A sazonalidade ambiental afeta a composição faunística de Ephemeroptera e Trichoptera em um riacho de Cerrado do Sudeste do Brasil?Does environmental seasonality affect the faunal composition of Ephemeroptera and Trichoptera in a Cerrado stream from

No presente trabalho, dados sobre Ephemeroptera e Trichoptera (ET) de um riacho de cerrado foram analisados, com o objetivo de testar a hipótese de que a alta sazonalidade neste bioma pode alterar a composição de ET entre as estações chuvosas e de seca. A estrutura da comunidade foi avaliada utilizando a Análise de Correspondência Destendenciada e a Análise de Agrupamento (Morisita-Horn, UPGMA). Os fatores ambientais foram submetidos à Análise de Componentes Principais. Para testar a influência das variáveis abióticas sobre a fauna, foram utilizados o Procrustean Randomization Test (Protest) e o Teste de Mantel. Os fatores ambientais registrados influenciaram significativamente a fauna de ET do Córrego do Pedregulho. A similaridade faunística foi alta ao longo do ano, indicando que, apesar de a densidade flutuar em resposta à variação da precipitação, a composição faunística apresentou baixa variabilidade temporal. Por outro lado, foi possível constatar que o gênero Lachlania (Ephemeroptera) ocorreu, preferencialmente, nos meses chuvosos e que a composição da fauna da estação seca variou menos do que aquela das demais estações. Portanto, a sazonalidade ambiental afetou parcialmente a composição da fauna de ET do Córrego do Pedregulho.Abstract In this paper, data on the fauna of Ephemeroptera and Trichoptera (ET) from a Cerrado stream was analysed in order to test the hypothesis that the high seasonality of this biome can influence the composition of ET between the wet and dry seasons. The community structure was evaluated using Detrended Correspondence Analysis and Cluster Analysis (Morisita Horn-UPGMA). Environmental factors were analyzed using the Principal Components Analysis. In order to test the effect of abiotic variables on the fauna, It was applied the Procrustean Randomization Test (Protest) and Mantel Test. The environmental factors recorded for this study had a significant effect on the ET fauna from Córrego do Pedregulho. Faunal similarity was high throughout the year, indicating that although there was density of fluctuation, due to rainfall variation, the faunal composition showed little temporal variability. On the other hand, it was possible to observe that the genus Lachlania (Ephemeroptera) occurred preferably during the rainy months and that the faunal composition during the dry season was less variable than those from other seasons. Therefore, environmental seasonality had a partial effect on the faunal composition of ET from Córrego do Pedregulho

SURVEYING THE TOPOGRAPHIC HEIGHT FROM SRTM DATA FOR CANOPY MAPPING IN THE BRAZILIAN PANTANAL

An algorithm was developed in Geographical Information Systems (GIS) for the extraction of topographic height from the Digital Elevation Models (DEM) of the Shuttle Radar Topography Mission (SRTM), C and X bands, applied to mapping vegetation canopy in the Pantanal Floodlands. According to previous studies, these bands are sensitive to surface vegetation and thus elevation values increase in relation to terrain proportional to the height of the canopy, known as canopy effect. The proposed algorithm identifies minimum elevations within a search radius, which are likely to represent bare earth values, to generate a reference surface. The topographic height results from the subtraction between the elevations of this surface and of the original DEM. It is expected that, in this region, the height values be related to the vegetation height. Whenever possible, the algorithm results were observed together with optical images and vegetation maps of RADAMBRASIL project, for the establishment of height slicing levels as related to vegetal groups. The visual examination and statistical analysis have provided three levels of slicing height, which would be related to herbaceous, shrub and tree (forest) vegetation communities. Although slicing levels could be related to general classes of vegetation canopy in this region, field data and, or, fine resolution optical data are required for more detailed mappings. Regardless of the classification approach, height estimates from SRTM DEM represent a subsidiary data for the remote characterization of the Pantanal Floodlands vegetation, which complements traditionally used optical dat

ANALYSIS OF THE TARGET DECOMPOSITION TECHNIQUE ATTRIBUTES AND POLARIMETRIC RATIOS TO DISCRIMINATE LAND USE AND LAND COVER CLASSES OF THE TAPAJÓS REGION

This study aims to analyze the capability of the target decomposition techniques and the polarimetric ratios applied to the ALOS/PALSAR-2 satellite polarimetric images to discriminate the land use and land cover classes in the Tapajós National Forest region, Pará State. Three full polarimetric ALOS/PALSAR-2, level 1 single look complex scenes were selected to generate the coherence and the covariance matrices to derive the Cloude-Pottier and the Freeman-Durden target decomposition attributes. From the radiometrically calibrated PALSAR-2 images, we generated the backscatter coefficients, the cross polarized ratio (RC; HV/HH), the parallel polarized ratio (RP; VV/HH) and the Radar Forest Degradation Index (RFDI). The images resulting from these polarimetric attributes were processed by the Maximum Likelihood (MAXVER) classifier coupled with the Iterated Conditional Modes (ICM) contextual algorithm. We found that the classifications derived from the target decomposition attributes, mainly from the CloudePottier technique, with a Kappa index of 0.75, presented a significant higher performance than those derived from the RC ratio, RP ratio, and RFDI

Efeito da topografia na resposta polarimétrica de floresta tropical em imagens PALSAR/ALOS Effect of topography on the polarimetric response of tropical forest in PALSAR / ALOS

A dependência do coeficiente de retroespalhamento (σ°) para diferentes alvos florestais não tem sido investigada quanto às variações topográficas, conforme se observa na literatura. Assim, o objetivo desse estudo é avaliar o efeito da topografia sobre o retroespalhamento derivado de áreas de floresta tropical em imagens PALSAR/ALOS (banda L). Para tal, foi realizada uma análise das respostas polarimétricas de áreas florestais, parcelas situadas em relevo plano, ondulado e fortemente ondulado. As respostas polarimétricas revelaram comportamento característico do efeito da topografia sobre o sinal do radar através da altura do pedestal, que indica a intensidade da despolarização da onda incidente. Observouse uma maior frequência de ocorrência e de intensidade da despolarização nas parcelas de terrenos ondulados a forte ondulado, com acentuada despolarização como comportamentos típicos nesta última condição.Abstract The dependence of backscatter coefficient (σ°) on different targets forest has not bee deeply investigated as affected by topographic variations, as seen in the literature. The objective of this study is to evaluate the effect of topography on backscatter derived from rainforest areas in PALSAR / ALOS (L-band) images. An analysis of the polarimetric responses of forest plots located in flat, gentle and undulated terrain was performed. The polarimetric responses showed a typical behavior of the topography effect on the radar signal through the height of the pedestal, which indicates the intensity of depolarization of the incident wave. There was a higher frequency of occurrence and intensity of depolarization in the plots on gentle and undulated terrains, with a marked depolarization as a typical pattern for the last condition

ANÁLISE DA SUSCETIBILIDADE AOS MOVIMENTOS DE MASSA EM SÃO SEBASTIÃO (SP) COM O USO DE MÉTODOS DE INFERÊNCIA ESPACIAL

Este trabalho tem como objetivo avaliar métodos de inferência espacial para modelar a suscetibilidade a movimentos de massa no município de São Sebastião (SP) e testar o efeito da inclusão de variáveis geomorfométricas (curvaturas vertical e horizontal) no modelo. Foram comparados três métodos de inferência espacial: booleano, bayesiano e fuzzy gama. Estes métodos foram testados com cinco variáveis (geomorfologia, geologia, pedologia, uso da terra e declividade) e, em seguida, com sete variáveis (incluindo as curvaturas vertical e horizontal). O método booleano não permitiu uma classificação detalhada das classes de suscetibilidade para ambos os casos (com cinco e com sete variáveis). O método fuzzy gama apresentou uma maior flexibilidade na identificação de áreas e na geração de cenários para ambos os casos, o que foi possível através da manipulação dos valores do índice gama. A adição das curvaturas no modelo permitiu um melhor desempenho, apresentando resultados mais satisfatórios para o seu refinamento. A inferência bayesiana utilizou efetivamente apenas a variável declividade (no caso de cinco variáveis) e, em uma segunda etapa, as variáveis declividade e curvatura horizontal (no caso de sete variáveis). Este método não se mostrou satisfatório na discriminação das classes de suscetibilidade aos movimentos de massa. Palavras-chave: métodos de inferência espacial, análise de risco, sistema de informações geográficas, movimentos de massa, geomorfometria

Evaluation of Environmental Naturalness: A Case Study in the Tietê-Jacaré Hydrographic Basin, São Paulo, Brazil

The connection between humanity and nature has an organizational impact on land use/land, often changing landscapes' patterns. In this context, our study aims to analyze the changes in the land-scape structure of the Tietê-Jacaré watershed, São Paulo state, Brazil, in 2007 and 2017, through the urbanity index. The landscape analysis described the temporal landscape patterns resulting from the influence of anthropogenic processes. This approach assumes that the environmental impacts are associated with the vulnerability of land use components. The urbanity index was utilized to analyze the landscape sustainability conditions in response to anthropogenic influence. We ob-served a reduction in vegetation areas (2.72%), representing 32,149 ha, followed by an expansion of crops (2.05%, 24,507.53 ha) and, as a result, a reduction of the landscape environmental quality with a growth of the areas with anthropic intervention. The development of anthropic activities, land use, and land cover changes could compromise the region’s ecosystems negatively, e.g., through effects on soils that provide sustenance vegetation and afford energy for terrestrial life. The ur-banity index expressed the conservation and natural state of the landscape studied. It is presented as an essential tool for diagnosing the environment and for the conservation of the ecosystem, allowing precise analysis of landscape elements and enabling accurate analysis of each fragment of the landscape

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

From MDPI via Jisc Publications RouterHistory: accepted 2021-12-01, pub-electronic 2021-12-05Publication status: PublishedIt is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques

Analysis of a Landscape Intensely Modified by Agriculture in the Tietê–Jacaré Watershed, Brazil

From MDPI via Jisc Publications RouterHistory: accepted 2021-08-04, pub-electronic 2021-08-19Publication status: PublishedFunder: São Paulo Research Support Foundation; Grant(s): 2015/19918-3, 2018/00162-4, 660020, PR140015Anthropogenic actions influence landscapes, and the resulting mosaic is a mix of natural and anthropogenic elements that vary in size, shape, and pattern. Considering this, our study aimed to analyse the land use and land cover changes in the Tietê–Jacaré watershed (São Paulo state, Brazil), using the random forest (RF) algorithm and Sentinel-2 satellite data from 2016 to 2018 to detect landscape changes. By overlapping the environmental data and the proposed model evaluation, it was possible to observe the landscape structure, produce information about the state of this region, and assess the environmental responses to anthropic impacts. The land use and land cover analysis identified eight classes: exposed soil, citriculture, pasture, silviculture, sugar cane, urban area, vegetation, and water. The RF classification for the three years reached high accuracy with a kappa index of 0.87 in 2016, 0.85 in 2017, and 0.85 in 2018. The model developed was essential for the temporal analysis since it allowed us to comprehend the driving forces that act in this landscape and contribute to the discussions about their impacts over time. The results showed a predominance of agricultural activities over the three years, with approximately 900.000 ha (76% of the area), mainly covered by sugarcane cultivation

Carbon Dynamics in a Human-Modified Tropical Forest: A Case Study Using Multi-Temporal LiDAR Data

Tropical forests hold significant amounts of carbon and play a critical role on Earth´s climate system. To date, carbon dynamics over tropical forests have been poorly assessed, especially over vast areas of the tropics that have been affected by some type of disturbance (e.g., selective logging, understory fires, and fragmentation). Understanding the multi-temporal dynamics of carbon stocks over human-modified tropical forests (HMTF) is crucial to close the carbon cycle balance in the tropics. Here, we used multi-temporal and high-spatial resolution airborne LiDAR data to quantify rates of carbon dynamics over a large patch of HMTF in eastern Amazon, Brazil. We described a robust approach to monitor changes in aboveground forest carbon stocks between 2012 and 2018. Our results showed that this particular HMTF lost 0.57 m·yr−1 in mean forest canopy height and 1.38 Mg·C·ha−1·yr−1 of forest carbon between 2012 and 2018. LiDAR-based estimates of Aboveground Carbon Density (ACD) showed progressive loss through the years, from 77.9 Mg·C·ha−1 in 2012 to 53.1 Mg·C·ha−1 in 2018, thus a decrease of 31.8%. Rates of carbon stock changes were negative for all time intervals analyzed, yielding average annual carbon loss rates of −1.34 Mg·C·ha−1·yr−1. This suggests that this HMTF is acting more as a source of carbon than a sink, having great negative implications for carbon emission scenarios in tropical forests. Although more studies of forest dynamics in HMTFs are necessary to reduce the current remaining uncertainties in the carbon cycle, our results highlight the persistent effects of carbon losses for the study area. HMTFs are likely to expand across the Amazon in the near future. The resultant carbon source conditions, directly associated with disturbances, may be essential when considering climate projections and carbon accounting methods

A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

From MDPI via Jisc Publications RouterHistory: accepted 2021-08-19, pub-electronic 2021-08-24Publication status: PublishedThe near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon