High mortality rates and lack of recruitment in the acacia populations throughout the Negev Desert and the Arava rift valley of Israel have been reported in previous studies. However, it is difficult to determine whether these reports can be evidence to a significant decline trend of the trees populations. This is because of the slow dynamic processes of acaia tree populations and the lack of long term continuous monitoring data. We suggest a new data analysis technique that expands the time scope of the field long term monitoring of trees in arid environments. This will enables us to improve our understanding of the spatial and temporal changes of these populations. <br><br> We implemented two different approaches in order to expand the time scope of the acacia population field survey: (1) individual based tree change detection using Corona satellite images and (2) spatial analysis of trees population, converting spatial data into temporal data. The next step was to integrate the results of the two analysis techniques (change detection and spatial analysis) with field monitoring. This technique can be implemented to other tree populations in arid environments to help assess the vegetation conditions and dynamics of those ecosystems.
Multi-Image and Multi-Sensor Change Detection for Long-Term Monitoring of Arid Environments With Landsat Series
