ResumoO uso de técnicas de sensoriamento remoto em apoio aos estudos ambientais se tornou comum nos últimos anos, e a disponibilidade de imagens de satélites de forma gratuita também tem impulsionado esse crescimento. A utilização de imagens orbitais para realização destes estudos permite uma redução dos custos envolvidos, maior agilidade e constância no acesso aos dados e, consente, uma avaliação holística, analisando com maior precisão e detalhamento diversas componentes ambientais presentes na região de interesse. Este artigo buscou analisar as respostas espectrais, nas bandas do visível e infravermelho e comparar valores do Índice de Vegetação de Diferença Normalizada (NDVI) obtidos pelos sensores OLI -Landsat 8 e MSI -Sentinel 2, na região semiárida que compreende parte do território da bacia hidrográfica do rio Moxotó. Ao se analisar a resposta espectral de cada um dos sensores fica evidenciado que existem diferenças entre as bandas que proporcionam o cômputo do NDVI, que são as bandas do vermelho e infravermelho próximo que poderiam gerar valores diferentes de um mesmo índice, numa mesma área. Apesar das diferenças espectrais e espaciais existentes entre os sensores ópticos Landsat-8/OLI e Sentinel-2/MSI, apresentaram momentos estatísticos próximos entre as bandas comparadas. O NDVI no período estudado apresentou valores médios para o Landsat-8 e Sentinel-2 iguais a 0,383 e 0,387, respectivamente, e uma correlação forte igual a 0,871 entre os sensores. O sensor MSI -Sentinel-2 permitiu um maior delineamento dos alvos devido à sua maior resolução espacial, possibilitando maior confiança para monitoramento e gestão do meio ambiente.
Background: In the last decades, everal runoff-erosion models have been proposed to estimate soil erosion, which may lead to loss of fertile land and increase sedimentation and pollution in water bodies. Physically-based erosion models are usually used for such purpose, but a major problem concerning their use is the difficulty to directly measure parameters in the field. This problem can be overcome by exploring empirical models, such as so-called Self-Organizing Maps (SOM). An SOM is a type of Artificial Neural Network (ANN) based on a competitive learning approach for clustering and modeling a variety of databases. Since studies on soil erosion modeling based on SOM are very incipient, we compared some structures of SOM with the purpose of estimating sediment yield based on runoff and climatological data at the micro-watershed scale. The case study was a micro-watershed within the Sumé Experimental Basin, which is located in a semiarid region of Brazil. Different from the conventional ANN, SOM-based models represent a multidimensional data set by means of a bidimensional matrix of features, which may be applied for analysis and estimation purposes. In order to calibrate and validate the proposed SOM structures, we used data from 117 rainfall events that occurred between 1985 and 1991. Results: Analyses of the results indicate that all SOM structures were efficiently calibrated with NASH coefficients (Nash & Sutcliffe 1970) varying from 0.88 to 0.90. The SOM structure with 6 × 8 neurons was the most effective for estimating sediment yields when considering the validation data set (NASH = 0.73). The generated maps showed that sediment yields were directly related to runoff and rainfall intensity and inversely correlated to average vegetation heights. The dry period length did not seem to influence the production of sediments. Conclusions: SOM were shown to be very practical and meant to be applied to specific locations. This type of methodology also demands long term data and dynamic recalibration with up-to-date information in order to account for changes in the watershed.
The study of energy, water, and carbon exchanges between ecosystems and the atmosphere is important in understanding the role of vegetation in regional microclimates. However, they are still relatively scarce when it comes to Caatinga vegetation. This study aims to identify differences in the dynamics of critical environmental variables such as net radiation (Rn), evapotranspiration (ET), and carbon fluxes (gross primary production, GPP) in contrasting recovered Caatinga (dense Caatinga, DC) and degraded Caatinga (sparse Caatinga, SC) in the state of Paraíba, northeastern Brazil. Estimates were performed using the Surface Energy Balance Algorithm for Land (SEBAL), and comparisons between estimated and measured data were conducted based on the coefficient of determination (R2). The fluxes were measured using the Eddy Covariance (EC) method for comparison with the same variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the Terra satellite. The estimates showed higher Rn values for the DC, indicating that this area should have greater energy availability for physical, biological, and chemical processes. The R2 between daily Rn estimates and observations was 0.93. The ET estimated using the SEBAL showed higher differences in relation to the observed values; however, it presented better spatial discrimination of the surface features. The MOD16A2 algorithm, however, presented ET values closer to the observed data and agreed with the seasonality of the Enhanced Vegetation Index (EVI). The DC generally showed higher ET values than the SC, while the MODIS data (GPP MOD17A2H) presented a temporal behavior closer to the observations. The difference between the two areas was more evident in the rainy season. The R2 values between GPP and GPP MOD17A2H were 0.76 and 0.65 for DC and SC, respectively. In addition, the R2 values for GPP Observed and GPP modeled were lower, i.e., 0.28 and 0.12 for the DC and SC, respectively. The capture of CO2 is more evident for the DC considering the whole year, with the SC showing a notable increase in CO2 absorption only in the rainy season. The GPP estimated from the MOD17A2H showed a predominant underestimation but evidenced the effects of land use and land cover changes over the two areas for all seasons.
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