Fredy Cabrera-Olivera
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Fredy Cabrera-Olivera has a total of 28 co-authors. Here are the top co-authors by number of shared publications:
- Edwin Pino-Vargas
Department of Civil Engineering, Jorge Basadre Grohmann National University, Tacna 23000, Peru
11 shared publications - German Huayna
Master’s Program in Water Resources, Jorge Basadre Grohmann National University, Tacna 23000, Peru
9 shared publications - Jorge Espinoza-Molina
Department of Architecture, Jorge Basadre Grohmann National University, Tacna 23000, Peru
6 shared publications - Karina Acosta-Caipa
Department of Architecture, Jorge Basadre Grohmann National University, Tacna 23000, Peru
5 shared publications - Bertha Silvana Vera Barrios
Faculty of Mining Engineering, National University of Moquegua, Moquegua 18001, Peru
4 shared publications - Lia Ramos-Fernández
Department of Water Resources, Universidad Nacional Agraria La Molina, Lima 15024, Peru
4 shared publications
Latest Publications
- Open Access
Range-Wide Habitat Suitability, Climate Change Exposure and Field Stand Structure in Two Andean Polylepis Species
Polylepis forests are the highest-elevation woody ecosystems on Earth and are severely threatened by climate change and land use, yet habitat models that go beyond binary presence/absence remain scarce. The current and future habitat suitability (2021–2100; CMIP6, four to five GCMs × four SSPs) of two co-occurring Andean species, Polylepis rugulosa and Polylepis tarapacana, was modelled with MaxEnt and Random Forest calibrated across their full range (Peru, Bolivia, Chile and Argentina) and clipped to the Tacna region of southern Peru. Internal spatial cross-validation was complemented by two external validations: 228 field-mapped stand polygons and 35 inventory plots (883 trees), the latter used to test whether suitability also tracks measured stand structure. Both algorithms performed well (AUC – across internal and external validation). Once within-plot non-independence and residual spatial autocorrelation were accounted for, no structural attribute was significantly associated with suitability: a positive suitability–multi-stemming trend persisted in Polylepis rugulosa (–) but not in Polylepis tarapacana, so this coupling is reported as a preliminary, hypothesis-generating result rather than as a validated predictive relationship. Within Tacna, suitable habitat above the maxSSS threshold is projected to contract by for Polylepis rugulosa and by for Polylepis tarapacana under SSP585 by 2081–2100 (MaxEnt ensemble; the near-total loss is unanimous across the contributing GCMs), revealing divergent climatic vulnerabilities. Reading suitability as a map of habitat quality rather than of habitat presence therefore remains a working hypothesis that larger plot networks and demographic data are needed to test.Open AccessRange-Wide Habitat Suitability, Climate Change Exposure and Field Stand Structure in Two Andean Polylepis Species
Polylepis forests are the highest-elevation woody ecosystems on Earth and are severely threatened by climate change and land use, yet habitat models that go beyond binary presence/absence remain scarce. The current and future habitat suitability (2021–2100; CMIP6, four to five GCMs × four SSPs) of two co-occurring Andean species, Polylepis rugulosa and Polylepis tarapacana, was modelled with MaxEnt and Random Forest calibrated across their full range (Peru, Bolivia, Chile and Argentina) and clipped to the Tacna region of southern Peru. Internal spatial cross-validation was complemented by two external validations: 228 field-mapped stand polygons and 35 inventory plots (883 trees), the latter used to test whether suitability also tracks measured stand structure. Both algorithms performed well (AUC – across internal and external validation). Once within-plot non-independence and residual spatial autocorrelation were accounted for, no structural attribute was significantly associated with suitability: a positive suitability–multi-stemming trend persisted in Polylepis rugulosa (–) but not in Polylepis tarapacana, so this coupling is reported as a preliminary, hypothesis-generating result rather than as a validated predictive relationship. Within Tacna, suitable habitat above the maxSSS threshold is projected to contract by for Polylepis rugulosa and by for Polylepis tarapacana under SSP585 by 2081–2100 (MaxEnt ensemble; the near-total loss is unanimous across the contributing GCMs), revealing divergent climatic vulnerabilities. Reading suitability as a map of habitat quality rather than of habitat presence therefore remains a working hypothesis that larger plot networks and demographic data are needed to test. - Open Access
Mountaintop-Extinction Risk for Polylepis tarapacana: An Elevational-Ceiling and No-Escape Framework in the South-Central Andes
Sustained warming is displacing montane species upslope, yet trees already at the upper limit of their mountains face a finite vertical refuge. This study assessed whether the world’s highest-elevation tree, Polylepis tarapacana, can remain within climatically suitable terrain as the climate warms across its South-Central Andean range (Peru, Bolivia, Chile, and Argentina), with field validation in Tacna, southern Peru. The aim was to quantify, under CMIP6 scenarios extending to 2100, how much suitable habitat the species retains and what fraction of any loss leaves no reachable higher-elevation refuge. A MaxEnt model, benchmarked against Random Forest, was calibrated on the full known range from GBIF occurrences and field plots using collinearity-filtered bioclimatic predictors and a sampling-bias-corrected background, projected as a multi-GCM ensemble under CMIP6 twenty-first-century scenarios, and validated externally against 148 field-mapped stand polygons (12,874 ha). A novel elevational-ceiling and “no-escape” framework quantified, for each scenario, the suitable area lost without any reachable higher-elevation refuge. The models discriminated suitable habitat well (external AUC , Boyce ). Under the highest-emission pathway, suitable habitat in Tacna contracts from (2030s) to (2090s) and the highly suitable class collapses by ; across the partial-loss scenarios, the suitability centroid shifts upslope by up to m. By 2081–2100 the no-escape fraction reaches at every tested dispersal buffer—to date the first quantitative evidence of mountaintop-extinction risk for the world’s highest-elevation tree. These results argue for anticipatory ex situ conservation and Peru–Bolivia cross-border protection of the highest-elevation Altiplano refugia, before the vertical escape route closes.Open AccessMountaintop-Extinction Risk for Polylepis tarapacana: An Elevational-Ceiling and No-Escape Framework in the South-Central Andes
Sustained warming is displacing montane species upslope, yet trees already at the upper limit of their mountains face a finite vertical refuge. This study assessed whether the world’s highest-elevation tree, Polylepis tarapacana, can remain within climatically suitable terrain as the climate warms across its South-Central Andean range (Peru, Bolivia, Chile, and Argentina), with field validation in Tacna, southern Peru. The aim was to quantify, under CMIP6 scenarios extending to 2100, how much suitable habitat the species retains and what fraction of any loss leaves no reachable higher-elevation refuge. A MaxEnt model, benchmarked against Random Forest, was calibrated on the full known range from GBIF occurrences and field plots using collinearity-filtered bioclimatic predictors and a sampling-bias-corrected background, projected as a multi-GCM ensemble under CMIP6 twenty-first-century scenarios, and validated externally against 148 field-mapped stand polygons (12,874 ha). A novel elevational-ceiling and “no-escape” framework quantified, for each scenario, the suitable area lost without any reachable higher-elevation refuge. The models discriminated suitable habitat well (external AUC , Boyce ). Under the highest-emission pathway, suitable habitat in Tacna contracts from (2030s) to (2090s) and the highly suitable class collapses by ; across the partial-loss scenarios, the suitability centroid shifts upslope by up to m. By 2081–2100 the no-escape fraction reaches at every tested dispersal buffer—to date the first quantitative evidence of mountaintop-extinction risk for the world’s highest-elevation tree. These results argue for anticipatory ex situ conservation and Peru–Bolivia cross-border protection of the highest-elevation Altiplano refugia, before the vertical escape route closes. - Open Access
- 24 March 2026
- Published by MDPI AG in Sustainability
- Vol. 18 (7) , 3179
Monitoring of Changes in Desertification in the High Andean Zone of Candarave: Case Study in Tacna, Perú, at the Headwaters of the Atacama Desert
Desertification is one of the main threats to high Andean ecosystems, particularly in arid and semi-arid regions subject to increasing climatic and anthropogenic pressures. This study evaluated the spatial-temporal dynamics of desertification in the province of Candarave (Tacna, Peru) by integrating the Remote Sensing-based Desertification Index (RSDI), constructed from a principal component analysis incorporating four biophysical indicators: vegetation greenness, surface moisture, soil grain size, and fraction of solar radiation reflected (albedo), derived from Landsat 5 and 8 satellite images processed in Google Earth Engine. Temporal trends were analyzed using the Mann–Kendall test, while system stability was evaluated using the coefficient of variation, allowing different degrees of stability and environmental degradation to be characterized during the period 2010–2025. The results show that moderate and severe desertification classes predominate in higher altitude areas, covering approximately 92% of the study area, and are characterized by insignificant to weakly significant negative trends associated with high to relatively high temporal volatility. In contrast, stable areas with no significant changes represent 5.3% of the territory, while restoration processes occupy a small proportion, close to 2.7%. The high variability observed in the high Andean sectors is mainly linked to the interaction between reduced water availability, climate variability, and extreme events, as well as anthropogenic pressures, particularly overgrazing and aquifer exploitation. This multitemporal analysis allows us to anticipate the evolution of desertification and highlights the need to strengthen conservation planning in order to reduce the degradation of strategic high Andean ecosystems in the Tacna region.
- 24 March 2026
- Published by MDPI AG in Sustainability
- Vol. 18 (7) , 3179
Open AccessMonitoring of Changes in Desertification in the High Andean Zone of Candarave: Case Study in Tacna, Perú, at the Headwaters of the Atacama Desert
Desertification is one of the main threats to high Andean ecosystems, particularly in arid and semi-arid regions subject to increasing climatic and anthropogenic pressures. This study evaluated the spatial-temporal dynamics of desertification in the province of Candarave (Tacna, Peru) by integrating the Remote Sensing-based Desertification Index (RSDI), constructed from a principal component analysis incorporating four biophysical indicators: vegetation greenness, surface moisture, soil grain size, and fraction of solar radiation reflected (albedo), derived from Landsat 5 and 8 satellite images processed in Google Earth Engine. Temporal trends were analyzed using the Mann–Kendall test, while system stability was evaluated using the coefficient of variation, allowing different degrees of stability and environmental degradation to be characterized during the period 2010–2025. The results show that moderate and severe desertification classes predominate in higher altitude areas, covering approximately 92% of the study area, and are characterized by insignificant to weakly significant negative trends associated with high to relatively high temporal volatility. In contrast, stable areas with no significant changes represent 5.3% of the territory, while restoration processes occupy a small proportion, close to 2.7%. The high variability observed in the high Andean sectors is mainly linked to the interaction between reduced water availability, climate variability, and extreme events, as well as anthropogenic pressures, particularly overgrazing and aquifer exploitation. This multitemporal analysis allows us to anticipate the evolution of desertification and highlights the need to strengthen conservation planning in order to reduce the degradation of strategic high Andean ecosystems in the Tacna region.
- Open Access
- 7 January 2026
- Published by Centro de Investigacion y Desarrollo Ecuador in Revista Alfa
- Vol. 10 (28) , 68-89
Clasificación de la calidad del agua subterránea utilizada en la agricultura mediante aprendizaje automático con datos desbalanceados
En zonas costeras áridas donde la sobreexplotación y la intrusión marina amenazan tanto la calidad como la disponibilidad del agua subterránea, esta es fundamental para la agricultura. Se utilizaron índices hidroquímicos y modelos de aprendizaje automático (AA) para determinar si el agua subterránea del acuífero Caplina (sur de Perú) era apta para el riego. El Índice de Calidad del Agua de Riego (ICAR) se calculó utilizando siete iones principales: Na⁺, Ca²⁺, Mg²⁺, K⁺, Cl⁻, SO₄²⁻ y HCO₃⁻. Este índice también se empleó para entrenar algoritmos de clasificación supervisada en un esquema binario (zonas críticas y no críticas). El clasificador XGBoost resultó ser el mejor modelo de los evaluados, con una puntuación F1 de 0,897, un área bajo la curva ROC (AUC-ROC) de 0,968 y una precisión de 0,927 mediante validación cruzada dejando uno fuera (n = 41). El análisis de sensibilidad reveló que los predictores más efectivos fueron Na⁺, Ca²⁺, Mg²⁺ y K⁺. Esto significa que el intercambio iónico y la congelación del agua ocurrieron simultáneamente. La precisión del modelo se mantuvo estable, ya que el número de predicciones disminuyó de siete a cuatro iones, mientras que el costo del monitoreo se redujo hasta en un 43 %. Esto demuestra que la red hidrológica puede mejorarse. Una combinación de modelado basado en datos y análisis geoquímico reveló indicios tempranos de salinización del agua en la zona costera del estuario. Este resultado demuestra la eficacia de los enfoques basados en aprendizaje automático (ML) como sistemas de alerta temprana para la exploración de aguas subterráneas en áreas con recursos limitados.
- 7 January 2026
- Published by Centro de Investigacion y Desarrollo Ecuador in Revista Alfa
- Vol. 10 (28) , 68-89
Open AccessClasificación de la calidad del agua subterránea utilizada en la agricultura mediante aprendizaje automático con datos desbalanceados
En zonas costeras áridas donde la sobreexplotación y la intrusión marina amenazan tanto la calidad como la disponibilidad del agua subterránea, esta es fundamental para la agricultura. Se utilizaron índices hidroquímicos y modelos de aprendizaje automático (AA) para determinar si el agua subterránea del acuífero Caplina (sur de Perú) era apta para el riego. El Índice de Calidad del Agua de Riego (ICAR) se calculó utilizando siete iones principales: Na⁺, Ca²⁺, Mg²⁺, K⁺, Cl⁻, SO₄²⁻ y HCO₃⁻. Este índice también se empleó para entrenar algoritmos de clasificación supervisada en un esquema binario (zonas críticas y no críticas). El clasificador XGBoost resultó ser el mejor modelo de los evaluados, con una puntuación F1 de 0,897, un área bajo la curva ROC (AUC-ROC) de 0,968 y una precisión de 0,927 mediante validación cruzada dejando uno fuera (n = 41). El análisis de sensibilidad reveló que los predictores más efectivos fueron Na⁺, Ca²⁺, Mg²⁺ y K⁺. Esto significa que el intercambio iónico y la congelación del agua ocurrieron simultáneamente. La precisión del modelo se mantuvo estable, ya que el número de predicciones disminuyó de siete a cuatro iones, mientras que el costo del monitoreo se redujo hasta en un 43 %. Esto demuestra que la red hidrológica puede mejorarse. Una combinación de modelado basado en datos y análisis geoquímico reveló indicios tempranos de salinización del agua en la zona costera del estuario. Este resultado demuestra la eficacia de los enfoques basados en aprendizaje automático (ML) como sistemas de alerta temprana para la exploración de aguas subterráneas en áreas con recursos limitados.
- Open Access
- 1 January 2026
- Published by MDPI AG in Agriengineering
- Vol. 8 (1) , 9
Mapping Olive Crops (Olea europaea L.) in the Atacama Desert (Peru): An Integration of UAV-Satellite Multispectral Images and Ensemble Machine Learning Models
Spatial monitoring of olive systems in arid regions is essential for understanding agricultural expansion, water pressure, and productive sustainability. This study aimed to map coverage and estimate olive plantation density (Olea europaea L.) in the Atacama Desert, Tacna (Peru) through the integration of UAV-satellite multispectral images and machine learning algorithms (CART, Random Forest, and Gradient Tree Boosting). Forty-eight optical, radar, and topographic covariates were analyzed. Fifteen were selected for coverage classification and 16 for plantation density, using Pearson’s correlation (|r| > 0.75). The classification maps reported an area of 23,059.87 ha (38.21%) of olive groves, followed by 5352.10 ha (8.87%) of oregano cultivation and 725.74 ha (1.20%) of orange cultivation, with respect to the total study area, with overall accuracy (OA) of 86.6% and a Kappa coefficient of 0.81. Meanwhile, the RF and GTB regression models showed R2 ≈ 0.89 and RPD > 2.8, demonstrating excellent predictive performance for estimating tree density (between 1 and 8 trees per 100 m2). Furthermore, the highest concentration of olive trees was found in the central and southern zones of the study area, associated with favorable soil and microclimatic conditions. This work constitutes the first comprehensive approach for olive mapping in southern Peru using UAV–satellite fusion, demonstrating the capability of ensemble models to improve agricultural mapping accuracy and support water and productive management in arid ecosystems.
- 1 January 2026
- Published by MDPI AG in Agriengineering
- Vol. 8 (1) , 9
Open AccessMapping Olive Crops (Olea europaea L.) in the Atacama Desert (Peru): An Integration of UAV-Satellite Multispectral Images and Ensemble Machine Learning Models
Spatial monitoring of olive systems in arid regions is essential for understanding agricultural expansion, water pressure, and productive sustainability. This study aimed to map coverage and estimate olive plantation density (Olea europaea L.) in the Atacama Desert, Tacna (Peru) through the integration of UAV-satellite multispectral images and machine learning algorithms (CART, Random Forest, and Gradient Tree Boosting). Forty-eight optical, radar, and topographic covariates were analyzed. Fifteen were selected for coverage classification and 16 for plantation density, using Pearson’s correlation (|r| > 0.75). The classification maps reported an area of 23,059.87 ha (38.21%) of olive groves, followed by 5352.10 ha (8.87%) of oregano cultivation and 725.74 ha (1.20%) of orange cultivation, with respect to the total study area, with overall accuracy (OA) of 86.6% and a Kappa coefficient of 0.81. Meanwhile, the RF and GTB regression models showed R2 ≈ 0.89 and RPD > 2.8, demonstrating excellent predictive performance for estimating tree density (between 1 and 8 trees per 100 m2). Furthermore, the highest concentration of olive trees was found in the central and southern zones of the study area, associated with favorable soil and microclimatic conditions. This work constitutes the first comprehensive approach for olive mapping in southern Peru using UAV–satellite fusion, demonstrating the capability of ensemble models to improve agricultural mapping accuracy and support water and productive management in arid ecosystems.
- Open Access
Historical Land Cover Dynamics and Projected Changes in the High Andean Zone of the Locumba Basin: A Predictive Approach Using Remote Sensing and Artificial Neural Network—Cellular Automata Model
The conservation and monitoring of land cover represent crucial elements for sustainable regional development, especially in fragile high Andean ecosystems. This study evaluates the spatiotemporal changes in land use and land cover (LULC) in the Locumba basin over the period of 1984–2023. A hybrid modeling approach combining artificial neural networks (ANN) and cellular automata (CA) was employed to project future changes for 2033, 2043, and 2053. The results reveal a significant reduction in glaciers and lagoons throughout the Locumba basin, with notable declines from 1984 to 2023, while vegetated areas, particularly grasslands and wetlands, experienced substantial expansion. Specifically, grasslands increased by 273.7% relative to their initial coverage, growing from 57.87 km2 in 1984 to over 220.31 km2 in 2023, with projections indicating continued growth to over 331.62 km2 by 2053. This multitemporal analysis provides crucial information for anticipating future land dynamics and underscores the urgent need for strategic conservation planning to mitigate the continued loss of strategic ecosystems in the high Andean region of Tacna.
Open AccessHistorical Land Cover Dynamics and Projected Changes in the High Andean Zone of the Locumba Basin: A Predictive Approach Using Remote Sensing and Artificial Neural Network—Cellular Automata Model
The conservation and monitoring of land cover represent crucial elements for sustainable regional development, especially in fragile high Andean ecosystems. This study evaluates the spatiotemporal changes in land use and land cover (LULC) in the Locumba basin over the period of 1984–2023. A hybrid modeling approach combining artificial neural networks (ANN) and cellular automata (CA) was employed to project future changes for 2033, 2043, and 2053. The results reveal a significant reduction in glaciers and lagoons throughout the Locumba basin, with notable declines from 1984 to 2023, while vegetated areas, particularly grasslands and wetlands, experienced substantial expansion. Specifically, grasslands increased by 273.7% relative to their initial coverage, growing from 57.87 km2 in 1984 to over 220.31 km2 in 2023, with projections indicating continued growth to over 331.62 km2 by 2053. This multitemporal analysis provides crucial information for anticipating future land dynamics and underscores the urgent need for strategic conservation planning to mitigate the continued loss of strategic ecosystems in the high Andean region of Tacna.
- Open Access
- 15 April 2025
- Published by Universidad Nacional de Trujillo in Scientia Agropecuaria
- Vol. 16 (2) , 249-261
Identification of vulnerable areas to flash floods using weighted sum analysis and unsupervised machine learning in arid regions of the northern Atacama Desert
In recent decades, global warming has triggered significant changes in the hydrological cycle, leading to various disasters, especially contrasting events such as droughts and floods. These occurrences have also been recorded in the Atacama Desert, resulting in considerable economic losses worldwide, in Latin America, in Peru, and within the study region. The primary objective of this study is to obtain fundamental morphometric parameters, including basic spatial, linear, shape, and landscape aspects through the integration of GIS tools and artificial intelligence, enabling the identification of flood-prone areas within micro-watersheds. The studied basin is located at the head of the Atacama Desert, in southern Peru, where various lithological and hydro-geomorphological structures influence its vulnerability to floods. To assess flood vulnerability in the Caplina River micro-watersheds, 16 morphometric parameters were precisely analyzed, identifying areas of high vulnerability that require basin management measures. The results show that the hydrological response of the Caplina Basin is strongly influenced by its morphometric characteristics, with micro-watersheds in the middle and lower sections exhibiting higher susceptibility to flash floods. These findings aim to support urban planning and watershed management, offering insights for policymakers to develop flood mitigation strategies and enhance infrastructure resilience.
- 15 April 2025
- Published by Universidad Nacional de Trujillo in Scientia Agropecuaria
- Vol. 16 (2) , 249-261
Open AccessIdentification of vulnerable areas to flash floods using weighted sum analysis and unsupervised machine learning in arid regions of the northern Atacama Desert
In recent decades, global warming has triggered significant changes in the hydrological cycle, leading to various disasters, especially contrasting events such as droughts and floods. These occurrences have also been recorded in the Atacama Desert, resulting in considerable economic losses worldwide, in Latin America, in Peru, and within the study region. The primary objective of this study is to obtain fundamental morphometric parameters, including basic spatial, linear, shape, and landscape aspects through the integration of GIS tools and artificial intelligence, enabling the identification of flood-prone areas within micro-watersheds. The studied basin is located at the head of the Atacama Desert, in southern Peru, where various lithological and hydro-geomorphological structures influence its vulnerability to floods. To assess flood vulnerability in the Caplina River micro-watersheds, 16 morphometric parameters were precisely analyzed, identifying areas of high vulnerability that require basin management measures. The results show that the hydrological response of the Caplina Basin is strongly influenced by its morphometric characteristics, with micro-watersheds in the middle and lower sections exhibiting higher susceptibility to flash floods. These findings aim to support urban planning and watershed management, offering insights for policymakers to develop flood mitigation strategies and enhance infrastructure resilience.
- Open Access
Glacier, Wetland, and Lagoon Dynamics in the Barroso Mountain Range, Atacama Desert: Past Trends and Future Projections Using CA-Markov
Glacial retreat is a major global challenge, particularly in arid and semi-arid regions where glaciers serve as critical water sources. This research focuses on glacial retreat and its impact on land cover and land use changes (LULC) in the Barroso Mountain range, Tacna, Peru, which is a critical area for water resources in the hyperarid Atacama Desert. Employing advanced remote sensing techniques through the Google Earth Engine (GEE) cloud computing platform, we analyzed historical trends (1985–2022) using Landsat satellite imagery. A normalized index classification was carried out to generate LULC maps for the years 1986, 2001, 2012, and 2022. Future projections until 2042 were developed using Cellular Automata–Markov (CA–Markov) modeling in QGIS, incorporating six predictive environmental variables. The resulting maps presented an overall accuracy (OA) greater than 83%. Historical analysis revealed a dramatic glacier reduction from 44.7 km2 in 1986 to 7.4 km2 in 2022. In contrast, wetlands expanded substantially from 5.70 km2 to 12.14 km2, indicating ecosystem shifts potentially driven by glacier meltwater availability. CA–Markov chain modeling projected further glacier loss to 3.07 km2 by 2042, while wetlands are expected to expand to 18.8 km2 and bodies of water will reach 4.63 km2. These future projections (with accuracies above 84%) underline urgent implications for water management, environmental sustainability, and climate adaptation strategies, particularly with regard to downstream hydrological risks and ecosystem resilience.
Open AccessGlacier, Wetland, and Lagoon Dynamics in the Barroso Mountain Range, Atacama Desert: Past Trends and Future Projections Using CA-Markov
Glacial retreat is a major global challenge, particularly in arid and semi-arid regions where glaciers serve as critical water sources. This research focuses on glacial retreat and its impact on land cover and land use changes (LULC) in the Barroso Mountain range, Tacna, Peru, which is a critical area for water resources in the hyperarid Atacama Desert. Employing advanced remote sensing techniques through the Google Earth Engine (GEE) cloud computing platform, we analyzed historical trends (1985–2022) using Landsat satellite imagery. A normalized index classification was carried out to generate LULC maps for the years 1986, 2001, 2012, and 2022. Future projections until 2042 were developed using Cellular Automata–Markov (CA–Markov) modeling in QGIS, incorporating six predictive environmental variables. The resulting maps presented an overall accuracy (OA) greater than 83%. Historical analysis revealed a dramatic glacier reduction from 44.7 km2 in 1986 to 7.4 km2 in 2022. In contrast, wetlands expanded substantially from 5.70 km2 to 12.14 km2, indicating ecosystem shifts potentially driven by glacier meltwater availability. CA–Markov chain modeling projected further glacier loss to 3.07 km2 by 2042, while wetlands are expected to expand to 18.8 km2 and bodies of water will reach 4.63 km2. These future projections (with accuracies above 84%) underline urgent implications for water management, environmental sustainability, and climate adaptation strategies, particularly with regard to downstream hydrological risks and ecosystem resilience.
- Open Access
- 25 April 2024
- Published by MDPI AG in Agriculture
- Vol. 14 (5) , 662
Spatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru
Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is to find an appropriate VI to analyse the spatio-temporal evolution of olive water stress using LST images and VIs derived from Landsat 5 and 8 satellites in the semi-arid region of southern Peru. For this purpose, VIs (Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2) and Soil Adjusted Vegetation Index (SAVI)) and LST were calculated. The information was processed in Google Earth Engine (GEE) for the period 1985 to 2024, with an interval of every five years for the summer season. The triangle method was applied based on the LST-VIs scatterplot analysis, a tool that establishes wet and dry boundary conditions for the Temperature Vegetation Dryness Index (TVDI). The results indicated a better appreciation of olive orchard water stress over time, with an average of 39% drought (TVDINDVI and TVDISAVI), 24% severe drought (TVDINDVI) and 25% (TVDISAVI) of the total area, compared to TVDIEVI2, which showed 37% drought and 16% severe drought. It is concluded that TVDINDVI and TVDISAVI provide a better visualisation of the water stress map of the olive crop and offer a range of options to address current and future problems in water resource management in the olive sector in semi-arid areas of southern Peru.
- 25 April 2024
- Published by MDPI AG in Agriculture
- Vol. 14 (5) , 662
Open AccessSpatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru
Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is to find an appropriate VI to analyse the spatio-temporal evolution of olive water stress using LST images and VIs derived from Landsat 5 and 8 satellites in the semi-arid region of southern Peru. For this purpose, VIs (Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2) and Soil Adjusted Vegetation Index (SAVI)) and LST were calculated. The information was processed in Google Earth Engine (GEE) for the period 1985 to 2024, with an interval of every five years for the summer season. The triangle method was applied based on the LST-VIs scatterplot analysis, a tool that establishes wet and dry boundary conditions for the Temperature Vegetation Dryness Index (TVDI). The results indicated a better appreciation of olive orchard water stress over time, with an average of 39% drought (TVDINDVI and TVDISAVI), 24% severe drought (TVDINDVI) and 25% (TVDISAVI) of the total area, compared to TVDIEVI2, which showed 37% drought and 16% severe drought. It is concluded that TVDINDVI and TVDISAVI provide a better visualisation of the water stress map of the olive crop and offer a range of options to address current and future problems in water resource management in the olive sector in semi-arid areas of southern Peru.
- Open Access
- 2 April 2024
- Published by Universidad Nacional de Tumbes in Manglar
- Vol. 21 (1) , 95-106
Machine Learning para la Clasificación y Análisis de los Índices de Biomasa y su relación con el Cambio Climático, Desierto de Atacama
En este trabajo usamos Machine Learning (Randon Forest) como herramienta para clasificar la biomasa y calcular los índices de vegetación buscando identificar las características de la cobertura vegetal en la cabecera del desierto Atacama. Se busca establecer la correlación entre los índices de vegetación y la precipitación, a fin de conocer su confiabilidad sobre la climatología en esta región. Fue importante el análisis geoespacial basado en Google Earth Engine (GEE) y el procesamiento de imágenes Landsat 5 ETM y Landsat 8 OLI/TIRS, para el período 1985 - 2022, lo que permitió caracterizar el cambio climático. El NDVI, SAVI, GVI y RVI han sido probados y validados en sistemas áridos. El NDVI responde positivamente a la precipitación en temporada húmeda y en forma débil en la temporada de lluvias invernales. Se confirma que el NDVI alto corresponde al verano, después de una sequía prolongada. Hacia los años 2020 y 2022, se registra un aumento de cobertura vegetal en lugares de mayor temperatura, evidenciando cambio climático y reflejado en los índices de biomasa.
- 2 April 2024
- Published by Universidad Nacional de Tumbes in Manglar
- Vol. 21 (1) , 95-106
Open AccessMachine Learning para la Clasificación y Análisis de los Índices de Biomasa y su relación con el Cambio Climático, Desierto de Atacama
En este trabajo usamos Machine Learning (Randon Forest) como herramienta para clasificar la biomasa y calcular los índices de vegetación buscando identificar las características de la cobertura vegetal en la cabecera del desierto Atacama. Se busca establecer la correlación entre los índices de vegetación y la precipitación, a fin de conocer su confiabilidad sobre la climatología en esta región. Fue importante el análisis geoespacial basado en Google Earth Engine (GEE) y el procesamiento de imágenes Landsat 5 ETM y Landsat 8 OLI/TIRS, para el período 1985 - 2022, lo que permitió caracterizar el cambio climático. El NDVI, SAVI, GVI y RVI han sido probados y validados en sistemas áridos. El NDVI responde positivamente a la precipitación en temporada húmeda y en forma débil en la temporada de lluvias invernales. Se confirma que el NDVI alto corresponde al verano, después de una sequía prolongada. Hacia los años 2020 y 2022, se registra un aumento de cobertura vegetal en lugares de mayor temperatura, evidenciando cambio climático y reflejado en los índices de biomasa.