An algorithm for chlorophyll using first difference transformations of AVIRIS reflectance spectra

Experimental results have shown the existence of a strong relationship between chlorophyll alpha concentration and remote sensing reflectance measured at lake level with a high resolution spectroradiometer. The objective of our study was to investigate the relationship between surface chlorophyll alpha concentration at Mono Lake and water reflectance retrieved from Airborne Visible - Infrared Imaging Spectrometer (AVIRIS) data obtained in october 7, 1992. AVIRIS data were atmospherically corrected as described by Green et al. A description of the lake-level sampling is found in Melack and Gastil. The relationship between chlorophyll concentration and both the single band reflectance and the first difference transformation of the reflectance spectra for the first 40 AVIRIS spectral bands (400 nm to 740 nm) was examined. The relationship was then used to produce a map of the surface chlorophyll distribution

Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide

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

Validation of high‐resolution MAIAC aerosol product over South America

Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm that combines time series approach and image processing to derive surface reflectance and atmosphere products, such as aerosol optical depth (AOD) and columnar water vapor (CWV). The quality assessment of MAIAC AOD at 1 km resolution is still lacking across South America. In the present study, critical assessment of MAIAC AOD550 was performed using ground‐truth data from 19 Aerosol Robotic Network (AERONET) sites over South America. Additionally, we validated the MAIAC CWV retrievals using the same AERONET sites. In general, MAIAC AOD Terra/Aqua retrievals show high agreement with ground‐based measurements, with a correlation coefficient (R) close to unity (RTerra:0.956 and RAqua: 0.949). MAIAC accuracy depends on the surface properties and comparisons revealed high confidence retrievals over cropland, forest, savanna, and grassland covers, where more than 2/3 (~66%) of retrievals are within the expected error (EE = ±(0.05 + 0.05 × AOD)) and R exceeding 0.86. However, AOD retrievals over bright surfaces show lower correlation than those over vegetated areas. Both MAIAC Terra and Aqua retrievals are similarly comparable to AERONET AOD over the MODIS lifetime (small bias offset ~0.006). Additionally, MAIAC CWV presents quantitative information with R ~ 0.97 and more than 70% of retrievals within error (±15%). Nonetheless, the time series validation shows an upward bias trend in CWV Terra retrievals and systematic negative bias for CWV Aqua. These results contribute to a comprehensive evaluation of MAIAC AOD retrievals as a new atmospheric product for future aerosol studies over South America

APLICAÇÃO DA TÉCNICA DE MODELO LINEAR DE MISTURA ESPECTRAL PARA O MAPEAMENTO DA PLUMA DO RIO AMAZONAS

This paper aims to verify the applicability of Spectral Mixture Analysis (SMA) for mapping the plume of the Amazonas River, a feature of great importance for the coastal dynamics at the South-American Northeastern coast. Remote sensing reflectance data acquired by Sea-viewing Wide Field-of-view Sensor (SeaWiFS) were utilized to identify 5 water masses with different spectral and color characteristics. Through one SeaWiFS image were identified 5 water masses with different spectral and color characteristics, of what mean spectral signatures were obtained. The 5 types of water masses were classified according to its spectral characteristics, of what demonstrated typical behavior of waters with (i) suspended sediment, (ii) dissolved organic matter, (iii) oceanic water, and (iv and v) with different chlorophyll concentration. The mean spectral signatures were applied as endmembers in the SMA resulting in 5 fraction images. The fraction image related to the oceanic water allowed the best classification and mapping of the plume. The mapped area in the image shows the great extension (510 x 103 km2) that the plume can reach in the Northwestern direction from the Amazonas River mouth and into the Equatorial Atlantic, driven by the North Equatorial Counter Current and the North Brazil Current, respectively. Key words: Spectral mixture analysis. Amazon River plume. Remote sensing, SeaWiFS.Este artigo tem como objetivo verificar a aplicação da técnica de Modelo Linear de Mistura Espectral (MLME) para o mapeamento da pluma do Rio Amazonas, uma feição de grande importância na dinâmica costeira da região nordeste da América do Sul. Foram utilizados dados de reflectância de sensoriamento remoto obtidos pelo sensor Sea-viewing Wide Field-of-view Sensor (SeaWiFS) para identificar 5 massas de água com características espectrais e de cor distintas, das quais se obtiveram assinaturas espectrais médias. Os 5 tipos de massas de água foram classificados de acordo com suas características espectrais, sendo estas, típicas de águas com (i) sedimentos em suspensão, (ii) matéria orgânica dissolvida, (iii) água oceânica e (iv e v) com diferentes concentrações de concentrações de clorofila. As assinaturas espectrais médias foram utilizadas como endmembers no MLME o que resultou em 5 imagens fração. A imagem fração referente à água oceânica foi a que possibilitou a melhor identificação e mapeamento da pluma. A área mapeada na imagem mostrou a grande extensão (510 x 103 km2) que a pluma alcança na direção noroeste da desembocadura do Rio Amazonas e para o Oceano Atlântico sob o efeito da Contra Corrente Norte Equatorial e Corrente Norte do Brasil, respectivamente. Palavras chave: Modelo linear de mistura espectral. Pluma do Rio Amazonas. Sensoriamento remoto. SeaWiFS

Vegetation index and spectral linear mixing model for monitoring the Pantanal region

This paper presents the contribution of the normalized difference vegetation index (NDVI) and the fraction images derived from linear mixing model for monitoring the dynamic of the land cover in the Pantanal region. The vegetation index and the vegetation, soil, and shade or water fraction images were derived from Landsat TM (Thematic Mapper) digital data acquired over the alto Taquari (MS) region, on December 22, 1992 (dry season) and on March 12, 1993 (rainy season). These images permitted to analyze the land cover in this region for the image dates, as well as to detect changes occurred during the period of image acquisition. The results indicated an apparent higher sensitivity of vegetation fraction image to land cover variation when compared to vegetation index image. The radiometric rectification method presented a good performance, indicating that the selected dark and bright targets in the images have not changed in the interval between dates of image acquisition.Este trabalho apresenta a contribuição da imagem índice de vegetação de diferença normalizada (NDVI) e das imagens-fração derivadas de modelo linear de mistura espectral para o monitoramento da dinâmica da cobertura do solo na região do Pantanal. As imagens índice de vegetação e as imagens-fração (vegetação, solo e sombra ou água) foram derivadas dos dados digitais do TM (Thematic Mapper) do Landsat obtidos sobre a região do alto Taquari (MS), em 22 de dezembro de 1992 (período seco) e 12 de março de 1993 (período chuvoso). Estas imagens permitiram analisar a cobertura de solo da região nas datas das imagens, bem como detectar as mudanças ocorridas entre o período de aquisição dessas imagens. Aparentemente, os resultados indicaram maior sensibilidade da imagem-fração de vegetação às variações de cobertura vegetal do solo quando comparada com a imagem índice de vegetação (NDVI). O método de retificação radiométrica apresentou bom desempenho, indicando que tanto os alvos escuros quanto os alvos claros, selecionados nas imagens, não sofreram mudanças durante o intervalo de aquisição das imagens

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

APLICAÇÃO DE SISTEMAS RADAR NO MONITORAMENTO DE INFESTAÇÕES DE PLANTAS AQUÁTICAS EM RESERVATÓRIOS: VANTAGENS E LIMITAÇÕES

O monitoramento de processos dinâmicos em reservatórios artificiais é uma necessidade para a manutenção da estabilidade da cadeia trófica e da qualidade da água nos reservatórios. O uso de técnicas de sensoriamento remoto tem-se apresentado como uma ferramenta importante para tal, devido à sua capacidade de proporcionar visão sinóptica do estado do reservatório. Imagens de radar mostramse particularmente interessantes para o monitoramento de processos dinâmicos em reservatórios, principalmente em regiões tropicais, visto que estas imagens não sofrem interferência das condições meteorológicas. Este trabalho apresenta uma discussão sobre o potencial e as limitações do uso de dados de radar no estudo de plantas aquáticas em reservatórios. Radar System Application For The Management Of Aquatic Plant Infestation In Reservoirs: Advantages And Disadvantages Abstract Reservoir monitoring is the key to food web and water quality protection. Remote sensing techniques can provide a synoptic view necessary for accurate assessment of the entire reservoir. Radar imaging systems are particularly important in tropical regions because of day and night acquisition, independent of weather conditions. This paper discusses the advantages and disadvantages of radar data for assessing aquatic plant infestation in tropical reservoirs

Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations

A practical atmospheric correction algorithm, called Coupled Moderate Products for Atmospheric Correction (CMPAC), was developed and implemented for the Multispectral Camera (MUX) on-board the China-Brazil Earth Resources Satellite (CBERS-4). This algorithm uses a scene-based processing and sliding window technique to derive MUX surface reflectance (SR) at continental scale. Unlike other optical sensors, MUX instrument imposes constraints for atmospheric correction due to the absence of spectral bands for aerosol estimation from imagery itself. To overcome this limitation, the proposed algorithm performs a further processing of atmospheric products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as input parameters for radiative transfer calculations. The success of CMPAC algorithm was fully assessed and confirmed by comparison of MUX SR data with the Landsat-8 OLI Level-2 and Aerosol Robotic Network (AERONET)-derived SR products. The spectral adjustment was performed to compensate for the differences of relative spectral response between MUX and OLI sensors. The results show that MUX SR values are fairly similar to operational Landsat-8 SR products (mean difference \u3c 0.0062, expressed in reflectance). There is a slight underestimation of MUX SR compared to OLI product (except the NIR band), but the error metrics are typically low and scattered points are around the line 1:1. These results suggest the potential of combining these datasets (MUX and OLI) for quantitative studies. Further, the robust agreement of MUX and AERONET-derived SR values emphasizes the quality of moderate atmospheric products as input parameters in this application, with root-mean-square deviation lower than 0.0047. These findings confirm that (i) CMPAC is a suitable tool for estimating surface reflectance of CBERS MUX data, and (ii) ancillary products support the application of atmospheric correction by filling the gap of atmospheric information. The uncertainties of atmospheric products, negligence of the bidirectional effects, and two aerosol models were also identified as a limitation. Finally, this study presents a framework basis for atmospheric correction of CBERS-4 MUX images. The utility of CBERS data comes from its use, and this new product enables the quantitative remote sensing for land monitoring and environmental assessment at 20 m spatial resolution