Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework

This paper presents an application of a novel machine-learning framework on MODIS (moderate-resolution imaging spectroradiometer) data to map burned areas over tropical forests of South America and South-east Asia. The RAPT (RAre Class Prediction in the absence of True labels) framework is able to build data adaptive classification models using noisy training labels. It is particularly suitable when expert annotated training samples are difficult to obtain as in the case of wild fires in the tropics. This framework has been used to build burned area maps from MODIS surface reflectance data as features and Active Fire hotspots as training labels that are known to have high commission and omission errors due to the prevalence of cloud cover and smoke, especially in the tropics. Using the RAPT framework we report burned areas for 16 MODIS tiles from 2001 to 2014. The total burned area detected in the tropical forests of South America and South-east Asia during these years is 2,071,378 MODIS (500 m) pixels (approximately 520 K sq. km.), which is almost three times compared to the estimates from collection 5 MODIS MCD64A1 (783,468 MODIS pixels). An evaluation using Landsat-based reference burned area maps indicates that our product has an average user’s accuracy of 53% and producer’s accuracy of 55% while collection 5 MCD64A1 burned area product has an average user’s accuracy of 61% and producer’s accuracy of 27%. Our analysis also indicates that the two products can be complimentary and a combination of the two approaches is likely to provide a more comprehensive assessment of tropical fires. Finally, we have created a publicly accessible web-based viewer that helps the community to visualize the burned area maps produced using RAPT and examine various validation sources corresponding to every detected MODIS pixel

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Aplicações de modelos de deep learning para monitoramento ambiental e agrícola no Brasil

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.Algoritmos do novo campo de aprendizado de máquina conhecido como Deep Learning têm se popularizado recentemente, mostrando resultados superiores a modelos tradicionais em métodos de classificação e regressão. O histórico de sua utilização no campo do sensoriamento remoto ainda é breve, porém eles têm mostrado resultados similarmente superiores em processos como a classificação de uso e cobertura da terra e detecção de mudança. Esta tese teve como objetivo o desenvolvimento de metodologias utilizando estes algoritmos com um enfoque no monitoramento de alvos críticos no Brasil por via de imagens de satélite a fim de buscar modelos de alta precisão e acurácia para substituir metodologias utilizadas atualmente. Ao longo de seu desenvolvimento, foram produzidos três artigos onde foi avaliado o uso destes algoritmos para a detecção de três alvos distintos: (a) áreas queimadas no Cerrado brasileiro, (b) áreas desmatadas na região da Amazônia e (c) plantios de arroz no sul do Brasil. Apesar do objetivo similar na produção dos artigos, procurou-se distinguir suficientemente suas metodologias a fim de expandir o espaço metodológico conhecido para fornecer uma base teórica para facilitar e incentivar a adoção destes algoritmos em contexto nacional. O primeiro artigo avaliou diferentes dimensões de amostras para a classificação de áreas queimadas em imagens Landsat-8. O segundo artigo avaliou a utilização de séries temporais binárias de imagens Landsat para a detecção de novas áreas desmatadas entre os anos de 2017, 2018 e 2019. O último artigo utilizou imagens de radar Sentinel-1 (SAR) em uma série temporal contínua para a delimitação dos plantios de arroz no Rio Grande do Sul. Modelos similares foram utilizados em todos os artigos, porém certos modelos foram exclusivos a cada publicação, produzindo diferentes resultados. De maneira geral, os resultados encontrados mostram que algoritmos de Deep Learning são não só viáveis para detecção destes alvos mas também oferecem desempenho superior a métodos existentes na literatura, representando uma alternativa altamente eficiente para classificação e detecção de mudança dos alvos avaliados.Algorithms belonging to the new field of machine learning called Deep Learning have been gaining popularity recently, showing superior results when compared to traditional classification and regression methods. The history of their use in the field of remote sensing is not long, however they have been showing similarly superior results in processes such as land use classification and change detection. This thesis had as its objective the development of methodologies using these algorithms with a focus on monitoring critical targets in Brazil through satellite imagery in order to find high accuracy and precision models to substitute methods used currently. Through the development of this thesis, articles were produced evaluating their use for the detection of three distinct targets: (a) burnt areas in the Brazilian Cerrado, (b) deforested areas in the Amazon region and (c) rice fields in the south of Brazil. Despite the similar objective in the production of these articles, the methodologies in each of them was made sufficiently distinct in order to expand the methodological space known. The first article evaluated the use of differently sized samples to classify burnt areas in Landsat-8 imagery. The second article evaluated the use of binary Landsat time series to detect new deforested areas between the years of 2017, 2018 and 2019. The last article used continuous radar Sentinel-1 (SAR) time series to map rice fields in the state of Rio Grande do Sul. Similar models were used in all articles, however certain models were exclusive to each one. In general, the results show that not only are the Deep Learning models viable but also offer better results in comparison to other existing methods, representing an efficient alternative when it comes to the classification and change detection of the targets evaluated

Computational Techniques to Identify Rare Events in Spatio-temporal Data

University of Minnesota Ph.D. dissertation.May 2018. Major: Computer Science. Advisor: Vipin Kumar. 1 computer file (PDF); xi, 96 pages.Recent attention on the potential impacts of land cover changes to the environment as well as long-term climate change has increased the focus on automated tools for global-scale land surface monitoring. Advancements in remote sensing and data collection technologies have produced large earth science data sets that can now be used to build such tools. However, new data mining methods are needed to address the unique characteristics of earth science data and problems. In this dissertation, we explore two of these interesting problems, which are (1) build predictive models to identify rare classes when high quality annotated training samples are not available, and (2) classification enhancement of existing imperfect classification maps using physics-guided constraints. We study the problem of identifying land cover changes such as forest fires as a supervised binary classification task with the following characteristics: (i) instead of true labels only imperfect labels are available for training samples. These imperfect labels can be quite poor approximation of the true labels and thus may have little utility in practice. (ii) the imperfect labels are available for all instances (not just the training samples). (iii) the target class is a very small fraction of the total number of samples (traditionally referred to as the rare class problem). In our approach, we focus on leveraging imperfect labels and show how they, in conjunction with attributes associated with instances, open up exciting opportunities for performing rare class prediction. We applied this approach to identify burned areas using data from earth observing satellites, and have produced a database, which is more reliable and comprehensive (three times more burned area in tropical forests) compared to the state-of-art NASA product. We explore approaches to reduce errors in remote sensing based classification products, which are common due to poor data quality (eg., instrument failure, atmospheric interference) as well as limitations of the classification models. We present classification enhancement approaches, which aim to improve the input (imperfect) classification by using some implicit physics-based constraints related to the phenomena under consideration. Specifically, our approach can be applied in domains where (i) physical properties can be used to correct the imperfections in the initial classification products, and (ii) if clean labels are available, they can be used to construct the physical properties