Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest

[1]  Nicholas C. Coops,et al.  Updating stand-level forest inventories using airborne laser scanning and Landsat time series data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[2]  M. L. Guillén-Climent,et al.  High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density lidar , 2018 .

[3]  E. Næsset,et al.  Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference , 2018 .

[4]  Lars M. H. Ulander,et al.  Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data , 2017, Remote. Sens..

[5]  Severino G. Salmo,et al.  Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery , 2017 .

[6]  M. Rautiainen,et al.  Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index , 2017 .

[7]  J. Carreiras,et al.  Mapping major land cover types and retrieving the age of secondary forests in the Brazilian Amazon by combining single-date optical and radar remote sensing data , 2017 .

[8]  G. Mallinis,et al.  Assessing the relationships between growing stock volume and Sentinel-2 imagery in a Mediterranean forest ecosystem , 2017 .

[9]  Joanne C. White,et al.  Assessing Precision in Conventional Field Measurements of Individual Tree Attributes , 2017 .

[10]  S. Tuominen,et al.  Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging , 2017 .

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

[12]  Andreas Kääb,et al.  Glacier Remote Sensing Using Sentinel-2. Part II: Mapping Glacier Extents and Surface Facies, and Comparison to Landsat 8 , 2016, Remote. Sens..

[13]  Xiaodong Li,et al.  Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band , 2016, Remote. Sens..

[14]  Pierre Soille,et al.  Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas , 2016, Remote. Sens..

[15]  M. Rautiainen,et al.  The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: a simulation study , 2016 .

[16]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[17]  Juha Hyyppä,et al.  Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes , 2015, Remote. Sens..

[18]  Xiaolin Zhu,et al.  Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series , 2015 .

[19]  M. Vastaranta,et al.  Tandem-X interferometry in the prediction of forest inventory attributes in managed boreal forests , 2015 .

[20]  Arturo Sanchez-Azofeifa,et al.  Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada , 2015, Remote. Sens..

[21]  M. Vastaranta,et al.  Combining Lidar and Synthetic Aperture Radar Data to Estimate Forest Biomass: Status and Prospects , 2015 .

[22]  Hideki Saito,et al.  Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables , 2014, Remote. Sens..

[23]  A. Günlü,et al.  Estimating aboveground biomass using Landsat TM imagery: A case study of Anatolian Crimean pine forests in Turkey , 2014 .

[24]  Joanne C. White,et al.  A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach , 2013 .

[25]  Joanne C. White,et al.  Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update , 2013 .

[26]  B. Koetz,et al.  Capability of the Sentinel 2 mission for tropical coral reef mapping and coral bleaching detection , 2012 .

[27]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[28]  M. Vastaranta,et al.  Prediction of plot-level forest variables using TerraSAR-X stereo SAR data , 2012 .

[29]  M. Vastaranta,et al.  Predicting individual tree attributes from airborne laser point clouds based on the random forests technique , 2011 .

[30]  J. Repola.  Biomass equations for Scots pine and Norway spruce in Finland , 2009 .

[31]  M. Nilsson,et al.  Combining national forest inventory field plots and remote sensing data for forest databases , 2008 .

[32]  J. Hyyppä,et al.  Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests , 2008 .

[33]  K. Kraus.  Photogrammetry: Geometry from Images and Laser Scans , 2007 .

[34]  R. McRoberts,et al.  Remote sensing support for national forest inventories , 2007 .

[35]  S. Franklin,et al.  OBJECT-BASED ANALYSIS OF IKONOS-2 IMAGERY FOR EXTRACTION OF FOREST INVENTORY PARAMETERS , 2006 .

[36]  J. Hyyppä,et al.  DETECTING AND ESTIMATING ATTRIBUTES FOR SINGLE TREES USING LASER SCANNER , 2006 .

[37]  Darrel L. Williams,et al.  Landsat: Yesterday, Today, and Tomorrow , 2006 .

[38]  M. Gillis,et al.  Monitoring Canada's forests: The National Forest Inventory , 2005 .

[39]  Darrel L. Williams,et al.  Landsat sensor performance: history and current status , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[40]  A. Pekkarinen,et al.  Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data , 2004 .

[41]  J. Hyyppä,et al.  Automatic detection of harvested trees and determination of forest growth using airborne laser scanning , 2004 .

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

[43]  Konstantinos Papathanassiou,et al.  Single-baseline polarimetric SAR interferometry , 2001, IEEE Trans. Geosci. Remote. Sens..

[44]  E. Tomppo,et al.  Selecting estimation parameters for the Finnish multisource National Forest Inventory , 2001 .

[45]  J. Gove,et al.  Forest inventory , 2001 .

[46]  W. Cohen,et al.  An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon , 2001 .

[47]  J. Hyyppä,et al.  Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes , 2000 .

[48]  Michael A. Wulder,et al.  Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters , 1998 .

[49]  Unfccc.  Kyoto Protocol to the United Nations Framework Convention on Climate Change , 1997 .

[50]  J. Chen,et al.  Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images , 1996 .

[51]  E. Tomppo.  Multi-source national forest inventory of Finland. , 1994 .

[52]  Thuy Le Toan,et al.  Relating forest biomass to SAR data , 1992, IEEE Trans. Geosci. Remote. Sens..

[53]  E. Tomppo,et al.  Satellite image-based national forest inventory of finland for publication in the igarss'91 digest , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[54]  Jouko Laasasenaho.  Taper curve and volume functions for pine, spruce and birch [Pinus sylvestris, Picea abies, Betula pendula, Betula pubescens] , 1982 .

[55]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .