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

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach

Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil

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

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Uncertainties in Digital Elevation Models: Evaluation and Effects on Landform and Soil Type Classification

Digital elevation models (DEMs) are a widely used source for the digital representation of the Earth's surface in a wide range of scientific, industrial and military applications. Since many processes on Earth are influenced by the shape of the relief, a variety of different applications rely on accurate information about the topography. For instance, DEMs are used for the prediction of geohazards, climate modelling, or planning-relevant issues, such as the identification of suitable locations for renewable energies. Nowadays, DEMs can be acquired with a high geometric resolution and over large areas using various remote sensing techniques, such as photogrammetry, RADAR, or laser scanning (LiDAR). However, they are subject to uncertainties and may contain erroneous representations of the terrain. The quality and accuracy of the topographic representation in the DEM is crucial, as the use of an inaccurate dataset can negatively affect further results, such as the underestimation of landslide hazards due to a too flat representation of relief in the elevation model. Therefore, it is important for users to gain more knowledge about the accuracy of a terrain model to better assess the negative consequences of DEM uncertainties on further analysis results of a certain research application. A proper assessment of whether the purchase or acquisition of a highly accurate DEM is necessary or the use of an already existing and freely available DEM is sufficient to achieve accurate results is of great qualitative and economic importance. In this context, the first part of this thesis focuses on extending knowledge about the behaviour and presence of uncertainties in DEMs concerning terrain and land cover. Thus, the first two studies of this dissertation provide a comprehensive vertical accuracy analysis of twelve DEMs acquired from space with spatial resolutions ranging from 5 m to 90 m. The accuracy of these DEMs was investigated in two different regions of the world that are substantially different in terms of relief and land cover. The first study was conducted in the hyperarid Chilean Atacama Desert in northern Chile, with very sparse land cover and high elevation differences. The second case study was conducted in a mid-latitude region, the Rur catchment in the western part of Germany. This area has a predominantly flat to hilly terrain with relatively diverse and dense vegetation and land cover. The DEMs in both studies were evaluated with particular attention to the influence of relief and land cover on vertical accuracy. The change of error due to changing slope and land cover was quantified to determine an average loss of accuracy as a function of slope for each DEM. Additionally, these values were used to derive relief-adjusted error values for different land cover classes. The second part of this dissertation addresses the consequences that different spatial resolutions and accuracies in DEMs have on specific applications. These implications were examined in two exemplary case studies. In a geomorphometric case study, several DEMs were used to classify landforms by different approaches. The results were subsequently compared and the accuracy of the classification results with different DEMs was analysed. The second case study is settled within the field of digital soil mapping. Various soil types were predicted with machine learning algorithms (random forest and artificial neural networks) using numerous relief parameters derived from DEMs of different spatial resolutions. Subsequently, the influence of high and low resolution DEMs with the respectively derived land surface parameters on the prediction results was evaluated. The results on the vertical accuracy show that uncertainties in DEMs can have diverse reasons. Besides the spatial resolution, the acquisition technique and the degree of improvements made to the dataset significantly impact the occurrence of errors in a DEM. Furthermore, the relief and physical objects on the surface play a major role for uncertainties in DEMs. Overall, the results in steeper areas show that the loss of vertical accuracy is two to three times higher for a 90 m DEM than for DEMs of higher spatial resolutions. While very high resolution DEMs of 12 m spatial resolution or higher only lose about 1 m accuracy per 10° increase in slope steepness, 30 m DEMs lose about 2 m on average, and 90 m DEMs lose more than 3 m up to 6 m accuracy. However, the results also show significant differences for DEMs of identical spatial resolution depending on relief and land cover. With regard to different land cover classes, it can be stated that mid-latitude forested and water areas cause uncertainties in DEMs of about 6 m on average. Other tested land cover classes produced minor errors of about 1 – 2 m on average. The results of the second part of this contribution prove that a careful selection of an appropriate DEM is more crucial for certain applications than for others. The choice of different DEMs greatly impacted the landform classification results. Results from medium resolution DEMs (30 m) achieved up to 30 % lower overall accuracies than results from high resolution DEMs with a spatial resolution of 5 m. In contrast to the landform classification results, the predicted soil types in the second case study showed only minor accuracy differences of less than 2 % between the usage of a spatial high resolution DEM (15 m) and a low resolution 90 m DEM. Finally, the results of these two case studies were compared and discussed with other results from the literature in other application areas. A summary and assessment of the current state of knowledge about the impact of a particular chosen terrain model on the results of different applications was made. In summary, the vertical accuracy measures obtained for each DEM are a first attempt to determine individual error values for each DEM that can be interpreted independently of relief and land cover and can be better applied to other regions. This may help users in the future to better estimate the accuracy of a tested DEM in a particular landscape. The consequences of elevation model selection on further results are highly dependent on the topic of the study and the study area's level of detail. The current state of knowledge on the impact of uncertainties in DEMs on various applications could be established. However, the results of this work can be seen as a first step and more work is needed in the future to extend the knowledge of the effects of DEM uncertainties on further topics that have not been investigated to date

The Blind Oracle, eXplainable Artififical Intelligence (XAI) and human agency

An explainable machine learning model is a requirement for trust. Without it the human operator cannot form a correct mental model and will distrust and reject the machine learning model. Nobody will ever trust a system which exhibit an apparent erratic behaviour. The development of eXplainable AI (XAI) techniques try to uncover how a model works internally and the reasons why they make some predictions and not others. But the ultimate objective is to use these techniques to guide the training and deployment of fair automated decision systems that support human agency and are beneficial to humanity. In addition, automated decision systems based on Machine Learning models are being used for an increasingly number of purposes. However, the use of black-box models and massive quantities of data to train them make the deployed models inscrutable. Consequently, predictions made by systems integrating these models might provoke rejection by their users when they made seemingly arbitrary predictions. Moreover, the risk is compounded by the use of models in high-risk environments or in situations when the predictions might have serious consequences.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Máster en Ingeniería Informátic

Ethnoarchaeology of the middle Tanana Valley, Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2020This study explores the shifting anthropological constructs of identity for the Middle Tanana people through time. It first summarizes this theme through contemporary regional Native American internal and external influences. A discussion is then given on how these constructs became formed through historical processes. Next, it provides an in-depth look into how identity became shaped prior to the Euro-American influence through an ethnographic reconstruction. These are framed in a way to form relevant hypotheses to study the regional prehistoric archaeological record. The Historical Linguistics analytical approach used here confirms that there is very little, if any, evidence in the languages of the Tanana Valley from any non-Dene or other hypothetical pre-existing linguistic group. Language forms an integral unit of community identity. This study also frames the linguistic argument for deep regional cultural antiquity and identity through an extensive survey of traditional place names. A brief comparative study of the processes and effects of the incursion of the Indo-European languages into traditional Dene territory is discussed to demonstrate this argument. Next, the research explores the middle and later Holocene archaeological record of the Shaw Creek basin, located deep within the Middle Tanana homelands, using innovative approaches framing traditional Optimal Foraging theory arguments through the lens of Complexity theory. It focuses on the household archaeology and spatial artifact analysis of two archaeological sites, Swan Point (three Holocene components) and Pickupsticks (one Holocene component). In these case studies, cultural identity analogs, social structure, and agency are discussed using the material cultural record as a proxy. Finally, a dynamic, seasonal, ecological landscape-use model informed by predator/prey interactions is used to inform hypothetical human foraging movements. It models decision-making and risk-mitigation processes through resource shortfalls, predicting raw materials' movements from their source locations to their discard locations at these two archaeological sites. The conclusions support the theory that Dene presence in the Middle Tanana Valley is an ancient phenomenon that has at least early Holocene roots. Further, the period between 2,000 and 1,000 years ago appears to have been a critical period of additional cultural intensification processes. The processes leading to the development of the Athabascan archaeological tradition are considered to be the result of demographic expansion, increased territoriality, and a critical reinterpretation of the roles of kinship and non-related partnerships.Tanana Chief's Conference, Otto William Geist Fund, David and Rachel Hopkins FellowshipChapter 1: Introduction -- Chapter 2: Theoretical methods -- Chapter 3: The Middle Tanana People: modern and historical identity -- Chapter 4: The ethnographic reconstruction of the past: The Middle Tanana Valley -- Chapter 5: Historical linguistic and ethno-geographic perspectives of the Alaskan Dene -- Chapter 6: Archaeological and ecological reconstructions: The Holocene epoch -- Chapter 7: The project history of the Swan Point and Pickupsticks archaeological sites -- Chapter 8: Radiocarbon analysis -- Chapter 9: Feature analysis -- Chapter 10: Spatial artifact analysis -- Chapter 11: Inferences from top-predator data for modeling seasonal lithic procurement strategies as a dynamic system -- Chapter 12: Discussion -- Chapter 13: Conclusion -- References -- Appendix