The Use of Remote Sensing Indices for Land Cover Change Detection
| Autor: | Jaíza Santos Motta, Gustavo Facincani Dourado, Antonio Conceição Paranhos Filho, D. F. Scott, Sandra Garcia Gabas |
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| Rok vydání: | 2019 |
| Předmět: |
geography
geography.geographical_feature_category Geography Planning and Development Geology Wetland Vegetation Land cover Development Normalized Difference Vegetation Index Landsat Multi-temporal NDVI NDWI QGIS Time-series Environmental science Economic Geology Water cycle Scale (map) Change detection General Environmental Science Riparian zone Remote sensing |
| Zdroj: | Anuário do Instituto de Geociências; Vol 42, No 2 (2019); 72-85 Anuário do Instituto de Geociências Universidade Federal do Rio de Janeiro (UFRJ) instacron:UFRJ |
| ISSN: | 1982-3908 0101-9759 |
| DOI: | 10.11137/2019_2_72_85 |
| Popis: | Remote sensing technology has been applied to monitor anthropogenic changes in the landscape that produce impacts on natural resources, such as environmental degradation, changes in the hydrological cycle and in ecosystems structure and functioning. As digital change detection may be a difficult task to perform, this study proposes a simple and logical technique to display land cover changes using Landsat imagery. Open source geoprocessing tools were used to acquire information for mapping changes on the land surface. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite images of four dates between 1984 and 2016 were used in RGB composites. The method was used to map gains and losses of vegetation cover and liquid water content in a spatiotemporal scale. The results indicate that this change detection method can effectively reflect the variations occurred over the years. Although both indices have similar responses, NDWI may provide opposite information to NDVI in certain areas, such as in wetlands and riparian zones, presenting wetness losses even in places that exhibit gains in vegetation. This method has applicability to other regions for deriving historical changes. |
| Databáze: | OpenAIRE |
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