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.