Flood Inundated Agricultural Damage and Loss Assessment Using Earth Observation Technique

International Journal of Excellence Innovation and Development
||Volume 1, Issue 1, Nov. 2018||Page No. 060-069||
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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,
3
Lecturer, Department of Disaster Science and Management, Faculty of Earth and Environmental Sciences,
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
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 [6-
9]. 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,

Flood inundated agricultural damage and loss assessment Rabby et al.
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].
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.
MATERIALS AND METHODS
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.
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).
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).
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).
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.

Table 1: Specification of utilized Sentinel 1 imagery.
Dataset Sentinel-1A Sentinel-1A Sentinel-1A
Granule
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 IW IW IW
Path 114 150 114
Frame 76 511 76
Ascending/Descending Ascending Descending Ascending
Absolute Orbit 17511 14747 16986
Table 2: Specification of utilized Sentinel 2 imagery.
Field Value
Platform SENTINEL-2A SENTINEL-2A SENTINEL-2A SENTINEL-2A
Vendor Product ID
S2A_MSIL1C_2017
0115T044121_N020
4_R033_T45QYG_2
0170115T044124
S2A_MSIL1C_2017011
5T044121_N0204_R033
_T45RYH_20170115T0
44124
S2A_MSIL1C_2017071
4T043701_N0205_R033
_T45RYH_20170714T0
44656
S2A_MSIL1C_20170
714T043701_N0205_
R033_T45QYG_2017
0714T044656
Entity ID
L1C_T45QYG_A00
8181_20170115T04
4124
L1C_T45RYH_A008181
_20170115T044124
L1C_T45RYH_A010755
_20170714T044656
L1C_T45QYG_A010
755_20170714T04
4656
Acquisition Date 1/15/2017 1/15/2017 7/14/2017 7/14/2017
Tile Number T45QYG T45RYH T45RYH T45QYG
Cloud Cover 0 0 43.6721 38.1859
Orbit Number 33 33 33 33
Orbit Direction Descending Descending Descending Descending
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 post-
processing. 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
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

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

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
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.
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
Water 11 4 2 0 0 17
Cropland 1 42 1 1 1 46
Soil 0 1 32 2 0 35
Tree 0 2 0 13 0 15
Urban 0 0 0 2 5 7
Total column 12 49 35 18 6 120
(a) (b)

Table 4: User and producer accuracy of land use classification.
User accuracy Producer accuracy
Water 64.71% Water 91.66%
Cropland 91.30% Cropland 85.71%
Soil 91.43% Soil 91.43%
Tree 86.67% Tree 72.22%
Urban 71.43% Urban 83.33%
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. Field-
level 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.
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.
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
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.
Discussion
This research compared the damage and loss of EO
technique with the information collected from the field
(Figure 5). From Figure 5, it’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].
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
(a) (b)

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.
Filter/ parameter VH Frost VV Frost VH Gamma VV Gamma VH Lee VV Lee
VH Refined
Lee
VV Refined
Lee
Mean 0.0261 0.1287 0.0264 0.1309 0.0264 0.131 0.0254 0.1153
SD 0.0762 0.5584 0.0409 0.2687 0.0408 0.2689 0.0457 0.3039
ENL 0.1172 0.0532 0.4164 0.2373 0.4202 0.2372 0.2871 0.1441
Bias 0.0527 0.2779 0.0083 0.0294 0.0056 0.0294 0.0173 0.0929
SD/M 2.9216 4.337 1.5497 2.0527 1.5427 2.0533 1.8663 2.6344
*Bold indicates better value/performance
Fig. 5: Damaged paddy field in hectares and the corresponding estimated loss.
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.
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.
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