Earth Observation Based Monitoring of Forests in Germany: A Review

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.

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[27]  Cornelius Senf,et al.  A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series , 2017 .

[28]  Marco Heurich,et al.  Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest , 2016, Remote. Sens..

[29]  Marco Heurich,et al.  Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[30]  A. Elatawneh,et al.  Forest Cover Database Updates Using Multi-Seasonal RapidEye Data—Storm Event Assessment in the Bavarian Forest National Park , 2014 .

[31]  Marco Heurich,et al.  Integrating LiDAR and high-resolution imagery for object-based mapping of forest habitats in a heterogeneous temperate forest landscape , 2018, International Journal of Remote Sensing.

[32]  Johannes Breidenbach,et al.  The Influence of DEM Quality on Mapping Accuracy of Coniferous- and Deciduous-Dominated Forest Using TerraSAR-X Images , 2012, Remote. Sens..

[33]  Joachim Hill,et al.  Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume , 2015, Remote. Sens..

[34]  Christiane Schmullius,et al.  Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape , 2012, Remote. Sens..

[35]  Marco Heurich,et al.  LiDAR derived topography and forest stand characteristics largely explain the spatial variability observed in MODIS land surface phenology , 2018, Remote Sensing of Environment.

[36]  Andrew T. Hudak,et al.  Taxonomic, functional, and phylogenetic diversity of bird assemblages are oppositely associated to productivity and heterogeneity in temperate forests , 2018, Remote Sensing of Environment.

[37]  A. Rigling,et al.  A first assessment of the impact of the extreme 2018 summer drought on Central European forests , 2020, Basic and Applied Ecology.

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[39]  Marco Heurich,et al.  Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest , 2019, Remote. Sens..

[40]  Andrew K. Skidmore,et al.  Estimation of forest leaf water content through inversion of a radiative transfer model from LiDAR and hyperspectral data , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[41]  Jiaojiao Tian,et al.  Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection , 2017, Remote. Sens..

[42]  Marco Heurich,et al.  LiDAR‐derived canopy structure supports the more‐individuals hypothesis for arthropod diversity in temperate forests , 2018 .

[43]  Wolfgang Stümer,et al.  Spatial interpolation of in situ data by self-organizing map algorithms (neural networks) for the assessment of carbon stocks in European forests , 2010 .

[44]  C. Bigler,et al.  Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species , 2019, Global change biology.

[45]  N. Buchmann,et al.  Species-specific differences in water uptake depth of mature temperate trees vary with water availability in the soil. , 2018, Plant biology.

[46]  Arthur P. Cracknell,et al.  An overview of small satellites in remote sensing , 2008 .

[47]  Leif Martin Schroeder,et al.  Colonization of storm gaps by the spruce bark beetle: influence of gap and landscape characteristics , 2010 .

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[50]  Joachim Hill,et al.  Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands , 2013 .

[51]  Jan-Peter Mund,et al.  UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring , 2019, Remote. Sens..

[52]  Markus Tum,et al.  Validation of modelled forest biomass in Germany using BETHY/DLR , 2011 .

[53]  Marco Heurich,et al.  Biodiversity along temperate forest succession , 2018, Journal of Applied Ecology.

[54]  Jing Liu,et al.  Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests , 2017 .

[55]  Henning Buddenbaum,et al.  Comparison of Feature Reduction Algorithms for Classifying Tree Species With Hyperspectral Data on Three Central European Test Sites , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[56]  Sungho Choi,et al.  A Comparative Study of Predicting DBH and Stem Volume of Individual Trees in a Temperate Forest Using Airborne Waveform LiDAR , 2015, IEEE Geoscience and Remote Sensing Letters.

[57]  Clement Atzberger,et al.  Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[58]  Johannes Breidenbach,et al.  Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data , 2013, Remote. Sens..

[59]  Miroslav Svoboda,et al.  Forest disturbances under climate change. , 2017, Nature climate change.

[60]  Florian Detsch,et al.  Analyzing the relationship between historic canopy dynamics and current plant species diversity in the herb layer of temperate forests using long-term Landsat time series , 2019, Remote Sensing of Environment.

[61]  S. Shataee,et al.  Forest Attributes Estimation Using Aerial Laser Scanner and TM Data , 2013 .

[62]  Claudia Kuenzer,et al.  European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products , 2012, Remote. Sens..

[63]  Markus Hollaus,et al.  Operational forest structure monitoring using airborne laser scanning Flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring , 2013 .

[64]  Katarzyna Zielewska-Büttner,et al.  Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus) , 2018, Remote. Sens..

[65]  Birgit Kleinschmit,et al.  Significance Analysis of Different Types of Ancillary Geodata Utilized in a Multisource Classification Process for Forest Identification in Germany , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[66]  Joanne C. White,et al.  Remote Sensing Technologies for Enhancing Forest Inventories: A Review , 2016 .

[67]  Markus Immitzer,et al.  Estimating stand density, biomass and tree species from very high resolution stereo-imagery- towards an all-in-one sensor for forestry applications? , 2017 .

[68]  Marco Heurich,et al.  Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles , 2018 .

[69]  Barbara Koch,et al.  Enhancement of bioenergy estimations within forests using airborne laser scanning and multispectral line scanner data , 2011 .

[70]  Sarah Asam,et al.  The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics , 2019, Remote. Sens..

[71]  Robert S. Nuske,et al.  Using Unmanned Aerial Vehicles (UAV) to Quantify Spatial Gap Patterns in Forests , 2014, Remote. Sens..

[72]  C. Beierkuhnlein,et al.  Spatial variation in leaf damage of forest trees and the regeneration after the extreme spring frost event in May 2011. , 2012 .

[73]  Terje Gobakken,et al.  A functional regression model for inventories supported by aerial laser scanner data or photogrammetric point clouds , 2016 .

[74]  Barbara Koch,et al.  Mapping forest biomass from space - Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[75]  A. Skidmore,et al.  Important LiDAR metrics for discriminating forest tree species in Central Europe , 2018 .

[76]  Marco Heurich,et al.  Using airborne laser scanning to model potential abundance and assemblages of forest passerines. , 2009 .

[77]  B. Koch,et al.  Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors , 2010 .

[78]  Patrick Hostert,et al.  Urban vegetation classification: Benefits of multitemporal RapidEye satellite data , 2013 .

[79]  M. Heurich Automatic recognition and measurement of single trees based on data from airborne laser scanning over the richly structured natural forests of the Bavarian Forest National Park , 2008 .

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[81]  P. Reinartz,et al.  Assessment of Cartosat-1 and WorldView-2 stereo imagery in combination with a LiDAR-DTM for timber volume estimation in a highly structured forest in Germany , 2013 .

[82]  Stefan Erasmi,et al.  Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests , 2019, Remote. Sens..

[83]  Jürgen Bauhus,et al.  Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements , 2020, Remote. Sens..

[84]  Martin Gutsch,et al.  Balancing trade-offs between ecosystem services in Germany’s forests under climate change , 2018 .

[85]  Tobia Lakes,et al.  High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments , 2017, Carbon Balance and Management.

[86]  Jörg Müller,et al.  Modelling Forest α-Diversity and Floristic Composition - On the Added Value of LiDAR plus Hyperspectral Remote Sensing , 2012, Remote. Sens..

[87]  Marian Kazda,et al.  Priority assessment for conversion of Norway spruce forests through introduction of broadleaf species , 1998 .

[88]  Ralf Münnich,et al.  Non‐parametric small area models using shape‐constrained penalized B‐splines , 2017 .

[89]  Jonas Fridman,et al.  Factors affecting the probability of windthrow at stand level as a result of Gudrun winter storm in southern Sweden , 2011 .

[90]  Marco Heurich,et al.  Estimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data , 2016 .

[91]  Marco Heurich,et al.  Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[92]  Konstantinos Papathanassiou,et al.  Forest Structure Characterization From SAR Tomography at L-Band , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[93]  Katarzyna Zielewska-Büttner,et al.  Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery , 2016, Remote. Sens..

[94]  Michael Matiu,et al.  Monitoring succession after a non-cleared windthrow in a Norway spruce mountain forest using webcam, satellite vegetation indices and turbulent CO2 exchange , 2017 .

[95]  Marco Heurich,et al.  Mapping a ‘cryptic kingdom’: Performance of lidar derived environmental variables in modelling the occurrence of forest fungi , 2016 .

[96]  Massimo Faccoli,et al.  Composition and Elevation of Spruce Forests Affect Susceptibility to Bark Beetle Attacks: Implications for Forest Management , 2014 .

[97]  Johannes Schumacher,et al.  Considerations towards a Novel Approach for Integrating Angle-Count Sampling Data in Remote Sensing Based Forest Inventories , 2017 .

[98]  Marco Heurich,et al.  Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[99]  Hans Pretzsch,et al.  Prediction of stem volume in complex temperate forest stands using TanDEM-X SAR data , 2016 .

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[101]  Manfred Reich,et al.  Forest monitoring with TerraSAR-X: first results , 2010, European Journal of Forest Research.

[102]  Andrew K. Skidmore,et al.  Integration of Landsat-8 Thermal and Visible-Short Wave Infrared Data for Improving Prediction Accuracy of Forest Leaf Area Index , 2019, Remote. Sens..

[103]  Birgit Kleinschmit,et al.  Approaches to utilising QuickBird data for the monitoring of NATURA 2000 habitats , 2008 .

[104]  P. Pellikka,et al.  Application of vertical skyward wide-angle photography and airborne video data for phenological studies of beech forests in the German Alps , 2001 .

[105]  Marco Heurich,et al.  LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer , 2014 .

[106]  Johann G. Goldammer,et al.  Airborne forest fire mapping with an adaptive infrared sensor , 2003 .

[107]  Mathias Schardt,et al.  An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data - A case study in complex temperate forest stands , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[108]  Roland Brandl,et al.  Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblages , 2009 .

[109]  M. Hansen,et al.  Classifying drivers of global forest loss , 2018, Science.

[110]  Beat Wermelinger,et al.  Ecology and management of the spruce bark beetle Ips typographus—a review of recent research , 2004 .

[111]  Florian Hartig,et al.  Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass , 2014 .

[112]  Joachim Saborowski,et al.  Spatial prediction of forest stand variables , 2009, European Journal of Forest Research.

[113]  Stefan Erasmi,et al.  Canopy height estimation with TanDEM-X in temperate and boreal forests , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[114]  Barbara Koch,et al.  Airborne laser data for stand delineation and information extraction , 2009 .

[115]  Ingolf Kühn,et al.  Phase difference analysis of temperature and vegetation phenology for beech forest: a wavelet approach , 2013, Stochastic Environmental Research and Risk Assessment.

[116]  M. Scheuber,et al.  Potentials and limits of the k-nearest-neighbour method for regionalising sample-based data in forestry , 2009, European Journal of Forest Research.

[117]  Barbara Koch,et al.  Estimating Single Tree Stem Volume of Pinus sylvestris Using Airborne Laser Scanner and Multispectral Line Scanner Data , 2011, Remote. Sens..

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[119]  David Small,et al.  Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data , 2019, Remote. Sens..

[120]  Roland Brandl,et al.  Composition versus physiognomy of vegetation as predictors of bird assemblages: the role of lidar. , 2010 .

[121]  Cornelius Senf,et al.  Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe. , 2017, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[122]  Stefan Dech,et al.  Spatial characterization of bark beetle infestations by a multidate synergy of SPOT and Landsat imagery , 2013, Environmental Monitoring and Assessment.

[123]  Kerstin Stahl,et al.  How well do meteorological indicators represent agricultural and forest drought across Europe? , 2018 .

[124]  Christoph Straub,et al.  Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data , 2019, Remote. Sens..

[125]  M. Schlerf,et al.  Remote sensing of forest biophysical variables using HyMap imaging spectrometer data , 2005 .

[126]  Johannes Schumacher,et al.  Combination of Multi-Temporal Sentinel 2 Images and Aerial Image Based Canopy Height Models for Timber Volume Modelling , 2019, Forests.

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[128]  Barbara Koch,et al.  Modelling the standing timber volume of Baden-Württemberg - A large-scale approach using a fusion of Landsat, airborne LiDAR and National Forest Inventory data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[129]  J. Hill,et al.  Satellite-based stand-wise forest cover type mapping using a spatially adaptive classification approach , 2012, European Journal of Forest Research.

[130]  Marco Heurich,et al.  An experimental test of the habitat-amount hypothesis for saproxylic beetles in a forested region. , 2017, Ecology.

[131]  Marco Heurich,et al.  Small beetle, large-scale drivers: how regional and landscape factors affect outbreaks of the European spruce bark beetle. , 2016, The Journal of applied ecology.

[132]  Laurent Tits,et al.  Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale , 2015, Remote. Sens..

[133]  Clement Atzberger,et al.  First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe , 2016, Remote. Sens..

[134]  Barbara Koch,et al.  Modelling stratified forest attributes using optical/LiDAR features in a central European landscape , 2012, Int. J. Digit. Earth.

[135]  Manfred J. Lexer,et al.  Impact of bark beetle (Ips typographus L.) disturbance on timber production and carbon sequestration in different management strategies under climate change , 2008 .

[136]  J. Koricheva,et al.  Tree Diversity Drives Forest Stand Resistance to Natural Disturbances , 2017, Current Forestry Reports.

[137]  Henning Buddenbaum,et al.  Combining canopy height and tree species map information for large-scale timber volume estimations under strong heterogeneity of auxiliary data and variable sample plot sizes , 2018, European Journal of Forest Research.

[138]  M. Turner Disturbance and landscape dynamics in a changing world. , 2010, Ecology.

[139]  Marco Heurich,et al.  Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales , 2013 .

[140]  Marco Heurich,et al.  Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation , 2019, Remote. Sens..

[141]  C. Straub,et al.  Assessing height changes in a highly structured forest using regularly acquired aerial image data , 2015 .

[142]  M. Heurich,et al.  Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests , 2008 .

[143]  Stefan Erasmi,et al.  High Resolution Forest Maps from Interferometric TanDEM-X and Multitemporal Sentinel-1 SAR Data , 2017, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

[144]  Andrew K. Skidmore,et al.  Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[145]  Marco Heurich,et al.  Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[146]  Lars T. Waser,et al.  Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality , 2014, Remote. Sens..

[147]  Tobia Lakes,et al.  Modeling above-ground carbon storage: a remote sensing approach to derive individual tree species information in urban settings , 2017, Urban Ecosystems.

[148]  Andreas Hill,et al.  A Double-Sampling Extension of the German National Forest Inventory for Design-Based Small Area Estimation on Forest District Levels , 2018, Remote. Sens..

[149]  Grant M. Domke,et al.  Applications of the United States Forest Inventory and Analysis dataset: a review and future directions , 2018, Canadian Journal of Forest Research.

[150]  C. Atzberger,et al.  Windthrow Detection in European Forests with Very High-Resolution Optical Data , 2017 .

[151]  Andreas Hill,et al.  A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data , 2017, Remote. Sens..

[152]  Marco Heurich,et al.  Tree species classification using plant functional traits from LiDAR and hyperspectral data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[153]  Alexandre Bouvet,et al.  Detection of windthrows and insect outbreaks by L-band SAR: A case study in the Bavarian Forest National Park , 2018 .

[154]  Barbara Koch,et al.  Estimating the spatial distribution, extent and potential lignocellulosic biomass supply of Trees Outside Forests in Baden-Wuerttemberg using airborne LiDAR and OpenStreetMap data , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[155]  Anne Chao,et al.  Airborne LiDAR reveals context dependence in the effects of canopy architecture on arthropod diversity , 2014 .

[156]  Hans Pretzsch,et al.  Using canopy heights from digital aerial photogrammetry to enable spatial transfer of forest attribute models: a case study in central Europe , 2017 .

[157]  B. Koch,et al.  A comparison of different methods for forest resource estimation using information from airborne laser scanning and CIR orthophotos , 2010, European Journal of Forest Research.

[158]  Daniel Doktor,et al.  Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations , 2009 .

[159]  Pavel Propastin,et al.  Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[160]  Marco Heurich,et al.  Radar vision in the mapping of forest biodiversity from space , 2019, Nature Communications.

[161]  Fabian Ewald Fassnacht,et al.  Forest structure modeling with combined airborne hyperspectral and LiDAR data , 2012 .

[162]  Lars T. Waser,et al.  Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests , 2013 .

[163]  Gustau Camps-Valls,et al.  Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data , 2015 .

[164]  Uwe Stilla,et al.  Detection of fallen trees in ALS point clouds using a Normalized Cut approach trained by simulation , 2015 .

[165]  Marco Heurich,et al.  Automatic Mapping of Standing Dead Trees after an Insect Outbreak Using the Window Independent Context Segmentation Method , 2014 .

[166]  Tek Narayan Maraseni,et al.  Global trend of forest ecosystem services valuation – An analysis of publications , 2019, Ecosystem Services.

[167]  Stefan Dech,et al.  Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery , 2014 .

[168]  Thomas Giesecke,et al.  Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model , 2012 .

[169]  Andreas Huth,et al.  The effects of tree species grouping in tropical rainforest modelling: Simulations with the individual-based model Formind , 1998 .

[170]  Shaun R. Levick,et al.  Scaling wood volume estimates from inventory plots to landscapes with airborne LiDAR in temperate deciduous forest , 2016, Carbon Balance and Management.

[171]  Matteo Pardini,et al.  Comparison of Tomographic SAR Reflectivity Reconstruction Algorithms for Forest Applications at L-band , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[172]  C. Stepper,et al.  Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: application for a broadleaf-dominated forest in central Europe , 2015 .

[173]  Marco Heurich,et al.  Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[174]  Johannes Schumacher,et al.  Potential of remote sensing-based forest attribute models for harmonising large-scale forest inventories on regional level: a case study in Southwest Germany , 2019, Annals of Forest Science.

[175]  Thuy Le Toan,et al.  Relating Radar Remote Sensing of Biomass to Modelling of Forest Carbon Budgets , 2004 .

[176]  Andrew K. Skidmore,et al.  Retrieval of forest leaf functional traits from HySpex imagery using radiative transfer models and continuous wavelet analysis , 2016 .

[177]  J. Reitberger,et al.  3D segmentation of single trees exploiting full waveform LIDAR data , 2009 .

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