Geo-spatial assessment of agricultural drought Remote Sensing & GISTecnnique

1

IDENTIFICATION OF MANGROVE ZONATION AND SUCCESSION PATTERN IN THE DELTAIC ISLANDS BY REMOTE

SENSING AND GIS TECHNIQUE

A CASE STUDY ON LOTHIAN AND PRENTICE ISLAND, SUNDAR BAN, WEST BENGAL

SUBMITED

BY

BISWAJIT DAS

UNDER GUIDENCE

OF

Dr. JATISANKAR BANDYOPADHYAY

DEPERTMENT OF REMOTE SENSING & GIS

VIDYASAGAR UNIVERSITY,

MIDNAPURE-721102

IN 2013

GEO-SPATIAL ASSESSMENT OF AGRICULTURAL DROUGHT BY REMOTE SENSING & GIS TECNNIQUE

(A CASE STUDY OF BANKURA, WEST BENGAL)

2

GEO-SPATIAL ASSESSMENT AGRICULTURAL DROUGHT BY REMOTE SENSING & GIS TECNNIQUE

(A CASE STUDY OF BANKURA, WEST BENGAL)

SUBMITED

BY

BISWAJIT DAS

UNDER GUIDENCE

OF

Dr. JATISANKAR BANDYOPADHYAY

DEPERTMENT OF REMOTE SENSING & GIS

VIDYASAGAR UNIVERSITY

MIDNAPURE-721102

2013

3

ACKNOWLEDGEMENT

I would like to thanks Dr. Jatisankar Bandyopadhyay (Faculty of

Remote Sensing & GIS dept., Vidyasagar University), Dr. Abhisek Chakraborty

(Faculty of Remote Sensing & GIS dept., Vidyasagar University) and Dr.

Sikhendra Kishor De (Visiting Faculty, Retired Director G.S.I) for their

valuable thought and knowledge given throughout the study of M.Sc. course

I wish to express my deep sense of gratitude to Mr. Ratnadeep Ray

(Faculty of Remote Sensing & GIS dept., Vidyasagar University) for his advice,

support and assistance throughout my dissertation work.

Finally, yet importantly, I would like to express my heartfelt thanks to my

beloved parents for their blessings, my friends/classmates for their help and

wishes for the successful completion of this project work.

……………………………………

Biswajit Das

Dept of Remote Sensing and GIS

Vidyasagar University

4

ABSTRACT

Climate has always been a dynamic entity affecting natural systems through the

consequence of its variability and change. Agriculture is the most vulnerable and sensitive

sector that is seriously affected by the impact of climate variability and change, which is

usually manifested through rainfall variability and recurrent drought. Drought is the most

complex but least understood of all natural hazards. Low rainfall and fall in agricultural

production has mainly caused droughts.

In recent years, Geospatial techniques have played a key role in studying different

types of hazards either natural or manmade. This study stresses upon the use of RS & GIS in

the field of drought risk assessment. In Bankura District, West Bengal agricultural drought

and crop failure have been common, and farmers inhabiting the area experience extreme

temporal and spatial variability of rainfall in cropping season with frequent and longer dry

spells. This makes them vulnerable to the risk of agricultural drought. Thus, in order to adapt

and/or mitigate the impact of agricultural drought, agricultural drought assessment has to

form one dimension of research to be done whereas the use of remote sensing and GIS

techniques provides wide scope in drought risk detection and mapping. Consequently, this

study is conducting with the objective of assessing agricultural drought risk and preparing

agricultural drought risk zone map using satellite data to assess and examine spatiotemporal

variation of seasonal agricultural drought patterns and severity, the digital indices are useful

for agricultural drought assessment namely, NDVI (Normalized difference Vegetation index)

and from the value of NDVI ,NDVI anomaly can be prepared to assess the severity of

drought. Apart from that, VCI(Vegetation condition Index), TCI(Temperature condition

Index) and SPI(Standardized Precipitation Index) is very essential to show the severity of

agricultural drought. Temporal satellite data of three years 2000, 2005 and 2010 are used to

monitor and assess the drought severity and the impact of agricultural drought on crop

production can be measured through estimation of yield reduction. Thus, this agricultural

drought risk mapping can be useful to guide decision making process in drought monitoring

and to reduce the risk of drought on agricultural production and productivity.

Key words: Agricultural drought, GIS, NDVI, Remote Sensing, VCI, TCI, Standardized precipitation Index.

5

CONTENT CHAPTER NO. TITLE PAGE NO.

Title I

Bonafide Certificate II

Abstract III

Acknowledgement IV

Content V

List of Figures VI

List of Tables VII

I INTRODUCTION

1.1 General Introduction 1-5

1.2 Scope of the Study 5

1.3 Literature Review 5-6

II MATERIALS AND METHODS

2.1 Objective 7

2.2 Methodology 7-12

2.3 Materials Required 13

2.4 Software Used 13

III STUDY AREA

3.1 Introduction of the Study area 14-18

IV RESULTS AND DISCUSSIONS

4.1 Results 19-32

V CONCLUSIONS

5.1 Conclusion 33

6

REFERENCES 34

LIST OF FIGURES

FIG. NO. TITLE PAGE NO.

1 Relationship between meteorological drought, Hydrological drought and agricultural drought 2 2 Multidimensional Impacts of Drought 4 3 Flow chart of the Methodology 7 4 Collected Satellite data of the Study area at various time scale 8 5 Before atmospherically corrected image and after atmospheric correction 10 6 Location Map of the Study area 17 7 Base map of the study area 18 8 Soil(Textural) Map of the Study area 20 9 Hydrological soil group map of the study area 22 10 Land use and Land cover Classification Map of the study area (year-2000) 23 11 Graphical representation of Land use and Land cover areas(in %) of 2000 24 12 Land use and Land cover Classification Map of the study area (year-2005) 25 13 Graphical representation of Land use and Land cover areas(in %) of 2005 26 14 Land use and Land cover Classification Map of the study area (year-2010) 27 15 Graphical representation of Land use and Land cover areas(in %) of 2010 28 16 Temporal changes of land use land cover classes of Bankura District over the years(2000-10) 28

7

LIST OF TABLES TABLE NO. TITLE PAGE NO.

1 Characteristics of Satellite Imageries used in this study 13 2 Different Classes of Hydrological Soil Group Contains respective soil type(Texture) 21 3 Net change (in %) of LULC areas of Bankura District from 2000 to 2010 29

8

CHAPTER I

INTRODUCTION

1.1 GENERAL INTRODUCTION

Drought may be defined as an extended period – a season, a year or more - of deficient

rainfall relative to the statistical multi-year average for a region. It is a normal and recurrent

feature of climate and may occur anywhere in the world, in all climatic zones. Its features or

characteristics, of course, vary from region to region.

Simply put, drought is a period of drier-than-normal conditions that lead to water

related problems. When rainfall is below normal for weeks, months or even years, it brings

about a decline in the flow of rivers and streams and a drop in water levels in reservoirs and

wells. If dry weather persists and water supply-related problems increase, the dry period can

be called a drought. Drought cannot be confined to a single all-encompassing definition. It

depends on differences in regions, needs and disciplinary perspectives.

1.1.1 Drought classification:

There are a number of classifications for drought. A permanent drought is

characterised by extremely dry climate, drought vegetation and agriculture that is possible

only by irrigation; seasonal drought requires crop durations to be synchronised with the rainy

season; contingent drought is of irregular occurrence; and invisible drought occurs even when

there is frequent rainfall, in humid regions (Thornthwaite-1947). Physical aspects are also

used to classify drought. They may be clubbed into three or Three major groups:

• Meteorological drought, is related to deficiencies in rainfall compared to the average

mean annual rainfall in an area. There is, however, no consensus on the threshold of

deficit that makes a dry spell an official drought. According to the India

Meteorological Department (IMD), meteorological drought occurs when the seasonal

rainfall received over an area is less than 75% of its long-term average value. If the

rainfall deficit is between 26-50%, the drought is classified as 'moderate', and 'severe'

if the deficit exceeds 50%.

.

9

• Hydrological drought, is a deficiency in surface and sub-surface water supply. It is

measured as stream flows and also as lake, reservoir and groundwater levels.

• Agricultural drought, occurs when there is insufficient soil moisture to meet the

needs of a particular crop at a particular point in time. Deficit rainfall over cropped

areas during their growth cycle can destroy crops or lead to poor crop yields.

Agricultural drought is typically witnessed after a meteorological drought, but before

a hydrological drought.

A relationship between meteorological drought, Hydrological drought and agricultural

drought can be displayed in the below figure.

Fig.1: Relationship between meteorological drought, Hydrological drought and agricultural drought

Source: NDMC, USA

10

1.1.2 Impacts of drought:

Drought has a direct and indirect impact on the economic, social and environmental

fabric of the country. Depending on its reach and scale it could bring about social unrest. The

Employment Guarantee Scheme (EGS) that the Maharashtra government put in place was a

direct outcome of the 1971-72 drought. The immediate visible impact of monsoon failure

leading to drought is felt by the agricultural sector. The impact passes on to other sectors,

including industry, through one or more of the following routes:

• A shortage of raw material supplies to agro-based industries.

• Reduced rural demand for industrial/consumer products due to reduced agricultural

incomes.

• Potential shift in public sector resource allocation from investment expenditure to

financing of drought relief measures.

� Economic Impact: Many economic impacts occur in agriculture and related sectors, including forestry and fisheries, because of the reliance of these sectors on surface and subsurface water supplies. In addition to obvious losses in yields in crop and livestock production. Drought is associated with increases in insect infections, plant disease and wind erosion. The incidence of forest and range fires increases substantially during extended droughts, which in turn places both human and wildlife populations at higher levels of risk. � Environmental Impact: Environmental losses are the result of damages to plant and animal species, wildlife habitat and air and water qualiy; forest and range fires; degradation of landscapes; loss of biodiversity and soil erosion. Some of the effects are short term and conditions quickly return to normal following the end of the drought. Other environmental effects linger for some time or may even become permanent, wildlife habitat. For example, may be degraded through the loss of wetlands, lakes and vegetation. The degradation of landscape quality, including increased soil erosion may lead to a more permanent loss of biological productivity of the landscape. � Social Impact: Social impacts involve public safety, health, conflicts between water users, reduced quality of life and inequities in the distribution of impacts and disaster relief. Many of the impacts identified as economic and environmental have social components as well. Population migration is a significant problem in many countries, often stimulated by greater supply of food and water elsewhere. Migration is usually to urban areas within the stressed area or to regions outside the drought area. Migration may even be to adjacent countries. The drought migrants place increasing pressure on the social infrasttructure of the urban areas, leading to increased poverty and social unrest.

11

Fig. 2: Multidimensional Impacts of Drought

1.1.3 Agricultural drought impact on agricultural sector:

Agricultural drought produce a complex web of impacts that span many economic

sectors. Among, agriculture is the primary economic sector affected by agricultural drought

and particularly, short term agricultural drought at the critical growth stages has severe

impacts on agriculture (Wu and Wilhite, 2004 cited in Mokhtari, 2005). Agriculture is the

largest consumer of water and, therefore, the most sensitive to agricultural drought. Moisture

deficit is often the most limiting factor for crop production. In Ethiopia, rainfall in main rainy

season (Kiremt) is the most important for agricultural activities as nearly 95 percent of crop

production is in this season (Workneh Degefu, 1987). Thus the occurrence of agricultural

drought during the main rainy season has greater impact on country.s food production. This

impact is largely prominent in dry land semiarid areas.

Agricultural drought has either direct or indirect impact on agricultural activities.

Direct impact includes reduced crop yield, rangeland and forest productivities. The

12

consequence of these impacts result in reduction of income of farmers and agro based

industries, increased price for food and other agricultural products such as forest products.

Besides, losses in crop production, agricultural drought is associated with increases in insect

infestation, plant disease and wind erosion (Mokhtari, 2005). Agricultural drought induced

physiological stress increases a plant’s susceptibility to disease and insects, and reduces crop

survival. Furthermore, the loss of soil organic matter can lessen cropland productivity and

facilitate for wind erosion. On the other extreme, agricultural drought has also social impact

particularly on farmers that drive the agricultural sectors. It involves food shortage and

migration to urban areas. This makes drought migrants increase pressure on social

infrastructure of the urban areas and leads to increased poverty.

1.2 SCOPE OF THE STUDY

� Geospatial techniques can assist for detection and mapping of the agricultural drought

prone areas including monitoring appropriate site for specific mitigation actions.

Thus, agricultural drought risk zone map will be produced from this study, can be

useful for policy makers to take appropriate actions depending upon the risk level.

� On the other hand it is helpful for researchers to generate the information including

selection of drought prone area as well crop management and soil moisture

conservation practices.

� Moreover, it may be helpful for development agencies and Government Organization

(NGO) for drought management.

1.3 LITERATURE REVIEW

� National Institute of Disaster Management’s “Manual for Drought Management

of India” , November, 2009 suggested basic concepts of drought and a system for

monitoring and assessing the drought and proper management policy including

preparedness and mitigation.

Reference: Department of Agriculture and cooperation, Ministry of agriculture, Govt. of

India (New Delhi), pp 1-202

13

� Dr. Brian Wardlow’s study on “Remote Sensing and Drought Monitoring”,

September,2009 has explored the potential of Remote sensing and Image processing

technique for drought analysis.

� In this study drought has determined by different types of Indices such as, NDVI,

VHI, SPI and crop performance index(WRSI).

Reference: Drought Monitor Workshop (Bamako, Mali) September 14-17, 2009

� Hasan Murad et.al (2011) were worked on “Drought Assessment using remote

Sensing and GIS in North-West region of Bangladesh” and apply RS and GIS

techniques for drought detection. In this study:

• Meteorological drought and Agricultural drought were determined based on

Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index

(NDVI) during 2000-2008.

• Meteorological and agricultural drought risk maps were prepared by integrating the

various classes of drought.

• Finally, a resultant risk map was obtained by integrating agriculture and

meteorological drought risk maps which indicates the areas facing a combined

drought.

Reference: 3rd International Conference on Water & Flood Management (ICWFM-2011)

14

CHAPTER II

MATERIALS AND METHODS

2.1 OBJECTIVES The first and foremost objective of the study is to assess the spatiotemporal

occurrence of agricultural drought and their impacts on agricultural production.

For achieving the objective, the following analyses are required:

� Drought risk assessment using remotely sensed image based vegetation,

precipitation, Temperature and soil adjusted indices.

� Impact of drought on agriculture

� Drought risk zone map showing the severity of drought condition at various levels

2.2 METHODOLOGY

Fig. 3: Flow chart of the Methodology

15

2.2.1 Data Collection:

The first phase of the methodology is data collection. The relevant data has been

collected from different sources. Remote sensing data is having a immense role in

agricultural drought study. In this study three years of satellite data has used between 2000-

2010 time period with 5 years interval. All the satellite images (LANDSAT ETM+, 2000),

(LANDSAT TM, 2005) and LANDSAT TM, 2010) has been downloaded from Global Land

cover Facility(GLCF) and GLOVIS site. Toposheet has been collected from UTEXAS site

with 1:2, 50000 scales. Toposheet is very essential for delineating the Area of Interest(AOI)

and base map preparation. Seasonal rainfall data has collected from Indian Meteorological

Dept.(IMD) from 2000-2010. And other ancillary data like soil data has collected from NBSS

to prepare soil map of the area.

Fig.4: Collected Satellite data of the Study area at various time scale 2.2.2 Image Pre-processing:

Image pre-processing is very necessary to further use of the data for processing. Here

geometric correction and atmospheric correction has been done as image pre-processing

techniques.

2.2.2.1 Geometric Corrections:

In geometric correction, Toposheet has been georeferenced first in ERDAS Imagine

9.1 software and On the basis of georeferenced Toposheet all the satellite images has been

16

georeferenced by using Image to Image georeferencing technique in Erdas Imagine 9.1

software one by one.

2.2.2.2 Atmospheric Corrections:

However, there is no commonly accepted correction for Landsat data for operational

applications at regional scale. Here atmospheric correction model was adapted from

"Atmospheric and radiometric correction of Landsat TM 5 Data using the COST model

(Chavez, 1996", S. M. Skirvin).

The inputs to the COST model are the Earth-Sun Distance, sun elevation angle, and

minimum DN values for each band. The model first converts each minimum DN value to

minimum spectral radiance value:

Lλ�� = LMINλ + QCAL ∗ (LMAXλ − LMINλ)/QCALMAX ............................. Eq. 1

Lλsensor =apparent at-satellite radiance, LMAXλ =the spectral radiance that is scaled to

QCALMIN in watts/(meter squared * ster * µm), LMAXλ=the spectral radiance that is scaled

to QCALMAX in watts/(meter squared * ster * µm),QCAL= the quantized calibrated pixel

value in DN, QCALMAX=the minimum quantized calibrated pixel value(corresponding to

LMIN λ) in DN. The value of QCALMAX,QCALMIN,LMAXλand LMAXλ values can be

obtained from the Metadata file of the respective image. For each band, the theoretical

radiance of a dark object (assumed to have a reflectance of 1% by Chavez 1996 and Moran et

al. 1992) is computed:

Lλ,�% = .01 ∗ d�cos�θ/(π ∗ ESUNλ) ............................................. Eq. 2

Where, ESUNλ=mean solar exatmosperic spectral radiance from Table 4 of Markham and

Barker (1986), d is the sun-earth distance can be obtained from http://ssd.jpl.nasa.gov/cgi-

bin/eph, and theta(θ) is the solar zenith angle (90-sun elev).The value of sun elevation angle

can be found in Metadata of the image. A haze correction is computed using the computed

dark object values (Chavez 1996):

Lλ,&'(� = Lλ�� − Lλ,�% ..................................................... Eq. 3

The fundamental radiance to surface reflectance (rho) equation ( Chavez 1996) is:

ρ = π ∗ d�(L�� − Lλ,&'(�)/ESUNλ ∗ COS�θ ........................................................ Eq. 4

The output from the model is in reflectance units, which should range from 0 to 1.

Occasionally, values greater than 1 are obtained, and these generally correspond to bright

objects (e.g., clouds, snow, playa) or sometimes noise (randomly or systematically scattered

saturated pixels). The Landsat 5 Atmospheric and Radiometric Correction output data format

17

is float single. If we plan to compute indices or classify using reflectance data, we should

keep the images in this float-single format. If you have some other use that does not require

this radiometric resolution, you may want to rescale the images to 8-bit format to save disk

space.

a. b.

Fig. 5: Figure showing before atmospherically corrected image(a) and

after atmospheric correction(b)

2.2.3 Image Processing:

In Image processing phase supervised Classification of the satellite image and digital

indices method has followed. The supervised classification has done in ERDAS IMAGINE

9.1 software by using Maximum Likelihood algorithm to show the basic landuse and

landcover classes of different time scale.

2.2.4 Digital indices for Agricultural drought assessment:

Different types of drought require different indices that can be used to quantify the

moisture condition of a region and thereby detect the onset and measure the severity of

drought events, and to quantify the spatial extent of a drought event thereby allowing a

comparison of moisture supply condition between regions (Quiring and Papakryiakou, 2003;

Beyene Ergogo, 2007). It has become clear that no single indicator or index is adequate for

18

monitoring drought on regional scale. Instead, a combination of monitoring tools integrated

together is preferable for producing regional or national maps (Martini et al., 2004). Thus,

spatiotemporal patterns of seasonal drought can be detected using meteorological, vegetative

as well as crop performance indices among others.

2.2.4.1 Normalized Difference vegetation Index:

NDVI is an index of vegetation health and density and computed from the satellite Image

using spectral radiance in red and near infrared reflectance using the formula:

NDVI= (NIR-R) / (NIR+R); Where NIR= near infrared band, R= Red band

NDVI is a powerful indicator to monitor the vegetation cover of wide areas, and to detect the

frequent occurrence and persistence of droughts (Thavorntam and Mongkolsawat, 2006). It

provides a measure of the amount and vigor of vegetation at the land surface. The magnitude

of NDVI is related to the level of photosynthetic activity in the observed vegetation. In

general, higher values of NDVI indicate greater vigor and amounts of vegetation. Tucker first

suggested NDVI in 1979 as an index of vegetation health and density (Thenkabail et al.,

2004) and it has been considered as the most important index for mapping of agricultural

drought (Vogt, 2000 cited in Mokhtari, 2005).

NDVI is a nonlinear function that varies between -1 and +1 and values of NDVI for

vegetated land generally range from about 0.1 to 0.7, with values greater than 0.5 indicating

dense vegetation (FEWSnet). NDVI is good indicator of green biomass, leaf area index and

patterns of production (Thenkabail et al., 2004). Furthermore, NDVI can be used not only for

accurate description of vegetation vigor, vegetation classification and continental land cover

but is also effective for monitoring rainfall and drought, estimating net primary production of

vegetation, crop growth conditions and crop yields, detecting weather impacts and other

events important for agriculture, ecology and economics (Ramesh et al., 2003).

2.2.4.2 Vegetation Condition Index:

Kogan (1990) developed Vegetation Condition Index (VCI) using the range of NDVI which

is a good indicator for assessing the severity of agricultural drought.

VCI = (NDVI-NDVI min) / (NDVI max-NDVI min)*100

19

Where, NDVI is the actual value of NDVI , NDVI max and NDVI min are smoothed weekly

NDVI absolute maximum and its minimum.

The VCI values between 50 to 100 % indicate optimal or above-normal conditions.

At the VCI value of 100% the NDVI value for this month (or week) is equal to NDVImax.

Different degrees of a drought severity are indicated by VCI values below 50%.

2.2.4.3 Temperature Condition Index:

TCI is also suggested by Kogan(1997). It was developed to reflect vegetation response to

temperature i.e. higher the temperature more extreme the drought. TCI is besed on brightness

temperature and represents the deviation of current month’s value from the recorded

maximum. It is defined as:

TCI = (BT max-BT) / (BT max-BT min)*100

Where, BT, BT max and BT min are smoothed weekly brightness

temperature absolute maximum and its minimum.

The TCI value of 50 % indicates the normal condition or no drought. At the TCI

value of 100% represents the optimal/above normal condition and Different degrees of a

drought severity are indicated by TCI values below 50%.

2.2.4.4 Standardized Precipitation Index:

Standardized Precipitation Index (SPI) is an index that was developed to quantify

precipitation deficit at different time scales, and can also help assess drought severity. It is

defined as:

SPI = {(Xij − Xim) /ó }

where, (Xij= is the seasonal precipitation and, Xim is its long-term seasonal mean and ó is it’s

standard deviation.)

20

2.3 MATERIALS REQUIRED

2.3.1 Spatial Data:

� Satellite data

� LANDSAT ETM+ (2000)

� LANDSAT TM (2005)

� LANDSAT TM (2010)

� Toposheet of the study area( 1: 50,000)

2.3.2 Non-spatial Data:

� Agricultural data

� Soil data

� Monthly rainfall data(2000-2010)

Table. 1: Characteristics of Satellite Imageries used in this study

Satellite and Sensor

Year No. of bands

Spatial resolution(m)

Projection Path Row

Landsat ETM+ 2000-03-29 9 30 UTM45 139 044

Landsat TM 2005-11-14 7 30 UTM45 139 044

Landsat TM 2010-04-02 7 30 UTM45 139 044

2.4 SOFTWARE USED

� ARC GIS 9.3

� ERDAS Imagine 9.1

21

CHAPTER III STUDY AREA

3.1 INTRODUCTION OF THE STUDY AREA

Bankura district is one of the seven districts of Burdwan Division in the Indian state

of West Bengal. The district has been described as the “connecting link between the plains of

Bengal on the east and Chota Nagpur plateau on the west.” The areas to the east and north-

east are low lying alluvial plains. To the west the surface gradually rises, giving way to

undulating country, interspersed with rocky hillocks. Centre of the famous Malla kingdom of

western Bengal, Bankura and its surrounding regions are identified with its historical and

cultural significance for the later period of Middle Ages. Vaishnavism, which gained the

status of state religion of the Malla kingdom in seventeenth century AD, shaped the culture of

the region. The Malla Kingdom was annexed by British East India Company in 1765 and the

modern Bankura district took its form in 1881 and was named after its headquarters.

Geography:

Bankura district is situated between 22° 38’ and 23° 38’ north latitude and between 86°

36’ and 87° 46’ east longitude. It has an area of 6,882 square kilometres (2,657 sq mi). On

the north and north-east the district is bounded by Bardhaman district, from which it is

separated mostly by the Damodar River. On the south-east it is bounded by Hooghly district,

on the south by Paschim Medinipur district and on the west by Purulia district. Bankura

district has been described as the “connecting link between the plains of Bengal on the east

and Chota Nagpur plateau on the west.” The areas to the east and north-east are low lying

alluvial plains, similar to predominating rice lands of Bengal. To the west the surface

gradually rises, giving way to undulating country, interspersed with rocky hillocks. Much of

the country is covered with jungles.

The western part of the district has poor, ferruginous soil and hard beds of laterite with

scrub jungles and sal woods. Long broken ridges with irregular patches of more recent

alluvium have marks of seasonal cultivation. During the long dry season large extents of red

soil with hardly any trees lend the country a scorched and dreary appearance. In the eastern

part the eye constantly rests on wide expanses of rice fields, green in the rains but parched

and dry in summer.

22

The Gondwana system is represented in the northern portion of the district, south of the

Damodar, between Mejia and Biharinath Hill. The beds covered with alluvium contains

seams of coal belonging to the Raniganj system.

Hills and River System:

The hills of the district consist of outliers of the Chota Nagpur plateau and only two are

of any great height – Biharinath and Susunia. While the former rises to a height of 448 metres

(1,470 ft), the latter attains a height of 440 metres (1,440 ft).

The rivers of the area flow from the north-east to the south-west in courses roughly

parallel to one another. They are mostly hill streams, originating in the hills in the west. The

rivers come down in floods after heavy rains and subside as rapidly as they rise. In summer,

their sand beds are almost always dry. The principal rivers are: Damodar, Dwarakeswar,

Shilabati, Kangsabati, Sali, Gandheswari, Kukhra, Birai, Jaypanda and Bhairabbanki. There

are some small but picturesque water falls along the course of the Shilabati near Harmasra,

and along the course of the Kangsabati in the Raipur area.

Kangsabati Project was started during the second five year plan period (1956–1961).

The dam across the Kangsabati has a length of 10,098 metres (33,130 ft) and a height of 38

metres (125 ft).

Climate:

The climate, especially in the upland tracts to the west, is much drier than in eastern or

southern Bengal. From the beginning of March to early June, hot westerly winds prevail, the

thermometer in the shade rising to around 45 °C (113 °F). The monsoon months, June to

September, are comparatively pleasant. The total average rainfall is 1,400 millimetres (55 in),

the bulk of the rain coming in the months of June to September. Winters are pleasant with

temperatures dropping down to below 27 °C (81 °F) in December.

Soil:

According to soil texture, 60207 hec. is Clay area, 81944 hec. is Loamy-Clay area and

the rest part is described as Sandy-Clay area.

23

Flora:

The eastern portion of the district forms part of the rice plains of West Bengal. The

land under rice cultivation contains the usual marsh weeds of Gangetic plain. Aquatic plants

and water weeds are found in ponds, ditches and still streams. Around human habitations

there are shrub species such as Glycosmis, Polyalthia suberosa, Clerodenaron infortunatum,

Solanum torvum, and various other species of the same genus, besides Trema, Streblus and

Ficus hispida. The larger trees are papal, banyan, red cotton tree (Bombax malabaricum),

mango (Mangifera indica), jiyal (Odina Wodier), Phoenix dactylifera, and Borassus

flabellifer. Other plants found include Jatropha gossypifolia, Urena, Heliotropium and Sida.

Forests or scrub jungles contain Wendlandia exserta, Gmelina arborea, Adina Cordifolia,

Holarrhena antidysenterica, Wrightia tomentosa, Vitex negundo and Stephegyne parvifolia.

The western portion of the district is higher. The uplands either bare or are covered

with scrub jungle of Zizyphus and other thorny shrubs. This thorny forest gradually merges

into sal (Shorea robusta) forest. Low hills are covered with Miliusa, Schleichera, Diospyros

and other trees.

Some of the common trees of economic interest found in the district are: Alkushi

(Mucuna pruriens), amaltas (Cassia Fistula), asan (Terminaliat omentosa), babul (Acacia

Arabica), bair (Zizyphus Jujuba), bael (Aegle Marmelos), bag bherenda (Jatropha curcas),

bichuti (Tragia involucrate), bahera (Terminalia belerica), dhatura (Datura stramonium),

dhaman (Cordia Macleoidii), gab (Diospyros Embyopteris), harra (Terminalia chebula), imli

(Tamarindus indica), kuchila (Strychnos Nux vomica), mahua (Bassia latifolia), palas (Butea

frondosa), sajina (Moringa pterygosperma), kend (Diospyros melanoxylon), mango, date-

palm, nim, papal, banyan, red cotton tree and jiyal.

24

Fig.6: Location Map of the Study area

25

3.1.1 Base Map:

The spatial features of the study area is represented through base map. It also indicates

the spatial extent of the study area and it is prepared from toposheet. The base map(fig.7) is

containing Administrative boundary, Location, major and minor roads, drainage and

waterbodies, railway tracks and district headquarter.

Fig. 7: Base map of the study area

26

CHAPTER IV

RESULTS AND DISCUSSION

4.1 RESULTS

The following results are obtained at the end of phase-I project. The work has performed

basically preparation soil type map and hydrological soil group map through digitization and

database generation in ARC GIS 9.3 software and the supervised classification of the

temporal images and identified the basic landuse and landcover types and patterns over the

study area and also NDVI has performed to determine the vegetation condition and

healthiness over the area in ERDAS Imagine 9.1 software.

4.1.1(a) Soil(texture) Map:

The soil map (Fig. 8) of Bankura District represents the general soil type(textural) over

the entire area. These are mainly Coarse Loamy, Fine Loamy, Sandy Loam, Sandy Clay,

Silty Clay, Silty Loam and Silty Clay Loam. The soil attribute data has collected from NBSS,

LUP and the soil map is prepared.

27

Fig.8: Soil (Textural) Map of the Study area

4.1.1(b) Hydrological soil group Map:

Hydrologic soil group define and describe the rate at which water enters the soil

surface and the rate at which it transmits through the soil. The transmission rate is expressed

as in numerical range. A, B and D three hydrological soil group(Fig. 9) have found in this

study.

• Group A Soil:

Group A soil has low runoff potential and a high infiltration rate even when wet. The group

consists of soil that is deep and well-to-excessively draining. The soil ranges from sandy

28

loam to high gravel content. The water transmission rate is greater than 0.30 inch per hour.

Sandy loam, coarse and fine loamy soil falls in this hydrologic soil group .

• Group B Soil:

Group B hydrologic soil classification indicates silt loam or loam soil. It has moderately fine-

to-moderately course texture created by its mineral particle content relative to its organic

matter content. It has small amounts of sand and clay in silt-size particles. It has moderate

water infiltration rates 0.15 to 0.30 inch per hour. They are moderately well draining soils.

• Group D Soil:

Hydrologic soil classification Group D consists of soils with high clay content and very low

rate of water transmission. The rate of water infiltration and transmission is 0 to 0.05 inch per

hour. Clay loam, silty clay loam, sandy clay, silty clay soil falls in this group.

Table. 2: Different Classes of Hydrological Soil Group contains respective soil type (Texture)

HSG Soil Textures A Coarse loamy, fine loamy and sandy loam

B Silt loam

D silty clay loam, sandy clay and silty clay

29

Fig. 9: Hydrological soil group map of the study area

4.1.2 Land use and Land cover Map:

As far as agricultural activity is concerned, Land use pattern is an important factor that

influences agricultural production and productivity. The land use/ land cover map (Fig. 10,12

& 14) has been prepared of all the three years(2000, 2005 and 2010) as temporal basis using

Erdas Imagine 9.1 software by supervised classification techniques for understanding the

basic land use land cover types and it’s patterns of the study area and it also indicates the

trend of changes of LULC areas for temporal basis. (Fig. 11, 13 and 15) is showing the

percentage coverage of land use and land cover areas within the total area of each years

graphically. these include Agricultural land(mature stage), Seedlings, Harvested land, Dense

30

Vegetation, Mixed Vegetation, Shrubs, Vegetated land, Water bodies, Sand areas, Fallow

land, Barren Land and Wetlands.

Fig. 10: Land use and Land cover Classification Map of the study area (2000)

31

In the below chart(fig. 11), the pie diagram is showing the percentage of individual land use/ land cover classes of each sector is covering how much area within the entire area. From this figure it is clearly observed that, the shrub and seedlings are covering the maximum portion of the total sector, 40% and 32% respectively. But the other classes (wetland, barren land, sand, water bodies, harvested land, agricultural land and vegetation) are covering very less portion of the total area(in average <5%) in the year of 2000.

Fig. 11: Graphical representation of Land use and Land cover areas (in %) of 2000

32


33

The below Chart (fig. 13), is also showing that, the shrub, seedlings and wetland are covering the maximum portion of the total sector, 21%, 27% and 18% respectively. But the other classes (barren land, sand, water bodies, harvested land, agricultural land and vegetation) are covering very less portion of the total area (in average <10%) in the year of 2005.

Fig. 13: Graphical representation of Land use and Land cover areas(in %) of 2005

34


35

The below chart (fig. 13), is showing that, the seedlings is covering the maximum portion (25%) of the total sector. and shrub, barren lands, degraded vegetation and wet land are covering above 50% of the total area But dense vegetation, sand area, agricultural land(mature stage) and clear water bodies are covering < 1% in average of the total area in the year of 2010.

Fig. 15: Graphical representation of Land use and Land cover areas (in %) of 2010

The below figure(fig. 16) is showing the temporal changes of land use and land cover areas graphically over the entire study area from 2000-2010 due to the severe impacts of drought. It is clearly shown that the agricultural land, crop land and vegetated area is immensely degraded over the years and fallow land and barren lands are increased massively. The net changes( in %) of LULC classes from 2000 to 2010 is calculated quantitatively through ARC GIS 9.3 software and it is showing in below table( table. 3).

Fig. 16: Temporal changes of land use land cover classes of Bankura District over the years(2000-10)

36

Table 3. Net change (in %) of LULC areas of Bankura District from 2000 to 2010

LULC classes Year

2000

(area in

sq.km)

Year 2005

(area in

sq.km)

Rate of

change in %

(2000-2005)

Year 2010

(area in

sq.km)

Rate of

change in %

(2005-2010)

Net change in

% (2000-2010)

Clear water 29.55

58.83

.2928 42.06

-.1677 -.1251

Shallow water 92.33

179.51

.8718 215.39

.3588 .513

shrub 1794.62

941.68

-8.5294 734.87

-2.0681 -6.4613

vegetation 330.27

337.15

.0688 92.98

-2.4417 -2.3729

Dense

vegetation

75.54

102.53

.2699 54.09

-.4844 -.2145

Mixed

vegetation

52.57

290.5

2.3793 245.47

-.4503 1.929

seedlings 1452.78

1200.14

-2.5264 1114.26

-.8588 -1.6676

Agricultural

land (mature

stage)

56.47

124.67

.682 99.22

-.2545 .4275

Harvested land 136.32

287.26

1.5094 209.34

-.7792 .7302

Wetland 302.93

793.95

4.9102 436.56

-3.5739 1.3363

sand 77.94

82.78

.0484 30.26

-.5252 -.4768

Barren land 120.23

81.85

-.3838 596.24

5.1439 4.7601

4.1.3 NDVI (Normalized Difference Vegetation Index):

LANDSAT ETM+ and TM data is used for computation of NDVI (Fig. 17, 18 & 19)

for three different years (2000, 2005 and 2010) that represents the healthy condition of

vegetation. From NDVI values NDVI anomaly can also be prepared which is a good

indicator of assessment of drought. NDVI value lies between -1 to +1. It has been observed

that, in the year of 2000 the minimum NDVI value is -1 and the maximum value shows

.9362. Similarly, in 2005 the minimum NDVI value is -1 and the maximum is .9921 and in

2010, the minimum NDVI value is -1 and the maximum value is .8593. From this NDVI

value we can assess the vegetation healthiness. From this NDVI study it is clearly observed

37

that, 2010 year is indicating the maximum unhealthy condition of vegetation rather than 2000

and 2005 and 2005 is showing the maximum healthy condition between three years.

Fig. 17: NDVI Map of the Study area(2000)

38


39


40

CHAPTER V CONCLUSION 5.1 CONCLUSION

The major work carried out in the phase- I project is the data collection for agricultural

drought assessment over the entire Bankura District, West Bengal along with that the

preparation of objective and methodology for reaching to the tentative conclusion by using

Optimal GIS and Remote Sensing techniques. Thus in major findings soil, base map were

prepared along with that indices based approach to analyze the spatio - temporal scenario

over the study area due to drought, classification were adopted to separate land use/land

cover classes and to view the area conversion because of drought over the study area from the

year 2000 to 2010. The base map showing spatial features such as roads, railways with

administrative boundary and geographical areas and location provide the vision about the

study area. Soil map showing the variation in the soil strata and textural components over the

area with different types and characterization on the basis of texture and structure and soil

hydrological group. The Land use/ Land cover maps over the years showing the basic LULC

types , patterns and temporal variation over the years due to the impact of drought and NDVI

maps are showing the vegetation conditions and healthiness and variation due to the severity

of drought and Remote Sensing and GIS is the best tool to temporally and quantitatively

analyze the drought severity over the agriculture and assessment.

41

REFERENCES

♣ Anyamba, A. and Tucker, C.J. (2005). Analysis of Sahelian vegetation dynamics

using NOAA-AVHRR NDVI data from 1981-2003. Journal of Arid Environment,

63:596.614.

♣ Aslam, M., Khan, I., M., Saleem, A. and Ali, Z. (2006). Assessment of water stress

tolerance in different maize accessions at germination and early growth stage.

Pakistan Journal of Botany, 38: 1571- 1579

♣ Bastiaanssen, 1998, Remote Sensing in Water Resources Management: The state of

the art, IWMI publication, p118.

♣ Batista TT, Shimabukuro YE and Lawrence WT, 1997, The long term monitoring of

vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR

data, International Journal of Remote Sensing, 18: 3195-3210.

♣ Chopra, P. (2006). Drought risk assessment using remote sensing and GIS: A case

study of Gujarat. Msc. Thesis, ITC, Enschede.

♣ Comenetz, J. and Caviedes, C. (2002). Climate variability, political crises, and

historical population displacements in Ethiopia. Environmental Hazards, 4: 113-127.

♣ Dracup, J. A., Lee, K. S. and Paulson, E. G. (1980). On the definition of droughts.

Water Resources Research, 16:297-302

♣ Goldberg I, Ed., 1972, Agro climatic atlas of the World, Hydrometizdat, p 133

♣ Jeyaseelan, A.T. (2004). Drought and flood assessment and monitoring using remote

sensing and GIS.URL: http://www.wamis.org/agm/pubs/agm8/Paper-14.pdf

♣ Kogan,F.N. (1997). Contribution of Remote Sensing to Drought Early Warning.

42

♣ URL: http://drought.unl.edu/monitor/EWS/ch7_Kogan.pdf

♣ Ball, M.C. 1998. Mangrove species richness in relation to salinity and water logging. Global Ecology and Biogeography Letters7:73-82.

♣ Banerjee, L.K., A.R.K. Sastry & M.P. Nayar. 1989. Mangroves in India, Identification Manual. Botanical Survey of India, Kolkata.

♣ B. Meza D´ıaz and G. A. Blackburn Remote sensing of mangrove biophysical properties: evidence from a laboratory simulation of the possible effects of background variation on spectral vegetation indices, INT.J.Remote Sensing, 2003,vol 24, No 1, 53 - 73

♣ Chen, R. & R.R. Twilley. 1998. A gap dynamic model of mangrove forest development along gradients of soil salinity and nutrient resources. Journal of Ecology 86: 37-52.

♣ Chen, R. & R.R. Twilley. 1999. Patterns of mangrove forest structure and soil nutrient dynamics along the Shark River estuary, Florida. Estuaries 22: 955-970.

♣ Jordan, C. F., 1969, Derivation of leaf area index from quality of light at the forest floor. Ecology, 50, 663–666.

♣ http://glovis.usgs.gov/

♣ http://reverb.echo.nasa.gov

♣ http://glcf.gov.in

♣ http://bhuvan.gov.in

♣ http://earth.exploer.gov.in

♣ Kathiresan, K., N. Rajendran & G. Thangadurai. 1996. Growth of mangrove seedlings in intertidal area of Vellar estuary southeast coast of India. Indian Journal of Marine Sciences 25: 240-243.

♣ Kennealy, K.F. 1982. Mangroves of Western Australia. pp. 95-110. In: B.F. Clough (ed.) Mangrove Ecosystem in Australia: Structure, Function and Management. Australian Institute of Marine Sciences, Australia.

♣ Lugo, A.E. & S.C. Snedaker. 1974. The ecology of mangroves. Annual Review of Ecology and Systematics 5: 39-64.

43

♣ Landsat 7 Science User Data Handbook Chap.11, (2002),

♣ Macnae, W. 1968. A general account of the fauna and flora of mangrove swamps and forests in the Indo West Pacific Region. Advances in Marine Biology 6: 72-270.

♣ Mckee, K.L. 1993. Soil physicochemical properties and mangrove species distribution – reciprocal effects? Journal of Ecology 81: 477-487.

♣ Ormsby, J. P., Choudhury, B. J., and Owe, M., 1987, Vegetation spatial variability and its effect on vegetation indices. International Journal of Remote Sensing, 8, 1301–1306.

♣ Pal, D., A.K. Das, S.K. Gupta & A.K. Sahoo. 1996. Vegetation pattern and soil characteristics of some mangrove forest zones of the Sundarbans, West Bengal. Indian Agriculturist 40: 71-78.

o By � [email protected]

Geo-spatial assessment of agricultural drought Remote Sensing & GISTecnnique

Documents