Reconstrução histórica de mudanças na cobertura florestal em várzeas do Baixo Amazonas utilizando o algoritmo LandTrendr

The Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzea forest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain

A floristic survey of angiosperm species occurring at three landscapes of the Central Amazon várzea, Brazil

The Amazonian floodplains harbor highly diverse wetland forests, with angiosperms adapted to survive extreme floods and droughts. About 14% of the Amazon Basin is covered by floodplains, which are fundamental to river productivity, biogeochemical cycling and trophic flow, and have been subject to human occupation since Pre-Colombian times. The botanical knowledge about these forests is still incomplete, and current forest degradation rates are much higher than the rate of new botanical surveys. Herein we report the results of three years of botanical surveys in floodplain forests of the Central Amazon. This checklist contains 432 tree species comprising 193 genera and 57 families. The most represented families are Fabaceae, Myrtaceae, Lauraceae, Sapotaceae, Annonaceae, and Moraceae representing 53% of the identified species. This checklist also documents the occurrence of approximately 236 species that have been rarely recorded as occurring in white-water floodplain forests

Classificação da cobertura da terra na planície de inundação do Lago Grande de Curuai (Amazônia, Brasil) utilizando dados multisensor e fusão de imagens

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea

Monitoring Water Siltation Caused by Small-Scale Gold Mining in Amazonian Rivers Using Multi-Satellite Images

The small-scale mining techniques applied all over the Amazon river basin use water from streams, including digging and riverbed suctioning, rarely preventing environmental impacts or recovery of the impacted areas. As a consequence, thousands of tons of inorganic sediment (which can contain mercury) have been discharged directly into the rivers creating sediment plumes that travel hundreds of kilometers downstream with unknown consequences to the water quality and aquatic biota. We hypothesize that because of intensification of mining activities in the Brazilian Amazon, clear water rivers such as the Tapajós and Xingu rivers and its tributaries are becoming permanently turbid waters (so-called white waters in the Amazonian context). To investigate this hypothesis, satellite images have been used to monitor the sediment plume caused by gold mining in Amazonian rivers. Given the threat of intense water siltation of the Amazonian rivers combined with the technological capacity of detecting it from satellite images, the objective of this chapter is to inform the main activities carried out to develop a monitoring system for quantifying water siltation caused by small-scale gold mining (SSGM) in the Amazon rivers using multi-satellite data

NORMALIZAÇÃO RADIOMÉTRICA DE IMAGENS: UM MEIO DE INTEGRAR DADOS MULTI TEMPORAIS DE SENSORIAMENTO REMOTO PARA MONITORAMENTO AMBIENTAL

As características espaciais, espectrais, radiométricas e temporais das imagens orbitais de sensoriamento remoto pennitem sua utilização em muitas propostas de monitoramento ambiental, tomando-as uma ferramenta efetiva para a detecção de mudanças temporais na paisagem. Entretanto, na análise de dados multitemporais é necessário considerar as variações decorrentes dos efeitos atmosféricos, de iluminação e dos parâmetros do sensor. Nesse sentido, apresenta-se um método de normalização radiométrica de dados multitemporais o qual, apesar de não remover os efeitos das diferentes fontes de variação, utiliza os próprios parâmetros da cena para tornar as imagens comparáveis em relação a uma data de referência. Além disso, com base em um experimento de aplicação da técnica, mostra-se que uma escolha adequada da imagem de referência para o procedimento de normalização, pode melhorar consideravelmente a visualização de cenas de baixo contraste e realçar feições que eram imperceptíveis na imagem original

Sunglint correction in airborne hyperspectral images over inland waters

This study assessed sunglint effects in airborne high spatial and high spectral resolution images acquired by the SpecTIR sensor under different view-illumination geometries over the Brazilian Ibitinga reservoir (Case II waters). These effects were corrected using the Goodman et al. (2008) and the Kutser et al. (2009) methods, and a variant that used the continuum removal technique to calculate the oxygen absorption band depth. The performance of each method to removing sunglint effects was evaluated by a quantitative analysis of pre- and post-sunglint correction reflectance values (residual reflectance images). Furthermore, the analysis was supported by inspection of the reflectance differences along transects placed over homogeneous masses of waters or over specific portions of the scenes affected and non-affected by sunglint. Results showed that the algorithm of Goodman et al. (2008) produced better results than the other two methods, as it approached to zero the amplitude of the reflectance values between homogenous water masses free and contaminated by sunglint. The Kutser et al. (2009) method had also good performance, except for the most contaminated sunglint portions of the scenes. When the continuum removal technique was incorporated to the Kutser et al. (2009) method, results varied with the scene and were more sensitive to atmospheric correction artifacts and instrumental signal-to-noise ratio

INTEGRAÇÃO DE DADOS TM/LANDSAT E MEDIDAS IN SITU PARA ESTIMATIVA DE SEDIMENTOS EM SUSPENSÃO EM RIOS AMAZÔNICOS: UM ESTUDO DE VIABILIDADE

A base de dados in situ de concentração de sedimentos em suspensão (CSS) nos rios amazônicos possui baixa densidade de amostras (2,72 coletas por ano) distribuídas de modo não sistematizado. O uso de imagens de satélite pode aumentar essa densidade devido à relação direta entre CSS e a reflectância espectral da água na região do visível-infravermelho próximo. O objetivo desse trabalho é, portanto, avaliar a viabilidade de integração de dados do sensor TM/Landsat 5 (TM-5) e dados sedimentológicos para gerar modelos de estimativa da CSS de rios amazônicos. Analisam-se as limitações espaciais e radiométricas do sensor TM-5 e seu impacto sobre os modelos para diferentes tipos de água. Avalia-se também a frequência de dados in situ e orbitais e o ganho de informação com o uso de dados de sensoriamento remoto. Os resultados mostram que apenas 35 das 97 estações de coleta de CSS podem ser utilizadas. Apesar do reduzido número de amostras, os resultados mostram que nos casos mais restritivos de cobertura de nuvens, poderia haver um aumento de até 127% na base de dados se fossem desenvolvidos modelos empíricos baseados em imagens de satélite. Conclui-se que existem dados suficientes para testar o desenvolvimento de modelos empíricos e semi-empíricos baseados na integração de dados TM-5 e medidas in situ de modo a aumentar a densidade de dados de CSS dos rios da bacia amazônica. A próxima etapa dessa pesquisa é então o desenvolvimento e teste desses modelos

PADRÕES ESPACIAIS DO TRANSPORTE, PRODUÇÃO E VARIABILIDADE DE SEDIMENTOS SUSPENSOS DOS RIOS AMAZÔNICOS DE ÁGUAS BRANCAS

A produção e transporte de sedimentos suspensos são importantes processos geomorfológicos atuantes nos sistemas fluviais amazônicos, especialmente nos denominados Rios Amazônicos de Águas Brancas (RAAB). O estudo sintetizado neste artigo foca na representação e compreensão das variações espaciais do transporte de sedimentos desses rios. Este trabalho utilizou uma nova série de valores de concentração de sedimentos suspensos, estimados por meio de imagens do satélite Landsat 5/TM, para estender e preencher as séries de dados coletadas in situ. A partir dessa compilação de dados, foram geradas séries temporais de transporte de sedimentos suspensos para nove estações nos RAAB. Grandes rios amazônicos como o Amazonas, Solimões e Madeira possuem agora séries de aproximadamente 30 anos de dados de transporte de sedimentos suspensos de alta frequência temporal. Foram feitas análises sobre: i) diferenças regionais de produção e transporte de sedimentos; ii) variabilidade da produção de sedimentos nas escalas intranual e interanual; iii) relações entre variáveis espaciais, como latitude, longitude e área da bacia, e a produção de sedimentos e sua variabilidade. Os resultados obtidos permitem as seguintes conclusões em relação aos padrões espaciais e relações determinantes: i) quanto mais à jusante, e maior a área da bacia de drenagem, maior é o transporte de sedimentos, conforme uma relação logarítmica; ii) enquanto o transporte de sedimentos suspensos é maior nas estações de jusante, a produção média das bacias possui um padrão contrário, sendo maior nas estações de montante; iii) considerando toda a bacia, os menores valores de produção de sedimentos concentram-se no período entre os meses de agosto e outubro, e o período que possui os maiores valores é entre janeiro e março. A região que produz a maior quantidade de sedimentos é a bacia do alto rio Madeira, em que grande parte da massa é transportada no período chuvoso, entre os meses de janeiro a abril. Esta bacia, no entanto, possui valores extremamente baixos nos meses secos; iv) as estações que estão no centro da bacia possuem uma menor variabilidade temporal, tanto interanual quanto mensal. As variabilidades anuais e mensais possuem uma forte relação e foram explicadas pelas mesmas variáveis (área de drenagem e longitude). Os coeficientes da regressão linear mostram que, quanto maior a área da bacia, e quanto mais a oeste, menor a variabilidade temporal da produção de sedimentos. </p

The tree species pool of Amazonian wetland forests: Which species can assemble in periodically waterlogged habitats?

We determined the filtered tree species pool of Amazonian wetland forests, based on confirmed occurrence records, to better understand how tree diversity in wetland environments compares to tree diversity in the entire Amazon region. The tree species pool was determined using data from two main sources: 1) a compilation of published tree species lists plus one unpublished list of our own, derived from tree plot inventories and floristic surveys; 2) queries on botanical collections that include Amazonian flora, curated by herbaria and available through the SpeciesLink digital biodiversity database. We applied taxonomic name resolution and determined sample-based species accumulation curves for both datasets, to estimate sampling effort and predict the expected species richness using Chao’s analytical estimators. We report a total of 3 615 valid tree species occurring in Amazonian wetland forests. After surveying almost 70 years of research efforts to inventory the diversity of Amazonian wetland trees, we found that 74% these records were registered in published species lists (2 688 tree species). Tree species richness estimates predicted from either single dataset underestimated the total pooled species richness recorded as occurring in Amazonian wetlands, with only 41% of the species shared by both datasets. The filtered tree species pool of Amazonian wetland forests comprises 53% of the 6 727 tree species taxonomically confirmed for the Amazonian tree flora to date. This large proportion is likely to be the result of significant species interchange among forest habitats within the Amazon region, as well as in situ speciation processes due to strong ecological filtering. The provided tree species pool raises the number of tree species previously reported as occurring in Amazonian wetlands by a factor of 3.2

Simulation of spectral bands of the MERIS sensor to estimate chlorophyll-a concentrations in a reservoir of the semi-arid region

Nowadays, the monitoring of water is essential for the sustainability and better management of water resources. The use of remote sensing data is important, since it allows evaluation of dynamic problems in aquatic systems, such as the eutrophication of bodies of water and suspended sediment. The aim of this study was to estimate chlorophyll-a concentrations in a reservoir of the semi-arid region of Brazil using simulated orbital-sensor data, as an aid in the management of water resources. The study area corresponded to the Orós reservoir, in the State of Ceará, Brazil. Water samples for analysis of the chlorophyll-a and measurements of the spectral radiance of the aquatic system were collected from 20 points. The radiance was measured by spectroradiometer. The data were collected in June and August of 2011. The model using three bands of the MERIS sensor (7, 9 and 10) presented an R2 of 0.84. For the two-band model (7 and 9), the value of R2 was 0.85. The waters of the Orós reservoir were all classified as eutrophic. The main optically active component in modelling the shape of the spectra was chlorophyll-a. The models showed a mean absolute error (MAE) of 3.45 and 3.61 μg L-1 for the three- and two-band models respectively. The models displayed high coefficients of determination, i.e. the simulations show the feasibility of estimating chlorophyll-a concentration from the data of the MERIS orbital sensor