Estimation of Individual Tree Stem Biomass in an Uneven-Aged Structured Coniferous Forest Using Multispectral LiDAR Data

被引:9
作者
Georgopoulos, Nikos [1 ]
Gitas, Ioannis Z. [1 ]
Stefanidou, Alexandra [1 ]
Korhonen, Lauri [2 ]
Stavrakoudis, Dimitris [1 ]
机构
[1] Aristotle Univ Thessaloniki, Sch Forestry & Nat Environm, Lab Forest Management & Remote Sensing, P.O. Box 248, Thessaloniki 54124, Greece
[2] Univ Eastern Finland, Sch Forest Sci, P.O. Box 111, FI-80101 Joensuu, Finland
关键词
stem biomass; multispectral LiDAR; remote sensing; regression analysis; ABOVEGROUND BIOMASS; AIRBORNE LIDAR; ALLOMETRIC EQUATIONS; GROUND BIOMASS; AERIAL PHOTOGRAPHS; SATELLITE DATA; PINE TREES; VOLUME; HEIGHT; EXTRACTION;
D O I
10.3390/rs13234827
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Stem biomass is a fundamental component of the global carbon cycle that is essential for forest productivity estimation. Over the last few decades, Light Detection and Ranging (LiDAR) has proven to be a useful tool for accurate carbon stock and biomass estimation in various biomes. The aim of this study was to investigate the potential of multispectral LiDAR data for the reliable estimation of single-tree total and barkless stem biomass (TSB and BSB) in an uneven-aged structured forest with complex topography. Destructive and non-destructive field measurements were collected for a total of 67 dominant and co-dominant Abies borisii-regis trees located in a mountainous area in Greece. Subsequently, two allometric equations were constructed to enrich the reference data with non-destructively sampled trees. Five different regression algorithms were tested for single-tree BSB and TSB estimation using height (height percentiles and bicentiles, max and average height) and intensity (skewness, standard deviation and average intensity) LiDAR-derived metrics: Generalized Linear Models (GLMs), Gaussian Process (GP), Random Forest (RF), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The results showcased that the RF algorithm provided the best overall predictive performance in both BSB (i.e., RMSE = 175.76 kg and R-2 = 0.78) and TSB (i.e., RMSE = 211.16 kg and R-2 = 0.65) cases. Our work demonstrates that BSB can be estimated with moderate to high accuracy using all the tested algorithms, contrary to the TSB, where only three algorithms (RF, SVR and GP) can adequately provide accurate TSB predictions due to bark irregularities along the stems. Overall, the multispectral LiDAR data provide accurate stem biomass estimates, the general applicability of which should be further tested in different biomes and ecosystems.
引用
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页数:27
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