@Article{ShimabukuroArDuJoSaGaDu:2019:MoDeFo,
author = "Shimabukuro, Yosio Edemir and Arai, Egidio and Duarte, Valdete and
Jorge, Anderson and Santos, Erone Ghyizoni dos and Gasparini, Kaio
Allan Cruz and Dutra, Andeise Cerqueira",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Monitoring deforestation and forest degradation using
multi-temporal fraction images derived from Landsat sensor data in
the Brazilian Amazon",
journal = "International Journal of Remote Sensing",
year = "2019",
volume = "40",
number = "4",
pages = "5475--5496",
month = "July",
abstract = "Deforestation is the replacement of forest by other land use while
degradation is a reduction of long-term canopy cover and/or forest
stock. Forest degradation in the Brazilian Amazon is mainly due to
selective logging of intact/un-managed forests and to uncontrolled
fires. The deforestation contribution to carbon emission is
already known but determining the contribution of forest
degradation remains a challenge. Discrimination of logging from
fires, both of which produce different levels of forest damage, is
important for the UNFCCC (United Nations Framework Convention on
Climate Change) REDD+ (Reducing Emissions from Deforestation and
Forest Degradation) program. This work presents a semi-automated
procedure for monitoring deforestation and forest degradation in
the Brazilian Amazon using fraction images derived from Linear
Spectral Mixing Model (LSMM). Part of a Landsat Thematic Mapper
(TM) scene (path/row 226/068) covering part of Mato Grosso State
in the Brazilian Amazon, was selected to develop the proposed
method. First, the approach consisted of mapping deforested areas
and mapping forest degraded by fires using image segmentation.
Next, degraded areas due to selective logging activities were
mapped using a pixel-based classifier. The results showed that the
vegetation, soil, and shade fraction images allowed deforested
areas to be mapped and monitored and to separate degraded forest
areas caused by selective logging and by fires. The comparison of
Landsat Operational Land Imager (OLI) and RapidEye results for the
year 2013 showed an overall accuracy of 94%. We concluded that
spatial resolution plays an important role for mapping selective
logging features due to their characteristics. Therefore, when
compared to Landsat data, the current availability of higher
spatial and temporal resolution data, such as provided by
Sentinel-2, is expected to improve the assessment of deforestation
and forest degradation, especially caused by selective logging.
This will facilitate the implementation of actions for forest
protection.",
doi = "10.1080/01431161.2019.1579943",
url = "http://dx.doi.org/10.1080/01431161.2019.1579943",
issn = "0143-1161",
language = "en",
targetfile = "Monitoring deforestation and forest degradation using multi
temporal fraction images derived from Landsat sensor data in the
Brazilian Amazon.pdf",
urlaccessdate = "05 maio 2024"
}