Search

Lake Burlan, a lentic ecosystem artificially created by untreated runoff from adjacent rice fields, is located in the Amazon region within the dry forests of northern Peru. This body of water plays a fundamental role in agriculture and recreational activities in the area, which are fundamental to the local economy. This research aimed to evaluate the water quality of Lake Burlan using the Water Quality Index of Peru (WQI-PE). In addition, both spatial and depth variations of limnological parameters and trace elements were determined. The WQI-PE was calculated at seven sampling stations at two depths (surface level and one meter), using 18 limnological parameters and nine trace elements. The WQI-PE assessment indicated that the lake water quality ranged from poor to fair for both depths. Statistical analysis showed that nine limnological parameters and five trace elements showed spatial differences across seven sampling stations, while three limnological parameters and two trace elements showed depth-dependent variations. Concentrations of arsenic, cadmium, mercury, and lead were in exceedance of the national and international standards on environmental water quality. Therefore, the water quality of Lake Burlan is affected mainly by the impact of the surrounding rice fields and recreational activities. This research establishes a starting point for future monitoring to assist in the implementation of prevention and mitigation. [ABSTRACT FROM AUTHOR]

One of the world's major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the "salt and pepper" effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management. [ABSTRACT FROM AUTHOR]

Summary: The wetlands of the jalca ecoregion in the Andes of northern Peru form peat and play a major role in the hydrological ecosystem services of the ecoregion. Although peat is globally valued for carbon sequestration and storage, peatlands have not yet been mapped in the jalca. In this region, the Gocta waterfall, one of the 20 highest waterfalls in the world, depends on the jalca 's wetlands ecosystem. The local population depends on tourism to the waterfall and is concerned about preserving its drainage area. To inform conservation planning, in this study we delimited the drainage area of the Gocta waterfall and identified land tenure by applying Geographic Information System (GIS), remote sensing and participatory mapping techniques. Then, by classifying optical, radar and digital elevation models data, we mapped peatland in the jalca of the Gocta drainage area with an overall accuracy of 97.1%. Our results will inform conservation strategy in this complex area of communal, private and informal land tenure systems. At a regional level, this appears to be the first attempt at mapping peatlands using remote sensing imagery in the jalca ecoregion, and it represents a milestone for future efforts to map and conserve peatlands in other tropical mountain areas of the world. [ABSTRACT FROM AUTHOR]

Peru is one of the world's leading exporters of cocoa beans, which directly impacts the household economy of millions of small farmers. Currently, the expansion and modernization of the cocoa-growing area require the zoning of the territory with suitable biophysical and infrastructural conditions to facilitate optimizing productivity factors. Therefore, we analyzed land suitability for cocoa (Theobroma cacao L.) production on the Peruvian mainland as a support measure for sustainable agriculture. To this end, the climatological, edaphological, orographic, and socioeconomic criteria determining sustainable cocoa cultivation were identified and mapped. Three modeling approaches (Analytic Hierarchy Process—AHP, Maximum Entropy—MaxEnt, and AHP—MaxEnt combined) were further used to hierarchize the importance of the criteria and to model the potential territory for sustainable cocoa cultivation. In all three modeling approaches, climatological criteria stood out among the five most important criteria. Elevation (orographic criteria) is also featured in this group. On the other hand, San Martin and Amazonas emerged as the five regions with the largest area 'Highly suitable' for cocoa cultivation in all three modeling approaches, followed by Loreto, Ucayali, Madre de Dios, Cusco, Junín, and Puno, which alternated according to modeling approach. From most to least restrictive, the AHP, MaxEnt, and AHP–MaxEnt modeling approaches indicate that 1.5%, 5.3%, and 23.0% of the Peruvian territory is 'Highly suitable' for cocoa cultivation, respectively. [ABSTRACT FROM AUTHOR]

Land degradation is a permanent global threat that requires an interdisciplinary approach to addressing solutions in a given territory. This study, therefore, analyses environmentally sensitive areas to land degradation using the Mediterranean Desertification and Land Use (MEDALUS) and Geographic Information System (GIS) method through a multi-criteria approach in the district of Florida (Peru). For the method, we considered the main quality indicators such as: Climate Quality Index (CQI), Soil Quality Index (SQI), Vegetation Quality Index (VQI), and Management Quality Index (MQI). There were also identified groups of parameters for each of the quality indicators analyzed. The results showed that 2.96% of the study area is classified as critical; 48.85% of the surface is classified as fragile; 15.48% of the areas are potentially endangered, and 30.46% are not threatened by degradation processes. Furthermore, SQI, VQI, and MQI induced degradation processes in the area. Based on the results, five restoration proposals were made in the study area: (i) organic manure production, (ii) cultivated and improved pastures and livestock improvement, (iii) native forest restoration, (iv) construction of reservoirs in the top hills and (v) uses of new technologies. The findings and proposals can be a basic support and further improved by decision-makers when implemented in situ to mitigate degradation for a sustainable use of the territory. [ABSTRACT FROM AUTHOR]

Amazonas is a mountain region in Peru with high cloud cover, so using optical data in the analysis of surface changes of water bodies (such as the Burlan and Pomacochas lakes in Peru) is difficult, on the other hand, SAR images are suitable for the extraction of water bodies and delineation of contours. Therefore, in this research, to determine the surface changes of Burlan and Pomacochas lakes, we used Sentinel-1 A/B products to analyse the dynamics from 2014 to 2020, in addition to evaluating the procedure we performed a photogrammetric flight and compared the shapes and geometric attributes from each lake. For this, in Google Earth Engine (GEE), we processed 517 SAR images for each lake using the following algorithms: a classification and regression tree (CART), Random Forest (RF) and support vector machine (SVM).) 2021-02-10, then; the same value was validated by comparing the area and perimeter values obtained from a photogrammetric flight, and the classification of a SAR image of the same date. During the first months of the year, there were slight increases in the area and perimeter of each lake, influenced by the increase in rainfall in the area. CART and Random Forest obtained better results for image classification, and for regression analysis, Support Vector Regression (SVR) and Random Forest Regression (RFR) were a better fit to the data (higher R2), for Burlan and Pomacochas lakes, respectively. The shape of the lakes obtained by classification was similar to that of the photogrammetric flight. For 2021-02-10, for Burlan Lake, all 3 classifiers had area values between 42.48 and 43.53, RFR 44.47 and RPAS 45.63 hectares. For Pomacohas Lake, the 3 classifiers had area values between 414.23 and 434.89, SVR 411.89 and RPAS 429.09 hectares. Ultimately, we seek to provide a rapid methodology to classify SAR images into two categories and thus obtain the shape of water bodies and analyze their changes over short periods. A methodological scheme is also provided to perform a regression analysis in GC using five methods that can be replicated in different thematic areas. [ABSTRACT FROM AUTHOR]

The evaluation of suitable landfill sites is a complex process and requires various legislative, technical, social, and environmental criteria. Therefore, this study provides a management tool for identifying suitable sites for landfills through the integrated use of the analytic hierarchy process (AHP), geographic information systems (GISs), and remote sensing (RS). Accordingly, fourteen subcriteria were identified and grouped into physical (7), environmental (3), and socioeconomic (4) criteria and were weighed using pairwise comparison matrices (PCMs). The weighted linear combination (WLC) approach of maps allowed us to generate models and submodels of land suitability. From the territory of the districts of Chachapoyas and Huancas, 0.9% (1.71 km2), 71.1% (141.89 km2), 21.0% (41.86 km2), 0.0%, and 7.7% (14.21 km2) have highly suitable, moderately suitable, marginally suitable, unsuitable, and restricted conditions, respectively, for a landfill site. Twelve highly suitable sites were identified, of which three were selected based on their shape and the minimum area required for the operation of the landfill until 2040. In fact, this study proposes a management tool for decision-makers (DMs) that improve the process of selecting landfill sites, supported by engineering and its applications for territorial sustainability. [ABSTRACT FROM AUTHOR]

Copyright of Boletín de la Asociación de Geógrafos Españoles is the property of Asociacion de Geografos Espanoles and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Meteorological observations play a major role in land management; thus, it is vital to properly plan the monitoring network of weather stations (WS). This study, therefore, selected 'highly suitable' sites with the objective of replanning the WS network in Amazonas, NW Peru. A set of 11 selection criteria for WS sites were identified and mapped in a Geographic Information System, as well as their importance weights were determined using Analytic Hierarchy Process and experts. A map of the suitability of the territory for WS sites was constructed by weighted superimposition of the criteria maps. On this map, the suitability status of the 20 existing WS sites was then assessed and, if necessary, relocated. New 'highly suitable' sites were determined by the Near Analysis method using existing WS (some relocated). The territory suitability map for WS showed that 0.3% (108.55 km2) of Amazonas has 'highly suitable' characteristics to establish WS. This 'highly suitable' territory corresponds to 26,683 polygons (of ≥30 × 30 m each), from which 100 polygons were selected in 11 possible distributions of new WS networks in Amazonas, with different number and distance of new WS in each distribution. The implementation of this methodology will be a useful support tool for WS network planning. [ABSTRACT FROM AUTHOR]

The increasing demand for tropical timber from natural forests has reduced the population sizes of native species such as Cedrela spp. because of their high economic value. To prevent the decline of population sizes of the species, all Cedrela species have been incorporated into Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). The study presents information about the modeled distribution of the genus Cedrela in Peru that aims to identify potential habitat distribution of the genus, its availability in areas protected by national service of protected areas, and highlighted some areas because of their conservation relevance and the potential need for restoration. We modeled the distribution of the genus Cedrela in Peru using 947 occurrence records that included 10 species (C. odorata, C. montana, C. fissilis, C. longipetiolulata, C. angustifolia, C. nebulosa, C. kuelapensis, C. saltensis, C. weberbaueri, and C. molinensis). We aim to identify areas environmentally suitable for the occurrence of Cedrela that are legally protected by the National Service of Protected Areas (PAs) and those that are ideal for research and restoration projects. We used various environmental variables (19 bioclimatic variables, 3 topographic factors, 9 edaphic factors, solar radiation, and relative humidity) and the maximum entropy model (MaxEnt) to predict the probability of occurrence. We observed that 6.7% (86,916.2 km2) of Peru presents a high distribution probability of occurrence of Cedrela, distributed in 17 departments, with 4.4% (10,171.03 km2) of the area protected by PAs mainly under the category of protection forests. Another 11.65% (21,345.16 km2) of distribution covers areas highly prone to degradation, distributed mainly in the departments Ucayali, Loreto, and Madre de Dios, and needs immediate attention for its protection and restoration. We believe that the study will contribute significantly to conserve Cedrela and other endangered species, as well as to promote the sustainable use and management of timber species as a whole. [ABSTRACT FROM AUTHOR]

1 Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM), Chachapoyas 01001, Peru; rsalas@indes-ces.edu.pe (R.S.L.); nrojas@indes-ces.edu.pe (N.B.R.B.); jsilva@indes-ces.edu.pe (J.O.S.L.); miguel.barrena@untrm.edu.pe (M.Á.B.G.); We would also like to thank the Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES) of the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM). Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: The spectacled, or Andean, bear (Tremarctos ornatus) is threatened by human activities, despite being a key species for conservation. In our study, the sightings and tracks for spectacled bear were collected, key environmental variables affecting its distribution were identified, and its distribution was predicted under both current and future (2050 and 2070) conditions in Amazonas, northeastern Peru. Under current conditions, areas with "high", "moderate" and "low" probability for the spectacled bear distribution cover about 1.99% (836.22 km2), 14.46% (6081.88 km2) and 20.73% (8718.98 km2) of Amazonas, respectively. Under all future conditions, the "high" probability area will increase, while the "moderate" and "low" probability areas, as well as total area (sum of "high", "moderate" and "low"), will decrease. The, protected natural areas in Amazonas, currently and in the future, do not cover most of the important habitats for the spectacled bear. Therefore, to effectively conserve this species, it is strongly recommended that areas with "high" (even "moderate") probability and the main ecosystems it inhabits should be designated as priority areas for research and conservation (even in natural protected areas). We assume that our study will make a strong contribution towards the sustainable conservation for spectacled bear under such threaten conditions. The spectacled, or Andean, bear (Tremarctos ornatus) is classified as vulnerable by the IUCN due to climate change and human-induced habitat fragmentation. There is an urgent need for the conservation of spectacled bear at real time. However, the lack of knowledge about the distribution of this species is considered as one of the major limitations for decision-making and sustainable conservation. In this study, 92 geo-referenced records of the spectacled bear, 12 environmental variables and the MaxEnt entropy modelling have been used for predictive modelling for the current and future (2050 and 2070) potential distribution of the spectacled bear in Amazonas, northeastern Peru. The areas of "high", "moderate" and "low" potential habitat under current conditions cover 1.99% (836.22 km2), 14.46% (6081.88 km2) and 20.73% (8718.98 km2) of the Amazon, respectively. "High" potential habitat will increase under all climate change scenarios, while "moderate" and "low" potential habitat, as well as total habitat, will decrease over the time. The "moderate", "low" and total potential habitat are distributed mainly in Yunga montane forest, combined grasslands/rangelands and secondary vegetation and Yunga altimontane (rain) forest, while "high" potential habitat is also concentrated in the Jalca. The overall outcome showed that the most of the important habitats of the spectacled bear are not part of the protected natural areas of Amazonas, under current as well as under future scenarios. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Anthropic activity affects the hydrogeomorphological quality of fluvial systems. River and valley classifications are fundamental preliminary steps in determining their ecological status, and their prioritization is essential for the proper planning and management of soil and water resources. Given the importance of the High Andean livestock micro-watershed (HAL-MWs) ecosystems in Peru, an integrated methodological framework is presented for morphometric prioritization that uses a Principal Component Analysis (PCA) and Weighted Sum Approach (WSA), geomorphological fluvial classifications (channel, slope, and valley), and hydrogeomorphological evaluations using the Hydrogeomorphological Index (IHG). Of six HAL-MWs studied in Leimebamba and Molinopampa (Amazonas region), the PCWSA hybrid model identified the San Antonio HAL-MW as a top priority, needing the rapid adoption of appropriate conservation practices. Thirty-nine types of river course were identified, by combining 13 types of valley and 11 types of riverbed. The total assessment of the IHG indicated that 7.6% (21.8 km), 14.5% (41.6 km), 27.9% (80.0 km), and 50.0% (143.2 km) of the basin lengths have "Poor", "Moderate", "Good", and "Very good" quality rankings, respectively. The increase in the artificial use of river channels and flood plains is closely linked to the decrease in hydrogeomorphological quality. [ABSTRACT FROM AUTHOR]

Peruvian aquaculture, specifically trout production, has had significant growth over the past ten years. However, the establishment and expansion of small-scale aquaculture have been carried out without considering the suitability of the land. In Peru, such land suitability studies have yet to be reported. Therefore, a methodological framework is presented for inland aquaculture, which may be replicated, with the necessary complements, for the entire Fisheries and Aquaculture sector in Peru. This work modeled the suitability of land for sustainable rainbow trout aquaculture in the Molinopampa district (Peru). Fifteen key criteria (socioeconomic, environmental and physicochemical) were identified for the proper development of fish activity. These were mapped using Remote Sensing (RS) and Geographic Information Systems (GIS). The Analytical Hierarchy Process (AHP) was applied to build peer-to-peer comparison matrices and weight the importance of the criteria. The weighted overlay of maps (criteria) made it possible to determine that 4.26%, 23.03% and 69.73% of the territory is "very suitable", "moderately suitable" and "marginally suitable", respectively, for the development of aquaculture. The implementation of this methodology will contribute to more effective investment planning and efforts, both by the government and by private initiative. [ABSTRACT FROM AUTHOR]