Journal article
Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
Remote Sensing of Environment, Vol.264, pp.1-13
2021
Abstract
Remote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance our understanding of changes in ecosystem composition, structure, and function of forests, offering multi-scale monitoring of forest restoration. UAV systems can generate high-resolution images that provide accurate information on forest ecosystems to aid decision-making in restoration projects. However, UAV technological advances have outpaced practical application; thus, we explored combining UAV-borne lidar and hyperspectral data to evaluate the diversity and structure of restoration plantings. We developed novel analytical approaches to assess twelve 13-year-old restoration plots experimentally established with 20, 60 or 120 native tree species in the Brazilian Atlantic Forest. We assessed (1) the congruence and complementarity of lidar and hyperspectral-derived variables, (2) their ability to distinguish tree richness levels and (3) their ability to predict aboveground biomass (AGB). We analyzed three structural attributes derived from lidar data—canopy height, leaf area index (LAI), and understory LAI—and eighteen variables derived from hyperspectral data—15 vegetation indices (VIs), two components of the minimum noise fraction (related to spectral composition) and the spectral angle (related to spectral variability). We found that VIs were positively correlated with LAI for low LAI values, but stabilized for LAI greater than 2 m2/m2. LAI and structural VIs increased with increasing species richness, and hyperspectral variability was significantly related to species richness. While lidar-derived canopy height better predicted AGB than hyperspectral-derived VIs, it was the fusion of UAV-borne hyperspectral and lidar data that allowed effective co-monitoring of both forest structural attributes and tree diversity in restoration plantings. Furthermore, considering lidar and hyperspectral data together more broadly supported the expectations of biodiversity theory, showing that diversity enhanced biomass capture and canopy functional attributes in restoration. The use of UAV-borne remote sensors can play an essential role during the UN Decade of Ecosystem Restoration, which requires detailed forest monitoring on an unprecedented scale.
Details
- Title
- Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
- Authors
- Danilo Roberti Alves de Almeida (Author) - University of Sao PauloEben North Broadbent (Author) - University of FloridaMatheus Pinheiro Ferreira (Author) - Military Institute of EngineeringPaula Meli (Author) - Universidad de La FronteraAngelica Maria Almeyda Zambrano (Author) - University of FloridaEric Bastos Gorgens (Author) - Universidade Federal dos Vales do Jequitinhonha e MucuriAngelica Faria Resende (Author) - Universidade Federal dos Vales do Jequitinhonha e MucuriCatherine Torres de Almeida (Author) - University of Sao PauloCibele Hummel do Amaral (Author) - Universidade Federal de ViçosaAna Paula Dalla Corte (Author) - Universidade Federal do ParanáCarlos Alberto Silva (Author) - University of FloridaJoão P Romanelli (Author) - University of Sao PauloGabriel Atticciati Prata (Author) - University of FloridaDaniel de Almeida Papa (Author) - Embrapa Acre, Rio Branco, Acre, BrazilScott C Stark (Author) - Michigan State UniversityRuben Valbuena (Author) - Bangor UniversityBruce Walker Nelson (Author) - National Institute of Amazonian ResearchJoannes Guillemot (Author) - University of Sao PauloJean-Baptiste Féret (Author) - University of Sao PauloRobin Chazdon (Author) - University of the Sunshine Coast, Queensland, Tropical Forests & People Research CentrePedro H.S Brancalion (Author) - University of Sao Paulo
- Publication details
- Remote Sensing of Environment, Vol.264, pp.1-13
- Publisher
- Elsevier BV
- DOI
- 10.1016/j.rse.2021.112582
- ISSN
- 1879-0704
- Organisation Unit
- University of the Sunshine Coast, Queensland; Tropical Forests & People Research Centre; Forest Research Institute
- Language
- English
- Record Identifier
- 99569198302621
- Output Type
- Journal article
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- Domestic collaboration
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- Environmental Sciences
- Imaging Science & Photographic Technology
- Remote Sensing
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