Abstract
ABSTRACT 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 (σ²-overall spatial variability of the surface property) and range (φ-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.
Highlights
Recent years have seen a dramatic increase in the attention being given to the condition of tropical forests
Remote sensing based approaches play a key role in forest monitoring, as they are of low cost and provide an opportunity for mapping forest change over large areas (DEVRIES et al, 2015)
For forested and human modified areas, the minimum Normalized Difference Vegetation Index (NDVI) values were obtained from samples of the SPOT 6 image; this is probably due to the presence of shadows that are captured by high resolution images, causing a reduction in NDVI values
Summary
Recent years have seen a dramatic increase in the attention being given to the condition of tropical forests. Remote sensing based approaches play a key role in forest monitoring, as they are of low cost and provide an opportunity for mapping forest change over large areas (DEVRIES et al, 2015). Remote sensing using satellite imagery has emerged as a key geospatial tool to meet the growing information needs of landscape and forest managers (COSTANTINI et al, 2012). Different methods to quantify changes in landscape complexity have been developed in the last few decades (WU, 2013). Most of these involve the use of remotely sensed images and geospatial techniques (MONMANY et al, 2015; BERBEROGLU et al, 2000; BERBEROGLU; AKIN, 2009; GARCIA-PEDRERO et al, 2015). Several works have investigated environmental changes, using spatial heterogeneity derived from various types of remote sensing data
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