Deforestación en la Amazonía peruana: índices de cambios de cobertura y uso del suelo basado en SIG

Briceño, Niltón B. Rojas; Castillo, Elgar Barboza; Maicelo, Jorge L.; Oliva‐Cruz, Manuel; López, Rolando Salas

doi:10.21138/bage.2538a

Cited by 32 publications

(41 citation statements)

References 47 publications

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“…In fact, the Peruvian Amazon has lost 22,848.88 km 2 of forest between 2001 and 2018, and Amazonas ranks eighth with 880 km 2 of forest loss [ 61 ]. Studies in Amazonas [ 62 ], indicate that deforestation has maintained increasing rates in recent years. Figueroa et al [ 17 ] highlights that the loss of the habitat of the spectacled bear is due to the construction of road and hydroelectric networks in the dry forest of the Marañón (border between Amazonas and Cajamarca).…”

Section: Discussionmentioning

confidence: 99%

Predictive Modelling of Current and Future Potential Distribution of the Spectacled Bear (Tremarctos ornatus) in Amazonas, Northeast Peru

Mori

Castillo

Guzmán

et al. 2020

Animals

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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.

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Section: Discussionmentioning

confidence: 99%

Predictive Modelling of Current and Future Potential Distribution of the Spectacled Bear (Tremarctos ornatus) in Amazonas, Northeast Peru

Mori

Castillo

Guzmán

et al. 2020

Animals

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“…With the aim of creating a continuous surface able to represent the wildfires intensity at the Amazonas department level, Kernel Density Estimation (KDE) was used [75]. The KDE [76,77]. Next, the density map was reclassified according to the Jenks natural breaks classification method [78] in five classes related to different levels of BA density: very low, low, moderate, high, and very high [28,41,50].…”

Section: Identification Of Ba Patternsmentioning

confidence: 99%

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Castillo

Cayo

Almeida

et al. 2020

IJGI

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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.

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“…A Sentinel 2A satellite image (Path/Row: 17MRP; 2 August 2019; Bands: 2-8A, 11-12) acquired from the European Space Agency's (ESA) Copernicus Services Data Hub platform, was used to develop a map of Land Cover and Land Use (LC/LU), through QGIS' Semi-Automatic Classification Plugin (SCP) [40]. The digital processing of the image was carried out following the methodology of Rojas et al [41] at the SCP. Based on the CORINE Land Cover methodology adapted for Peru [42], prior knowledge of the study area and 68 training areas, 6 classes of LC/LU were ranked: artificialized area, scrubland and grassland, shrub vegetation, grass and crops, waterbody, and forests.…”

Section: Software Base Mapping and Satellite Imagerymentioning

confidence: 99%

Land Suitability for Sustainable Aquaculture of Rainbow Trout (Oncorhynchus mykiss) in Molinopampa (Peru) Based on RS, GIS, and AHP

Yunis

López

Oliva‐Cruz

et al. 2020

IJGI

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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.

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Deforestación en la Amazonía peruana: índices de cambios de cobertura y uso del suelo basado en SIG

Cited by 32 publications

References 47 publications

Predictive Modelling of Current and Future Potential Distribution of the Spectacled Bear (Tremarctos ornatus) in Amazonas, Northeast Peru

Predictive Modelling of Current and Future Potential Distribution of the Spectacled Bear (Tremarctos ornatus) in Amazonas, Northeast Peru

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Land Suitability for Sustainable Aquaculture of Rainbow Trout (Oncorhynchus mykiss) in Molinopampa (Peru) Based on RS, GIS, and AHP

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