Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe

Abstract The study presents three methods for estimation of forest aboveground biomass (AGB) at tree and plot levels using different categories of airborne data. The first method estimates AGB from high spatial resolution hyperspectral (HS) data. The second method estimates AGB from airborne laser scanning data. The third method explores the synergy between hyperspectral and LiDAR data to estimate AGB. The results are compared with AGB estimated from field measurements. The results demonstrate that: 1) The biomass estimation from the HS data showed a good correlation with field biomass values for spruce, beech and mixture of these species at tree and plot levels, but also the highest uncertainties in comparison with the other two methods; 2) The biomass estimation from the LiDAR data had a strong correlation with field biomass values for spruce for tree level and a good correlation for spruce, beech and mixture of these species for plot level; 3) The biomass estimation from fused HS and LiDAR data showed the best results for tree and plot levels for the study sites. This study expands on previous research assessing the applicability of HS, LiDAR and fused datasets for AGB assessment. It proves the efficiency of using fused HS and LiDAR data and suggests the use of HS-based methods for biomass assessment when laser scanning data are not available.

[1]  Christian Wirth,et al.  Generic biomass functions for Norway spruce in Central Europe--a meta-analysis approach toward prediction and uncertainty estimation. , 2004, Tree physiology.

[2]  Pablo Cruz,et al.  The effect of spectral and spatial degradation of hyperspectral imagery for the Sclerophyll tree species classification , 2013 .

[3]  C. Proisy,et al.  Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images , 2007 .

[4]  D. Leckie.  Stand delineation and composition estimation using semi-automated individual tree crown analysis , 2003 .

[5]  Markus Hollaus,et al.  Tree species classification based on full-waveform airborne laser scanning data , 2009 .

[6]  Barbara Koch,et al.  Exploring full-waveform LiDAR parameters for tree species classification , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[7]  K. Itten,et al.  Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats , 2011 .

[8]  P. Štěpánek,et al.  Discerning environmental factors affecting current tree growth in Central Europe. , 2016, The Science of the total environment.

[9]  Mary E. Martin,et al.  Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data , 1998 .

[10]  Andrew K. Skidmore,et al.  Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Juha Hyyppä,et al.  Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR , 2013, Remote. Sens..

[12]  C. Wirth,et al.  Generic biomass functions for Common beech (Fagus sylvatica) in Central Europe: predictions and components of uncertainty , 2008 .

[13]  M. Schlerf,et al.  Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods , 2005 .

[14]  Jan Novotný,et al.  INDIVIDUAL TREE CROWNS DELINEATION USING LOCAL MAXIMA APPROACH AND SEEDED REGION GROWING TECHNIQUE , 2011 .

[15]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[16]  R. Valentini,et al.  Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data , 2014 .

[17]  Hans Pretzsch,et al.  Forest Dynamics, Growth and Yield: From Measurement to Model , 2009 .

[18]  Richard A. Fournier,et al.  Predicting wood quantity and quality attributes of balsam fir and black spruce using airborne laser scanner data , 2014 .

[19]  C. Gleason,et al.  A Review of Remote Sensing of Forest Biomass and Biofuel: Options for Small-Area Applications , 2011 .

[20]  M. Schlerf.  Determination of structural and chemical forest attributes using hyperspectral remote sensing data : case studies in Norway spruce forests , 2006 .

[21]  F. Gougeon.  A Crown-Following Approach to the Automatic Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images , 1995 .

[22]  R. Lucas,et al.  The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data , 2006 .

[23]  Irshad A. Mohammed,et al.  Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops , 2014, Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation.

[24]  P. Gessler,et al.  Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data , 2006 .

[25]  E. Næsset,et al.  UTILIZING AIRBORNE LASER INTENSITY FOR TREE SPECIES CLASSIFICATION , 2007 .

[26]  R. Dubayah,et al.  Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest , 2008 .

[27]  Steffen Fritz,et al.  A Global Forest Growing Stock, Biomass and Carbon Map Based on FAO Statistics , 2008 .

[28]  Barbara Koch,et al.  Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment , 2010 .

[29]  Y. Yue,et al.  Biomass and carbon storage in an age-sequence of Cyclobalanopsis glauca plantations in southwest China , 2014 .

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

[31]  Michael A. Wulder,et al.  Modeling Stand Height, Volume, and Biomass from Very High Spatial Resolution Satellite Imagery and Samples of Airborne LiDAR , 2013, Remote. Sens..

[32]  K. Lim,et al.  Lidar remote sensing of biophysical properties of tolerant northern hardwood forests , 2003 .

[33]  D. Leckie,et al.  Automated tree recognition in old growth conifer stands with high resolution digital imagery , 2005 .

[34]  Peter Bunting,et al.  Retrieving forest biomass through integration of CASI and LiDAR data , 2008 .

[35]  Ross Nelson,et al.  Measuring biomass and carbon in delaware using an airborne profiling LIDAR , 2004 .

[36]  G. H. Rosenfield,et al.  A coefficient of agreement as a measure of thematic classification accuracy. , 1986 .

[37]  M. Lefsky,et al.  Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California , 2010 .

[38]  S. Popescu,et al.  Lidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers , 2009 .

[39]  Hao Chen,et al.  Comparison of Aviris and AISA Airborne Hyperspectral Sensing for Above-Ground Forest Carbon Mapping , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[40]  R. J. Olson,et al.  NET PRIMARY PRODUCTION AND CARBON ALLOCATION PATTERNS OF BOREAL FOREST ECOSYSTEMS , 2001 .

[41]  S. Popescu,et al.  Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height , 2004 .

[42]  Olga V. Brovkina,et al.  Estimating average tree crown size using high-resolution airborne data , 2015 .

[43]  W. Cohen,et al.  Lidar remote sensing of above‐ground biomass in three biomes , 2002 .