Remote sensing technologies and machine learning (ML) algorithms play an increasingly
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …

Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single
system and has gained widespread acceptance as a non-destructive scientific instrument for …

In the last few decades, researchers have developed a plethora of hyperspectral Earth
Observation (EO) remote sensing techniques, analysis and applications. While …

Indigenous peoples (IPs) worldwide are confronted by the increasing threat of pollution.
Based on a comprehensive review of the literature (n= 686 studies), we present the current …

Natural disturbances induced by insect outbreaks have increased in forest ecosystems over
the past decades. To minimize economic loss and prevent a mass outbreak, early detection …

This review outlines the advances achieved in monitoring natural and anthropogenic plant
stressors by hyperspectral remote sensing over the last 50 years. A broad diversity of …

The surface soil and vegetation had been destroyed by opencast mining, leading the
decrease of the vegetation fractional coverage, which caused a severe damage to the …

In recent years, the hyperspectral image (HSI) classification based on generative adversarial
networks (GANs) has achieved great progress. GAN-based classification methods can …

Oil pollution harms terrestrial ecosystems. There is an urgent requirement to improve on
existing methods for detecting, map** and establishing the precise extent of oil-impacted …

Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and
environmental dimensions of the coastal ecosystem. However, comprehensive assessment …