Suchergebnisse

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 km2 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 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. ; This research was funded by the European Union under the EuropeAid Programme, as a part of the Across the River Transboundary Peace Park Project DCI/ENV/2008/151-577; by a Cambridge Conservation Initiative Collaborative Fund grant "Applications of airborne remote sensing to the conservation management of a West African National Park"; and by the ERC grant Africa GHG #247349. We would also like to thank the British Technion Society for the generous funding of the post-doctoral Coleman-Cohen fellowship of R. Kent. ; This is the final version of the article. It first appeared from MDPI via http://dx.doi.org/10.3390/rs70708348