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Title: Combining airborne lidar and acoustic remote sensing to characterize the impacts of Amazon forest degradation
Frontier forests in the Brazilian Amazon have been heavily altered by nearly a half-century of deforestation for agriculture and degradation from fire and logging. The long-term effects of forest degradation on habitat structure and habitat use remain poorly understood, largely due to the limitations of traditional field methods for characterizing heterogeneity at relevant spatial and temporal scales. This work demonstrates the opportunity to assess degradation impacts on ecosystem structure and biodiversity at landscape scales (200 km2) by combining airborne lidar and acoustic remote sensing across two municipalities in Mato Grosso, Feliz Natal and Nova Ubiratã. Among degradation classes, our results indicate that repeated fire events have the most destructive legacy for both habitat structure and habitat use. Lidar analyses reveal that repeated fire events can result in a total loss of original canopy trees. Similarly, our acoustic analyses suggest that repeated fires may fundamentally transform animal community composition. The combination of remote sensing approaches bridges the scale gap between ground-based and satellite observations to support a regional-scale investigation into the complex consequences of Amazon forest degradation.  more » « less
Award ID(s):
1634168
NSF-PAR ID:
10059221
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Anais do Simpósio Brasileiro de Sensoriamento Remoto
Page Range / eLocation ID:
4040-4047
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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