Large-scale Monitoring of Forest Disturbances – a Future CLMS Prototype

Forest change detection and monitoring is a key part of the Copernicus Land Monitoring Service (CLMS) (https://land.copernicus.eu/). Various methodologies already implement near real-time (NRT) forest monitoring in tropical regions (e.g. Reiche et al., 2021) with the focus on timely detection of deforestation activities. However, there is not yet an operational pan-European product tracking forest dynamics at such temporal frequency, which has moreover the capability to separate also subtle disturbances of the tree canopy from signal noise. This kind of product is under demand by the user community, hence a new CLMS prototype on “Continuous Forest Monitoring”, with the goal to capture natural and human-induced forest disturbances by detecting tree cover vitality loss on a monthly basis is tested and implemented within the Horizon Europe project Evolution of the Copernicus Land Service portfolio (EvoLand). In a second instance, the feasibility to detect disturbance agents, i.e., (i) windthrow/storm damage, (ii) wildfire, (iii) insect infestations, as well as (iv) human-induced disturbances (e.g., forest clearing, clear-cutting, and thinning activities) is tested.

Dense time series from Sentinel-2 serve as main input for both prototypes, supported by forest masks from the CLMS High Resolution Vegetated Land Cover Characteristics (HRL VLCC) and ancillary data on forest disturbance locations and agents. From a benchmarking of various tools, the Exponentially Weighted Moving Average (EWMA) – proposed by Brooks et al. (2014) for Landsat time series data and implemented as part of the JRC-NRT tool (https://github.com/ec-jrc/nrt) – yielded the most promising results, especially considering the balance between accuracy, NRT capability, and computational effort. It is an unsupervised data-driven approach using univariate input indices to detect location and timing of disturbances. A supervised classification to derive the disturbance agents is added on top.

This study describes the implementation and results of this prototype and compares the detected forest disturbance locations and dates to the radar-based Tree Cover Disturbance Monitoring (TCDM) product and the 3-yearly VLCC forest change product. Two large EvoLand European sites were chosen for a first phase implementation: one in Germany (analysis years 2019-2021) and another in Spain (analysis years 2020-2022). The evaluation is carried by disturbance agent, concluding to different effects on either the physical structure of the trees and/or the spectral signal of the canopy, and hence also on the suitability of a method of detection. Products are delivered at pixel level (10m spatial resolution), improving the 20m resolution of the currently available CLMS forest change products, while increasing the change frequency from 3-yearly or yearly to monthly.

The resulting information can be utilized to enhance forest management and planning, aid forest-related decision-making or contribute to reporting on forest-related EU policies. These two prototypes are proposed within EvoLand to enhance the CLMS forest portfolio and to meet or go beyond users' requirements and demands.