Incorporating satellite remote sensing for improving potential habitat simulation of Prosopis cineraria (L.) Druce in United Arab Emirates

Abstract

Predicting the suitable habitat of a species is one of the adequate and enhancing approach in biodiversity conservation planning and implementation. Species habitat distribution is closely linked with environmental factors and bioclimatic variables used as prediction variables in species distribution models (SDMs). Recent application of satellite remote sensing data and the bioclimatic variables has created an advanced way to improve the SDMs performance. In this study, MaxEnt was used to predict species habitat distribution. Our objectives are to assess the application of satellite remote sensing data in predicting the potential habitat suitability of the arid plant species. We have selected Prosopis cineraria (L.) Druce (Ghaf), the national tree of the UAE. We have chosen 33 environmental variables along with 90 species occurrences and for the final model simulation we have used three modeling scenarios. MaxEnt results showed that the model simulation with all key variables has substantially improved the potential habitat suitability prediction with a mean Area Under receiver operating Curve (AUC) value of 0.984, indicating a better predictive accuracy in the integration of satellite remote sensing data, edaphic variables and topographic parameters. Model results showed that the spatial proportions of the potential habitat suitability in the UAE consisted of high (2%), medium (6%) and low (9%) habitat suitability classes. The MaxEnt results revealed that precipitation of the coldest quarter (32.5%), NDVI (12.1%) and elevation (8%), had significant contribution to the potential habitat distribution of UAE Ghaf trees. Cold season precipitation is the most significant climate constraint on the habitat distribution. The most habitat suitability of Ghaf tree in the UAE was within a certain range of NDVI, LST, elevation, aspect, and precipitation patterns. This paper finding could be useful for environmental managers for implementing reforestation not only in UAE but also for the entire middle eastern regions with similar hyper arid conditions.