Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

Abstract

Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.

Advances in Vegetation Structure Modelling Using Remote Sensing to Support the Acquisition of Sustainable Development Goals through Forest Management

“…Coniferous stands of central Germany were most heavily affected by forest loss due to natural disturbances and sanitation logging. Kacic et al [10] proposed a multi-sensor approach combining the Global Ecosystem Dynamics Investigation (GEDI) mission as ground truth and Sentinel-1 and Sentinel-2 datasets to analyse changes in forest structure (cover, height, and biomass) between 2017 and 2022 at a national scale. The authors found a major decline in tree height, cover, and subsequent loss of aboveground biomass (AGB) in central and western Germany.…”

“…Technically, the contributions included in this Special Issue have used a variety of remote sensing passive (i.e., multispectral) and active (i.e., LiDAR and Synthetic Aperture Radar (SAR)) sensors mounted in terrestrial, unmanned aerial vehicles, airplanes, and satellite platforms to perform analyses at different spatial and temporal resolutions. Three contributions combined passive and active sensors for multipurpose applications serving not only to improve the accuracy of forest change [10], mushroom yield production [5], and microclimate [6] predictions, but also to provide a greater link to biological processes, including vegetation structure, or the possibility to increase temporal resolution in often cloudy environments. The remaining six contributions use only one remote sensing sensor.…”

“…Although RTMs provide broader universality and have been previously used for forest structure analysis [13,14], the contributions of this Special Issue focus on the use of empirical models. In particular, four contributions used machine learning algorithms, such as random forest [7,8,10], for classification and forest change analysis, and a support vector machine [11] for selecting edges in tree crown delineation. Furthermore, parametric models, such as linear regression, were used for forest inventory metric prediction [4,10], generalized linear models for microclimate prediction [6], and generalized additive mixed models, which combine the flexibility of generalized additive models while accounting for random effects (i.e., plot), were used for predicting yield mushrooms.…”

“…In particular, four contributions used machine learning algorithms, such as random forest [7,8,10], for classification and forest change analysis, and a support vector machine [11] for selecting edges in tree crown delineation. Furthermore, parametric models, such as linear regression, were used for forest inventory metric prediction [4,10], generalized linear models for microclimate prediction [6], and generalized additive mixed models, which combine the flexibility of generalized additive models while accounting for random effects (i.e., plot), were used for predicting yield mushrooms. The choice of different algorithms may be linked to dataset size, feasibility, or even the application, but should be parsimonious and pursue a biological meaning [12], so as to enhance sustainable forest management.…”

Forestry Applications of Space-Borne LiDAR Sensors: A Worldwide Bibliometric Analysis

“…Overall, it is mandatory to combine space-borne LiDAR data with other imaging remote sensing technologies (Sentinel-1, Sentinel-2, etc.) to obtain wall-to-wall forest height for resource management, policy development, and decision-making at regional or even nationwide studies [ 27 , 32 ]. In this way, the recent launch in August 2022 of the Terrestrial Ecosystem Carbon Monitoring Satellite (TECMS; China State Administration of Forestry and Grassland), coupled with the introduction of a new generation of space-borne active sensors such as Multi-footprint Observation LiDAR and Imager (MOLI; Japan Aerospace Exploration Agency), BIOMASS P-band Synthetic Aperture Radar (SAR) (European Space Agency), and LiDAR Surface Topography (LIST; NASA) (see Table 1 ), will enhance the array of space-based sensors available for mapping and monitoring extensive forest systems.…”