Onboard Real Time Image Processing System for Water Bodies Extraction

Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

To select an optimal method for Nantong region, the study compared normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and relation of spectrum to extract water information with Landsat ETM+ data. Supervised classification that needs training site was used to evaluate the results. Relation of spectrum could accurately extract wide water body such as Tong-Lu canal, but it was difficult to extract a smaller water body. Moreover, relation of spectrum could not extract water information covered by sparse vegetation. In contrast, NDWI and MNDWI did better. The water information by NDWI always mixed with built-up land noise and thus the area of a water body was overestimated. MNDWI could reveal more subtle features such as the distribution of suspended sediment, water quality changes than NDWI. MNDWI could significantly highlight the water information, especially Haohe river that surrounds the city center. MNDWI could depress the buildings' effect while highlighting water information and accurately extract the water information. MNDWI revealed successfully significant non-pollution of the water. Also, MNDWI could easily distinguish shadow and water body and solve the problem to eliminate shadow in extracting water information. MNDWI was a better method for extracting water information in Nantong city.

Abstract. This study proposes a multi-source methodology for monitoring bathymetry in continental water bodies in central Mexico by integrating in-situ and satellite-based techniques. A 3D-printed Unmanned Surface Vehicle (USV), equipped with echo sounders and GPS, was used to collect high-resolution depth data from five dams: Cointzio and Queréndaro (Michoacán) and Mata, Soledad, and Esperanza (Guanajuato). This data was correlated with Sentinel-2 imagery accessed through the Microsoft Planetary Computer, which provided multispectral, spatio-temporal data across different seasons. To assess water body delineation accuracy, several water indices were compared, including the Normalized Difference Water Index (NDWI), Automated Water Extraction Index with shadows (AWEI_sh), without shadows (AWEI_nsh), Modified NDWI (MNDWI), Sentinel Multi-Band Water Index (SMBWI), and Sentinel-2 Water Index (SWI), along with the Scene Classification Layer (SCL). SWI consistently yielded the most reliable contours. Although the SCL layer struggled in areas with dense aquatic vegetation, misclassifying water surfaces, it proved useful when combined with SWI. This integration produced the most accurate results for most dams— except in Queréndaro, where dense hyacinth cover in parts of the water body and irrigation agriculture in the surroundings impedes the correct detection of water body boundaries. A strong correlation between USV data and satellite-derived contours confirms that combining in-situ and remote sensing sources offers a robust and precise framework for bathymetric mapping in inland waters of Mexico.

This study evaluates various water extraction indices to identify surface features using Sentinel-2 satellite images. Eight study sites were selected with three indices employed in this study, such as the water ratio index (WRI), the modified normalized difference water index (MNDWI), and the normalized difference water index (NDWI). The results of the accuracy assessment revealed that the producer accuracy (PA) for the NDWI ranged from 81.11 to 94.56%, that for the MNDWI ranged from 85.3 to 96.7%, and that for the WRI ranged from 96.98 to 97.98%. The user accuracies for the NDWI, MNDWI, and WRI results are 68.1–81.6%, 74.81–86.14%, and 80.33–88.78%, respectively. This study reveals that the WRI has greater PA (97.98%), user accuracy (88.78%), and kappa statistic (0.88) than the MNDWI and NDWI. The WRI is particularly suitable for extracting surface bodies surrounded by bare lands with minimal vegetation cover. Conversely, the NDWI is more applicable in areas with moderate vegetation and shadows, such as agricultural regions or wetlands. The MNDWI is suitable to avoid confusion water bodies with built-up areas and used to extract water bodies with various land cover. Therefore, establishing optimal thresholds is essential for policymakers and planners to effectively monitor water surface features and changes in water sources.

In riverine flood-prone areas, the delineation of the spatial pattern of flood extents and durations allow flood planners to anticipate likely threats from floods and to formulate actions to mitigate these events. Rapid flood mapping is crucial for flood disaster estimation and evaluation in the early stage. Accurate and timely updates of flood inundation have been made possible by remote sensing techniques. The present study applies the Water indexes and Classification method to analyzes and estimates the riverine Spatio-temporal flood-2014 extent changes using Landsat-8 imagery in Lower Chenab Plain, Pakistan. The lower Chenab plain is particularly prone to frequent riverine flooding but is understudied. It has experienced history worst flooding in September 2014. Optical Landsat-8 data can be used for flood inundation mapping when the flooded areas are clouds free. Cloud free Landsat-8 data was acquired for pre-flood, during-flood, and post-flood periods for detailed analysis. We used different water indexes including Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) for the delineation of inundated areas based on increase water index value from pre-flood and post-flood Landsat-8 images. Satellite-derived Water Indexes which are mostly utilized for flood extent estimation that separates the flooded water area from non-flooded areas based on a threshold value. Further, we also used supervised classification to detect flooded areas and compare them with water indexes. The resulted analysis allowed us to compute flood extent area, duration, and flood recession. The inundated area values of used water indexes are higher in the post-flood instance as compared to the pre-flood instance. The proposed RS technique provides an empirical basis for the rapid identification of inundated areas, which would enable emergency response and relief efforts on newly flooded areas in future events. Thus, our study provides another perspective and substantial contributions to flood monitoring using free satellite data in Pakistan.

Water is an important natural resource in our daily life. Water resources play a major role in sustainable development. Surveying and continuous monitoring of water bodies in a vast area is a tedious and timeconsuming task by conventional surveying techniques. Keeping track of changes in the areas of waterbodies due to urbanization and industrialization is very important. The present study aims at determining the spatiotemporal changes in the water bodies from 2008 to 2018 in the drought-hit area of Mahbubnagar, Telangana using satellite-derived spectral index namely,Modified Normalized Difference Water Index (MNDWI) and Support Vector Machine (SVM) technique using Resourcesat-1 Data. Extraction of water bodies using MNDWI and SVM can be done very easily with very little effort as compared to conventional surveying techniques. Using the MNDWI technique,the total area of waterbodies is 322.64 square kilometers in the year 2008 and 362.61 square kilometers in the year 2018. Using the SVM technique, the area of waterbodies is 368.17 square kilometers in the year 2008 and 395.72 in the year 2018. The kappa index values of 2018 by MNDWI technique and SVM technique are 0.86 and 0.91 respectively. The kappa index values of 2008 by MNDWI technique and SVM technique are 0.85 and 0.89 respectively. It is observed that the SVM technique is more accurate than the MNDWI technique. The area of water bodies has increased during the study period mainly due to increased awareness among the public on the importance of water and also initiatives taken by the Government of Telangana, like Mission Kakatiya for the revival of water bodies in the study area.

Extracting surface land-cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic tasks is to identify and map surface water boundaries. Spectral water indexes have been successfully used in the extraction of water bodies in multispectral images. However, directly applying a water index method to hyperspectral images disregards the abundant spectral information and involves difficulty in selecting appropriate spectral bands. It is also a challenge for a spectral water index to distinguish water from shadowed regions. The purpose of this study is therefore to develop an index that is suitable for water extraction by the use of hyperspectral images, and with the capability to mitigate the effects of shadow and low-albedo surfaces, especially in urban areas. Thus, we introduce a new hyperspectral difference water index (HDWI) to improve the water classification accuracy in areas that include shadow over water, shadow over other ground surfaces, and low-albedo ground surfaces. We tested the new method using PHI-2, HyMAP, and ROSIS hyperspectral images of Shanghai, Munich, and Pavia. The performance of the water index was compared with the normalized difference water index (NDWI) and the Mahalanobis distance classifier (MDC). With all three test images, the accuracy of HDWI was significantly higher than that of NDWI and MDC. Therefore, HDWI can be used for extracting water with a high degree of accuracy, especially in urban areas, where shadow caused by high buildings is an important source of classification error.

Several water index-based methods have been proposed in the literature, which, combine satellite multispectral bands in an algebraic expression. The objective of these water index-based methods is to increase the intensity contrast between water-pixels (surface water-body) and non-water pixels (built-up, soil, vegetation, etc.). The present investigation evaluates the Modified Normalized Difference Water Index (MNDWI) and the Automated Water Extraction Index (AWEI) using the Satellite data from Landsat 5 TM, Landsat 8 and Sentinel 2A at different time scenes. Based on visual inspection of the Lake Metztitlan water body mapping results, a high performance of AWEI approached via the OLI and the MSI sensors is observed. In the selected study area of [9210x9380]m, a statistical water pixel percentage of 30.703616% is observed in a flooding season and 9.884537% for a dry season of the year.

A novel water extraction method is proposed to improve the ability of delineation of the narrow water for the modified normalized difference water index (MNDWI) derived from remotely sensed images. This method introduces a morphological white top-hat transforming operation on the MNDWI to construct a morphological narrow water index (MNWI). The MNWI can effectively enhance the local contrast of linear objects, such that the narrow water can be easily separated from mountain shadows, bare patches and other land covers in the MNDWI image. Furthermore, a dual-threshold segmentation method was also used to extract the potential water bodies by combining an empirical threshold segmentation on the MNDWI and an automatic threshold segmentation on the MNWI. The effectiveness of the proposed method was validated with three experimental datasets, clipped from two different Landsat images, and the results demonstrate that the narrow water bodies can be effectively extracted with the overall accuracy being over 90%. Visually, most of the narrow streams or rivers keep continuous shapes in space and their water boundaries are also precisely delineated.

The accumulation of sediment in reservoirs poses a major challenge that impacts the storage capacity, quality of water, and efficiency of hydroelectric power generation systems. Geospatial methods, including Geographic Information Systems (GIS) and Remote Sensing (RS), were used to assess Dukan Reservoir sediment quantities. Satellite and reservoir water level data from 2010 to 2022 were used for sedimentation assessment. The satellite data was used to analyze the water spread area, employing the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) to enhance the water surface in the satellite imagery of Dukan Reservoir. The cone formula was employed to calculate the live storage capacity of the reservoir within two elevations. According to the study results, the live storage capacity of Dukan Reservoir at elevation 511.78 m had decreased from 8000 MCM to 7007.77MCM and 6923.53 MCM using NDWI and MNDWI respectively, due to sedimentation, resulting in a capacity loss of 14.59% and 15.83% for NDWI and MNDWI respectively. The annual sedimentation was 13.78 MCM and 14.95 MCM for NDWI and MNDWI, respectively. Joglekar's equation and Khosla's formula have demonstrated that the sedimentation rate in the Dukan reservoir exceeds the critical rate. The findings of this study will inform the development of sediment management strategies aimed at preserving the reservoir's capacity.

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

<p>The damage caused by a natural disaster in rural areas differs in nature, extent, landscape and in structure, from the damage in urban environments. Previous and current studies focus mainly on mapping damaged structures in urban areas after catastrophe events such as an earthquake or tsunami. Yet, research focusing on the damage level or its distribution in rural areas is absent. In order to apply an emergency response and for effective disaster management, it is necessary to understand and characterize the nature of the damage in each different environment. </p><p>Havivi et al. (2018), published a damage assessment algorithm that makes use of SAR images combined with optical data, for rapid mapping and compiling a damage assessment map following a natural disaster. The affected areas are analyzed using interferometric SAR (InSAR) coherence. To overcome the loss of coherence caused by changes in vegetation, optical images are used to produce a mask by computing the Normalized Difference Vegetation Index (NDVI) and removing the vegetated area from the scene. Due to the differences in geomorphological settings and landuse\landcover between rural and urban settlements, the above algorithm is modified and adjusted by inserting the Modified Normalized Difference Water Index (MNDWI) to better suit rural environments and their unique response after a disaster. MNDWI is used for detection, identification and extraction of waterbodies (such as irrigation canals, streams, rivers, lakes, etc.), allowing their removal which causes lack of coherence at the post stage of the event. Furthermore, it is used as an indicator for highlighting prone regions that might be severely affected pre disaster event. Thresholds are determined for the co-event coherence map (≤ 0.5), the NDVI (≥ 0.4) and the MNDWI (≥ 0), and the three layers are combined into one. Based on the combined map, a damage assessment map is generated. </p><p>As a case study, this algorithm was applied to the areas affected by multi-hazard event, following the Sulawesi earthquake and subsequent tsunami in Palu, Indonesia, which occurred on September 28th, 2018. High-resolution COSMO-SkyMed images pre and post the event, alongside a Sentinel-2 image pre- event are used as inputs. The output damage assessment map provides a quantitative assessment and spatial distribution of the damage in both the rural and urban environments. The results highlight the applicability of the algorithm for a variety of disaster events and sensors. In addition, the results enhance the contribution of the water component to the analysis pre and post the event in rural areas. Thus, while in urban regions the spatial extent of the damage will occur in its proximity to the coastline or the fault, rural regions, even in significant distance will experience extensive damage due secondary hazards as liquefaction processes.     </p>

Deep learning empowers the Google Earth Engine for automated water extraction in the Lake Baikal Basin

Use of multi-temporal satellite data for monitoring pool surface areas occurring in non-perennial rivers in semi-arid environments of the Western Cape, South Africa