Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review

TL;DR

This review discusses current optical remote sensing methods for mapping forest disturbance and degradation, emphasizing recent advances with Sentinel-2a and Landsat data, and highlights future research directions.

Contribution

It provides a comprehensive overview of available sensors and approaches, compares classical change detection with time series analysis, and discusses recent studies across different ecosystems.

Findings

Time series analysis is the most promising approach for degradation mapping.

Four classification methods for time series are distinguished and analyzed.

Future work should focus on high-resolution data integration and real-time disturbance detection.

Abstract

Purpose of review: This paper presents a review of the current state of the art in remote sensing based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing approaches: classical image-to-image change detection and time series analysis. Recent findings: With the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has become the most promising but also most demanding category of degradation mapping approaches. Four time series classification methods are distinguished. The methods are explained and their benefits and drawbacks are discussed. A separate chapter presents a number of recent forest degradation mapping studies for two different ecosystems: temperate forests with a geographical focus on Europe and tropical forests with a geographical focus on Africa. Summary: The review revealed that a wide variety of methods for the detection of forest degradation is already available. Today, the main challenge is to transfer these approaches to high resolution time series data from multiple sensors. Future research should also focus on the classification of disturbance types and the development of robust up-scalable methods to enable near real time disturbance mapping in support of operational reactive measures.