Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests

Abstract

Abstract• Introduction Accurate estimation of aboveground biomass is essential to better understand the carbon cycle in forest ecosystems.• Methods The objective of this study was to determine whether biomass in temperate hardwood forests is better estimated using very high-frequency radar data (from BioSAR) alone or in combination with small-footprint discrete-return lidar data (both profiling and scanning). The study area was in the Appomattox-Buckingham State Forest, Virginia, USA (78°41′W, 37°25′N). Aboveground biomass for 28 stands was estimated using 131 basal area factor 10 point samples. The resulting stand biomass estimates were used as the dependent variable in a multiple linear regression. Descriptors of the lidar distributions (both profiling and scanning) and averaged normalized radar cross-sections in each of these stands were used as independent variables.• Results Regression results revealed the following: (1) neither BioSAR nor scanning lidar data alone are good predictors of stand biomass (R 2 =0.57, root mean squared error (RMSE)= 31.0 tonnes/ha and R 2 =0.64, RMSE=28.5 tonnes/ha, respectively); (2) BioSAR data combined with small-footprint discrete lidar data (either profiling or scanning) are the best predictors of stand biomass (R 2 =0.80, RMSE=21.3 tonnes/ ha and R 2 =0.76, RMSE=24.2 tonnes/ha, respectively); and (3) when used with BioSAR data for stand biomass estimation, less costly profiling lidar data convey the same information as more costly scanning lidar data.• Conclusion Useful synergy can be realized by considering lidar and radar measurements jointly in estimating aboveground biomass in hardwood and mixed forests.

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

“…The M2sp.autopls model showed a good estimation of AGB, with a lower RMSE (11.17) than the mean RSE (39.4) of all the DRL studies, and a RSE% of 8.9% lower than 20% (or ±20 t·ha −1 , the greater of the two), which is considered the accuracy requirement of a global forest biomass mapping mission for at least 80% of grid cells [22]. Compared to other studies [22], the M2sp.autopls model showed better results than those found for the following: coniferous forest (R 2 = 0.64, RMSE = 28.5 t·ha −1 [74] and R 2 = 0.75, RMSE = 45.6 t·ha −1 [75]); deciduous forest (R 2 = 0.71, RMSE = 39.3 t·ha −1 ) [75]; taiga forest (R 2 = 0.72, RMSE = 14.2 t·ha −1 ) [76]; and tropical forest (R 2 = 0.36, RMSE = 22.8 t·ha −1 [77]; R 2 = 0.78 [78]; and R 2 = 0.72, RMSE = 40.2 t·ha −1 [33]). Other studies reported higher accuracies, such as in rainforest (R 2 = 0.90, RMSE = 38.3 t·ha −1 ) [79] and temperate forest (R 2 = 0.89, RMSE = 50.2 t·ha −1 [80] and R 2 = 0.93, RMSE = 33.9 t·ha −1 [81]), among other results reported in the literature.…”

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

“…Nichol and Sarker (2011) recently presented a study where texture feature ratios extracted from AVNIR-2 and HRG data were successfully employed in modelling biomass with R 2 up to 0.939. LiDAR, SAR, and even Landsat data have been also employed in biomass estimation studies (Kronseder et al 2012;Banskota et al 2011;Vastaranta et al 2014;Langner et al 2012;Sandberg et al 2011). Stem volume and basal area have been estimated through a synergy of ALS with airborne colour infrared (CIR) and AVNIR-2 data (Hou et al 2011).…”

Combining Lidar and Synthetic Aperture Radar Data to Estimate Forest Biomass: Status and Prospects

“…As forests are one of the major carbon sinks in the global ecosystem, the aboveground biomass (AGB) is a primary variable related to the amount of carbon flowing in the cycle. Therefore, more accurate AGB estimations are needed to understand the carbon cycle in forest ecosystems [1][2][3]. Spatially explicit measurement of global biomass also supports the Reduction of Emissions due to Deforestation and Forest Degradation (REDD) mechanism and the international effort to reduce anthropogenic greenhouse gas emissions [4][5][6][7].…”