International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 1, January 2019, pp.143–154, Article ID: IJCIET_10_01_014 Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 ©IAEME Publication Scopus Indexed DETECTION WETLAND DEHYDRATION EXTENT WITH MULTI-TEMPORAL REMOTELY SENSED DATA USING REMOTE SENSING ANALYSIS AND GIS TECHNIQUES Hayder Dibs Hydraulic Structures Department, Water resources Engineering Faculty, AL-Qasim Green University, Babylon, Iraq Suhad AL-Hedny Department of Environment, Faculty of Environmental Science, Al-Qasim Green University, Babylon, Iraq ABSTRACT To prevent losing water resources and wetlands, and conserve existing wetlands ecosystem for ecosystem and biodiversity services, good, wetlands habitats forstart any sustainable development programs, it is necessary to detect, monitor and inventory water resources and their surround uplands. Recently, AL-Razaza Lake suffer from a critical situation because of the decreasing in the water level and increase a salinity. We have propose a method to monitor and model the spatial and multi-temporal changes of AL-Razaza Lake in the period 1992–2018. This study includes pre-processing, processing and post-processing stages. In Addition, a supervised classification was used to classify the satellite images. Validation result reveals that the overall accuracies and kappa coefficients of the supervised classifications were 88, 90.79, 95.94 and 87.67 respectively, and 82%, 86%, 93% and 79% respectively. The results showed that the percentage change was significant during this period, such that the decreased surface area was from 1313.87 km2 in 1992 to 224.85 km2 in 2018.The noticeable results show the rapidly decreasing in the Lake area by 82.8% with area about 1089.02 km2 over the last three decades. All the dehydration extended area of the Lake was replaced by soil. Keywords: Wetland Change Detection, Dehydration Extent, Multispectral Image Classification, Landsat Satellite Images, and Supervised Classification. http://www.iaeme.com/IJCIET/index.asp 143 editor@iaeme.com Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis and GIS Techniques Cite this Article: Hayder Dibs and Suhad AL-Hedny, Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis and GIS Techniques, International Journal of Civil Engineering and Technology (IJCIET), 10 (1), 2018, pp. 143–154. http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1 1. INTRODUCTION Study the water resources degradation and/ or dehydration extent play a big role to investigate and analysis wetland global change to monitor and manage the water resources around the world (Dibs, 2018). Non-organize the international water resources policies, Global warming, Land use-Land cover (LU-LC), low education and human modifications paly a big role in wetland changing and lead to loss the biodiversity, increase of environmental problems and reveal of different types of natural disaster such as desertification, loss of natural resources, wildlife habitats, biodiversity and water resources (Dwivedi et al., 2005; Abd and Husam, 2013). Al-Razaza Lake provides many services including control the flood water retention of Euphrates river flood, maintenance of water quality, wildlife habitat, and control of soils erosion, and it is also important for human beings for food crops, and balance of the ecosystems (Atasoy et al., 2011; Sun et al., 2012). Its water level was started to decrease from the 1980s and accelerated since 1990s due to many reasons as climate change, rising atmospheric temperatures and continuing evaporation process during Iraq’s dry in hot summers and decrease the water levels in the Euphrates River, which is the most important main water sources of AL-Razaza lake, all these factors together led to a decrease in the water elevation and the lake surface area, revealing and increasing soil salinity that become a real disaster (Nawal et al., 2012and K.N.Kadhim, 2018). The lake water elevations have been decreased to be only (5-10) m deep were reported by local reports (Atasoy et al., 2011). Both remote sensing and geographical information system (GIS) are powerful tools to obtain accurate and update to day information on the distribution of water resources degradation over large areas (Brisco et al., 2013and K.N.Kadhim&Noor S.,2018). Remotely sensed imagery is very important data resources to analysis and process through the GIS, It uses for conduct object recognition (Hayder et al., 2014). Many studies studied and performed towards the determination of the water surface area extraction, wetland change, water body detection, degradation and mapping (Demir et al., 2013; Dibs et al., 2015; Dibs, 2016;Dibs et al., 2017; Dibs et al., 2018a; Dibs et al., 2018b), disaster monitoring (Volpi et al., 2013; Brisco et al., 2013), forest and vegetation change(Markogianni et al., 2013), urban sprawl (Raja et al., 2013), and hydrology (Zhu et al., 2011).Remote sensing degradation algorithms based on imagery differencing that including transformations like (1) principal component analysis, (2) modeling of spectral mixture, (2) using different vegetation indices, and (3) change vector analysis have accurate result for mapping earth’ surface changes (Dahl, 2006; Selçuk, 2008; Lu et al., 2004; Hayder, 2018). Water resources detection is well represented (Abd et al., 2011; Liya and Karsten, 2015). This study propose a technique for investigating the spatiotemporal degradation changes and dehydration extent of Al-Razaza Lake for the period time 1990 up to 2018 using digital image processing and GIS analysis on satellite imagery. Recommendations are made in the context of the water surface extraction, monitor and manage of water bodies, surface water dehydration extent, Wetland change, desertification, natural resources loss, water resources loss, water quality maintenance, wildlife habitat, and soil erosion control http://www.iaeme.com/IJCIET/index.asp 144 editor@iaeme.com Hayder Dibs and Suhad AL-Hedny 2. MATERIAL AND METHODS Extraction and detection technique of water resources was initially tested to map the spatial and temporal water body changes (dehydration extent) of the Al-Razaza Lake. For the purpose of this study, Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 operation land imager (OLI) data acquired from 1992 to 2018 were used. In this research, supervised classification (Mahalanobis distance algorithm) were applied on the four collected Landsat images of 1992, 2001, 2010, and 2018 for water resources degradation. The accuracy assessment, and a statistical analysis of the expected results were conducted to get an accurate spatiotemporal degradation and dehydration extent of Al-Razaza Lake. Figure 1 shows the overall adopted methodology to perform this study. 2.1. Study area description Al-Razaza Lake was generated to divert the annual floodwaters of the Euphrates River into a desert to prevent flooding across southern Iraq side. It is a few miles from North West of Karbala province and north east of Al-Anbar province, Iraq as shown in Figure 2. It is bounded by 33°10′N to 32°20′N and 43°55′ E to 43°15′ E (Nawal et al., 2012). Al-Razaza Lake is a part of a wide valley that consists of AL-Tharthar, AL-Habbaniya, Al-Razzaza and Bahr Al-Najaf (Atasoy et al., 2011). The water surface area of this lake about 1810 km2 when the water level at 40m above sea level. It holds about of 26 billion m3 of water with length (Nawal et al., 2012). The lake is supplied by different water sources, including the Euphrates River; Lake Habaniya; Rashidiya; groundwater springs; rainwater and seasonal flows. The climate of the lake basin is characterized by cold winters and dry and hot summers, being influenced by the desert surrounding the lake (Nawal et al., 2012). Figure 2 describes the study area location. 2.2. Satellite Data Four Landsat satellite images were applied to conduct this study from 1992 to 2018. Two images downloaded from TM sensor of Landsat 4 and 5 in August 1992 and February 2010, respectively. The third imagery was obtained from ETM+ sensor in January 2001, and the last image of OLI sensor acquired in May 2018.The TM scenes consist of seven 7 bands with 30m as a spatial resolution for bands (1 – 5) and 7. Band 6 spatial resolution is 120m. The ETM+ imagery consist of 8 spectral bands with 30m as a spatial resolution for bands (1 – 5) and 7. Band 6 spatial resolution is 60 m. However, the newly satellite Landsat 8 OLI launched (on 11 February 2013) has 9 spectral bands and 2 thermal bands. All spectral its bands have spatial resolution equal to 30m, and the thermal bands (10 & 11) are obtained at 100m spatial resolution and then resampled to 30m. All of the obtained Landsat images of 1992, 2001, 2010 and 2018 were collected from the path 169 and row 37. These time series of Landsat satellite images are freely downloaded from the website of United States geological survey (USGS) (http://glovis.usgs.gov). http://www.iaeme.com/IJCIET/index.asp 145 editor@iaeme.com Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis and GIS Techniques Landsat7 ETM+2001 Landsat MSS 1992 Landsat7 ETM+2010 Landsat 8 OLI 2018 Pre-processing stage for 1992, 2001, 2010 & 2018 Geometric Correction Radiometric Correction Images Co-registration Images Resampling Processing stage for 1992, 2001, 2010 & 2018 Images Layer stacking Images Sub-setting Image Classification (Mahalanobis Distance) for 1992, 2001, 2010 & 2018 Selecting Training Sites N Thematic Maps of 1992, 2001, 2010 & 2018 Ye Accuracy assessment of 1992, 2001, 2010 & 2018 classifications Visual Interpretation Collecting Testing sites Wetland Change Detection 1992, 2001, 2010 & 2018 Figure 1. Flowchart showing the overall methods adopted in the study area. 2.3. Image Pre-Processing To make the input satellite images ready for further analyzing and processing, the following pre-processing steps were performed; radiometric calibration, atmospheric correction, geometric correction, co-registration, and resampling. As reported by Song et al., (2001) atmospheric correction of satellite image might not be needed in case of using a single image for image classification. However, when multi-temporal or multi-sensor images are used, the atmospheric correction become a mandatory (Lu and Weng, 2007). So, radiometric and atmospheric correction were performed as reported by (Schroeder et al., 2006). The Landsat calibration perform using ENVI v 5.0 software. A pre-georeferenced was performed to UTM zone 38 north projection using WGS-84 datum. The geometric correction of satellite images was performed to a reference image (Landsat 8 OLI image 2018) to register the all other images, with using the root mean square error (RMSE) < 0.5 pixel from using the image to image co-registration technique, the Arc map software has been used for performing geometric and co-registration. The 2018 image had previously been georeferenced from using ground control points (GCPs) and topographic map (RMSE < 0.4 pixel). Around 22 control points (CPs) were used for co-registration all the images to the reference image. Finally, the images were resampled using the nearest neighbor method. Figure 3 (a) indicates the Landsat time series images after radiometric calibration, atmospheric correction, geometric correction, co-registration, and resampling were performed. http://www.iaeme.com/IJCIET/index.asp 146 editor@iaeme.com Hayder Dibs and Suhad AL-Hedny Figure 2 Location map of the study area in the western part of Iraq 2.4. Image Processing For further satellite images processing and analysis stages, the following processing steps were performed: (1) Layers stacking, and (2) Images resizing. Layer stacking was the next step of processing stage, it is applied to all of the Landsat images using the Layer Stacking tool under Envi v 5.0 software. After that, the satellite scenes were resized. The steps of images Layer stacking and resizing were conducted in order to saving time when processing and analyzing data and reduce the storage size (Abd, 2013; Dibs et al., 2015). Figure 3 (b) indicts the final time series images of Landsat that ready to further analysis to identify the water dehydration extent of Al- Razaza Lake after performed the layer stacking and resizing. 2.5. Multi-Temporal Supervised Classifications The classification techniques and approaches are developed by scientists and practitioners for increase the accuracy of classification (San et al., 1997). The classification of remote sensing data is very attractive to researchers who deals with applications of environment or socioeconomic (Dibs et al., 2016). In this study totally, four classes were established as water bodies, shallow water, soil-1, and soil-2. Description of these classes are presented in Table 1. Table 1. Land cover classification Land cover classes Soil-1 Soil-2 Water bodies Shallow water Description Areas with no vegetation cover, stony area Areas with no vegetation cover, sandy area Deep water of Al-Razaza lake Coastal and the shallow water areas surrounding the of Al-Razaza lake = ݐݏ݅ܦටሺܺ − ܯ ሻ ∗ Vିଵ ∗ ሺܺ − ܯ ሻ http://www.iaeme.com/IJCIET/index.asp 147 editor@iaeme.com Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis and GIS Techniques Where (X) is the input variable vector and (Mi) represent the class (i) mean vector for over all pixels, (Vi) is the (variance/covariance) matrix for class (i), and (T) is the matrix transpose. Ranging from (0 - 1), the MDC typicality identify the absolute strength of class membership (San et al., 1997; Eastman et al., 2005). Figure 3. (a) Landsat satellite images with all corrections, (b) Landsat satellite images after performing layer stacking and resizing 2.5.1. Training and testing sites Training site another step was performed to this research over the study area. The training sites of each class is selected by using a topographic map, and visual image interpretation. The training sets size has a great effect on the result of classification accuracy (Mather and Koch, 2010). By following the recommendation of (Foody et al., 2006; Gao& Liu, 2010), the size of each training sets should not select to be fewer than 10-30 observations for each class. Training and testing sites were selected for each class by delimiting polygons around representative site. From using this pixels enclose by these polygons, we obtained spectral signature for the four classes type and recorded by the satellite images. Then we entered the spectral signatures into the processing of classification. Regarding to the reference image and visual image interpretation, the interesting regions (ROI) were generated for each class of the study area. The samples screening were conducted for each class type in the ENVI v 5.0 software. The principle is to get the evenly distributed training sites over the catchment area and to keep the size to be similar as possible for both the Landsat 8 image and the to the series of images. In this paper, the size of the training sites of water bodies, shallow water, soil-1, and soil-2 categories were set to be greater than 1500 pixels. However, only 200 pixels for the water body class. Figure 4 demonstrates the thematic maps of time series of Landsat images from 1992-2018. http://www.iaeme.com/IJCIET/index.asp 148 editor@iaeme.com Hayder Dibs and Suhad AL-Hedny 2.6. Water dehydration extent detection After the thematic maps generated for each individual satellite image, a statistical comparison was performed to determine the dehydration of Al-Razaza Lake. This method is perhaps the most common technique to use for change detection (Jensen, 2004), and has been used by Yang (2002) to monitor water resources change in the Atlanta. Figure 4 shows the thematic maps and the spatial distribution of their classes. Figure 4 (a) demonstrates the four classes distribution in 1992. The images classification results are summarized for all images, whereas Tables 3 and 4 respectively display the percentage, relative change and area in square kilometer of the four classes for the period from 1992 to 2018. In 1992 the dominant class is soil (soil-1 & soil-2) as indicates in Table 3 & 4, which represent approximately 67.40 % of the study area, with area was 3157.03 km2 (1534.01 km2 for soil1-1 and 1623.02 km2 for soil2). In other hand, area of soil-1 class is bigger than area of soil-2 class, and the percentage 32.75 % for soil-1 and 34.65 % for soil-2 in 1992. But, in 2001 the total soil classes represent approximately 74.65 % (49.05 % for soil-1 and 25.60 % for soil-2) of the study area or 3496.6 km2 (2297.50 km2 for soil-1 and 1199.10 km2 for soil-2), and that indicate the dominant class is also soil, the soil class area in increasing. In contrast, the shallow water class decreased and covered the smallest area of approximately 213.10 km2, which represents 4.55 % of the study area in1992, while the shallow water class in 2001 decreased to cover the smallest area of approximately 138.18 km2, which represents 2.95 % of the study area. The last class was the water bodies, it has the second highest percentage of the study area after soil class at approximately 28.05% or 1313.87 km2 in 1992. However, the water bodies is the second most prevalent class in the study area and its area decreased by 2001 to become 22.40 % or 1049.22 km2. The changes in land cover classes for the period 1992 to 2001 as seen in Figure 4 ( a & b) and Table 3 & 4. For the 2010 image classification, the thematic map indicates in Figure 4 (c) and Table 3 & 4 indicate the statistical results. For 2010 still the dominant class is also soil class, which represents approximately 78.5 % (36.30 % for soil-1 and 42.20 % for soil-2) of the study area or 3676.96 km2 (1700.30 km2 for soil-1 and 1976.66 km2 for soil-2). The shallow water is became the second most prevalent class in the study area, covering 12.00 % or 257.60 km2. The water bodies’ class covers the smallest area of approximately 224.85 km2, which represents 9.5 % of the study area. However, the statistical result of image classification in 2018 as seen in Table 2 and its spatial distribution in Figure 4 (d) indicate increase the soil class as which represents approximately 89.70 % (46.60 % for soil-1 and 43.10 % for soil-2) of the study area, which represents area of 4201.55 km2 (2182.75 km2 for soil-1 and 2018.80 km2 for soil-2). Whereas the shallow water is became the second most prevalent class in the study area, covering 05.50%, which covers about 257.60 km2. The water bodies’ class decreased to cover the smallest area of approximately 224.85 km2, which represents 04.80 % of the study area. Table 3 indicates the statistical results in course of time (1992 to 2018), the soil -1 and soil-2 classes increase by +13.85 and 8.45 respectively, with area represents approximately 0648.74 km2 and + 0395.78 km2 respectively. In the other hand, area of shallow water decreased for the same course of time from percentage of 5.5 % in to become 4.55 %, with relative change equal to 0.95% with relative change of area equal to 044.5 in of the study area. In contrast, the area of water bodies class has dramatically decreasing and the dehydration extent increase. The Al-Razaza lake water surface area start to decrease for the three interval of time 1992 - 2001, 2001 - 2010 and 2010 - 2018 to have 264.65 km2, 604.24 km2 and 220.13 km2, respectively. The lake surface area change of water bodies decreased with relative change percentage of 23.25 %, with relative change in area approximately to 1089.02 km2 of the study area as indicate in Table 3. The statistical results indicate rapidly decreasing trend in Al-Razaza Lake surface area in the period of 1992–2018, this change equal to 1089.02 km2 and that means the lake lose around 82.8% of its total area in very short http://www.iaeme.com/IJCIET/index.asp 149 editor@iaeme.com Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis and GIS Techniques time (over lost than three decades) as indicates in Table 4. The dehydration extent increase dramatically and it has negative impact on the climate of surrounding area, rising atmospheric temperature, and inadequate water contained the lake, biodiversity and the ecosystem. Table 2 Comparison of classification classes area and percent. 1992 Class Soil-1 Soil-2 Water bodies Shallow water Total area in Km2 2001 2 Area in Km 1534.01 1623.02 1313.87 0213.10 2 (%) Area in Km 32.75 2297.50 34.65 1199.10 28.05 1049.22 04.55 0138.18 2010 2018 2 (%) Area in Km 49.05 1700.30 25.60 1976.66 22.40 0444.98 02.95 0562.06 (%) Area in Km2 36.30 2182.75 42.20 2018.80 09.50 0224.85 12.00 0257.60 (%) 46.60 43.10 04.80 05.50 Total pixels of image = 5204474 5204474 pixels * 30m *30m/1000000 = 4,684.0266 Figure 4. Four classified thematic maps of Landsat satellite images of the study area: (a) The classified image in 1992; (b) the classified image in 2001; (c) The classified image in 2010; (d) The classified image in 2018. Table 3. Statistical summary of Landsat classification area for 1992 and 2018 Classes 2018 (%) 1992 (%) Soil-1 Soil-2 Water bodies Shallow water 46.60 43.10 04.80 05.50 32.75 34.65 28.05 04.55 http://www.iaeme.com/IJCIET/index.asp Relative change (2018 -1992) (%) + 13.85 + 08.45 - 23.25 + 00.95 150 Relative change of Area in Km2 + 0648.74 + 0395.78 - 1089.02 + 0044.5 editor@iaeme.com Hayder Dibs and Suhad AL-Hedny Table 4. Statistic result of Al-Razaza lake surface water area change. Year Lake surface area (km2) 1313.87 1992 Lake surface area change (km2) Total change area in (km2) - 264.65 1049.22 2001 -1089.02 - 604.24 2010 0444.98 2018 0224.85 - 220.13 2.7. Classification accuracy assessment Accuracy assessment is critical for any generated map from remote sensing data (Hayder and Thulfekar, 2018. Error matrix is commonly method to present the classification accuracy (Jensen, 2004). Overall’s, user’s and producer’s accuracies, and the Kappa statistic were calculated from the error matrices as reported by (Yuan et al., 2005). An independent sample of an average of 275 polygons, with about 90 pixels randomly selected for each polygon, for each classification to calculate the classification accuracies. Error matrix as cross tabulations of the generated class vs. the corresponding class were used to obtain classification accuracy (Congalton& Green, 1999). Table 5 summaries the accuracy assessment of all the classified images in the course of time 1992 - 2018 using ENVI v 5.0software. The overall accuracy assessment, kappa coefficient, producer’s and user’s accuracies are illustrated in Table 5.Post classification were applied to reduce the classification errors caused due to the similarity in the spectral responses of classes such as soil, shallow water and water body. Table 5 Summary of Lands at classification accuracies for 1992, 2001, 2010, and 2018 Land cover class Soil-1 Soil-2 Water bodies Shallow water 1992 Producer’s 85.30 80.87 99.77 79.99 Overall accuracy Kappa coefficient 88.00 00.82 User’s 75.72 89.26 99.10 81.10 2001 Producer’s 85.69 80.50 99.98 92.59 User’s 81.93 86.60 99.88 76.49 90.79 00.86 2010 Producer’s 96.30 94.55 100.00 99.97 95.94 00.93 User’s 95.55 98.16 99.99 79.66 2018 Producer’s 90.40 87.34 80.43 76.96 User’s 83.23 91.74 100.00 76.68 87.67 00.79 3. CONCLUSION Mapping water resources changes at regional scales is very important for different of applications including desertification, loss of natural resources, wildlife habitats, land planning, and increase the global warming. Al-Razaza suffered from dehydration extent from 1980s till recent years due to decreasing surface water and international and national water policies. This study aimed to model the spatial and temporal changes of surface water area of AL-Razaza Lake for 1992, 2001, 2010 and 2018 by performing a time series of supervised classification on Landsat satellite images. Validation of this study conducted by calculation the accuracy assessment of each image classification. The statistical results of validation for this method reveals that the overall accuracies and kappa coefficients of the supervised classifications were 88, 90.79, 95.94 and 87.67 respectively, and 82%, 86%, 93% and 79% respectively. The results demonstrated an intense decreasing and rapidly changing trend in the http://www.iaeme.com/IJCIET/index.asp 151 editor@iaeme.com Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis and GIS Techniques lake surface area in the period 1992–2018, the Lake lost 82.8% of its total area 1089.02 km2 compare to Lake surface area in 1992. If such a trend in AL-Razaza Lake continues, it is very likely the lake will lose its entire water in the near future. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] Atasoy, M.; Karslı, F.; Bıyık, C.; Demir, O. Determining Land Use Changes with Digital Photogrammetric Techniques. Environmental Engineering Science 2006, 23 (4), 712-721. Abd El-Kawy, O.R. Rød, J.K. Ismail, H.A. . Suliman, A.S. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data, Applied Geography, 31 (2011) 483-494. Abd, Hayder, Al-RazzaqAbd, and HusamAbdulrasoolAlnajjar. 2013. “Maximum Likelihood for Land-Use/Land-Cover Mapping and Change Detection Using Landsat Satellite Images: A Case Study South Of Johor.” International Journal of Computational Engineering Research 3 (6): 26–33. www.ijceronline.com. Brisco, B.; Schmitt, A.; Murnaghan, K.; Kaya, S.; Roth, A. Sarpolarimetric change detection for flooded vegetation. Int. J. Digit. Earth 2013, 6, 103–114. Congalton, R. G., & Green, K. (1999). Assessing the accuracy of remotely sensed data: Principles and practices (pp. 43–64). Boca Rotan, Florida’ Lewis Publishers. Carrão, H.; Gonçalves, P.; Caetano, M. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sens. Environ. 2008, 112, 986–997. Demir, B.; Bovolo, F.; Bruzzone, L. Updating land-cover maps by classification of image time series: A novel change-detection-driven transfer learning approach. IEEE Trans. Geosci. Remote Sens. 2013, 51, 300–312. Dahl, T. E. (2006). Status and trends of wetlands in the conterminous United States 1998 to 2004. Washington, DC: U.S. Department of Interior, Fish and Wildlife Service 112 pp. Dibs, H, S AL-Hedny, and H S Abed Karkoosh. 2018a. “Extracting Detailed Buildings 3D Model with Using High Resolution Satellite Imagery by Remote Sensing and GIS Analysis; Al-Qasim Green University a Case Study.” International Journal of Civil Engineering and Technology 9 (7): 1097–1108. https://www.scopus.com/inward/record.uri?eid=2-s2.0 85052391926&partnerID=40&md5=454e0f42cf2bbc67ff32fe38a4a6b7b4. Dibs, Hayder. 2018. “Estimating and Mapping the Rubber Trees Growth Distribution Using Multi Sensor Imagery with Remote Sensing and GIS Analysis.” Journal of University of Babylon, Pure and Applied Sciences 26 (6): 109–23. Dibs, Hayder, ShattriMansor, Noordin Ahmad, and Biswajeet Pradhan. 2015. “Band-toBand Registration Model for near-Equatorial Earth Observation Satellite Images with the Use of Automatic Control Point Extraction.” International Journal of Remote Sensing 36 (8): 2184–2200. https://doi.org/10.1080/01431161.2015.1034891. Dibs, Hayder, Mohammed OludareIdrees, and Goma Bedawi Ahmed Alsalhin. 2017. “Hierarchical Classification Approach for Mapping Rubber Tree Growth Using Per-Pixel and Object-Oriented Classifiers with SPOT-5 Imagery.” Egyptian Journal of Remote Sensing and Space Science 20 (1): 21–30. https://doi.org/10.1016/j.ejrs.2017.01.004. Dibs, H., Idrees, M. O., Saeidi, V. and Mansor, S. 2016. Automatic Keypoints Extraction from UAV Image with Refine and Improved Scale Invariant Features Transform (RISIFT), International Journal of Geoinformatics, Vol. 12, No.3, September. Dwivedi, R.S.; Sreenivas K.; Ramana, K.V. Land-use/land-cover change analysis in part of Ethiopia using Landsat Thematic Mapper data. International Journal of Remote Sensing 2005, 26 (7), 1285-1287. http://www.iaeme.com/IJCIET/index.asp 152 editor@iaeme.com Hayder Dibs and Suhad AL-Hedny [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] Dibs, H, S Mansor, and Airbus Defence and Space; Asia Air Survey (AAS); DigitalGlobe; JAXA; Suntac Technologies; Surrey Satellite Technology Ltd. 2014. “Mapping Rubber Tree Growth by Spectral Angle Mapper Spectral-Based and Pixel-Based Classification Using SPOT-5 Image.” 35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014, no. September. https://www.scopus.com/inward/record.uri?eid=2-s2.084925437415&partnerID=40&md5=39078b723ad7445d3132e395966d5911. Dibs, Hayder, Ahmed Al-Janabi, and Chandima Gomes. 2018b. “Easy To Use Remote Sensing and GIS Analysis for Landslide Risk Assessment.” Journal of University of Babylon 26 (1): 42–54.El-Asmar, H.M.; Hereher, M.E. Change detection of the coastal zone east of the Nile Delta using remote sensing. Environ. Earth Sci. 2011, 62, 769–777. Eastman, J. R. Toledano, J. Crema, S. Zhu H. and Jiang, H. In-Process Classification Assessment of Remotely Sensed Imagery,” GeoCarto International 2005, 4: 3344. Foody, G.M.; Mathur, A.; Sanchez-Hernandez, C.; Boyd, D.S. Training set size requirements for the classification of a specific class. Remote Sens. Environ. 2006, 104, 1–14. Gao, J., & Liu, Y. (2010). Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection. International Journal of Applied Earth Observation and Geoinformation, 12(1), 9-16 Heydari, S.S., Mountrakis, G., 2017. Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sens. Environ. http://dx.doi.org/10.1016/j.rse.2017.09.035. Jensen, J. R. (2004). Digital change detection. Introductory digital image processing: A remote sensing perspective (pp. 467– 494). New Jersey’ Prentice-Hall. K.N.Kadhim and Noor S."Geospatial Technology For Ground Water Quality ParametersAssessment In Al-Kifl Districtbabylon–Iraq". (IJCIET) Volume 9, Issue 8, August 2018. K.N.Kadhim" Geospatial Technology for ground Water quality parameters assessment in Dhi-Qar governorate-Iraq by using (GIS)” (IJCIET), Volume 9, Issue 1, (Jan 2018) Lopez-Caloca, A.; Tapia-Silva, F.O.; Escalante-Ramirez, B. Lake Chapala change detection using time series. Remote Sens. Agric. Ecosyst. Hydrol. 2008, 7104, 1–11. Liya Sun, Karsten Schulz. The Improvement of Land Cover Classification by Thermal Remote Sensing, Remote Sens. 2015, 7, 8368-8390; doi: 10.3390/rs70708368. Mather, P.; Koch, M. Computer Processing of Remotely-Sensed Images: An Introduction; Wiley: New York, NY, USA, 2010. Markogianni, V.; Dimitriou, E.; Kalivas, D.P. Land-use and vegetation change detection in plastira artificial lake catchment (Greece) by using remote-sensing and GIS techniques. Int. J. Remote Sens. 2013, 34, 1265–1281. Murai, H.; Omatu, S. Remote sensing image analysis using a neural network and Knowledge-based processing. Int. J. Remote Sens. 1997, 18, 811–828. Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.; Hoffer, R.M. An automated approach to mappingcorn from Landsat imagery. Comput. Electron. Agric. 2004, 43, 43–54. Nawal K. Ghazal, Auday H. Shaban, Fouad K. Mashi, Abdulhadi M. Raihan, Change Detection Study Of Al Razaza Lake Region Utilizing Remote Sensing And GIS Technique Iraqi Journal of Science, December 2012, Vol. 53, No. 4, Pp. 950-957 Raja, R.A.A.; Anand, V.; Kumar, A.S.; Maithani, S.; Kumar, V.A. Wavelet based post classification change detection technique for urban growth monitoring. J. Indian Soc. Remote Sens. 2013, 41, 35–43. http://www.iaeme.com/IJCIET/index.asp 153 editor@iaeme.com Detection Wetland Dehydration Extent with Multi-Temporal Remotely Sensed Data Using Remote Sensing Analysis and GIS Techniques [32] [33] [34] [35] [36] [37] [38] [39] [40] San M. J. and G. S. Biging. Comparison of single-stage and multi-stage classification approaches for cover type mapping with TM and SPOT data. Remote Sens Environ 1997; 59:92. Sun, F.; Sun, W.; Chen, J.; Gong, P. Comparison and improvement of methods for identifying, Water bodies in remotely sensed imagery. Int. J. Remote Sens. 2012, 33, 6854–6875. Song, C.; Woodcock, C.E.; Seto, K.C.; Lenney, M.P.; Macomber, S.A. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sens. Environ. 2001, 75, 230–244. Schroeder, T.A.; Cohen, W.B.; Song, C.; Canty, M.J.; Yang, Z. Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in westernOregon. Remote Sens. Environ. 2006, 103, 16–26. Selçuk Reis. Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey. Sensors 2008, 8, 6188-6202; DOI: 10.3390/s8106188 Yang, X. (2002). Satellite monitoring of urban spatial growth in the Atlanta metropolitan area. Photogrammetric Engineering and Remote Sensing, 68 (7), 725–734. Yuan, F.; Sawaya, K.E.; Loeffelholz, B.C.; Bauer, M.E. Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan areas by multi-temporal Landsat remote sensing. Remote Sensing of Environment 2005, 98, 317-328. Zhu, X.; Cao, J.; Dai, Y. A Decision Tree Model for Meteorological Disasters Grade Evaluation of Flood. In Proceedings of 4th International Joint Conference on Computational Sciences and Optimization 2011, Kunming and Lijiang, Yunnan, China, 15–19 April 2011; Institute of Electrical and Electronics Engineers: New York NY, USA, 2011; pp. 916–919. Volpi, M.; Petropoulos, G.P.; Kanevski, M. Flooding extent cartography with Landsat TM imagery and regularized Kernel Fisher’s discriminant analysis. Comput. Geosci. 2013, 57, 24–31. http://www.iaeme.com/IJCIET/index.asp 154 editor@iaeme.com