Pitahaya (Hylocereus megalanthus), commonly known as dragon fruit, is grown in tropical areas and has a promising future in the world market. At present, it is a crop developed by small-scale farmers. However, finding optimal areas for installing this crop is a major challenge. In this study, we evaluated the suitability of land for pitahaya cultivation in the department of Amazonas using integrated multi-criteria techniques such as geographic information systems (GISs) and remote sensing (RS). The analytic hierarchy process (AHP) method was used to select and rank the suitability criteria. The fuzzy-AHP (F-AHP) method was then applied to perform pairwise comparisons and determine the linguistic scaling of the requirements, and, using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the requirements with the highest preference for land suitability were selected. The results reported that for pitahaya cultivation, the most important criterion was mean annual temperature (20.70%), followed by soil organic matter (11.8%), mean annual rainfall (9.50%), and proximity to roads (9.0%). The final suitability map indicated that 0.006% (2.39 km²) was very suitable, 4.60% (1661.97 km²) moderately suitable, 0.10% (34.65 km²) marginally suitable, and 95.30% (34,459.31 km²) of the study area was not suitable.

Understanding how land use and climate change jointly affect water availability is essential for sustainable planning in tropical Andean–Amazonian basins. This study focuses on the Utcubamba River Basin in northeastern Peru, a data-scarce and socioecologically strategic watershed where land transformation and climate variability converge. A multi-temporal land use/land cover (LULC) analysis (1990–2024) was conducted, coupled with hydrological modeling using the SWAT model under historical and future climate scenarios (SSP2–4.5 and SSP5–8.5), including the spatial overlay of the LULC change concentration with key hydrological indicators. LULC classifications revealed forest loss and the expansion of pasture, agriculture, and shrubland areas, particularly in the upper basin. Hydrological projections showed significant changes in water flow, including reductions in minimum monthly flows by up to 73.9% and increases in peak flows by 14.8% under the SSP5–8.5 scenario. The water balance is expected to shift, with increased percolation and reduced lateral flow, suggesting decreased storage capacity. By identifying critical sub-basins where land degradation and water insecurity converge, the study supports adaptive strategies for land restoration, aquifer recharge planning, and ecosystem conservation. Despite limited hydrological infrastructure, the SWAT model effectively simulated water dynamics, aiding climate resilience and water security efforts in the Amazonian Andes.

Habitat fragmentation and loss seriously threaten Ceroxylon palms, a key and vulnerable species in Andean forests. Given the need for efficient tools for their monitoring and conservation, this study aimed to evaluate the effectiveness of deep learning YOLO models for the automatic detection of Ceroxylon individuals in high-resolution UAV images. Three versions of YOLO (v8, v10, and v11) were analyzed, each in nano (“n”), medium (“m”), and extra-high (“x”) configurations, considering both processing time and detection accuracy. Difficulties in orthomosaic reconstruction were addressed by specific adjustments to the photogrammetric software parameters. The nine resulting models were tested in seven study plots, with the YOLOv8-m configuration standing out as the one that best balanced processing speed and accuracy, achieving the following outstanding metrics: F1 = 0.91; mAP50 = 0.98; and mAP50-95 = 0.62. These results demonstrate the practical value of YOLO model automatic detection for the informed management and effective conservation of Ceroxylon in mountain ecosystems.

Accurate estimation of aboveground biomass (AGB) is essential for monitoring forage availability and guiding sustainable management in high-altitude pastures, where grazing sustains livelihoods but also drives ecological degradation. Although remote sensing has advanced biomass modeling in rangelands, applications in Andean–Amazonian ecosystems remain limited, particularly using UAV-based structural and spectral data. This study evaluated the potential of UAV LiDAR and multispectral imagery to estimate fresh and dry AGB in ryegrass (Lolium multiflorum Lam.) pastures of Amazonas, Peru. Field data were collected from subplots within 13 plots across two sites (Atuen and Molinopampa) and modeled using Random Forest (RF), Support Vector Machines, and Elastic Net. AGB maps were generated at 0.2 m and 1 m resolutions. Results revealed clear site- and month-specific contrasts, with Atuen yielding higher AGB than Molinopampa, linked to differences in climate, topography, and grazing intensity. RF achieved the best accuracy, with chlorophyll-sensitive indices dominating fresh biomass estimation, while LiDAR-derived height metrics contributed more to dry biomass prediction. Predicted maps captured grazing-induced heterogeneity at fine scales, while aggregated products retained broader gradients. Overall, this study shows the feasibility of UAV-based multi-sensor integration for biomass monitoring and supports adaptive grazing strategies for sustainable management in Andean–Amazonian ecosystems.

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has emerged as a promising tool for monitoring crop physiological status and supporting precision disease management. However, the capacity of multispectral vegetation indices to detect foliar diseases under commercial field conditions remains insufficiently understood, particularly in perennial crops grown in humid tropical environments. This study evaluated the potential of UAV-derived multispectral vegetation indices to assess the physiological response of avocado (Persea americana Mill.) canopies affected by anthracnose caused by Colletotrichum fructicola in Amazonas, Peru. Disease incidence and severity were assessed through field evaluations, while multispectral imagery was acquired using a UAV equipped with a MicaSense RedEdge-MX Dual sensor. Vegetation indices including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge Index (NDRE), Red Edge Chlorophyll Index (CIred-edge), Plant Senescence Reflectance Index (PSRI), and Visible Atmospherically Resistant Index (VARI) were calculated, and their relationships with anthracnose incidence were analyzed using Spearman’s rank correlation. Field observations confirmed a high incidence of foliar anthracnose with a heterogeneous spatial distribution across the orchard. Multispectral imagery successfully characterized spatial variability in the canopy physiological condition, revealing differences in vegetation vigor, chlorophyll-related reflectance, and senescence among experimental blocks. Nevertheless, none of the evaluated vegetation indices showed statistically significant correlations with anthracnose incidence (p > 0.05), indicating that spectral variability primarily reflected the general canopy physiological status rather than a disease-specific spectral response. These findings demonstrate that UAV-derived multispectral vegetation indices are valuable for monitoring spatial variability in the avocado canopy condition but have limited capability for independently detecting anthracnose under humid tropical field conditions. Future studies integrating multi-temporal UAV acquisitions, hyperspectral and thermal imagery, LiDAR, environmental variables, and machine-learning approaches are expected to improve the early detection and spatial prediction of anthracnose in avocado production systems.