Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests

Abstract

Estimates of forest aboveground biomass are fundamental for carbon monitoring and accounting; delivering information at very high spatial resolution is especially valuable for local management, conservationand selective logging purposes. In tropical areas, hosting large biomass and biodiversity resources whichare often threatened by unsustainable anthropogenic pressures, frequent forest resources monitoring isneeded. Lidar is a powerful tool to estimate aboveground biomass at fine resolution; however its applica-tion in tropical forests has been limited, with high variability in the accuracy of results. Lidar pulses scanthe forest vertical profile, and can provide structure information which is also linked to biodiversity. Inthe last decade the remote sensing of biodiversity has received great attention, but few studies focusedon the use of lidar for assessing tree species richness in tropical forests.This research aims at estimating aboveground biomass and tree species richness using discrete returnairborne lidar in Ghana forests. We tested an advanced statistical technique, Multivariate AdaptiveRegression Splines (MARS), which does not require assumptions on data distribution or on the rela-tionships between variables, being suitable for studying ecological variables.We compared the MARS regression results with those obtained by multilinear regression and foundthat both algorithms were effective, but MARS provided higher accuracy either for biomass (R2= 0.72)and species richness (R2= 0.64). We also noted strong correlation between biodiversity and biomass fieldvalues. Even if the forest areas under analysis are limited in extent and represent peculiar ecosystems, thepreliminary indications produced by our study suggest that instrument such as lidar, specifically usefulfor pinpointing forest structure, can also be exploited as a support for tree species richness assessment

Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions

“…Second, total biomass should include tree trunks, branches, and foliage, but the optical imagery we used captured only the signals from the vegetation canopy, and even contained noise from soil and other environmental backgrounds, which resulted in great uncertainty in the biomass estimation. Lidar has been widely used as it has the ability to provide vertical information that is closely related to AGB [48,49]. Third, the random forest algorithm was used to produce the AGB map and rank the relative importance of selected variables, but it is still a black box in which the interaction mechanism between remote sensing data and forest biochemical parameters is unrevealed.…”

Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

“…Secondly, it provides measures of variables describing the structure of the forest canopy (average height, dominant height, or mean diameter) [13][14][15], even allowing the discrimination between tree species [16]. Forest biomass can be estimated on the basis of these variables by diverse approaches [17][18][19][20], which can be categorized depending on the footprint size and the object under study (plot size or individual tree). Early studies used small-footprint (discrete) LiDAR data to estimate biophysical properties of forest stands at plot size.…”

A Geospatial Decision Support System Tool for Supporting Integrated Forest Knowledge at the Landscape Scale

“…Currently, Airborne Laser Scanner technology represents one of the most promising and effective innovation for a wide range of forestry applications, in particular, it allows a valuable estimation of above-ground biomass [16,39]. Within SOILCONSWEB activities, discrete-return aerial LiDAR data, collected during 2011 leaf-off condition were used to distinguish forest stand parameters and structural diversity in the study area.…”