Remote sensing images are suitable for quantifying and analyzing land-cover dynamics,
particularly for forest-cover change. In this study, the methodology used the supervised …

Remote sensing is an effective way in monitoring desertification dynamics in arid and semi-
arid regions. In this study, we used a decision tree method based on NDVI (normalized …

Spatial data mining discovers patterns and knowledge in spatial data. The geospatial data
analysis plays a decisive role in framing essential policies related to the environment at the …

The links between climate change, environmental degradation, and conflict are highly
complex and poorly understood as yet. Resources always constitute risks of conflict in …

Reference to geographic scale and geographic space representation are characteristics of
geospatial data. This work has discussed two issues related to satellite image data, namely …

Classification of remote sensing images faces several challenges due to mixed pixels. Such
pixels that are wrongly classified are called mixed pixels. There is uncertainty about the …

Remote sensing has been widely used in monitoring vegetation and in detecting agricultural
droughts. The most commonly used data for this purpose is the coarse spatial and the high …

The emerging threat for eco-sustainability has led to a breakthrough in satellite image
analyses and such instantaneous monitoring of hazards could replenish the rejuvenation of …

Lack of labelled class information in data, huge data size and spatial autocorrelation pose
challenge in the classification of remote sensing images. The concepts of clustering …

Geospatial applications have invaded most web-and IT-based services, adding value to
information-based solutions. But there are many challenges associated with the analysis of …