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Forest loss estimation by remote sensing analysis and drone multispectral imagery in zoonotic landscapes under deforestation in the Amazon

Grant number: 23/08053-8
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): December 01, 2023
Effective date (End): November 30, 2027
Field of knowledge:Biological Sciences - Parasitology - Entomology and Malacology of Parasites and Vectors
Principal Investigator:Gabriel Zorello Laporta
Grantee:Roberto Cardoso Ilacqua
Host Institution: Centro Universitário. Faculdade de Medicina do ABC (FMABC). Organização Social de Saúde. Fundação do ABC. Santo André , SP, Brazil
Associated research grant:21/06669-6 - Follow-up of zoonotic landscapes under deforestation and land-use changes in the Amazon, AP.JP2

Abstract

Remote sensing data are important for deforestation estimates and environmental monitoring of the Amazon rainforest. In a previous project, we used data from LANDSAT, SENTINEL and CBERS satellite images to produce an analytical protocol for measuring land use and occupation in rural settlements in the Brazilian Amazon. We have shown in published articles from my Scientific Initiation and my Masters that the incidence of cases of malaria and yellow fever are associated with a variation in the composition and configuration of the remaining tropical forest in the Amazon and Atlantic Forest biomes, respectively. However, the major limitation of these studies was the spatial resolution of satellite images, which affected the precision and accuracy of measured parameters of the sampled terrain. This could also affect the measure of the effect of deforestation on the risk of malaria for the local population. The objectives of the present work are: (1) to compare the accuracy and precision of different satellite and multispectral drone sensors in estimating Amazonian Forest loss and forest degradation over 5 years; (2) to assess variation in deforestation estimates on the measurement of malaria risk. The present project will use the logistical, operational, and intellectual supports of the Main Project (FAPESP-JP2 21/06669-6). The first cross-sectional study of the main project was undertaken, and preliminary results generated are presented in the present proposal, including the analytical rational for selection of 40 landscape sites under study in the Santa Luzia settlement, municipality of Cruzeiro do Sul, state of Acre, Brazil. Composition and configuration estimates of land use and cover in all these landscapes at three scales (7-3 Km2) were carried out for the period of July 2022 (the main project's first cross-sectional study). Here it is sought to apply cutting-edge multispectral drone flight plan for capturing orthomosaic images of the 40 study landscape sites in the second (July 2024) and third (July 2026) cross-sectional studies. These images will be classified using artificial intelligence and machine learning to produce estimates of deforestation with a minimum spatial detection of 0.04 meters between 2024-2026. We will apply the same classification to LANDSAT, SENTINEL and CBERS satellite images to compare sensors on deforestation estimates, using kappa concordance coefficient and linear and logistic regression models. The estimated variance of deforestation will be evaluated for its predictive association with the risk of malaria incidence. This risk will be estimated as an integral part of the main project in which there are other researchers and graduate students working in national and international collaboration networks. The expected results will be able to contribute to the monitoring of deforestation in the Amazon rainforest and will provide important information for the current plan to eliminate malaria in Brazil. The results will be disseminated through scientific publications, scientific meetings, congress presentations and in accordance with the dissemination plan of the Amazônia+10 FAPESP program.

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