1.Remote Sensing and Geospatial Technologies for Land Use Cover Changes (LUCC) and Monitoring
In the literature, Multiple studies have employed remote sensing and geospatial techniques for LUCC monitoring and within this special issue, an array of articles highlights these aspects. For example, Hassan et al. (2020) employed satellite images from SENTINEL-2A, RAPIDEYE, WORLD VIEW-2, multi-date UAV, and a digital elevation model from the Shuttle Radar Topographic Mission to assess forest cover degradation in the Cox’s Bazar area, Bangladesh, related to Rohingya emigration. They used a supervised classification method for multi-date land-use/cover data and dynamic modeling with the Markov chain and cellular automata technique to predict forest-cover loss, highlighting the role of various satellite data in assessing spatiotemporal changes. Gilani et al. (2020) utilized MODIS 1km data to comprehensively determine soil erosion dynamics in Pakistan. Employing a Revised Universal Soil Loss Equation (RUSLE) and MODIS data, they evaluated soil erosion patterns between 2005 and 2015, enabling the identification of soil erosion classes and transitions, and underlining the contribution of remote sensing in understanding soil erosion dynamics. Further, Tran et al. (2021) proposed a method involving spatial analysis approaches and enhanced vegetation index (EVI) data from LANDSAT via Google Earth Engine to estimate vegetation dynamics. Their study showcased the applicability of EVI data for monitoring spatiotemporal changes in vegetation coverage, highlighting the role of remote sensing in assessing ecological health. A study by Williams et al. (2021) focused on mapping smallholder forest plantations in India using multitemporal visible and near-infrared (VNIR) bands from Sentinel-2 multispectral instruments. They demonstrated the effectiveness of Sentinel-2 VNIR bands and multitemporal data for accurately distinguishing forest plantations from natural forests, showcasing the potential of these remote sensing techniques. In addition, Kumar et al. (2021) utilized very high-resolution (VHR) Indian Remote Sensing (IRS) satellite data, specifically CARTOSAT-1 panchromatic and multispectral LISS-IV datasets, to quantify Trees outside Forest (ToF) in Haryana State. Their innovative classification scheme and VHR satellite data played a pivotal role in accurately quantifying ToF, illustrating the value of remote sensing in assessing complex landscapes. These studies collectively highlight how specific satellite data, such as MODIS, SENTINEL-2A, RAPIDEYE, WORLD VIEW-2, LANDSAT, and VHR IRS data, coupled with various geospatial techniques, play a critical role in monitoring and analyzing diverse environmental aspects.