Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

[1]  Hanyue Chen,et al.  High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Marc Simard,et al.  Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California , 2020, Remote Sensing of Environment.

[3]  Scott J. Goetz,et al.  The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography , 2020, Science of Remote Sensing.

[4]  Göran Ståhl,et al.  Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data , 2019, Remote Sensing of Environment.

[5]  Lori A. Magruder,et al.  Canopy and Terrain Height Retrievals with ICESat-2: A First Look , 2019, Remote. Sens..

[6]  Sorin C. Popescu,et al.  Synergy of ICESat-2 and Landsat for Mapping Forest Aboveground Biomass with Deep Learning , 2019, Remote. Sens..

[7]  Klaus Scipal,et al.  The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space , 2019, Remote Sensing of Environment.

[8]  Fausto W. Acerbi-Junior,et al.  Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Amy L. Neuenschwander,et al.  Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data , 2019, Remote Sensing of Environment.

[10]  Marc Simard,et al.  Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels , 2019, Remote. Sens..

[11]  Amy L. Neuenschwander,et al.  The ATL08 land and vegetation product for the ICESat-2 Mission , 2019, Remote Sensing of Environment.

[12]  Xiaoli Sun,et al.  The GEDI Simulator: A Large‐Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions , 2019, Earth and space science.

[13]  Seung-Kuk Lee,et al.  Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data , 2019, Remote Sensing of Environment.

[14]  Göran Ståhl,et al.  Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data , 2018, Remote. Sens..

[15]  Kristofer D. Johnson,et al.  LiDAR Derived Biomass, Canopy Height and Cover for Tri-State (MD, PA, DE) Region, V2 , 2018 .

[16]  Klaus Scipal,et al.  Assessment of a Power Law Relationship Between P-Band SAR Backscatter and Aboveground Biomass and Its Implications for BIOMASS Mission Performance , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Marwan Younis,et al.  Tandem-L: Project Status and Main Findings of the Phase Bl Study , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[18]  Maurizio Santoro,et al.  Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations , 2018, Remote. Sens..

[19]  Victoria Meyer,et al.  Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study From Central Gabon , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  G. Asner,et al.  An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR , 2018 .

[21]  David Gwenzi,et al.  Estimating Tree Crown Area and Aboveground Biomass in Miombo Woodlands From High-Resolution RGB-Only Imagery , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Hannah M. Cooper,et al.  Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data , 2018 .

[23]  Klaus Scipal,et al.  Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions , 2017 .

[24]  Grant M. Domke,et al.  Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA , 2017, Remote. Sens..

[25]  H. Balzter,et al.  Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data , 2017, Journal of geophysical research. Biogeosciences.

[26]  Kevin J. Gaston,et al.  Measurement of fine-spatial-resolution 3D vegetation structure with airborne waveform lidar: Calibration and validation with voxelised terrestrial lidar , 2017 .

[27]  Xiao Xiang Zhu,et al.  Data Fusion and Remote Sensing: An ever-growing relationship , 2016, IEEE Geoscience and Remote Sensing Magazine.

[28]  Nancy F. Glenn,et al.  Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass , 2016 .

[29]  R. McRoberts,et al.  Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference , 2016 .

[30]  C. Silva,et al.  A principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne LiDAR data , 2016 .

[31]  R. Dubayah,et al.  Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar , 2016, Scientific Reports.

[32]  Sassan Saatchi,et al.  Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests , 2016, Remote. Sens..

[33]  Zhenfeng Shao,et al.  Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China , 2016, Sensors.

[34]  Marc Simard,et al.  Radiometric Correction of Airborne Radar Images Over Forested Terrain With Topography , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Guoqing Sun,et al.  The uncertainty of biomass estimates from modeled ICESat-2 returns across a boreal forest gradient , 2015 .

[36]  Mahendra Singh Nathawat,et al.  A review of radar remote sensing for biomass estimation , 2015, International Journal of Environmental Science and Technology.

[37]  Richard M. Lucas,et al.  A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables , 2014, Remote. Sens..

[38]  Johan E. S. Fransson,et al.  Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests , 2014, Remote. Sens..

[39]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[40]  Richard M. Lucas,et al.  The Remote Sensing and GIS Software Library (RSGISLib) , 2014, Comput. Geosci..

[41]  S. Goetz,et al.  A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing , 2013 .

[42]  Jianping Guo,et al.  Reprint of: Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[43]  Maxim Neumann,et al.  Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Jianping Guo,et al.  Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[45]  Philip Lewis,et al.  A threshold insensitive method for locating the forest canopy top with waveform lidar , 2011 .

[46]  S. Saatchi,et al.  Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass , 2011 .

[47]  Guoqing Sun,et al.  Forest biomass mapping from lidar and radar synergies , 2011 .

[48]  A. Hudak,et al.  A Comparison of Accuracy and Cost of LiDAR versus Stand Exam Data for Landscape Management on the Malheur National Forest , 2011, Journal of Forestry.

[49]  Masanobu Shimada,et al.  An Evaluation of the ALOS PALSAR L-Band Backscatter—Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[50]  Masanobu Shimada,et al.  Generating Large-Scale High-Quality SAR Mosaic Datasets: Application to PALSAR Data for Global Monitoring , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  M. Lefsky A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System , 2010 .

[52]  Gerhard Krieger,et al.  Tandem-L: A Mission for Monitoring Earth System Dynamics with High Resolution SAR Interferometry , 2010 .

[53]  I. Woodhouse,et al.  Using satellite radar backscatter to predict above‐ground woody biomass: A consistent relationship across four different African landscapes , 2009 .

[54]  Andrew Thomas Hudak,et al.  LiDAR Utility for Natural Resource Managers , 2009, Remote. Sens..

[55]  Scott Hensley,et al.  First deformation results using the NASA/JPL UAVSAR instrument , 2009, 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar.

[56]  S. Goetz,et al.  Importance of biomass in the global carbon cycle , 2009 .

[57]  G. Krieger,et al.  The tandem-L mission proposal: Monitoring earth's dynamics with high resolution SAR interferometry , 2009, 2009 IEEE Radar Conference.

[58]  Nicholas L. Crookston,et al.  yaImpute: An R Package for kNN Imputation , 2008 .

[59]  Wolfgang Lucht,et al.  Global biomass mapping for an improved understanding of the CO2 balance—the Earth observation mission Carbon-3D , 2005 .

[60]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[61]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .