2021•Caminhos de GeografiaOpen access

ANALYSIS OF THE DEGREE OF GRASSLAND DEGRADATION USING REMOTE SENSING

Jaíza Santos Motta, César Claudio Cáceres Encina, Eliane Guaraldo, Ariadne Barbosa Gonçalves, Roberto Macedo Gamarra, Antônio Conceição Paranhos Filho

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Abstract

The objective of this study is to adapt the calculations of the Pasture Degradation Index (GDI) to the Brazilian savannah using medium spatial resolution satellite image for the dry season. Vegetation cover is the main evaluation parameter used to calculate the GDI. The extreme ranges of the grazing class were determined by the NDVI histogram of a single date. Pasture cover was distinguished into five classes called Vegetable Pasture Cover (GVC), derived from NDVI and compared with five other classes derived from field photographs, named Green Coverage Percentage (GCP). The similarity between GVC and GVP demonstrated that GVC can be used to classify pasture cover. As a product of GVC, GDI was obtained. The GDI showed that pasture degradation in Paraíso das Águas is very serious. Extremely severe and Severe degradation occupy 9.28% and 25.22% of the study area, moderate and light degradation of pasture occupy 8.29% and 4.50%, respectively, and the non-degradation area covers 1.43 % of pastures. The results suggest the possibility of applying the GDI, originally developed for natural fields and multitemporal remote sensing data, to evaluate the conditions of the tropical savanna planted fields by means of a unique image.

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The objective of this study is to adapt the calculations of the Pasture Degradation Index (GDI) to the Brazilian savannah using medium spatial resolution satellite image for the dry season. Vegetation cover is the main evaluation parameter used to calculate the GDI. The extreme ranges of the grazing class were determined by the NDVI histogram of a single date. Pasture cover was distinguished into five classes called Vegetable Pasture Cover (GVC), derived from NDVI and compared with five other classes derived from field photographs, named Green Coverage Percentage (GCP). The similarity between GVC and GVP demonstrated that GVC can be used to classify pasture cover. As a product of GVC, GDI was obtained. The GDI showed that pasture degradation in Paraíso das Águas is very serious. Extremely severe and Severe degradation occupy 9.28% and 25.22% of the study area, moderate and light degradation of pasture occupy 8.29% and 4.50%, respectively, and the non-degradation area covers 1.43 % of pastures. The results suggest the possibility of applying the GDI, originally developed for natural fields and multitemporal remote sensing data, to evaluate the conditions of the tropical savanna planted fields by means of a unique image.

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Available abstract

The objective of this study is to adapt the calculations of the Pasture Degradation Index (GDI) to the Brazilian savannah using medium spatial resolution satellite image for the dry season. Vegetation cover is the main evaluation parameter used to calculate the GDI. The extreme ranges of the grazing class were determined by the NDVI histogram of a single date. Pasture cover was distinguished into five classes called Vegetable Pasture Cover (GVC), derived from NDVI and compared with five other classes derived from field photographs, named Green Coverage Percentage (GCP). The similarity between GVC and GVP demonstrated that GVC can be used to classify pasture cover. As a product of GVC, GDI was obtained. The GDI showed that pasture degradation in Paraíso das Águas is very serious. Extremely severe and Severe degradation occupy 9.28% and 25.22% of the study area, moderate and light degradation of pasture occupy 8.29% and 4.50%, respectively, and the non-degradation area covers 1.43 % of pastures. The results suggest the possibility of applying the GDI, originally developed for natural fields and multitemporal remote sensing data, to evaluate the conditions of the tropical savanna planted fields by means of a unique image.

Key concepts: Pasture, Normalized Difference Vegetation Index, Grassland degradation, Grassland, Environmental science, Remote sensing, Grazing, Land degradation

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