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Pfeifer M, Boyle MJ, Dunning S, Olivier P.2018. Forest floor temperature and greenness link significantly to canopy attributes in South Africa’s fragmented coastal forests. PeerJ Preprints6:e27168v1https://doi.org/10.7287/peerj.preprints.27168v1
Tropical landscapes are changing rapidly due to changes in land use and land management. Being able to predict and monitor land use change impacts on species for conservation or food security concerns requires the use of habitat quality metrics, that are consistent, can be mapped using above - ground sensor data and are relevant for species performance. Here, we focus on ground surface temperature (Thermalground) and ground vegetation greenness (NDVIdown) as potentially suitable metrics of habitat quality. We measure both across habitats differing in tree cover (natural grassland to forest edges to forests and tree plantations) in the human-modified coastal forested landscapes of Kwa-Zulua Natal, South Africa. We show that both habitat quality metrics decline linearly as a function of increasing canopy closure (FCover, %) and canopy leaf area index (LAI). Opening canopies by about 20% or reducing canopy leaf area by 1% would result in an increase of temperatures on the ground by more than 1°C, and an increase in ground vegetation greenness by 0.2 and 0.14 respectively. Upscaling LAI and FCover to develop maps from Landsat imagery using random forest models allowed us to map Thermalground and NDVIdown using the linear relationships. However, map accuracy was constrained by the predictive capacity of the random forest models predicting canopy attributes and the linear models linking canopy attributes to the habitat quality metrics. Accounting for micro-scale variation in temperature is seen as essential to improve biodiversity impact predictions. Our upscaling approach suggests that mapping ground surface temperature based on radiation and vegetation properties might be possible, and that canopy cover maps could provide a useful tool for mapping habitat quality metrics that matter to species. However, we need to increase sampling of surface temperature spatially and temporally to improve and validate upscaled models. We also need to link surface temperature maps to demographic traits of species of different threat status or functions in landscapes with different disturbance and management histories testing for generalities in relationships. The derived understanding could then be exploited for targeted landscape restoration that benefits biodiversity conservation and food security sustainably at the landscape scale.
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Canopy structure data acquired at the level of plots