An automated ice-mapping algorithm has been developed and evaluated using data from the GOES-13 imager. The approach includes cloud-free image compositing as well as image classification using spectral criteria. The algorithm uses an alternative snow index to the Normalized Difference Snow Index (NDSI). The GOES-13 imager does not have a 1.6 μm band, a requirement for NDSI; however, the newly p...

The accurate spatial information of snow cover is useful for understanding the impact global warming, and it high significance hydrological disaster prediction, water resources management, climate change research. Normalized Difference Snow Index (NDSI) based approach has been used extensively around world mapping snow, they displayed accuracy in open areas. However, capturing forests remains p...

This work presents a new algorithm designed to detect clouds in satellite visible and infrared (IR) imagery of ice sheets. The approach identifies possible cloud pixels through the use of the normalized difference snow index (NDSI). Possible cloud pixels are grown into regions and edges are determined. Possible cloud edges are then matched with possible cloud shadow regions using knowledge of t...

Identifying managed flooding in paddy fields is commonly used in remote sensing to detect rice. Such flooding, followed by rapid vegetation growth, is a reliable indicator to discriminate rice. Spectral indices (SIs) are often used to perform this task. However, little work has been done on determining which spectral combination in the form of Normalised Difference Spectral Indices (NDSIs) is m...

Accurate monitoring of glaciers’ extents and their dynamics is essential for improving our understanding the impacts climate environmental changes in cold regions. The satellite-based Normalized Difference Snow Index (NDSI) has been widely used mapping snow cover glaciers around globe. However, snow-covered areas using existing indices remains a challenging task due to incapabilities separating...