Water Body Detection Research Articles

Detection of Surface Water Bodies and Cyanobacterial Blooms using Satellite based Remote Sensing

For many environmental applications, accurate surface water maps are crucial. It is possible to construct surface water maps by merging data from multiple sources. Due to sensor limitations, complex land cover, geography, and atmospheric conditions, accurate surface water estimation using satellite imagery is still a difficult undertaking. In this study, Sentinel 1 images from Google Earth Engine (GEE) was used to derive a high-resolution water mask. To overcome the limitation of visible satellite images which is the presence of clouds, on this research was focused on using Synthetic-Aperture Radar (SAR) sensors, like Sentinel-1, which penetrate clouds and permitting to get a view of the earth surface, even when clouds are presence. To validate the proposed method, it was applied to three study cases which are located in different climate zones in Sri Lanka. In the meanwhile, this research implemented a time series of Sentinel 2 satellite images from GEE to monitor the distribution of cyanobacterial blooms on those lakes. Recent years have seen an increase in the frequency and severity of cyanobacteria in recreational lakes and reservoirs, which has become a serious concern for the public’s health and a serious environmental danger. Monitoring cyanobacterial blooms serves as the foundation for early detection and treatment. Therefore, the results of this study could be used as a benchmark for future environmental monitoring and management of these Sri Lankan lakes and reservoirs.

A Novel Approach to the Detection and Imaging of Candidate Martian Subglacial Water Bodies by Radar Sounder Data

Research based on Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) data detected unusual radar bright basal reflections located at about 1.5 km depth in a Mars region denoted as Ultimi Scopuli. These reflections were interpreted as a signature of subglacial liquid water even though this interpretation is still being debated in the literature. In this article, we propose a novel approach to the detection and imaging of candidate subglacial liquid water from radar sounding data. The approach combines the radar echo power traces with a suitable digital elevation model to provide a bidimensional representation of the surface. Even if the imaging method reconstructs a representation of the surface, we prove that it can be used to identify subsurface bright reflections in icy regions. Imaging is feasible even if the basal interface is not directly included in the processed data for image generation. To support this experimental evidence, we show that a relationship exists between the value of the reflected echo power originating from the englacial layers and the basal-to-surface-echo-power ratio. The observed relationship holds on both Ultimi Scopuli radar sounding data acquired on Mars and Lake Vostok data acquired on Earth. Our results show that the 2-D imaging provides an alternative way for locating candidate subglacial liquid water bodies on Mars over large areas also where the basal interface is not directly measured. The proposed approach complements previous research for further evaluation of the actual presence of liquid water on Mars.

Analysis of modified water index (MWI) for extraction of water bodies in landsat-8 imagery

Remote sensing techniques play an important role in exploring and management of water resources on the surface of the earth. Water bodies' identification from multispectral imagery is mainly done using several water indices. Water indices evolved based on the reflectance variations in multispectral imagery from different water bodies. Normalized Difference Water Index (NDWI) is mostly popularly used water index for detection of water bodies. The NDWI water index is used to classify the clear water bodies from non-water bodies and mixed water bodies. Modified Normalized Difference Water Index (MNDWI) is used to classify the clear water bodies along with mixed water bodies from non-water bodies. The selection of appropriate indices significantly affects the performance accuracy in extraction of water bodies. This paper aims in proposing simple and effective water index based on multi bands, Blue, Green, NIR and SWIR2.The objective of Modified Water Index (MWI) is better handling classification of the mixed water pixels. The Blue, Green bands has high reflectance values and Near Infrared (NIR), Short Wave Infrared (SWIR2) bands has low reflectance from water bodies. The significant reflectance variance feature of SWIR2 from clear water to mixed water bodies is useful in the classification of the mixed water pixels in the MWI. The performance of the Modified Water Index is analyzed and compared with the existing several water indices. The performance metrics such as Water Spread Area, mean and standard deviation are used to compare the effectiveness of water body indices.

Airborne GNSS Reflectometry for Water Body Detection

This article is dedicated to the study of airborne GNSS-R signal processing techniques for water body detection and edge localization using a low-altitude airborne carrier with high rate reflectivity measurements. A GNSS-R setup on-board a carrier with reduced size and weight was developed for this application. We develop a radar technique for automatic GNSS signal segmentation in order to differentiate in-land water body surfaces based on the reflectivity measurements associated to different areas of reflection. Such measurements are derived from the GNSS signal amplitudes. We adapt a transitional model to characterize the changes in the measurements of the reflected GNSS signals from one area to another. We propose an on-line/off-line change detection algorithm for GNSS signal segmentation. A real flight experimentation took place in the context of this work obtaining reflections from different surfaces and landforms. We show, using the airborne GNSS measurements obtained, that the proposed radar technique detects in-land water body surfaces along the flight trajectory with high temporal (50 Hz ) and spatial resolution (order of 10 to 100 m2). We also show that we can localize the edges of the detected water body surfaces at meter accuracy.

Enhanced index for water body delineation and area calculation using Google Earth Engine: a case study of the Manchar Lake

Abstract The sustainability of the hydrological and ecological ecosystems of any region requires continuous monitoring of the water bodies. Recent advancements in satellite-based remote optical sensors, big data analysis and cloud computing have given new dimensions to the field of water body studies including their detection as well as analysis. The present study extends the existing methods to assess the contemporary surface water detection and monitoring techniques via remote sensing. The proposed technique implies an improved hybrid approach for the purpose along with the calculation of the boundary areas. The study has been carried out on the Manchar Lake, the largest natural freshwater lake in Pakistan as well as in South Asia. The proposed hybrid water index along with the different existing water body detection indices and spectral bands have been worked out on the satellite images retrieved from the Google Earth Engine to detect and analyze the area/flow changes in the water body. Based on the 7 years of data, the proposed algorithm calculates the water body area more precisely. With limited availability of metadata about the study area, the results have been validated both qualitatively through national-met data and statistically. These results help to better preserve and improve the quality of the water resource.

Uncertainties Involved in the Use of Thresholds for the Detection of Water Bodies in Multitemporal Analysis from Landsat-8 and Sentinel-2 Images.

Although the single threshold is still considered a suitable and easy-to-do technique to extract water features in spatiotemporal analysis, it leads to unavoidable errors. This paper uses an enumerative search to optimize thresholds over satellite-derived modified normalized difference water index (MNDWI). We employed a cross-validation approach and treated accuracy as a random variable in order to: (a) investigate uncertainty related to its application; (b) estimate non-optimistic errors involving single thresholding; (c) investigate the main factors that affect the accuracy’s model, and (d) compare satellite sensors performance. We also used a high-resolution digital elevation model to extract water elevations values, making it possible to remove topographic effects and estimate non-optimistic errors exclusively from orbital imagery. Our findings evidenced that there is a region where thresholds values can vary without causing accuracy loss. Moreover, by constraining thresholds variation between these limits, accuracy is dramatically improved and outperformed the Otsu method. Finally, the number of scenes employed to optimize a single threshold drastically affects the accuracy, being not appropriate using a single scene once it leads to overfitted threshold values. More than three scenes are recommended.

Correlation of normalized difference water index and baseflow index in small island watershed landscapes

The utilization of remote sensing data in the field of environmental hydrology is experiencing rapid progress. The Normalized Difference Water Index (NDWI) approach to transforming the water content of various land cover types and its implications for small island watersheds' hydrological characteristics is essential. NDWI is an algorithm used to detect water bodies, with the capacity to absorb visible and infrared wavelengths strongly. This study aims to analyze the correlation between the NDWI water index and the BFI baseflow index in the small island landscape of Ambon City. The Landsat 7 ETM + and Landsat 8 OLI image processing methods use ENVI 5.3 software to transform the NDWI algorithm and the BFI + 3.0 digital recursive filtering (RDF) method for hydrological characterization. The results showed that there was a strong correlation between the NDWI water index and the baseflow index (BFI) for the small island watershed of Ambon city. This result is relevant to the geographic area of Ambon City, which is dominated by the ocean 95% and land area 5%, so the application of the NDWI water index and the hydrological conditions of small island watersheds are significant.

Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia

The carrying capacity of water resources is key to the sustainable development of arid and semi-arid regions. There are important challenges related to the detection of discontinuous and crooked water bodies in the vast Mongolian Plateau, despite the availability of remote sensing technology which has the advantage of facilitating water observations over large areas and timelines. Given the high cost and low coverage of high-resolution images and the low resolution of images with high coverage, this study proposes a pixel-based convolutional neural network (CNN) method for the application of water extracted from the region of interest (ROI) to medium-resolution Landsat images. The pixel-based CNN method combines the texture and spectral features of the ground object by connecting the center pixels of the images to the surrounding pixels. ROI is used instead of full-label datasets, reduce the difficulty of building labels in low-to-medium-resolution images. Taking the Tuul River in Mongolia as a case, the pixel-based CNN method, the normalized difference water index threshold (NDWI) method, the modified normalized difference water index (MNDWI) threshold method, U-net model in deep learning, and the pixel-based deep neural network (DNN) method were used with medium-resolution Landsat 8 images with ROI labels. The pixel-based CNN method shows better water extraction results for the cloud, cloud shadows, and building areas, compared with other methods. The method proposed in this study had the highest verification accuracy (92.07%). It also has the advantages of fewer training parameters and shorter training time. The training accuracies of the pixel-based CNN, pixel-based DNN, and U-net were 99.90%, 96.98%, and 93.70%, respectively. All training models and calling methods were uploaded to GitHub (https://github.com/CaryLee17/Pixel-based-CNN).

Electrochemical impedance biosensor for detection of saxitoxin in aqueous solution.

Saxitoxin is a cyanotoxin which is very harmful to human health; the concentration limit in drinking water is only 3μg/L. Therefore, a simple, fast, sensitive, low-cost, and specific method for its detection, quantification, and monitoring in water bodies is needed to avoid adverse effects on animal and human health. In this work, we developed an electrochemical impedimetric biosensor using a specific aptamer as recognition element for saxitoxin detection. This method allies the superior sensing characteristics of aptamers with the nondestructive, label-free, and easy working principles of the electrochemical impedance technique. The device presented sensitivity for detecting saxitoxin concentrations above 0.3μg/L, with high selectivity in negative control experiments, demonstrating a promising alternative for water toxin detection.

MSResNet: Multiscale Residual Network via Self-Supervised Learning for Water-Body Detection in Remote Sensing Imagery

Driven by the urgent demand for flood monitoring, water resource management and environmental protection, water-body detection in remote sensing imagery has attracted increasing research attention. Deep semantic segmentation networks (DSSNs) have gradually become the mainstream technology used for remote sensing image water-body detection, but two vital problems remain. One problem is that the traditional structure of DSSNs does not consider multiscale and multishape characteristics of water bodies. Another problem is that a large amount of unlabeled data is not fully utilized during the training process, but the unlabeled data often contain meaningful supervision information. In this paper, we propose a novel multiscale residual network (MSResNet) that uses self-supervised learning (SSL) for water-body detection. More specifically, our well-designed MSResNet distinguishes water bodies with different scales and shapes and helps retain the detailed boundaries of water bodies. In addition, the optimization of MSResNet with our SSL strategy can improve the stability and universality of the method, and the presented SSL approach can be flexibly extended to practical applications. Extensive experiments on two publicly open datasets, including the 2020 Gaofen Challenge water-body segmentation dataset and the GID dataset, demonstrate that our MSResNet can obviously outperform state-of-the-art deep learning backbones and that our SSL strategy can further improve the water-body detection performance.

Towards user-adaptive remote sensing: Knowledge-driven automatic classification of Sentinel-2 time series

Land cover mapping over large areas is essential to address a wide spectrum of socio-environmental challenges. For this reason, many global or regional land cover products are regularly released to the scientific community. Yet, the remote sensing community has not fully addressed the challenge to extract useful information from vast volumes of satellite data. Especially, major limitations concern the use of inadequate classification schemes and “black box” methods that may not match with end-users conceptualization of geographic features. In this paper, we introduce a knowledge-driven methodological approach to automatically process Sentinel-2 time series in order to produce pre-classifications that can be adapted by end-users to match their requirements. The approach relies on a conceptual framework inspired from ontologies of scientific observation and geographic information to describe the representation of geographic entities in remote sensing images. The implementation consists in a three-stage classification system including an initial stage, a dichotomous stage and a modular stage. At each stage, the system firstly relies on natural language semantic descriptions of time series of spectral signatures before assigning labels of land cover classes. The implementation was tested on 75 time series of Sentinel-2 images (i.e. 2069 images) in the Southern Brazilian Amazon to map natural vegetation and water bodies as required by a local end-user, i.e. a non-governmental organization. The results confirmed the potential of the method to accurately detect water bodies (F-score = 0.874 for bodies larger than 10 m) and map natural vegetation (max F-score = 0.875), yet emphasizing the spatial heterogeneity of accuracy results. In addition, it proved to be efficient to provide rapid estimates of degraded riparian forests at watershed level (R2 = 0.871). Finally, we discuss potential improvements both in the system's implementation, e.g. considering additional characteristics, and in the conceptual framework, e.g. moving from pixel- to object-based image analysis and evolving towards a hybrid system combining data- and knowledge-driven approaches.

Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery

Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40–0.56 m yr−1 for lake sizes of 0.10 ha–23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.

Sentinel-1 SAR image-based waterbody detection technique for estimating the water storage in agricultural reservoirs

Sentinel-1 SAR image-based waterbody detection technique for estimating the water storage in agricultural reservoirs

Reduced Graphene Oxide Fabricated Molecular Imprinted Polymeric Layers for Improved Recognition of Ciprofloxacin

Introduction Ciprofloxacin is a broad spectrum synthetic antibiotic that is frequently used against gram negative and gram positive bacteria. The increasing use of ciprofloxacin by humans has also results its release in environment as it is detected in different water bodies [1]. The presence of antibiotics in aquatic environment could result the development of antibiotic resistant strains [2], which is a serious concern. The current analytical tools for its sensitive detection are mainly based on spectrometric or chromatographic techniques. Nevertheless these methods are time consuming and require extensive sample preparation steps. It is imperative to design sensitive and robust sensor setups for its rapid detection in water bodies. In this study, we designed molecular imprinted polymeric layers incorporated with graphene derivatives for electrochemical sensing of ciprofloxacin using interdigitated capacitors as transducer. These devices offer rapid response and have low fabrication costs. The combination of reduced graphene oxide (rGO) with molecular imprinted polymer (MIP) has shown enhanced sensitivity and selectivity thus, developing smart sensor devices for straightforward detection of ciprofloxacin. Experimental Graphene oxide (GO) was synthesized by oxidation of graphite powder following modified Hummer’s method. For preparing reduced graphene oxide (rGO), the as-prepared GO was dispersed in distilled water and treated with sodium borohydride as reducing agent at 70°C for about 12 hours. The resultant product was subjected to several washings and dried overnight at 65°C. Molecular imprinted polymer (MIP) was prepared using methacrylic acid (MAA) and ethyleneglycol dimethacrylate (EGDMA) along with ciprofloxacin as template structure in dimethylformamide (DMF). The as-prepared GO/rGO powder was added to above pre-polymer mixture to prepare respective composite coatings. Azoisobutyrnitrile (AIBN) was used to carry out thermally initiated free radical polymerization at 60 °C for 30 minutes. The resultant polymer mixtures were integrated with IDCs by spin coating. The non-imprinted polymer was prepared under same manner without adding template. Moreover, its composite layers with GO/rGO were prepared under similar conditions and integrated with IDCs. Results and Conclusion The sensor response as capacitance shift for rGO-MIP, GO-MIP, MIP and their corresponding non-imprinted layers i.e., rGO-NIP, GO-NIP and NIP was measured for varying concentrations of ciprofloxacin as shown in figure 1. Comparing the response of imprinted layers, it can be noticed that rGO-MIP coated IDCs showed highest sensor shift i.e. 43 µF, while GO-MIP is second best (34 µF) and finally, the pristine MIP is at third place (25 µF). This suggests that incorporation of rGO in MIP [3] enhances the sensor shifts and thus, is a suitable strategy for improving the sensitivity. On other hand, all the non-imprinted layers i.e., rGO-NIP, GO-NIP and pristine NIP showed much lower sensor shifts. Moreover, these layers showed similar sensor shifts indicating that these layers does not possess any specific recognition sites and thus, can be taken as control surface.The selectivity of rGO-MIP sensor is tested by exposing the device against structurally analogous antibiotic drugs. The sensor shifts of rGO-MIP for ciprofloxacin, levofloxacin and moxifloxacin in the concentration range 10-50 ppm is shown in figure 2. It is evident that sensor response for ciprofloxacin is at least 3 times higher than levofloxacin and moxifloxacin. The significantly higher sensor shifts for ciprofloxacin is due to the presence of complementary interaction sites in MIP layer. While levofloxacin and moxifloxacin despite of their similar structures to ciprofloxacin showed much lower sensor shifts. The results suggested that the incorporation of rGO to MIP not only improves sensitivity but also contribute in enhanced selectivity as well.

Inland Water Body Mapping Using CYGNSS Coherence Detection

This work demonstrates the creation of dynamic inland water body masks at spatial resolutions ranging from 1 to 3 km through the use of a recently developed coherence detector for the delay-Doppler maps produced by the cyclone global navigation satellite system (CYGNSS) constellation. The use of the coherence of the observed measurements reduces many of the uncertainties associated with previous signal-to-noise ratio-based water body detection approaches for CYGNSS. Using data from January 2018 to February 2020 and producing maps representing time intervals ranging from 3 months to 2 years, the water body masks created are found to be associated with a probability of detection that exceeds 80% as compared to the Pekel water mask developed from Landsat observations. The analysis presented in this work highlights the potential of using spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) systems for dynamic inland water body mapping.

Feature Extraction of Marine Water Pollution Based on Data Mining

The ocean occupies more than two-thirds of the earth’s area, providing a lot of oxygen and materials for human survival and development. However, with human activities, a large number of sewage, plastic bags, and other wastes are discharged into the ocean, and the problem of marine water pollution has become a hot topic in the world. In order to extract the characteristics of marine water pollution, this study proposed K-means clustering technology based on cosine distance and discrimination to study the polluted water. In this study, the polygonal area method combined with six parameters of water quality is used to analyze the marine water body anomalies, so as to realize the rapid and real-time monitoring of marine water body anomalies. At the same time, the cosine distance method and discrimination are used to classify marine water pollutants, so as to improve the classification accuracy. The results show that the detection rate of water quality anomalies is more than 88.2%, and the overall classification accuracy of water pollution is 96.3%, which proves the effectiveness of the method. It is hoped that this study can provide timely and effective data support for the detection of marine water bodies.

Deep-Learning-Based Multispectral Satellite Image Segmentation for Water Body Detection

Automated water body detection from satellite imagery is a fundamental stage for urban hydrological studies. In recent years, various deep convolutional neural network (DCNN)-based methods have been proposed to segment remote sensing data collected by conventional RGB or multispectral imagery for such studies. However, how to effectively explore the wider spectrum bands of multispectral sensors to achieve significantly better performance compared to the use of only RGB bands has been left underexplored. In this article, we propose a novel DCNN model-multichannel water body detection network (MC-WBDN)-that incorporates three innovative components, i.e., a multichannel fusion module, an Enhanced Atrous Spatial Pyramid Pooling module, and Space-to-Depth/Depth-to-Space operations, to outperform state-of-the-art DCNN-based water body detection methods. Experimental results convincingly show that our MC-WBDN model achieves remarkable water body detection performance, is more robust to light and weather variations, and can better distinguish tiny water bodies compared to other DCNN models.

Detecting Traces of 17α-Ethinylestradiol in Complex Water Matrices

Hormones have a harmful impact on the environment and their detection in water bodies is an urgent matter. In this work, we present and analyze a sensor device able to detect traces of the synthetic hormone 17α-ethinylestradiol (EE2) below 10−9 M in media of different complexities, namely, ultrapure, mineral and tap waters. This device consists of solid supports with interdigitated electrodes without and with a polyethylenimine (PEI) and poly (sodium 4-styrenesulfonate) (PSS) layer-by-layer film deposited on it. Device response was evaluated through capacitance, loss tangent and electric modulus spectra and the data were analyzed by principal component analysis method. While the three types of spectra were demonstrated to be able to clearly discriminate the different media, loss tangent spectra allow for the detection of EE2 concentration, with a sensitivity of −0.072 ± 0.009 and −0.44 ± 0.03 per decade of concentration, for mineral and tap water, respectively. Detection limits values were found to be lower than the ones present in the literature and presenting values of 8.6 fM (2.6 pg/L) and of 7.5 fM (22.2 pg/L) for tap and mineral waters, respectively. Moreover, the obtained response values follow the same behavior with EE2 concentration in any medium, meaning that loss tangent spectra allow the quantification of EE2 concentration in aqueous complex matrices.

Manual-Based Improvement Method for the ASTER Global Water Body Data Base

A water body detection technique is an essential part of digital elevation model (DEM) generation to delineate land–water boundaries and to set flattened elevations. The initial tile-based water body data that are created during production of the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) GDEM, as a by-product, are incorporated into ASTER GDEM V3 to improve the quality. At the same time as ASTER GDEM V3, the Global Water Body Data Base (ASTWBD) Version 1 is also released to the public. The ASTWBD generation consists of two parts: separation from land area, and classification into three categories: sea, lake, and river. Sea water bodies have zero elevation. Lake water bodies have flattened elevations. River water bodies have a gradual step-down from upstream to downstream with a step of one meter. The separation process from land area is carried out automatically using an algorithm, except for sea-ice removal, to delineate the real seashore lines in the high latitude areas; almost all of the water bodies are created through this process. The classification process into three categories, i.e., sea, river, and lake, is carried out, and incorporated into ASTER GDEM V3. For inland water bodies, it is not possible to perfectly detect all water bodies using reflectance and spectral index, which are the only available parameters for optical sensors. The only way available to identify the undetected inland water bodies is to manually copy them with visual inspection from the earth’s surface images, like Landsat images. GeoCover2000 images are the main part of the object images. Color–Land ASTER MosaicS (CLAMS) images are used to cover the deficiency of the GeoCover2000 images. This kind of time-consuming, unsophisticated way is inevitable as it is a manual-based method to improve the quality of the ASTWBD. This paper describes the manual-based improvement method; specifically, how deficient water body images are efficiently copied as rasterized images from the earth’s surface images to obtain a more complete global water body data set.

Hybrid modelling approach for water body change detection at Chalan Beel area in northern Bangladesh

Water is a strategic resource for both socio-economic development and human life. The current study has been carried out for spatio-temporal change detection of surface water bodies during winter period using hybrid modelling approach. The study area has fallen in the northern part of Bangladesh and is locally called Chalan Beel with 5 million of in habitants, a prominent intensive crop production, surface and groundwater irrigation, high evapotranspiration, and water scarcity. For the detection of water body changes, satellite images of 1999 and 2011 were used, and the following image fusion techniques were applied: (a) Gram-Schmidt (GS), (b) modified intensity hue saturation (IHS), (c) high-pass filter (HPF), and (d) wavelet. Landsat 7/ETM + panchromatic (PAN) band of 15 m × 15 m resolution in 1999 and Landsat 5/TM multispectral (MS) bands of 30 m × 30 m resolution in 2011 were allied each other to generate high-resolution image that contains information of two different years. The fused images were classified to extract the water bodies using four classification methods: (a) artificial neural network (ANN), (b) support vector machine (SVM) and (c) maximum likelihood (ML). To analyze the quality of the fused images, statistical calculation (quantitatively) and Laplacian edge detection (qualitatively) were used. To validate the fused image classification results, the multispectral images from 1999 and 2011 were again individually classified using principal component analysis (PCA), normalized difference water index (NDWI), and image differencing (ID) processes and compared with the previous classification. Surprisingly, the results showed that two-third of the areas dried up in 10 years.