Airborne LiDAR Detects Selectively Logged Tropical Forest Even in an Advanced Stage of Recovery

Abstract

Identifying historical forest disturbances is difficult, especially in selectively logged areas. LiDAR is able to measure fine-scale variations in forest structure over multiple kilometers. We use LiDAR data from ca. 16 km 2 of forest in Sierra Leone, West Africa, to discriminate areas of old-growth from areas recovering from selective logging for 23 years. We examined canopy height variation and gap size distributions. We found that though recovering blocks of forest differed little in height from old-growth forest (up to 3 m), they had a greater area of canopy gaps (average 10.2% gap fraction in logged areas, compared to 5.6% in unlogged area); and greater numbers of gaps penetrating to the forest floor (162 gaps at 2 m height in logged blocks, and 101 in an unlogged block). Comparison of LiDAR measurements with field data demonstrated that LiDAR delivered accurate results. We found that gap size distributions deviated from power-laws reported previously, with substantially fewer large gaps than predicted by power-law functions. Our OPEN ACCESSRemote Sens. 2015, 7 8349 analyses demonstrate that LiDAR is a useful tool for distinguishing structural differences between old-growth and old-secondary forests. That makes LiDAR a powerful tool for REDD+ (Reduction of Emissions from Deforestation and Forest Degradation) programs implementation and conservation planning.

Lidar detection of individual tree size in tropical forests

“…In fact, there is limited ground-based analysis on those relationships over tropical regions, but they report probability density functions with shapes similar to the ones shown in Fig. 8c and d. In particular, tropical tree height distributions are characterized by a right-skewed distribution with the peak corresponding to mid-range trees (Kent, Lindsell, Laurin, Valentini, & Coomes, 2015;Shimizu et al, 2014), which is around 10-15 m over the BCI 50ha plot according to the AMS3D (Fig. 8c).…”

Drivers of aboveground wood production in a lowland tropical forest of West Africa: teasing apart the roles of tree density, tree diversity, soil phosphorus, and historical logging

“…Supporting these findings, Kent et al. () recently used airborne LiDAR imagery covering a vast swathe of Gola forest to show that these same logging operations left a clear and detectable fingerprint on the vertical structure of the forest canopy. Conversely, our results do not suggest that logging activities have had a long‐lasting impact on tree diversity in Gola (Fig.…”

“…Logging, for example, can impact AWP in a number of ways, including damaging live trees and altering the structure of the canopy (Okuda et al 2003;Asner et al 2004;Blanc et al 2009;West et al 2014), through soil impoverishment as a result of erosion and nutrient leaching (Chazdon 2003), and by facilitating the establishment of lianas (Schnitzer and Bongers 2011;Dur an et al 2013). One process in particularthe removal of large diameter trees (Okuda et al 2003;Bonnell et al 2011;Osazuwa-Peters et al 2015)can have a sizable and long-lasting impact on AWP, as large trees contribute disproportionately to productivity (Slik et al 2013;Michaletz et al 2014;Stephenson et al 2014) and it can take decades for surviving trees to take their place in the canopy (Martin et al 2013;Kent et al 2015;Osazuwa-Peters et al 2015).…”

Wavelet Based Analysis of TanDEM-X and LiDAR DEMs across a Tropical Vegetation Heterogeneity Gradient Driven by Fire Disturbance in Indonesia

“…Forest spatial structure from LiDAR datasets has been extensively studied with the aim of characterizing forest types using texture metrics [23]. Also forest disturbance has been addressed using these techniques (e.g., selective logging: [52,53]). Canopy grain analysis from FOTO applied to LiDAR derived Canopy Height Model (CHM) and Digital Surface Model (DSM) was also used to generate metrics related to structure and to ultimately improve above-ground biomass prediction [32,54].…”