Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2

Jara, Carlos; Delegido, Jesús; Ayala, Johanna E; Lozano, Patricio; Armas, Aracely; Flores, V.

doi:10.4995/raet.2019.11715

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…A major benefit of the RF method is the ability to incorporate co‐variables to improve the classification (Belgiu & Drăguţ, 2016; Ham et al, 2005). Here, we used a total of 20 vegetation and 7 topographic indices (Appendix A, Tables A1, A2) that potentially enhance the spectral differentiation of vegetation from satellite imagery to identify potential bofedales extent (Jara et al, 2019; Mahdavi et al, 2018). Topographic indices were derived from the NASADEM dataset, which has a 30 m spatial resolution (Buckley et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…This method has been proven to provide uniform and high accuracy results when applied over large areas (Mutanga & Kumar, 2019) and has been successfully applied to map tropical high-Andean wetlands (cf. Chimner et al, 2019;Hribljan et al, 2017;Jara et al, 2019). RF uses bootstrap aggregation (bagging) to generate an ensemble of 'trees' in which each classifier is trained on a random subspace of features (Belgiu & Dr agut ¸, 2016).…”

Section: Seasonal Bofedales Extent Mappingmentioning

confidence: 99%

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Seasonal water storage and release dynamics of bofedal wetlands in the Central Andes

Ross

Mendoza

Drenkhan

et al. 2023

Hydrological Processes

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Tropical high‐Andean wetlands, locally known as ‘bofedales’, are key ecosystems sustaining biodiversity, carbon sequestration, water provision and livestock farming. Bofedales' contribution to dry season baseflows and sustaining water quality is crucial for downstream water security. The sensitivity of bofedales to climatic and anthropogenic disturbances is therefore of growing concern for watershed management. This study aims to understand seasonal water storage and release characteristics of bofedales by combining remote sensing analysis and ground‐based monitoring for the wet and dry seasons of late 2019 to early 2021, using the glacierised Vilcanota‐Urubamba basin (Southern Peru) as a case study. A network of five ultrasound loggers was installed to obtain discharge and water table data from bofedal sites across two headwater catchments. The seasonal extent of bofedales was mapped by applying a supervised machine learning model using Random Forest on imagery from Sentinel‐2 and NASADEM. We identified high seasonal variability in bofedal area with a total of 3.5% and 10.6% of each catchment area, respectively, at the end of the dry season (2020), which increased to 15.1% and 16.9%, respectively, at the end of the following wet season (2021). The hydrological observations and bofedal maps were combined into a hydrological conceptual model to estimate the storage and release characteristics of the bofedales, and their contribution to runoff at the catchment scale. Estimated lag times between 1 and 32 days indicate a prolonged bofedal flow contribution throughout the dry season (about 74% of total flow). Thus, our results suggest that bofedales provide substantial contribution to dry season baseflow, water flow regulation and storage. These findings highlight the importance of including bofedales in local water management strategies and adaptation interventions including nature‐based solutions that seek to support long‐term water security in seasonally dry and rapidly changing Andean catchments.

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Section: Methodsmentioning

confidence: 99%

Section: Seasonal Bofedales Extent Mappingmentioning

confidence: 99%

Seasonal water storage and release dynamics of bofedal wetlands in the Central Andes

Ross

Mendoza

Drenkhan

et al. 2023

Hydrological Processes

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“…The soil brightness correction factor ranges from the lowest vegetation value of 0 to a high vegetation cover value of 1 (Li et al, 2015). The average study defines the L value for accommodating each land cover type (Jara et al, 2019) Where: NIR = band 5 RED = band 4 L = the adjustment for the canopy background is 0.5 2.3.3.1.2. Enhanced Vegetation Index (EVI)…”

Section: Processingmentioning

confidence: 99%

Vegetation Indices From Landsat-8 Data in Palabuhanratu

Setiawan

Manessa

Setiadi

2023

IJReSES

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Land cover will change due to population pressure, resource use, and human interest in space. Measuring the land area is important to determine how much-converted land is positive and negative. The vegetation on land was determined by how densely the plants were spread out. This study is conducted in Palabuhanratu, Sukabumi Regency. Aims to test and compare how accurate EVI and SAVI are at seeing vegetation density. The images used are from Landsat 8 in 2018 and 2022. Calibration is performed using high-resolution images, followed by field surveys with 98 points from polygon sampling. The average accuracy of the results from EVI is 49%, while the average accuracy of the results from SAVI is 45%. So, we can say that the EVI or SAVI based-input gives a similar result on observing the vegetation density in Palabuhanratu.

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“…The combination of optical spectral and SAR indices has proven to be suitable for wetland mapping, as indicated by studies conducted by Kulawardhana et al 14 , García and Lleellish 15 ; Farda et al 16 ; Alves et al 17 ; Sun et al 18 ; López-Tapia et al 19 ; Islam et al 20 ; Saha et al 21 ; and Pham et al 10 to mention just a few. For decades, Synthetic Aperture Radar (SAR) are currently integrated for flood process, wetlands mapping, and vegetation monitoring 22 – 27 .…”

Section: Introductionmentioning

confidence: 99%

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

Géant,

Gustave,

Schmitz

2023

Sci Rep

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There are several techniques for mapping wetlands. In this study, we examined four statistical models to assess the potential distribution of wetlands in the South-Kivu province by combining optical and SAR images. The approach involved integrating topographic, hydrological, and vegetation indices into the four most used classifiers, namely Artificial Neural Network (ANN), Random Forest (RF), Boosted Regression Tree (BRT), and Maximum Entropy (MaxEnt). A wetland distribution map was generated and classified into 'wetland' and 'non-wetland.' The results showed variations in predictions among the different models. RF exhibited the most accurate predictions, achieving an overall classification accuracy of 95.67% and AUC and TSS values of 82.4%. Integrating SAR data improved accuracy and precision, particularly for mapping small inland wetlands. Our estimations indicate that wetlands cover approximately 13.5% (898,690 ha) of the entire province. BRT estimated wetland areas to be ~ 16% (1,106,080 ha), while ANN estimated ~ 14% (967,820 ha), MaxEnt ~ 15% (1,036,950 ha), and RF approximately ~ 10% (691,300 ha). The distribution of these areas varied across different territories, with higher values observed in Mwenga, Shabunda, and Fizi. Many of these areas are permanently flooded, while others experience seasonal inundation. Through digitization, the delineation process revealed variations in wetland areas, ranging from tens to thousands of hectares. The geographical distribution of wetlands generated in this study will serve as an essential reference for future investigations and pave the way for further research on characterizing and categorizing these areas.

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Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2

Cited by 14 publications

References 30 publications

Seasonal water storage and release dynamics of bofedal wetlands in the Central Andes

Seasonal water storage and release dynamics of bofedal wetlands in the Central Andes

Vegetation Indices From Landsat-8 Data in Palabuhanratu

Mapping small inland wetlands in the South-Kivu province by integrating optical and SAR data with statistical models for accurate distribution assessment

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