550 Geowissenschaften
Refine
Has Fulltext
- yes (108)
Is part of the Bibliography
- yes (108)
Year of publication
Document Type
- Journal article (108) (remove)
Keywords
- remote sensing (15)
- Geographie (8)
- climate change (6)
- time series (6)
- Niger (5)
- earth observation (5)
- forest (5)
- review (5)
- MODIS (4)
- Sentinel-1 (4)
- drought (4)
- machine learning (4)
- Earth Observation (3)
- Earth observation (3)
- Landsat (3)
- South Africa (3)
- dynamics (3)
- land cover (3)
- permafrost (3)
- Antarctic ice sheet (2)
- Antarctica (2)
- Geologie (2)
- Germany (2)
- Google Earth Engine (2)
- Kilombero (2)
- NDVI (2)
- SAR (2)
- Sahara (2)
- Sentinel-2 (2)
- TanDEM-X (2)
- biodiversity (2)
- change detection (2)
- deep learning (2)
- forecast (2)
- forest ecology (2)
- geomorphology (2)
- glaciers (2)
- global change (2)
- hydrology (2)
- land use (2)
- movement ecology (2)
- object-based classification (2)
- optical remote sensing (2)
- probability (2)
- random forest (2)
- satellite data (2)
- supraglacial lakes (2)
- time series analysis (2)
- wetland (2)
- 3D (1)
- 3D remote sensing (1)
- 3‐D electrical resistivity imaging (1)
- AVHRR (1)
- Aggeneys (1)
- Alps (1)
- Analyse (1)
- Angola (1)
- Animal Tracking (1)
- Antarktis (1)
- Asia (1)
- Bavaria (1)
- Bilma <Region> (1)
- Biostratigraphy (1)
- Blue Spot Analysis (1)
- Broken Hill (1)
- CORDEX Africa (1)
- Cambrian (1)
- Covid‐19 (1)
- DEM (1)
- DEUQUA (1)
- DInSAR (1)
- DSM (1)
- Dongting Lake (1)
- ERT (1)
- Einzelhandel (1)
- El Niño (1)
- Elissen-Palm flux (1)
- Erbendorf (1)
- Erholungsplanung (1)
- Europe (1)
- Extreme flows (1)
- Eyjafjallajökull 2010 (1)
- Fractional cover analysis (1)
- Fulgurite (1)
- GEDI (1)
- GPS-Tracking (1)
- GSV (1)
- Gamsberg (1)
- Ghana (1)
- GlobALS (1)
- Global Ecosystem Dynamics Investigation (1)
- Google Earth Engine (GEE) (1)
- Greenland ice sheet (1)
- Herodotus (1)
- Himalaya Karakoram (1)
- Holocene (1)
- Holozän (1)
- Hunsrueck (1)
- InSAR (1)
- InSAR height (1)
- Indus-Ganges-Brahmaputra-Meghna (1)
- Isheru (1)
- Karst (1)
- Karstverfüllungen (1)
- Kontinentales Tiefbohrprogramm der Bundesrepublik Deutschland (1)
- Kunduz River Basin (1)
- LST (1)
- Land Change Modeler (1)
- Landsat archive (1)
- Landsat time series (1)
- Lantana camara (1)
- LiDAR (1)
- MODIS time-series (1)
- Mann-Kendall test (1)
- Markov chains (1)
- Mekong (1)
- Mikrosonde (1)
- Mineralogie (1)
- Morocco (1)
- NDVI thresholds (1)
- Nachhaltigkeitstransformation (1)
- Namibia (1)
- Neolithic (1)
- Niger <Ost> (1)
- Nile delta (1)
- Nile flow (1)
- Nordvictorialand (1)
- Oman (1)
- Oshana (1)
- Ostniger (1)
- PEST (1)
- Pakistan (1)
- Paläoklima (1)
- PlanetScope (1)
- Pleistozän (1)
- R (1)
- Ramsar Convention on Wetlands (1)
- RapidEye (1)
- Reliefgeschichte (1)
- SBAS (1)
- SDG 11.3.1 (1)
- SOC content prediction (1)
- SPOT-6 (1)
- SWAT (1)
- SWAT model (1)
- Sahel (1)
- Sandstein (1)
- Scenario analysis (1)
- Schmuckperle (1)
- Sebennitic (1)
- Sentine-1 (1)
- Sentinel–1 (1)
- Silicate (1)
- Snow Line Elevation (1)
- Soil and Water Assessment Tool (SWAT) (1)
- Southeast Asia (1)
- Swabian Alb (1)
- Systematics (1)
- Sápmi (1)
- TIMELINE (1)
- Tanzania (1)
- Tell Basta (1)
- Tepl-Taus (1)
- Tian Shan (1)
- Trilobita (1)
- UAV (1)
- Uzbekistan (1)
- Vohenstrauß (1)
- WaSiM-ETH (1)
- West Africa (1)
- West Gondwana (1)
- Western Cape (1)
- Western Europe (1)
- Wilson Terrane (1)
- Wilson Terrane ; intrusions ; mafic composition ; relative age ; petrographic analysis ; gabbroic composition ; subduction zones (1)
- Zambia (1)
- accuracy (1)
- agricultural drought (1)
- agricultural mapping (1)
- agriculture (1)
- air quality (1)
- alpha diversity (1)
- ancient Egypt (1)
- anthroposphere (1)
- aquaculture (1)
- atmospheric circulation (1)
- atmospheric correction (1)
- atmospheric waves (1)
- automatic processing (1)
- base metal deposit (1)
- beech (1)
- beta diversity (1)
- big earth data (1)
- biosphere (1)
- black carbon AOD (1)
- boreholes (1)
- burned area (1)
- calc-silicate rocks; fluid behaviour; P-T path; reaction textures; Variscan basement; very high-pressure metamorphism (1)
- canopy height (1)
- causal networks (1)
- change vector analysis (1)
- circulation patterns (1)
- circulation type (1)
- circum-Arctic (1)
- class homogeneity (1)
- climate extremes (1)
- climate related trends (1)
- climate scenarios (1)
- climatic change (1)
- coal (1)
- coal fire (1)
- coal mining area (1)
- coastal erosion (1)
- coastal zone (1)
- coastline dynamics (1)
- composition (1)
- conservation (1)
- consumptive water use (1)
- convolutional neural network (1)
- crop statistics (1)
- cryosphere (1)
- culturable command area (1)
- damage assessment disaster (1)
- database (1)
- debris-covered glaciers (1)
- digitalisation initiative (1)
- disaster (1)
- distributary (1)
- diurnal (1)
- drainage ratio (1)
- drilling (1)
- driving forces (1)
- drought impact (1)
- drought stress indicators (1)
- eCognition (1)
- earthquake (1)
- electrical resistivity tomography (1)
- emissivity (1)
- energy (1)
- entrainment (1)
- environmental justice (1)
- environmental modeling (1)
- error estimation (1)
- eruption rate (1)
- evapotranspiration (1)
- explosive volcanism (1)
- e‐commerce (1)
- feature tracking (1)
- fire (1)
- flood (1)
- floodpath lake (1)
- food production (1)
- forest disturbances (1)
- forest hydrology (1)
- forest monitoring (1)
- forest resources inventory (1)
- forest structure Germany (1)
- framing (1)
- fulgurites (1)
- function (1)
- galamsey (1)
- gamma diversity (1)
- general circulation model (1)
- geoarchaeology (1)
- geomorphologie (1)
- gis (1)
- global (1)
- global warming (1)
- grassland (1)
- ground penetrating radar (1)
- groundwater (1)
- ground‐penetrating radar (1)
- harmonization (1)
- hazard maps (1)
- heat wave (1)
- historical (1)
- hotspot analysis (1)
- human disturbance (1)
- human pressure (1)
- hydrological drought (1)
- hydrological modelling (1)
- hydrological regime (1)
- ice sheet dynamics (1)
- ice sheet hydrology (1)
- image (1)
- image artifacts (1)
- impervious surface (1)
- indicator importance assessment (1)
- infrasound (1)
- integration (1)
- intercomparison (1)
- interferometry (1)
- interpolation (1)
- inundation (1)
- inverse parameterization (1)
- irrigated agriculture (1)
- irrigation (1)
- irrigation pricing (1)
- jet stream (1)
- jets (1)
- karst siliceux (1)
- land cover change (1)
- land surface (1)
- land surface temperature (1)
- land surface temperature (LST) (1)
- land use change (1)
- land use/cover pattern (LUCP) (1)
- land-use/land-cover change (1)
- landcover changes (1)
- landsat (1)
- landscape metrics (1)
- landslides (1)
- large‐scale atmospheric circulation modes (1)
- lava (1)
- letzte Meile (1)
- lightning (1)
- loess plateau (1)
- lokaler Onlinemarktplatz (1)
- loss (1)
- low-cost applications (1)
- management (1)
- mass (1)
- metamorphic sulfidation (1)
- meteorological drought (1)
- mineralization (1)
- mining (1)
- modeling (1)
- models (1)
- mountains (1)
- multi-sensor (1)
- multi-spectral (1)
- multispectral VNIR (1)
- multitemporal metrics (1)
- multi‐model ensemble (1)
- nature conservation (1)
- near-field monitoring (1)
- near-surface geophysics (1)
- networking (1)
- nu SVR (1)
- object-based image analysis (1)
- oil spill (1)
- optical diversity (1)
- optimization (1)
- palaeoclimatology (1)
- palaeontology (1)
- palaeosols (1)
- paleoclimate (1)
- paleoenvironment (1)
- palsa development (1)
- paléoclimat (1)
- pan (1)
- partial correlation (1)
- peatland (1)
- penetration bias (1)
- performance assessment (1)
- periglacial (1)
- periurban (1)
- phenology (1)
- pilot-point-approach (1)
- platform economy (1)
- plumes (1)
- polarimetery (1)
- pollution (1)
- ponds (1)
- population change (1)
- post-classification comparison (1)
- predictive performance (1)
- preface (1)
- protection status (1)
- pulsating explosive eruptions (1)
- radar (1)
- random forest regression (1)
- regional climate model (1)
- reliability (1)
- renewable energy (1)
- resource mapping (1)
- resource suitability (1)
- retrogressive thaw slump (1)
- river discharge (1)
- robust change vector analysis (1)
- rock glaciers (1)
- sacred lakes (1)
- sar (1)
- satellite remote sensing (1)
- scenario analysis (1)
- seasonal (1)
- seasonal dynamics (1)
- seasonality (1)
- sedimentology (1)
- segmentation (1)
- semantic segmentation (1)
- sensitivity analysis (1)
- sentinel (1)
- sentinel-2 (1)
- silicate karst (1)
- slope bogs (1)
- snow cover area (1)
- snow hydrology (1)
- snow parameters (1)
- snow variability (1)
- snowmelt runoff model (1)
- soil matric potential (1)
- source parameters (1)
- southern annular mode (1)
- spatial analysis (1)
- spatial scale (1)
- spatial water balance (1)
- spatiotemporal slump development (1)
- species (1)
- spectral diversity (1)
- spectral variation hypothesis (1)
- spring flood (1)
- statistical modeling (1)
- storage volume (1)
- stream flow (1)
- structure (1)
- sub-pixel coastline extraction (1)
- subpixel (1)
- subsidence (1)
- subsurface hydrology (1)
- sulfide inclusions (1)
- surface melt (1)
- surface reflectances (1)
- surface urban heat island (SUHI) (1)
- surface water (1)
- surface water area (1)
- sustainable irrigation system (1)
- synthetic aperture RADAR (1)
- tasselled cap (1)
- temperature (1)
- thermal infrared (1)
- thunderstorms (1)
- tikhonov regularization (1)
- time-series features (1)
- training sample migration (1)
- trend analysis (1)
- trends (1)
- two‐sided markets (1)
- uncertainties (1)
- uncertainty (1)
- uneven-aged mountainous (1)
- urban climate (1)
- urban environments (1)
- urbane Logistik (1)
- vDEUQUA2021 (1)
- validation (1)
- value of water (1)
- variability (1)
- vegetation indices (1)
- vegetation restoration (1)
- volcano (1)
- volcanoes (1)
- water (1)
- water balance (1)
- water dynamics (1)
- water management (1)
- water retention (1)
- water yield (1)
- wetland mapping (1)
- wind speed (1)
Institute
EU-Project number / Contract (GA) number
- 20-3044-2-11 (1)
- 308377 (1)
- 776019 (1)
Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product's quality with respect to its use for modeling and the geoscientific community.
Forests in Germany cover around 11.4 million hectares and, thus, a share of 32% of Germany's surface area. Therefore, forests shape the character of the country's cultural landscape. Germany's forests fulfil a variety of functions for nature and society, and also play an important role in the context of climate levelling. Climate change, manifested via rising temperatures and current weather extremes, has a negative impact on the health and development of forests. Within the last five years, severe storms, extreme drought, and heat waves, and the subsequent mass reproduction of bark beetles have all seriously affected Germany’s forests. Facing the current dramatic extent of forest damage and the emerging long-term consequences, the effort to preserve forests in Germany, along with their diversity and productivity, is an indispensable task for the government. Several German ministries have and plan to initiate measures supporting forest health. Quantitative data is one means for sound decision-making to ensure the monitoring of the forest and to improve the monitoring of forest damage. In addition to existing forest monitoring systems, such as the federal forest inventory, the national crown condition survey, and the national forest soil inventory, systematic surveys of forest condition and vulnerability at the national scale can be expanded with the help of a satellite-based earth observation. In this review, we analysed and categorized all research studies published in the last 20 years that focus on the remote sensing of forests in Germany. For this study, 166 citation indexed research publications have been thoroughly analysed with respect to publication frequency, location of studies undertaken, spatial and temporal scale, coverage of the studies, satellite sensors employed, thematic foci of the studies, and overall outcomes, allowing us to identify major research and geoinformation product gaps.
Die Covid-19-Pandemie gilt in vielen gesellschaftlichen Teilbereichen als Beschleuniger für Transformationsprozesse. Auch im Bereich der Organisation urbaner Logistik und Einzelhandelslandschaften etablieren sich neue Akteur*innen und Funktionen. Logistiker*innen integrieren lokale Onlinemarktplätze in ihre Profile und der stationäre Einzelhandel generiert Wettbewerbsfähigkeit gegenüber großen Onlinehändler*innen über die Nutzung lokaler Radlogistiknetzwerke, mittels derer Lieferungen noch am Tag der Bestellung (Same-Day-Delivery) verteilt werden können. Damit leisten die involvierten Akteur*innen potenziell auch einen Beitrag zur Nachhaltigkeitstransformation im Bereich urbaner Logistiksysteme. Im Fokus steht das Fallbeispiel WüLivery, ein Kooperationsprojekt des Stadtmarketingvereins, der Wirtschaftsförderung, Radlogistiker*innen sowie Einzelhändler*innen in Würzburg, welches während des zweiten coronabedingten Lockdowns im November 2020 umgesetzt wurde. Die entstehenden Dynamiken und Organisationsformen werden auf Basis von 11 Expert*inneninterviews dargestellt und analysiert. Es kann gezeigt werden, dass städtische Akteur*innen grundlegende Mediator*innen für Transformationsprozesse darstellen und Einzelhändler*innen und lokale Onlinemarktplätze als Katalysator*innen fungieren können. Das ist auch vor dem Hintergrund planerischer und politischer Kommunikationsprozesse zur Legitimation neuer Verkehrsinfrastrukturen nutzbar, da die einzelnen Akteur*innengruppen in Austausch kommen und ein gesteigertes Bewusstsein für die jeweiligen Bedarfe entsteht.
The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.
Der vorliegende Beitrag faßt den derzeitigen Stand der Untersuchungen von Hangrutschungen im Bereich der Frankenhöhe, die im Rahmen des EPOCH-Programmes durchgeführt wurden, zusammen. Nach einer Inventarisierung der regionalen Rutschungsereignisse wird die Rutschung bei Obergailnau in einer geomorphologischen Detailkartierung vorgestellt. Für die Auslösung der Rutschung kommen mehrere Faktoren in Betracht: neben einer erhöhten Rutschungsanfälligkeit aufgrund der geologisch-tektonischen Verhältnisse muß v.a. auch eine Einflußnahme durch die Landnutzung mit berücksichtigt werden. Dazu zählen Steinbrucharbeiten in unmittelbarer Nähe der Rutschung, aber auch Wasserbaumaßnahmen am Schloßberg. In diesem potentielllabilisierten Gebiet kam es nach überdurchschnittlichen Niederschlägen Anfang 1958 zu einer Überschreitung der Belastungsgrenze des Hanges, die sich in einer großflächigen Rutschung äußerte. Die weiteren Untersuchungen sollen zeigen, ob sich die für Obergailnau herausgestellten Faktorenkomplexe im regionalen Rahmen verifizieren lassen.
Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet
(2019)
Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.
Estimating flood risks and managing disasters combines knowledge in climatology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography - thus a complex multi-faceted problem. This study focuses on the capabilities of multi-source remote sensing data to support decision-making before, during and after a flood event. With our focus on urbanized areas, sample methods and applications show multi-scale products from the hazard and vulnerability perspective of the risk framework. From the hazard side, we present capabilities with which to assess flood-prone areas before an expected disaster. Then we map the spatial impact during or after a flood and finally, we analyze damage grades after a flood disaster. From the vulnerability side, we monitor urbanization over time on an urban footprint level, classify urban structures on an individual building level, assess building stability and quantify probably affected people. The results show a large database for sustainable development and for developing mitigation strategies, ad-hoc coordination of relief measures and organizing rehabilitation.
Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review
(2020)
Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.
Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts “vegetation indices”, “spectral information content”, and “spectral species” for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.
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.