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Urban growth and Land Use/Land Cover (LULC) changes have increased in recent decades due to anthropogenic activities. This study explored past and projected future LULC changes and urban growth patterns in the Bagua Grande and Chachapoyas districts using Landsat imagery, cloud computing, and predictive models for 1990 to 2031. The analysis of satellite images was grouped into four time periods (1990–2000, 2000–2011, 2011–2021 and 2021–2031). The Google Earth Engine (GEE) cloud-based system facilitated the classification of Landsat 5 ETM (1990, 2000, and 2011) and Landsat 8 OLI (2021) images using the Random Forest (RF) model. A simulation model integrating Cellular Automata (CA) and an Artificial Neural Network (ANN) Multilayer Perceptron (MLP) in the MOLUSCE plugin of QGIS was used to forecast urban sprawl to 2031. The resulting maps showed an overall accuracy (OA) of over 92%. A decrease in forested area was observed, from 20,807.97 ha in 1990 to 14,629.44 ha in 2021 in Bagua Grande and from 7,796.08 ha to 3,598.19 ha in Chachapoyas. In contrast, urban areas experienced a significant increase, from 287.49 to 1,128.77 ha in Bagua Grande and from 185.65 to 924.50 ha in Chachapoyas between 1990 and 2021. By 2031, the urban area of Bagua Grande is expected to increase from 1,128.77 to 1,459.25 ha (29%) in a southeast, south, southwest, west, and northwest direction. Chachapoyas expanded from 924.50 to 1138.05 ha (23%) in the southwest, north, northeast, and southeast directions. The study presents an analytical method integrating cloud processing, GIS, and change simulation modeling to evaluate urban growth spatio-temporal patterns and LULC changes. This approach effectively identified the main LULC changes and trends in the study area. In addition, potential urbanization areas are highlighted where there are still opportunities for developing planned and managed urban settlements.

Pastures provide different ecosystem services, such as carbon dioxide fixation, are indicators of climate change and prevent soil erosion. However, anthropogenic activities degrade and decrease soil diversity. Therefore, the objective was to evaluate pasture degradation using vegetation indices. For this purpose, the floristic composition was evaluated by means of linear transects (50 m) to determine the percentage of area covered by forages and weeds; the Normalized Vegetation Index (NDVI) and the Pasture Vegetation Cover (PVC) assessment methodology were used to determine pasture degradation, and the chemical properties of the soils were characterized. The degradation map showed that the range "S4-Strongly degraded (CVP, <40%) for the Pomacochas micro-watershed is 2.60% (93.43 ha); however, in Ventilla, 0.40% (8.95 ha) was obtained. An equation was generated from a multiple linear regression model and a principal component analysis (PCA) of the main regressor variables. The results were obtained for both micro-watersheds with an R2greater than 60% determined by CVP + Bands 3, 4, 5, 6, 8 and pH. In addition, the PCA showed that at acidic pH, the percentage of degradation increased, reaching the level of advanced degradation in both micro-watersheds under study, and when the pH values were close to neutrality, they indicated optimal zones for pasture production. This study provides a new source of information regarding the degradation of high Andean grasslands. It is also the basis for further research in the agricultural sector.