Tendências temporais de índices de vegetação nos campos do Pampa do Brasil e do Uruguai

O objetivo deste trabalho foi avaliar a redução do vigor vegetativo da cobertura vegetal do Pampa do Brasil e do Uruguai, por meio da identificação de tendências negativas em séries temporais de imagens. Utilizaram-se séries temporais de imagens de NDVI/EVI do sensor Modis, de 2000 a 2011; imagens de índices de umidade do solo do "climate forecast system reanalysis"; e dados de precipitação pluvial de estações meteorológicas. O estudo quantificou tendências lineares e não lineares nas séries de NDVI e EVI, em áreas de campos. Na tendência monotônica de Mann-Kendall, a 5% de probabilidade, 81,9% da área total estudada foi significativa com o NDVI, e 74,8%, com o EVI; no entanto, o EVI apresentou contraste superior na estimativa dos parâmetros. Os resultados mostraram maior sinal negativo a oeste, com valores médios de R²>0,15, r<-0,3 e τ <-0,15 na tendência dos índices de vegetação, e tendência decrescente para NDVI, EVI e precipitação pluvial, com menores valores médios de umidade do solo. A tendência negativa dos índices de vegetação, relacionada à combinação da ocorrência de deficit hídrico em solos rasos com o sobrepastoreio, indica alterações no padrão de cobertura vegetal do Pampa, com redução do vigor vegetativo

Comparison of different fusion algorithms in urban and agricultural areas using sar (palsar and radarsat) and optical (spot) images

Image fusion techniques of remote sensing data are formal frameworks for merging and using images originating from different sources. This research investigates the quality assessment of Synthetic Aperture Radar (SAR) data fusion with optical imagery. Two different SAR data from different sensors namely RADARSAT-1 and PALSAR were fused with SPOT-2 data. Both SAR data have the same resolutions and polarisations; however images were gathered in different frequencies as C band and L band respectively. This paper contributes to the comparative evaluation of fused data for understanding the performance of implemented image fusion algorithms such as Ehlers, IHS (Intensity-Hue-Saturation), HPF (High Pass Frequency), two dimensional DWT (Discrete Wavelet Transformation), and PCA (Principal Component Analysis) techniques. Quality assessments of fused images were performed both qualitatively and quantitatively. For the statistical analysis; bias, correlation coefficient (CC), difference in variance (DIV), standard deviation difference (SDD), universal image quality index (UIQI) methods were applied on the fused images. The evaluations were performed by categorizing the test area into two as "urban" and "agricultural". It has been observed that some of the methods have enhanced either the spatial quality or preserved spectral quality of the original SPOT XS image to various degrees while some approaches have introduced distortions. In general we noted that Ehlers' spectral quality is far better than those of the other methods. HPF performs almost best in agricultural areas for both SAR images

Estimating texture independently of tone in simulated images of forest canopies

Tone and texture are two fundamental characteristics of remotely sensed images. Current research on the remote sensing of tropical forest biomass uses the tone (i.e., backscatter) of Synthetic Aperture Radar (SAR) images as this is related directly to biomass (albeit up to the backscatter/biomass asymptote). As a tropical forest canopy ages so its unevenness increases, progressing from smooth to rough. Therefore a measure of SAR texture that is independent of SAR tone has the potential of increasing the biomass maxima that can be estimated with SAR data. This experiment used simulated SAR images designed to reproduce forest canopies and different patterns of tone (or contrast) and texture (or clumpiness). Twenty six texture measures (derived from local statistics, the grey-level co-occurrence matrix (GLCM) and variograms) were calculated for these simulated images. Measures sensitive to texture (clumpiness) and/or tone (contrast) were identified using Analysis of Variance (ANOVA). Seven texture measures were recommended for the estimation of tropical forest biomass with SAR images

Relating SAR image texture to the biomass of regenerating tropical forests

An accurate global carbon budget requires information on terrestrial carbon sink strength. Regenerating tropical forests are known to be important terrestrial carbon sinks but information on their location, extent and biomass (from which carbon content can be estimated) is incomplete. The use of remotely sensed data in optical wavelengths has been of limited use due to both the weak relationship between optical radiation and forest biomass and near-constant cloud cover in the tropics. L-band Synthetic Aperture Radar (SAR) backscatter, however, is related positively to biomass (but only up to an asymptote of around 40�90 Tha21) and can be obtained independently of cloud cover. Both canopy structure and biomass change over time as pioneer species are replaced by early and late regenerating species. These structural changes are related to an increase in (i) tree height, (ii) tree species richness and (iii) canopy thickness and influence the roughness of the canopy surface and consequently SAR image texture. Therefore, we investigated the degree to which textural information could be used to increase the correlation between image tone (backscatter) and biomass. Field data were used to estimate the biomass of 37 regenerating forests plots in Brazilian Amazonia. Texture measures derived from local statistics, the grey level co-occurrence matrix (GLCM) and the variogram were evaluated using simulated images on the basis of their ability to identify significant differences in image texture independently of image contrast. Theselected texture measures were applied to L-band JERS-1 (Japanese Earth Resources Satellite) SAR images and the correlation between backscatter and biomass was determined for regenerating tropical forests. A strong correlation was found for the texture measures and biomass. The ra2 (adjusted coefficient of determination), measuring the correlation between backscatter and biomass, increased from 0.74 to 0.82 with the addition of GLCM-derived contrast. The addition of image texture (GLCM-derived contrast) to image tone (backscatter) potentially increases the accuracy with which JERS-1 SAR data can be used to estimate biomass in tropical forests