Automatic detection of surface-water bodies from Sentinel-1 images for effective mosquito larvae control

Ovakoglou, Georgios; Cherif, Ines; Alexandridis, Thomas; Pantazi, Xanthoula Eirini; Tamouridou, Afroditi-Alexandra; Moshou, Dimitrios; Tseni, Xanthi; Raptis, Iason; Kalaitzopoulou, Stella; Mourelatos, Spiros

doi:10.1117/1.jrs.15.014507

Cited by 18 publications

(16 citation statements)

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“…Sentinel-1 data and four different ML models (K-nearest neighbors classifier (KNN), fuzzy-rules classification, Haralick’s textural features of dissimilarity, Otsu valley-emphasis) were employed to classify water bodies in [ 54 ]. It involved many different ML methods in tandem (i.e., the output of one ML model was fed into other processing steps), which complicates interpretability.…”

Section: The State Of the Art: Advances In Intelligent Waterbody Info...mentioning

confidence: 99%

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Yang

Driscol

Sarigai

et al. 2022

Sensors

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Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.

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Section: The State Of the Art: Advances In Intelligent Waterbody Info...mentioning

confidence: 99%

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Yang

Driscol

Sarigai

et al. 2022

Sensors

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“…All 15 images were of Ground Range Detection (GRD) type and in Interferometric Wide (IW) swath instrument mode. Only the Vertical-Vertical (VV) polarized bands were pre-processed 9,11 using the following steps: orbit application, thermal and GRD border noise removal, calibration, terrain correction, speckle filtering, and conversion to backscattering sigma0 coefficient 11 . The download and pre-processing steps using the ESA SNAP Graph Processing Tool (GPT) are described in 9 .…”

Section: Sar Datamentioning

confidence: 99%

“…To detect the water pixels from the SAR backscatter VV image, the Otsu Valley Emphasis histogram thresholding method was used as described by Ovakoglou et al 9 . A threshold is automatically found in each bimodal histogram for each Sentinel-1 image without the need for reference data or supervision.…”

Section: Water Detectionmentioning

confidence: 99%

“…Water detection techniques like classification and machine learning score high accuracies after training with reference data 3,4,6,7 . Simpler methods which differentiate water from land and vegetation in the histogram of SAR backscatter have also been applied 8,9 . In mountainous landscapes water detection methods are less performant due to slope orientation and hill shadows, as well as surface roughness, presence of vegetation.…”

Section: Introductionmentioning

confidence: 99%

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Improving water bodies detection from Sentinel-1 in South Africa using drainage and terrain data

Cherif

Ovakoglou

Alexandridis

et al. 2021

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII

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In areas with extensive, nomadic, or transhumant livestock farming, it is important to access regular information on the location of ephemeral surface water bodies. SAR satellite images are used for water bodies mapping at high-resolution. Unlike optical data their use is not restricted to cloud-free conditions. Nevertheless, surface roughness, hill shadows, and presence of vegetation are known to affect the SAR backscatter and lead to false positives while detecting water. In this study, surface water was automatically mapped in hilly landscapes using Sentinel-1 images and the Otsu Valley Emphasis method to detect a threshold for water in the histogram of backscatter. In order to reduce the false positive rate in the steep areas, five different water masks using terrain and drainage information with different thresholds are compared in the mountainous province of KwaZulu-Natal (KZN) in South-Africa. The quantitative assessment shows that the overall accuracy ranged between 0.865 and 0.958 with the highest value obtained with a threshold of Height Above the Nearest Drainage index of 10m, leading to the lowest specificity of 0.037. The visual inspection over two reservoirs (Midmar Dam and Wagendrift Dam) shows that there is high agreement between the produced map and the reference data despite differences in their spatial and temporal coverage. Besides, radiometrically terrain corrected SAR data, which could be advantageous in such landscapes were recently made available by the ASF vertex platform. Even though they are not available in NRT, the potential of using such data for water detection is investigated.

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“…Numerous studies show SARs effectiveness for water inundation and flood mapping [37,[46][47][48][49][50], however, the separation of land and water based on backscatter values alone is rarely perfect. Inundated vegetation can cause complex scattering mechanism and is a frequently reported source of omission errors [51,52]. Similarly, wind and rain may cause omission errors by increasing waters' surface roughness, resulting in higher backscatter coefficients than expected.…”

Section: Introductionmentioning

confidence: 99%

An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China

Druce

Tong

Lei³

et al. 2021

Remote Sensing

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Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting.

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Automatic detection of surface-water bodies from Sentinel-1 images for effective mosquito larvae control

Cited by 18 publications

References 0 publications

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Improving water bodies detection from Sentinel-1 in South Africa using drainage and terrain data

An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China

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