CHARACTERIZING LANDSCAPE SPATIAL HETEROGENEITY USING SEMIVARIOGRAM PARAMETERS DERIVED FROM NDVI IMAGES

• Autor(a) principal: Silveira, Eduarda Martiniano de Oliveira
• Data de Publicação: 2018
• Outros Autores: Mello, José Márcio de, Acerbi Junior, Fausto Weimar, Reis, Aliny Aparecida dos, Withey, Kieran Daniel, Ruiz, Luis Angel
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Cerne (Online)
• Texto Completo: https://cerne.ufla.br/site/index.php/CERNE/article/view/1582
• topic: Remote sensing; Geostatistics; Forested areas; Human modified landscapes

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 SPOT 6, LANDSAT 8 and MODIS TERRA, to describe spatial landscape heterogeneity to identify forested and human modified areas. The NDVI (Normalized Difference Vegetation Index) was generated in a large area of Brazilian amazon tropical forest. 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 by fitting mathematical models to the experimental semivariogram using the weighted least squares method. The analysis revealed that image 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 and lowest within-class variation was provided by LANDSAT 8, indicating that this image resolution is the most appropriate to derive these parameters for describing the landscape spatial heterogeneity.  By combining geostatistical and remote sensing techniques, we show that the sill and range parameters of semivariogram derived NDVI images can be used as a simple indicator of landscape heterogeneity to identify fragile areas. 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.