Sammanfattning

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.

Nyckelord

Atlantic forest; Forest landscape restoration; UAV-borne LiDAR; Structural attributes; Forest understory; Forest succession; GatorEye; Structural variation

Publicerad i

Remote Sensing of Environment
2023, Volym: 290, artikelnummer: 113533
Utgivare: ELSEVIER SCIENCE INC

SLU författare

• Valbuena, Ruben

  • Institutionen för skoglig resurshushållning, Sveriges lantbruksuniversitet
    

Globala målen

SDG15 Skydda, återställa och främja ett hållbart nyttjande av landbaserade ekosystem, hållbart bruka skogar, bekämpa ökenspridning, hejda och vrida tillbaka markförstöringen samt hejda förlusten av biologisk mångfald
SDG13 Vidta omedelbara åtgärder för att bekämpa klimatförändringarna och dess konsekvenser


UKÄ forskningsämne

Fjärranalysteknik
Skogsvetenskap


Publikationens identifierare

DOI: https://doi.org/10.1016/j.rse.2023.113533

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/121910