The development of a temporal-BRDF model-based approach to change detection, an application to the identification and delineation of fire affected areas.

Although large quantities of southern Africa burn every year, minimal information is available relating to the fire regimes of this area. This study develops a new, generic approach to change detection, applicable to the identification of land cover change from high temporal and moderate spatial resolution satellite data. Traditional change detection techniques have several key limitations which are identified and addressed in this work. In particular these approaches fail to account for directional effects in the remote sensing signal introduced by variations in the solar and sensing geometry, and are sensitive to underlying phenological changes in the surface as well as noise in the data due to cloud or atmospheric contamination. This research develops a bi-directional, model-based change detection algorithm. An empirical temporal component is incorporated into a semi-empirical linear BRDF model. This may be fitted to a long time series of reflectance with less sensitivity to the presence of underlying phenological change. Outliers are identified based on an estimation of noise in the data and the calculation of uncertainty in the model parameters and are removed from the sequence. A "step function kernel" is incorporated into the formulation in order to detect explicitly sudden step-like changes in the surface reflectance induced by burning. The change detection model is applied to the problem of locating and mapping fire affected areas from daily moderate spatial resolution satellite data, and an indicator of burn severity is introduced. Monthly burned area datasets for a 2400km by 1200km area of southern Africa detailing the day and severity of burning are created for a five year period (2000-2004). These data are analysed and the fire regimes of southern African ecosystems during this time are characterised. The results highlight the extent of the burning which is taking place within southern Africa, with between 27-32% of the study area burning during each of the five years of observation. Higher fire frequencies are exhibited by savanna and grassland ecosystems, while more dense vegetation types such as shrublands and deciduous broadleaf forests burn less frequently. In addition the areas which burn more frequently do so with a greater severity, with a positive relationship identified between the frequency and the severity of burning

Improving the MODIS LAI compositing using prior time-series information

80NSSC21K1960 - NASA; 80NSSC22K0052 - NASA/Goddard Space Flight Center; 80NSSC22K0052 - NASAFirst author draf

Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

Procedures for the analysis and use of multiple view angle image data

It is recognised that the majority of vegetative cover types have anisotropic reflectance characteristics that are largely a function of their canopy geometry. Several studies have made attempts at formulating methods for the use of data remotely sensed from off-nadir directions. The best of these methods attempt to utilise the "extra" information implicitly contained in off-nadir image datasets. In this study, an attempt is made to extract information concerning agro-physical parameters of a number of vegetative cover types using imagery acquired by an airborne sensor, the Daedalus Airborne Thematic Mapper (ATM). It is also recognised in the literature that the nature of spatial variance in images is related to the size and distribution of the objects in the scene and the sampling characteristics of the sensor. In previous work this relationship has been explored by examining scenes using images of varying spatial resolutions, using a number of measurements of spatial variance. The underlying trend of these measurements is then used to interpret the nature of the objects in the scene. No previous work exists which attempts to utilise the change in variance of images acquired at different off-nadir view angles. In this study, the understanding of this relationship is developed by examining the change in variance of a number of vegetative cover types from multiple view angle image datasets. The geometry of the ATM sensor is derived to allow an understanding of the sampling characteristics of the instrument. Two important geometric factors are established: first, the area of the ground resolution element increases with view angle, which effectively reduces spatial resolution at off-nadir angles; and second, overlap between adjacent ground resolution elements increases with view angle, increasing the spatial auto-correlation between these samples. The effects of illumination, atmosphere and topography can all influence variance in an image. A parametric procedure for normalising multiple view angle (and therefore multitemporal) datasets for these factors is developed, based upon the production of reflectance images using a sky radiance model of the spectral and spatial distributions of irradiance, ground measurements of irradiance, and a digital terrain model of the study site. Finally, it is shown that image variance is likely to decrease at off-nadir view angles, the magnitude of this decrease being related to the sensor geometry and (more importantly) the geometry of the canopy. By a simple statistical analytical procedure it is possible to construct broad classes within which the nature of the canopy can be classified

Coupled canopy-atmosphere modelling for radiance-based estimation of vegetation properties

Vegetation is an important component of the Earth’s biosphere and therefore plays a crucial role in the carbon exchange of terrestrial ecosystems. Vegetation variables, such as leaf area index (LAI) and leaf chlorophyll content (Cab), can be monitored at global scale using remote sensing (RS). There are two main categories of approaches for estimating the vegetation variables from RS data: empirical and physically-based approaches. Physically-based approaches are more widely applicable because they rely on radiative transfer (RT) models, which can be adapted to the observation conditions and to the observed vegetation. For estimating the vegetation variables, however, the RT model has to be inverted, and this inversion is usually an ill-posed and under-determined problem. Several regularization methods have been proposed to allow finding stable and unique solutions: model coupling, using multi-angular data, using a priori information, as well as applying spatial or temporal constraints. Traditionally, radiance data measured at top-of the atmosphere (TOA) are pre-processed to top-of-canopy (TOC) reflectances. Corrections for atmospheric effects, and, if needed, for adjacency, directional, or topographic effects are usually applied sequentially and independently. Physically, however, these effects are inter-related, and each correction introduces errors. These errors propagate to the TOC reflectance data, which are used to invert the canopy RT model. The performance of the TOC approach is therefore limited by the errors introduced in the data during the pre-processing steps. This thesis proposes to minimize these errors by directly using measured TOA radiance data. In such a TOA approach, the atmospheric RT model, which is normally inverted to perform the atmospheric correction, is coupled to the canopy RT model. The coupled canopy-atmosphere model is inverted directly using the measured radiance data. Adjacency, directional and topographic effects can then be included in the coupled RT model. The same regularization methods as used for TOC approaches can be applied to obtain stable and unique estimates. The TOA approach was tested using four case studies based on mono-temporal data. A) The performance of the TOA approach was compared to a TOC approach for three Norway spruce stands in the Czech Republic, using near-nadir Compact High Resolution Imaging Spectrometer (CHRIS) data. The coupled model included canopy directional effects and simulated the CHRIS radiance data with similar accuracy as the canopy model simulated the atmospherically-corrected CHRIS data. Local sensitivity analyses showed that the atmospheric parameters had much less influence on the simulations than the vegetation parameters, and that the sensitivity profiles of the latter were very similar for both TOC and TOA approaches. The dimensionality of the estimation problem was evaluated to be 3 for both approaches. Canopy cover (Cv), fraction of bark material (fB), Cab, and leaf dry matter content (Cdm) were estimated using look-up tables (LUT) with similar accuracy with both approaches. B) Regularization using multi-angular data was tested for the TOA approach, using four angular CHRIS datasets, for the same three stands as used in A). The coupled model provided good simulations for all angles. The dimensionality increased from 3 to 6 when using all four angles. Two LUTs were built for each stand: a 4-variable LUT with fB, Cv, Cdm, and Cab, and a 7-variable LUT where leaf brown pigment concentration (Cs), dissociation factor (D), and tree shape factor (Zeta) were added. The results did not fully match the expectation that the more angles used, the more accurate the estimates become. Although their exploitation remains challenging, multi-angular data have higher potential than mono-angular data at TOA level. C) A Bayesian object-based approach was developed and tested on at-sensor Airborne Prism Experiment (APEX) radiance data for an agricultural area in Switzerland. This approach consists of two steps. First, up to six variables were estimated for each crop field object using a Bayesian optimization algorithm, using a priori information. Second, a LUT was built for each object with only LAI and Cab as free variables, thus spatially constraining the values of all other variables to the values obtained in the first step. The Bayesian object-based approach estimated LAI more accurately than a LUT with a Bayesian cost function approach. This case study relied on extensive field data allowing defining the objects and a priori data. D) The Bayesian object-based approach proposed in C) was applied to a simulated TOA Sentinel-2 scene, covering the area around Zurich, Switzerland. The simulated scene was mosaicked using seven APEX flight lines, which allowed including all spatial and spectral characteristics of Sentinel-2. Automatic multi-resolution segmentation and classification of the vegetated objects in four levels of brightness in the visible domain enabled defining the objects and a priori data without field data, allowing successful implementation of the Bayesian object-based approach. The research conducted in this thesis contributes to the improvement of the use of regularization methods in ill-posed RT model inversions. Three major areas were identified for further research: 1) inclusion of adjacency and topography effects in the coupled model, 2) addition of temporal constraints in the inversion, and 3) better inclusion of observation and model uncertainties in the cost function. The TOA approach proposed here will facilitate the exploitation of multi-angular, multi-temporal and multi-sensor data, leading to more accurate RS vegetation products. These higher quality products will support many vegetation-related applications.</p

Changes in Tall Shrub Abundance on the North Slope of Alaska, 2000-2010

The observed greening of Arctic vegetation and the expansion of shrubs in the last few decades has likely had profound implications for the tundra ecosystem, including feedbacks to climate. Uncertainty surrounding the magnitude, direction, and implications of this vegetation shift calls for monitoring of vegetation structural parameters, such as fractional cover of shrubs. Due to the extent of the North Slope of Alaska and its extreme environments, remote sensing may be the most suitable tool to produce wall-to-wall fractional shrub cover maps for the entire region, however, most regional maps have relied on vegetation indices or needed many years worth of data to cover the whole region. Here, a new mapping approach is presented that uses satellite imagery from the Multi-angle Imaging SpectroRadiometer (MISR) sensor and some landscape variables to predict tall shrub (\u3e 0.5 m) cover with the ultimate goal of evaluating temporal changes in tall shrub fractional cover during the period of 2010-2000. Specifically, we: 1) undertook two field surveys in the North Slope of Alaska to obtain estimates of tall shrub cover, canopy height, crown radius, and total number of shrubs at 26 sites (250 m × 250 m each); 2) evaluated the ability of the semi-automated image interpretation algorithm CANAPI - CANopy Analysis from Panchromatic Imagery, to derive structural data for tall (\u3e 0.5 m) shrubs in the Arctic; 3) constructed a robust reference database with estimates of shrub structural parameters; 4) trained and validated the boosted regression tree model to predict tall shrub fractional cover from moderate resolution imagery; 5) created the 2000 and the 2010 tall shrub fractional cover map for the North Slope of Alaska; and 6) evaluated the changes in shrub abundance during the period 2010-2000 in the North Slope of Alaska. Results from the field surveys suggested that tall shrub fractional cover was less than 5% at 250 m scales. The evaluation of the CANAPI algorithm showed that CANAPI could successfully retrieve fractional cover (R2 = 0.83, P \u3c 0.001), mean crown radius (R2 = 0.81, P \u3c 0.001), and total number of shrubs (R2 = 0.54, P \u3c 0.001) from very-high resolution imagery. As a result, a robust reference database was constructed with estimates of tall shrub fractional cover, canopy radius, and total number of shrubs for 1,039 sites across the domain of the North Slope. After the training and validation of the Boosted Regression Tree (BRT), the best model used 14 predictor variables and explained 52% of the variation in the response variable, fractional cover. The red reflectance, slope, nadir Bidirectional Reflectance Distribution Function (BRDF) adjusted weight of determination, and isotropic scattering kernel were the variables more often used to generate the regression trees, and therefore they contributed the most to the model. The trained BRT model was used to construct the tall shrub fractional cover map for the year 2000 and 2010 using moderate resolution imagery. The maps revealed that cover ranged from 0.00 to 0.21 and about 75% of the sites had a fractional cover less than 0.013. High cover values were predicted along floodplains, creeks, and sloped terrain. The 2000 MISR-derived fractional cover map presented here outperformed the 2000 Landsat-derived tall shrub fractional cover map when compared to the robust validation data set (R2= 0.38, Root Mean Square Error (RMSE) = 0.08). Temporal comparisons of tall shrub abundance in the MISR-derived maps suggested that shrubs expanded during the period 2000-2010. The extent of the area that unequivocally experienced a robust change in tall shrub cover was less than 1 % (1,487 km2) of the total area of the North Slope of Alaska (213,090 km2). It is possible that tall shrubs may have expanded throughout a larger area but there is insufficient precision in the MISR-based estimates to make an unequivocal determination. Nevertheless, it seems that there was a positive trend toward an increase in shrub cover considering that 95% of the locations that had a robust change saw an increase. The tall shrub cover expansion rate varied between 0.006 yr-1 and 0.017 yr-1, being higher along the forest-tundra ecotone, north of the Brooks Range. More research is necessary to determine if the increase in cover corresponded to the advance of the tree line, or to the expansion of the tall shrubs, or both

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Human and environmental exposure to hydrocarbon pollution in the Niger Delta:A geospatial approach

This study undertook an integrated geospatial assessment of human and environmental exposure to oil pollution in the Niger Delta using primary and secondary spatial data. This thesis begins by presenting a clear rationale for the study of extensive oil pollution in the Niger Delta, followed by a critical literature review of the potential application of geospatial techniques for monitoring and managing the problem. Three analytical chapters report on the methodological developments and applications of geospatial techniques that contribute to achieving the aim of the study. Firstly, a quantitative assessment of human and environmental exposure to oil pollution in the Niger Delta was performed using a government spill database. This was carried out using Spatial Analysis along Networks (SANET), a geostatistical tool, since oil spills in the region tend to follow the linear patterns of the pipelines. Spatial data on pipelines, oil spills, population and land cover data were analysed in order to quantify the extent of human and environmental exposure to oil pollution. The major causes of spills and spatial factors potentially reinforcing reported causes were analysed. Results show extensive general exposure and sabotage as the leading cause of oil pollution in the Niger Delta. Secondly, a method of delineating the river network in the Niger Delta using Sentinel-1 SAR data was developed, as a basis for modelling potential flow of pollutants in the distributary pathways of the network. The cloud penetration capabilities of SAR sensing are particularly valuable for this application since the Niger Delta is notorious for cloud cover. Vector and raster-based river networks derived from Sentinel-1 were compared to alternative river map products including those from the USGS and ESA. This demonstrated the superiority of the Sentinel-1 derived river network, which was subsequently used in a flow routing analysis to demonstrate the potential for understanding oil spill dispersion. Thirdly, the study applied optical remote sensing for indirect detection and mapping of oil spill impacts on vegetation. Multi-temporal Landsat data was used to delineate the spill impact footprint of a notable 2008 oil spill incident in Ogoniland and population exposure was evaluated. The optical data was effective in impact area delineation, demonstrating extensive and long-lasting population exposure to oil pollution. Overall, this study has successfully assembled and produced relevant spatial and attribute data sets and applied integrated geostatistical analytical techniques to understand the distribution and impacts of oil spills in the Niger Delta. The study has revealed the extensive level of human and environmental exposure to hydrocarbon pollution in the Niger Delta and introduced new methods that will be valuable fo

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