Impacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence

Author(s)

Neves d'Oliveira, Marcus Vinicio ^[1] ; Figueiredo, Evandro Orfano ^[1] ; Alves de Almeida, Danilo Roberti ^[2] ; Oliveira, Luis Claudio ^[1] ; Silva, Carlos Alberto ^[3] ; Nelson, Bruce Walker ^[4] ; da Cunha, Renato Mesquita ^[5] ; Papa, Daniel de Almeida ^[1] ; Stark, Scott C. ^[6] ; Valbuena, Ruben ^[7]

Affiliation

^[1] Embrapa Acre, Rodovia BR-364, Km 14, BR-69900056 Rio Branco, Acre - Brazil

^[2] Univ Sao Paulo USP ESALQ, Coll Agr, Dept Forest Sci Luiz de Queiroz, Piracicaba, SP - Brazil

^[3] Univ Florida, Sch Forest Fisheries & Geomat Sci, Forest Biometr & Remote Sensing Lab, Silva Lab, Gainesville, FL 32611 - USA

^[4] Natl Inst Amazon Res INPA, Manaus, Amazonas - Brazil

^[5] Inst Meio Ambiente Acre, Rio Branco, Acre - Brazil

^[6] Michigan State Univ, Dept Forestry, E Lansing, MI 48824 - USA

^[7] Bangor Univ, Sch Nat Sci, Bangor, Gwynedd - Wales

• Document type: Journal article
• Source: FOREST ECOLOGY AND MANAGEMENT; v. 500, NOV 15 2021.
• Web of Science Citations: 0

Abstract

Sustainable forest management relies on good knowledge of forest structure obtained from ground surveys combined with remote sensing. Capable of detecting both the forest floor and canopy elements, airborne LiDAR can estimate forest structure parameters with accuracy and precision, but is still difficult to acquire due to the lake of service provider in remote regions of developing countries. Alternatively if ground surface elevations are known (e.g., from LiDAR), they can be tied to a canopy surface model derived from stereo photogrammetry using RGB images from unmanned aerial vehicles (UAV). Here we assessed whether such photogrammetric canopy measurements offer aboveground biomass (AGB) and disturbance impact estimates from logging that are comparable to LiDAR, and whether the use of both in sequence can provide an efficient post-harvest monitoring system. Specifically, through a combination of forest inventory ground plots, airborne LiDAR data, and a UAVRGB camera system we (i) automatically located and measured canopy disturbance caused by logging, (ii) compared AGB models produced by LiDAR alone and the combination of LiDAR (for terrain elevation model) and RGB-photogrammetry (for forest surface model), and (iii) estimated the AGB stock loss from logging. The study was carried out in the Antimary State forest located in the southwestern Brazilian Amazon. Our results demonstrate that the use of RGB-photogrammetry in regions where the terrain elevation has already been estimated can be an effective way to rapidly identify selective logging and to accurately monitor its impact. (AU)

• FAPESP's process: 18/21338-3 - Monitoring forest landscape restoration from unmanned aerial vehicles using Lidar and hyperspectral remote sensing
• Grantee: Danilo Roberti Alves de Almeida
• Support Opportunities: Scholarships in Brazil - Post-Doctoral