| dc.contributor.author | Chiloane, Chantel | |
| dc.contributor.author | Dube, Timothy | |
| dc.contributor.author | Shoko, Cletah | |
| dc.date.accessioned | 2023-03-29T10:16:56Z | |
| dc.date.available | 2023-03-29T10:16:56Z | |
| dc.date.issued | 2023 | |
| dc.identifier.citation | Chiloane, C. et al. (2023). Multispectral remote sensing of potential groundwater dependent vegetation in the greater Floristic region of the Western Cape, South Africa. South African Geographical Journal,. https://doi.org/10.1080/03736245.2023.2183890 | en_US |
| dc.identifier.issn | 2151-2418 | |
| dc.identifier.uri | https://doi.org/10.1080/03736245.2023.2183890 | |
| dc.identifier.uri | http://hdl.handle.net/10566/8696 | |
| dc.description.abstract | Groundwater dependent vegetation (GDV) is increasingly threa-tened by the transformation of the natural environment. An under-standing of the nature of GDV at the appropriate scale helps environmental managers make suitable decisions. This study assesses the potential for mapping the distribution of GDV within the Heuningnes Catchment using multispectral remotely sensed data (i.e., Landsat 8 (L8) and Sentinel 2 (S2)), the derived vegetation indices (Normalised Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI)) and in-situ data. The GDV distribution maps were produced by integrating vegetation pro-ductivity, landcover, and topographic layers as GDV indicators. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Taylor and Francis Group | en_US |
| dc.subject | Fynbos | en_US |
| dc.subject | Satellite data | en_US |
| dc.subject | Water resources management | en_US |
| dc.subject | Geology | en_US |
| dc.subject | Geophysics | en_US |
| dc.subject | South Africa | en_US |
| dc.title | Multispectral remote sensing of potential groundwater dependent vegetation in the greater Floristic region of the Western Cape, South Africa | en_US |
| dc.type | Article | en_US |
