investigated their usability for N status monitoring [12,28]. Firstly, the capability of digital cameras to monitor wheat LNC at di fferent growth stages remains poorly understood. Since the composition of canopy components (e.g., leaves, tassels) and background materials (e.g., soil) varied sharply during whole growth stages of winter wheat [29], the performance of digital cameras on estimating LNC is necessary to be tested across di fferent growth stages. Secondly, it is crucial to investigate the saturation problem of VIs, with varying LNC values. Because the application of N fertilizer has increased recently in China, it is important to e ffectively monitor LNC under middle to high application levels. Thirdly, the capability of digital cameras for LNC estimation under di fferent conditions is unclear. The relationships among VIs and LNC in cereal crops have been investigated in terms of the mechanisms involved based on hyperspectral remote sensing. Many studies have proposed e ffective VIs that can be adjusted to variations in growth stage [30] and geographic

location [31], and that can reduce the effects of the soil background [32]. Therefore, there is also a need to assess the capability of digital cameras for estimating wheat LNC under different conditions.

The overall objective of this study was to evaluate whether digital cameras mounted on UAVs could be applied to monitor LNC in winter wheat. Five typical VIs derived from RGB images and two widely used VIs derived from CIR images were selected to estimate LNC. Additionally, we developed the true color vegetation index (TCVI) and the false color vegetation index (FCVI) from RGB and CIR images, respectively. Experiments with different wheat varieties, planting densities, and N application rates were conducted in the field to: (1) quantify the relationship between LNC and the VIs from digital imagery at different growth stages, (2) evaluate the saturation sensitivity of the VIs under various LNC levels, and (3) validate the applicability of the LNC estimation models under different treatments.

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

#### *2.1. Study Site and Experimental Design*

The study site was located in Rugao City, Jiangsu Province, China (120◦45 E, 32◦16 N), as shown in Figure 1. The regional annual precipitation of this area is around 927.53 mm, with an average annual temperature of 16.59 ◦C. Two field experiments using winter wheat (*Triticum aestivum* L.) were designed that included three N application rates, two planting densities (D), and two varieties (V) in two growing seasons (Table 1). In each experiment, a split design was used with three replications and there were 36 plots, each with a plot area of 35 m2 (Figure 1). The basal fertilizer included 120 kg/ha P2O5 and 120 kg/ha K2O and there were three N application rates (0, 150, and 300 kg/ha as urea) applied at the end of October 2013/2014. Compound fertilizer was applied in early March 2014/2015, including N applications at the three different rates. The N fertilizers were applied in 50% as basal fertilizer at the sowing day and in 50% at the jointing stage. All other agronomic management was undertaken according to local wheat production practices.

**Figure 1.** The study site of field experiments with two winter wheat varieties (V1, V2), two planting densities (D1, D2) and three nitrogen application rates (N0, N1, N2) in Rugao City, Jiangsu Province, China.

**Table 1.** Experimental Designs and Sampling Dates.

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

#### 2.2.1. Color Images from Unmanned Aerial Vehicle (UAV)

In this study, an eight-rotor ARF-MikroKopter UAV (Figure 2a) was used as the platform for the UAV-camera system and its specifications are listed in Table 2. A Canon 5D Mark III (Canon Inc., Japan) commercial digital camera (Figure 2b) was mounted on the UAV and took RGB images in continuous mode. The CIR camera was modified from a Canon SX260HS camera (Canon Inc.) by changing the original red channel to a near-infrared channel. The main parameters of the two cameras are described in Table 3. An MC-32 remote control module and a ThinkPad laptop were used to control the autonomous UAV flight. During each flight, the camera was fixed on a two-axis gimbal, with the lens positioned vertically downward at 50 m altitude. Considering the lighting conditions, the exposure time and shutter speed were fixed for each campaign. UAV campaigns were conducted at noon in clear weather under stable light conditions. The spatial resolution of the RGB and CIR imagery was 3 cm. The acquisition dates of these images are listed in Table 4.

**Figure 2.** The unmanned aerial vehicle (UAV) camera system: (**a**) ARF-MikroKopter UAV, (**b**) normal color (RGB) camera (Canon 5D Mark III) and (**c**) color near-infrared (CIR) camera (Canon SX260HS).

**Table 2.** Specifications of ARF-MikroKopter Unmanned Aerial Vehicle (UAV).

**Table 3.** Main Parameters of Normal Color (RGB) and Near Infrared (NIR-G-B) Cameras.

**Table 4.** Acquisition Dates of Unmanned Aerial Vehicle (UAV)-Based Digital Camera Images.

Before image pre-processing, the original digital images were screened. We selected images with a heading overlap rate of ~70% and a side overlap rate of ~30%, and excluded images with excessive repetition. The selected images were then pre-processed, including a lens distortion correction, image mosaicking, image registration, and ortho-rectification. First, lens distortion was corrected based on the Brown Model and the correction coefficients were calculated by an Agisoft Lens. Second, image mosaicking was conducted in Photoscan (Airsoft LLC, Russia). Third, image registration was referred to ground control points (GCPs) in the experimental area (see Figure 1). The GCPs were painted on a road surface as black annuluses, with inner and outer diameters of 10 and 50 cm, respectively. The geographic coordinates of the GCPs were determined using a real time kinematic (RTK) GPS system, with an error less than 2 cm in the horizontal direction and less than 3 cm in the vertical direction. Finally, ortho-rectification was automatically performed using Photoscan. Figure 3 shows the processed images from RGB and CIR cameras at the four growth stages.

**Figure 3.** The digital images from normal color (RGB) (**a**–**d**) camera and color near-infrared (CIR) (**e**–**h**) at the booting (**a**,**e**), heading (**b**,**f**), anthesis (**c**,**g**) and filling (**d**,**h**) stages. Red-green-blue and near infrared (NIR)-red-green channels are presented as RGB for RGB and CIR cameras.

#### 2.2.2. Determination of Leaf N Concentration (LNC)

Common measures of crop LNC are either area-based (LNCarea, g/m2) or mass-based (LNCmass, %). LNCarea is the N mass per unit leaf area and LNCmass is the ratio of N mass to leaf dry mass. These two measures can be converted between each other through the leaf mass per area (LMA), i.e., LNCarea = LNCmass × LMA [33]. Therefore, LNCarea should be determined based on both LNCmass and LMA measures, while LNCmass can be measured directly in the laboratory. Given its strong connection with photosynthetic capacity [34] and its widespread use in fertilization management [35,36], LNCmass has received more attention and has been estimated from remotely sensed data more often than its counterpart LNCarea [37–40]. The term LNC hereafter refers to LNCmass.

Ground destructive samplings were taken at critical growth stages of winter wheat (Table 1) on the same dates as the UAV campaigns. Thirty hills of wheat plants were randomly cut above the ground surface of each plot and separated into leaves, stems, and panicles. All leaves were oven-dried at 105 ◦C for 30 min and then at 80 ◦C until a constant weight. We then grounded and stored all samples in plastic bags for chemical analysis. The LNC was determined based on the micro-Keldjahl method [41] with SEAL AutoAnalyzer 3 HR (SEAL Analytical, Ltd., German).

#### 2.2.3. Derivation of Vegetation Indices (VIs)

The main VIs derived from digital camera images are summarized in Table 5. For RGB cameras, five widely used color indices were chosen in this study: the NGRDI [42], Kawashima index (IKaw) [17], red green ratio index (RGRI) [43], visible atmospherically resistance index (VARI) [42], and ExG [18]. For the CIR camera, we used the GNDVI [44] and enhanced normalized di fference vegetation index (ENDVI), because GNDVI is related to chlorophyll concentration, while the ENDVI was recommended by the company that manufactured the modified cameras (www.maxmax.com) and also can monitor vegetation vigor [27]. The VIs could be categorized into two groups according to the number of channels used. Some were constructed by two channels, such as NGRDI, IKaw, RGRI, and GNDVI. The others (i.e., VARI, ExG, and ENDVI) were established using three channels.

**Table 5.** Formulas and References of Possible Vegetation Indices (VIs).

1 TCVI is the True Color Vegetation Index derived from RGB images. 2 FCVI is the False Color Vegetation Index from CIR images. They are newly conducted in this paper.

Color vegetation index (CVI) derived from digital imagery is often calculated as ratios of DN values. However, the previous VIs listed in Table 5 were proposed without considering background material, such as soil in the field. Previous studies have suggested that the influence of the soil background can be reduced by adjusting the VIs with a term representing soil brightness [22,28]. Based on the ratio form and an equivalent soil adjustment, the commonly used CVIs from digital cameras can be expressed as:

$$CVI = \frac{(1+L)(a\_1R + a\_2G + a\_3B)}{(a\_4R + a\_5G + a\_6B + 255 \ast L)}\tag{1}$$

where *G* and *B* are the DN values of the green and blue channels, respectively; *R* represents the red component of RGB imagery or the near-infrared component of CIR imagery; *ai* is the coe fficient of each channel; and *L* is a soil background adjustment parameter.

To explore the capability of digital cameras for estimating LNC in wheat, we constructed new CVI from both RGB and CIR images. They were determined by optimizing the values of *ai* and *L* based on the cost function *J* defined as:

$$J = 1 - \frac{\sum\_{i=1}^{n} \left( LN\mathbb{C}\_{m,i} - LN\mathbb{C}\_{p,i} \right)^2}{\sum\_{i=1}^{n} \left( LN\mathbb{C}\_{m,i} - \overline{LN\mathbb{C}\_{m,i}} \right)^2} \tag{2}$$

where *LNCm* is the measured LNC. *LNCp* was predicted by the best fitted function of CVI and *LNCp* is the average value of *LNCp*. According to the imaging principle of a Bayer filter, we set the value of *ai* as an element of {−2, −1, 0, 1, 2}. Based on the setting rules of a soil background adjustment parameter in [22], *L* ranges from 0 to 1, with intervals of 0.1. The value of *L* varies by the amount or cover of green vegetation: in very high vegetation regions, *L* = 0; and in areas with no green vegetation, *L* = 1. Given that the maximum value of the DN was 255, *L* was multiplied by 255 in the denominator. All data from Exp. 2 were used in Equation (2). All possible combinations of variables (i.e., *ai* and *L*) were traversed, and the corresponding values of *J* were compared. The values of *ai* and *L* in Equation (1) that yield the best *J* were the optimal variables. Consequently, the TCVI from RGB images and the FCVI from CIR images were determined as follows:

$$TCVI = \frac{(1 + 0.4) \* (2R - 2B)}{(2R - G - 2B + 255 \* 0.4)} \tag{3}$$

$$FCVI = \frac{(1+0.5) \ast (2NIR + B - 2G)}{(2G + 2B - 2NIR + 255 \ast 0.5)} \tag{4}$$

#### *2.3. Data Analysis and Evaluation*

The channel information from digital cameras that constituted the VIs was first analyzed based on the data from Exp. 2. We analyzed how the DN values of di fferent channels changed with the changes of LNC in wheat. To compare and evaluate the performances of di fferent VIs for estimating LNC, the quantitative relationship between the VIs and LNC was analyzed at di fferent growth stages. For all growth stages of winter wheat, the LNC–VI models from both RGB and CIR images were calibrated and validated with a 10-fold cross-validation procedure using the data from Exp. 2. The whole dataset was randomly divided into three equal-sized sub-datasets with two sub-datasets used as the calibration (training) dataset and the rest as the validation (testing) dataset. The process was repeated 10 times [45]. For the CIR cameras, the estimation models were also validated independently with the data from Exp. 1. The performance of the di fferent VIs and models were evaluated using the determination coe fficient (R2) and the relative root mean square error (RRMSE).

$$R^2 = 1 - \frac{\sum\_{i=1}^{n} (O\_i - P\_i)^2}{\sum\_{i=1}^{n} (O\_i - \overline{O\_i})^2} \tag{5}$$

$$RRMSE = \sqrt{\frac{1}{n} \sum\_{i=1}^{n} \left(O\_i - P\_i\right)^2} \times \frac{1}{\overline{O\_i}} \times 100\% \tag{6}$$

where *n* is the number of samples; *Oi* is the observed LNC value; and *Pi* is the estimated value. The saturation sensitivity of the VI versus LNC was evaluated using the index of noise equivalent (NE LNC) [46,47].

$$\text{NEALNC} = \frac{\text{RMSE} \| VI \text{vs.LNC}}{d(VI) / d(L\text{NC})} \tag{7}$$

where *RMSE*{*VIvs.LNC*} is the root mean square error (RMSE) of the best fit function and the actual LNC value, and *d(VI)*/*d(LNC)* is the first derivative of VI with respect to LNC. A higher NE LNC indicates a lower sensitivity to LNC. The accuracies of the optimal estimation models from RGB and CIR images were compared under di fferent treatments (i.e., di fferent varieties, N application rates, and planting densities) using RRMSE.

## **3. Results**

#### *3.1. Changes of Digital Number (DN) Values in Di*ff*erent Channels*

Figure 4 shows the changes of DN values in di fferent channels with the variation in LNC for images from the RGB and CIR cameras. The DN values of all channels from the RGB camera decreased when the LNC increased to 3.0%, and then became flatter as the LNC continually increased (Figure 4a). Conversely, the DN values of the near infrared channel from the CIR camera increased as the LNC increased (Figure 4b). The variations of the DN values in the blue and green channels from both cameras were similar, but the values were di fferent. For the RGB camera, the values of the green channel were higher than those of the blue channel. For the CIR camera, the DN values of the green and blue channels were similar.

**Figure 4.** Changes of digital number values in di fferent channels of (**a**) normal color (RGB) images and (**b**) color near-infrared (CIR) images with leaf N concentration (LNC) in winter wheat.

#### *3.2. Leaf N Concentration (LNC) Estimation Model Constraction and Validation*

3.2.1. Quantitative Relationships between Leaf N Concentration (LNC) and Vegetation Indices (VIs) at Di fferent Growth Stages

Figure 5 shows the relationship between LNC and the VIs determined from both RGB and CIR images at di fferent growth stages of winter wheat. For the VIs derived from RGB images, the results were quite di fferent (Figure 5a–f). When the LNC increased, the NGRDI, VARI, and ExG increased, while the IKaw, RGRI, and TCVI decreased. The exponential relationship between the ExG and LNC was weak at all four stages (Figure 5e). The relationship between the NGRDI and LNC (Figure 5a) was similar to that between the VARI and LNC, but the fitting curves of the VARI and LNC at the booting and heading stages were stronger than those of the NGRDI and LNC (Figure 5d). Unlike the other VIs, the relationship between the TCVI and LNC was almost identical at all four stages (Figure 5f). For the VIs derived from CIR images, the GNDVI, ENDVI, and FCVI all increased as the LNC increased (Figure 5g–i). The results for the GNDVI and FCVI at each growth stage were similar (Figure 5g,i). The sample points for the relationship between the ENDVI and LNC were distributed along the fitting curves at the booting, anthesis, and filling stages, but at the heading stage the results were scattered (Figure 5h). At intermediate to high LNC levels, the NGRDI, RGRI, VARI, and GNDVI were not sensitive to changes in LNC > 3.0%, especially at the heading stage.

**Figure 5.** Leaf N concentration (LNC) (%) plotted against different vegetation indices (VIs) at different growth stages of winter wheat. (**a**) normalized green-red difference index (NGRDI), (**b**) Kawashima index (IKaw), (**c**) red green ratio index (RGRI), (**d**) visible atmospherically resistance index (VARI), (**e**) excess green vegetation index (ExG), (**f**) true color vegetation index (TCVI), (**g**) green normalized difference vegetation index (GNDVI), (**h**) enhanced normalized difference vegetation index (ENDVI), (**i**) false color vegetation index (FCVI). Statistics are given in Table 6.

**Table 6.** Relationship between leaf N concentration (LNC) and vegetation indices (VIs) at different growth stages of winter wheat. *x* and *y* are VI and LNC, respectively. R2 is the determination coefficient. Relative root mean square error (RRMSE) (%) is the relative root mean square error. The bold values mean the most accurate result of normal color (RGB) and color near-infrared (CIR) cameras for each term.

To quantitatively analyze the ability of the VIs to estimate LNC at di fferent growth stages, the R2 and RRMSE values at each growth stage and over all four stages are listed in Table 6. Generally, the results at the filling stage were much worse than at the other three stages, with an RRMSE higher than 15%. In the first three stages, the RRMSE values of most VIs were around 10%. However, the performances of the ExG and ENDVI were much poorer than those of the other VIs, especially at the heading and filling stages. The results for the TCVI and FCVI were not remarkably better than the other VIs at each growth stage, but they were obviously better for all four stages combined. For the RGB camera, the IKaw performed best at the booting and heading stages, and the VARI had the highest R2 and lowest RRMSE values at the anthesis stage. Although the TCVI results were not the most accurate in the first three stages, they were superior to those of the other VIs derived from RGB images at the filling stage. For the CIR camera, the performance of the ENDVI was much poorer than that of the GNDVI and FCVI. The FCVI performed best at each stage, although the RRMSE of 9.5% at the booting stage was slightly higher than the value of 9.1% for the GNDVI. Compared to the VIs from RGB images, the FCVI results were less accurate than those of the IKaw at the first two stages, but were the most accurate at the anthesis and filling stages. For all four stages combined, the performance of the TCVI was remarkable, with an R2 value of 0.852 and RRMSE of 12.1%, followed by the FCVI with an R2 value of 0.792 and RRMSE of 14.0%.

#### 3.2.2. Validation of the Leaf N Concentration (LNC) Models for Wheat

The validation statistics for the LNC models constructed from the di fferent VIs are presented in Table 7. According to the results of the 10-fold cross-validation, the estimation models constructed from the TCVI had the highest accuracy. Compared to the other VIs from the RGB camera, the RRMSEs were improved by 6.32% to 15.76% for LNC based on the TCVI. For CIR images, the statistical values of the FCVI were the best for LNC, followed by the GNDVI. The independent validation indicated that the FCVI was capable of producing accurate LNC estimations at all stages.

**Table 7.** Validation statistics for the leaf N concentration (LNC) estimation models from di fferent vegetation indices (VIs). The bold values mean the most accurate result of normal color (RGB) and color near-infrared (CIR) cameras for each term.

#### *3.3. Saturation Sensitivity of Vegetation Indices (VIs) at Di*ff*erent Leaf N Concentrations (LNCs)*

As shown in Figure 6, the saturation of the selected VIs was tested under the LNC range of 0 to 5%. The NE LNC of ExG increased sharply as the LNC increased. The ExG was the VI with the fastest and largest increase, followed by the ENDVI. When LNC < 3%, the NE LNC of the IKAW, RGRI, and VARI were similar. However, when LNC > 3%, the NE LNC of the IKAW increased more slowly than that of the RGRI and VARI. Compared to the other VIs, the NE LNC of the TCVI and FCVI were

much lower, with that of the TCVI being the smallest. The NE LNC of the TCVI was still the lowest of all the VIs at high LNCs.

**Figure 6.** Sensitivity of the selected vegetation indices (VIs) to leaf N concentration (LNC).

#### *3.4. Applicability of the Leaf N Concentration (LNC) Models under Di*ff*erent Treatments*

According to the results in Table 6, the optimal LNC estimation models for the whole season from RGB and CIR images were constructed by the TCVI and FCVI, respectively. Table 8 shows a comparison of the optimal estimation models from RGB and CIR images under the different treatments. Generally, the RRMSE values of LNC estimations under the various treatments were lower than 15%.

For the different wheat varieties, the RRMSE values of LNC estimations from RGB images were lower than those from CIR images, with the lowest RRMSE of 9.88% for Shengxuan 6. Under the different N application rates, the LNC models from RGB images performed better than those from CIR images. For both RGB and CIR images, the RRMSE values for N application rates of 150 kg/ha were lower than for the other N application rates. As planting density decreased, the accuracy of LNC estimation from RGB images decreased, while for CIR images it increased. The best results for the lowest planting density were obtained from CIR images, and the lowest RRMSE values for the highest planting density were obtained from RGB images.

## **4. Discussion**

#### *4.1. Performance of the Vegetation Indices (VIs) Derived from Digital Imagery in Estimating Wheat Leaf N Concentration (LNC)*

The estimation of VIs is the most common and simplest way to extract information on crop growth status from digital images [27]. Di fferent VIs calculated from di fferent wavelengths highlight various vegetation properties [48–50]. Due to the di fferent band combinations and di fferent formulas used, the accuracy of N status estimation varies between the di fferent VIs.

In this study, seven commonly used VIs (i.e., NGRDI, IKAW, RGRI, VARI, ExG, GNDVI, and ENDVI) and two new VIs (i.e., TCVI and FCVI) derived from both RGB and CIR images for estimating LNC were analyzed in wheat. As shown in Table 9, the numerator of the VIs from RGB images consisted of two channels, except ExG. Among them, the numerator of IKaw and TCVI contained the red and blue bands, while the red-green VIs (i.e., NGRDI, RGRI, and VARI) used the red and green bands as the numerator. The results in Figures 5 and 6 indicated that the ability to mitigate the saturation of the red-green VIs under high LNC levels was weakest for IKaw and TCVI. This might be because the red and blue bands are the chlorophyll and carotenoid absorption bands [51]. Given that crops respond to N status mainly by a change in chlorophyll concentration in the leaves [52], VIs using ratios or normalized di fferences of values acquired in the red and blue bands were significantly related to the N status of crops [53–55]. The GNDVI, which is calculated from the normalized di fference between values of the green and NIR bands, was also limited by the saturation under intermediate to high LNC levels (Figures 5g and 6). Unlike the GNDVI, the proposed FCVI acquired information in the blue band, which reduced the saturation. Although the ENDVI also used all three bands, its performance in estimating LNC was relatively poor. Therefore, we cannot conclude that a VI incorporating more bands is able to produce more reliable LNC results. The accuracy is dependent on both the band configuration and VI formulation.

**Table 9.** Coe fficients for Di fferent Vegetation Indices (VIs).

#### *4.2. Accuracy and Universality of Leaf N Concentration (LNC) Estimation Models in Wheat*

In this study, the LNC estimation models constructed with the TCVI and FCVI were sensitive under varying LNCs (from 0 to 5%) and had a better accuracy under the di fferent treatments. Moreover, they were generalizable from the booting to filling stages of wheat. As the growth stage progressed, dramatic changes in the composition of canopy components and background materials can occur. These changes pose a critical challenge for the timely monitoring of crop N status. For the late stages (booting to filling) in the reproductive period, a single LNC–TCVI or LNC–FCVI model could be fitted, with a high e fficiency and low error (Figure 5 and Table 6). Although ExG is mainly used to extract vegetation from di fferent backgrounds and has been widely cited [12,42,56], its performance for estimating N status was very poor, which was consistent with previous results [12]. Previous studies have suggested that adjusting VIs with a term representing soil brightness could reduce the e ffect of the soil background [22,28]. Given that a soil background adjustment parameter (*L*) was added to the TCVI and FCVI, they had the potential to reduce the significant e ffect of soil background during the early stages of the vegetative period.

Due to the limited experiment, we only made an independent validation for the LNC–VI models from CIR images based on Exp. 1, and the LNC–VI models from both RGB images were calibrated and validated with a 10-fold cross-validation procedure using the data from Exp. 2. Since the FCVI and TCVI were developed based on the relationships between CVI and LNC from Exp.2, there is a need to validate their performance with independent measurements under di fferent conditions (i.e., varied crop types, di fferent site conditions and other cameras). Although the coe fficients of FCVI and TCVI had been modified, the accuracy of LNC–TCVI or LNC–FCVI models was not always the highest (see Table 6). For example, the IKaw performed best at the booting and heading stages, and the VARI had the highest R2 and lowest RRMSE values at the anthesis stage. However, these relationships may vary with crop type, site condition, and growth stage as influenced by the variation in physiological processes. Therefore, the universality of the LNC estimation models needs to be further verified during the early growth stages and under various conditions.

#### *4.3. Capability of Commercial Digital Cameras for Wheat Leaf N Concentration (LNC) Estimation*

The development of simple but e fficient methods to monitor crop growth across a wide range of LNCs is urgently needed for precision agriculture in China. This is because the application of N fertilizer has increased in recent years in an attempt to boost production. Therefore, the timely monitoring of crop N status under intermediate to high N application levels is essential to maximize yield. In addition, easy-to-operate and low-cost equipment is required to help the owners of large farms or smallholders with fertilization management. Due to the low price and convenient operation of digital cameras, they have potential application prospects. In this study, UAV campaigns were conducted at noon in clear weather under stable lighting conditions. However, radiation correction should be considered in the future work, especially when experiencing changeable light intensity (e.g., for rice monitoring in summer).

To explore the capability of commercial digital cameras to estimate LNC in wheat, we constructed new VIs (i.e., TCVI and FCVI) from both RGB and CIR images. Because the TCVI and FCVI were based on large amounts of field experimental data under di fferent conditions, they performed reliably in indicating the LNC in wheat. It is suggested that commercial digital cameras have the capability to derive optimum VIs for LNC monitoring in winter wheat. In addition, the performance of RGB images was generally better than that of CIR images (see Table 8). This study supported the widespread agreement that digital cameras are powerful tools for assessing crop growth status and further proved the applicability of UAV-based RGB and CIR cameras for the monitoring of crop N status.

## **5. Conclusions**

The applicability of digital cameras mounted on UAVs for monitoring the LNC of winter wheat was evaluated in this study. Seven commonly used VIs (i.e., NGRDI, IKaw, RGRI, VARI, ExG, GNDVI, and ENDVI) and two proposed VIs (i.e., TCVI and FCVI) were used to estimate LNC at di fferent growth stages of winter wheat. The performances of NGRDI, RGRI, VARI, and GNDVI were limited by saturation at intermediate to high LNCs (i.e., LNC > 3.0%). The accuracy of LNC estimation using ExG was the poorest among the VIs tested, while the optimal models were constructed by the TCVI and FCVI for all stages. The models were then cross-validated with datasets from di fferent cultivars, N application rates, and planting densities. Compared to the other VIs derived from the RGB camera, the RRMSE values of the TCVI were improved by 6.32% to 15.76% for LNC. For the CIR camera, the statistical values of the FCVI were the best for determining LNC ( R2 = 0.756, RRMSE = 14.18%). The independent validation also indicated that the FCVI was capable of accurately estimating LNC at all growth stages. In summary, commercial digital cameras mounted on an UAV are feasible for monitoring wheat LNC at the farm-scale, especially under the high N fertilizer applications and different treatments typically used in fields in modern China.

In practical terms, commercial digital cameras are low-cost and easy to operate for researchers and farmers. Although the equipment is applicable, the LNC–VI relationship may vary with crop type, site condition, and growth stage, and may be influenced by variations in physiological processes. Therefore, additional calibrations are needed for different conditions before extending this method to other crops. For example, the use of UAV-based digital cameras for crop N status monitoring should be further investigated during the early stages of the vegetative period. Moreover, fluctuating ambient lighting conditions are an issue that should be addressed in future studies.

**Author Contributions:** X.Y. and T.C. conceived and designed the experiments. J.J. and H.Z. performed the experiments. J.J. analyzed the data. J.J. and X.Y. wrote the paper. All authors contributed to the interpretation of results and editing of the manuscript.

**Funding:** This work was supported by grants from the Key Projects (Advanced Technology) of Jiangsu Province (BE2019383), National Natural Science Foundation of China (31671582, 31971780), Jiangsu Qinglan Project, Jiangsu Collaborative Innovation Center for Modern Crop Production (JCICMCP), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Qinghai Project of Transformation of Scientific and Technological Achievements (2018-NK-126); Xinjiang Corps Great Science and Technology Projects (2018AA00403), and the 111 project (B16026).

**Acknowledgments:** We would like to thank former graduate students Yong Liu and Ni Wang for their help with field data collection.

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


**Abstract**

Melatonin (N-acetyl-5-methoxytryptamine) is an antioxidant active pharmaceutical ingredient with numerous applications as medicine and nutraceutical. Melatonin, a hormone synthesized by the pineal gland, has a significant role in the regulation of the circadian biological clock. The aim of this chapter is to present the conventional solid and liquid forms (i.e., tables, capsules, suspensions, etc.) and the nanoformulations (i.e., liposomes, niosomes, polymeric nanoparticles, chitosomes, calcium alginate beads, etc.) of melatonin and to give special attention to its release kinetics from the pharmaceutical vehicle. These systems have been designed and developed as platforms for the delivery and release of melatonin. In all cases, the controlled release of melatonin is the main goal of its loading into drug delivery platforms. Fractal analysis is a mathematical tool to quantify nature and physical systems' complexity. These systems have been characterized as fractal objects, due to their fractional dimensions. In this chapter, we are probing the interrelationship between the fractal dimension of pharmaceutical vehicle and the release profile of melatonin. Several examples will be given in order to understand in depth the reason of controlledrelease profile of melatonin and its added value for the development of a new medicine and/or nutraceutical.

**Keywords:** nanosystems, drug delivery, kinetics, release, fractals


**4.3.1 Magic number sequence of nana-clusters for liquid metal Al**

For liquid metal Al, for simplicity, we only analyze ten groups in the system in turn by the numbers of basic clusters contained in each group for two cases of liquid state at 943K and solid state at 350K, as shown in Table 5. From Table 5, it can be clearly seen that there is a peak value (maximum) of the numbers of clusters for each group and this is shown with a short underline in the table. As we compare this peak value with the abundance usually used in the research of cluster configurations, it is found that the two concepts are

Formation and Evolution Characteristics of Nano-Clusters (For Large-Scale Systems of 106

elements, especially in different states.

13

Liquid Metal Atoms) 189

750K

45 48 51 59 65

943K

350K

phase given by Harris et al is 13, 19, 23, 26, 29, 32, 34, 43, 46, 49, 55, 61, 64, 66... (see Fig.1 in Ref. ( Harris, Kidwell & Northby, 1984), and the sequence obtained from the mass spectra of Xe clusters formed in a supersaturated vapor phase given by Echt et al (Echt, Sattler & Recknagle, 1981) is 13, 19, 23, 25, 29, 55, 71... (see Fig.1 in this Ref.), these results are also in good agreement with our sequence in the same error range. That is to say that the this simulation result from metal Al is similar to those from inert gases Ar and Xe, and this similarity should reflect in certain degree some essential relations between different

Fig. 9. Variation of the number of clusters in system of Al with sizes of clusters (i. e. the

25-27

25 27

31 33

38 40 42

**4.3.2 Magic number sequence of nana-clusters for liquid metal Na**

It is highly interesting that this magic number sequence is also in good agreement with the results, obtained by using MD simulation and other model potentials from Solov'yov's and Doye's works, such as 13, 19, 23, 26, 29, 32, 34, 43, 46, 49, 55, 61, 64, 71, … (see Fig.1 and 2 in Ref. (Solov'yov I A, Solov'yov A V & Greiner, 2003)), and 13, 19, 23, 26, 29, 34, 45, 51, 55,… (see Fig.1 and 2 in Ref. (Doye & Meyer, 2005)) , respectively. From these, it can be explained that as long as the methods used to solve the similar problem are reasonable, the results

Number of atom in cluster 10 20 30 40 50 60

Number of atom in cluster 20 30 40 50 60 70

For liquid metal Na, for deep understanding the size distribution of the clusters mentioned above, we also only analyze ten group levels in the system in turn by the numbers of basic clusters contained in each group level for two cases of liquid state at 573 K and solid state at

number of atoms contained in the cluster) at 350K.

19

Number of cluster in total groups

should also be similar.

0

2000

4000

6000

8000

10000

12000

Number of

cluster in

system

completely consistent with each other. As we display the relations of the numbers of clusters formed in system with the size (the number of atoms contained in them) of these clusters, it is further found that the positions of the peak value points of the numbers of clusters also correspond to the magic number points. It is also clearly seen that the numbers of clusters at 943K are much less than those for the same group level at 350K for the former five group levels and there are few or almost none for the latter five group levels; and the front five peak value positions of clusters at 943K are not all consistent with those at 350K, for convenience of discussion for magic numbers, we only show the simulation results at 350K in figure 9.

It is clear from figure 9 that the quantity of various clusters is sensitive to the size of a cluster, and the magic numbers do exist. In the solid state at 350K, the total magic number sequence of all groups are in turn as 13, 19, 25, 27, 31, 33, 38, 40, 42, 45, 48, 51, 59, 65, 67…. However, when the number of atoms contained in a cluster is more than 70, the position of its magic number would be ambiguous.

In order to further reveal the magic number characteristics of the above-mentioned groups, we show the variation of the numbers of clusters in the system with the numbers of atoms contained in the clusters for ten groups in Figure 10, respectively.

It is observed in Fig.10 that although the ranges of neighboring groups have overlapped each other, one or two partial magic numbers still can be obviously distinguished for each group, and all the partial magic numbers for the ten groups rather correspond to the total magic number sequence for the whole system as shown in Figure 10. Going further, the total magic number sequence can be classified again according to the order of the ten groups of clusters in the following sequence: 13 (first magic number), 19 (second), 25-27(third), 31- 33(fourth), 38-40(fifth), 42-45(sixth), 48-51(seventh), 55-59(eighth), 61-65(ninth) and 67(tenth). The ninth and tenth magic numbers are not so obvious in figure 10 because the numbers of clusters containing 9 and 10 basic clusters are insufficient, however, they stand out in figure 10 (c). For simplicity, the magic number sequence corresponding to the order of the ten groups of clusters can be listed again as 13, 19, 25(27), 31(33), 38(40), 42(45), 48(51), 55(59), 61(65) and 67, where the numbers in bracket are the secondary magic numbers of the corresponding groups of clusters. We think the above-mentioned analysis is very important for searching the origin of the magic number of clusters formed in the system.

We compare the total magic number sequence mentioned above to the experimental results of the photo-ionization mass spectra of clusters, formed through supersonic deposition from supersaturated gaseous phase Al, obtained by Schriver et al as shown in Fig.3 of Ref. (Schriver et al., 1990), it can be clearly seen that the magic numbers reported (14, 17, 23, 29, 37, 43, 47, 55, 67…), and those not reported (19, 21, 25, 33, and 39) (they can be clearly seen in the same Fig.3, maybe the authors thought those numbers were not consistent with the magic number rule at that time), are almost all consistent with our magic number sequence (in the error range of ±1). Thus, it can be said that the magic number sequence from our simulation is supported by the experimental results, but their clusters are produced by both different formation processes even though they are of the same element, Al.

In particular, as we further compare the magic number sequence from our simulation to the experimental results of inert gas clusters, it can be also clearly seen that the magic number sequence obtained from the mass spectra of Ar clusters formed in a supersaturated ionic

completely consistent with each other. As we display the relations of the numbers of clusters formed in system with the size (the number of atoms contained in them) of these clusters, it is further found that the positions of the peak value points of the numbers of clusters also correspond to the magic number points. It is also clearly seen that the numbers of clusters at 943K are much less than those for the same group level at 350K for the former five group levels and there are few or almost none for the latter five group levels; and the front five peak value positions of clusters at 943K are not all consistent with those at 350K, for convenience of discussion for magic numbers, we only show the simulation results at 350K

It is clear from figure 9 that the quantity of various clusters is sensitive to the size of a cluster, and the magic numbers do exist. In the solid state at 350K, the total magic number sequence of all groups are in turn as 13, 19, 25, 27, 31, 33, 38, 40, 42, 45, 48, 51, 59, 65, 67…. However, when the number of atoms contained in a cluster is more than 70, the position of

In order to further reveal the magic number characteristics of the above-mentioned groups, we show the variation of the numbers of clusters in the system with the numbers of atoms

It is observed in Fig.10 that although the ranges of neighboring groups have overlapped each other, one or two partial magic numbers still can be obviously distinguished for each group, and all the partial magic numbers for the ten groups rather correspond to the total magic number sequence for the whole system as shown in Figure 10. Going further, the total magic number sequence can be classified again according to the order of the ten groups of clusters in the following sequence: 13 (first magic number), 19 (second), 25-27(third), 31- 33(fourth), 38-40(fifth), 42-45(sixth), 48-51(seventh), 55-59(eighth), 61-65(ninth) and 67(tenth). The ninth and tenth magic numbers are not so obvious in figure 10 because the numbers of clusters containing 9 and 10 basic clusters are insufficient, however, they stand out in figure 10 (c). For simplicity, the magic number sequence corresponding to the order of the ten groups of clusters can be listed again as 13, 19, 25(27), 31(33), 38(40), 42(45), 48(51), 55(59), 61(65) and 67, where the numbers in bracket are the secondary magic numbers of the corresponding groups of clusters. We think the above-mentioned analysis is very important

in figure 9.

its magic number would be ambiguous.

contained in the clusters for ten groups in Figure 10, respectively.

for searching the origin of the magic number of clusters formed in the system.

different formation processes even though they are of the same element, Al.

We compare the total magic number sequence mentioned above to the experimental results of the photo-ionization mass spectra of clusters, formed through supersonic deposition from supersaturated gaseous phase Al, obtained by Schriver et al as shown in Fig.3 of Ref. (Schriver et al., 1990), it can be clearly seen that the magic numbers reported (14, 17, 23, 29, 37, 43, 47, 55, 67…), and those not reported (19, 21, 25, 33, and 39) (they can be clearly seen in the same Fig.3, maybe the authors thought those numbers were not consistent with the magic number rule at that time), are almost all consistent with our magic number sequence (in the error range of ±1). Thus, it can be said that the magic number sequence from our simulation is supported by the experimental results, but their clusters are produced by both

In particular, as we further compare the magic number sequence from our simulation to the experimental results of inert gas clusters, it can be also clearly seen that the magic number sequence obtained from the mass spectra of Ar clusters formed in a supersaturated ionic phase given by Harris et al is 13, 19, 23, 26, 29, 32, 34, 43, 46, 49, 55, 61, 64, 66... (see Fig.1 in Ref. ( Harris, Kidwell & Northby, 1984), and the sequence obtained from the mass spectra of Xe clusters formed in a supersaturated vapor phase given by Echt et al (Echt, Sattler & Recknagle, 1981) is 13, 19, 23, 25, 29, 55, 71... (see Fig.1 in this Ref.), these results are also in good agreement with our sequence in the same error range. That is to say that the this simulation result from metal Al is similar to those from inert gases Ar and Xe, and this similarity should reflect in certain degree some essential relations between different elements, especially in different states.

Fig. 9. Variation of the number of clusters in system of Al with sizes of clusters (i. e. the number of atoms contained in the cluster) at 350K.

It is highly interesting that this magic number sequence is also in good agreement with the results, obtained by using MD simulation and other model potentials from Solov'yov's and Doye's works, such as 13, 19, 23, 26, 29, 32, 34, 43, 46, 49, 55, 61, 64, 71, … (see Fig.1 and 2 in Ref. (Solov'yov I A, Solov'yov A V & Greiner, 2003)), and 13, 19, 23, 26, 29, 34, 45, 51, 55,… (see Fig.1 and 2 in Ref. (Doye & Meyer, 2005)) , respectively. From these, it can be explained that as long as the methods used to solve the similar problem are reasonable, the results should also be similar.