Next Article in Journal
Spatiotemporal Image Fusion in Remote Sensing
Next Article in Special Issue
Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest–Agriculture Mosaics in Temperate and Tropical Landscapes
Previous Article in Journal
Alternative Vegetation States in Tropical Forests and Savannas: The Search for Consistent Signals in Diverse Remote Sensing Data
Previous Article in Special Issue
Effective Band Ratio of Landsat 8 Images Based on VNIR-SWIR Reflectance Spectra of Topsoils for Soil Moisture Mapping in a Tropical Region
Open AccessArticle

Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR

1
Remote Sensing Division, National Institute for Space Research—INPE, São José dos Campos SP 12227-010, Brazil
2
School of Geography, University of Leeds, Leeds LS2 9JT, UK
3
Brazilian Forest Service, Brasília DF 70818-900, Brazil
4
Geography, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 817; https://doi.org/10.3390/rs11070817
Received: 13 February 2019 / Revised: 26 March 2019 / Accepted: 3 April 2019 / Published: 4 April 2019
(This article belongs to the Special Issue Remote Sensing of Tropical Environmental Change)
Logging, including selective and illegal activities, is widespread, affecting the carbon cycle and the biodiversity of tropical forests. However, automated approaches using very high resolution (VHR) satellite data (≤1 m spatial resolution) to accurately track these small-scale human disturbances over large and remote areas are not readily available. The main constraint for performing this type of analysis is the lack of spatially accurate tree-scale validation data. In this study, we assessed the potential of VHR satellite imagery to detect canopy tree loss related to selective logging in closed-canopy tropical forests. To do this, we compared the tree loss detection capability of WorldView-2 and GeoEye-1 satellites with airborne LiDAR, which acquired pre- and post-logging data at the Jamari National Forest in the Brazilian Amazon. We found that logging drove changes in canopy height ranging from −5.6 to −42.2 m, with a mean reduction of −23.5 m. A simple LiDAR height difference threshold of −10 m was enough to map 97% of the logged trees. Compared to LiDAR, tree losses can be detected using VHR satellite imagery and a random forest (RF) model with an average precision of 64%, while mapping 60% of the total tree loss. Tree losses associated with large gap openings or tall trees were more successfully detected. In general, the most important remote sensing metrics for the RF model were standard deviation statistics, especially those extracted from the reflectance of the visible bands (R, G, B), and the shadow fraction. While most small canopy gaps closed within ~2 years, larger gaps could still be observed over a longer time. Nevertheless, the use of annual imagery is advised to reach acceptable detectability. Our study shows that VHR satellite imagery has the potential for monitoring the logging in tropical forests and detecting hotspots of natural disturbance with a low cost at the regional scale. View Full-Text
Keywords: remote sensing; forest management; disturbance monitoring; forest dynamics; multi-temporal analysis; WorldView-2; GeoEye-1; random forest; Amazon; Jamari National Forest remote sensing; forest management; disturbance monitoring; forest dynamics; multi-temporal analysis; WorldView-2; GeoEye-1; random forest; Amazon; Jamari National Forest
Show Figures

Graphical abstract

MDPI and ACS Style

Dalagnol, R.; Phillips, O.L.; Gloor, E.; Galvão, L.S.; Wagner, F.H.; Locks, C.J.; Aragão, L.E.O.C. Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR. Remote Sens. 2019, 11, 817.

AMA Style

Dalagnol R, Phillips OL, Gloor E, Galvão LS, Wagner FH, Locks CJ, Aragão LEOC. Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR. Remote Sensing. 2019; 11(7):817.

Chicago/Turabian Style

Dalagnol, Ricardo; Phillips, Oliver L.; Gloor, Emanuel; Galvão, Lênio S.; Wagner, Fabien H.; Locks, Charton J.; Aragão, Luiz E.O.C. 2019. "Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR" Remote Sens. 11, no. 7: 817.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop