Forest floor temperature and greenness link significantly to canopy attributes in South Africa’s fragmented coastal forests
- Published
- Accepted
- Subject Areas
- Ecosystem Science, Climate Change Biology, Forestry, Spatial and Geographic Information Science
- Keywords
- coastal forests, forest edges, Eucalyptus plantations, ground surface temperature, NDVI, habitat microclimate, fragmented landscapes, South Africa, remote sensing, thermal mapping
- Copyright
- © 2018 Pfeifer et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Forest floor temperature and greenness link significantly to canopy attributes in South Africa’s fragmented coastal forests. PeerJ Preprints 6:e27168v1 https://doi.org/10.7287/peerj.preprints.27168v1
Abstract
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.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Canopy structure data acquired at the level of plots
Sensor data acquired overall during the fieldwork
Data acquired at the level of sample points: NDVI and Thermal data
R code for reading all datasets and implementing the analyses
Permission from Figure Subject
Note: Pieter Olivier, who features on these images, is co-author on the manuscript and has contributed to the development of both Figures 1 and 6 and has approved the manuscript.