| _version_ | 1866902011802288128 |
|---|---|
| author | KAPOOR, ADITYA |
| author_facet | KAPOOR, ADITYA |
| contents | <p><strong>1. Introduction</strong></p> <p>Quantitative and qualitative analysis of water resources in a region is important for performing water resources management planning exercises. Such analysis involves studying the extent of water bodies (inundation area), chlorophyl content for assessing water quality and hydrological factors such as precipitation, evapotranspiration and runoff (Hasan et al. 2024). Traditionally, such analysis is performed by performing in-situ (field) studies which is time consuming and logistically expensive. However, with advancement in the field of remote sensing, performing such analysis has become easier. Furthermore, with the introduction of Google Earth Engine (GEE) – a cloud based, parallelized geospatial analysis platform, such analysis is computationally faster and facilitates community driven development.</p> <p>This script enables the user to perform such analysis in a seasonal manner (say monsoon and non-monsoon) for a group of water bodies for a given set of years. Vector data for water bodies can be ingested as a feature collection. Apart from the aforementioned factors, the output also encapsulates the mean of major spectral indices such as NDVI, MNDWI and NDTI. The seasonal spatial mean of the output factors is encapsulated as a CSV file that user can export to google drive. To demonstrate its capability, four features from HydroLakes V 1.0 (Messager et al. 2016) datasets have been selected and analysis has been performed for monsoon (June to October) and Non-Monsoon (November to May) over the years 2013 to 2024 using Landsat 8 dataset. Users may alter the years, datasets, factors. I will include more capabilities to this script over the course of time in the upcoming versions.</p> <p> </p> <p><strong><em><span>The complete script is accompanied with this document in the repository. User may copy and paste it in Google Earth Engine JavaScript based code editor</span></em></strong></p> <p> </p> <p><strong>2. References</strong></p> <p><span><span>·<span> </span></span></span><span>Hasan, R., Kapoor, A., Singh, R. and Yadav, B.K., 2024. A state-of-the-art review on the quantitative and qualitative assessment of water resources using google earth engine. <em>Environmental Monitoring and Assessment</em>, <em>196</em>(12), p.1266.</span></p> <p><span> </span></p> <p><span><span>·<span> </span></span></span><span>Messager, Mathis Loïc, Bernhard Lehner, Günther Grill, Irena Nedeva, and Oliver Schmitt. "Estimating the volume and<a name="__codelineno-0-2"></a> age of water stored in global lakes using a geo-statistical approach."<a name="__codelineno-0-3"></a>. <em>Nature communications</em> 7, no. 1 (2016): 1-11.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14708839 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Spatio-Temporal Quantitative and Qualitative Assessment of Water Resources Using Google Earth Engine KAPOOR, ADITYA Google Earth Engine Remote Sensing Water resources engineering <p><strong>1. Introduction</strong></p> <p>Quantitative and qualitative analysis of water resources in a region is important for performing water resources management planning exercises. Such analysis involves studying the extent of water bodies (inundation area), chlorophyl content for assessing water quality and hydrological factors such as precipitation, evapotranspiration and runoff (Hasan et al. 2024). Traditionally, such analysis is performed by performing in-situ (field) studies which is time consuming and logistically expensive. However, with advancement in the field of remote sensing, performing such analysis has become easier. Furthermore, with the introduction of Google Earth Engine (GEE) – a cloud based, parallelized geospatial analysis platform, such analysis is computationally faster and facilitates community driven development.</p> <p>This script enables the user to perform such analysis in a seasonal manner (say monsoon and non-monsoon) for a group of water bodies for a given set of years. Vector data for water bodies can be ingested as a feature collection. Apart from the aforementioned factors, the output also encapsulates the mean of major spectral indices such as NDVI, MNDWI and NDTI. The seasonal spatial mean of the output factors is encapsulated as a CSV file that user can export to google drive. To demonstrate its capability, four features from HydroLakes V 1.0 (Messager et al. 2016) datasets have been selected and analysis has been performed for monsoon (June to October) and Non-Monsoon (November to May) over the years 2013 to 2024 using Landsat 8 dataset. Users may alter the years, datasets, factors. I will include more capabilities to this script over the course of time in the upcoming versions.</p> <p> </p> <p><strong><em><span>The complete script is accompanied with this document in the repository. User may copy and paste it in Google Earth Engine JavaScript based code editor</span></em></strong></p> <p> </p> <p><strong>2. References</strong></p> <p><span><span>·<span> </span></span></span><span>Hasan, R., Kapoor, A., Singh, R. and Yadav, B.K., 2024. A state-of-the-art review on the quantitative and qualitative assessment of water resources using google earth engine. <em>Environmental Monitoring and Assessment</em>, <em>196</em>(12), p.1266.</span></p> <p><span> </span></p> <p><span><span>·<span> </span></span></span><span>Messager, Mathis Loïc, Bernhard Lehner, Günther Grill, Irena Nedeva, and Oliver Schmitt. "Estimating the volume and<a name="__codelineno-0-2"></a> age of water stored in global lakes using a geo-statistical approach."<a name="__codelineno-0-3"></a>. <em>Nature communications</em> 7, no. 1 (2016): 1-11.</span></p> |
| title | Spatio-Temporal Quantitative and Qualitative Assessment of Water Resources Using Google Earth Engine |
| topic | Google Earth Engine Remote Sensing Water resources engineering |
| url | https://doi.org/10.5281/zenodo.14708839 |