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

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

Modelling aboveground biomass in forest remnants of the Brazilian Atlantic Forest using remote sensing, environmental and terrain-related data

The Brazilian Atlantic Forest, one of the most threatened tropical regions in the world, exhibits high levels of terrestrial aboveground biomass (AGB). We propose a random forest approach to model, map and assess whether public lands provide protection for AGB in the Rio Doce watershed, one of the most important watercourses of the Atlantic Forest biome. We used 188 field plots and individual and hybrid features from remote sensing, environmental and terrain-related data. The hybrid model improved the AGB prediction by reducing the root mean square error to 33.43 Mg/ha and increasing the coefficient of determination (R2) to 0.57. The total estimated AGB was 178,967,656.73 Mg, ranging from 20.40 to 167.72 Mg/ha following the seasonal precipitation pattern and anthropogenic disturbance effects. Only 5.76% of the total AGB was located on public protected lands, totalling 10,305,501 Mg, while most of the remaining AGB were located on private properties

Unseen rare tree species in southeast Brazilian forests: a species abundance distribution approach

Rarity is an important aspect of biodiversity often neglected in ecological studies. Species abundance distributions (SADs) are useful tools to describe patterns of commonness–rarity in ecological communities. Most studies assume field observa- tions of species relative abundances are approximately equal to their true relative abundances, thus dismissing the potential for, and importance of unseen rare species. Here, we adopted the approach proposed by Chao et al. (Ecol, 96:1189–1201, 2015) to estimate the number and abundance of unseen species, and thus the true SADs, for tree species in 48 forest sites in Minas Gerais state, Brazil (4 rainforests, 35 semideciduous forests, and 9 deciduous forests). Also, we assessed the correla- tions between both unseen and rare species and sampling protocol and environment characteristics (climate, terrain, terrain heterogeneity). We found estimated true SADs invariably had higher species richness values than observed in the surveys, due to the increase in rare species. We estimate that up to 55.6% of tree species per site were unseen (8.5–55.6%), with an average of 26.6%. The estimated percentage of rare species per site was between 31.9% and 72.8%, with an average of 57.78%. We found rarity to be most strongly correlated with the percentage of unidentified trees, local terrain conditions and hetero- geneity at site-level. Semideciduous forest and rainforest had similar higher percentages of unseen species (c. 27.2%) when compared to deciduous forests, probably due to the relatively higher local heterogeneity of these forests, which may provide more niches for rare species. Future studies should consider estimating true species abundances to better assess biodiversity