Land cover changes and comparison of current landscape metrics in a region of the Central Andes affected by population migration

Abstract

ABSTRACTThe Andes region has a rich history of environmental and human interactions that has shaped the landscape for millennia. Our study quantified the land cover changes in the districts of Huanta and Luricocha after the human population abandoned the region due to the armed conflict with Peruvian authorities (1980 – 1990) and their progressive return (1990 – 2020). We analysed satellite images of various resolutions, including Landsat-5 for the years 1986, 1990, 2000 and 2010; Landsat-8 for the year 2020, Sentinel-2 for the years 2017 and 2020; PeruSat-1 for the years 2017 and 2020, to assess the temporal change of land cover and landscape metrics. The classification was based on the pixel approach for Landsat images and Sentinel-2 with an average accuracy of 88.29% and 92.13% respectively, and an object-based (GEOBIA) approach for Perusat-1 images, with an average accuracy of 87.75%.The dominant coverages are high Andean grasslands, scrublands and croplands. According to the obtained data, high Andean grasslands and scrublands have decreased, which is associated with an increase in population over time. Likewise, there has been an increase in the area of lagoons and the expansion of nearby wetlands due to the construction of dams, driven by an increase in demand for water for human consumption and agriculture. However, areas with degraded soils, rocky outcrops, and forest plantations have expanded. The results suggest that abandoned agricultural lands did not recover quickly enough and more research and action is needed for restoration of the high-mountain ecosystem.Landscape metrics such as Edge Density (ED), Euclidean Distance to Nearest Neighbor (ENN) and Aggregation Index (AI) were calculated for different land cover (LC/LU) to characterize composition and structure, showing their variation at different spatial resolutions. This indicates that increasing spatial resolution makes it possible to better define the landscape characteristics.

Keywords

• Social conflicts
• land cover
• support vector machine
• GEOBIA
• multi-resolution