MAPPING PRIORITY AREAS FOR FOREST RECOVERY USING MULTICRITERIA ANALYSIS IN THE BRAZILIAN ATLANTIC FOREST

The collapse of a mining dam with 62 million cubic meters of mud in the Rio Doce basin resulted in the destruction of whole communities and large areas of the Atlantic Forest. As forest restoration activities are among the most costly conservation strategies, prioritization of restoration efforts is crucial. In the present article, this study mapped priority areas for forest recovery in a portion of the Rio Doce Basin (DO1) using a GIS-based (geographic information system) multicriteria decision analysis (MCDA) employing the weighted linear combination (WLC) method. Five factors with different weights were taken into consideration according to their level of importance: distance from the drainage network, distance from the native vegetation patches, slope, soil class and precipitation. A map of priority areas was produced in which 1.73% of the area was classified as very high priority for forest recovery, while 5.18% of the area was classified as high priority, 57.88% as medium priority, 1.34% as low priority and 0.00% as very low priority. The highest weights were attributed to the distance from the drainage network and the distance from native vegetation, revealing that areas of permanent preservation and those closer to forest fragments are priority areas for forest recovery. MCDA is a flexible and easy-to-implement method which generates maps with suitable solutions for forest recovery. The chosen approach can be replicated in regions that require support for decision making in environmental planning, such as the Pantanal biome, which is under considerable process of deforestation for the expansion of pastures.The collapse of a mining dam with 62 million cubic meters of mud in the Rio Doce basin resulted in the destruction of whole communities and large areas of Atlantic Forest. As forest restoration activities are among the most costly conservation strategies, prioritization of restoration efforts is crucial. In the present article, we mapped priority areas for forest recovery in a portion of the Rio Doce Basin (DO1) using a GIS-based (geographic information system) multicriteria decision analysis (MCDA) employing the weighted linear combination (WLC) method. Five factors with different weights were taken into consideration according to their level of importance: distance from the drainage network, distance from the native vegetation patches, slope, soil class and precipitation. A map of priority areas was produced where 1.73% of the area was classified as very high priority for forest recovery, while 5.18% of the area was classified as high priority, 57.88% as medium priority, 1.34% as low priority and 0.00% as very low priority. The highest weights were both for the distance from the drainage network and the distance from native vegetation, revealing that areas of permanent preservation and those closer to forest fragments are priority areas for forest recovery. MCDA is a flexible and easy-to-implement method generating maps with suitable solutions for forest recovery. The approach taken can be replicated in regions that require support for decision making in environmental planning, such as the Pantanal biome, which is under considerable pressure from deforestation for the expansion of pastures

Estimating Aboveground Biomass Loss from Deforestation in the Savanna and Semi-arid Biomes of Brazil between 2007 and 2017

Brazilian Savannas and Semi-arid woodlands biomes exhibit high levels of aboveground biomass (AGB) associated with high rates of deforestation. The state of Minas Gerais (MG), southeast of Brazil, encompasses landscape variations ranging from Savanna and Atlantic Forest to Semiarid woodlands. The understanding of land-cover changes in these biomes is limited due to the fact that most of the efforts for estimating forest cover changes has been focused on the tropical rain forests. Hence, the question is: What is the total amount of AGB loss across Savanna and Semi-arid woodland biomes in MG state, during the period 2007–2017? We first used a total of 1914 field plots from a forest inventory to model the AGB using a combination of remote sensing and spatio-environmental predictor variables to produce a spatial-explicit AGB map. Second, from a global map of forest cover change (GFC), we obtained deforestation patches. As a result, from 2007 to 2017, the Savanna and the Semiarid woodland biomes lost together 508,042 ha of native vegetation in MG state, leading to 21,182,150 Mg of AGB loss (4.65% of total AGB). In Savannas and Semi-arid woodland biomes in MG state, conservation initiatives must be implemented to increase the forests protection and expand AGB

Estoque do potencial produtivo do Cerrado utilizando geotecnologias

Monitoring natural resources of our planet is essential to gather information to support strategies for conservation and a sustainable use of these natural resources. This monitoring is even more important in endangered biomes, as is the case of brazilian savannas, one of the most threatened and richest, in terms of biodiversity, savannas in the world. Usually, forest monitoring is accomplish by measuring the trees on the field, the so-called forest inventories, which are expensive, costly and extremely difficult to be performed periodically in extensive forests, as is the case of the Brazilian savannas. Therefore, this study aimed to update the information about volume and carbon stocks in savanna fragments in order to reduce tree measurements in the field. We used data from 61 savanna remnants, where 25 of them had 2 measurements with 5 years interval (monitoring). Multiple linear models, based on field data and reflectance values of Landsat images, were fitted and validated. Subsequently, the best models were applied to remnants that had only one measured, and then volume and carbon estimates were obtained for all 61 remnants in the second year of measurement. Additionally, maps updating the productivity of these remnants were generated.O monitoramento dos recursos naturais do nosso planeta é essencial para a obtenção de informações que possam subsidiar estratégias de conservação e utilização sustentável desses recursos. Tais estratégias se tornam ainda mais importantes em biomas ameaçados, como é o caso do Cerrado brasileiro, uma das savanas mais ricas e ameaçadas do mundo. Comumente o monitoramento das florestas é realizado por meio de inventários florestais, atividade onerosa, cara e extremamente difícil de ser realizada periodicamente em florestas extensas, como o Cerrado. Nesse sentido, o presente estudo teve como objetivo atualizar o mapeamento do estoque de volume e carbono em fragmentos de Cerrado, diminuindo a quantidade de levantamentos de campo por meio da aplicação de geotecnologias. Utilizaram-se dados de levantamento de campo de 61 fragmentos de Cerrado, sendo que em 25 deles foram realizadas 2 medições em um intervalo de 5 anos. Através dos dados de campo e valores de reflectância de imagens Landsat, modelos lineares múltiplos foram ajustados e validados. Posteriormente, esses modelos foram aplicados aos fragmentos que foram mensurados apenas uma vez. Dessa forma, foram obtidos os valores de volume e carbono para todos os 61 fragmentos, no ano da segunda medição, gerando mapas de produtividade atualizados

Caracterizacão da heterogeneidade espacial da paisagem utilizando parâmetros do semivariograma derivados de imagens NDVI

[EN] Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landscape spatial heterogeneity of forested and human modified areas. The NDVI (Normalized Difference Vegetation Index) was generated in an area of Brazilian amazon tropical forest (1,000 km²). We selected samples (1 x 1 km) from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (¿²-overall spatial variability of the surface property) and range (¿-the length scale of the spatial structures of objects) parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA) approaches.[PT] Assumindo a existência de uma relação entre a heterogeneidade da paisagem e medidas de dependência espacial obtidas de dados de sensoriamento remoto, o objetivo deste estudo foi avaliar o potencial dos parâmetros do semivariograma derivados de imagens de satélite com diferentes resoluções espaciais, para caracterizar áreas cobertas por floresta e áreas sob ação antrópica. Para isso, o NDVI (Índice de Vegetação da Diferença Normalizada) de cada umas das imagens (SPOT 6, Landsat 8 e MODIS Terra) foi gerado em uma área de floresta tropical Amazônica (1.000 km²), onde foram selecionadas amostras (1 x 1 km) de áreas florestadas e áreas antrópicas. A partir destes dados, foram gerados os semivariogramas e extraídos os parâmetros patamar (¿²-variabilidade espacial total) e alcance (¿-distância dentro da qual as amostras apresentam-se estruturadas espacialmente). A análise revelou que a resolução espacial das imagens influencia os parâmetros ¿² e ¿, apresentando significativo aumento das áreas de florestas para as áreas sob ação antrópica. A maior variação entre estas classes foi obtida com as imagens Landsat 8, indicando estas imagens, com resolução espacial de 30 metros, a mais apropriada para a obtenção dos parâmetros do semivariograma objetivando a caracterização da heterogeneidade espacial da paisagem. Combinando o sensoriamento remoto e técnicas geostatisticas, demonstrou-se que os parâmetros do semivariograma derivados de imagens NDVI podem ser utilizados como um simples indicador de heterogeneidade da paisagem, gerando mapas que permitem aos pesquisadores delinearem com maior eficácia o regime de amostragem. Outras aplicações combinando estas duas técnicas devem ser investigadas, como por exemplo a detecção de mudanças na cobertura do solo e a classificação de imagens utilizando análises orientada a objetos (OBIA).The authors are grateful to the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Department of Forest Science of the Federal University of Lavras (UFLA) and the ONF Brazil group for supporting this work.De Oliveira Silveira, EM.; De Mello, JM.; Acerbi Junior, FW.; Dos Reis, AA.; Withey, KD.; Ruiz Fernández, LÁ. (2017). Characterizing Landscape Spatial Heterogeneity Using Semivariogram Parameters Derived from NDVI Images. Cerne. 23(4):413-422. https://doi.org/10.1590/01047760201723042370S41342223

ANÁLISE MULTICRITÉRIO NA DEFINIÇÃO DE ÁREAS PRIORITÁRIAS PARA RECUPERAÇÃO FLORESTAL NA BACIA DO RIO DOCE, EM MINAS GERAIS

A Floresta Atlântica é um dos ecossistemas mais fragmentado e explorado. Como atividades de restauração florestal são dispendiosas, a Análise de Decisão Multicritério (ADMC) integrada ao SIG (Sistema de Informações Geográficas) fornece um satisfatório suporte de decisão espacial para produção de mapas de forma eficiente. O colapso de uma barragem de mineração em áreas de floresta Atlântica, resultou na destruição de comunidades por rejeitos de mineração na bacia hidrográfica do Rio Doce. Assim, o objetivo deste estudo foi mapear áreas prioritárias para recuperação florestal na bacia do Rio Doce, Minas Gerais. Utilizou-se a ADMC baseada em SIG, e associada ao método do Processo Analítico Hierárquico (AHP) e Combinação Linear Ponderada (CLP). Cinco fatores foram utilizados com distintos pesos: distância da rede de drenagem, distância do fragmento de vegetação nativa, declividade, classe de solo e precipitação. De acordo com o mapa de áreas prioritárias produzido, 92,69% da área foi classificado como área de importância baixa ou muito baixa para recuperação florestal e, 7,31% como área de média, alta e muito alta prioridade. A ADMC é de fácil implementação, produzindo mapas que podem predizer as soluções adequadas para conduzir ações de recuperação, desde que a base de dados seja fidedigna para obter resultados satisfatórios.Palavras-chave: manejo de ecossistemas; combinação linear ponderada; processo analítico hierárquico. MULTRICRITERIA ANALYSIS TO DEFINE PRIORITY AREAS FOR FOREST RECOVERY IN THE RIO DOCE BASIN, MINAS GERAIS ABSTRACT: The Brazilian Atlantic forest is one of the most fragmented ecosystems and exploited Brazilian biome. As restoration activities are expensive, multicriteria decision analysis (MCDA) integrated with GIS (geographic information system) provide a satisfactory spatial decision support system to efficiently produce maps. The collapse of a mining dam in a region of Brazilian Atlantic forest, resulted in the destruction of communities by a river of mud and mining waste. Thus, the objective of this study was to map and identify priority areas for forest recover in the Rio Doce Basin, Minas Gerais. We used GIS-based multicriteria decision analysis associated with the analytic hierarchy process (AHP) and weighted linear combination (WLC) method in the aggregation of criteria. Five factors were used, receiving different weights: distance from the drainage network, distance from the native vegetation patches, slope, soil class and precipitation. According to the priority areas map, 92.69% of the area was classified as an area of low or very low importance for forest recovery and the remained (2.92%) of the Rio Doce basin was mapped as an area with high and very high priority for forest recovery. The ADMC is easy to implement, producing maps that can predict the right solutions to conduct recovery actions, provided the database is trusted for satisfactory results.Keywords: ecossystem management; linear weighted combination; analytical hierarchical process

Determinação do volume de madeira em povoamento de eucalipto por escâner a laser aerotransportado

The objective of this work was to evaluate the possibility of estimating the diameter at breast height (DBH) with tree height and number data derived from airborne laser scanning (LiDAR, light detection and ranging) dataset, and to determine the timber volume of an Eucalyptus sp. stand from these variables. The total number of detected trees was obtained using a local maxima filtering. Plant height estimated by LiDAR showed a nonsignificant tendency to underestimation. The estimate for DBH was coherent with the results found in the forest inventory; however, it also showed a tendency towards underestimation due to the observed behavior for height. The variable number of stems showed values close to the ones observed in the inventory plots. LiDAR underestimated the total timber volume in the stand in 11.4%, compared to the total volume delivered to the industry. The underestimation tendency of tree height (5% mean value) impacted the individual tree volume estimate and, consequently, the stand volume estimate. However, it is possible to obtain regression equations that estimate DBH with good precision, from the LiDAR plant height derived data. The parabolic model is the one that provides the best estimates for timber volumetric yield of eucalyptus stands.O objetivo deste trabalho foi avaliar a possibilidade de se estimar o diâmetro à altura do peito (DAP) com os dados de altura e de número de árvores derivados do escâner a laser aerotransportado (LiDAR, “light detection and ranging”), e determinar o volume de madeira de talhão de Eucalyptus sp. a partir dessas variáveis. O número total de árvores detectadas foi obtido com uso da filtragem de máxima local. A altura de plantas estimada pelo LiDAR apresentou tendência não significativa à subestimativa. A estimativa do DAP foi coerente com os valores encontrados no inventário florestal; porém, também mostrou tendência à subestimativa, em razão do comportamento observado quanto à altura. A variável número de fustes apresentou valores próximos aos observados nas parcelas do inventário. O LiDAR subestimou o volume total de madeira do talhão em 11,4%, em comparação ao volume posto na fábrica. A tendência de subestimação da altura das árvores (em média, cerca de 5%) impactou a estimativa do volume individual de árvores e, consequentemente, a do volume do talhão. No entanto, é possível gerar equações de regressão que estimam o DAP com boa precisão, a partir de dados de altura de plantas obtidos pelo LiDAR. O modelo parabólico é o que possibilita as melhores estimativas da produção volumétrica dos talhões de eucalipto

Reducing the effects of vegetation phenology on change detection in tropical seasonal biomes

Tropical seasonal biomes (TSBs), such as the savannas (Cerrado) and semi-arid woodlands (Caatinga) of Brazil, are vulnerable ecosystems to human-induced disturbances. Remote sensing can detect disturbances such as deforestation and fires, but the analysis of change detection in TSBs is affected by seasonal modifications in vegetation indices due to phenology. To reduce the effects of vegetation phenology on changes caused by deforestation and fires, we developed a novel object-based change detection method. The approach combines both the spatial and spectral domains of the normalized difference vegetation index (NDVI), using a pair of Operational Land Imager (OLI)/Landsat-8 images acquired in 2015 and 2016. We used semivariogram indices (SIs) as spatial features and descriptive statistics as spectral features (SFs). We tested the performance of the method using three machine-learning algorithms: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The results showed that the combination of spatial and spectral information improved change detection by correctly classifying areas with seasonal changes in NDVI caused by vegetation phenology and areas with NDVI changes caused by human-induced disturbances. The use of semivariogram indices reduced the effects of vegetation phenology on change detection. The performance of the classifiers was generally comparable, but the SVM presented the highest overall classification accuracy (92.27%) when using the hybrid set of NDVI-derived spectral-spatial features. From the vegetated areas, 18.71% of changes were caused by human-induced disturbances between 2015 and 2016. The method is particularly useful for TSBs where vegetation exhibits strong seasonality and regularly spaced time series of satellite images are difficult to obtain due to persistent cloud cover

Remote Sensing and Geostatistics Applied to Post-stratification of Eucalyptus Stands

ABSTRACT Brazil has many rural properties with unmanaged eucalyptus stands. These plantations are heterogeneous, presenting different tree sizes, advanced ages, and large wood volumes that can be quantified using forest inventories. The prediction error of dendrometric variables, mainly in highly heterogeneous areas, can be associated with inadequate forest inventory procedures, i.e. low intensity of sampling plots. However, a larger number of plots increases the cost of inventorying. Therefore, a promising alternative is forest stratification into homogeneous sub areas. Accordingly, the aim of this study was to analyze the reduction of volume estimate errors by post-stratification procedures. We used the normalized difference vegetation index (NDVI) derived from Landsat 8 and Spot 6 images and geostatistical techniques, such as kriging the volume (V) and diameter at breast height (DBH). The most precise method to estimate the total volume was the stratified random sampling (STS), based on geostatistical interpolation, using the DBH (error lower than 10%)