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Habitat condition is a vital ecological attribute in wildlife conservation and management in protected areas, including the Burunge wildlife management areas in Tanzania. Traditional techniques, including satellite remote sensing and ground-based techniques used to assess habitat condition, have limitations in terms of costs and low resolution of satellite platforms. The Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) have potential for assessing habitat condition, e.g., forage quantity and quality, vegetation cover and degradation, soil erosion and salinization, fire, and pollution of vegetation cover. We, therefore, examined how the recently emerged Unmanned Aerial Vehicle (UAV) platform and the traditional Sentinel-2 differs in indications of habitat condition using NDVI and GNDVI. We assigned 13 survey plots to random locations in the major land cover types: three survey plots in grasslands, shrublands, and woodlands, and two in riverine and mosaics cover types. We used a UAV-mounted, multi-spectral sensor and obtained Sentinel-2 imagery between February and March 2020. We categorized NDVI and GNDVI values into habitat condition classes (very good, good, poor, and very poor). We analyzed data using descriptive statistics and linear regression model in R-software. The results revealed higher sensitivity and ability of UAV to provide the necessary preliminary diagnostic indications of habitat condition. The UAV-based NDVI and GNDVI maps showed more details of all classes of habitat conditions than the Sentinel-2 maps. The linear regressions results showed strong positive correlations between the two platforms (p < 0.001). The differences were attributed primarily to spatial resolution and minor atmospheric effects. We recommend further studies to test other vegetation indices.
Mangewa et al. (Tue,) studied this question.