Estimation of Voxel-Based Above-Ground Biomass Using Airborne LiDAR Data in an Intact Tropical Rain Forest, Brunei

Abstract

Abstract:The advancement of LiDAR technology has enabled more detailed evaluations of forest structures. The so-called "Volumetric pixel (voxel)" has emerged as a new comprehensive approach. The purpose of this study was to estimate plot-level above-ground biomass (AGB) in different plot sizes of 20 m × 20 m and 30 m × 30 m, and to develop a regression model for AGB prediction. Both point cloud-based (PCB) and voxel-based (VB) metrics were used to maximize the efficiency of low-density LiDAR data within a dense forest. Multiple regression model AGB prediction performance was found to be greatest in the 30 m × 30 m plots, with R 2 , adjusted R 2 , and standard deviation values of 0.92, 0.87, and 35.13 Mg·ha −1 , respectively. Five out of the eight selected independent variables were derived from VB metrics and the other three were derived from PCB metrics. Validation of accuracy yielded RMSE and NRMSE values of 27.8 Mg·ha −1 and 9.2%, respectively, which is a reasonable estimate for this structurally complex intact forest that has shown high NRMSE values in previous studies. This voxel-based approach enables a greater understanding of complex forest structure and is expected to contribute to the advancement of forest carbon quantification techniques.

Estimation of Above Ground Biomass in a Tropical Mountain Forest in Southern Ecuador Using Airborne LiDAR Data

“…This can be determined by multispectral remote sensing observations [32], which facilitate information in very high spatial resolution. However, multispectral satellite data only provides two-dimensional information from the upper forest canopy, which can be used for forest type classifications [33,34] but hardly to detect the vertical structure of the forest for AGB and C stock estimations [35,36]. Furthermore, the availability of multispectral satellite data is limited, because areas covered by cloud cannot be classified, which is especially problematic in tropical regions, where cloud frequency is extraordinarily high [9,37].…”

“…LiDAR data permits the generation of high resolution Digital Terrain Models (DTM) and high resolution Digital Surface Models (DSM), from which Canopy Height Models (CHM) can be obtained. These data can be used to derive different topographical indices and vegetation properties, such as the height of the vegetation at tree level (e.g., [36,38,44,45]). In general, the LiDAR measurements facilitate information about forest structure at tree level as well as information about the tree canopies, which can be used to detect the forest structure at fine scale and, in combination with field plot measurements, to estimate the AGB of the entire forest stand [46].…”

Modelling vegetation understory cover using LiDAR metrics

“…We identified four metrics for our analysis. Three of these metrics are used in the literature: fractional cover (FRAC, modified from Wing et al (17)), leaf area density (LAD, (24)), and voxel cover (VOX1m, (25)). The fourth metric considered was normalized cover (NORM), because it is an easily interpretable and easily calculated alternative.…”

AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

“…Nonetheless, coarse to moderate resolution satellite images facilitate temporal information and historical data, at least for the last few decades (NOAA-AVHRR, Aster, MODIS or Landsat) [37][38][39]. This information can be used for forest classification, forest cover determination, and deforestation rate estimation [25,40] but hardly for accurate local AGB calculation and natural TMF monitoring due to high cloudiness and the fast-changing topography in tropical high mountains [41].A third, more accessible technology is drones or unmanned aerial vehicles (UAV), which allow for the detection of surface data in very high spatial and temporal resolution [42][43][44]. The available sensors can provide 3D and multispectral information [4,45], which permit AGB estimation as well as ecosystem monitoring at small scale [28].…”

“…Nonetheless, coarse to moderate resolution satellite images facilitate temporal information and historical data, at least for the last few decades (NOAA-AVHRR, Aster, MODIS or Landsat) [37][38][39]. This information can be used for forest classification, forest cover determination, and deforestation rate estimation [25,40] but hardly for accurate local AGB calculation and natural TMF monitoring due to high cloudiness and the fast-changing topography in tropical high mountains [41].…”