The appetite for up-to-date information about earth's surface is ever increasing, as such
information provides a base for a large number of applications, including local, regional and …

Unique among Earth observation programs, the Landsat program has provided continuous
earth observation data for the past 41 years. Landsat data are systematically collected and …

This study aims to develop a deep learning based classification framework for remotely
sensed time series. The experiment was carried out in Yolo County, California, which has a …

New remote sensing sensors will acquire High spectral, spatial and temporal Resolution
Satellite Image Time Series (HR-SITS). These new data are of great interest to map land …

A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover
using all available Landsat data is developed. It is capable of detecting many kinds of land …

Urban greening is increasingly viewed by cities worldwide as an effective measure to
mitigate adverse impacts of urban heat island (UHI) effect. Despite the recognition of the …

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has …

Timely and accurate change detection of Earth's surface features is extremely important for
understanding relationships and interactions between human and natural phenomena in …

This book provides a comprehensive discussion on urban growth and sprawl, and how they
can be analyzed using remote sensing imageries. It compiles views of numerous …

Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have
demonstrated potential as a means to detect, identify, map and monitor ecosystem changes …