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Title: The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration
Authors: Almeida, Danilo Roberti Alves de
Stark, Scott C.
Chazdon, Robin L.
Nelson, Bruce Walker
César, Ricardo Gomes
Meli, Paula
Görgens, Eric Bastos
Duarte, Marina Melo
Valbuena, Rubén
Moreno, Vanessa Sousa
Mendes, Alex Fernando
Amazonas, Nino Tavares
Gonçalves, Nathan Borges
Silva, Carlos Alberto
Schietti, Juliana
Brancalion, Pedro Henrique Santin
Keywords: Biodiversity
Cost Effectiveness
Land Reclamation
Optical Radar
Remote Sensing
Atlantic Forest
Forest Canopies
Forest Regeneration
Forest Succession
Tropical Forest
Forest Canopy
Forest Cover
Forest Ecosystem
Laser Method
Leaf Area
Remote Sensing
Restoration Ecology
Secondary Forest
Species Diversity
Species Richness
Tropical Forest
Cost Effectiveness
Land Reclamation
Remote Sensing
Atlantic Forest
Issue Date: 2019
metadata.dc.publisher.journal: Forest Ecology and Management
metadata.dc.relation.ispartof: Volume 438, Pags. 34-43
Abstract: Ambitious pledges to restore over 400 million hectares of degraded lands by 2030 have been made by several countries within the Global Partnership for Forest Landscape Restoration (FLR). Monitoring restoration outcomes at this scale requires cost-effective methods to quantify not only forest cover, but also forest structure and the diversity of useful species. Here we obtain and analyze structural attributes of forest canopies undergoing restoration in the Atlantic Forest of Brazil using a portable ground lidar remote sensing device as a proxy for airborne laser scanners. We assess the ability of these attributes to distinguish forest cover types, to estimate aboveground dry woody biomass (AGB) and to estimate tree species diversity (Shannon index and richness). A set of six canopy structure attributes were able to classify five cover types with an overall accuracy of 75%, increasing to 87% when combining two secondary forest classes. Canopy height and the unprecedented “leaf area height volume” (a cumulative product of canopy height and vegetation density) were good predictors of AGB. An index based on the height and evenness of the leaf area density profile was weakly related to the Shannon Index of tree species diversity and showed no relationship to species richness or to change in species composition. These findings illustrate the potential and limitations of lidar remote sensing for monitoring compliance of FLR goals of landscape multifunctionality, beyond a simple assessment of forest cover gain and loss. © 2019 Elsevier B.V.
metadata.dc.identifier.doi: 10.1016/j.foreco.2019.02.002
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