Index-based methods for water body extraction in satellite data

Arreola-Esquivel, M.; Delgadillo-Herrera, M.; Toxqui-Quitl, C.; Padilla-Vivanco, Alfonso

doi:10.1117/12.2529756

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Multiple red-green-blue composites and standard spectral indices were created to employ in the ML classification as input variables (Table 2). The spectral indices include the Normalized Difference Vegetation Index (NDVI), which measures vegetation greenness as a proxy for health [51]; Normalized Difference Moisture Index (NDMI), which quantifies vegetation with high water content [52]; Modified Normalized Difference Water Index (MNDWI), which distinguishes open water features [53]; Green Chlorophyll Vegetation Index (GCVI), to quantify leaf chlorophyll content [54]; and modified Radar Forest Degradation Index (mRFDI), which can distinguish between different vegetation varieties using the difference between the VV and VH polarizations in SAR data [49] to potentially distinguish mangroves from other vegetation types. We also calculated additional indices, including the simple ratio (SR), to quantify vegetation density [55]; Band Ratio 54, which is helpful in distinguishing water bodies from vegetation [56]; and Band Ratio 35, to enhance urban areas and distinguish water from vegetation [57].…”

Section: Spectral and Backscatter Indicesmentioning

confidence: 99%

Integrating SAR, Optical, and Machine Learning for Enhanced Coastal Mangrove Monitoring in Guyana

Chan-Bagot,

Herndon,

Puzzi Nicolau

et al. 2024

Remote Sensing

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Mangrove forests are a biodiverse ecosystem known for a wide variety of crucial ecological services, including carbon sequestration, coastal erosion control, and prevention of saltwater intrusion. Given the ecological importance of mangrove forests, a comprehensive and up-to-date mangrove extent mapping at broad geographic scales is needed to define mangrove forest changes, assess their implications, and support restoration activities and decision making. The main objective of this study is to evaluate mangrove classifications derived from a combination of Landsat-8 OLI, Sentinel-2, and Sentinel-1 observations using a random forest (RF) machine learning (ML) algorithm to identify the best approach for monitoring Guyana’s mangrove forests on an annual basis. Algorithm accuracy was tested using high-resolution planet imagery in Collect Earth Online. Results varied widely across the different combinations of input data (overall accuracy, 88–95%; producer’s accuracy for mangroves, 50–87%; user’s accuracy for mangroves, 13–69%). The combined optical–radar classification demonstrated the best performance with an overall accuracy of 95%. Area estimates of mangrove extent ranged from 908.4 to 3645.0 hectares. A ground-based validation exercise confirmed the extent of several large, previously undocumented areas of mangrove forest loss. The results establish that a data fusion approach combining optical and radar data performs marginally better than optical-only approaches to mangrove classification. This ML approach, which leverages free and open data and a cloud-based analytics platform, can be applied to mapping other areas of mangrove forests in Guyana. This approach can also support the operational monitoring of mangrove restoration areas managed by Guyana’s National Agricultural and Research Extension Institute (NAREI).

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Section: Spectral and Backscatter Indicesmentioning

confidence: 99%

Integrating SAR, Optical, and Machine Learning for Enhanced Coastal Mangrove Monitoring in Guyana

Chan-Bagot,

Herndon,

Puzzi Nicolau

et al. 2024

Remote Sensing

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“…Partiendo de la teoría de que el agua absorbe casi todo el flujo radiante incidente y que la superficie terrestre refleja cantidades significativas de energía en las bandas del infrarrojo de onda corta y cercana, la reflectancia medida en la banda verde es mucho mayor para el agua en comparación con la reflectancia de la superficie terrestre (Arreola-Esquivel et al, 2019).…”

Section: Monitoreo De Recursos Hídricos Superficialesunclassified

“…El índice AWEI ha sido utilizado en México para detectar cambios en cuerpos de agua en el Lago de Chapala, Jalisco (Calvario et al, 2017), para el monitoreo (Wang et al, 2018) y discriminación de cuerpos de agua (Calvario et al, 2018), así como en la extracción (Arreola-Esquivel et al, 2019) y detección (Soria-Ruiz et al, 2022) de cuerpos de agua.…”

Section: íNdice De Aguas Superficiales Terrestres (Lswi)unclassified

Índices para el monitoreo de cuerpos de agua usando sensores remotos

Henrández Lozano,

Pavón

2024

Acta Universitaria

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La sobreexplotación de los cuerpos de agua aunado a las sequías y el impacto del cambio climático reducen el agua disponible para actividades humanas, lo cual genera serios problemas económicos y sociales. Por tanto, una tarea imprescindible es el monitoreo del estado de los cuerpos de agua superficiales, y una alternativa rápida, precisa y económica es hacerlo mediante técnicas de teledetección usando sensores remotos satelitales. Estas técnicas ayudan a obtener información a distancia de un determinado objeto situado sobre la superficie terrestre. El objetivo de este estudio fue, mediante el método PRISMA, realizar una revisión de las aplicaciones de los sensores remotos en el monitoreo de cuerpos de agua para dar alternativas de uso de los índices de agua. El índice de agua modificado de diferencia normalizada (MNDWI, por sus siglas en inglés) y el índice de extracción de agua automatizado (AWEI, por sus siglas en inglés) son los más adecuados debido a que son fáciles de construir e interpretar, además de que tienen alta precisión.

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“…A fixed threshold can lead to large uncertainties in the resulting snow-cover outcomes at the local scale. For snow-cover mapping based on higher-resolution imagery, a threshold value variable in space and time would be needed to improve the snow-cover map quality [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Non-Binary Snow Index for Multi-Component Surfaces

et al. 2021

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A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.

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Index-based methods for water body extraction in satellite data

Cited by 8 publications

References 10 publications

Integrating SAR, Optical, and Machine Learning for Enhanced Coastal Mangrove Monitoring in Guyana

Integrating SAR, Optical, and Machine Learning for Enhanced Coastal Mangrove Monitoring in Guyana

Índices para el monitoreo de cuerpos de agua usando sensores remotos

Non-Binary Snow Index for Multi-Component Surfaces

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