Extraction of Water-body Area from High-resolution Landsat Imagery

Naik, B. Balaji; Anuradha, B.

doi:10.11591/ijece.v8i6.pp4111-4119

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…The The stages in this study are described in a flowchart (Figure 2), where the process begins with filtering Landsat 9 2022 imagery on the Google Earth Engine using the following script: Drought analysis was performed using NDDI derived from the Normalized Difference Water Index (NDWI) and NDVI formulas. NDWI is used to see open water features in satellite imagery, which allow the body of water to stand out from the ground and vegetation [23,24].…”

Section: Methodsmentioning

confidence: 99%

Soil moisture mapping for drought monitoring in urban areas

Arif,

Susena

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Soil moisture is an important indicator for drought monitoring. Mapping of soil moisture in this study uses remote sensing, namely Landsat 9 OLI imagery, because it can be relied upon as a cheap source of information, and its good temporal resolution or revisit period. Two parameters indirectly related to soil moisture, namely vegetation were analyzed using the Normalized Difference Vegetation Index (NDVI) approach, and land surface temperature (LST). Drought analysis was verified using the Normalized Difference Drought Index (NDDI). The remote sensing imagery used in this study is Landsat 9 OLI imagery by selecting images with 30% cloud cover from 1 January 2022 to 31 December 2022 with the support of the cloud-based Google Earth Engine computing platform. The results of the analysis indicate high LST values in the southern part of the study area whose dominant land use is built-up areas, namely in the sub-districts of Depok, Gamping, Ngaglik, and Mlati. The effect of vegetation on soil moisture is indicated by the NDVI value, which has a relatively strong positive correlation with SMI (R= 0.46). The SMI value is in contrast to LST, where the spatial distribution of high SMI is spread in the northern part, namely Pakem, Cangkringan, and Turi districts. On the other hand, a low SMI is spread across the central and southern parts of the study area, which have a high drought index (extreme moderate). Overall, it is concluded that the SMI has the potential to map drought and is a reliable index for initial analysis of drought risk management.

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Section: Methodsmentioning

confidence: 99%

Soil moisture mapping for drought monitoring in urban areas

Arif,

Susena

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…On the other hand, one of the primary data sources that empower environmental research is satellite imagery [8]. Satellite programs such as landsat [9], sentinel, aqua/terra, among others, provide a wealth of freely available data sets for the masses. This tremendous progress has unlocked many innovations that extract valuable insights [10][11] from satellite imagery using big data pipelines and advanced machine learning systems [12].…”

Section: Introductionmentioning

confidence: 99%

Satellite image inpainting with deep generative adversarial neural networks

Zaytar

Amrani

2021

IJ-AI

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This work addresses the problem of recovering lost or damaged satellite image pixels (gaps) caused by sensor processing errors or by natural phenomena like cloud presence. Such errors decrease our ability to monitor regions of interest and significantly increase the average revisit time for all satellites. This paper presents a novel neural system based on conditional deep generative adversarial networks (cGAN) optimized to fill satellite imagery gaps using surrounding pixel values and static high-resolution visual priors. Experimental results show that the proposed system outperforms traditional and neural network baselines. It achieves a normalized least absolute deviations error of ( & decrease in error compared with the two baselines) and a mean squared error loss of ( & decrease in error) over the test set. The model can be deployed within a remote sensing data pipeline to reconstruct missing pixel measurements for near-real-time monitoring and inference purposes, thus empowering policymakers and users to make environmentally informed decisions.

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“…Research through remote sensing is widely used in different areas, such as water body analysis, coastal zone management, erosion monitoring (water, wind, gravitational, relief or desertification), natural disasters, among others; However, the information posed by monitoring bodies of water by means of Landsat images is sufficient compared to other topics of interest [8]. The use of the Standardized Water Index (NDWI) for water body analysis is carried out through multispectral sensors, which maximize the utilization for spatial analysis of surface water bodies, and therefore direct efforts for the correct use of information and the generation of timely actions for mitigation [9].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Climate Indices and NDWI Analysis in Lerma Chapala Basin

Castro-Lazcarro¹,

Belitskaya²,

Tristán³

2021

Preprint

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Lake Chapala is a natural ecosystem of Mexico, declared Ramsar site, currently, is an area with importance in the agricultural sector, tourism, and important source of drinking water in the Metropolitan area of Guadalajara, considered as a thermo regulatory body. This study used tools such as Rhtest, as well as analyzing extreme climate indices using RClimdex. In addition, the relationship with the change in water surface of Lake Chapala was assessed through the NDWI index, using Landsat images, between 1985 and 2018. According to the obtained results, the signs of climatic variability are detected in the study region, which, directly affects the basin runoff and water levels in the lake, as well as, the factor of water overexploitation in the basin, so it is considered that the lake conditions mostly depend on the type of management that is given to the resource. Finally, the results will help the understanding of climate variability and its water resources influence of the Lerma Chapala basin, which will assist in future research.

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Extraction of Water-body Area from High-resolution Landsat Imagery

Cited by 11 publications

References 15 publications

Soil moisture mapping for drought monitoring in urban areas

Soil moisture mapping for drought monitoring in urban areas

Satellite image inpainting with deep generative adversarial neural networks

Assessment of Climate Indices and NDWI Analysis in Lerma Chapala Basin

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