Deforestation is the replacement of forest by other land use, while degradation is a reduction of long-term canopy cover and/or forest stock. In the Brazilian Amazon, forest degradation mainly results from selective logging of intact/unmanaged forests and uncontrolled fires. While the contribution of deforestation to carbon emissions is well-known, determining the impact of forest degradation remains challenging. This work presents a semi-automated procedure for classifying forest degradation in Mato Grosso State using fraction images derived from the Linear Spectral Mixing Model (LSMM). Sentinel-2 MSI and Landsat OLI images acquired in 2023 over the study area were selected to develop the proposed method. Discrimination against logging from fires, which produce different levels of forest damage, is important for the UNFCCC (United Nations Framework Convention on Climate Change) REDD+ (Reducing Emissions from Deforestation and Forest Degradation) program.

Amazon is recognized for its biodiversity and has a crucial role in the global climate balance. However, it is currently facing drought, one of the most pressing challenges in its history. Then the objective of this study was to estimate the extent of water coverage in the Amazon biome during the seasonal pulses of rising and descending waters. For this, we used NPP VIIRS and Sentinel-2 MSI mosaics which were acquired during the 2023 peak flood and the peak drought events ever recorded in the Rio Negro, Amazonas. To classify water coverage using images from both sensors, a script was developed in GEE, which applies the Linear Spectral Mixing Model (LSMM). The estimated areas were: 109,169.00 km2(January to July 2023) and 61,477.50 km2(August to October). The drought event in Amazon this year is due to the 2023 strong El Niño and the temperature increase in the North Atlantic.

This article presents a new method for monitoring forest cover in the state of Rondônia, in the Brazilian Amazon. The proposed method applies the Linear Spectral Mixing Model (LSMM) to Landsat datasets (MSS, TM and OLI) to derive annual vegetation, soil, and shade fraction images for the period 1980 – 2020. These fraction images have the advantages of reducing the volume of data to be analyzed and highlighting the target characteristics. Then, we applied a threshold method to classify forest, non-forest, hydrography, and deforestation areas. The proposed method showed to be consistent and flexible allowing to change the threshold values according to the fraction images to obtain the results with high accuracy. The results obtained by the proposed method can be easily checked over the RGB image mosaic. This kind of information is very important for environmental and climate change studies and for supporting government conservation efforts.

This article presents a land use and land cover (LULC) classification map based on Random Forest (RF) classifier algorithm in the São Paulo State (Brazil), using Landsat-8 OLI data. The method consists in using time series images from January to December of 2020 based on the spectral and temporal characteristics of the LULC classes. We performed the classification class by class considering: water, urban area, forest, agriculture, forest plantation and pasture. Then, we pre-processed the selected images based on the spectral characteristics of the targets to highlight each LULC class. After that, the classification was performed using RF for each class individually and then we composed the final map with all LULC classes. The results showed a global accuracy of 89.10%, kappa value of 0.8692, producer accuracies greater than 79.80% and user accuracies greater than 76.82% for the classes mapped. Therefore, the method is consistent allowing to minimize the classification errors facilitating the pos-classification edition of individual classes mapped.

This article presents a method to map the extent of forest plantation in an area located in the São Paulo State (Brazil). The proposed method applies the Linear Spectral Mixing Model (LSMM) to Landsat Thematic Mapper (TM) datasets to derive annually vegetation, soil and shade fraction images for local analysis. We used 30 m annual mosaics of TM images during the 1985 to 1995 time period. These fraction images have the advantage to reduce the volume of data to be analyzed highlighting the target characteristics. Then, we generated only one mosaic for each fraction images for TM dataset computing de maximum value through this period, facilitating the classification of areas occupied by forest plantation. The proposed method allowed to classify two forest plantation classes: Eucalypt and Pine. In addition, it allowed to monitor the phenological stages of Eucalypt according to its growth cycle. The results are very important for planning and management by the commercial companies and can contribute to develop an automatic method to map forest plantation areas in a regional and global scales.

This article presents a land use and land cover (LULC) classification map using Random Forest algorithm in the São Paulo State (Brazil), and an assessment of burned areas using two products (MCD64A1 and MapBiomas Fire). The method uses Landsat Operational Land Imager (OLI) time series images from January to December of 2020. We performed the classification class by class considering: water, urban area, forest formation, sugarcane, agriculture, forest plantation and pasture. For each class, we used different spectral bands and image fraction according to the best response for the class. For 2020, the top three areas mapped in São Paulo State were pasture (40.49%), sugarcane (24.74%) and forest formation (20.60%). Comparing the two burned area products, MCD64A1 mapped more burned areas as it uses MODIS images combined with 1 km active fire observations with higher temporal resolution than MapBiomas Fire. About 60% of the burned areas mapped in 2020 occurred in the sugarcane class. The results show the importance of land use and land cover classification for better understanding fire-prone classes given the spatial distribution. It turns as an environmental tool for environmental strategies of planning and monitoring burned area assessment over regional scales.

There are several polarimetric filters in the literature developed for the most diverse applications. Here, we present an evaluation of different filter sizes and types for improving the AGB estimates in the Brazilian Amazon, varying from none to 21×21 px filter size in full polarimetric ALOS/PALSAR-2. The optimal window size was chosen by the highest coefficient of determination (R2) between forest AGB and backscattering coefficient (σ°). After that, six polarimetric filters were evaluated: BoxCar, Refined Lee, Improved Sigma Lee, Intensity Driven Adaptive Neighbourhood (IDAN), Scattering Model-Based (SMB), and Model-based (MB). The comparison criterion was a set of statistics aimed at preserving the three basic principles of the filtering process. The optimal window size was 11×11 px. The best was Refined Lee, followed by BoxCar and SMB. The R2differences in the filter choice can be up to 15% for retrieving forest AGB.

Implementation of complex SAR speckle filtering algorithms is usually limited to experimental settings, that make use of heavy-duty computers or processing clusters. Operational applications of SAR imagery, such as those used to map flash-flooded areas, or to flag on-going deforestation, usually are not able to take advantage of these advanced filtering techniques. Here we introduce SDNLM3D, a fast and effective 3D filtering algorithm based on the non-local paradigm, suitable to be applied on cloud environments, such as the Google Earth Engine (GEE). A systematic, real-world based benchmark of more than 700 variations of the SDNLM3D revealed that the optimized version of the SDNLM3D filter outperforms the usual filters used operationally.

Unmanned aerial vehicles (UAVs) have been advancing in precision and cost-benefit for remote sensing studies, including height and biomass estimations. This article presents a preliminary experiment to explore UAV photogrammetry to estimate canopy height in savanna and grassland phytophysiognomies in the Brazilian Cerrado biome. For this purpose, it was generated dense cloud points to obtain digital terrain and surface models used to calculate the canopy height. The spatial distribution of canopy height ranged from 0 to 4 meters, in which the most values were under 50 cm, commonly found in grasslands and observed in vegetation regrowth of post-fire event in savanna areas.

Fire dynamics in the Brazilian Savannas (Cerrado) is related to climatic conditions and management interventions by human activities. Thus, the fire occurrence in conservation units (UCs) may be different when compared with their buffer zones. Our results, obtained by burned area analysis, demonstrate that buffer zones have the most significant variation in the burned area over the years when compared to the burned area inside the UC, such as the Jalapão State Park. In contrast, when the buffer zone presents agricultural activities, as occurs in the Chapada dos Veadeiros and Chapada das Mesas National Parks, or urban occupation, such as the Brasilia National Park, the proportions of burned area are lower than found inside the UCs, according to data obtained from the MODIS MCD64A1 product from 2001 to 2018 years.

This article presents a method to map the extent of annual land-use and land-cover (LULC) in Mato Grosso State, located in the Brazilian Legal Amazon. The proposed method applies the Linear Spectral Mixing Model (LSMM) to VIIRS NPP dataset to derive monthly vegetation, soil and shade fraction images for regional analysis. We used 500 m monthly image mosaics for VIIRS in 2015 year. These fraction images have the advantage to reduce the volume of data to be analyzed highlighting the target characteristics. Then we generated only one mosaic for each fraction images for VIIRS dataset computing de maximum value through the year, facilitating the classification of LULC classes. The proposed method allowed to classify three LULC classes: forest, cropland and non-forest (Savannah and pasture) areas. In addition, it allowed to map burned areas occurred during the study period. The results are very important for planning and management by the government and non-governmental organizations.

In this work we present a procedure to analyze the forest degradation by fire in the Brazilian Amazon using the Landsat-8 Operational Land Imager (OLI) time series, taking advantage of the resources of the Google Earth Engine platform. The study area is the municipality of Porto dos Gaúchos located in the state of Mato Grosso, in the "arc of deforestation" of the Brazilian Legal Amazon. We used OLI images acquired between January 1st, 2017 and the last available image from 2018. We generated fraction images of soil, vegetation and shade using the Linear Spectral Mixing Model to highlight burned forest that we visited in the field in September 2018. Our analysis showed that forest degradation by fire can be detected using time series and the Google Earth Engine platform.

Secondary forests (SFs) are one of the major carbon sink in the Neotropics due to the rapid carbon assimilating in their aboveground biomass (AGB). However, the accurate contribution of the SFs to the carbon cycle is a great challenge because of the uncertainty in AGB estimates. In this context, the main objective of this work is to explore polarimetric Alos/Palsar-2 data from to model AGB in the SFs of the Central Amazon, Amazonas State. Forest inventory was conducted in 2014 with the measured of 23 field plots. Multiple linear regression analysis was performed to select the best model by corrected AICwand validated by leave-one-out bootstrapping method. The best fitted model has six parameters and explained 65% of the aboveground biomass variability. The prediction error was calculated to be RMSEP = 8.8 ± 2.98 Mg.ha-1(8.75%). The main polarimetric attributes in the model were those direct related to multiple scattering mechanisms as the Shannon Entropy and the volumetric mechanism of Bhattacharya decomposition; and those related to increase in double-bounce as the co-polarization ratio (VV/HH) resulted of soil-trunk interactions. Such models are intended to improve accuracy for mapping SFs AGB in often cloudy environments as in the Brazilian Amazon.

The objective of this paper is to present a method to assess the extent of annual land use/land cover in Brazil, South America. The proposed method applies the Linear Spectral Mixing Model (LSMM) to PROBA-V datasets to derive vegetation, soil and shade fraction images for global and regional analysis. We used 1 km composites of 10 days (S10-TOC - 10-daily global composites, Top-Of-Canopy) for the South America and 100 m composites of 5 days (S5-TOC - 5-daily global composites, Top-Of-Canopy) for the Mato Grosso State, Brazilian Amazon. Then we built the 1km and 100m composites corresponding to the three endmembers with the highest fraction values during the year 2015. In that manner we could detect and map the areas occupied by main crops in Brazil, during the 2015 year, using the vegetation fraction composites. Also, PROBA-V images were acquired in the dry season, on 21 June, 26 July and 11 August 2015 to show the potentiality of these images to assess the land cover changes due to deforestation and forest degradation by fire. The agricultural areas mapped using 1km dataset were compared with 100m results for the Mato Grosso State showing a difference of 12% (58,834 km2and 66,490 km2, respectively). The results are very important for the government and nongovernmental organizations for planning and management of the tropical environment.

In this work, we used hyperspectral remote sensing and a simplified three-dimensional radiative transfer approach to retrieve structural and chemical properties of individual tree crowns (ITCs) from a tropical forest area. First, a Look-Up-Table of simulated ITC reflectance was built by randomly varying parameters of the DART and PROSPECT models. Then, simulated and experimental reflectance of ITCs were compared in terms of spectral similarity. Finally, model parameters that yielded simulations spectrally similar to experimental data were related to sub-pixel fractions and narrow-band vegetation indices computed from the hyperspectral images. DART canopy structural parameters were related to the proportion of non-photosynthetic vegetation (NPV) (R2=0.65), green photosynthetic vegetation (GV) (R2=0.72) and shade (R2=0.34) estimated within ITCs. PROSPECT parameters describing foliar chemical traits such as Chlorophyll a+b (Cab) and Carotenoids (Cxc) were related to the ratio of TCARI/OSAVI (R2=0.77) indices and to the simple ratio between reflectance at 515 nm and 570 nm (R515/R570) (R2=0.42), respectively.

This study aimed to characterize and analyze, based on MOD17 data, the spatio-temporal dynamics of GPP in the northern region of Para state, Brazilian Amazon, during a 6-year period (2001 to 2006). The study area encompasses two river basins (Upper Tapajos and Curua-Una) and covers ~74,190 km2. The spatial variation of GPP was primarily related to the larger presence of forested areas in Upper Tapajos River basin in comparison with Curua-Una River basin. Temporally, GPP varied with the dry and wet seasons in the region. There was a decrease of ~4% in GPP during the dry season, which was related to the fact that the water limitation during the dry season in Amazonia leads to a decrease in photosynthesis, affecting vegetation productivity. It was observed a reasonable interannual variation of GPP in the study area, which corresponded to ~10%.

This work presents a semi-automated procedure for monitoring deforestation and forest degradation in the Brazilian Amazon using a multi-temporal dataset of satellite imagery. Degradation in forest cover in the Brazilian Amazon region is mainly due to selective logging of intact/un-managed forests and to uncontrolled fires. For this study, part of a Landsat TM scene located in the State of Mato Grosso, in the “deforestation arc” of the Brazilian Amazon was selected. Landsat TM images acquired in years 2005, 2006, 2007, 2008, 2009, 2010 and 2011 and one RapidEye image acquired in 2013 was used in this study. The proposed approach can be used for monitoring deforestation and forest degradation activities by selective logging and fires. The current availability of high spatial resolution data such as Sentinel-2 is expected to allow improving the assessment of deforestation and forest degradation processes using the proposed method and, consequently, facilitating the implementation of actions of forest protection.

This paper presents a multidisciplinary approach used to assess forest degradation in the Brazilian Amazon based on remote sensing and spatial pattern analysis techniques. The selected study area is located in the Mato Grosso State, which is one of the states of the Brazilian legal Amazon with the highest deforestation rates and with a high concentration of selective logging activities and forest fires. We used an object-based image analysis for mapping degraded forest areas and compared their spatial distribution with that of fragmentation and edge indicators and the distance to roads. Our results show that the majority of these disturbed forest areas occur within a distance of less than 5 km from the main roads, are located between 100m and 5 km from the forest edge, and show higher entropy (used as a measure of fragmentation). However, circa 30% of the degradation occurred in areas considered as “core areas”.

This paper presents a new paradigm for object based classification of multispectral images. Instead of classifying objects only after the segmentation process is completed, it is proposed to intercept the early stages of the segmentation by iteratively performing classification tests to under growing regions. By applying this simultaneous analysis, mislabeling of objects considered only after segmentation is completely done can be avoided. The proposed technique assumes that some growing regions can present higher membership to a particular class when comparing to the final object in which it is included. A Bayesian framework was applied in classification tests performed by pixel based, traditional object based, and the proposed technique were performed. The results show the soundness of the proposed method when comparing overall accuracies with a reference map.

In this paper, we present a novel domain adaptation technique aimed at providing reliable change detection maps for a series of image pairs acquired on the same area at different times. The proposed technique exploits the polar change vector analysis method and assumes that the reference data for characterizing a specific change of interest are available only for a pair of images (source domain). Then, it exploits the knowledge learned from the source domain and adapts it to other pairs of images belonging to the time series (target domains) to be analyzed. The proposed technique is able to handle possible radiometric differences among images adapting in an unsupervised way the decision rule estimated on the source domain to the target domains through variables estimated directly on the target images. The proposed approach has been applied to two data sets made up of time series of Landsat Thematic Mapper images. In one case, the change of interest is related to evolution of deforestation, while in the other case, it is related to burned area detection. Experimental results show the effectiveness of the proposed technique.

This paper aims to use unique features of hyperspectral data on an automatic process for outlining individual tree crowns (ITCs) in a tropical forest area, with special focus on semi-deciduous species. In order to enhance biophysical and biochemical properties of canopy species, a set of vegetation indices were computed. These indices served as input for a region growing segmentation algorithm that takes into account mutual similarity of pixels and spectral separability between neighbor segments. Segmentation output was evaluated on the basis of a score computed with the proportion of the area of the segments located within manually delineated ITCs. Results show that the segmentation approach is able to automatically delineate up to 70% of the control ITCs.

This paper presents a method for mapping and assessing burned areas at a regional scale, using a systematic sample of medium spatial resolution satellite images (Landsat). The State of Mato Grosso, located in the Brazilian Amazon region, comprising an area of approximately 903,366 km2, was selected for this study. 77 sample sites (20km × 20km in size) located at each full degree confluence of latitude and longitude were analyzed. The results showed that 52,663 km2or approximately 5.8% of the State land cover was burned in 2010. Our method produced results comparable with PanAmazonia Project data and useful for evaluating burned area products based on coarse spatial resolution imagery like MODIS or SPOT-VEGETATION.

This paper presents an alternative object based classification for multispectral remote sensing images. Instead of classifying the images after the segmentation process, it is suggested to involve some steps of objects recognition during the segmentation process in order to improve the final classification results. The methodology is based on the statistical distribution of object classes. Experiments were performed with a TM-Landsat image and the results were compared with a reference data. The results indicate the soundness of the proposed methodology.

A problem that is frequently arising in the spectral mixture analysis is how to correctly identify the endmembers present in the scene. In the analysis of image data covering natural scenes, vegetation, bare soil, and shade/water are commonly assumed as endmembers, but other endmembers may also be present. This paper investigates an approach based on the analysis of residuals produced by the linear spectral mixing model for identifying potential missing endmembers. The basic proposition consists in assuming that larger residuals are caused by missing endmembers. The image is segmented in terms of the residuals, and the Kolmogorov-Smirnov test is applied to group segments that show similar residuals and are thus likely to include the same missing endmember. An approach to estimate the spectral response of the missing endmembers is also investigated. The proposed methodology is tested by using Thematic Mapper Landsat and Coupled Charge Device China-Brazil Earth Resources Satellite image data. In addition to vegetation, bare soil, and shade/water, two additional endmembers were included as missing endmembers (clouds and water bodies with a large load of suspended sediments). The tests have shown that the proposed methodology is capable of detecting image regions that include missing endmembers and of correctly estimating the corresponding spectral responses.

This paper investigates the spectral variability of Brazilian Atlantic Forest trees. Spectroscopy data (reflectance and transmittance) of full sunlight leaves were collected. The root mean square difference between pairs of spectra were used to estimate within- and between-species variability. It was also performed a feature selection procedure to identify spectral regions where the species most differ. Results show that between- is greater than within-species variability, evidencing the potential of spectroscopy measurements to species discrimination. Moreover, it was verified that spectral diversity of species is concentrated on the optical and near-infrared domain. Differences in leaf internal structure and leaf pigments (chlorophylls and carotenoids) concentration may be responsible to the observed variation.

In this paper, a methodology for automatic detection of burned areas is suggested. The classification criterion is performed using Bayesian statistical parameter (mean and covariance matrix) extracted automatically using the Expectation Maximization algorithm and taking into account the spectral similarity between burned and flooded areas. In this work the final process involves the application of morphological operators of erosion and dilation of images in order to insert information from the spatial context, refining the final map. Experiments were conducted to a TM-Landsat scene with areas affected by fires and seasonal flooding. The results show that the accuracy is increased with the consideration of flooding mask and the subsequent application of spatial context, reaching values up to 97% of accuracy when compared with a reference map.

Burned areas map is essential in many applications and the orbital sensors have been used to monitor fires for many years, providing a better understanding of processes at different scales and being the only practical technique to estimate fires in large areas. However, remote sensing methods have limitations that could cause errors in the final products. Therefore, the objective of this work is to evaluate the MCD45A1 burned area product derived from MODIS sensor in Amazon tropical forest by comparing this dataset with the reference data derived from the mapping of burned areas in Acre State/Brazil acquired by TM sensor aboard of Landsat 5 and with a fieldwork that took place in November 2011. The results showed that de MCD45A1 product presented 93% of omission errors in 2010 and 96% in 2011 year in relation to reference data, presenting a low confidence in identifying the burned areas in Amazon region.

The objective of this work was to identify and monitor forest regeneration for a period of eight years after deforestation events occurred in 2001 in the Eastern Amazon region. The study utilized Landsat Thematic Mapper (TM) images acquired for scene 224/65 (path/row), for the following dates: 07/06/2000 (Geocover product), 08/02/2001, 08/29/2002, 07/04/2005 (Geocover product) and 08/08/2009. Deforestation data was obtained from the PRODES deforestation mapping program for the year 2001. The results showed a large and increasing area of forest regeneration within the area and period of study. The first year after deforestation showed the highest proportion of regeneration (20%), which can be attributed to the known pattern of less intensive land use in the initial years after vegetation removal, associated with stem regeneration and germination from the remaining seed banks. The proportion of regenerated area decreased over the years (through new deforestation of regenerating areas), but still occupied 13% of the area deforested in 2001 at the end of the eight-year period (2001–2009). Another important result showed that from the total area regenerated by 2009, 60% started the recovery process immediately after the 2001 deforestation event, and had no evidence of economic use during the studied period (i.e. no justification exists for the observed deforestation in the first place). These results emphasize the importance of better understanding and quantifying the role of biophysical and socio-economic factors in the dynamics of forest regeneration in the Amazon, and could help improve carbon emission and biodiversity studies in the region.

Wildfires plays a fundamental intervention in global biogeochemical cycle, by the chemical reaction occurring in the combustion process, and the organic compounds present in vegetation returns to the atmosphere and soil in a cyclical behavior. Therefore, the main objective of this work is to develop a method to estimate the instantaneous fire size in Brazil using Thematic Mapper (TM) aboard of Landsat 5 and Enhanced Thematic Mapper Plus (ETM+) aboard of Landsat 7. The results indicate that active fire to pastures/grasslands is approximately 38% higher than that found for forest areas, 31% higher than the same coefficient used to estimate the fire size in areas of herbaceous and shrub vegetation and 11% higher than the coefficient used in agricultural areas.

The objective of this work was to evaluate a series of ScanSAR images of the Advanced Land Observation Satellite ALOS/PALSAR for monitoring the Brazilian Amazon rainforest. For this, a study area with approximately 6,814 km2was selected in the Mato Grosso State, Brazilian Amazon. This area was covered by two orbits of ALOS/PALSAR providing 16 scenes during the March 2009 to March 2011 time period. The maps available from PRODES (Monitoring of Brazilian Amazon Rainforest) and DETER (Detection of Deforested Areas in Real Time) projects were used for supporting the proposed method. The results showed that there is a need for pre-processing procedures such as balancing the series of ALOS/PALSAR images affected by precipitation occurred before the acquisition date. Then the ALOS/PALSAR could be incorporated into the PRODES and DETER projects for monitoring the Amazon region.

This paper proposes an analysis on the residual term resulting from the Linear Spectral Mixing Model (SLMM) solution in order to access model uncertainty. The framework employed here is based on analysis of data produced initially by unmixing of vegetation, bare soil and shade/water, whose are commonly used as standard endmembers. We suggest procedures to identify missing components in the mixture problem and automatically compute the spectral endmember values for these components directly from image data and residual information. The techniques proposed have been tested on real TM-Landsat. The results obtained promises and confirm the validity of the proposed approach.

Remote sensing is based on the interaction of electromagnetic radiation with the portion of the electromagnetic radiation that interacts with Earth surface targets. Studies to analyze the spectrum of reflectance of surface targets by sensor systems could be done by terrestrial (laboratory and field), aerial and orbital acquisitions. Consequently, for vegetation studies through remote sensing observations is necessary to know the physiology of the plant studied and the reflectance spectrum, considering that the solar radiation reaches the earth's surface and results in three fractions: one part is absorbed, another is reflected and a third part is transmitted. The leaves are the principal absorber of electromagnetic radiation in a canopy and represents the element that more contribute to the signal detected by orbital sensors. The present work has as its main goal the analysis of the spectral signature responses of common species of several forest functional types in a tropical forest area in the Amazon.

In this work, the computational operation based on Gradient Pattern Analysis - GPA was applied for the first time in MODIS spatial-temporal images over the Amazon region. The study area is located in the Pará State, eastern Brazilian Amazonia. Using MOD09 8-day composite product from 2000 to 2009 was elaborated the EVI2 spatial-temporal series of the study area. For each pixel we performed smooth time-series applying wavelets transform method for noise reduction. The GPA objective was characterizing small symmetry breaking, amplitude and phase disorder due to spatial-temporal fluctuations driven by the deforestation and flooded changes detected by MODIS images. For the characterization of spatial-temporal series the Gradient Pattern Analysis showed a new approach to understand LULC changes in the remote sensing images.

There is an increasing need for fast and accurate data on tropical land cover status, and a baseline for land cover monitoring. Remotely sensed SAR data are not sensitive to cloud cover and can be useful for such purpose. Polarimetric SAR data are available in orbital systems, such as RADARSAT-2, and still have to be tested for the classification of tropical land cover and the detection of land cover change, particularly forest conversion. This work presents a study of RADARSAT-2 polarimetric images, acquired in two different dates (September 2008 and October 2009), to assess their potential in classifying forest and non-forest classes in Brazilian Amazonia. SAR images were acquired following different orbit and incidence angles, which anticipated varied conditions for images interpretation and classes discrimination. The complex SAR data were classified based on the distance of Wishart, and information from field campaigns was used for the training and test samples. Classification results were compared to evaluate possibilities for change detection in the forest cover. Classification accuracy figures were around 80%. The use of RADARSAT-2 images allowed the mapping of land cover and land cover change, considering forest and non-forest classes.

In this study, we present an approach for phenology analysis of Amazon green-up using Linear Spectral Mixing Model applied to Hyperion multitemporal data. The study area was selected in the Tapajo¿s National Forest located in Para¿ State, Brazilian Amazonia. The region has well-defined dry and wet seasons with yearly rain about 2, 100 mm a dry season occurring from June to October. The study area is primarily covered by dense tropical rain forest (¿Floresta Ombro¿fila Densa¿) with a high number of emergent tree species. The EO-1 Hyperion data were acquired in July, August and September 2001, corresponding to the dry season in this region. The Linear Spectral Mixing Model was applied on each calibrated surface reflectance data, generating vegetation, soil, and shade fraction images. Then fundamental statistical analyses were carried out to evaluate the differences within the vegetation and shade fraction images derived from medium spatial resolution Hyperion images for rainforest phenology analysis.

Polarimetric signatures for different tropical land covers were extracted from RADARSAT-2 data. Subsequently, the data were classified. The objective of this work was to assess the potential of RADARSAT-2 polarimetric C band data on land cover mapping. RADARSAT-2 data were acquired over Tapajos National Forest, a tropical forest reserve in Brazil, and surroundings, in September 2008. A field campaign was conducted during the same week of the SAR data recording. Polarimetric signatures for the different land covers were extracted for co- and cross-polarised bands and results indicated the variety of scattering mechanisms in the study area. Following that, the coherence and covariance matrices were used for the Freeman-Durden target decomposition, which decomposed the image targets in new bands representing the main scattering mechanism in the resolution cells - corner reflection, volumetric and superficial. Data were later classified by a k-means-Wishart classifier. The bands representing volumetric and superficial scattering helped discriminating vegetated and non-vegetated areas. Classification accuracy reached around 80% for forest and pasture/bare soil classes. For the remaining classes, the classification accuracy results did not reach 50%.

Remote sensing techniques provide information about volume, biomass and other biophysical parameters of forest stands. The estimation of biomass by satellite remote sensing has been tested considering a wide range of spatial scales, environments and methods. Although both its spatial resolution (20×20 m) and spectral resolution (5 bands) enable important applications in forestry research, Chinese-Brazilian Earth Resources Satellite (CBERS) data had not been used for this purpose in Europe yet. In this work, we examined the potential of the fraction images obtained by unmixing the Charge-Coupled Device (CCD) CBERS data for estimating Pinus radiata (D. Don) stand attributes, especially biomass, in a Northwestern Spain region. Fraction images from spectral unmixing show biophysics properties more easily than original bands because they represent physics aspects of ground covers. Preliminary results are satisfactory and reinforce the conviction of usefulness of CCD CBERS fraction images in assessing forestry systems in Spain.

This paper presents the use of multitemporal remote sensing data for monitoring land cover changes in Acre State, Brazilian Amazônia. The 2000 Landsat ETM+, the 1990 Landsat TM, and 1980 Landsat MSS were used. The 2005 and 2007 MODIS images were also used to map deforestation occurred during the recent years and to map burned areas occurred in the 2005 dry year. The Landsat and MODIS images were converted to vegetation, soil, and shade fraction images. Then land cover maps were obtained by digital classification of these fraction images. The deforestation increased 7, 114 km2from 1980 to 1990, 4, 900 km2from 1990 to 2000, and 3, 258 km2from 2000 to 2007. It also showed that about 2, 815 km2of regrowth areas were observed in the 2000 ETM+ images. The analysis of MODIS images showed that 3, 700 km2of deforested areas and 2, 800 km2of forested areas were burned in Acre State in 2005. These information are critical for regional and global environmental studies and for efforts to control such burning and deforestation in the future.

The objective of this paper was to estimate turbidity in the Curuai floodplain during the high water level period. Spatial regression models were developed by using fraction images derived from a linear spectral mixture model applied to a Moderate Resolution Imaging Spectroradiometer/Terra image and turbidityinsitudata. As the turbidity in situ data showed spatial autocorrelation, they were divided into four spatial regimes (clusters). Thus, a spatial regression model was developed for each spatial regime. Through the Akaike information criterion, it was verified which spatial regime showed the best fit in the spatial regression model. The best fit was presented by the spatial regime 4 (R2= 0.80,p < 0.05). Then, the spatial regression model developed for the spatial regime 4 was applied to all floodplain lakes. The spatial regression models show potential for assessing the water turbidity in aquatic systems by considering a spatial dependence between samples.

This work presents a methodology that uses fraction images derived from Linear Spectral Mixture Model and wavelets transform from MODIS time-series for land cover change analysis. Our approach uses MODIS/Terra surface reflectance images acquired from 2000 to 2006 time period. For this study, a test site was selected in the Mato Grosso State, Brazilian Amazonia, encompassing several landscape types as tropical forest, savanna, transitional forest, regrowth, deforested areas, croplands and pasture. The samples of land cover classes were collected during four field campaigns (2003, 2004, 2005, and 2006) to be used as ground truth. The linear spectral mixture model was applied to the MODIS surface reflectance images of RED, NIR and MIR spectral bands. This model generated the vegetation, shade, and soil fraction images. In the next step, the Meyer orthogonal Discrete Wavelets Transform was used for filtering the time-series of MODIS fraction images. The filtered signal was reconstructed excluding high frequencies for each pixel in the fraction images (soil, vegetation, and shade) of the time-series. This procedure allows to observe the original signal without clouds and other noises. The accumulated precipitation data were used for dynamic phenological analysis, which showed the temporal lags between wet season and vegetation growing stages. The results show that wavelets transform can provide a gain in multitemporal analysis and visualization on inter-annual fraction images variability patterns.

This study deals with some applications of the concepts developed by the Theory of Evidence, in remote sensing digital image classification. Data from different sources are used in addition to multispectral image data in order to increase the accuracy of the thematic map. Data from different sources as well as probability images estimated from the multispectral image data are arranged in form of layers in a GIS-like structure. Layers representing belief and plausibility concerning the labeling of pixels across the image are then derived to help detect errors of omission and of commission respectively, in the thematic image. In this study, a new approach to introduce the information conveyed by belief and plausibility into the classification process is proposed and tested. Preliminary tests were performed over an area covered by natural forest of Araucaria showing some promising results.

This paper discusses the use of a JERS-1 synthetic aperture radar (SAR) time-series for mapping and monitoring land cover in a test site in the region of Corumbiara, Rondonia State, western Brazilian Amazonia. In order to support JERS-1 data analysis, land cover maps were obtained by digital classification of Landsat TM images acquired from 1993 to 1997 period, following a procedure based on image segmentation, unsupervised classification, and post-classification image edition. The comparison of these products with JERS-1 images shows that clean deforested areas are well identified presenting a low backscattering response as expected. However areas that have been cleared and even burned but with remaining forest material left on the ground present high backscattering response opposed to expected. Considering these observations and user interpretation expertise, JERS-1 SAR images can be used to map and monitor land cover changes in Amazonia.

The objective of this paper was to estimate turbidity in the Curuai floodplain during high water level. Spatial regression models were developed using fraction images derived from a Linear Spectral Mixture Model (LSMM) applied to a MODIS/Terra image and turbidity in-situ data. As the turbidity in-situ data showed spatial autocorrelation, they had been divided into four spatial regimes (clusters). Thus, a spatial regression model was developed for each spatial regime. Through the Akaike information criterion (AIC) it was verified which spatial regime showed the best fit in the spatial regression model. The results showed that the best fit was presented by the spatial regime 4 (r2= 0.80, p<0.05). The spatial regression model developed for the spatial regime 4 was then applied to all floodplain lakes. Spatial regression models show potential for turbidity studies in aquatic systems for considering spatial dependence between samples.

Satellite data such as the vegetation indices are a crucial tool for studying vegetation phenology patterns from regional to global scales. In this study, we investigated the relationship of the fraction images, derived from the linear spectral mixture model, with the NDVI and EVI, the most used indices to evaluate the phenological response using remote sensing data from the MODIS sensor. Our objectives were to understand how the vegetation indices are related with the vegetation fraction and to evaluate if the information provided by the shade and soil fraction images can be used to explain the vegetation indices behavior. We used a temporal series data of the MOD13A1 product for the 2002 year, the precipitation data from 125 meteorological stations, and a land cover map generated based on the 2002 images. We studied two different vegetation physiognomies to analyse if the fraction images were landscape dependent. Our results showed that for the open tropical forest, the vegetation fraction image presented a significant correlation with the EVI (r2=0.84) but not with the NDVI. For the Cerrado grassland landscape, the vegetation fraction image presented high correlation with the NDVI (r2=0.93) and EVI (r2=0.98). Significant correlations were also found for the shade and soil fraction images for the land cover studied, showing that these additional information are a useful source of data to understand the vegetation canopy structural changes and to analyze the responses provided by the vegetation indices correctly.

In this study we investigated the relationships between landscape features such as terrain elevation and slope with two variables that drives forest productivity, soil texture and leaf area index (LAI). The study was carried out at the Tapajos region in Para State, eastern Amazonia. Twenty-four 0.25 ha plots were sampled along a ~150 km north-south transect in October 2002. Soil samples were collected (0-10 cm) in three random points in each plot for texture analysis. LAI was measured at 25 points regularly distributed in each plot. The geomorphologic attributes for each plot were extracted from the Shuttle Radar Topography Mission (SRTM) data linearly resample to 10 m spatial resolution. Terrain slope was linear and negatively related to the soil clay content (r2=0.73). Soil sand content had an expected opposite pattern (r2=0.72). The soil content of clay and sand along the elevation gradient can be strongly explained by a cubic polynomial curve (r2=0.82 and 0.81, respectively). LAI showed to be a logarithmic function of slope (r2=0.61), excluding plots located in the Valley regions. Moreover, LAI showed a linear and positive relationship with soil clay content (r2=0.52). Similarly to the relationships found between terrain elevation and soil texture, the 3rd order polynomial could explain 64% of the LAI variability over the Tapajos. Therefore, we concluded that topography is a major driver of the patterns of soil texture at the landscape scale and the combined effect of topography and soil can largely explain the patterns of LAI over the Tapajos. The combination of SRTM data and field-based information has the potential to increase the accuracy of ecosystem scale estimations of forest productivity in the Amazonia.

Japanese Earth Resources Satellite 1 (JERS-1) synthetic aperture radar (SAR) data were evaluated to map areas of deforestation in a Brazilian Amazo/spl circ/nia test-site. The results were compared with information derived from a Landsat TM multitemporal series. Unambiguous detection of deforested areas was observed only when the entire deforestation process (slash, burning, and terrain clearing) had already occurred. This result recommends further investigations on the effectiveness of horizontal polarization SAR data to map deforestation in a consistent basis. The cross-polarized (horizontal-vertical) channel designed to be in the ALOS/PALSAR system is expected to improve the distinction between forested and recently deforested areas.

We present a methodology for rapidly assessing deforestation over the Amazon region needed for policy intervention. We use soil fraction images generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 250-m spatial resolution. Results showed reasonable agreement with higher resolution Landsat data (r/sup 2/=0.73) for our study area. MODIS data are promising for near real-time deforestation monitoring, previously not practical with Landsat data.

This paper reports on the validation of the Collection 4 MODIS leaf area index (LAI) product over the Tapajo/spl acute/s region, eastern Amazonia. The validation site is enclosed in tile h12v09 of the MODIS LAI product. The methodology to assess MODIS LAI accuracy included two main steps: (1) a multiple regression analysis for the generation of LAI surfaces, based on the relationships between field data and remote sensing information from the Enhanced Thematic Mapper Plus sensor, and between field data and topographic information from a digital elevation model; (2) the direct comparison of these LAI surfaces with the MODIS LAI surfaces. The analysis indicated that MODIS LAI is significantly overestimated for the Tapajo/spl acute/s region by a factor of 1.18. No relationships between MODIS LAI and the validation surfaces were found. These results are indicative of a predominance of LAI retrievals by the backup algorithm, which is overcompensating LAI values at the saturation domain. The overgeneralization of the land cover layer (MOD12Q1) can be a source of uncertainties for the lookup table parameterization. Further validation efforts must be carried out over Amazonia for a quantitative quality assessment of the MODIS LAI surfaces in order to improve its accuracy.

Different ways to estimate the spectral reflectance for the component classes in a mixture problem have been proposed in the literature (pure pixels, spectral library, field measurements). One of the most common approaches consists in the use of pure pixels, i.e., pixels that are covered by a single component class. This approach presents the advantage of allowing the extraction of the components' reflectance directly from the image data. This approach, however, is generally not feasible in the case of low spatial resolution image data, due to the large ground area covered by a single pixel. In this paper, a methodology aiming to overcome this limitation is proposed. The proposed approach makes use of the spectral linear mixing model. In the proposed methodology, the components' proportions in image data are estimated using a medium spatial resolution image as auxiliary data. The linear mixing model is then solved for the unknown spectral reflectances. Experiments are presented, using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus, as low and medium spatial resolution image data, respectively, acquired on the same date over the Tapajos study site, Brazilian Amazon. Three component classes or endmembers are present in the scene covered by the experiment, namely vegetation, exposed soil, and shade. The components' spectral reflectance for the Terra MODIS spectral bands were then estimated by applying the proposed methodology. The reliability of these estimates is appraised by analyzing scatter diagrams produced by the Terra MODIS spectral bands and also by comparing the fraction images produced using both image datasets. This methodology appears appropriate for up-scaling information for regional and global studies.

Broadleaf forest is a major type of Earth's land cover with the highest observable vegetation density. Retrievals of biophysical parameters, such as leaf area index (LAI), of broadleaf forests at global scale constitute a major challenge to modern remote sensing techniques in view of low sensitivity (saturation) of surface reflectances to such parameters over dense vegetation. The goal of the performed research is to demonstrate physical principles of LAI retrievals over broadleaf forests with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI algorithm and to establish a basis for algorithm refinement. To sample natural variability in biophysical parameters of broadleaf forests, we selected MODIS data subsets covering deciduous broadleaf forests of the eastern part of North America and evergreen broadleaf forests of Amazonia. The analysis of an annual course of the Terra MODIS Collection 4 LAI product over broadleaf forests indicated a low portion of best quality main radiative transfer-based algorithm retrievals and dominance of low-reliable backup algorithm retrievals during the growing season. We found that this retrieval anomaly was due to an inconsistency between simulated and MODIS surface reflectances. LAI retrievals over dense vegetation are mostly performed over a compact location in the spectral space of saturated surface reflectances, which need to be accurately modeled. New simulations were performed with the stochastic radiative transfer model, which poses high numerical accuracy at the condition of saturation. Separate sets of parameters of the LAI algorithm were generated for deciduous and evergreen broadleaf forests to account for the differences in the corresponding surface reflectance properties. The optimized algorithm closely captures physics of seasonal variations in surface reflectances and delivers a majority of LAI retrievals during a phenological cycle, consistent with field measurements. The analysis of the optimized retrievals indicates that the precision of MODIS surface reflectances, the natural variability, and mixture of species set a limit to improvements of the accuracy of LAI retrievals over broadleaf forests.

The aim of this study consists in proposing and testing a methodology to estimate the spectral reflectance of the component classes in the mixed pixel problem, for the case of low spatial resolution image data. The well known linear mixing model is modified in order to estimate the components spectral reflectance. Terra-MODIS image data, with a pixel size of 500 m is used to test the results.

The general aim of this study was to produce a continuous field of LAI to evaluate the LAI surface (MODIS product) derived from the moderate resolution imaging spectroradiometer (MODIS), for the Tapajos region, eastern Amazonia. Our method consisted in generating regression models combining spectral data derived from Enhanced Thematic Mapper Plus (ETM+) sensor (07/30/2001) and terrain slope and altimetry information extracted from a digital terrain model. The spectral variables considered for this study are reflectance, vegetation indices (NDVI and SR) and fraction images. Using a multiple comparison test, we compared the mean LAI estimated by the three models generated in this study (270 m spatial resolution) with the 8 days LAI composition (08/13/2001) derived from MODIS sensor (1 km spatial resolution) and also with field data. The MODIS LAI surface for the Tapajos region showed a more homogenous surface and little information about land cover and land use when we visually compared with our estimations. This fact occurs due to the 1 km resolution from de MODIS LAI against the 270 m resolution used in our approach. The statistics indicated that the mean LAI derived from MODIS sensor is significantly overestimated (P<0.05) in relation to both field and modeled data. We conclude that the approach employed here is promising for generating LAI surfaces, based on field data, for MODIS LAI validation purposes

This work presents a methodology for detecting deforestation activities in a near real time using Terra MODIS daily data. This work is part of the operational PRODES Project of INPE (Brazil) that estimates the annual deforestation in Brazilian Amazon. The detection of deforested areas by MODIS daily data is a useful information for enforcement of forest protection laws. The proposed methodology, named "DETER" Project, will follow the dynamics of deforestation activities while the PRODES Digital project is evaluating the total area deforested during the study year using higher spatial resolution data. For the development of this methodology, it was used a temporal series of MODIS MOD09 product (surface reflectance) from June to October over an area covered by one Landsat ETM+ scene located in Mato Grosso State, Brazilian Amazon region. The obtained results were compared to the corresponding Landsat ETM+ images acquired in the same dates of MODIS images. The small deforestation areas (lower than 15 ha) are detected with less accuracy by MODIS. As the areas deforested increases, the MODIS results come near to the ETM+ results. The results showed that MODIS sensor daily data can be used for an operational deforestation detection in the Brazilian Amazon.

This paper shows the classification obtained with an artificial neural network to map land cover areas in Brazilian Amazon region. The new approach is based on fraction images generated by linear spectral mixture modeling and used as input to the network. It identified with good accuracy the following classes: water, deforested areas, forests, and areas without predominant forest physiognomy (savannah and regeneration areas).

The aim of this study consists in investigating a new methodology to detect changes in land cover that have occurred over a given period of time, based on the concept of mixture pixels. This approach allows the interpretation of remote sensing digital image data at sub-pixel level. It is expected that this feature will create the right conditions for a more accurate identification of changes in land-cover as compared with the results obtained directly from multispectral image data. The Mixture Models allow the creation of fraction images, which convey the information regarding the proportion of each component class within any pixel. By subtracting fraction images associated with the same component at two different dates, fraction-change images can be obtained. As the magnitude of the changes may be either positive or negative, each component will originate two fraction-images, one displaying positive changes in the magnitude of component's proportion and the other the negative changes. Therefore 2m fraction-change images will be generated, m being the number of component classes. The proposed methodology to detect land-cover changes is then performed by analyzing the fraction-change image data in this 2m-dimensional space.

The lack of sustainability in the exploitation of the tropical Amazon forest has caused a severely impacted environment and biodiversity. In this context, the objective of this study is to detect, characterize and quantify the forest areas affected by timber exploitation in the years 2001 and 2002. This research was developed in the north of the Mato Grosso state, Brazil, utilizing fraction images that were generated by a linear spectral mixture model referring to the mentioned years. Change vector analysis was applied to the difference image of the fraction images from the two years. One image representing magnitude of change, and two images representing the angles of the change vectors were generated. These images are efficient for detection of intensity and type of change that occurred in the forested areas, which are subject to selective logging.

Constrained-least-squares (CLS) and weighted-least-squares (WLS) mixing models for generating fraction images derived from remote sensing multispectral data are presented. An experiment considering three components within the pixels-eucalyptus, soil (understory), and shade-was performed. The generated fraction images for shade (shade image) derived from these two methods were compared by considering the performance and computer time. The derived shade images are related to the observed variation in forest structure, i.e. the fraction of inferred shade in the pixel is related to different eucalyptus ages.>