Linear mixing model applied to coarse resolution satellite data

A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies

Shade images of forested areas obtained from LANDSAT MSS data

The pixel size in the present day Remote Sensing systems is large enough to include different types of land cover. Depending upon the target area, several components may be present within the pixel. In forested areas, generally, three main components are present: tree canopy, soil (understory), and shadow. The objective is to generate a shade (shadow) image of forested areas from multispectral measurements of LANDSAT MSS (Multispectral Scanner) data by implementing a linear mixing model, where shadow is considered as one of the primary components in a pixel. The shade images are related to the observed variation in forest structure, i.e., the proportion of inferred shadow in a pixel is related to different forest ages, forest types, and tree crown cover. The Constrained Least Squares (CLS) method is used to generate shade images for forest of eucalyptus and vegetation of cerrado using LANDSAT MSS imagery over Itapeva study area in Brazil. The resulted shade images may explain the difference on ages for forest of eucalyptus and the difference on three crown cover for vegetation of cerrado

LINEAR SPECTRAL MIXING MODEL APPLIED IN IMAGES FROM PROBA-V SENSOR: A SPATIAL MULTIRESOLUTION APPROACH

The complexity of pixel composition of orbital images has been commonly referred to the spectral mixture problem. The acquisition of endmembers (pure pixels) direct from image under study is one of the most commonly employed approaches. However, it becomes limited in low or moderate spatial resolutions due to the lower probability of finding those pixels. In this way, this work proposes the combined use of images with different spatial resolutions to estimate the spectral responses of the endmembers in low spatial resolution image, from the obtained proportions derived from the spatial higher-resolution images. The proposed methodology was applied to products provided by PROBA-V satellite with spatial resolution of 100 m and 1 km in the Pantanal region of Mato Grosso state. Initially, the fraction images (proportions) were generated from the 100 m dataset using the endmembers selected directly in the image, considering the higher probability of finding pure pixels in such images. Following the spectral responses of the endmembers in 1 km were estimated by multiple linear regression, using the proportions of the endmembers in the pixels derived from 100 m images. For the evaluation, the endmembers fraction images were compared and field data was used. These analyses indicated that the spectral responses estimated allowed to improve the results with regard to error, to variability, and to the identification of endmembers proportions, considering that inadequate choice of pixels considered as pure in low spatial resolution images can affect the quality of the fraction images for operational use

Fractal properties of forest fires in Amazonia as a basis for modelling pan-tropical burnt area

Journal ArticleCurrent methods for modelling burnt area in dynamic global vegetation models (DGVMs) involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation, but undeniably useful for modelling specific, small-scale burns. Using observed fractal distributions of fire scars in Brazilian Amazonia in 2005, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. This model is intended for predicting large-scale burnt area rather than looking at individual fire events. A simple parameterization of a tapered fractal distribution is calibrated at multiple spatial resolutions using a satellite-derived burnt area map. The model is capable of accurately reproducing the total area burnt (16 387 km2) and its spatial distribution. When tested pan-tropically using the MODIS MCD14ML active fire product, the model accurately predicts temporal and spatial fire trends, but the magnitude of the differences between these estimates and the GFED3.1 burnt area products varies per continent. © Author(s) 2014.Natural Environment Research Council (NERC)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)CAPESScience without Borders programme fellowshi

PROCESSAMENTO DIGITAL DE IMAGENS MULTITEMPORAIS LANDSAT-5 TM E JERS-1 SAR APLICADO AO MAPEAMENTO E MONITORAMENTO DE ÁREAS DE ALTERAÇÃO ANTRÓPICA NA AMAZÔNIA

Landsat-5 TM and JERS-1 SAR images were used to map and monitor areas of anthropic disturbance in the region of the Tepequém plateau and surroundings, Brazilian Amazônia. The study area, performing approximately 400 km2, is covered by tropical rain forest and savanna grassland. Part of the vegetation cover has been continuously replaced by cultivated areas (agriculture and pastures), and disturbed by independent gold mining activities. For the development of this work were used Landsat-5 TM images (acquired in 1987, 1991, 1994, and 1996) and JERS-1 SAR images (acquired in 1993, 1994, and 1996). Landsat-5 TM images were converted to soil, vegetation, and shade components, through a spectral linear mixture modeling. An approach based on image segmentation, region-classification, and map-editing techniques was used to map degraded areas. Over the period, the results showed an increase from 341 hectares to 1,986 hectares in the deforested areas due to agricultural activities in the forested terrain. Concerning to the areas disturbed by mining activities, predominant in the savanna grassland areas, it was identified an increase of 94 hectares to 537 hectares, over the same period. JERS-1 SAR images were investigated as an attempt to supply information referent to the years in which Landsat-5 TM images could not be used, due to the presence of cloud cover. Contrast stretched SAR images were able to detect only recent clear cut and areas under regeneration process in the domain of the rain forest. They do not provide any information regarding areas of gold mining activities in the savanna grassland domain. However, such information was only partially provided by the generation of difference images between different dates. The adopted procedure showed to be very efficient for the case of the Landsat images, producing very accurate maps. Although this procedure has been applied in a small region, it is applicable to map and monitor broad region in the Amazônia. Key words: Remote Sensing, Deforestation, Mining Activities, Image Digital Processing, Landsat-5 TM, JERS-1 SAR.Imagens Landsat-5 TM e JERS-1 SAR foram usadas para mapear e monitorar áreas de alterações antrópicas na região da serra do Tepequém e arredores, no estado de Roraima. A área de estudo, perfazendo aproximadamente 400 km2, é coberta por floresta tropical e campos abertos. Parte da cobertura vegetal nativa tem sido continuamente substituída por áreas cultivadas (agricultura e pastagens), e alteradas por atividades de garimpo. Para o desenvolvimento deste trabalho foram utilizadas imagens Landsat-5 TM (adquiridas em 1987, 1991, 1994 e 1996) e as imagens JERS-1 SAR (adquiridas em 1993, 1994 e 1996). As imagens Landsat-5 TM foram decompostas em componentes solo, vegetação e sombra, através de um modelo linear de mistura espectral. Técnicas de segmentação, classificação por região e edição de imagens foram utilizadas para mapear áreas degradadas. Os resultados mostraram no período um crescimento de 341 hectares para 1.986 hectares, devido a atividades agropastoris em áreas de florestas. Com relação às atividades de garimpo, predominantes em áreas de campos abertos, identificou-se um crescimento de 94 hectares para 537 hectares. Imagens JERS-1 SAR foram analisadas como uma tentativa de prover informações referentes aos anos em que imagens Landsat não puderam ser usadas, devido à presença de cobertura de nuvens. Informações extraídas dessas imagens, realçadas com ampliação linear de contraste, permitiram detectar apenas áreas recém desmatadas, ou em estágio de regeneração no domínio da floresta tropical, não fornecendo qualquer informação relativa às áreas de garimpo, no domínio de campos abertos. Informações relativas às áreas desmatadas por garimpos foram apenas parcialmente fornecidas, através da geração de imagens diferença entre datas distintas. O procedimento adotado mostrou-se eficiente, para o caso das imagens Landsat, produzindo mapas acurados. Embora tenha sido aplicado em uma pequena região, este procedimento pode ser aplicável para mapear e monitorar amplas regiões na Amazônia. Palavras-chave: Sensoriamento Remoto, Desmatamento, Garimpo, Processamento Digital de Imagens, Landsat-5 TM, JERS-1 SAR

Assessing land cover changes in the Brazilian Cerrado between 1990 and 2010 using a remote sensing sampling approach

We present a remote sensing sampling approach to assess land cover changes between years 1990 and 2010 for the Cerrado biome. Despite the fact that natural vegetation cover of this biome has been heavily converted into agricultural lands over the past decades, there is still a lack of detailed and historical information about vegetation cover changes at the biome scale. The sampling design and image processing techniques were developed by the Joint Research Centre (JRC) Tropical Ecosystem Environment Observation by Satellite (TREES-3) project. A set of 175 regularly distributed sample units (with10 km x 10 km size) located at every full degree confluence point of latitude and longitude were assessed. For each sample unit, (E)TM Landsat images from three target years (1990, 2000 and 2010) were selected, pre-processed, segmented and classified into five land cover classes (Tree Cover - TC, Tree Cover Mosaic - TCM, Other Wooded Land - OWL, Other Land Cover - OLC and Water -W). The results showed that the Cerrado had a net loss of natural vegetation (TC + OWL) of about 12 million hectares between 1990 and 2010, or an average rate of change of -0.6% y-1. However, the rates of change decreased from the first (1990-2000) to the second (2000-2010) decade. By 2010, the percentage of natural vegetation cover remaining in the Cerrado was 47%.JRC.H.3-Forest Resources and Climat

Produtividade de soja estimada por modelo agrometeorológico num SIG

Os modelos agrometeorológicos integrados em Sistemas de Informação Geográfica - SIG são uma alternativa para simular e quantificar o efeito da variabilidade espacial e temporal do clima sobre a produtividade agrícola. O objetivo deste trabalho foi adaptar e integrar um modelo agrometeorológico num SIG para estimar a produtividade da soja [Glycine max (L.) Merr.]. Foram geradas estimativas de produtividade para 144 municípios do Estado do Paraná, responsáveis por 90% da produção de soja no Estado, em cinco anos-safra no período de 1996/1997 a 2000/2001. O modelo utiliza parâmetros agronômicos e dados meteorológicos para o cálculo da produtividade máxima, a qual é penalizada quando ocorre estresse hídrico. A análise da comparação entre as estimativas municipais obtidas pelo modelo e aquelas divulgadas pela Secretaria de Estado da Agricultura e do Abastecimento (SEAB) do Paraná foi feita através do teste "t" para pares de observação. No ano safra 1996/1997 o modelo superestimou a produtividade em 10,8% em relação à SEAB, o que pode ser atribuído à ocorrência de oídio, cujo efeito não é considerado no modelo. Nos anos safras de 1997/1998, 1998/1999 e 1999/2000 não foram identificadas diferenças (P >; 0,05) entre as estimativas do modelo e da SEAB. Em 2000/2001 a produtividade foi subestimada pelo modelo em 10,5%, sendo que as causas desta diferença precisam ser melhor investigadas. O modelo integrado no SIG mostrou ser uma ferramenta viável para acompanhar a cultura da soja ao longo da estação de crescimento, e estimar a produtividade em municípios do Estado do Paraná.Agrometeorological models interfaced with the Geographic Information System - GIS are an alternative to simulate and quantify the effect of weather spatial and temporal variability on crop yield. The objective of this work was to adapt and interface an agrometeorological model with a GIS to estimate soybean [Glycine max (L.) Merr.] yield. Yield estimates were generated for 144 municipalities in the State of Paraná, Brazil, responsible for 90% of the soybean production in the State, from 1996/1997 to 2000/2001. The model uses agronomical parameters and meteorological data to calculate maximum yield which will be penalized under drought stress. Comparative analyses between the yield estimated by the model and that reported by the Paraná State Department of Agriculture (SEAB) were performed using the "t" test for paired observations. For the 1996/1997 year the model overestimated yield by 10.8%, which may be attributed to the occurrence of fungal diseases not considered by the model. For 1997/1998, 1998/1999 and 1999/2000 no differences (P >; 0.05) were found between the yield estimated by the model and SEAB's data. For 2000/2001 the model underestimated yield by 10.5% and the cause for this difference needs further investigation. The model interfaced with a GIS is an useful tool to monitor soybean crop during growing season to estimate crop yield

Mapping major land cover types and retrieving the age of secondary forests in the Brazilian Amazon by combining single-date optical and radar remote sensing data

Secondary forests play an important role in restoring carbon and biodiversity lost previously through deforestation and degradation and yet there is little information available on the extent of different successional stages. Such knowledge is particularly needed in tropical regions where past and current disturbance rates have been high but regeneration is rapid. Focusing on three areas in the Brazilian Amazon (Manaus, Santarém, Machadinho d'Oeste), this study aimed to evaluate the use of single-date Landsat Thematic Mapper (TM) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data in the 2007–2010 period for i) discriminating mature forest, non-forest and secondary forest, and ii) retrieving the age of secondary forests (ASF), with 100 m × 100 m training areas obtained by the analysis of an extensive time-series of Landsat sensor data over the three sites. A machine learning algorithm (random forests) was used in combination with ALOS PALSAR backscatter intensity at HH and HV polarizations and Landsat 5 TM surface reflectance in the visible, near-infrared and shortwave infrared spectral regions. Overall accuracy when discriminating mature forest, non-forest and secondary forest is high (95–96%), with the highest errors in the secondary forest class (omission and commission errors in the range 4–6% and 12–20% respectively) because of misclassification as mature forest. Root mean square error (RMSE) and bias when retrieving ASF ranged between 4.3–4.7 years (relative RMSE = 25.5–32.0%) and 0.04–0.08 years respectively. On average, unbiased ASF estimates can be obtained using the method proposed here (Wilcoxon test, p-value > 0.05). However, the bias decomposition by 5-year interval ASF classes showed that most age estimates are biased, with consistent overestimation in secondary forests up to 10–15 years of age and underestimation in secondary forests of at least 20 years of age. Comparison with the classification results obtained from the analysis of extensive time-series of Landsat sensor data showed a good agreement, with Pearson's coefficient of correlation (R) of the proportion of mature forest, non-forest and secondary forest at 1-km grid cells ranging between 0.97–0.98, 0.96–0.98 and 0.84–0.90 in the 2007–2010 period, respectively. The agreement was lower (R = 0.82–0.85) when using the same dataset to compare the ability of ALOS PALSAR and Landsat 5 TM data to retrieve ASF. This was also dependent on the study area, especially when considering mapping secondary forest and retrieving ASF, with Manaus displaying better agreement when compared to the results at Santarém and Machadinho d'Oeste

Sensor modis : características gerais e aplicações

O presente trabalho tem como propósito realizar uma abordagem geral sobre um dos principais sensores, denominado MODIS (Moderate Resolution ImagingSpectroradiometer), desenvolvido pela NASA, com o objetivo de determinar como aTerra está mudando e quais as conseqüências para a vida neste planeta, desenvolvendo um entendimento de seu funcionamento como um sistema único e interligado. Seusprodutos permitirão um monitoramento de longa duração da superfície, necessários para o entendimento de mudanças globais. Neste propósito, realiza-se uma descrição sobre este sensor e por fim, uma síntese dos principais produtos gerados por ele e suas aplicações. _________________________________________________________________________________ ABSTRACTThe present work has as purpose to accomplish a general descriptionon one of main sensor, denominated MODIS (Moderate Resolution ImagingSpectroradiometer), developed by NASA, with the objective of determining how theEarth is changing and which the consequences for the life in this planet, developing an understanding of its functioning as an only and interlinked system. Its products willallow a long term monitoring of long duration of the surface, necessary for theunderstanding of global changes. In this purpose, it is presented a description of its systems and finally, a synthesis of the main products generated by this sensor and its applications