Abstract The study introduces an automated approach for extracting water bodies from satellite images using the Faster R-CNN algorithm. was tested on two datasets consisting of body collected Sentinel-2 and Landsat-8 (OLI) images, totaling over 3500 images. results showed that proposed achieved accuracy 98.7% 96.1% datasets, respectively. This is significantly higher than by convolutional neura...

We assessed vegetation cover density and distribution in Eastern Jordanian rangeland reserves (2411 km2) using a series of Landsat images (34 year’s period) ground survey (2020) to identify proper sites for reintroducing Arabian Oryx (Oryx leucoryx). The natural were Burqu Nature Reserve site-1, site-2 (Dmetheh), Al Dahek Reserve. Satellite sensor data from Landsat-8-OLI Landsat- 5-TM used deri...

Background and Purpose: Rapid assessments of forest damage caused by natural disasters such as ice-break, wind, flooding, hurricane, or forest fires are necessary for mitigation and forest management. Forest damage directly impacts carbon uptake and biogeochemical cycles, and thus, has an impact on climate change. It intensifies erosion and flooding, and influences socio-economic well-being of ...

The modernization of computational resources and application artificial intelligence algorithms have led to advancements in studies regarding the evapotranspiration crops by remote sensing. Therefore, this research proposed machine learning estimate ETrF (Evapotranspiration Fraction) sugar can crop using METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model ...