Christian Ginzler

Accurate and comprehensive tree cover mapping plays a vital role in forest management and ecosystem assessment. However, the lack of spectral information and the low quality of available black and white (B&W) imagery makes it difficult to retroactively predict an historical tree cover map using common methods. Here, a network structure called B&WTreeNet, based on semantic segmentation, is proposed to make full use of the limited labeled training remote sensing data to obtain tree cover information from historical datasets. The proposed B&WTreeNet is capable of cross-temporal semantic segmentation, including various data augmentation methods, to address inconsistent tree cover features caused by variations in image quality. The luminance enhancer and other modules in B&WTreeNet can extract the characteristics of tree cover effectively, successfully compensating for the limited spectral information in B&W image datasets. The countrywide historical tree cover map in Switzerland generated for the 1980s using a limited training dataset from 2018 and 2019 agrees well with manual interpretation results.

Information regarding the spatial arrangement and extent of past habitats is important for understanding present biodiversity, restoration potential, and fighting extinction-debt effects. European landscapes have changed profoundly over recent decades, with the trend accelerating following World War 2. We develop a proof of concept for mapping historic habitat distribution for Switzerland from black and white aerial imagery compatible with the present-day habitat map. Recently available orthorectified 1946 aerial imagery (1 m resolution) was segmented based on spectral and shape characteristics for training areas (320–508 km²) representing the main biogeographical regions of Switzerland. Initial training data was derived by manual aerial orthoimage interpretation differentiating 15 habitat classes. A random forest model was trained to classify the segments using variables describing spectral information, image texture, segment shape, topography, climate, and anthropogenic influence. Classification accuracy was improved with additional training samples derived in a stepwise approach, applying three different sampling techniques. Highest class accuracies (producer's and user's accuracies ≥ 0.75) were achieved for the habitats ‘Standing water’, ‘Flowing water’, ‘Glaciers, permanent ice and snow’, and ‘Forests and other wooded land’. Particularly low user's accuracies were found for ‘Wetlands’, ‘Hedges and tree rows’ and ‘Buildings’. The comparison to independent data further revealed minor differences in overall accuracy for the three different sampling strategies. Yet, map predictions sometimes varied substantially, indicating that the sampling strategies address different classification issues. Hence, we conclude that combining different sampling strategies for training data collection has the potential to improve the mapping, particularly in the case of multi-class classifications.

Close-range remote sensing technologies show great potential to support national forest inventories (NFIs). Mobile laser scanning (MLS) has performed especially well in recent studies, as it is capable of fast data acquisition while still delivering accurate information on NFI-relevant variables (e.g. tree position, diameter at breast height [DBH]). However, the performance of MLS acquisition under challenging conditions (e.g. steep terrain, dense understorey vegetation) has rarely been addressed. Here, on 29 NFI plots distributed over the entire country of Switzerland and representing various terrain and forest conditions, we evaluated the use and performance of handheld MLS acquisition, using a GeoSLAM ZebHorizon laser scanner, to deliver analysis-ready (with minimal occlusion) point clouds and to estimate primary tree variables such as tree position and DBH. Data acquisition was successful for all tested Swiss NFI plots. The average time needed for point cloud acquisition of a 50 × 50 m sample plot was 13.13 min. Over all tested NFI plots, we achieved a tree detection rate of 82.6%, with a relative root mean square error (RMSE) of 7.25%, a mean absolute error of 2.0 cm and a systematic prediction error (SPE) of 1.0 cm for DBH estimation. The terrain gradient had an especially strong influence on the accuracy of DBH estimation. Occlusion, as a metric for point cloud capacity to hold forest structural information for the entire canopy volume, was minimal when the suggested acquisition pattern was followed, at 9% on average for the entire canopy volume. However, the level of occlusion increased in the upper part of the canopy (> 10 m above ground), to 15% on average.
Overall, handheld MLS effectively produced analysis-ready point clouds within the 15-minute acquisition time limitation for a 50 x 50 m area, even under challenging conditions, showing promise for future use in the Swiss NFI. However, the current accuracy of MLS-estimated forest variables like DBH and tree detection is insufficient to replace conventional measurements within an established NFI. Despite this, estimating NFI-relevant variables, even with lower accuracy, and expanding the area of measured tree variables could improve information on forest functions and services (e.g., protection and biodiversity), currently not possible to derive from NFI data due to the limited plot size. In the future, MLS-acquired point clouds could (1) shift from expert-assessed to more quantitative and reproducible variable estimation, and (2) enhance NFI with variables difficult to measure conventionally. This could open new pathways for assessing and monitoring forest structural diversity nationally and provide valuable calibration and validation data for large-scale forest assessments from airborne or space-borne acquisitions.

Forest edges represent the transition zone (ecotone) between the forest interior and the surrounding open land. Due to their great ecological importance, the value of assessing the structure of forest edges has been recognized. In Switzerland, for example, forest edge structure is assessed during field surveys of the Swiss National Forest Inventory (NFI). However, these assessments are time consuming and limited to sample plots. Publicly available countrywide airborne laser scanning (ALS) data, in contrast, offers possibilities to retrieve forest edge structure information over large spatial extents. In this study, we derived five metrics from ALS point clouds, namely the canopy height variability; ratios of the areas of the three edge components, i.e. shrub belt, shelterbelt and forest layer; the sky-view fraction; the shelterbelt slope; and the front density of the forest edge. These metrics describe the three-dimensional edge structure and therefore could enhance existing NFI edge metrics, which focus on two-dimensional structure characteristics. An expert assessment of the ALS edge metrics demonstrated the ability of the defined set of edge metrics to capture ecologically relevant and indicative characteristics of forest edges. Understanding this relationship between edge metrics and ecological functions is a prerequisite if ALS metrics are to be integrated into NFIs. We subsequently used the ALS edge metrics to group 284 forest edge into three classes with respect to their structural complexity using k-means clustering. The results indicated that the structual complexity was low for 173, medium for 46 and high for 65 forest edges, respectively. Applied to countrywide ALS data sets, our approach allows to retrieve area-wide, spatially continuous information on the forest edge conditions, and, if multitemporal ALS data is available, to monitor the development of the forest edges.

Forests are under pressure and going through rapid changes. However, current inventorying and monitoring (IM) programs are often either disjointed, too narrow in their scope and/or do not operate at fine enough temporal resolutions, which may hinder scientific understanding, the timely supply of information, fast decision making, and may result in the sub-optimal use of resources. For these reasons, there is an urgent need for Advanced Forest Inventorying and Monitoring (AIM) programs to (i) achieve expanded relevance (by augmenting data/information across ecosystem properties and trophic levels), (ii) have increased temporal resolution (by tailored data collection frequency), and (iii) make use of technological advances (by incorporating novel tools and technologies). The Advanced Inventorying and Monitoring for Swiss Forests (SwissAIM) initiative was launched in 2020 to address these needs. SwissAIM builds upon the foundation offered by the existing programs (e.g., national forest inventory, long-term forest ecosystem research, biodiversity monitoring). It aims to offer a collaborative and adaptive framework to enable integrated data collection, evaluation, interpretation, analysis, and modeling. Ideally, it will result in a more responsive system with respect to current and predicted biotic/abiotic stressors that will challenge Swiss forests. Developing such a system implies identifying the information needs of different stakeholders (e.g., science, policy, practice), related technical requirements, and governance frameworks. Here, we present (i) the main features of the SwissAIM initiative (vision, scientific questions and variables, governance and engagement), (ii) the main outcomes of the participatory design process (measurements, sampling, and plot design), (iii) the potential transferability of AIM initiatives outside Switzerland (timing, relevance, practicability), and (iv) the key messages that emerged (i.e., need for advancement, integration and transdisciplinarity, statistical underpinning). Since similar needs related to forest inventorying and monitoring are emerging throughout Europe and elsewhere, the objective of this opinion paper is to share our experience and promote a dialog with those interested in developing AIM initiatives in other countries and regions.

Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies.

Europe's semi-natural grasslands support notably high levels of temperate biodiversity across multiple taxonomic groups. However, these ecosystems face unique conservation challenges. Contemporary agricultural practices have replaced historical traditional low-intensity agriculture in many regions, resulting in a spectrum of management intensities within these ecosystems, ranging from highly intensive methods to complete abandonment. Paradoxically, both extremes along this spectrum of management intensity can be detrimental to biodiversity of semi-natural grasslands. Moreover, while anthropogenic climate change is an overarching threat to these ecosystems, rapid changes in land use and its intensity often present more immediate pressures. Often occurring at a faster rate than climate change itself, these land-use changes have the potential to rapidly impact the biodiversity of these grasslands. Here, we divide the ecological processes, threats, and developments to semi-natural grasslands into three sections. First, we examine the different impacts of agricultural intensification and abandonment on these ecosystems, considering their different consequences for biodiversity. Second, we review seminal works on various evidence-based management practices and offer a concise summary that provides support for various conservation and management strategies. However, the socio-economic factors that drive both abandonment and intensification in semi-natural grasslands can also be used to develop solutions through strategic governmental and non-governmental interventions. Accordingly, we conclude with a way forward by providing several key policy recommendations. By synthesizing existing knowledge and identifying research gaps, this essay aims to provide valuable insights for advancing the sustainable management of semi-natural grasslands.

Copernicus ist das europäische Erdbeobachtungsprogramm und liefert wertvolle Informationen für das Land Monitoring, auch für die Schweiz. Das bekannteste Produkt ist CORINE Land Cover, ein über mehrere Jahrzehnte konsistentes Inventar von 44 Bodenbedeckungs- und Bodennutzungsklassen für ganz Europa. Koordiniert durch die Europäische Umweltagentur wird eine Vielzahl weiterer Produkte angeboten. Dieser Artikel gibt einen Überblick über die verfügbaren Produkte und ihre Anwendungsmöglichkeiten. Es werden laufende Entwicklungen, geplante Produkte und neue Möglichkeiten vorgestellt.

Copernicus est le programme européen d'observation terrestre et fournit des informations précieuses pour le monitoring terrestre, également pour la Suisse. Le produit le plus connu est CORINE Land Cover, un inventaire consistant depuis plusieurs décennies de 44 classes de couvertures et d'utilisations du sol pour toute l'Europe. Coordonnée par l'Agence européenne de l'environnement une multitude d'autres produits est offerte. Cet article donne un aperçu des produits disponibles et de leurs possibilités d'application. Des développements en cours, des produits planifiés et des nouvelles possibilités y sont présentés.

Copernico è il programma europeo di osservazione della Terra e fornisce preziose informazioni per il monitoraggio del territorio, anche per la Svizzera. Il prodotto più noto è CORINE Land Cover, un copioso inventario raccolto durante diversi decenni, con 44 classi copertura e utilizzo del suolo in tutt'Europa. L'Agenzia europea dell'ambiente coordina anche l'offerta di tutta una serie di altri prodotti. Quest'articolo fornisce uno spaccato dei prodotti disponibili e delle loro possibilità di applicazione. In aggiunta, si presenta una carrellata sugli sviluppi attuali, i prodotti progettati e le nuove possibilità.

Land-use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information on grassland-use intensity is difficult to acquire and restricted to the local or regional level. Recent studies have shown that mowing events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially to mountain areas, has been little explored, however, and the relevance for ecological applications in biodiversity and conservation has hardly been investigated. Here, we used a rule-based algorithm to produce annual maps for 2018–2021 of grassland-management events, that is, mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite data. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further examined the relationships between the generated grassland-use intensity measures and plant species richness and ecological indicator values derived from a nationwide field survey. The webcam-based verification for 2020 and 2021 revealed that most detected management events were actual mowing/grazing events (≥78%), but that a substantial number of events were not detected (up to 57%), particularly grazing events at higher elevations. We found lower plant species richness and higher mean ecological indicator values for nutrients and mowing tolerance with more frequent management events and those starting earlier in the year. A large proportion of the variance was explained by our use-intensity measures. Our findings therefore highlight that remotely assessed management events can characterise land-use intensity at fine spatial and temporal resolutions across broad scales and can explain plant biodiversity patterns in grasslands.

Die Intensität der Landnutzung hat einen grossen Einfluss auf die Biodiversität unseres Grünlands. Es ist jedoch aufwändig, sie flächendeckend und detailliert zu erfassen. Wir haben die Nutzungsintensität in der ganzen Schweiz mit Satellitenbildern über mehrere Jahre kartiert. Die frei verfügbaren Karten des Schweizer Grünlands haben ein grosses Potenzial für verschiedene ökologische Anwendungen und die Biodiversitätsforschung.

L'intensité d'exploitation du sol a de grands effets sur la biodiversité de nos surfaces herbagères. Toutefois, recenser cette intensité sur tout le territoire et de manière détaillée nécessite des moyens. Grâce à des images satellite, nous l'avons cartographiée sur plusieurs années et dans toute la Suisse. Les cartes, en libre accès, recèlent un grand potentiel pour différentes applications écologiques et pour la recherche dans le domaine de la biodiversité.

Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate change impacts on biodiversity and ecosystem functioning. Here we combine a network of microclimate temperature measurements across different habitats and vertical heights with a novel radiative transfer model to map daily temperatures during the vegetation period at 10 m spatial resolution across Switzerland. Our results reveal strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 ^∘C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 ^∘C, respectively. We also found that, in open grasslands, maximum temperatures at 5 cm above ground are, on average, 3.4 ^∘C warmer than those of the macroclimate, suggesting that, in such habitats, heat exposure close to the ground is often underestimated when using macroclimatic data. Spatial interpolation was achieved by using a hybrid approach based on linear mixed-effect models with input from detailed radiation estimates from radiative transfer models that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography, and vegetation height. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by root mean squared errors ranging from 1.18 to 3.43 ^∘C, with minimum temperatures being predicted more accurately overall than maximum temperatures. The microclimate-mapping methodology presented here enables a biologically relevant perspective when analysing climate–species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split-sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert-based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat-type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient-poor to nutrient-rich grasslands, which is highly important for biodiversity conservation.

Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-m ground sampling distance for the years 2017–2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 m². Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.

Green spaces and natural environments are essential for quality of life, as they influence health behaviours and outcomes positively. The quantification of "greenness" observable from the human perspective is of great interest, especially in urban environments where built-up areas and infill are expanding. In this paper, we present a method based on airborne laser scanning (ALS) point clouds that makes it possible to simulate a space observed from the human perspective and to quantify the surrounding vegetation. The method can be applied over various spatial scales, in urban, suburban and rural regions. We employed this new method to analyse a set of specific locations in Switzerland that are important for people to recover from everyday stress. The greenness quantification can be used to compare the perceived restorative quality of landscape characteristics with physical landscape qualities. Our approach provides a viable methodological solution for spatial planning and large-scale socio-ecological studies on the influence of natural and green spaces on health and wellbeing, and we recommend that it be applied in the natural landscape and in urban areas.

Grünflächen und naturnahe Flächen sind für die Lebensqualität von wesentlicher Bedeutung, da sie sich positiv auf die Gesundheit und das Wohlbefinden auswirken. In der Schweiz leben heute ungefähr drei Viertel der Einwohner in Städten und Agglomerationen, daher ist die Qualität und Zugänglichkeit von Grün- und Naturräumen (GNR) von grossem Interesse. In dieser Studie haben wir Orte analysiert, an denen sich die Schweizer Bevölkerung vom Stress im Alltag erholt. Das ganzheitliche GIS-basierte Konzept zur Beschreibung dieser Orte folgt einem dreistufigen Top-Down-Ansatz und bezieht sich auf verschiedene Massstäbe, Aspekte und Details der Beschreibung. Die vorliegende Studie befasst sich ausschliesslich mit der detailliertesten Ebene. Auf der Grundlage von Airborne Laserscanning (ALS)-Punktwolken haben wir eine Methode entwickelt, um den Raum aus der menschlichen Perspektive zu simulieren und die sichtbare nahegelegene Vegetation zu quantifizieren. Dieser Beitrag gibt einen Überblick über die entwickelte Methode und eine Erläuterung der Interpretation der Ergebnisse und zeigt Anwendungsmöglichkeiten auf.

Green spaces and natural environments are essential for quality of life as they have a positive impact on health and well-being. In Switzerland, about three quarters of the population now live in cities and agglomerations, so the quality and accessibility of green and natural spaces (GNS) is of great interest there. In this study, we analysed places where the Swiss population recovers from stress in everyday life. The holistic GIS-based concept for describing these places follows a three-level top-down approach and refers to different scales, aspects and details of the description. The present study deals exclusively with the most detailed level. Based on airborne laser scanning (ALS) point clouds, we have developed an effective method to simulate the space from a human perspective and quantify the visible nearby vegetation. This paper gives an overview of the developed method and a detailed explanation of the interpretation of the results, and shows its possible applications.

Die Veränderungen der klimatischen Bedingungen sind auch zunehmend in den Waldökosystemen der Schweiz sichtbar. Dies nicht nur in Hinblick auf die langfristigen Auswirkungen auf den Waldzustand durch die zunehmenden Temperaturen und die sich regional verändernden Wasserverfügbarkeiten, sondern auch in Bezug auf das erhöhte Risiko von unmittelbaren Waldschäden durch biotische und abiotische Faktoren. Mit ExoSilva, einem satellitengestützten Ansatz, der zusammen mit der ETHZ, der WSL und dem UZH Spin-off ExoLabs entwickelt wurde, können sowohl langfristige Trends als auch kurzfristige Veränderungen des Waldzustandes in der Schweiz erfasst werden. Dabei ermöglicht, neben der Erfassung des Waldzustandes in einer hohen räumlichen Auflösung von 20 × 20 m Pixelgrösse und einer wöchentlichen Aktualisierung, vor allem der pixel-basierte Abgleich mit der langjährigen Dynamik neue Ansätze für das Waldmonitoring. In diesem Beitrag wird die Datenaufbereitung, die Evaluierung der Ergebnisse sowie das Anwendungspotenzial der entwickelten Methodik präsentiert.

Changes in climatic conditions are increasingly visible in Switzerland’s forest ecosystems. This is the case not only regarding long-term effects on the condition of the forests caused by increasing temperatures and regionally changing water availability, but also with respect to the increased risk of immediate forest damage due to biotic and abiotic factors. ExoSilva, a satellite-based approach developed collaboratively by ETHZ, WSL and the UZH spin-off ExoLabs, can be used to record both long-term trends and short-term changes in the condition of forests in Switzerland. In addition to recording the forest condition at a high spatial resolution of 20 m × 20 m pixel size and being updated weekly, the pixel-based comparison with the long-term dynamics enables new approaches to forest monitoring. In this paper we present the data preparation, the evaluation of the results, and the application potential of the developed methodology.

The launch of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission in 2018 opens new opportunities to quantitatively describe forest ecosystems across large scales. While GEDI's height-related metrics have already been extensively evaluated, the utility of GEDI for assessing the full spectrum of structural variability - particularly in topographically complex terrain - remains incompletely understood. Here, we quantified GEDI's potential to estimate forest structure in mountain landscapes at the plot and landscape level, with a focus on variables of high relevance in ecological applications. We compared five GEDI metrics including relative height percentiles, plant area index, cover and understory cover to airborne laser scanning (ALS) data in two contrasting mountain landscapes in the European Alps. At the plot level, we investigated the impact of leaf phenology and topography on GEDI's accuracy. At the landscape-scale, we evaluated the ability of GEDIs sample-based approach to characterize complex mountain landscapes by comparing it to wall-to-wall ALS estimates and evaluated the capacity of GEDI to quantify important indicators of ecosystem functions and services (i.e., avalanche protection, habitat provision, carbon storage). Our results revealed only weak to moderate agreement between GEDI and ALS at the plot level (R² from 0.03 to 0.61), with GEDI uncertainties increasing with slope. At the landscape-level, however, the agreement between GEDI and ALS was generally high, with R² values ranging between 0.51 and 0.79. Both GEDI and ALS agreed in identifying areas of high avalanche protection, habitat provision, and carbon storage, highlighting the potential of GEDI for landscape-scale analyses in the context of ecosystem dynamics and management.