Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR

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Scheeres, Janneke ; de Jong, Johan ; Brede, Benjamin ; Brancalion, Pedro H. S. ; Broadbent, Eben Noth ; Zambrano, Angelica Maria Almeyda ; Gorgens, Eric Bastos ; Silva, Carlos Alberto ; Valbuena, Ruben ; Molin, Paulo ; Stark, Scott ; Rodrigues, Ricardo Ribeiro ; Rodrigues, Ribeiro ; Santoro, Giulio Brossi ; de Almeida, Catherine Torres ; de Almeida, Danilo Roberti Alves

• Document type: Journal article
• Source: REMOTE SENSING OF ENVIRONMENT; v. 290, p. 14-pg., 2023-03-15.

Abstract

Forest landscape restoration is a global priority to mitigate negative effects of climate change, conserve biodiversity, and ensure future sustainability of forests, with international pledges concentrated in tropical forest regions. To hold restoration efforts accountable and monitor their outcomes, traditional strategies for monitoring tree cover increase by field surveys are falling short, because they are labor-intensive and costly. Meanwhile remote sensing approaches have not been able to distinguish different forest types that result from utilizing different restoration approaches (conservation versus production focus). Unoccupied Aerial Vehicles (UAV) with light detection and ranging (LiDAR) sensors can observe forests` vertical and horizontal structural variation, which has the potential to distinguish forest types. In this study, we explored this potential of UAV-borne LiDAR to distinguish forest types in landscapes under restoration in southeastern Brazil by using a supervised classification method. The study area encompassed 150 forest plots with six forest types divided in two forest groups: conservation (remnant forests, natural regrowth, and active restoration plantings) and production (monoculture, mixed, and abandoned plantations) forests. UAV-borne LiDAR data was used to extract several Canopy Height Model (CHM), voxel, and point cloud statistic based metrics at a high resolution for analysis. Using a random forest classification model we could successfully classify conservation and production forests (90% accuracy). Classification of the entire set of six types was less accurate (62%) and the confusion matrix showed a divide between conservation and production types. Understory Leaf Area Index (LAI) and the variation in vegetation density in the upper half of the canopy were the most important classification metrics. In particular, LAI understory showed the most variation, and may help advance ecological understanding in restoration. The difference in classification success underlines the difficulty of distinguishing individual forest types that are very similar in management, regeneration dynamics, and structure. In a restoration context, we showed the ability of UAV-borne LiDAR to identify complex forest structures at a plot scale and identify groups and types widely distributed across different restored landscapes with medium to high accuracy. Future research may explore a fusion of UAV-borne LiDAR with optical sensors , include successional stages in the analyses to further characterize , distinguish forest types and their contributions to landscape restoration. (AU)

• FAPESP's process: 20/06734-0 - Unravelling landscape drivers of forest recovery in a successional perspective
• Grantee: Catherine Torres de Almeida
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• 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
• FAPESP's process: 19/24049-5 - Monitoring São Paulo State restoration forests: application of new remote sensing tools and subsidies for public policies
• Grantee: Angelica Faria de Resende
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/18416-2 - Understanding restored forests for benefiting people and nature - NewFor
• Grantee: Pedro Henrique Santin Brancalion
• Support Opportunities: BIOTA-FAPESP Program - Thematic Grants
• FAPESP's process: 20/15792-3 - Creating spaces for ecosystem restoration by increasing operational efficiency in the harvest of sugarcane
• Grantee: Giulio Brossi Santoro
• Support Opportunities: Scholarships in Brazil - Master