@Article{DalagnolPhGlGaWaLoAr:2019:QuCaTr,
author = "Dalagnol, Ricardo and Phillips, Oliver L. and Gloor, Emanuel and
Galv{\~a}o, L{\^e}nio Soares and Wagner, Fabien Hubert and
Locks, Charton J. and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz
de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University
of Leeds} and {University of Leeds} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Servi{\c{c}}o Florestal Brasileiro} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Quantifying canopy tree loss and gap recovery in tropical forests
under low-intensity logging using VHR satellite imagery and
airborne LiDAR",
journal = "Remote Sensing",
year = "2019",
volume = "11",
number = "7",
month = "Apr.",
keywords = "remote sensing, forest management, disturbance monitoring, forest
dynamics, multi-temporal analysis, WorldView-2, GeoEye-1, random
forest, Amazon, Jamari National Forest.",
abstract = "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.",
doi = "10.3390/rs11070817",
url = "http://dx.doi.org/10.3390/rs11070817",
issn = "2072-4292",
language = "en",
targetfile = "remotesensing-11-00817-v2.pdf",
urlaccessdate = "27 abr. 2024"
}