Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China

Abstract

Abstract:The ultimate goal of our multi-article series is to demonstrate the Allometric Scaling and Resource Limitation (ASRL) approach for mapping tree heights and biomass. This third article tests the feasibility of the optimized ASRL model over China at both site (14 meteorological stations) and continental scales. Tree heights from the Geoscience Laser Altimeter System (GLAS) waveform data are used for the model optimizations. Three selected ASRL parameters (area of single leaf, α; exponent for canopy radius, η; and OPEN ACCESS Remote Sens. 2014, 6 3534 root absorption efficiency, γ) are iteratively adjusted to minimize differences between the references and predicted tree heights. Key climatic variables (e.g., temperature, precipitation, and solar radiation) are needed for the model simulations. We also exploit the independent GLAS and in situ tree heights to examine the model performance. The predicted tree heights at the site scale are evaluated against the GLAS tree heights using a two-fold cross validation (RMSE = 1.72 m; R 2 = 0.97) and bootstrapping (RMSE = 4.39 m; R 2 = 0.81). The modeled tree heights at the continental scale (1 km spatial resolution) are compared to both GLAS (RMSE = 6.63 m; R 2 = 0.63) and in situ (RMSE = 6.70 m; R 2 = 0.52) measurements. Further, inter-comparisons against the existing satellite-based forest height maps have resulted in a moderate degree of agreements. Our results show that the optimized ASRL model is capable of satisfactorily retrieving tree heights over continental China at both scales. Subsequent studies will focus on the estimation of woody biomass after alleviating the discussed limitations.

Mapping Forest Canopy Height over Continental China Using Multi-Source Remote Sensing Data

“…represent the values of the beginning signal and the ground peak of the GLAS full-waveform, d is the GLAS footprint size of 70 m [3,16], and is the topographic slope.…”

“…Forests store carbon in their above-and below-ground biomass [1]. As an important predictor of forest biomass and carbon stock, the vertical structure of forests has been well monitored in previous studies [2][3][4]. Light detection and ranging (LiDAR) remote sensing, is useful in the large-scale investigations of forest structural attributes, such as forest canopy height [2,[5][6][7][8][9][10].…”

“…However, the lack of valid GLAS data in some regions is problematic. Because the GLAS data are spatially incomplete, multiple geospatial predictors were supplemented to obtain spatially-continuous forest height maps in recently-published approaches [2,3,5,13]. In addition, the GLAS data are highly sensitive to topographic features due to its large footprint size, which causes overestimations of forest height [14].…”

Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data

“…The climatic variables used in this study mainly include precipitation and temperature, from China Meteorological Data Sharing Service System (CMDSSS), which include hourly and daily meteorological records observed from 754 benchmark surface weather stations and automatic weather station since 1951 [30][31][32]. The accuracy of these meteorological station data has been found to be over 99.9%.…”

Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests

“…Data from the SLICER sensor were used successfully to predict 91% of the variance in AGB in Douglas fir and western hemlock forests using canopy filled canopy volume and canopy height metrics; the former actually resulted in being the most important variable for predicting total AGB [7]. The spaceborne sensor, GLAS (Geoscience Laser Altimeter System), on-board ICESat (Ice, Cloud and Land Elevation Satellite), was used successfully to extract biomass via height metrics over large spatial scales [21,22].…”