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Analysis of forest cover change and its influence on sustainability indicators in Ecuadorian Amazon.
* 1, 2 , 3 , 4, 5 , 6 , 7 , 5, 8
1  Remote Sensing | Spatial Analysis Lab (REMOSA) Department of Environment Ghent University 9000 Ghent, Belgium, Gladysmaria.villegasrugel@ugent.be
2  Facultad de Ingeniería en Eléctrica y Computación, ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador.
3  Remote Sensing | Spatial Analysis Lab (REMOSA) Department of Environment Ghent University 9000 Ghent, Belgium;frieke.vancoillie@ugent.be
4  AgSystems, Ceigram, itdUPM, Centro de Innovación en Tecnología para el Desarrollo, Universidad Politécnica de Madrid, 28040, España; mageher@gmail.com
5  Universidad Regional Amazónica IKIAM, Km 7 Vía Muyuna, Tena 150150, Ecuador;bolier.torres@ikiam.edu.ec
6  Facultad de Ingeniería en Eléctrica y Computación, ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador, dochoa@fiec.espol.edu.ec
7  School of Agricultural and Environmental Sciences, Pontificia Universidad Católica del Ecuador SedeIbarra (PUCESI), Imbabura 100112, Ecuador; jmcayambe@pucesi.edu.ec
8  Departamento de Ciencias de la Vida, Universidad Estatal Amazónica (UEA), Pastaza 160101, Ecuador
Academic Editor: Lotus Guo

https://doi.org/10.3390/IECF2021-10815 (registering DOI)
Abstract:

The degradation of forest areas in the Amazon region, where many indigenous communities live, has shown a marked deterioration in recent years. The Yasuní Biosphere Reserve (YBR), placed on the Ecuadorian Amazon and settled by several indigenous groups, is considered a hotspot of natural and cultural diversity. One of the most well-known communities in this region is the Kichwa, which is characterised by its traditional production systems, which in turn represents a means of subsistence and socio-ecological integration. In this study, we draw attention to the issue of forest cover management in the transition of cover zones on the YBR in the context of determining a relationship with anthropogenic activities. In our analysis, we use long-term vegetation data, from 2013 to 2020, and both Landsat 5 TM and Landsat 8 OLI/TIRS imagery to estimate changes in forest cover, grasslands, other lands and water, through a supervised classification technique that uses a random forest classification algorithm and a transition matrix. To determine the relationship between the Kichwa community sustainability indicator and vegetation changes, a multiple regression model was used which is based on a socio-productive survey completed by 133 Kichwa households. The results show that forest lost more than 11% of the areas between 2013 and 2020 and grasslands gained more than 10%. Annual changes in NDVI were mainly driven by land uses, economic viability and quality of life. This study is important in order to promote the continued use of green projects to address environmental change and improve the lives of indigenous communities.

Keywords: Forest cover change; LULC; Indigenous communities;
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