Mapping and monitoring forest degradation using remote sensing data with medium and moderate spatial resolution

• Grant number: 16/19806-3
• Support type: Regular Research Grants
• Duration: February 01, 2017 - January 31, 2019
• Field of knowledge: Agronomical Sciences - Forestry Resources and Forestry Engineering
• Principal researcher: Yosio Edemir Shimabukuro
• Grantee: Yosio Edemir Shimabukuro
• Home Institution: Instituto Nacional de Pesquisas Espaciais (INPE). Ministério da Ciência, Tecnologia e Inovações (Brasil). São José dos Campos , SP, Brazil
• Assoc. researchers: Egidio Arai ; Francielle da Silva Cardozo ; Gabriel Pereira ; Liana Oighenstein Anderson ; Luiz Eduardo Oliveira e Cruz de Aragão ; Valdete Duarte

Abstract

This project presents a methodological approach for mapping and monitoring forest degraded areas due to selective logging and forest fires. In this context, the State of Mato Grosso was selected for the study area, a region that has several vegetation types and has suffered major changes in the land use and land cover due to the advance of agriculture in the cerrado regions, as well as the activities of deforestation and burning in the legal Amazon region, by natural or anthropogenic causes. For this, it will be used remote sensing images with medium spatial resolution of Landsat acquired during the years 2000 (TM), 2010 (TM) and 2015 (OLI). In addition, it will be used moderate spatial resolution images of MODIS (2001 to 2015) and PROBA-V (2015) sensors. The general objectives of this study are: 1) to develop a methodology for detection of degraded forest areas due to selective logging and burning; 2) to generate a land cover map of the region in a medium spatial resolution for the year 2015; 3) to generate maps of degraded forest areas for the study years; and 4) to analyse the fire incidence in deforested burned areas and to estimate the burned biomass and its greenhouse gases emitted into atmosphere. For the validation of the results it will be used high spatial resolution images from RapidEye, Sentinel-2 and LiDAR in test areas, and four field works will be carried out to support the image interpretations. The informations obtained by the project are important as a base for planning and management of natural resources in the region, as well as to improve the carbon emission estimates. (AU)

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GODINHO CASSOL, HENRIQUE LUIS; ARAI, EGIDIO; EYJI SANO, EDSON; DUTRA, ANDEISE CERQUEIRA; HOFFMANN, TANIA BEATRIZ; SHIMABUKURO, YOSIO EDEMIR. Maximum Fraction Images Derived from Year-Based Project for On-Board Autonomy-Vegetation (PROBA-V) Data for the Rapid Assessment of Land Use and Land Cover Areas in Mato Grosso State, Brazil. LAND, v. 9, n. 5 MAY 2020. Web of Science Citations: 0.

SHIMABUKURO, YOSIO EDEMIR; ARAI, EGIDIO; DUARTE, VALDETE; DUTRA, ANDEISE CERQUEIRA; GODINHO CASSOL, HENRIQUE LUIS; SANO, EDSON EYJI; HOFFMANN, TANIA BEATRIZ. Discriminating Land Use and Land Cover Classes in Brazil Based on the Annual PROBA-V 100 m Time Series. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, v. 13, p. 3409-3420, 2020. Web of Science Citations: 0.

SHIMABUKURO, YOSIO EDEMIR; ARAI, EGIDIO; DUARTE, VALDETE; JORGE, ANDERSON; DOS SANTOS, ERONE GHIZONI; CRUZ GASPARINI, KAIO ALLAN; DUTRA, ANDEISE CERQUEIRA. Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon. International Journal of Remote Sensing, v. 40, n. 14, p. 5475-5496, JUL 18 2019. Web of Science Citations: 3.

DOS SANTOS, ERONE GHIZONI; SHIMABUKURO, YOSIO EDEMIR; DE MOURA, YHASMIN MENDES; GONCALVES, FABIO GUIMARAES; JORGE, ANDERSON; GASPARINI, KAIO ALAN; ARAI, EGIDIO; DUARTE, VALDETE; OMETTO, JEAN PIERRE. Multi-scale approach to estimating aboveground biomass in the Brazilian Amazon using Landsat and LiDAR data. International Journal of Remote Sensing, v. 40, n. 22 JUN 2019. Web of Science Citations: 0.