Los bosques de la Amazonia peruana han experimentado importantes transformaciones desde mediados del siglo pasado. Este trabajo evaluó los Cambios de Cobertura y Uso del Suelo (CCUS) en la provincia de Rodríguez de Mendoza, mediante los métodos de clasificación supervisada de máxima probabilidad e interpretación visual interdependiente de imágenes del satélite Landsat, entre los períodos 1987–2001 y 2001–2016. Se construyeron matrices de tabulación cruzada y se calcularon tasas e índices anuales de cambio. Los resultados muestran una pérdida acumulada de 918,59 km2 de cobertura boscosa. La intensidad de CCUS y la tasa de deforestación fueron mayores en el segundo período de análisis. Se evidenció que las altas concentraciones de pérdida de cobertura boscosa están próximas a la red vial e hídrica. Las principales causas de la pérdida de bosques fueron la actividad ganadera y la expansión agrícola migratoria a pequeña explotación, favorecidas por la accesibilidad de infraestructuras de transporte.

During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.

Agricultural productivity in the Peruvian region of Amazonas is being jeopardized by conflicts and inadequate land use, that are ultimately contributing to environmental degradation. Therefore, our aim is to assess land suitability for potato (Solanum tuberosum L.) farming in the Jucusbamba and Tincas microwatersheds located in Amazonas, in order to improve land-use planning and enhance the crop productivity of small-scale farmers. The site selection methodology involved a pair-wise comparison matrix (PCM) and a weighted multicriteria analysis using the Analytical Hierarchy Process (AHP) on selected biophysical and socioeconomical drivers. Simultaneously, land cover mapping was conducted using field samples, remote sensing (RS), geostatistics and geographic information systems (GIS). The results indicated that for potato crop farming, the most important criteria are climatological (30.14%), edaphological (29.16%), topographical (25.72%) and socioeconomical (14.98%) in nature. The final output map indicated that 8.2% (22.91 km2) was highly suitable, 68.5% (190.37 km2) was moderately suitable, 21.6% (60.11 km2) was marginally suitable and 0.0% was not suitable for potato farming. Built-up areas (archaeological sites, urban and road networks) and bodies of water were discarded from this study (4.64 km2). This study intends to promote and guide sustainable agriculture through agricultural land planning.

Peru is one of the world’s main coffee exporters, whose production is driven mainly by five regions and, among these, the Amazonas region. However, a combined negative factor, including, among others, climate crisis, the incidence of diseases and pests, and poor land-use planning, have led to a decline in coffee yields, impacting on the family economy. Therefore, this research assesses land suitability for coffee production (Coffea arabica) in Amazonas region, in order to support the development of sustainable agriculture. For this purpose, a hierarchical structure was developed based on six climatological sub-criteria, five edaphological sub-criteria, three physiographical sub-criteria, four socio-economic sub-criteria, and three restrictions (coffee diseases and pests). These were integrated using the Analytical Hierarchy Process (AHP), Geographic Information Systems (GIS) and Remote Sensing (RS). Of the Amazonas region, 11.4% (4803.17 km2), 87.9% (36,952.27 km2) and 0.7% (295.47 km2) are “optimal”, “suboptimal” and “unsuitable” for the coffee growing, respectively. It is recommended to orient coffee growing in 912.48 km2 of territory in Amazonas, which presents “optimal” suitability for coffee and is “unsuitable” for diseases and pests. This research aims to support coffee farmers and local governments in the region of Amazonas to implement new strategies for land management in coffee growing. Furthermore, the methodology used can be applied to assess land suitability for other crops of economic interest in Andean Amazonian areas.

Forest and land degradation is a serious problem worldwide and the Peruvian National Map of Degraded Areas indicates that 13.78% (177,592.82 km2) of the country’s territory is degraded. Forest plantations can be a restoration strategy, while conserving economically important species affected by climate change and providing forestry material for markets. This study modelled the species distribution under current conditions and climate change scenarios of five Timber Forest Species (TFS) in the Amazonas Department, northeastern Peru. Modelling was conducted with Maximum Entropy (MaxEnt) using 26 environmental variables. Of the total distribution under current conditions of Cedrelinga cateniformis, Ceiba pentandra, Apuleia leiocarpa, Cariniana decandra and Cedrela montana, 34.64% (2985.51 km2), 37.96% (2155.86 km2), 35.34% (2132.57 km2), 33.30% (1848.51 km2), and 35.81% (6125.44 km2), respectively, correspond to degraded areas and, therefore, there is restoration potential with these species. By 2050 and 2070, all TFS are projected to change their distribution compared to their current ranges, regardless of whether it will be an expansion and/or a contraction. Consequently, this methodology is intended to guide the economic and ecological success of forest plantations in reducing areas degraded by deforestation or similar activities.