Environmental Armed Conflict Assessment Using Satellite Imagery

Armed conflicts not only affect human populations but can also cause considerable damage to the environment. Its consequences are as diverse as its causes, including; water pollution from oil spills, land degradation due to the destruction of infrastructure, poisoning of soils and fields, destruction of crops and forests, over-exploitation of natural resources and paradoxically and occasionally reforestation. In this way, the environment in the war can be approached as beneficiary, stage, victim or/and spoil of war. Although there are few papers that assess the use of remote sensing methods in areas affected by warfare, we found a gap in these studies, being both outdated and lacking the correlation of remote sensing analysis with the causes-consequences, biome features and scale. Thus, this paper presents a methodical approach focused on the assessment of the existing datasets and the analysis of the connection between geographical conditions (biomes), drivers and the assessment using remote sensing methods in areas affected by armed conflicts. We aimed to find; weaknesses, tendencies, patterns, points of convergence and divergence. Then we consider variables such as biome, forest cover affectation, scale, and satellite imagery sensors to determine the relationship between warfare drivers with geographical location assessed by remote sensing methods. We collected data from 44 studies from international peer-reviewed journals from 1998 to 2019 that are indexed using scientific search engines. We found that 62% of the studies were focused on the analysis of torrid biomes as; Tropical Rainforest, Monsoon Forest / Dry Forest, Tree Savanna and Grass Savanna, using the 64% Moderate-resolution satellite imagery sensors as; Landsat 4-5 TM and Landsat 7 ETM+. Quantitative analysis of the trends identified within these areas contributes to an understanding of the reasons behind these conflicts

Multi-source data fusion of optical satellite imagery to characterize habitat selection from wildlife tracking data

This work was supported by CAPES (Coordination for the Improvement of Higher Education Personnel) [BEX-13438-13-1].Wildlife tracking data allow monitoring of how organisms respond to spatio-temporal changes in resource availability. Remote sensing data can be used to quantify and qualify these variations to understand how movement is related to these changes. The use of remote sensing data with concurrent high levels of spatial and temporal detail may hold potential to improve our understanding of habitat selection. However, no current orbital sensor produces data with simultaneous high temporal and high spatial resolution, therefore alternative methods are required to generate remote sensing data that matches the high spatial-temporal resolution of modern wildlife tracking data. We present an analytical framework, not yet used in movement ecology, for data fusion of optical remote sensing data from multiple satellites and wildlife tracking data to study the impact of seasonal vegetation patterns on the movement of maned wolves (Chrysocyon brachyurus). We use multi-source data fusion to combine MODIS data with higher spatial resolution data (ASTER, Landsat 4-5-7-8, CBERS 2-2B) and create a synthetic NDVI product with a 15 m spatial detail and daily temporal resolution. We also use the higher spatial resolution data to create a multi-source NDVI product with same level of spatial detail but coarser temporal resolution and data from MODIS to create a single-source NDVI product with high temporal resolution but coarse spatial resolution. We combine the three different spatial-temporal resolution NDVI products with GPS tracking data of maned wolves to create step-selection functions (SSF), which are models used in ecology to investigate and predict habitat selection by animals. The SSF model based on multi-source NDVI had the best performance predicting the probability of use of visited locations given its NDVI value. The SSF based on the raw MODIS NDVI product, one which is commonly employed by ecologists, had the poorest performance for our study species. These findings indicate that, in contrast with current practice in movement ecology, a detailed spatial resolution of contextual environmental variable may be more important than a detailed temporal resolution, when investigating wildlife habitat selection regarding vegetation, although this result will be highly dependent on species. The choice of data set should therefore take into account not only the scale of movement but also the spatial and temporal scales at which dynamic environmental variables are changing.PostprintPostprintPeer reviewe

Study of the urban heat island (UHI) using remote sensing data/techniques: a systematic review.

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus andWeb of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020. This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020.info:eu-repo/semantics/publishedVersio

Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations

A practical atmospheric correction algorithm, called Coupled Moderate Products for Atmospheric Correction (CMPAC), was developed and implemented for the Multispectral Camera (MUX) on-board the China-Brazil Earth Resources Satellite (CBERS-4). This algorithm uses a scene-based processing and sliding window technique to derive MUX surface reflectance (SR) at continental scale. Unlike other optical sensors, MUX instrument imposes constraints for atmospheric correction due to the absence of spectral bands for aerosol estimation from imagery itself. To overcome this limitation, the proposed algorithm performs a further processing of atmospheric products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as input parameters for radiative transfer calculations. The success of CMPAC algorithm was fully assessed and confirmed by comparison of MUX SR data with the Landsat-8 OLI Level-2 and Aerosol Robotic Network (AERONET)-derived SR products. The spectral adjustment was performed to compensate for the differences of relative spectral response between MUX and OLI sensors. The results show that MUX SR values are fairly similar to operational Landsat-8 SR products (mean difference \u3c 0.0062, expressed in reflectance). There is a slight underestimation of MUX SR compared to OLI product (except the NIR band), but the error metrics are typically low and scattered points are around the line 1:1. These results suggest the potential of combining these datasets (MUX and OLI) for quantitative studies. Further, the robust agreement of MUX and AERONET-derived SR values emphasizes the quality of moderate atmospheric products as input parameters in this application, with root-mean-square deviation lower than 0.0047. These findings confirm that (i) CMPAC is a suitable tool for estimating surface reflectance of CBERS MUX data, and (ii) ancillary products support the application of atmospheric correction by filling the gap of atmospheric information. The uncertainties of atmospheric products, negligence of the bidirectional effects, and two aerosol models were also identified as a limitation. Finally, this study presents a framework basis for atmospheric correction of CBERS-4 MUX images. The utility of CBERS data comes from its use, and this new product enables the quantitative remote sensing for land monitoring and environmental assessment at 20 m spatial resolution

Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status

Comparison of object and pixel-based classifications for land-use and land cover mapping in the mountainous Mokhotlong District of Lesotho using high spatial resolution imagery

Research Report submitted in partial fulfilment for the degree of Master of Science (Geographical Information Systems and Remote Sensing) School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg. August 2016.The thematic classification of land use and land cover (LULC) from remotely sensed imagery data is one of the most common research branches of applied remote sensing sciences. The performances of the pixel-based image analysis (PBIA) and object-based image analysis (OBIA) Support Vector Machine (SVM) learning algorithms were subjected to comparative assessment using WorldView-2 and SPOT-6 multispectral images of the Mokhotlong District in Lesotho covering approximately an area of 100 km2. For this purpose, four LULC classification models were developed using the combination of SVM –based image analysis approach (i.e. OBIA and/or PBIA) on high resolution images (WorldView-2 and/or SPOT-6) and the results were subjected to comparisons with one another. Of the four LULC models, the OBIA and WorldView-2 model (overall accuracy 93.2%) was found to be more appropriate and reliable for remote sensing application purposes in this environment. The OBIA-WorldView-2 LULC model was subjected to spatial overlay analysis with DEM derived topographic variables in order to evaluate the relationship between the spatial distribution of LULC types and topography, particularly for topographically-controlled patterns. It was discovered that although that there are traces of the relationship between the LULC types distributions and topography, it was significantly convoluted due to both natural and anthropogenic forces such that the topographic-induced patterns for most of the LULC types had been substantial disrupted.LG201

Using middle-infrared reflectance for burned area detection

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Meteorologia), Universidade de Lisboa, Faculdade de Ciências, 2011A strategy is presented that allows deriving a new index for burned area discrimination over the Amazon and Cerrado regions of Brazil. The index is based on information from the near-infrared (NIR) and middle-infrared (MIR) channels of the Moderate Resolution Imaging Spectroradiometer (MODIS). A thorough review is undertaken of existing methods for retrieving MIR reflectance and an assessment is performed, using simulated and real data, about the added value obtained when using the radiative transfer equation (RTE) instead of the simplified algorithm (KR94) developed by Kaufman and Remer (1994), the most used in the context of burned area studies. It is shown that use of KR94 in tropical environments to retrieve vegetation reflectance may lead to errors that are at least of the same order of magnitude of the reflectance to be retrieved and considerably higher for large values of land surface temperature (LST) and solar zenith angle (SZA). Use of the RTE approach leads to better estimates in virtually all cases, with the exception of high values of LST and SZA, where results from KR94 are also not usable. A transformation is finally defined on the MIR/NIR reflectance space aiming to enhance the spectral information such that vegetated and burned surfaces may be effectively discriminated. The transformation is based on the difference between MIR and NIR in conjunction with the distance from a convergence point in the MIR/NIR space, representative of a totally burnt surface. The transformation allows defining a system of coordinates, one coordinate having a small scatter for pixels associated to vegetation, burned surfaces and soils containing organic matter and the other coordinate covering a wide range of values, from green and dry/stressed vegetation to burned surfaces. The new set of coordinates opens interesting perspectives to applications like drought monitoring and burned area discrimination using remote-sensed information.O coberto vegetal da superfície da Terra tem vindo a sofrer mudanças, por vezes drásticas, que conduzem a alterações tanto na rugosidade da superfície terrestre como no seu albedo, afectando directamente as trocas de calor sensível e latente e de dióxido de carbono entre a superfície terrestre e a atmosfera (Sellers et al., 1996). Neste contexto, as queimadas assumem um papel de extremo relevo (Nobre et al., 1991; O’Brien, 1996; Xue, 1996) na medida em que constituem uma das mais importantes fontes de alteração do coberto vegetal, resultando na destruição de florestas e de recursos naturais, libertando carbono da superfície continental para a atmosfera (Sellers et al., 1995) e perturbando as interacções biosfera-atmosfera (Levine et al., 1995; Scholes, 1995) através de mudanças na rugosidade do solo, na área foliar e noutros parâmetros biofísicos associados ao coberto vegetal. Ora, neste particular, a Amazónia Brasileira constitui um exemplo notável de mudanças no uso da terra e do coberto vegetal nas últimas décadas, como resultado da desflorestação induzida pelo homem bem como por causas naturais (Gedney e Valdes, 2000; Houghton, 2000; Houghton et al., 2000; Lucas et al., 2000), estimando-se que as regiões tropicais sejam responsáveis por cerca de 32% da emissão global de carbono para a atmosfera (Andreae, 1991). Neste contexto, a disponibilidade de informações pormenorizadas e actualizadas sobre as distribuições espacial e temporal de queimadas e de áreas ardidas em regiões tropicais afigura-se crucial, não só para uma melhor gestão dos recursos naturais, mas também para estudos da química da atmosfera e de mudanças climáticas (Zhan et al., 2002). A detecção remota constitui, neste âmbito, uma ferramenta indispensável na medida em que permite uma monitorização em tempo quase real, a qual se revela especialmente útil em áreas extensas e/ou de difícil acesso afectadas pelo fogo (Pereira et al., 1997). Diversos instrumentos, tais como o Land Remote Sensing Satellite/Thematic Mapper (LANDSAT/TM) e o National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) têm vindo a ser extensivamente utilizados na gestão dos fogos florestais, em particular aos níveis da detecção de focos de incêndio e da monitorização de áreas queimadas. Mais recentemente, o instrumento VEGETATION a bordo do Satellite Pour l'Observation de la Terre (SPOT) tem vindo a ser utilizado com sucesso na monitorização de fogos. Finalmente, são de referir os sensores da série Along Track Scanning Radiometer (ATSR) para os quais têm vindo a ser desenvolvidos algoritmos de identificação de focos de incêndio, e ainda o sensor Moderate Resolution Imaging Spectroradiometer (MODIS) que tem vindo a demonstrar capacidades óptimas no que respeita à observação global de fogos, plumas e áreas queimadas. Neste contexto, os métodos actuais de detecção de áreas ardidas através da detecção remota têm vindo a dar prioridade à utilização das regiões do vermelho (0.64 μm) e infravermelho-próximo (0.84 μm) do espectro eletromagnético. No entanto, tanto a região do vermelho quanto a do infravermelho-próximo apresentam a desvantagem de serem sensíveis à presença de aerossóis na atmosfera (Fraser e Kaufman, 1985; Holben et. al., 1986). Desta forma, em regiões tropicais como a Amazónia, onde existem grandes camadas de fumo devido à queima de biomassa, a utlização destas duas regiões do espectro eletromagnético torna-se insatisfatória para a detecção de áreas ardidas. Por outro lado, a região do infravermelho médio (3.7 – 3.9 μm) tem a vantagem de não ser sensível à presença da maior parte dos aerossóis, exceptuando a poeira (Kaufman e Remer, 1994) mostrando-se, ao mesmo tempo, sensível a mudanças na vegetação devido à absorção de água líquida. Com efeito, estudos acerca dos efeitos do vapor de água na atenuação do espectro eletromagnético demonstraram que a região do infravermelho médio é uma das únicas regiões com relativamente pouca atenuação (Kerber e Schut, 1986). Acresce que a região do infravermelho médio apresenta uma baixa variação da irradiância solar (Lean, 1991), tendo-se ainda que a influência das incertezas da emissividade na estimativa da temperatura da superfície é pequena quando comparada com outras regiões térmicas tais como as de 10.5 e 11.5 μm (Salysbury e D’Aria, 1994). A utilização da radiância medida através de satélites na região do infravermelho médio é, no entanto, dificultada pelo facto de esta ser afectada tanto pelo fluxo térmico quanto pelo fluxo solar, contendo, desta forma, duas componentes, uma emitida e outra reflectida, tendo-se que a componente reflectiva contém os fluxos térmico e solar reflectidos pela atmosfera e pela superfície enquanto que as emissões térmicas são oriundas da atmosfera e da superfície. Ora, a componente solar reflectida é de especial interesse para a detecção de áreas ardidas pelo que se torna necessário isolá-la do sinal total medido pelo sensor. Devido à ambiguidade deste sinal, a distinção dos efeitos da reflectância e da temperatura torna-se uma tarefa muito complexa, verificando-se que os métodos em que se não assume nenhuma simplificação, levando-se, portanto, em consideração todos os constituintes do sinal do infravermelho médio se tornam complexos e difíceis de serem aplicados na prática, na medida em que requerem dados auxiliares (e.g. perfis atmosféricos) e ferramentas computacionais (e.g. modelos de tranferência radiativa). Kaufman e Remer (1994) desenvolveram um método simples para estimar a reflectância do infravermelho médio o qual assenta em diversas hipóteses simplificadoras. Apesar do objectivo primário que levou ao desenvolvimento do método ser a identificação de áreas cobertas por vegetação densa e escura em regiões temperadas, este método tem sido lagarmente utilizado nos estudos acerca da discriminação de áreas queimadas, algumas das vezes em regiões tropicais (Roy et al., 1999; Barbosa et al., 1999; Pereira, 1999). Na literatura não existe, no entanto, nenhum estudo acerca da exactidão e precisão deste método quando aplicado com o objectivo de detectar áreas ardidas, em especial em regiões tropicais. Neste sentido, no presente trabalho procedeu-se a um estudo de viabilidade do método proposto por Kaufman e Remer (1994) em simultâneo com a análise da equação de tranferência radiativa na região do infravermelho médio, tendo sido realizados testes de sensibilidade dos algoritmos em relação aos erros nos perfis atmosféricos, ruído do sensor e erros nas estimativas da temperatura da superfície. Para tal recorreu-se ao modelo de transferência radiativa Moderate Spectral Resolution Atmospheric Transmittance and Radiance Code (MODTRAN), dando-se especial atenção ao caso do sensor MODIS. Os resultados demonstraram que a utilização do método proposto por Kaufman e Remer (1994) em regiões tropicais para a estimativa da reflectância no infravermelho médio, leva a erros que são pelo menos da mesma ordem de magnitude do parâmetro estimado e, em alguns casos, muito maior, quando ocorre a combinação de altas temperaturas da superfície terrestre com baixos ângulos zenitais solares. A utilização da equação de transferência radiativa mostrouse uma boa alternativa, desde que estejam disponíveis dados acerca da temperatura da superfíce terrestre assim como dos perfis atmosféricos. Entretanto, nas regiões onde ocorrem altos valores de temperatura da superfície terrestre e baixos ângulos zenitais solares, quaisquer dos dois métodos se mostra pouco utilizável, já que nesta região a estimativa da reflectância constitui um problema mal-posto. Em paralelo, utilizaram-se informações sobre aerossóis de queimada para efectuar simulações do MODTRAN que permitiram avaliar a reposta do canal do infravermelho-médio à este tipo de perturbação do sinal, muito comum na Amazónia Brasileira. A fim de tornar o estudo o mais realístico possível, procedeu-se à coleta de material resultante de queimadas na região Amazónica, mais especificamente em Alta Floresta, Mato Grosso, Brasil. Estes resultado foram então integrados nos estudos em questão, possibilitando a caracterização espectral das áreas ardidas. Com base nos resultados obtido definiu-se uma tranformação no espaço do infravermelho próximo e médio com o objetivo de maximizar a informação espectral de forma a que as superfícies vegetadas pudessem ser efectivamente discriminadas e as áreas ardidas identificadas. A tranformação baseia-se na diferença entre a reflectância nos infravermelhos próximo e médio, em conjunto com a distância a um ponto de convergência no espaço espectral dos infravermelhos próximo e médio, ponto esse representativo de uma área completamente ardida. A tranformação permitiu a definição de um novo sistema de coordenadas, o qual provou ser bastante útil no que diz respeito á identificação de áreas ardidas. Este novo espaço de coordenadas constitui uma inovação na área dos estudos de queimadas, já que permite ao mesmo tempo definir dois tipos de índices, o primeiro dos quais identifica superfícies que contém ou não biomassa e o segundo identifica, de entre as superfícies que contêm biomassa, a quantidade de água presente, podendo variar de vegetação verde (abundância de água) até áreas ardidas (ausência de água). Além de distiguir áreas ardidas, os índices desenvolvidos podem ainda ser aplicados em outros casos como, por exemplo, estudos de estresse hídrico e secas.DSA/INPE; Portuguese Foundation of Science and Technology (Fundação para a Ciência e Tecnologia / FCT)(SFRH/BD/21650/2005

Comparison of optical sensors discrimination ability using spectral libraries

In remote sensing, the ability to discriminate different land covers or material types is directly linked with the spectral resolution and sampling provided by the optical sensor. Previous studies showed that the spectral resolution is a critical issue, especially in complex environment. In spite of the increasing availability of hyperspectral data, multispectral optical sensors onboard various satellites are acquiring everyday a massive amount of data with a relatively poor spectral resolution (i.e. usually about 4 to 7 spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution. In this paper, we studied seven of these optical sensors: Pleiades, QuickBird, SPOT5, Ikonos, Landsat TM, Formosat and Meris. This study focuses on the ability of each sensor to discriminate different materials according to its spectral resolution. We used four different spectral libraries which contains around 2500 spectra of materials and land covers with a fine spectral resolution. These spectra were convolved with the Relative Spectral Responses (RSR) of each sensor to create spectra at the sensors’ resolutions. Then, these reduced spectra were compared using separability indexes (Divergence, Transformed divergence, Bhattacharyya, Jeffreys-Matusita) and machine learning tools. In the experiments, we highlighted that the spectral bands configuration could lead to important differences in classification accuracy according to the context of application (e.g. urban area)

Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014

A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.JRC.G.2-Global security and crisis managemen