Flood Inundated Agricultural Damage and Loss Assessment Using Earth Observation Technique by IJEID

International Journal of Excellence Innovation and Development ||Volume 1, Issue 1, Nov. 2018||Page No. 060-069||

Flood Inundated Agricultural Damage and Loss Assessment Using Earth Observation Technique Md. Fazle Rabby1, Dewan Mohammad Enamul Haque2, Md. Selim3 1

Masters Student, Department of Disaster Science and Management, Faculty of Earth and Environmental Sciences, University of Dhaka, Bangladesh. 2 Assistant Professor, Department of Disaster Science and Management, Faculty of Earth and Environmental Sciences, University of Dhaka, Bangladesh. 3 Lecturer, Department of Disaster Science and Management, Faculty of Earth and Environmental Sciences, University of Dhaka, Bangladesh.

Abstract––Earth observation technique is an efficient way for flood damage mapping and assessment. This paper describes a synergic use of high resolution optical and radar image for retrieving information regarding flood inundation and resulting damage to the paddy fields. To reach the goal, Ullapara Upazila of Sirajganj District in Bangladesh has been selected as a test site and 2017 flood is the concerned event. In this research, the cultivable area (paddy field) identification and the corresponding yield calculation have been done for estimating damage and loss. “Polarimetry” and “Spectral and spatial analysis” techniques have been applied to extract the inundated area from Sentinel 1 radar and Sentinel 2 optical image respectively. In both cases, images for flood time have been used to estimate the damage. Sensitivity analysis has been performed for best parameter selection. The research outcomes have also been validated by the field observation. A significant amount of area has been inundated and 4798 hectares damaged croplands have been found from the radar image and 3937 hectares from the optical image and the economic losses have been found 18.06 crores and 14.82 crores respectively. Keywords––Damage and loss analysis, earth observation technique, polarimetry, sensitivity analysis, spatio-temporal analysis.

INTRODUCTION Flood disaster is a major threat to the environment and is responsible for the economic loss worldwide. A single major flood event can affect several countries simultaneously and can pressure on risk reduction and transfer [1]. Damage and loss assessment (DALA) is important for flood risk & crisis management but it is always challenging considering its complexity in dealing with big data, damage types, spatial and temporal scales i.e. depth of analysis [2,3]. Often due to the limitation and availability of data and information, simple approaches are used. Damage assessment depends on an assumption like spatial and temporal boundary selection and economic evaluation like depreciated values or replacement cost, classification of the element at risk, quantification of the exposed asset values and approaches for describing susceptibility [4]. Cost of www.ijeid.com

different types of natural hazard includes direct cost, indirect cost, intangible effect and cost of mitigation [5]. Nowadays Earth Observation (EO) technique is being widely used for disaster damage and loss assessment [69]. Flood monitoring, early warning, and rapid damage assessment have improved greatly because of the advancement in the geographic information system (GIS) and remote sensing (RS) [10]. Actual flood extent cannot be assessed fully from field visit because of the area vastness and the restriction of the mobility, thus EO data is important [11]. EO gives advantages where data is limited, costly and hard to access and needs frequent revisit times [12]. This situation has greatly improved because of availability of high-resolution satellite images, cost-effective flood monitoring, large area coverage and no risk to human lives [11,13]. Optical and radar data is common for flood monitoring and damage assessment and proven to be efficient in flood inundation mapping because of their distinct properties. [14-18]. Both these two sensors have respective advantages and disadvantages. The optical data is widely used to identify the water body form other land covers because of its distinct water reflectance property as it absorbs most of the incident solar energy [11]. Vegetation can be efficiently delineated from the other cover classes utilizing the information contained in a near-infrared and red band of optical imagery [6]. On the other hand, bad weather condition and presence of cloud is a major problem of optical images as flood occur mainly rainy season [12,16,19,20]. Microwave spectral bands of radar sensors are sensitive to the physical roughness of the surface and water is certainly smothered than other land cover types [12,19]. Radar imagery has the advantage over bad weather condition. Radar microwave pulse can penetrate through the cloud and applicable for both day and night and detect water under vegetation which makes radar extremely good for flood water area extraction [12,20,21]. But it has some problems. Presence of heavy rain and wind can cause roughening of the water surface and backscatter to like surrounding land. Multiple reflections can occur due to building and emergent vegetation, reduce the accuracy [22]. Land cover classification may sometimes a bit of difficulty because of surface roughness, speckle,

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topography, and dielectric properties which seem similar sometimes for the forest, road etc. However, the reflection of a radar pulse is minimal for water to make it easy to identify [11,19,23]. Therefore, both optical and radar image are being used for agricultural damage and losses due to flood and the resulting damage assessment in the crop fields [24,25].

The area falls under a stable Precambrian platform. Active channel, abandoned channel, natural levee, crevasse splay, floodplain and flood basin deposits are the common features of the area. Flood usually occurs in monsoon time especially from June to September because of the geographic location [17]. The area is in the active Jamuna-Brahmaputra floodplain delta region with an elevation of only 10– 12 feet (3.0– 3.7 m) above mean sea level (MSL)(Figure 1).

Agriculture damage and loss assessment using EO is relatively a new concept. Several methods have been used so far. Different crop index (NDVI, VCI, MVCI) for crop condition and their effect in flood condition is found suitable for damage estimation [26]. Three crop prediction methods have been developed using satellite image and auxiliary data, applied and validated at the Havel River in Germany [27]. MODIS and SAR data has been used for rapid assessment of crop affected by Typhoon Haiyan in Philippines [28]. NDVI and field observation has been used using GIS analysis for agriculture damage assessment [29]. The objective of this study is to assess the damage and losses occurred in the paddy fields due to recent 2017 flood disaster using optical and radar image. Moreover, the crop field (paddy) has been delineated utilizing a radar image from other cover classes. Finally, the damage and loss occurred in the paddy fields is estimated integrating field observation with the results derived from earth observation technique.

Data SAR Data Sentinel 1 synthetic aperture radar (SAR) data is used for its imaging capabilities in different resolution and coverage with four exclusive modes. Its dual polarization and very short revisit time can offer reliable, wide area monitoring. Sentinel 1 carries instruments to provide imagery for all weather at all time with a revisit time of 12 days for one satellite and 6 days for two satellites at the equator (ESA, 2013). Both normal time (08/01/2017) and flood time (17/07/2017) image is used for flooded area identification and for cropland identification before the flood occurs (11/06/2017)(Table 1).

MATERIALS AND METHODS

Optical Data Sentinel 2 carries multispectral, high-resolution image of 13 different spectral bands. It has high innovative swath range of land and vegetation perspective. Sentinel 2 comprises two polar-orbiting satellite with frequent revisit time (10 days for one satellite and 5 days for 2 satellites at the equator [30]. Both normal time (15/01/2017) and flood time (14/07/2017) image is used (Table 1).

Location The study area Ullapara is situated in Sirajganj District of Rajshahi Division, Bangladesh, with a zone of 414 sq. kilometers (160 sq. mi). The area is located in between 24°12' and 24°26' N and in between 89°24' and 89°38' E.

Field Data Field data is collected from the field visit. FGD, KII, and personal interview are performed to collect the field data.

Fig. 1: Location map of the study area. www.ijeid.com

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International Journal of Excellence Innovation and Development ||Volume 1, Issue 1, Nov. 2018||Page No. 060-069||

Dataset

Table 1: Specification of utilized Sentinel 1 imagery. Sentinel-1A Sentinel-1A

Sentinel-1A

S1A_IW_GRDH_1SDV _20170717T120432_201 70717T120457_017511_ 01D472_6A1D

S1A_IW_GRDH_1SDV_201 70108T235529_20170108T2 35554_014747_018021_ 7FF3

S1A_IW_GRDH_1SDV_ 20170611T120430_201706 11T120455_016986_01C4 7D_4951

Acquisition Date

7/17/2017

1/8/2017

6/11/2017

Beam Mode Path Frame Ascending/Descending Absolute Orbit

IW 114 76 Ascending 17511

IW 150 511 Descending 14747

IW 114 76 Ascending 16986

Granule

Table 2: Specification of utilized Sentinel 2 imagery. Value SENTINEL-2A SENTINEL-2A SENTINEL-2A S2A_MSIL1C_2017 S2A_MSIL1C_2017011 S2A_MSIL1C_2017071 0115T044121_N020 5T044121_N0204_R033 4T043701_N0205_R033 Vendor Product ID 4_R033_T45QYG_2 _T45RYH_20170115T0 _T45RYH_20170714T0 0170115T044124 44124 44656 L1C_T45QYG_A00 L1C_T45RYH_A008181 L1C_T45RYH_A010755 Entity ID 8181_20170115T04 _20170115T044124 _20170714T044656 4124 Acquisition Date 1/15/2017 1/15/2017 7/14/2017 Tile Number T45QYG T45RYH T45RYH Cloud Cover 0 0 43.6721 Orbit Number 33 33 33 Orbit Direction Descending Descending Descending Field Platform

Processing Steps Polarimetric Synthetic Aperture Radar (PolSAR) Synthetic aperture radar (SAR) uses side looking effective long antenna by summing multiple returns for signal processing means without using an actual long physical antenna. Most of the case, single, the small physical antenna is used [10,31,32]. Radar polarimetry uses electromagnetic (EM) field for acquiring, process and analyze the polarization state [33]. Sentinel 1 SAR transmit signal and can receive both horizontally (H) and vertically (V) as it is dual polarization radar. Backscatter can be measured using a single polarization. [34]. Four common procedure exists for flood area identification using SAR imagery- histogram thresholding, the variance of image texture, visual interpretation and active contour [35]. In this research, histogram thresholding is applied for flood mapping. Using optimal grey threshold, flooded areas are mapped in this process [35]. PolSAR includes pre-processing, processing and postprocessing. Image pre-processing includes the subset of image, calibration and spackle filtering. Radiometric calibration is essential for comparing images of different sensors or for same sensors which are collected at different times. Uncalibrated SAR imagery can be used for qualitative use, but for quantitative use, calibration is necessary [36]. SAR image coherently gained speckle or noise because of diffuse scattering [32,37]. It makes SAR image a granular aspect which has random spatial www.ijeid.com

SENTINEL-2A S2A_MSIL1C_20170 714T043701_N0205_ R033_T45QYG_2017 0714T044656 L1C_T45QYG_A010 755_20170714T04 4656 7/14/2017 T45QYG 38.1859 33 Descending

variations. Speckle can be found constructively or destructively by creating light and dark pixels [38]. Spatial filtering is used for noise reduction which is the spatial averaging technique which uses the pixel value of a kernel and replaces the value of the central pixel with the mathematical calculation [38]. Binarization is performed for identification of water features from other features. In this study, histogram thresholding is selected for the filter of backscatter coefficient. The histogram can show one peak or more than one peak of different magnitude. Higher values of backscatter indicate the non-water class and lower values indicate water class [10,39]. Once the threshold is applied, water class of the study area produced. Post-processing includes terrain correction. SAR has the property of side looking observation system of the topography and because of that, geometric and radiometric distortion occurs. Radar and map geometry relationship is not homographic due to topographic effect. Foreshortening and layover may happen [40]. For finding the corresponding position on the Earth, SAR geocoding reconstruct the imagery for each pixel. Range-Doppler equation is used for estimated the pixel value estimation [41]. The geometry is reconstructed using a DEM and ready to perform geometric correction for distortions induced by terrain [42]. Terrain correction in SAR geocode image accounts for the geometric

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distortions using a digital elevation model (1 Arc Sec SRTM DEM) and produce a map projected product. Range doppler terrain correction using WGS84 was used to reproject the data.

objects. This helps for land use classification [57]. Accuracy is assessed by creating an error matrix [10].

Spectral and Spatial Analysis The multispectral image uses a specific wavelength of EM spectrum for image data. Filters or other instruments may separate the wavelengths including visible light range to beyond range like IR, UV etc. Spectral analysis deals with the DN value of the image. Spectral transformation is used to identify feature [43]. The spatial analysis uses the topographic, geometric or geographic properties. Spatial analysis performed mainly semi-automated and rapid advancement has been made recently [44]. Spectral and spatial information shows the promising result in flood monitoring [45,46]. Layer-stack and mosaic is performed to composite the image using different bands of Sentinel 2 image. In this study, spectral band 2 (Blue), 3 (Green) and 8 (NIR) has been selected for analysis because Blue (band 2) represents clear water, Green (band 3) represents clear vegetation and NIR (band 8) is absorbed in water strongly [47]. Stretching is used for enhancing contrast, good for qualitative analysis but not for quantitative analysis [43]. Stretching is done by different types of stretch function [48]. Percent clip stretch is used in this study which applies a linear stretch between the maximum and minimum pixel value. Thresholding is a process which manipulates contrast by converting an image into two categories using an optical threshold [43,49]. Otsuâ&#x20AC;&#x2122;s thresholding method is applied in this study [50]. Mean and variance of the pixel value is calculated for determining threshold and pixel intensities are kept in an array. The pixel values are set either 0 or 1. So the change can take place only one in an image [49]. Sensitivity Analysis To identify the best parameter and filtering for flood mapping and DALA in this research, sensitivity analysis of SAR is performed. Generally, cross-polarized data (VH/HV) shows less accuracy than co-polarized data (HH/VV) because of overlapping [51,52]. VV polarization accuracy decreases because of roughening of water surface because of rain or wind, resulting in inundation not being identified [51]. Every polarization needs knowledge about the environment for limitations [12]. Four speckle filter is used in this research for the best filter: Frost filter [53], Gamma filter [54], Lee filter [55], Refined Lee filter [56]. Speckle filtering should possess some characteristics and for achieving better result, some factors are considered for non-referenced image used in this research according to [37]. Cropland Classification and accuracy assessment The unsupervised classification has performed for cropland classification using Radar image before the flood occurs. Objects can be identified from the scatter from the ground and the texture differs with different www.ijeid.com

Damage and Loss Assessment Damage in agricultural sector due to flood includes damage and loss of crops, infrastructure, and farm [58]. Also, sometimes damage to the soil might be taken into account [59]. Price of the damaged crop can be determined from the market price which could be obtained if there were no flood [18,4]. After the extraction of flooded area with radar and optical images, damage croplands have been identified by overlaying with crop classification. Then the damaged area has been calculated. Economic loss for cropland is estimated in the following manner: Economic loss = Affected area * Average yield (M. ton/ hectare) * Price per hectare (Taka) The affected area is the inundated or damaged cropland area. Average yield has been estimated from the field data. Price of the paddy has been determined from the information of several local markets of that time.

RESULT AND DISCUSSION Result Extraction of flood inundated areas EO images showed a significant amount of inundated area. Figure 3 shows the flood inundated area of Ullapara Upazila of sentinel 1 SAR and sentinel 2 optical data. Some differences can be identified because of the presence of cloud in optical image in several areas. Also, there is a time difference of three days between optical and SAR data acquisition. Sentinel 1 SAR data shows 118.18 sq. km (11818 hectares) area inundated by the flood which is 28.40% of Ullapara Upazila. Sentinel 2 optical data shows 101.73 sq. km (10173 hectares) of the inundated area which is 24.45% of total area. Some parts of Sentinel 2 image were covered with the cloud (indicated by red circles) for which some information is missing (Figure 2). Identification of cropland areas and classification accuracy Unsupervised classification of radar image is performed for cropland identification before the flood occurred (11 July 2017) because optical images are covered with cloud (Figure 3). Cropland area was calculated 156.9 sq. km (15690 hectares) which were 37.71% of the total area. This information shows that agriculture is the main source of income for this area. Accuracy assessment of classification is performed by generating error matrix (Table 3 and 4).

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International Journal of Excellence Innovation and Development ||Volume 1, Issue 1, Nov. 2018||Page No. 060-069|| Inundated and damaged croplands Many croplands are inundated due to flood. Figure 5 shows the cropland inundation and damaged areas due to flood (Figure 4). Sentinel 1 SAR data shows 4798

(a)

hectares of cropland inundated due to flood which is 30.58% of total cropland and Sentinel 2 data shows 3937 hectares of cropland were inundated which is 25.09% of total cropland.

(b)

Fig. 2: Flood inundated area on 17 July 2017 of SAR sentinel 1 image (a) and 14 July 2017 of optical sentinel 2 images (b) of Ullapara Upazila.

Fig. 3: Cropland areas of Ullapara Upazila using radar image. Table 3: Error matrix of land cover classification. Water Cropland Soil Tree Urban Total row 11 4 2 0 0 17 Water 1 42 1 1 1 46 Cropland 0 1 32 2 0 35 Soil 0 2 0 13 0 15 Tree 0 0 0 2 5 7 Urban 49 35 18 6 120 Total column 12 www.ijeid.com

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Table 4: User and producer accuracy of land use classification. User accuracy Producer accuracy 64.71% Water 91.66% Water 85.71% Cropland 91.30% Cropland Soil Tree Urban

91.43% 86.67% 71.43%

(a)

Soil Tree Urban

91.43% 72.22% 83.33%

(b)

Fig. 4: Inundated and damaged area of agriculture croplands. Image (a) shows inundated croplands of sentinel 1 data and the image (b) shows inundated croplands of sentinel 2 data. Damage and loss estimation The economic damage and loss have been estimated from EO data and field visit. EO technique is showing the damaged area of 4798 hectares for sentinel 1 data and 3937 hectares for sentinel 2 data. The earth observation based economic loss is 18.06 crore for radar image and 14.82 crore taka for the optical image. Fieldlevel damage and loss assessment data have been collected from FGD, KII (e.g. Upazila agricultural officer). Based on the collected data, only transplant and broadcast Aman rice have been damaged during the 2017 flood. The average price of rice is determined 26250 takas per hectare using the average wholesale price of Aman rice at the current market price of the flood time. Field data shows that the total economic loss is approximately 18.24 crore taka.

proves the validity of this technique. For cloud coverage, optical image shows a bit lower amount of damage and loss. Also, land use classification accuracy proves the validity of the cropland classification. User and producer accuracy show some differences but still it can be considered good for the validation (Table 4). Total amount of croplands is also considerable for both EO and field data. Sensitivity analysis proves the selection of the best parameters.

Sensitivity analysis result Statistical analysis of parameters is given in table 5. From the table 5, it can be concluded that Lee filter with VH polarization is the most suitable for this research purpose as mentioned earlier, thus used to assess the agriculture damage and loss.

Discussion This research compared the damage and loss of EO technique with the information collected from the field (Figure 5). From Figure 5, itâ&#x20AC;&#x2122;s transparent that, the analysis based on optical imagery underestimate the damage and loss a bit, due to cloud presence and in contrast, radar image-based analysis shows almost similar to the field data. Overall accuracy is 85.83% and the kappa coefficient k is 80.39% for this research. K value greater than 80% means the strong relation between the land cover classification and the ground truthing and the unsupervised classification shows strong accuracy [10].

Validation The EO technique of agriculture DALA has been validated from the field visit conducted in Ullapara Upazila for 2017 flood. Radar image and field data show similar amount of damage and loss for this study which

This technique needs further improvement. More detailed damage data is necessary, and the quality and reliability of these data should be maintained. Quantification of uncertainties associated with damage modeling is not possible for this research due to the

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International Journal of Excellence Innovation and Development ||Volume 1, Issue 1, Nov. 2018||Page No. 060-069|| unavailability of damage database [60]. Flood depth, duration, flow velocity can be vital factors for damage and loss assessment [61], which are missing in this research. Accuracy assessment shows some uncertainties in cropland identification, misclassification to some

extent. Another uncertainty is that the study focuses only on direct damage and loss. Indirect losses are not considered as labor cost, fertilizer, irrigation cost etc. There may be a possibility of other factors causing damage and loss which is also ignored.

Table 5: Quantitative comparison of different speckle filters using Sentinel 1 data. VH Refined VV Refined Filter/ parameter VH Frost VV Frost VH Gamma VV Gamma VH Lee VV Lee Lee Lee Mean 0.1287 0.0264 0.1309 0.0264 0.131 0.0254 0.1153 0.0261 SD 0.0762 0.5584 0.0409 0.2687 0.0457 0.3039 0.0408 0.2689 ENL 0.1172 0.0532 0.4164 0.2373 0.2871 0.1441 0.4202 0.2372 Bias 0.0527 0.2779 0.0083 0.0294 0.0173 0.0929 0.0056 0.0294 SD/M 2.9216 4.337 1.5497 2.0527 1.8663 2.6344 1.5427 2.0533 *Bold indicates better value/performance

Fig. 5: Damaged paddy field in hectares and the corresponding estimated loss. [2]

CONCLUSION Earth observation technique is relatively a new concept for agricultural damage and loss assessment. Due to the availability of high-resolution imagery, it is becoming popular in assessing damage and losses. This research shows the promising results of damage and loss estimation using EO technique. This technique can provide rapid damage and loss information. For the vast inundated area and difficulties associated with mobility, EO gives an advantage in assessing damage and loss. Although the presence of some discrepancy, the estimated damage from the earth observation technique is quite comparable. This technique is less costly, no risk associated with it and easy to perform will certainly facilitate the policymakers in implementing actions and plans.

[3]

[4]

[5]

Acknowledgment We want to give our thanks to the local administration of Ullapara Upazila of Sirajganj District in Bangladesh for their support in data collection for this study.

[6]

REFERENCES [1]

Jongman, B., Hochrainer-Stigler, S., Feyen, L., Aerts, J. C. J. H., Mechler, R., Botzen, W. J. W., … Ward, P. J. (2014). Increasing stress on disaster-risk finance due to large floods. Nature Climate Change, 4(4), 264–268. https://doi.org/10.1038/nclimate2124

www.ijeid.com

Menoni, S., Molinari, D., Ballio, F., Minucci, G., Atun, F., Berni, N., & Pandolfo, C. (2016). Reporting flood damages : a model for consistent , complete and multi-purpose scenarios, (February), 1–27. https://doi.org/10.5194/nhess-2016-51 Dingtao Shen, Qiting Kuang, Jie Yang, Jianhua Ni, & Jian Jia. (2015). The progress in the research of flood damage loss assessment. In 2015 23rd International Conference on Geoinformatics (pp. 1–6). IEEE. https://doi.org/10.1109/GEOINF ORMATICS.2015.7378572 Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Review article “assessment of economic flood damage.” Natural Hazards and Earth System Science, 10(8), 1697–1724. https://doi.org/10.5194/nhess-10-1697-2010 Meyer, V., Becker, N., Markantonis, V., Schwarze, R., Aerts, J. C. J. H., Bergh, J. C. J. M. Van den, … Viavattene, C. (2012). Costs of Natural Hazards - A Synthesis. Retrieved from https://hal.archives-ouvertes.fr/hal-00803679/ Joyce, K. E., Belliss, S. E., Samsonov, S. V., McNeill, S. J., & Glassey, P. J. (2009). A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Progress in Physical Geography, 33(2), 183–207. https://doi.org/10.1177/0309133 309339563

Page | 66

Flood inundated agricultural damage and loss assessment [7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

Uddin, K., & Shrestha, B. (2011). Assessing flood and flood damage using Remote Sensing: a case study from Sunsari, Nepal. 3rd International Conference on Water & Flood Management (ICWFM-2011), (June 2016), 293–301. Retrieved from http://www.buet.ac.bd/iwfm/icwfm.php Atif, I., Mahboob, M. A., & Waheed, A. (2015). Spatio-temporal mapping and multi-sector damage assessment of 2014 flood in Pakistan using remote sensing and GIS. Indian Journal of Science and Technology, 8(35). https://doi.org/10.17485/ijst/20 15/v8i35/76780 Zeaieanfirouzabadi, P., Saroei, S., & MADANIAN, E. (2015). Flood Damage Assessment Using Remote Sensing Technologies., 291–299. Retrieved from https://www.researchga te.net/publication/279203642_Flood_Dama Adjei-Darko, P. (2017). Remote Sensing and Geographic Information Systems for Flood Risk Mapping and Near Real-time Flooding Extent Assessment in the Greater Accra Metropolitan Area. Retrieved from http://www.diva-portal.org/smash/record.jsf?pid= diva2%3A1087549&dswid=-4610 Hussain, E., & Shan, J. (2010). Mapping major floods with optical and sar satellite images. IEEE Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA, (July), 25–30. Retrieved from https://engineering.purdue.edu/~jshan/public ations/2010/IGARSS_flood_formatted_Hussain_S han.pdf. Clement, M. A., Kilsby, C. G., & Moore, P. (2017). Multi-temporal synthetic aperture radar flood mapping using change detection. Journal of Flood Risk Management, 0–17. https://doi.org/10.1111/jfr3.12303 Yesou, H., Sarti, F., Tholey, N., Mouratidis, A., Clandillon, S., Huber, C., … de Fraipont, P. (2013). Addressing Emergency Flood Mapping and Monitoring of Inland Water Bodies with Sentinel 1-2. Expectative and Perspectives. In ESA Living Planet Symposium 2013. Edinburgh, UK. Imhoff, M. L., Vermillion, C., Story, M. H., Choudhury, A. M., Gafoor, A., & Polcyn, F. (1987). Monsoon Flood Boundary Delineation and Damage Assessment Using Space Borne Imaging Radar and Landsat Data. Photogrammetric Engineering and Remote Sensing, 53(4), 405–413. Mayer, L., & Pearthree, P. A. (2002). Mapping Flood Inundation in Southwestern Arizona Using Landsat TM Data: A Method for Rapid Regional Flood Assessment Following Large Storms. In Ancient Floods, Modern Hazards (pp. 61–75). https://doi.org/10.1029/WS005p0061 Qi, S., Brown, D. G., Tian, Q., Jiang, L., Zhao, T., & Bergen, K. M. (2009). Inundation Extent and Flood Frequency Mapping Using LANDSAT Imagery and Digital Elevation Models. GIScience & Remote Sensing, 46(1), 101–127. https://doi.org/10.2747/1548-1603.46.1.101 Islam, A. S., Bala, S. K., & Haque, M. A. (2010). Flood inundation map of Bangladesh using MODIS time-series images. Journal of Flood Risk Management, 3(3), 210–222.

www.ijeid.com

Rabby et al. https://doi.org/10.1111/j.1753-318X.2010.01074.x [18] Citeau, J.-M. (2003). A New Flood Control Concept in the Oise Catchment Area : Definition and Assessment of Flood Compatible Agricultural Activities. FIG Working Week, 1–19. [19] Brivio, P. A., Colombo, R., Maggi, M., & Tomasoni, R. (2002). Integration of remote sensing data and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing, 23(3), 429–441. https://doi.org/10.1080/01431160010014729 [20] Jüssi, M. (2015). Synthetic Aperture Radar based flood mapping in the Alam-Pedja Nature Reserve in years 2005-2011. University of Tartu. Retrieved from https://engineering.purdue.edu/~jshan/public ations/2010/IGARSS_flood_formatted_Hussain_S han.pdf. [21] Schlaffer, S., Matgen, P., Hollaus, M., & Wagner, W. (2015). Flood detection from multi-temporal SAR data using harmonic analysis and change detection. International Journal of Applied Earth Observation and Geoinformation, 38, 15–24. https://doi.org/10.1016/j.jag.2014.12.001 [22] Mason, D. C., Speck, R., Devereux, B., Schumann, G. J. P., Neal, J. C., & Bates, P. D. (2010). Flood Detection in Urban Areas Using TerraSAR-X. Geoscience and Remote Sensing, IEEE Transactions On, 48(2), 882–894. https://doi.org/10.1109/TGRS.2009.2029236 [23] Townsend, P. A. (2001). Mapping Seasonal Flooding in Forested Wetlands Using MultiTemporal Radarsat SAR. Photogrammetric Engineering & Remote Sensing, 67(7), 857–864. Retrieved from https://www.asprs.org/wpcontent/uploads/pers/2001journal/july/2001_jul_8 57-864.pdf [24] Shan, J., Hussain, E., Kim, K., & Biehl, L. (2010). Flood Mapping and Damage Assessment – A Case Study in the State of Indiana. In Geospatial Technology for Earth Observation (pp. 473–495). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4419-0050-0_18 [25] Kwak, Y., Shrestha, B. B., Yorozuya, A., & Sawano, H. (2015). Rapid Damage Assessment of Rice Crop after Large-Scale Flood in the Cambodian Floodplain Using Temporal Spatial Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3700–3709. https://doi.org/10.1109/JSTARS.201 5.2440439 [26] Yu, G., Di, L., Zhang, B., Shao, Y., Shrestha, R., & Kang, L. (2013). Remote-sensing-based flood damage estimation using crop condition profiles. In 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 205–210). IEEE. https://doi.org/10.1109/ArgoGeoinformatics.2013.6621908 [27] Tapia-Silva, F.-O., Itzerott, S., Foerster, S., Kuhlmann, B., & Kreibich, H. (2011). Estimation of flood losses to agricultural crops using remote sensing. Physics and Chemistry of the Earth, Parts A/B/C, 36(7–8), 253–265. https://doi.org/10.101 6/J.PCE.2011.03.005

Page | 67

International Journal of Excellence Innovation and Development ||Volume 1, Issue 1, Nov. 2018||Page No. 060-069|| [28] Boschetti, M., Nelson, A., Nutini, F., Manfron, G., Busetto, L., Barbieri, M., … Quilang, E. J. P. (2015). Rapid assessment of crop status: An application of MODIS and SAR data to rice areas in Leyte, Philippines affected by Typhoon Haiyan. Remote Sensing, 7(6), 6535–6557. https://doi.org/10.3390/rs70606535 [29] Silleos, N., Perakis, K., & Petsanis, G. (2002). Assessment of crop damage using space remote sensing and GIS. International Journal of Remote Sensing, 23(3), 417–427. https://doi.org/10.1080/01431160110040026 [30] ESA. (2015). SENTINEL-2 User Handbook, (1), 64. https://doi.org/GMES-S1OP-EOPG-TN-130001 [31] Cutrona, L. J. (1990). Synthetic Aperture Radar. Radar Handbook, 21.1-21.23. [32] Kopp, C. (2007). Synthetic Aperture Radar. Technology, (October 2004), 50–53. https://doi.org/10.1002/9780470035085.ch6 [33] Boerner, W.-M., Yan, W.-L., Xi, A.-Q., & Yamaguchi, Y. (1992). Basic Concepts of Radar Polarimetry. In Direct and Inverse Methods in Radar Polarimetry (pp. 155–245). Dordrecht: Springer Netherlands. https://doi.org/10.1007/97894-010-9243-2_8 [34] ESA. (2013). ESA Sentinel 1 handbook. European Space Agency technical note. https://doi.org/10.1017/CBO9781107415324.004 [35] Di Baldassarre, G., Schumann, G., Brandimarte, L., & Bates, P. (2011). Timely Low Resolution SAR Imagery To Support Floodplain Modelling: A Case Study Review. Surveys in Geophysics, 32(3), 255–269. https://doi.org/10.1007/s10712011-9111-9 [36] Mahapatra, P., & Hanssen, R. (2011). Next ESA SAR Toolbox (NEST): A Cookbook. Delft. Retrieved from https://earth.esa.int/documents/507 513/1077921/2011_Tiger_NEST_cookbook.pdf/d a84f918-a9e4-4404-8515-b624b7a6210c [37] Medasani, S., & Reddy, G. U. (2017). Analysis and Evaluation of Speckle Filters for Polarimetric Synthetic Aperture Radar (PolSAR) Data. International Journal of Applied Engineering Research ISSN, 12(15), 973–4562. Retrieved from http://www.ripublication.com [38] Meenakshi, a V, & Punitham, V. (2011). Performance of Speckle Noise Reduction Filters on Active Radar and SAR Images. International Journal of Technology And Engineering System (IJTES), 2(1), 111–114. [39] Dumitrascu, N., Oniga, E., Statescu, F., & Msrcu, C. (2017). Floods Damage Estimation Using Sentinel-1 Satellite Images. Case Study-Galati County, Romania. RevCAD Journal of Geodesy and Cadastre, 22, 115–122. Retrieved from https://www.researchgate.net/publication/3171795 10_FLOODS_DAMAGE_ESTIMATION_USIN G_SENTINEL-1_SATELLITE_IMAGES_CASE _STUDY-GALATI_COUNTY_ROMANIA [40] Tian, B., Li, Z., & Zhu, Y. (2011). Terrain Correction of Polarimetric SAR Data and its Application to Mapping Mountain Glacier facies www.ijeid.com

[41]

[42]

[43]

[44]

[45]

[46]

[47]

[48]

[49]

[50]

[51]

with Radarsat-2 SAR. ISPRS Hannover Workshop 2011: High-Resolution Earth Imaging for Geospatial Information, 14. - 17. June, 2011, Hannover, Germany, XXXVIII(9), 6. Curlander, J. C., & McDonough, R. N. (1991). Synthetic aperture radar : systems and signal processing. Wiley. Akbari, V., Larsen, Y., Doulgeris, A. P., & Eltoft, T. (2012). The impact of terrain correction of polarimetric SAR data on glacier change detection. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5129–5132). https://doi.org/10.1109/IGARSS.2012.6352456 Schowengerdt, R. A. (2007). Remote Sensing. Remote Sensing. Elsevier. https://doi.org/10.1016 /B978-0-12-369407-2.X5000-1 Armenakis, C., & Savopol, F. (2004). Image Processing and GIS Tools for Feature and Change Extraction. XXth Congress of the International Society for Photogrammetry and Remote Sensing (Istanbul, Turkey), Commission IV, WG IV/7., 6. Retrieved from http://www.isprs.org/proceedings/ xxxv/congress/comm4/papers/434.pdf Baatz, M., & Schape, A. (2000). Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation. Journal of Photogrammetry and Remote Sensing, 58(3–4), 12–23. https://doi.org/Export Date 6 May 2013 van der Sande, C. J., de Jong, S. M., & de Roo, A. P. J. (2003). A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation, 4(3), 217–229. https://doi.org/10.1016/S03032434(03)00003-5 Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sensing of Environment, 75(2), 230–244. https://doi.org/10.1016/S0034-4257(00)00169-3 ESRI. (2017). Stretch function. Retrieved December 16, 2017, from http://desktop.arcgis.com/en/arcmap/latest/manage -data/raster-and-images/stretch-function.htm Devi, H. K. A. (2006). Thresholding: A PixelLevel Image Processing Methodology Preprocessing Technique for an OCR System for the Brahmi Script. Ancient Asia, 1, 161. https://doi.org/10.5334/aa.06113 Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076 Manjusree, P., Prasanna Kumar, L., Bhatt, C. M., Rao, G. S., & Bhanumurthy, V. (2012). Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images. International Journal of Disaster Risk Science, 3(2), 113–122. https://doi.org/10.1007/s13753012-0011-5

Page | 68

Flood inundated agricultural damage and loss assessment

Rabby et al.

[52] Twele, A., Cao, W., Plank, S., & Martinis, S. (2016). Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990–3004. https://doi.org/10.1080/01431161.2016.1192304 [53] Frost, V. S., Stiles, J. A., Shanmugam, K. S., Holtzman, J. C., & Smith, S. A. (1981). An Adaptive Filter for Smoothing Noisy Radar Images. Proceedings of the IEEE, 69(1), 133–135. https://doi.org/10.1109/PROC.1981.11935 [54] Lopes, A., Nezry, E., Touzi, R., & Laur, H. (1993). Structure detection and statistical adaptive speckle filtering in SAR images. International Journal of Remote Sensing, 14(9), 1735–1758. https://doi.org/10.1080/01431169308953999 [55] Lee, J.-S. (1980). Digital Image Enhancement and Noise Filtering by Use of Local Statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-2(2), 165–168. https://doi.org/10.1109/TPAMI.1980.4766994 [56] Lee, J. Sen. (1981). Refined filtering of image noise using local statistics. Computer Graphics and Image Processing, 15(4), 380–389. https://doi.org/10.1016/S0146-664X(81)80018-4 [57] Chenghu, Z., Jiancheng, L., Cunjian, Y., Baolin, L., & Shixin, W. (2000). Flood monitoring using multi-temporal AVHRR and RADARSAT imagery. Photogrammetric Engineering and Remote Sensing, 66(5), 633–638.

[58] Dutta, D., Herath, S., & Musiake, K. (2003). A mathematical model for flood loss estimation. Journal of Hydrology, 277(1–2), 24–49. https://doi.org/10.1016/S0022-1694(03)00084-2 [59] Pivot, J. M., & Martin, P. (2002). Farms adaptation to changes in flood risk: A management approach. Journal of Hydrology, 267(1–2), 12–25. https://doi.org/10.1016/S00221694(02)00136-1 [60] Merz, B., Kreibich, H., Thieken, A., & Schmidtke, R. (2004). Estimation uncertainty of direct monetary flood damage to buildings. Natural Hazards and Earth System Science, 4(1), 153– 163. https://doi.org/10.5194/nhess-4-153-2004 [61] Smith, D. I. (1994). Flood damage estimation - a review of urban stage-damage curves and loss functions. Water SA, 20(3), 231–238. https://doi.org/10.1007/978-981-287-365-1_13 [62] UN-SPIDER. (2017). Step by Step: Recommended Practice Flood Mapping. Retrieved January 1, 2017, from http://www.unspider.org/advisory-support/recommendedpractices/recommended-practice-floodmapping/step-by-step [63] UNITAR. (2017). Rapid Response Mapping in Disaster Situations: For the training course: Specialization certificate for the assessment and management of geological and climate related risk. Geneva, Switzerland.

www.ijeid.com

Page | 69