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The loss of forests to suburbanisation continues to present a challenge to environmentalists. Lately, the analysis of remotely acquired data in Geographical Information Systems (GIS) for monitoring the changes in vegetation cover has proved efficacious. The most practical technique in this context is the normalised difference vegetation index (NDVI) and its correlation with land surface temperature (LST). Utilising remote sensing and GIS technologies in combination with a random forest classification algorithm, the study detected the spatiotemporal variations in forest cover for the period 1992–2022 in Maun. Multi-temporal Landsat Thematic Mapper and Landsat Operational Land Imager data were utilised to understand human impact on vegetation. The NDVI and LST were applied to quantify the impact of anthropological drivers affecting forest cover. The LST and NDVI were calculated based on the dynamics in land use and land cover and a strong correlation was observed between LST and NDVI for built-up, dense/thick forest, medium forest, light forest, and water bodies. Findings showed an inverse relationship between NDVI and LST in both densely built-up and densely vegetated areas. Thus, NDVI is low in densely built-up areas and LST is high in the same. NDVI and LST are high and low in densely vegetated areas, respectively.

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Anthropological Impact on Natural Forests in the Lower Okavango Delta, Botswana

  • 章节

  • 发表日期：

    2025年

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  • Reniko Gondo,
  • Oluwatoyin Dare Kolawole,
  • Chiedza Ngonidzashe Mutanga

摘要

The loss of forests to suburbanisation continues to present a challenge to environmentalists. Lately, the analysis of remotely acquired data in Geographical Information Systems (GIS) for monitoring the changes in vegetation cover has proved efficacious. The most practical technique in this context is the normalised difference vegetation index (NDVI) and its correlation with land surface temperature (LST). Utilising remote sensing and GIS technologies in combination with a random forest classification algorithm, the study detected the spatiotemporal variations in forest cover for the period 1992–2022 in Maun. Multi-temporal Landsat Thematic Mapper and Landsat Operational Land Imager data were utilised to understand human impact on vegetation. The NDVI and LST were applied to quantify the impact of anthropological drivers affecting forest cover. The LST and NDVI were calculated based on the dynamics in land use and land cover and a strong correlation was observed between LST and NDVI for built-up, dense/thick forest, medium forest, light forest, and water bodies. Findings showed an inverse relationship between NDVI and LST in both densely built-up and densely vegetated areas. Thus, NDVI is low in densely built-up areas and LST is high in the same. NDVI and LST are high and low in densely vegetated areas, respectively.