# BUILDING AND DELIVERING SUSTAINABILITY SOLUTIONS: INSIGHTS, METHODS, AND CASE-STUDIES

EDITED BY : Nathaniel K. Newlands, Tracy A. Porcelli, Andries B. Potgieter, Louis Kouadio, Alfredo Huete and Wei Guo PUBLISHED IN : Frontiers in Environmental Science and Frontiers in Plant Science

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# BUILDING AND DELIVERING SUSTAINABILITY SOLUTIONS: INSIGHTS, METHODS, AND CASE-STUDIES

Topic Editors:

Nathaniel K. Newlands, Agriculture and Agri-Food Canada, Canada Tracy A. Porcelli, P. Phys., Independent Researcher, Canada Andries B. Potgieter, University of Queensland, Australia Louis Kouadio, University of Southern Queensland, Australia Alfredo Huete, University of Technology Sydney, Australia Wei Guo, University of Tokyo, Japan

Image: Polina Rytova (TL), SpaceX (TR), Alex Perez (BL), Andreas Dress (BR). Unsplash, licenced under CC-BY (https://unsplash.com/)

Sustaining ecosystems to deliver what people need and value, while mitigating and adapting to global climate change and extreme event impacts, presents a complex set of environmental, economic, and social challenges in ensuring resilient and sustainable food production. The Climate Smart Landscape (CSL) approach has emerged as an integrated management strategy to address the increasing pressures on agricultural production, ecosystem conservation, rural livelihoods, climate change mitigation and adaptation. Deploying cheaper, more accurate, and efficient technology enables the harnessing of big data for use in solving sustainability challenges. With improved integrated analytical frameworks, statistical approaches, spatially-explicit models and indices, the CSL approach can be further developed and applied for more resilient, productive, and sustainable ecosystems. This eBook brings together original research, review, hypothesis, theory, and technology report articles, involving 87 authors from 9 countries across Asia, Europe, and North America. These articles present new methodological and technological innovation, findings, and insights across four themes: (1) landscape productivity and crop suitability, (2) variable crop requirements for water and nutrients, (3) crop health status, phenology, and phenotyping, and (4) crop disease assessment and prediction under integrated pest management (IPM).

Citation: Newlands, N. K., Porcelli, T. A., Potgieter, A. B., Kouadio, L., Huete, A., Guo, W., eds. (2019). Building and Delivering Sustainability Solutions: Insights, Methods, and Case-Studies. doi: 10.3389/978-2-88963-039-4

# Table of Contents

*06 Editorial: Building and Delivering Real-World, Integrated Sustainability Solutions: Insights, Methods and Case-Study Applications* Nathaniel K. Newlands, Tracy A. Porcelli, Andries B. Potgieter, Louis Kouadio, Alfredo Huete and Wei Guo

### SECTION 1

### LANDSCAPE PRODUCTIVITY AND CROP SUITABILITY

Sriram Ganapathi Subramanian and Mark Crowley

*37 Shifts in Plant Community Assembly Processes Across Growth Forms Along a Habitat Severity Gradient: A Test of the Plant Functional Trait Approach*

Jinshi Xu, Yongfu Chai, Mao Wang, Han Dang, Yaoxin Guo, Yu Chen, Chenguang Zhang, Ting Li, Lixia Zhang and Ming Yue

### SECTION 2

### VARIABLE CROP REQUIREMENTS FOR WATER AND NUTRIENTS

Karla Vilaça Martins, Durval Dourado-Neto, Klaus Reichardt, Quirijn de Jong van Lier, José Laércio Favarin, Felipe Fadel Sartori, Guilherme Felisberto and Simone da Costa Mello

### SECTION 3

### CROP HEALTH STATUS, PHENOLOGY, AND PHENOTYPING

*94 Multi-Scale Phenology of Temperate Grasslands: Improving Monitoring and Management With Near-Surface Phenocams*

Christopher J. Watson, Natalia Restrepo-Coupe and Alfredo R. Huete

Kakeru Watanabe, Wei Guo, Keigo Arai, Hideki Takanashi, Hiromi Kajiya-Kanegae, Masaaki Kobayashi, Kentaro Yano, Tsuyoshi Tokunaga, Toru Fujiwara, Nobuhiro Tsutsumi and Hiroyoshi Iwata

*143 Differentiating Wheat Genotypes by Bayesian Hierarchical Nonlinear Mixed Modeling of Wheat Root Density*

Anton P. Wasson, Grace S. Chiu, Alexander B. Zwart and Timothy R. Binns

### SECTION 4

### CROP DISEASE ASSESSMENT, PREDICTION

Nathaniel K. Newlands

# Editorial: Building and Delivering Real-World, Integrated Sustainability Solutions: Insights, Methods and Case-Study Applications

Nathaniel K. Newlands 1 \*, Tracy A. Porcelli 2 , Andries B. Potgieter 3 , Louis Kouadio4 , Alfredo Huete5 and Wei Guo6

*1 Agriculture and Agri-Food Canada, Science and Technology Branch, Summerland Research and Development Centre, Summerland, BC, Canada, 2 Newelli Consulting, Kelowna, BC, Canada, 3 Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Toowoomba, QLD, Australia, 4 International Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD, Australia, 5 School of Life Science, University of Technology Sydney, Ultimo, NSW, Australia, 6 Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Nishitokyo, Japan*

Keywords: adaptation, agriculture, big data, real-world, sustainability

### **Editorial on the Research Topic**

### **Building and Delivering Real-World, Integrated Sustainability Solutions: Insights, Methods and Case-Study Applications**

Sustaining ecosystems to deliver what people need and value, while mitigating and adapting

to global climate change and extreme event impacts, presents a complex set of environmental, economic, and social challenges in ensuring resilient and sustainable food production (Fuhrer, 2007; Newlands, 2016; Whitfield et al., 2018). Regional climate trends drive variability in land productivity, soil water and nutrient availability, crop calendars, and the prevalence of pests and pathogens. Abrupt extreme events that defy clear prediction and attribution to climate trends cause catastrophic crop damage through water-logging, soil erosion, nutrient leaching, heat waves and drought. Sayer et al. (2013) proposed 10 core principles for an integrated landscape approach for reconciling agriculture, conservation, and other competing land uses. Building on these principles, the Climate Smart Landscape (CSL) approach has emerged as an integrated management strategy to address the increasing pressures on agricultural production, ecosystem conservation, rural livelihoods, and climate change mitigation/adaptation (Scheer et al., 2012; Salvini et al., 2018). The CSL approach is strengthened by a broad array of different science-based indicators, metrics, frameworks and modeling systems that enhance its capability to make more informed, integrated decisions. This approach, however, needs to incorporate big data, statistical, and artificial intelligence (AI) methodological improvements, new sensors, and remote-sensing technological advancements (Lee et al., 2010; Wolfert et al., 2017; Willcock et al., 2018). Such improvements offer more flexible monitoring, newer and/or higher-resolution data, better prediction methods, and tools for decision support.

This Research Topic aims to showcase research, development and technology (RDT) work toward devising and delivering integrated solutions that support and enhance the CSL-based approach. This Research Topic comprises 13 articles, including 10 Original Research articles, 1 Review, 1 Hypothesis and Theory article, and 1 Technology Report. State-of-the-art modeling approaches and sampling technologies are showcased. Contributed papers present new methodological/technological innovation, findings, and/or insights across four themes: (1) landscape productivity and crop suitability, (2) variable crop requirements for water and nutrients,

Edited and reviewed by: *Hayley Jane Fowler, Newcastle University, United Kingdom*

> \*Correspondence: *Nathaniel K. Newlands<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Interdisciplinary Climate Studies, a section of the journal Frontiers in Environmental Science*

> Received: *12 April 2019* Accepted: *10 May 2019* Published: *28 May 2019*

#### Citation:

*Newlands NK, Porcelli TA, Potgieter AB, Kouadio L, Huete A and Guo W (2019) Editorial: Building and Delivering Real-World, Integrated Sustainability Solutions: Insights, Methods and Case-Study Applications. Front. Environ. Sci. 7:71. doi: 10.3389/fenvs.2019.00071* (3) crop health status, phenology and phenotyping, (4) crop disease assessment and prediction under integrated pest management (IPM) and the CSL approach.

### LANDSCAPE PRODUCTIVITY AND CROP SUITABILITY

Bock et al. provide a technology report on a Canadian Land Suitability Rating System (LSRS) is a rule-based algorithm integrating soil, climate, and landscape factors from accessible databases, calculating a classed suitability rating for a given landscape. It is used to support commercial field crop production, and as a spatial research tool for assessing climate change impacts. Subramanian and Crowley present a spatial-based reinforcement learning approach that uses satellite imagery to increase the predictive power of spatial dynamic models that predict, and learn better policies to manage and control spatially spreading processes. They apply this methodology in wildfire event prediction using Canadian data. Exploring landscape gradients, Xu et al. assess how measuring plant community assemblies along habitat severity gradients may improve our ability to understand and monitor community dynamics and species responses under future climate change.

### VARIABLE CROP REQUIREMENTS FOR WATER AND NUTRIENTS

Neilsen et al. showcase a landscape-based water demand model for agricultural water use that regional water managers can use to better manage water demand and supply in response to climate change. This model considers high-resolution land use, soil, elevation, historical/future climate scenario data, a digital elevation model, sub-basins, aquifers, and socio-political jurisdictional boundaries. They use this model to explore future scenarios of climate change, and historical effects of crop production systems on irrigation water demand. From water to nutrient requirements, Guo et al. investigate aerially estimating nitrogen update from multi-angular hyperspectral data on winter wheat to improve the efficiency of remotely-based techniques for non-destructive, rapid detection of wheat nitrogen (N) nutrient status. A novel, modified right-side peak area index (mRPA) is benchmarked against other widely used indices, and shown to have the highest predictive power. Using this index can increase accuracy in assessing crop N status and management. Martins et al. propose a novel methodology for tracking crop micronutrient composition over time and demonstrate it for predicting maize/corn maximum requirements under variation in nutrient uptake rates, potential evapotranspiration, and micronutrient partitioning over crop growth stages.

### CROP HEALTH STATUS, PHENOLOGY, AND PHENOTYPING

Watson et al. use time-lapsed "phenocam" cameras to track the phenology and identify phenological variability of native and exotic grasses across grassland areas in Australia. Their findings indicate C3/C4 species dominance to be the primary driver of phenological differences among grassland types, with the proportion of non-photosynthetic vegetation, grazing pressure, and species-dependent responses to rainfall and temperature being important biophysical drivers of grassland phenology. MODIS/Landsat satellite and field-based phenocam data were found to be in good agreement. A primary benefit of phenocam data is its higher temporal fidelity in capturing vegetation changes (i.e., increases/decreases in greenness) over periods of only 5 days, compared to coarser satellite or field measurement techniques. Using high-resolution images captured by unmanned aerial vehicles (UAVs), Guo et al. propose a two-step machine-learning based image processing method that can provide more reliable estimates of yield by detecting and counting the number of heads, than manual measurements in sorghum breeding trials, with potential for broader application in field experiments, and field production scouting operations. To better assess adaptation traits in large small-plot breeding trials, Potgieter et al. evaluate the use of a narrowband multi-spectral camera deployed on a UAV. Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Red Edge (NDRE) were evaluated. Despite variable emergence, these indices tracked canopy cover and LAI well over a large range of plant densities, with NDVI and EVI strongly correlated with plant number per plot, canopy cover, and LAI. NDRE (i.e., leaf chlorophyll content) was found to be most useful in characterizing the leaf area dynamics and senescence patterns in contrasting genotypes. In further addressing practical constraints in genomics-assisted breeding, Watanabe et al. demonstrate UAV remote sensing (with a RGB or near-infrared green and blue (NIR-GB) camera) for measuring sorghum plant height and nitrogen availability for faster and more cost-effective throughput phenotyping. For phenotyping based on root depth distributions, Wasson et al. propose a state-of-the-art Bayesian hierarchical' nonlinear mixed statistical modeling approach to estimate root depth distributions for wheat genotypes to enable breeders to select for whole root system distributions appropriate for sustainable intensification. This approach produces de-noised profiles that exhibit rigorously discernible phenotypic traits.

### CROP DISEASE ASSESSMENT, PREDICTION UNDER IPM AND CSL APPROACH

Pandey et al. provide a review of soil-borne and foliar fungal diseases of mungbean (Vigna radiata var. radiata), an important legume crop in South/Southeast Asia. They review pathogen characterization, economic impacts, and integrated management practices including host resistance, fungicides, biocontrol agents, natural plant products, and cultural practices. They highlight the need for longer-term studies to validate biological methods for commercial application. For wheat (yellow) stripe rust (Puccinia striiformis f.sp. tritici) fungal disease, the greatest global pathogen threat to wheat production worldwide, Newlands explores the feasibility of an integrated model-based framework for predicting and controlling across large agricultural regions, using a novel spatially-explicit complex model, climate reanalysis and weather station network data.

Deploying cheaper, more accurate, and efficient technology enables the harnessing big data for use in solving sustainability challenges. With improved integrated analytical frameworks, statistical approaches, spatiallyexplicit models and indices, the CSL approach can be further developed and applied for more resilient, productive, and sustainable ecosystems. Smarter models will however require sufficient training data and operational frameworks assimilating data across a broad range of sampling platforms and data types. Agri-business will likely play an increasing role in sustainable landscape management by involving stakeholders and monitoring progress and outcomes (Salvini et al., 2018). Collectively, we can move faster to confront such complex interplay involved in translating scientific

### REFERENCES

evidence into real-world operational or actionable solutions (Lamontagne et al., 2019).

### AUTHOR CONTRIBUTIONS

NN and TP co-wrote this editorial based on contributions to the Research Topic, incorporating editorial feedback provided by AP, LK, AH, and WG.

### ACKNOWLEDGMENTS

We thank all authors of contributions to this Research Topic for preparing high-quality manuscripts under a tight time schedule and the reviewers for critically evaluating the papers and for further improving them with their questions, comments, and suggestions. NKN acknowledges research funding support of the Canadian Agricultural Partnership (CAP), Agriculture and Agri-Food Canada (AAFC).

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Her Majesty the Queen in Right of Canada. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Land Suitability Rating System Is a Spatial Planning Tool to Assess Crop Suitability in Canada

Michael Bock 1 , Pierre-Yves Gasser 2 , Wayne W. Pettapiece1 , Anthony J. Brierley 1 , Andy Bootsma2 , Peter Schut 2 , Denise Neilsen3 and C. A. Scott Smith3 \*

*1 Science and Technology Branch, Agriculture and Agri-Food Canada, Edmonton, AB, Canada, 2 Science and Technology Branch, Agricultural and Agri-Food Canada, Ottawa, ON, Canada, 3 Science and Technology Branch, Agriculture and Agri-Food Canada, Summerland, BC, Canada*

#### Edited by:

*Tracy Ann Porcelli, Independent researcher, Canada*

#### Reviewed by:

*Jana Mrazova, Deakin University, Australia Sean Smukler, University of British Columbia, Canada*

> \*Correspondence: *C. A. Scott Smith<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Interdisciplinary Climate Studies, a section of the journal Frontiers in Environmental Science*

> Received: *09 March 2018* Accepted: *26 June 2018* Published: *19 July 2018*

#### Citation:

*Bock M, Gasser P-Y, Pettapiece WW, Brierley AJ, Bootsma A, Schut P, Neilsen D and Smith CAS (2018) The Land Suitability Rating System Is a Spatial Planning Tool to Assess Crop Suitability in Canada. Front. Environ. Sci. 6:77. doi: 10.3389/fenvs.2018.00077* The Land Suitability Rating System (LSRS) is a rule-based set of algorithms that integrate soil, climate and landscape factors to calculate a classed suitability rating for a given landscape to support commercial field crop production. The attributes used to define each of the factors are based on their proven ability to affect crop growth, their ability to be measured (or estimated by proxy) and their availability in accessible databases. The LSRS was first published in 1995 by Agriculture and Agri-Food Canada as a site-specific, manual calculator for spring-seeded small grains that incorporated sets of attribute point deduction curves based on expert knowledge. Since that time the system has been expanded to include additional crop modules and all data handling and calculations are automated through a set of web-based applications. The current version of LSRS (version 5) is implemented in Ruby on Rails® software as a suite of web services. The system runs against any soil map with standardized Canadian Soil Information Service soil data tables to process soil attributes and calculate limitations to crop growth. A climate factor rating is based on crop-specific agro-climatic indices and thresholds. Climatic indices have historically been calculated from 30-year climate normal periods using monthly data but LSRS can now also utilize daily data records which facilitate trend analyses within annual historic records. The use of available gridded climate datasets enables direct overlay and extraction of climate attributes to the spatial extent of soil map polygons. Lastly, the system incorporates a landscape factor related to land erodibility and constraints to management. Each of the three suitability factors is assigned a class rating between 1 (no limitations) and 7 (unsuitable) with the final overall rating being the most limiting of the three factors. Recent improvements in the ability of the system to process multiple climate datasets mean outputs from Global Circulation Models may also be useful for the LSRS model in assessing possible impacts of climate change on crop suitability. LSRS is used increasingly as a spatial research tool in assessing potential changes in crop distributions at both national and regional scales.

Keywords: land suitability, climate change, soil maps, agro-climatic indices, Global Circulation Model, agricultural capability

### INTRODUCTION

The identification of soil landscapes suitable for production of food no doubt began with the dawn of arable agriculture. "Pedology and soil science in general have their rudimentary beginnings in attempts to group or classify soils on the basis of productivity. Early agrarian civilizations must have had some way to communicate differences and similarities among soils." Ahrens et al. (2002). In the early 1900's, German agronomist A. D. Thaer proposed a scale to describe the intrinsic fertility of soil based on attributes such as soil texture, calcium carbonate content and organic matter content (Feller et al., 2003). These early assessments were very subjective and qualitative based on general observations of features such as topography, stoniness, wetness and tree cover.

As agriculture became more mechanized and intensified in the twentieth century, there was a concomitant increase in scientific research into the agronomic requirements of cultivated crops. The evaluation of production potential became much more specific and quantitative with assessments such as the Storie Index Rating (Storie, 1933). This mathematical treatment of individual parameters such as texture, organic matter and pH was well accepted by the technical community of soil specialists. Ratings based on his procedure were incorporated into numerous soil reports produced as part of the National Cooperative Soil Survey Program in the United States (see, for example, Goodman, 1955; Arroues and Anderson, 1986). In the province of Alberta, Canada, soil survey reports from the late 1930s to the late 1950s included an interpretive land class map derived primarily from the physical characteristics of the study area. When compiling the map, soil physical data (texture, surface color, pH, soil taxon, mode of deposition, and degree of stoniness), landscape data (topographic form and relief) and climate data (rainfall and rainfall variability) were all taken into consideration (Wyatt et al., 1939). Each of these factors was assigned a numeric value for each soil area, and the multiplying together of these values gave the final index rating of the soil area. Using available pasture carrying capacity and wheat yield data, a seven-class productivity grouping of these rated areas was created. Some of the later soil survey reports produced through the end of the 1960s incorporated a stocking rate and/or wheat yield range for each group as an estimate of productivity (see, for example, Bowser et al., 1951; Odynsky et al., 1952).

By the middle of the century with greater intensification and further expansion into the less suitable fringe lands, there was recognition that some of the agricultural practices associated with the push for higher productivity were negatively affecting environmental sustainability (Standing Committee on Agriculture, Fisheries and Forestry, 1984). In Canada, this discussion had started with the wind erosion concerns in the 1920s and 30s. There was increasing competition with other land uses such as forestry, wildlife habitat, and recreation all associated with a growing population. It was into this setting that the broader concept of land capability, which included an aspect of sustainability, was introduced both in the United States (Klingebiel and Montgomery, 1961) and Canada (Agriculture and Rural Development Act, 1965).

The Canada Land Inventory (CLI) was introduced in Canada in the mid-1960s under the program of the federal Agriculture and Rural Development Act. The system used a general comparative capability approach based on severity of limitations for broadly defined land uses, specifically, agriculture, forestry, wildlife, and recreation. In the words of the authors of the first CLI report, the new system "is designed primarily for planning rather than for management. It is of a reconnaissance type, it provides information essential for land development planning at the municipal, provincial and federal levels of government. It does not provide the detailed information required for management of individual parcels of land, nor for land planning in small watersheds, local government units, etc." (Agriculture and Rural Development Act, 1965) The capability ratings were to be presented on maps at a scale of 1:250,000. At the time of its development, the then relatively new profession of land use planning was becoming important and quickly embraced this new rating system as capability was an intuitive and easily understandable concept. Municipal planners and realtors particularly liked the approach. The classification system incorporated seven classes with Class 1 being the best with no limitation for the intended use, Class 4 being marginal for the use and Class 7 being completely unsuitable. Under the umbrella of a cooperative federal-provincial program, approximately 2.5 million hectares covering all regions of Canada with multiple land use issues associated with agriculture were mapped from 1965 to 1980 using the CLI (Canada Land Inventory, 1998).

The CLI system worked very well for its intended regional development objectives; however, these regional assessments, while based on specific soil and land information still required a significant amount of subjective interpretation, extrapolation and amalgamation and climate was not considered in the ratings. In addition, the capability classes were categorical and while well-suited to land use planning functions they were less useful for more detailed analysis such as assessing the productivity function of soils (Mueller et al., 2010). Many land evaluators and managers required a much more detailed assessment at scales of 1:50,000 or larger (with units as small as several hectares), but in many areas there simply was not a more detailed soil database available. The CLI, however, was not intended for use at detailed or site-specific scales (Alberta Energy and Natural Resources, 1983). Attempts to extend the CLI approach to address the more detailed requirements provided variable results. Over the years, agencies in a number of jurisdictions either modified the original CLI system or developed entirely new systems of land capability classification for a variety of purposes. In other instances, soils specialists with different agencies within a province simply used two parallel systems following the CLI and Storie-type approaches (Alberta Soils Advisory Committee, 1983). If one person or one closely correlated group was providing the interpretations there was at least consistency within a region; however, as more people and agencies became involved, consistent evaluations became a problem (Alberta Soils Advisory Committee, 1987).

The overall result was that by the mid-1980s there were many agricultural rating systems being used across Canada. These included the systems used in the Atlantic Region (Atlantic Advisory Committee on Soil Survey, 1988), Quebec (Mailloux et al., 1984), the Ottawa area (Marshall et al., 1979), Ontario (Brooke and Presant, 1986), Alberta (Alberta Soils Advisory Committee, 1987), and British Columbia (Kenk and Cotic, 1983). With different parameters and thresholds, the same crops could have different absolute ratings under the different systems. The original CLI was also not consistent across Canada as the ratings were based on regionally important crops that differed from one region to another. For example, an area rated as CLI Class 1 for agriculture in Saskatchewan would be based on wheat and other small grains while an area rated as CLI Class 1 for agriculture in Ontario would have to grow corn.

The use of multiple capability classification systems as well as the inconsistent manner in which the CLI was being applied nationally resulted in confusion and disagreement. The topic was raised at the meeting of the Canadian Expert Committee on Soil Survey in 1986. Agriculture and Agri-Food Canada's then Land Resource Research Centre responded by creating an Agronomic Interpretations Working Group (AIWG) in 1987 to pursue the development of a national rating system for Canada. It was asked to address several specific concerns related to the CLI-Soil Capability for Agriculture, namely:

The result of the work was the publication in 1995 of the technical bulletin "Land Suitability Rating System for Agricultural Crops: 1. Spring-seeded small grains" (Agronomic Interpretations Working Group, 1995). The spring-seeded small grains report presented the details for the first module of LSRS. While it was anticipated by the original working group that additional crop modules would quickly follow, it would be a full decade before these would be developed.

It is the purpose of this paper then to present the story of the evolution of Canada's Land Suitability Rating System (LSRS) from its beginnings in the 1990's up to 2017. Specifically, the paper outlines the rationale associated with the initial development of LSRS, describes the model's structure and methods of implementation, describes the chronology and nature of upgrades (new versions, new modules) to the system, and presents recent examples of its application at national and regional scales.

### SYSTEM BACKGROUND AND DEVELOPMENT

In describing the approach and methods used in the development of LSRS, the differences and usages in the terms land evaluation, land capability and land suitability warrant some discussion. Capability assesses the nature of limitations and degree of limitations imposed on cultivated agriculture by the physical characteristics of a land unit. In the New Zealand Land Use Capability classification system capability of land is defined as "its suitability for productive use or uses after considering the physical limitations of the land" (Lynn et al., 2009). The land capability classification for agriculture in the UK developed by the Macauley Land Research Institute described capability as "the agricultural potential of land based on the degree of limitation imposed by its biophysical properties" (Wright et al., 2006). Land capability classification involves systematically categorizing units of the landscape in a way that reflects the inherent ability to produce sustainably into the future (Lynn et al., 2009). In Canada, the Canada Land Inventory (CLI) series of capability maps have been an important reference for assessing present and potential land use activities and planning across Canada. However, in terms of capability ratings there have been issues related to specificity. The agricultural capability classification rates soils for their ability to sustain production of common crops based on their potentials and limitations. The underlying assumption is that the better the capability rating (i.e., Class 1 vs. Class 4) a wider variety of agricultural crops may be grown; however, the number of crops successfully grown varies from one region of Canada to another. For example, corn may be grown in the CLI Class 1 or 2 areas in southern Ontario, but within the Prairie region areas of equivalent capability cannot sustain equivalent corn crops.

In comparison, the term "land suitability" is an estimate of the fitness of a soil and its landscape for production of a specific agricultural crop (FAO, 1976). As plants have specific requirements related to the functional status of soil, classifications based on production limitations and crop productivity must have a certain stratification or orientation specific to a single crop or groups of related crops (Mueller et al., 2010). Early federal German systems used soil suitability classification terms like "prime wheat soil," "rye soil," or "oats soil" to describe the suitability of specific areas (Feller et al., 2003).  Similarly, soil suitability rating systems have been developed for a variety of specific land management practices such as reduced tillage or direct drilling (Cannell et al., 1978), irrigation (Alberta Agriculture Food and Rural Development, 2000), the spreading of animal manure (Eilers and Buckley, 2002) and carcass burial (Carcass Burial Site Selection Technical Committee, 2004).

Modern land evaluation systems largely grew out of the older agricultural land capability classification. However, land evaluation is a much broader concept bringing into the assessment aspects of environmental sustainability (Smyth and Dumanski, 1995) and a range of economic and market factors that can influence land use decisions (Rossiter, 1996). Land suitability is typically one component of land evaluation. Methods of land evaluation (including land suitability) have become more sophisticated over time (Sonneveld et al., 2010) and with increased availability of large geographic and production datasets and geographic information systems, evaluations have become more quantitative and process oriented (Triantafilis et al., 2001; Joss et al., 2008; Elsheikh et al., 2013).

### Initial Concepts and Assumptions

Development of LSRS was based on an "expert system" approach that made use of existing sources of data and the collective knowledge and experience of professionals from the fields of land and plant science and those familiar with the evaluation of land suitability for crop production. The Agricultural Interpretations Working Group that developed the first version of LSRS was composed of soil surveyors, agronomists and agro-climatologists from across Canada. They examined a number of systems that were being used to rate land for the production of agricultural crops, keeping in mind the need for national consistency and the other concerns raised by the Expert Committee on Soil Survey. Climatic stratifications such as those by Chapman and Brown (1966), FAO (1976), and Williams (1983) were examined.

The initial assessment recommendations were:

The first recommendation provided the overall approach that should be taken while the next four addressed the major weaknesses that had been identified in the CLI approach. The final two recommendations addressed the issue of national consistency and provided further direction for system development.

It may be noted that no mention was made of scale. The reason was that the system was meant to be scale-independent– that it should have the ability to be used at any scale appropriate to the objectives of the project and available data (see section Applications of the Rating System for description of how this applies to the current use of LSRS). Preliminary discussion identified the assumptions or guidelines that were required to provide boundary conditions for the system:

• External economic factors such as distance to market, availability of transportation and size of farm would not be criteria. These are important for municipal property assessments and taxation but would not be part of this natural resource evaluation;

### Structure of the Land Suitability Rating System

### Classes, Factors and Parameters

The basic structure of the LSRS rating output uses two hierarchical categories–classes and subclasses. Each factor is assigned a class rating. Classes are broad in scope and are based on the degree of limitation of land for production of the specified crop. Seven classes are recognized (**Table 1**). Areas assigned to the same suitability class are similar only with respect to the degree, and not the kind of limitation. Different management may be required on lands of similar class which can be composed of different soil, climate and landscape.

In LSRS, the major rating factors are related to three of the major elements that describe crop production suitability:

LSRS assesses the climate, soil and landscape factors independently with a precisely defined index procedure that links the results to the seven-class system. This methodology required the development of clear relationships and guidelines for the assessment of the factors the lead to the assignment of class rating.

LSRS is an interpretive assessment based on the limitations controlling crop specific production. The rating factor that is most limiting ultimately determines the suitability class

rating. The components and measurable parameters identified to evaluate the major rating factors were selected from those used in earlier approaches (Huddleston, 1984) and acknowledged to be critical in crop production (**Table 2**). The following criteria were used in selecting the specific parameters used to characterize the major rating factors used in the LSRS model:

Although it is recognized that there are many instances of overlap and synergy between the major factors, there are advantages with respect to simplicity, clarity and the ability to highlight specific limitations by assessing each factor separately. Having built the major climatic-soil interactions into the soil factor, any other remaining discrepancies are considered small. This decision was reasonable given the constraint of available data at that time and the need for national coverage. This approach provided the greatest flexibility to assess various environmental, crop and climatic scenarios.

Each of the major factors is assessed a rating index between 0 (most limiting) and 100 (least limiting). Initially, each factor is assigned an index rating of 100. Limitations are assessed using the specific parameters identified for each factor and point values are deducted from the initial index ratings. The final index rating (i.e., class) assigned is that of the most limiting of the three factors. Subclasses identify the parameters (**Table 2**) that have the greatest limiting

*a see Agronomic Interpretations Working Group (1995) for definitions.* influence on the final class rating. They reflect the kind of climate, soil and landscape limitations that are present. The subclass information is critical for determining conservation and management practices and for land use planning. While Classes are numbered, Subclass designations used in LSRS are letters as follows: climate—temperature/aridity (H) and moisture (A); mineral soil–water supplying ability (M), structure and consistence (D), organic matter content (F), depth of topsoil (E), reaction (V), salinity (N), sodicity (Y), organic (peaty) surface (O), and drainage (W); organic soil—soil temperature (Z), water supplying ability (M), degree of decomposition (B), reaction and nutrient status (V), salinity (N), and drainage (W), and; landscape—basic landform slope (T), stoniness (P), wood content (J), landscape pattern (K), and flooding (I).

A few examples of complete LSRS ratings would therefore be: 2A–a landscape with only slight climatic moisture limitation, or, 4N–a marginal landscape limited by severe soil salinity, or, 6T–a landscape rendered unsuited for crop production due to steep slopes. A class 1 rating (landscape without limitation) would have no subclass denoted.

### Linkage of Deductions to the Suitability Class

As productivity is an important consideration in suitability assessments, studies of the relationships of Canada Land Inventory (CLI) classes to the yields of cereals in Alberta (Peters, 1977; Peters and Pettapiece, 1981) and apples in Ontario (van Vliet et al., 1979) provided the initial guidance in developing an assessment of the limitations-based linkage between the prescriptive numerical index ratings (0–100) and the descriptive suitability class. These studies reported a reasonably good correlation for the better classes with Class 1 (none to slight limitation) generally yielding 80% to 100% of the crop maximum. Class 3 areas (moderate limitation but still considered "Good") generally had yields about 50% of maximum or better. It was also noted that with increasing limitations-particularly landscape limitations, the yield relationships disappeared. That is, landscape features may be difficult or costly to manage but are not directly related to yield. Additional expert opinion suggested that index rating of less than approximately 1/3 of maximum (33 out of 100 points) should be considered a very severe limitation to the long-term sustainability of production.

Using these considerations as a guide, a relationship framework was formulated that provides the conceptual and mathematical linkage between the factor index ratings and LSRS classes. The calculation of LSRS class ratings required the development of the individual parameter indices. Examples of point deduction values for selected climate, soil and landscape parameters as they relate to spring seeded small grains (wheat, oats, barley) are given in **Figure 1**. The point deduction values were selected through expert opinion designed around class breaks. Originally depicted in tabular format for manual calculation, these "parameter to deduction point" relations were fit to curves to allow automated processing. Although each factor is calculated separately, LSRS is ultimately a most limiting

function tool. The final class rating given to a parcel of land is the lowest of the climate, soil or landscape rating.

Initially LSRS calculations were made site-specifically based on field observed soil and landscape properties and climate data from the closest weather station. Early calculations were based on climate normal data (30-year averages for temperature and precipitation). Early calibration was based on the period 1951– 1980, later updated to 1961–1990. To facilitate the calculation of climate suitability over large areas, national scale maps of growing degree days and climatic aridity (precipitation minus potential evaporation) were constructed specifically for use with LSRS. Details of initial parameter values and the calculation of land suitability class and subclass are given in Agronomic Interpretations Working Group (1995). Over the years, validation efforts have led to many adjustments to the way soil and climate factors are assessed and rated and details of these validation efforts are given in the Supplemental Data.

### Recent System Development

Over the last 20 years the LSRS has undergone developments in all aspects of the system (**Table 3**). The principle enhancements have been the introduction of additional crop modules in version 3, the transformation of the model platform to web-based processing and output (version 4), and most recently, the ability to integrate a range of gridded historic and future scenario climate datasets and calculate climate indices internally within LSRS (version 5). The requirement of the system to handle ever larger climate and soil datasets has led to implementation of more efficient data processing, storage and retrieval technologies and a more effective architecture for developing web services. The key major development areas are described below.

### Multiple Crop Modules

Crop modules for corn, soybeans, forages (alfalfa, grasses), and canola are part of the current system in addition to the spring-seeded small grains module that was part of the original system described in Agronomic Interpretations Working Group (1995). The development process was similar for each module. Agronomists with crop-specific expertise were consulted to help define climate, soil and landscape parameter values for each crop. The threshold values for class limits were tested against published production figures and calculations were programmed for user access and further testing. For each new crop module, the bulk of the work lay in defining crop-specific climate indices and thresholds (**Table 4**). For grains and forages, variations of the widely used growing degree-days above 5◦C index with adjustments for higher latitude growing areas (termed "effective" growing degree-days) was used to stratify climate requirements. For soybeans and corn, an adaptation of the corn heat unit which we termed "crop" heat unit was used. Some development

#### TABLE 3 | Outline of the version history of the land suitability rating system.

TABLE 4 | Heat indices and thresholds used in the LSRS crop modules.

concepts and improvisations around each of the crop modules follows. Full documentation of crop module development and testing are available as Supplemental Data.

### **Corn and soybeans**

For both corn and soybeans, the heat index is based on crop heat units (CHU). The average daily values of crop heat units are calculated using the following formula:

$$\text{Daily CHU} = (\text{Ymax} + \text{Ymin})/2.01$$

Where Ymax and Ymin are the contributions to CHU from average daily maximum (Tmax) and minimum (Tmin) air temperatures respectively and are calculated as The growing season length and hence the starting and stopping dates for accumulation of the CHU values are based on temperature thresholds. The starting date is based on the average mean daily temperature (Tmean). The stopping date is based on the average mean daily minimum temperature (Tmin). Both thresholds are calibrated to the average historic dates of planting in spring and the probability of killing frost (−2 ◦C) in the fall (Bootsma, 1991; Bootsma et al., 1999, 2005) and are presented in **Table 5**. The seasonally accumulated CHU index determined in this manner is called "CHUnorm." The original method for calculating CHUnorm used climate normal records, currently we are making the calculation from 30 years of daily climate data.

$$\begin{array}{ll} \text{Ymax} = 3.33 \text{(Tmax} - 10.0) - 0.084 \text{(Tmax} - 10.0); & \text{if } Tmax < 10.0, \text{Ymax} = 0.0; \\ \text{Ymin} = 1.8 \text{(Tmin} - 4.44); & \text{if } Tmin < 4.44, \text{Ymin} = 0.0 \end{array}$$

With respect to the climatic moisture assessment, the consensus was that precipitation minus potential evapotranspiration (P-PE) was a reasonable and well accepted index. Given the higher moisture demand to produce corn than cereal grains, the class limits for corn were developed by adjusting the values defined for small grains by about 100 mm.

The corn module rates soil and landscape factors and their point deductions in a similar way to that in the small grains module.

The soybean module development considerations were similar to those for corn. Both are considered warm season crops so heat requirements can be assessed within a CHU framework. Some soybean cultivars can withstand temperature limitations of relatively short, cool growing seasons but temperature requirements for soybeans are nonetheless very similar to those for corn (OMAFRA, 2002). The minimum heat requirement for the commercial production of soybeans is about 2,000 CHU.

Given the above, the agronomic consensus was that the CHU scale be used to rate the heat requirements of the crop with the same rankings as used for corn, and that P-PE should be used to characterize the moisture limitations but with the point deduction set slightly less than that for corn (**Figure 2**).

Most soil factors for soybean production are rated similarly as those for corn and the spring-seeded small grains. The only

TABLE 5 | Definition of start and finish of the growing season for corn in eastern Canada.

line) and soybeans (dashed line).

exception being the susceptibility to emergence problems relating to crusting. The soil factor therefore has an added deduction related to surface soil structure calculated as a function of the content of soil organic carbon and percentages of sand, silt and clay. The landscape factors are rated the same for soybean as for small grains as the erosion potentials and mechanical limitations are assumed to be the same as for both crops.

### **Canola**

In many respects, canola (Brassica spp.) falls within the general climatic, soils and landscape parameters used to assess small grain limitations. However, there were some refinements made in consideration of requirements for particular Brassica species.

Effective growing degree days (i.e., GDD base 5◦C adjusted for latitudes up to 60◦N) is used as the temperature index using the same scale used for small grains. The long season, higher yielding (Brassica napus) varieties perform best in the areas with more than about 110 frost free days which correlates with accumulations of greater than about 1,200 effective growing degree days (EGDDs). Brassica rapa is a shorter season variety that performs well down to about 1000 EGDDs. The baseline temperature for Brassica is approximately 4.4◦C (Morrison et al., 1989). This corresponds well with the 5◦C base temperature used to calculate EGDDs. However, included was a special consideration with respect to growing season heat. Flower abortion associated with temperatures greater than about 30◦C is a known phenomenon (Morrison and Stewart, 2002). To address this, a heat index (HI) was calculated as the number of days where daily maximum temperature >30◦C during the canola flowering period defined as between the dates of accumulation of 600 and 1100 EGDDs.

As with soybeans, the module takes into consideration the potential for soil crusting and additional deductions for soils with this potential. Otherwise, all other soil and landscape factors were rated the same for canola as for small grains.

#### **Forages**

Rating land for forage production has a fundamental difference from ratings for annual grain and oilseed crops. Typically forage crops are harvested multiple times during the growing season, often referred to as "cuts." Depending on the region of the country, forages may be harvested up to four times in a single season.

Two general types of forages (i.e., legumes and grasses) with somewhat different climatic and soil requirements are recognized within LSRS. A widely grown legume in Canada is alfalfa (Medicago sativa L.) and was chosen as the perennial legume to include in the system. As a perennial crop, there are no concerns with determining dates for spring seeding or for frost damage as with grain. As such, the forages have a longer growing season than the annual crops in other LSRS modules. This extended season is assumed to be the period with mean daily temperatures above 5◦C (Bootsma and Boisvert, 1991).

The climatic rating for alfalfa is based on two factors, the accumulated growing degree days >5 ◦C (GDD) and growing season length. Bootsma and Boisvert (1991) estimated about 480 GDDs to produce a first cut of alfalfa with an additional 450 GDDs for crop carryover requirements. Note that for the grass forages, LSRS uses a straight GDD >5 index that is not adjusted for high latitude day length as is the case with the effective growing degree day (EGGD) index used for small grains. Therefore, the minimum heat requirement for alfalfa is about 930 GDDs (480 GDD to obtain a harvest, 430 GDD to ensure carryover for the following year) and was used to establish the class 4-5 configuration point (**Table 6**). The climatic requirement to produce three cuts per year was used to define the Class 1 configuration point. This translates into (3 x 480) + 450 = 1,890 GDDs. The ability to support two cuts per year, [(2 × 480) + 450 = 1,410 GDDs] was set as the Class 3 threshold. The ability to produce one cut with no carry over (480 GDDs) was set as representing the Class 5-6 boundary. The maximum deduction was set at 90 points.

Length of season requirements were established using monthly climatic data and the GDD requirements for one, two and three cuts of alfalfa. Bootsma and Boisvert (1991) suggested a minimum of 45 days between cuts, 65 days to accumulate the heat to necessary to produce a first cut crop and 55 days carry-over for the crop to store enough energy to survive the winter. The relationship between growing season length and accumulated heat varies across the country. For instance, in coastal British Columbia there is a very long growing season but often insufficient heat to produce more than 2 cuts of hay.

Several soil related parameters required specific modifications for alfalfa. These include soil and subsoil pH. The most favorable soil pH range for alfalfa is between 6.5 and 7.5 (Goplen et al., 1987; Undersander et al., 1991). Therefore, a pH in this range was taken as no limitation and assigned a zero-point deduction. A pH of 5.0 is considered marginal for alfalfa and thus assigned a deduction of 70 points. The class 2 configuration point (and a deduction of 20 points) was set at pH 6.0. The class 3 configuration point (and a deduction of 40 points) was set at pH 5.5. A similar logic and set of deductions was developed for pH above 7.5.

In addition, there were modification in the module to the landscape factors of slope and stoniness. The continuous cover of perennial crops means that the risk of soil erosion is much reduced relative to annual crops. Because there is no annual cultivation requirement, internal soil coarse fragment content is not an issue from a landscape management perspective.

Climatic growth requirements and response is similar for most cool season C3 grasses (Moser et al., 1996). In humid eastern Canada, Timothy (Phleum pretense) is the most common

TABLE 6 | Point deductions assigned to GDDs and growing season length for alfalfa.

forage grass. The climate of the Great Plains of western Canada is semi-arid and soils can be saline. Under these conditions smooth bromegrass (Bromus inermis L.) is the preferred forage grass. As with alfalfa, about 480 GDDs are required to produce a first cut of timothy with an additional 400 GDDs or so for crop carryover requirements. Carry over heat requirements for grasses are less than for alfalfa but were included for the grass forage module. The minimum heat requirement for grass forages was set at approximately 880 GDDs and was set as the boundary threshold for the class 4-5 boundary, the marginal category. Using the same approach as used for alfalfa, the ability to support two cuts per year of grass forage require 1360 GDDs and for three cuts 1840 GDDs. Point deductions for grass forage follow a similar logic as was used for alfalfa.

Also important in rating land for forage grass production is the growing season length. A literature review indicated that about 65 days of temperatures averaging >5 C are required to mature the crop for cutting and another 55 days are required for carryover into the following year. For two cuts to be achieved, it would require 65 days of initial growth, followed by 55 days of growth for the second cut and another 55 days to achieve the carryover. These counts of days were then used to determine class boundaries and deductions points. The most limiting of growing season degree-day accumulation or growing season length is used to calculate overall climatic heat suitability

Soil requirements for the brome-timothy forage model are the same as for the small grains. This is reasonable considering that these forages, like the small grains, are C3 grasses. The landscape factor deductions are the same for brome-timothy as for alfalfa.

### Enhanced Processing of Climate Data and Climate Indices

The current LSRS version (version 5) carries forward all the web-based processing functionality developed in version 4 and adds to it a new climate indices calculator (**Table 3**). While version 4 could accept climate indices calculated externally, version 5 incorporates the calculation of climate indices and supports raster data processing. Several major new features and improvements are incorporated into this release. Key amongst these is the use of Redis as a data store for climate data. Redis is an open source in-memory data structure store, used as a database, cache and message broker. The climate calculator validates climate data, calculates climate indices, and stores the calculated indices in Redis which simplifies access, and enhances performance and output reliability. The calculator reads climate indices directly from Redis to simplify use and to eliminate a significant source of human errors.

As the map polygon is the unit of calculation for LSRS, it is necessary to assign climate attributes (daily Tmax, Tmin, and Precipitation) to each polygon. The basic conceptual approach is the spatial union between the climate grids and soil map polygons (**Figure 3**). Depending on the scale of the grids and map polygons, when several climate grid centroids fall into one soil map polygon, these are averaged for the polygon.

The agro-climatic indices used in LSRS are calculated from mean monthly or average mean daily data for each polygon. The climate data are loaded into LSRS using a custom file

FIGURE 3 | Allocation of gridded climate data to detailed soil survey (DSS) map polygons (adapted from Gasser et al., 2016, reproduced with the permission of the copyright holder, NRC Research Press).

summer temperatures is visible in southern Ontario and Quebec, central New Brunswick and Newfoundland. Climate data were transposed from a national 10 km grid to 1:1,000,000 soil polygons to facilitate subsequent LSRS rating.

format that incorporates both documentation and climate data. The file is divided into two sections. The first section contains the documentation in YAML format, and the second section contains the data as a set of tab-separated values. Index calculation methods are specific to each crop. The climate indices calculator within LSRS version 5 generates the following outputs: growing season start date, growing season stop date, growing degree days >5 C, crop heat unit and P-PE.

Prior to version 5, climate indices had to be calculated outside of LSRS and then imported into the system. Early versions of the system were designed to work with monthly data. Indices requiring daily time steps were calculated by deconstructing monthly values through a curve fitting step (Bootsma and

FIGURE 5 | National-scale map of land suitability classes for canola based on dominant soil types and the period 1981 to 2010. Highest suitability classes exist in Prince Edward Island (Charlottetown), in southern Quebec and Ontario and along the northern agricultural region of the Prairie provinces. Lower suitability classes result elsewhere in the country due to aridity in parts of western Canada and cool temperature in the Atlantic region.

FIGURE 6 | Shaded areas show the expansion of prime land (class 1 and 2) for spring seeded small grains based on the climate normal period 1951–1980 (A) and 1981-2010 (B).

Boisvert, 1991). To date we have not depicted results based on a single year of data (although is this possible) but have stayed with the original concept that LSRS rating are generated from climate averages over multiple years.

### Computational Upgrades

Major upgrading of the LSRS system began in 2009 with the rewriting of the core LSRS calculator in Ruby as a suite of web services. The program can run and store batch processes compliant with Web Processing Services (WPS), calculate results for multiple crops simultaneously and utilize a range of map scales and data structures that exist for Canadian soil maps. The complete details of each calculation are visible as a web page (HTML) but data are also output as CSV and GDAS formats, and the system can provide thematic maps via WMS and KML. Significant adjustments were made between 2011 and 2014 to the original crop ratings algorithms as the result of regional validation efforts across Canada.

For most calculations, Version 5 generates ratings directly from class boundary (configuration) points instead of curve formulas. This approach eliminates unintended effects that occurred in earlier version of LSRS at climate extremes, helps to improve consistency with intended class breaks, and simplifies the adjustment of deduction curves.

The internal data access mechanism and LSRS calculation requests are based on a REST (Representational State Transfer) architectural approach to simplify operations and facilitate interoperability with other systems. Our implementation creates separate access for climate, soil and landscape ratings, and stores calculation results in Redis so that the results can be reused in subsequent routines to improve the performance of raster calculations.

Finally, in order to simplify maintenance of the system, version 5 drops XSLT in favor of plain HTML for most humanreadable outputs.

### APPLICATIONS OF THE RATING SYSTEM

LSRS may be applied at scales based on the scale of the input soil, landscape and climate information. The scale of output

1981–2010 climate normal intersected and displayed on a:100,000 scale soil map of the agricultural region of Alberta.

is controlled by the scale of the soil map used as input. Ratings are calculated on soil profile attributes for each soil type reported to occur in a map polygon. Typically, results are depicted based on the dominant (spatially most common) soil. Climate indices are calculated from data grids at a variety of scales and intersected with the soil map polygons. LSRS may also be used on-site with attribute data coming from an observed profile, landscape parameters measured on-site and climate data obtained from a near-by weather station. In this case LSRS ratings would be site-specific. Examples of national and regional assessments are presented in the following sections.

### National Scale Assessments

LSRS can map a variety of outputs on a soil map polygon basis at regional and national scales. These include climate attributes, agro-climatic indices, and for a particular crop, climate class rating, soil class rating, landscape class rating and an overall LSRS class and subclass suitability rating. This is achieved by integrating climate data with the 1:1,000,000 Soil Landscapes of Canada mapping to produce actual LSRS ratings (Schut et al., 2011).

Outputs at the national scale help to explain historic and future projected shifts in cropping patterns and can help to interpret changes in agri-environmental performance in national policy assessments). Climate change impacts using IPCC AR3 climate scenario data downscaled to a 10 km national climate grid and linked to 1:1,000,000 Soil Landscapes of Canada polygons were assessed for the 1971 to 2000 normal period and three future 30-year periods for three regions of Canada–the Prairie provinces, central Canada (southern Quebec and Ontario) and Atlantic Canada (Chen et al., 2008). Based on suitability rating changes over time the authors speculated that grain and canola production would move onto climatically marginal crop land that is currently under forage production or livestock grazing and where aridity is not a limitation, corn and soybeans could expand into areas now mostly dominated by grain and oilseed production, especially in the northern regions of the Prairie provinces. Perhaps more importantly, was the recognition that seeding and maturity dates will shift earlier into the year as a necessary adaptation to mid-season aridity in western Canada.

LSRS has been used to examine climatic shifts within the agricultural region of the country. **Figure 4** depicts slight changes in July maximum temperatures in the agricultural portions of the country between the climate normal periods 1951–1980 and 1981 to 2010. Even though the temperature ranges are depicted at a scale of 1:1,000,000 the extent of July warming during the historical period is obvious.

Integration of climatic data with the soil and landform attributes contained with the databases linked to the Soil Landscapes of Canada map product, enables crop ratings to be depicted for the dominant soils on the landscape. **Figure 5** shows suitability ratings for canola based on climate normal data for the period 1981–2010. **Figure 6** illustrates the expansion of LSRS class 2 lands for spring seeded small grains in western Canada for two historical time periods. Due to slight climatic limitations for grain production there is no class 1 in this region of Canada.

FIGURE 9 | Comparison of future prime land extents in the Peace River district of northwestern Alberta for the period 2041–2070. Shown are results based on climate outputs for RCP4.5 from the CanESM2 (A) and INM\_CM4 (B) models intersected with 1:100,000 soil map polygons. The difference shown represents the degree of spatial uncertainty that exists with respect to future land suitability for grain production in the region.

## Regional Climate Change Assessments

LSRS has been used locally in many provinces to replace the older CLI capability maps that were drawn based on mid-twentieth century climate conditions. Regional LSRS spatial outputs utilize regional detailed and semi-detailed soil maps as their base. In Alberta, where much of the original LSRS validation work was conducted, a provincial 1: 100,000 scale soil map is used as the base for depicting provincial-scale crop ratings. The baseline for making this comparison was the map for spring seeded small grain (**Figure 7**). Spatial validation of this type of output was used when designing the early versions of LSRS.

Gasser et al. (2016) examined the impact of a range of IPCC AR4 SRES scenarios on future production of corn in coastal British Columbia. In this instance, projected increasing growing season aridity (a subclass of the climate rating) indicated the likely future requirement for the adoption of irrigation much more widely than is currently the practice. In all our climate change impact studies to date, a wide range of suitability outcomes are projected as a function of the model scenarios selected. This range is a measure of uncertainty. Mapped class areas for each scenario are typically summed and plotted over several time periods to see the spread in trends (**Figure 8**).

A recent application of LSRS has helped to evaluate climate change impacts on northern agricultural regions of northwest Canada. Downscaled data (Wang et al., 2016) from a subset of five climate models were selected from the IPCC CMIP5 ensemble (Cannon, 2015) to span the range of projected temperature and precipitation changes in the region. These were then used to drive LSRS for the RCP4.5 and RCP8.5 scenarios. One study area is the Peace River district of northwestern Alberta where earlier national-scale assessments indicated considerable potential change. Grain and oilseeds are the main crops in the region. We analyzed climate over both historic and future time periods. However, as with our work in British Columbia, different model outputs projected vastly different outcomes with respect to agricultural suitability by late century. **Figure 9** compares the outputs for two models, the Canadian model CanESM2 and the Russian model INM\_CM4. While both models project increasing heat, CanESM2 projects significant aridity such that the extent of prime land for spring seeded grains (without irrigation) is greatly diminished relative to current condition. However, INM\_CM4 projects increased precipitation along with the heating such that the extent of prime land is greatly increased relative to the current condition. The LSRS output mapping clearly demonstrates the spatial uncertainty of future land suitabilities and the range of possible crop distribution patterns.

### DISCUSSION

LSRS classification remains a largely qualitative pursuit utilizing parametric scores based on expert knowledge to calculate deductions based on measurable climate, soil and landscape attributes. The derived class ratings are relative rankings rather than absolute measures. Rossiter (1996) termed this approach as a "land index," a simple rating of the goodness of fit of a particular land activity (crop type) for a given land capability. The principle purpose of agriculture land suitability assessment is to predict the potential and limitations of the land for production of a specific crop or crops. LSRS generates a measure of both potential (as a class rating) and a limitation (as a subclass modifier). LSRS contains a set of tools that allow the user to calculate a suite of agro-climatic indices integrated to a soil map to allow spatial assessment at a range of scales. LSRS has evolved to be increasingly useful to climate change studies in that it is able to depict spatially future land use possibilities. While the LSRS modules currently cover only common annual crops and some forages, the suitability concept of integrating climate, soil and landscape attributes in a spatial framework has been extended to examine climate change on potential future extent of sweet cherries in the interior of British Columbia (Neilsen et al., 2017).

Validation of the rating system remains a challenge. Two obvious independent variables, crop yield and crop presence on the landscape are two attributes against which to try to relate to suitability. However, as both of these variables are very much controlled by market/economic factors and management inputs, neither of which are considered in the LSRS computations, finding a strong correlation between an LSRS class rating and crop yield remains elusive. The early development of the system was driven by a need to have LSRS, specifically the spring seeded small grains module, emulate the CLI agriculture capability ratings. This objective was achieved but the LSRS is not a crop yield model nor necessarily a predictor of crop presence. Validation of the outputs remains an area of study.

A drawback of using a class rating structure is the inability to often detect modest changes in point ratings that may be obscured by the relatively wide range of points that constitute any one class. We encountered this problem in our climate change work in British Columbia (Gasser et al., 2016) where in some cases small point changes generated class changes, in other instances relatively large point changes did not generate any class change at all. When analyzing change, it is often better to work with point deduction values directly rather than class values.

The current version of the rating system utilizes webbased technologies that optimize data handling and storage and facilitates user access to the tools and calculators that make up the system. The efficiency in generating agro-climatic indices from both daily and monthly data makes LSRS well suited to climate change studies in Canada where many existing crop production systems are limited by climate (Campbell et al., 2014).

### AUTHOR CONTRIBUTIONS

MB and CS compiled the first draft of the article and CS revised the manuscript following reviewer comments. DN was project lead at Summerland for land suitability research and provided funding and technical input to model developments. WP wrote an earlier version of the introduction and led the development of versions 1, 2, and 3 of LSRS, P-YG and MB conducted GIS work and produced the map figures, AB established the agro-climatic indices for the new crop modules and wrote an earlier version of the crop module section, AJB provided development guidance for version 3 and 4. PS built versions 4 and 5 of LSRS and currently manages the model calculators and model website.

### ACKNOWLEDGMENTS

We would like to acknowledge funding from the Science and Technology Branch of Agriculture and Agri-Food Canada to support the development of the LSRS. We also acknowledge the contributions of the original Agronomic Interpretations Working Group that initiated the develop of the system. We would also like to thank David Lee and Simon Kiley for support and help with preparation of map figures for the paper. Institutional support from the Alberta Agriculture, Food and Rural Development is gratefully acknowledged as well as the contributions of the many contractors, summer students and consultants who provided field testing, expertise, and feedback over the years.

### REFERENCES

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs. 2018.00077/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Bock, Gasser, Pettapiece, Brierley, Bootsma, Schut, Neilsen and Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Using spatial reinforcement learning to Build Forest Wildfire Dynamics Models From satellite images

#### *Sriram Ganapathi Subramanian and Mark Crowley\**

*Machine Learning Laboratory, Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada*

#### *Edited by:*

*Nathaniel K. Newlands, Agriculture and Agri-Food Canada (AAFC), Canada*

#### *Reviewed by:*

*Saumitra Mukherjee, Jawaharlal Nehru University, India Xinyan Huang, Hong Kong Polytechnic University, Hong Kong*

> *\*Correspondence: Mark Crowley<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Environmental Informatics, a section of the journal Frontiers in ICT*

*Received: 25 November 2017 Accepted: 15 March 2018 Published: 19 April 2018*

#### *Citation:*

*Ganapathi Subramanian S and Crowley M (2018) Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images. Front. ICT 5:6. doi: 10.3389/fict.2018.00006*

Machine learning algorithms have increased tremendously in power in recent years but have yet to be fully utilized in many ecology and sustainable resource management domains such as wildlife reserve design, forest fire management, and invasive species spread. One thing these domains have in common is that they contain dynamics that can be characterized as a spatially spreading process (SSP), which requires many parameters to be set precisely to model the dynamics, spread rates, and directional biases of the elements which are spreading. We present related work in artificial intelligence and machine learning for SSP sustainability domains including forest wildfire prediction. We then introduce a novel approach for learning in SSP domains using reinforcement learning (RL) where fire is the agent at any cell in the landscape and the set of actions the fire can take from a location at any point in time includes spreading north, south, east, or west or not spreading. This approach inverts the usual RL setup since the dynamics of the corresponding Markov Decision Process (MDP) is a known function for immediate wildfir e spread. Meanwhile, we learn an agent policy for a predictive model of the dynamics of a complex spatial process. Rewards are provided for correctly classifying which cells are on fire or not compared with satellite and other related data. We examine the behavior of five RL algorithms on this problem: value iteration, policy iteration, Q-learning, Monte Carlo Tree Search, and Asynchronous Advantage Actor-Critic (A3C). We compare to a Gaussian process-based supervised learning approach and also discuss the relation of our approach to manually constructed, state-of-the-art methods from forest wildfire modeling. We validate our approach with satellite image data of two massive wildfire events in Northern Alberta, Canada; the Fort McMurray fire of 2016 and the Richardson fire of 2011. The results show that we can learn predictive, agent-based policies as models of spatial dynamics using RL on readily available satellite images that other methods and have many additional advantages in terms of generalizability and interpretability.

Keywords: reinforcement learning, machine learning, deep learning, A3C, forest wildfire management, sustainability, spatially spreading processes

## 1. INTRODUCTION

There is a clear and growing need for advanced analytical and decision-making tools as demands for sustainable management increase and as the consequences of inadequate resources for decision-making become more profound. Artificial intelligence and machine learning methods provide ways to combine multiple modes of information such as spatial statistical ground data, weather data, and satellite imagery into a unified model for classification or prediction.

One high impact example of this potential is forest wildfire management. The risk, costs, and impacts of forest wildfires are a perennial and unavoidable concern in many parts of the world. A number of factors contribute to the increasing importance and difficulty of this domain in future years including climate change, growing urban sprawl into areas of high wildfire risk, and past fire management practices which focused on immediate suppression at the cost of increased future fire risk (Montgomery, 2014).

There are a wide range of challenging decision and optimization problems in the area of forest fire management (Martell, 2015), many of which would benefit from more responsive fire behavior models which could be run quickly and updated easily based on new data. For example, one simple decision problem is whether to allow a fire to burn or not, since burning is a natural form of fuel reduction treatment. Answering this question requires a great deal of expensive simulations to evaluate the policy options fully (Houtman et al., 2013).

These simulations are built by an active research community for forest and wildland fire behavior modeling. Data are collected using trials in real forest conditions, controlled lab burning experiments, physics-based fire modeling, and more (Finney et al., 2013). These hand crafted physics-based models' simulations have high accuracy but are expensive to create and update and computationally expensive to use. The question we ask is: **Can we learn a dynamics model from readily available satellite image data and treating wildfire as an agent spreading across a landscape in response to neighborhood environmental and landscape parameters?**

In this work, we provide evidence for an affirmative answer to this question by introducing a new approach for using reinforcement learning (RL) (Sutton and Barto, 1998) to automatically learn wildfire spread dynamics models by treating fire as an agent on the landscape taking spatial actions in reaction to its environment (Subramanian and Crowley, 2017).

Forest wildfire spread as a specific case of a more general problem which we call spatially spreading processes (SSPs), which occur when local features are changed over time by some dynamic process which is a function of properties at different locations in space and their proximity to the target location. This is to be distinguished from mere prediction of impact of a physical object moving across space as the SSP can be active in many, or all, locations at once. This is also more than merely spatial auto-correlation that measures the degree to which features at two locations are similar based on their proximity. Spatial autocorrelation can be an indicator of the presence of an SSP, but the dynamic changes over time between spatial locations may not result in values being similar or inversely correlated in a simple way. In many of these domains, the dynamics are often modeled by hand using agent-based models and geostatistics methods. In other areas, they are increasingly learned from data streams by being treated as videos. However, each of these approaches has their drawbacks. One of the goals of our research is to find novel representations of local dynamics for SSPs that can provide transparent and interpretative solutions to learning of dynamics models and decision-making across many domains. The approach proposed here offers the tantalizing possibility to represent and learn a *causal* agent-based policy representation using RL which would be a much easier model to interpret and analyze for human decision makers.

In this article, we carry out experiments on the accuracy of interpolation and forward prediction using five different RL algorithms in a new formulation of agent-based learning. We use easily accessible Landsat satellite data from the USGS satellite data portal and provide a prediction accuracy of resulting policies learned with each algorithm upon comparison with the same. The dataset is a set of satellite photos of massive wild fires in Northern Alberta in 2011 and 2016. We show that Monte Carlo Tree search and the Deep RL algorithm A3C (Mnih et al., 2013) perform the best but have advantages in different situations in this domain. For comparison, we also implemented a Gaussian Process (GP) classifier (Rasmussen and Williams, 2006) as a supervised learning model on the dataset. This classifier places a Gaussian Process prior on a latent function, which is then squashed to the domain [0, 1] through a link function to obtain the probabilistic classification.

This approach has applications in forest wildfire management and opens up possibilities for interpretative dynamics models as well as providing new challenging data sets for RL research. Our approach promises to provide another source of validation for existing wildfire models as well as gives us the opportunity to perform faster and more accurate prediction by learning patterns in the raw data. Another application of our approach would be learning transferable models from one data-rich region or time period and applying it to another region or time period where less data are available if there is evidence that fires in both regions behave similarly.

In prior work (Houtman et al., 2013), we have used standard, physics-based simulators such as Farsite (Finney, 1998) to carry out automated planning of fire control policies using Monte Carlo simulations and optimization. These simulations took on the order of 1 h to run for 100-year simulations of forest fire futures, which present a challenge when thousands of simulation trajectories are needed for statistical confidence. Thus, augmenting these detailed simulators with faster approximations learned from data could improve the ability to do automated planning.

### 2. REVIEW OF LITERATURE

We provide an overview of the relevant literature on the forest wildfire prediction and management problem in general and then on the previous use of machine learning algorithms for this domain.

## 2.1. Machine Learning on Satellite Imagery

Researchers have attempted to use machine learning in combination with satellite imagery in a series of ecological applications in the past. In Kubat et al. (1998), the authors use satellite radar images and machine learning for the detection of oil spills. Algorithms, such as C4.5 and 1-nearest neighbor, are used along with expert rules to train the classifier which the authors specify was hard and not completely accurate. Our work completely removes the need for such expert rules.

In Jean et al. (2016), satellite imagery and machine learning has been used to tackle the case of poverty. Convolutional neural nets and transfer learning have been used to derive models having good performances in terms of accuracy. This research demonstrated how machine learning tools, which are typically suited for data-rich domains, can be used for data-scarce settings too.

Coral reef research is another aspect that has been studied using satellite images and machine learning (Knudby et al., 2010). In Knudby et al. (2010), a series of statistical and machine learning models have been used on the IKONOS satellite imagery to produce spatially explicit predictions of species richness, biomass, and diversity of fish community. This research motivates the exploration of importance of different variables in the predictive models by using permutations techniques.

### 2.2. Forest Wildfire Prediction and Management

Montgomery (2014) discusses the increased future fire risk at the consequence of fire management practices, which focused on immediate suppression. The three core themes of externalities, incentives, and risk-based decision analysis in the case of wild fire suppression are described. The goal is to determine how the core themes contribute to the evolution of an effective future fire policy. This problem is also enumerated in Houtman et al. (2013).

The standard for wildland fire behavior and forest fire modeling is described in Finney et al. (2013), which carried out exhaustive lab experiments, real forest condition simulations, and trials to build sound and coherent fire spread theory for model reliability. The resulting models are used by the US Forest Service but are very computationally expensive to run. The model accuracy also varies widely across wild fires in different regions. Cellular automaton models are also widely used to predict wildfire spread (Yongzhong et al., 2004). Our approach is easier to apply than these methods and is shown to perform better than the same.

The work in Martell (2015) describes the need for a computationally simple and accurate model for wild fires. They highlight a number of challenging decision and optimization problems in the area of forest fire management and recent efforts to develop decision support tools to overcome them. The focus is on using methods of Operations Research to aid fire managers making complex decisions about fire suppression and resource allocation.

### 2.3. Machine Learning in Forest Fire Management

In Castelli et al. (2015), the authors discuss the application of an intelligent system based on genetic programming for the prediction of burned areas in a wild fire situation. They also compare the genetic programming methodology to state-of-the-art machine learning techniques in fire modeling and conclude that genetic programming techniques are better. The major machine learning techniques used are SVM with a polynomial kernel, random forests, radial basis function network, linear regression, isotonic regression, and neural networks.

Significant problems have arisen while dealing with large databases or long periods of observation (e.g., pattern recognition, geophysical monitoring, monitoring of rare events (natural hazards), etc.). The authors in Forsell et al. (2009) stipulate that the major problems in such cases are how to explore, analyze, and visualize the oceans of available information. Several important applications of machine learning algorithms for geospatial data are presented: regional classification of environmental data, mapping of continuous environmental data including automatic algorithms and optimization (design/redesign) of monitoring networks.

Machine learning algorithms use an automatic inductive approach to recognize patterns in data. Once learned, pattern relationships are applied to other similar data to generate predictions for data-driven classification and regression problems. The work in Cracknell and Reading (2014) takes a task of supervised lithology classification (geological mapping) using airborne images and multispectral satellite data and compares the application of popular machine learning techniques to the same. A 10-fold cross validation was used to select the optimal parameters to be used in all the methods. These selected parameters were used to train the machine Learning classification models on the entire set of samples.

In Sehgal et al. (2006), the authors use a machine learning approach for Geospatial Entity resolution which is the problem of consolidating data from diverse sources into a single data source referenced by location (in the form of coordinates). Several feature-based matching techniques like location name matching, coordinate matching, and location-type matching are introduced and evaluated. These feature-based matching techniques use each location feature independently. A new method integrating spatial and non-spatial features and learning a combined spatial similarity measure is introduced.

Modeling forest areas is concentrated upon in Garzón et al. (2006). The environmental variables consisting of both topographic and climatic factors are considered in this work. A modeling framework for habitat modeling is established to train, test, and validate the popular predictive machine learning methods. Neural Networks, Random Forests, and Tree-Based Classification are used as predictive models. A ROC curve (Specificity vs Sensitivity graph) analysis is done for parameter selection. Species distribution and habitat modeling are described to be complex problems much like the wild fire problem with many responsible factors and the authors admit that modeling all the factors are impossible with the current state of the art. Hence, having an agent-based approach that learns the relevant factors on its own seems to be the most suitable idea.

Machine learning is used for the spatial interpolation of environmental variables in Li et al. (2011). In this study, around 23 methods are considered including popular machine learning methods and their combinations. Along with machine learning, the considered methods were drawn from a large pool of categories including geostatistical methods, non geostatistical methods, statistical methods, and combined methods. The dataset consists of about 177 samples of sea bed mud content in the southwest of Australian Exclusive Economic Zone, and the problem is to determine the mud content in the other points by interpolation. Several primary and secondary variables are considered to influence this decision. Random forest seems to perform the best among the methods considered. RF performance was attributed to its relative robustness to outliers and noise, its nature of not over fitting with respect to the source data, and its ability to model complex interactions.

Recknagel (2001) discusses a summary of all the different ecological modeling applications of machine learning. Artificial Neural nets, Genetic Algorithm methods, and Adaptive Agents seem to perform the best, but they seem to have advantages for different problems. The conclusions are that Artificial Neural Nets perform well in problems of non-linear ordination, visualization, multiple regression, time series modeling, and image recognition and classification. Genetic algorithms are suitable to evolve causal rules, process equations, and optimize process parameters. Adaptive agents are suggested for providing a novel framework in the aid of the discovery and forecasting of emergent ecosystem structures and behaviors in response to environmental changes. This review demonstrates that while various classical AI and ML techniques have been applied to this domain in preliminary forms, there is a gap in the literature on attempting to use modern deep learning and RL techniques.

The existing state-of-the-art methods for building fire prediction models directly satellite images include the Forest Fire risk prediction model used in China (Zhang et al., 2011), the Canadian systems like Forest Fire Weather Index (FWI) System, and the Forest Fire Behavior Prediction (FBP) System (Stocks et al., 1989; Zhang et al., 2011). These systems are still quite difficult to implement due to the requirement of large amount of heterogeneous sources of data such as fuel property and fire characteristics (Zhang et al., 2011), which requires data from high resolution close-range sensors. The work by Alkhatib (2014) explains the advantages of the satellite-based fire prediction systems but specifies that the temporal resolution of available data serves as a serious impediment. Our work specifies a method to learn from available data and reliably interpolate to fill the missing spaces to get an acceptable overall performance.

We model the forest fire domain as a Markov Decision Process (MDP). This approach is becoming more common (Mcgregor et al., 2016), but these are usually focused on finding treatment actions to be taken to reduce or alter fire spread. We take the novel approach of modeling the fire itself as a decision agent attempting to minimize prediction error. This is related to work in Forsell et al. (2009), which investigated using the state variable to represent land vegetation cover and environmental characteristics but to use the action variable to represent interaction between characteristics of nearby locations. This was not on a domain as dynamic as forest fire, however, and our approach differs entirely in implementation.

Machine learning has also been used to detect and classify burn areas in Indian forests (Saranya and Hemalatha, 2012). Saranya and Hemalatha (2012) use spatial data mining to obtain useful information from a series of datasets and subsequently apply supervised algorithms such as Artificial Neural Nets (ANN) and Sequential minimal optimization (SMO) to quantify ignition risk of different regions and hence predict the occurrence of fires. Similarly, Sitanggang and Ismail (2011) use Decision Trees and a series of IF–THEN rules to develop a classification model for forest fires in Indonesia. We prove the superiority of RL techniques to such supervised methods in our work. For instance, the results in Sitanggang and Ismail (2011) show an accuracy of about 63%, whereas our best RL models perform much better than that in similar test cases. In addition, Angayarkkani and Radhakrishnan (2009) use fuzzy logic and fuzzy membership rules to make a forest fire detection system from satellite images. The methods outlined are only capable of predicting fires at the time of satellite image availability, and it is not possible to predict forward or inbetween as demonstrated in our work.

Our work also has similarities to the use of intelligent systems for predicting burned areas as suggested in Castelli et al. (2015). However, that work focused on burned area alone whereas we look at the more specific problem of prediction of actual fire spread location over the short term.

### 3. PROBLEM FORMULATION

The problem is formulated as a Markov Decision Process (MDP) <*S*, *A*, *P*, *R*> where the set of states *S* describes any location on the landscape. A state *s* ∈ *S* corresponds to the state of a cell in the landscape (*x, y, t, l, w, d, rh, r*) where *x* and *y* are the location of the cell, *t* is the temperature at the particular time and location, and *l* is the land cover type of the cell, which could be one of water, vegetation, built up, bare land, and other (derived from satellite images), and *w* and *d* are wind speed and direction, *rh* is relative humidity, and *r* is amount of rainfall. The "agent" taking actions is a fire spreading across the landscape. The action *a* ∈ *A* indicates the direction the fire at a particular cell "chooses" to move: north, south, east, or west or to stay put. These variables are considered to be the most contributing to fires as they are the primary variables in the Canadian Forest fire weather index as specified in Cortez and Morais (2007).

The dynamics function for any particular cell *P*(*s*′|*s, a*) is a mapping from one state *s* to the next *s*′ given an action *a*. Note, unlike most RL domains where the unknown dynamics of the system can be very complex, in our formulation the dynamics are actually straightforward. Most properties of the cell state do not change quickly, or at all, in response to our actions. So the action of spreading a fire into a neighboring cell directly alters *p* in that cell, the probability that the neighboring cell in that direction will move to a burn state.

The reward function *R* maps a cell state to a continuous value in the range [−1, 1]. Rewards are based on the land-cover, for example, a cell predominantly filled with water has less chance catching fire compared with one filled with vegetation. Thus, there is a negative reward for choosing to spread fire to a cell with high percentage of water and a small positive reward for choosing to spread to a cell covered with vegetation. Other reward function components, are derived from the ground truth data for the training sets. Thus, in the training phase, the fire is given a positive reward for taking action choices similar to the actual scenario and negative rewards otherwise. These are applied for particular experiments and algorithms as explained in the experimental setup.

 The task then is to learn a policy for this agent, which minimizes the total error at different future steps.

The initial state will come from satellite images that correspond to the beginning of a fire. We are focusing on fire growth rather than ignition detection, so we set certain cells to have just ignited fire and assign these to the initial state. As it is impossible to predict precisely from where a "fire starts," these ignited cells are decided based on thermal image data, media reports, and approximations based on the burning areas in the first day on which the satellite image is available. Wind speed and wind direction are assumed to be a constant for a small area at a fixed point of time.

The goal is to learn a policy for this agent that recreates the spread of the fire observed in later satellite images by maximizing discounted rewards designed to reward high accuracy simulation. Actions are constrained to not cross the boundary of the domain of study.

### and the coordinates of Richardson Backcountry is (57°22′02.3″ N, 111°19′27.1″ W). The satellite images are downloaded from the USGS Earth Explorer data portal1 for Alberta. The Landsat Enhanced Thematic Mapper Plus (ETM +) sensor carried on Landsat 7 was the most important source of data. A summary (Kanevski et al., 2008) of the merits of various software packages and tools (such as GeoMISC, GeoKNN, GeoMLP, etc.) in relation to machine learning on geospatial data was useful in developing this work. **Figure 2** shows an example where the smoke over burning areas can be seen. A series of visual and thermal images corresponding to the duration of occurrence of the Fort McMurray fire (approximately from April 2016 to September 2016) and Richardson fire (approximately from May 2011 to October 2011) were collected for the corresponding regions of Alberta. All the images were corrected for missing values and outliers. Additional pre-processing steps were carried out as outlined in Cracknell and Reading (2014). These images gave a clear description of the ground scenario in case of fire and are a reliable source of burn areas. The pixels on fire are clearly at a far higher temperature corresponding to the pixels not on fire, and this can be easily delineated from the thermal images. **Figure 3** shows the areas in darker shades of black are on fire. The spatial resolution of the satellite images was 30 m. This means that a pixel

### 3.1. Data Acquisition

The Richardson fire occurred in a region called Richardson Backcountry north of Fort McMurray in Northern Alberta in 2011. Fort McMurray was also affected by forest fires in 2016 during the massive Fort McMurray fires (Woo and Tait, 2016). The coordinates of Fort McMurray is (56°43′36″ N, 111°22′51″ W)

other cells will be considered for spread. The arrows from the white circle indicate that there is a strong wind blowing toward north, and the north action is the most likely action choice for these cells (effect of wind).

Figure 2 | Raw color image. Images obtained from USGS/NASA Landsat Program.

Figure 3 | Thermal image. Images obtained from USGS/NASA Landsat Program.

1https://earthexplorer.usgs.gov/ (Accessed: December 5, 2016).

in the satellite image corresponds to 30 m × 30 m on the ground. This also means that two objects, at least 30 m long or wide, sitting side by side, can be separated (resolved) on a Landsat image. The temporal resolution of this satellite is 16 days. Thus a 16-day periodic information of a particular area during the course of the fires are available. The Landsat Enhanced Thematic Mapper Plus (ETM +) sensor is capable of sensing in 8 different spectral bands, which are made available in its datasets. Only the bands in the visible spectrum (red, blue, and green) and the thermal band are used in this work. Landsat 8 imagery has a radiometric resolution of 12-bits (16-bits when processed into Level-1 data products).

The land-cover value is obtained by processing the satellite images in an open source geoprocessing software (Multispec). Temperature is obtained from processing thermal images from the same data source. Wind speed, rain, and wind direction are obtained from the historical datasets present in the Canada Information Portal2 and World Clim datasets for the region of study. Relative humidity is obtained from processing satellite datasets from the USGS as explained in Peng et al. (2006).

The resolution for all the inputs was fixed to be 30 m in accordance with the spatial resolution of the satellite images. As all the other information came from data sources that have lesser resolution that 30 m all the inputs were comfortably generalized to 30 m. For example, the initial state of the Richardson fire contained the following values for a particular spatial location (fixed x, y) and calendar day. Date = May 8, 2011, geographic coordinates (x, y) = (57°24′11.2″ N, 111°13′20.8″ W), temperature (t) = 10.1 C, land cover (l) = vegetation (black spruce), wind speed (w) = 8.05 mph, wind direction (d) = NW, rainfall (r) = 35.1 mm, and relative humidity (rh) = 50%.

### 4. DEFINITION OF ALGORITHMS

We compare five very different, widely used RL algorithms on this domain to see the range and application of the idea. We begin with three classic approaches for iteratively solving MDPs, value iteration, policy iteration, and Q-learning. We then consider the recent and very different approaches of Monte Carlo Tree Search and A3C, a recent policy gradient RL algorithm that utilizes deep Learning for value function representation. These algorithms will be reviewed briefly with modifications needed for our problem highlighted.

For all of the following methods each cell in the target area that is visited has the resulting value estimate stored in a hash data structure so it expands only as new states are encountered.

### 4.1. Asynchronous Value Iteration (VI)

The optimal value of the state *V*\*(*s*) under the greedy fire spread policy is given by the following Bellman equation:

$$V^\*(s) = R(s) + \max\_{a'} \gamma \sum\_{s'} P(s' \mid s, a) V^\*(s') \tag{1}$$

where *s*′ is the successor state and *γ* denotes the discount factor which we set to 0.9. States are randomly sampled to update the value function according to equation (1) and updates are stopped when the change in value made by the successive iterations is less than 0.1. To provide a signal for training, a pixel in the center of the region of consideration that is currently burning in the next time step is given a high positive reward. This can be considered the goal state to reach for the burning cell as in a classic navigation problem using RL. This approach is utilized for training both value and policy iteration algorithms.

### 4.2. Policy Iteration (PI)

We begin with an initial random policy for acting in each state and iteratively improve it through alternating policy evaluation 2 and improvement 3 steps defined as follows:

$$V\_{\pi}(s) = R(s, \pi\_{(s)}) + \gamma \sum\_{s' \in S} T(s, \pi(s), s') V\_{\pi}(s') \tag{2}$$

$$\pi\_{k+1}(s) = \operatorname{argmax}\_{a} \sum\_{s'} T(s, a, s') \left[ R(s, a, s') + \gamma V\_i^{\pi\_k}(s') \right] \tag{3}$$

The discount factor *γ* was fixed to be 0.9. For this algorithm, the value function *Vπ*(*s*) is an array of **Net Policy Worth** values for all states, which is the highest cumulative reward that could be obtained for an optimal policy passing through that state. This is required to maintain single-step dynamics since each cell could take more than one action at a time as fire spreads in multiple directions.

### 4.3. Q-Learning (QL)

This algorithm (Watkins, 1989) performs off-policy exploration and uses temporal differences to estimate the optimal policy. In Q-learning, the agent maintains a state-action value function *Q*(*S,A*) instead of a state value function. This is updated as follows:

$$\triangle \dot{Q}(s, a) = \left( r + \gamma(\max Q(s', a')) \right) \tag{4}$$

and

$$Q(s,a) = Q(s,a) + \alpha(\triangle(s,a) - Q(s,a))\tag{5}$$

where *α* denotes the learning rate and *γ* denotes the relative value of delayed vs immediate rewards. *S*′ is the new state after action *a*. *a* and *a*′ are actions taken in states *s* and *s*′, respectively. maxa *Q*(*s*′, *a*′) denotes the estimate of maximum discounted future reward expected.

The learning rate for Q-learning was chosen to be 0.9 as we need the most recent information to have a higher impact in a continuous spatial environment like that of forest fire. There is a high positive spatial auto-correlation in spatial datasets corresponding to fire as the similar pixels(on fire or not fire) tend to cluster together. The Q-learning is made to exploit this property using the appropriate learning rate. The discount factor is determined to be 0.9 as the long-term rewards are more important than the short-term rewards in our model and we want the model to converge. Using a lower discount rate decreases the level of exploration and the risk of falling into a local optimum becomes high. Each state-action pair is considered as a step taken in the real world. For every valid state, the highest Q value (utility value) for the state is recorded. The burned area is determined to be all the states having utility values over the threshold. Further, for each successive time step in the real world, a suitable number of

2 https://www.canada.ca/en/services/environment/weather.html (Accessed: December 5, 2016).

steps taken by the fire are determined approximately using the difference between the total number of cells being burnt between successive time steps and is used to cap the total number of steps (state—action pairs analyzed) in the Q-learning implementation for the time step at which the learning is taking place. This is done to obtain a faster convergence.

### 4.4. Monte Carlo Tree Search (MCTS)

These are a class of algorithms that perform approximate, but confidence-bounded, RL by performing short roll-outs of the current policy to obtain enough statistical information about a state to keep it or discard it as belonging to the optimal path. A good survey is provided in Browne et al. (2012).

In our implementation of MCTS, each node in the search tree is a valid cell state in the fire model. From each state, any possible action could be taken, which is modeled as branches in the tree. The nodes in the tree are made to be selected by the **Upper Confidence Bound for trees (UCT)** (Kocsis and Szepesvári, 2006) method to minimize the cumulative regret. Each "step" in the MCTS tree is defined as a possible action taken from any valid state. A correlation between number of steps a fire can take in a 16-day period has been worked out empirically to be about 1,000. So, the MCTS roll-out policy is forced to stop after taking 1,000 steps. Note that there is a stay action defined for every fire location. Thus, in this implementation, it is possible to go a few levels down the tree without that corresponding to any real action in the world. Rewards are given based at each step of the roll-out and when complete the combined reward is used to update the value for the initial state at the root of the roll-out tree. For computational simplicity, during the roll-out a simpler state representation is used which focuses only on number of cells burning.

### 4.5. Deep RL

The final comparison is using the **Asynchronous Advantage Actor-Critic (A3C)** algorithm (Mnih et al., 2016), which represents the state-action value function using a Deep Q-Network (DQN) (Mnih et al., 2013). This algorithm defines a global network in addition to multiple worker agents with individual sets of parameters. The input to this global network was formalized as a grid of 100 × 100 cells with each cell having state values, which is an average of the state values of several pixels derived from satellite images. For our problem, A3C has the advantage of defining multiple worker agents. Each separate instance of a fire (unconnected to other fires) in a neighborhood is given its environment as an input, and the fire is defined as an individual worker. In our data, there are 96 instances of fire (thus, 96 worker agents) considered for training and testing. Each worker would then update the global environment, and we have plotted the result obtained. The deep network used is based on the DQN network given in Mnih et al. (2013), which uses an input layer of 100 × 100 pixel windows from the satellite image for the start date. Then there is a convolution layer of 16, 8 × 8 filters with a stride of 4 followed by a rectifier nonlinear unit (ReLU) layer. The second hidden layer involves 32 4 × 4 filters using a stride of 2 also followed by a ReLU layer. The final fully connected layer uses 256 ReLUs, which is output to a layer of threshold functions for the output of each of the five possible actions.

### 5. SUPERVISED CLASSIFICATION— GAUSSIAN PROCESS

In this supervised classification method of performing the experiments, the Gaussian process algorithm (Rasmussen and Williams, 2006) is used to estimate if a particular cell is burning or not based on the burning conditions of other cells in the scene. For each particular cell, the attributes of temperature, rain, relative humidity, land-cover type, wind speed, and wind direction are considered to influence the decision.

A distribution over function *f* can be specified by the Gaussian process as given by:

$$p(f) = GP(f \mid \mathbf{0}, k(\mathbf{x}, \mathbf{x}')),$$

where the mean function is 0, and the covariance is defined by some kernel function. We use the RBF kernel in our experiments because we have an assumption of 2D proximity of features being relevant in any direction.

The GP prior is a multivariate normal given by:

$$p(\boldsymbol{y}) = \operatorname{Normal}(\boldsymbol{y} \mid \mathbf{0}, K),$$

where *K* is a covariance matrix given by evaluating *k*(*xn*, *xm*) for each pair of inputs. The mean of the GP prior is assumed to be 0 in accordance with the above equations.

To obtain a probability the output response is condensed into the range [0, 1], which is an appropriate choice for classification. The probability is given by the condensed value, and a Bernoulli distribution is used to determine the label.

The likelihood of an observation (*xn*, *yn*) is given by:

$$p(\boldsymbol{y}\_n \mid \boldsymbol{z}, \boldsymbol{x}\_n) = Bernoulli(\boldsymbol{y}\_n \mid \text{logit}^{-1}(\boldsymbol{z}\_n)).$$

Gaussian processes were chosen for this problem as they are known to work well for modeling non-linear relationships between variables especially in spatial domains. For the GP classifier, we use the logistic (sigmoid) function, whose integral is approximated for the binary case to map output to a probability. For the covariance function, we use an RBF kernel (stationary kernel) because we have an assumption of 2D proximity of features being relevant in any direction. The hyper-parameters of the kernel are optimized by maximizing the log-marginal-likelihood of the optimizer. A Cholesky decomposition was used to decompose the kernel matrix. The result is a binary output response, which can be used for classification.

### 6. EXPERIMENTAL SETUP

To evaluate this idea of agent-based RL for SSP prediction, we performed a cross-comparison of five RL algorithms and a supervised spatial GP approach using a two-stage training process. In the first stage, we learn the cell-based fire spread policy directly using the satellite images from the start of the fire and the image at its next successive time step. This is the policy from the MDP, which describes spread of fire from one cell to another based on local conditions. The training data images are spaced 16 days apart, so many successive calls to this policy are required to reach the fire spread for 16 days. Thus, the second stage of training is to choose a cap *C* for the number of calls to this policy to obtain the correct spread area. We use another future satellite image to choose this cap based on maximum number of cells the policy got correct. The cap is used to set the upper limit of the total number of moves in any experiment.

In experiment (A), we test the ability of the algorithms to learn the spread of fire in an intermediate time step if the previous and next time step are known. Thus, we optimize *C* based on the ground truth data for time step 3. Now, the learned policy and the cap are used to determine the fire spread at the intermediate step 2 given that this is halfway between the state reached in step 3.

In experiment (B), we start with the initial state of the fire, we provide rewards based on the time step 2 of the fire, and we ask the algorithm to predict time step 3. This experiment is similar to asking the question: Where will the fire spread in the next 16 days, given its position currently? In this case, the cap value *C* is tuned for a 16-day duration.

In experiments (C)–(F), we apply the learned policy from the Richardson fire to the Fort McMurray fire over four 16-day time steps but tune the cap value *C* using the first transition in the Fort McMurray data. This was a fire that happened in Northern Alberta 5 years after the Richardson fire used for training. As the regions are similar and very near each other, the general properties that the model encapsulates should remain relevant. The initial state for the Fort McMurray fire is provided in a similar way to the Richardson fire.

After limiting the number of calls in any experiment based on the cap, we need a policy that determines if a fire should spread from one burning cell to a nearby non-burning cell. For the RL algorithms, this is done by applying a threshold to the value function to determine spread or non-spread. This threshold is determined for each algorithm to balance true positives and false positives based on the training data. For all the experiments, the result from the algorithms in the form of burned areas is compared against the actual scenario, using the satellite images corresponding to the predicted time step.

All experiments were run on an Intel core i7-6700 CPU with 32GB RAM.

### 7. ANALYSIS OF RESULTS

The task in these experiments is essentially to classify each cell at each time step correctly in terms of burning or not burning. So we can use the true positive rate and false positive rate to compute a receiver operating curve (ROC) and a corresponding area under the curve (AUC) metric. The AUC for experiment (A) is shown in **Table 1** for all algorithms and shows that the A3C algorithm provides an excellent threshold while classic value iteration is the least favorable. This seems to augur well with the overall accuracy for experiment (A) seen in **Table 2**.

**Figures 4** and **5** show the visualization of some of the results for the different experiments. For all the images, the red pixels correspond where fire was classified correctly, blue pixels represent those which were classified as burning but were not yet Table 1 | AUC for all the methods.

Table 2 | Average accuracy for each algorithm on the different test scenarios.

*Bold font indicates the best performing algorithms in the corresponding experiments.*

at that point. White pixels represent false negatives where the policy predicted no fire but fire was indeed present. Black pixels represent true negatives, the pixels that were not on fire and correctly classified.

From all the algorithms considered, A3C seems to be the best overall, which is not surprising as it has the most flexible stateaction value function representation. Policy Iteration in general comes second best in most tests. Q-learning a model free method of learning gives a lesser accuracy than model-based approaches like Value and Policy Iteration. In a spatial domain, the environment has a high influence on the spread of the agent. Thus, the strong consideration of the model of the spatial environment helps the model-based approaches.

The output images in **Figures 5G,H** show the two most successful algorithms MCTS and A3C on the region on which they were trained, filling in fire predictions between the start state and the reward target. Looking at the output images in **Figures 4E,F**, we see these two algorithms applied to the Fort McMurray fire (similar region, different year, and not part of the training data) at the same number of days forward after the start state. We can see that the model trained on one set images applies quite well to a different start state it has never seen, but A3C has much fewer false positives than MCTS. **Figure 5** visualizes the results of all the RL algorithms on predicting the next time step after the training data on the Richardson fire, here A3C clearly produces the most accurate prediction.

Somewhat surprisingly, MCTS outperforms A3C in experiment (B) for forward prediction of the next 16-day period on the same location as the training data. It seems that the MCTS rollouts have fit a better model to the sense of real world "time" to predict the extent of a fire in the next time period. As can be seen for the results for (C) and (D) in **Table 3**, the A3C algorithm outperforms all others on prediction when the learned policy for one location is applied to another location in the same region. MCTS does significantly worse in this challenge, meaning its model is not as good at generalizing the essentials of the policy for transfer to a new location even though it seems to have a better model of dynamics.

Note that the accuracy of all algorithms reduces with increasing time after the start of a fire as can be see by looking at experiments (C)–(F) in **Table 3**. This is unsurprising, as predicting multiple steps into the future is inherently harder. However, it is also partially due to the fact that later fires are larger, more intense, and spread faster, making learning more difficult. In the first time step, all algorithms do very well as it is easier to predict the first few moves of the fire. The accuracy goes down rapidly, and in the last time step it is only around 10%.

It is also interesting to note that even though MCTS starts of with a low accuracy compared with the other methods in the first time step, its cautious approach causes its accuracy to decrease much slower than other methods so that it seems tied with A3C when predicting three steps ahead. Similarly, the supervised GP method that loses out in most comparisons does far better for the hardest problem of predicting four time steps (about 2 months) ahead after all other methods have degraded. The GP approach is purely spatial pattern learning, with no element of dynamics. So one possible explanation is that, GP's with only a spatial model and no notion of time loses out when changes over time are relevant, but once we are so far ahead after the start of the fire a notion of time actually proves to be detrimental as the behavior of a very intense fire is difficult to correlate with time and the GP's more accurate spatial model wins out.

Turning our attention to the running times, we ran all experiments on similar scale problems running with the same system resources and training took between 5 and 7.5 h for all algorithms. Q-learning was the fastest to finish execution and obtained reasonably results even when even training time was subjected to a threshold cutoff. So Q-learning is a good candidate algorithm for fast approximations for testing purposes. One unique aspect of A3C is that it can exploit multithreading of CPU cores rather than GPU acceleration. Each fire in our dataset could be run as its own thread allowing A3C use similar training time to obtain

superior results. MCTS was the slowest algorithm we tried since it is not multithreaded and requires extra roll-out simulations and back propagation at every iteration.

There are several reasons why the best RL algorithms are more suited to such domains than supervised learning algorithms. The first reason is that, RL can model the spatial dynamics along with time in such domains. This enables RL to predict action choices using a policy tuned to a particular time of fire spread test as compared with supervised learning which estimates a model based on inputs and outputs only. The second reason being RL prepares a policy for the agent that takes actions which model the underlying causal fire behavior. The supervised learning algorithms do not have such a state-action mapping. Thus this RL approach should be able to learn a reasonable policy in data-scarce scenarios by focusing on the reachable state-action space only.

### 8. CHALLENGES AND FUTURE WORK

As expounded in Malarz et al. (2002), forest fire prediction requires additional information consisting of firefighting intervention (such as fire fighting strategy and time elapsed), which are not taken into consideration in this study, as we chose a study region having very minimal fire fighting. In future work, we aim to incorporate this kind of information as well as enriching the model by including more land characteristics such as moisture,

Table 3 | Average accuracy of each algorithm trained on the Richardson fire but applied on the Fort McMurray fire for different time durations.

*They are the best performing algorithms in the corresponding experiments.*

slope, and directional aspect as state variables in individual cells. We will also perform a wider comparison against different existing wildfire models algorithms such as those in Castelli et al. (2015).

We also plan to investigate improvements to the structure of the Deep Neural Network policy representation to tailor it more closely to this kind of spatially spreading problem. For example, the relatively better behavior of the supervised GP approach on distance predictions where the dynamics approximation may be hindrance, suggest trying a hybrid approaches, learning a pair of models, one temporal and atemporal to achieve the best of both.