LAND COVER DYNAMICS AND LANDSCAPE STRUCTURE IN THE AREA SURROUNDING WATER RESERVOIRS, MOUNTAINOUS REGION OF RIO DE JANEIRO

The aim of this study is to evaluate land cover dynamics and landscape structure in the area surrounding two water reservoirs built-in 2009 for energy production, in the mountainous region of the State of Rio de Janeiro (Serra Fluminense). The analysis was developed through the interpretation of Landsat images from 2003, 2009, and 2013, considering the following land cover classes: early successional forest, mid successional forest, pasture, pasture with shrubs and trees, geological outcrop, urban area, and water area. We used thematic maps to determine landscape metrics of size and proximity in the reservoirs catchment area and the Permanent Preservation Area (PPA). At catchment level, pasture was predominant, a consequence of the extensive livestock production carried out in the whole watershed. During the evaluated period, the forest area remained consistent, however, fragmented in many small patches of mid successional forest. The average patch area of mid successional forest is three times the size of the early successional forest patches. For neither forest land cover classes, no significant variations through time in area or isolation were identified. On the PPA, an overall reduction of the forest cover was registered before the construction of the reservoir. However, from 2009 to 2013, after the enclosure of PPA areas, the forest cover increased 35% via assisted natural regeneration, suggesting a high potential for cost-effective restoration in the region

A DESIGUALDADE AMBIENTAL EM RIO DAS OSTRAS-RJ, BRASIL

Este artigo analisou a desigualdade ambiental em Rio das Ostras-RJ por meio do mapeamento das condições adequadas de habitação, baseada nos índices de abastecimento de água, de esgotamento sanitário, de alfabetização, de renda, de disposição do lixo e de presença de vegetação, oriundos do Censo IBGE de 2010 e de imagens de satélite Cbers-2b e Landsat-5, de 2008. Os índices foram ponderados pelo método Analitic Hierarchy Process e agregados no software ArcGis 10.0, construindo o índice de desigualdade ambiental. O município de Rio das Ostras foi selecionado por apresentar um elevado crescimento populacional entre 2000 e 2010, seguido de um significativo crescimento econômico derivado da exploração de petróleo e gás na Bacia de Campos. Nesse sentido, tornou-se importante verificar se o crescimento populacional e econômico vinha acompanhando de condições de habitação adequadas para toda a população. O resultado expôs a presença de desigualdade ambiental em Rio das Ostras, afetando principalmente a população de menor renda, com maiores porcentagens de analfabetos e localizados no limite da área urbanizada e na área rural, cujas áreas apresentaram deficiência na oferta de serviços e infraestrutura. Concluiu-se que a distribuição condições adequadas de habitação avaliada por este trabalho não é homogênea e segue a lógica de valorização da terra urbana para o processo de especulação imobiliária em Rio das Ostras. A presença da desigualdade ambiental também representa dificuldades na garantia da sustentabilidade urbana, com diversas áreas desprovidas de condições de habitação em que, por isso, acabam por impactar o meio ambiente

AVALIAÇÃO DOS VALORES DE ERRO DO MODELO LINEAR DE MISTURA ESPECTRAL EM IMAGENS ETM+/LANDSAT 7 A PARTIR DE REAMOSTRAGENS PELO VIZINHO MAIS PRÓXIMO E CONVOLUÇÃO CÚBICA: Evaluation of the error values of the spectral mixture linear model in ETM+/Landsat 7 images from research by the nearest neighborhood and cubic convolution

This work compared the influence of two resamplings on orbital images using the linear spectral mixture model. The ETM+/Landsat 7 scene, originally available by the method of the nearest neighborhood, had it resampling changed to cubic convolution, prompting whether this change, in the face of changing values ​​of digital numbers, would influence the classification of images. 30 random samples and 30 manual samples were extracted from the transition areas of the fractions in the error images (B3, B4 and B5) of each resulting model, and the paired Student's t test for means was applied. The statistical results proved that there is not enough evidence, at a significance level of 5%, that the average of the error values ​​of the images generated by the two resampling methods in the linear spectral mixture model are different, indicating that the application of the model and the analysis of its fractions in future classifications will not be influenced using this methodology. Furthermore, the supervised classification of images and fractions, for both resamplings, found that through the confusion matrix, with an average of  99% of global accuracy, the classifications are practically identical, legitimizing that the application of different resamplings, through this methodology, did not influence the final cartography.Este trabajo comparó la influencia de dos resamples en imágenes orbitales usando el modelo de mezcla espectral lineal. La escena ETM + / Landsat 7, originalmente disponible por el método del vecino más cercano, cambió su remuestreo a convolución cúbica, lo que provocó si este cambio, frente a los valores cambiantes de los números digitales, influiría en la clasificación de las imágenes. Se extrajeron 30 muestras aleatorias y 30 muestras manuales de las áreas de transición de las fracciones en las imágenes de error (B3, B4 y B5) de cada modelo resultante, y se aplicó la prueba t de Student emparejada para medias. Los resultados estadísticos demostraron que no hay evidencia suficiente, con un nivel de significancia del 5%, de que el promedio de los valores de error de las imágenes generadas por los dos métodos de remuestreo en el modelo de mezcla espectral lineal son diferentes, lo que indica que la aplicación de El modelo y el análisis de sus fracciones para futuras clasificaciones no se verán influenciados con esta metodología. Además, la clasificación supervisada de imágenes y fracciones, para ambos remuestreos, encontró que a través de la matriz de confusión, con un promedio del 99% de precisión global, las clasificaciones son prácticamente idénticas, legitimando que la aplicación de diferentes muestreos, a través de este metodología, no influyó en la cartografía final.Este trabalho comparou a influência de duas reamostragens em imagens orbitais usando o modelo linear de mistura espectral. A cena ETM+/Landsat 7, originalmente, disponibilizada pelo método do vizinho mais próximo, teve sua reamostragem alterada para convolução cúbica, incitando se essa alteração, em face da mudança dos valores de números digitais, influenciaria na classificação das imagens. Foram extraídas 30 amostras aleatórias e 30 amostras manuais das áreas de transição das frações nas imagens erro (B3, B4 e B5) de cada modelo resultante, e aplicou-se o teste t de Student pareado para médias. Os resultados estatísticos comprovaram, que não existem evidências suficientes, a um nível de significância de 5%, de que a média dos valores de erro das imagens geradas pelos dois métodos de reamostragem no modelo linear de mistura espectral são diferentes, indicando que a aplicação do modelo e da análise de suas frações para futuras classificações não serão influenciadas utilizando-se desta metodologia. Ademais, a classificação supervisionada das imagens e das frações, para ambas reamostragens, constataram que por meio da matriz de confusão, com média de 99% de exatidão global, as classificações são praticamente idênticas, legitimando que a aplicação de diferentes reamostragens, por meio desta metodologia, não influenciaram na cartografia final

Forest restoration monitoring through digital processing of high resolution images

Monitoring and evaluating forest restoration projects is a challenge especially in large-scale, but the remote monitoring of indicators with the use of synoptic, multispectral and multitemporal data allows us to gauge the restoration success with more accurately and in small time. The objective of this study was to elaborate and compare methods of remote monitoring of forest restoration using Light Detection and Ranging (LIDAR) data and multispectral imaging from Unmanned Aerial Vehicle (UAV) camera, in addition to comparing the efficiency of supervised classification algorithms Maximum Likelihood (ML) and Random Forest (RF). The study was carried out in a restoration area with about 74 ha and five years of implementation, owned by Fibria Celulose S.A., in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, Near Infrared) on UAV and LIDAR data composition (intensity image, Digital Surface Model, Digital Terrain Model, normalized Digital Surface Model). The monitored restoration indicator was the land cover separated in three classes: canopy cover, bare soil and grass cover. The images were classified using the ML and RF algorithms. To evaluate the accuracy of the classifications, the Overall Accuracy (OA) and the Kappa index were used, and the last was compared by Z test. The area occupied by different land cover classes was calculated using ArcGIS and R. The results of OA, Kappa and visual evaluation of the images were excellent in all combinations of the imaging methods and algorithms analyzed. When Kappa values for the two algorithms were compared, RF presented better performance than ML with significant difference, but when sensors (UAV camera and LIDAR) were compared, there were no significant differences. There was little difference between the area occupied by each land cover classes generated by UAV and LIDAR images. The highest cover was generated for canopy cover followed by grass cover and bare soil in all classified images, indicating the need of adaptive management interventions to correct the area trajectory towards the restoration success. The methods employed in this study are efficient to monitor restoration areas, especially on a large scale, allowing us to save time, fieldwork and invested resources

Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field datainfo:eu-repo/semantics/publishedVersio

Management Recommendation Generation for Areas Under Forest Restoration Process through Images Obtained by UAV and LiDAR

Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was “weed control + plant seedlings” (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations

Escaneamento a laser aerotransportado aplicado a inventário de povoamentos de eucalipto ao nível de árvores individuais

The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field data.O objetivo deste trabalho foi avaliar a aplicação do escaneamento a laser aerotransportado (ALS) na realização de inventário de povoamentos de eucalipto, em larga escala, por meio do método de individualização de árvores, bem como propor um novo método para estimativa dos diâmetros das árvores em função das alturas obtidas a partir da nuvem de pontos. O estudo foi conduzido em floresta de 1,681 ha, composta por oito povoamentos de eucalipto com idades entre quatro e sete anos. Após o escaneamento, foram obtidas as alturas das árvores pelo algoritmo de máximos locais, e o estoque total de madeira, pela soma dos volumes individuais. Para determinar os diâmetros das árvores, foram utilizadas regressões ajustadas a partir de dados medidos em parcelas de inventário. Os resultados foram comparados às estimativas obtidas via amostragem de campo. O sistema de equações proposto é adequado para ser aplicado aos dados de altura derivados da nuvem de pontos do ALS. O método de individualização de árvores com o filtro de máximos locais é eficiente para estimar o número de árvores e o estoque de madeira a partir dos dados do ALS, desde que os resultados sejam previamente calibrados com o inventário de campo