An artificial intelligent framework for prediction of wildlife vehicle collision hotspots based on geographic information systems and multispectral imagery

Prediction of Sites with a High Probability of Wild Mammal Roadkill Using a Favourability Function

“…Using the favourability function, models were obtained that provided high predictive and explanatory power according to the applied evaluations. There are research studies that used various mathematical algorithms (e.g., generalized linear models, Random Forest, Support Vector Machine, Habitat Suitability) to explain and predict the effect of roadkill on wildlife [18,21,24,[46][47][48][49][50][51][52]. However, there is no research where the favourability function was applied to predict and explain the roadkill effect; moreover, it has usually been used as a tool for the identification of favourable habitats for biodiversity [23,27,50].…”

Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe

“…This is because changes in forest carbon stocks influence the atmospheric carbon dioxide (CO 2 ) concentration (Millington & Townsend, 1989;Fan et al, 2022). Quantifying the uncertainty associated with forest carbon stock estimation and prediction can be enhanced by the inclusion of historical information such as scales of spatial variability known for characterising carbon stock dynamics in particular forest ecosystems (Do et al, 2022;González-Vélez et al, 2021). Surveys involving the collection of biomass data from forest plantations are time consuming and expensive.…”

A Bayesian geo-statistical approach for plantation forest productivity assessment after the fast-track land reform in Zimbabwe.

“…This is because dynamics in forest carbon stocks influence the atmospheric carbon dioxide (CO2) concentration (Millington & Townsend, 1989;Fan, Wang and Yang, 2022). Quantifying the uncertainty associated with forest carbon stock estimation and prediction can be enhanced by the inclusion of historical information such as scales of spatial variability known for characterising carbon stock dynamics in particular forest ecosystems (Do et al, 2022;González-Vélez et al, 2021).…”

“…Framework Convention on Climate Change (UNFCCC) have recognised and stressed the importance of C sinks and the subsequent need for monitoring, preserving and expanding global C sinks (Millington, A.;Townsend, 1989). As such, quantification of uncertainties associated with C stock prediction and estimation can greatly be improved through the incorporation of pre-experimental or historical data known for influencing forest biomass dynamics in specific forest ecosystems (González-Vélez et al, 2021;Do et al, 2022).…”