j02 tuul beny friend project report

8/14/2019 J02 Tuul BENY FRIEND Project Report

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CHARACTERIZATION OF DESERTIFICATION STATUS BY

INTEGRATED USE OF SATELITE REMOTE SENSING AND GIS

A CASE STUDY OF EASTERN PART OF RAJASTHAN STATE

Submitted for the Partial Fulfillment of Requirement for the P. G. Diploma

in Remote Sensing and GIS Application in Agriculture and Soils.

BY

Miss Tuul Batbaldan

Institute of Meteorology and Hydrology, Mongolia.

[email protected]

SUPERVISED BY

Dr .S. K. Saha, Agriculture and Soils Division, IIRS

[email protected]

RESOURCE PERSON

Dr .R. D. Garg, Photogrammetry & Remote Sensing Division, IIRS

[email protected]

COURSE CONDUCTED AT

INDIAN INSTITUTE OF REMOTE SENSING (IIRS)

National Remote Sensing Agency (NRSA), Dehradun, INDIA

CENTE FOR SPACE SCIENCE AND TECHNOLOGY

EDUCATION IN ASIA AND THE PACIFIC (CSSTEAP)

(Affiliated to the United Nations)

IIRS CAMPUS, DEHRADUN, INDIA

JUNE 2005


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CENTRE FOR SPACE SCIENCE AND TECHNOLOGY EDUCATION

IN ASIA AND THE PACIFIC (CSSTEAP)

(Affiliated to the United Nations)

CERTIFICATE

This is to certify that Ms. Tuul Batbaldan has carried out Pilot Project study

entitled CHARACTERIZATION OF DESERTIFICATION STATUS BY

INTEGRATED USE OF SATELITE REMOTE SENSING AND GIS -CASE

STUDY OF EASTERN PART OF RAJASTHAN STATE for the fulfillment

of Post Graduate Course in Remote Sensing and Geographic Information System

of CENTRE FOR SPACE SCIENCE AND TECHNOLOGY EDUCATION IN

ASIA AND THE PACIFIC (CSSTE-AP). This work has been carried out at Indian

Institute of Remote Sensing, Dehra Dun.

Supervisor

Dr. S. K.Saha

Head, Agriculture & Soils Division

IIRS, Dehradun

Prof (Dr. Karl Harmsen) Dr. V. K.Dadhwal

Director, CSSTEAP Dean, IIRS


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ABSTRACT

Desertification is land degradation in arid, semi-arid and dry sub-humid areas

resulting from the complex interaction of physical, meteorological, biological, socio-economic and cultural factors. Desertification is one of the serious environmental-

problems faced by many countries in the world. It not only deteriorates the productivity of

the fragile ecosystems but also causes serious environmental and social problems. Satellite

Remote Sensing is a very effective tool for mapping and monitoring desertification over

large areas because of its unique capability of collecting data in multi-spectral, multi-

spatial resolutions, repetitive and synoptically.

The major objective of this pilot project is to map, assess and characterize

desertification status using satellite derived desertification indicators. The study area

consists of 21 districts of eastern part of Rajasthan State, India. Digital data of IRS-1D:

WiFS sensor belonging to Kharif (rainy) and Rabi (winter) crop seasons (October, 2004and February, 2005) of normal rainfall year were used as major data source.

Desertification status map showing various degree of desertification induced ecosystem

degradation was generated by GIS aided integration of satellite derived cropping system

and land use, climatic water balance and soil desertification indicators characteristics viz.

texture, soil available moisture, salinity/ sodicity and erosion hazard.

Various MODIS biophysical parameters monthly data products (May, 2004 to April,

2005) viz. albedo, vegetation indices (NDVI, EVI), land surface temperature, LAI (Leaf

Area Index), NPP (Net Primary Productivity) etc. are also used for characterizing district

wise and desertification status zone wise bio-physical conditions for the current cropseasons.


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ACKNOWLEDGEMENTS

I am happy to place on record, my gratitude, and sincere thanks to Prof.

Karl Harmsen, Director, CSSTEAP (Center for Space Science Technology and

Education for Asia-Pacific), for giving me opportunity to undergo Post Graduate

Diploma Course on Remote Sensing and GIS conducted by CSSTEAP, affiliated

to United Nations.

I also express my heartfelt thank to Dr .V. K. Dadhwal, Dean, IIRS (Indian

Institute of Remote Sensing) for providing necessary comfortable facilities and

encouragement.

I would like to express my deep sincere thanks to Dr.S.K.Saha, my project

guide, Head, Agriculture and Soil Division, IIRS, for his creative and valuable

comments, moral support, constant guidance in all the stages of this project work

and preparation of this report. I am grateful for Dr. Saha kindly supporting me to

do the postgraduate diploma course.

I am thankful to Dr. R. D. Garg, Scientist, Photogrammetry Division, IIRS

for providing technical help during field data collection and image processing of

satellite data as a resource person of the project.

It is also a pleasure to record my appreciation of the excellent support in my

study in IIRS by all IIRS teaching faculty members especially thanks to Dr. N. R.

Patel, Dr. Suresh Kumar, Dr. A.Velmurugan and faculty members of Agriculture

& Soils Division.

Lastly, I am thankful to my family members, especially my mommy and

dad who have always will be a constant source of support, joy, and motivation in

my life.

Thank you all.

Dehradun,India Miss Tuul Batbaldan.Date: June 29.2005


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CONTENTS

ABSTRACT....................................................................................................................... iii

ACKNOWLEDGEMENTS............................................................................................... iv

CONTENTS.v

FIGURE LIST.. .vii

TABLE LIST ...viii

CHAPTER-I........................................................................................................................ 1

1.0 INTRODUCTION .....................................................................................................1

1.1 Definitions and Impact of Desertification..................................................................1

1.2 Desertification Status in India....................................................................................4

1.3 Role of Remote Sensing and GIS in Desertification Study.......................................5

1.4 Objectives ..................................................................................................................6

CHAPTER-II ...................................................................................................................... 7

2.0 REVIEW OF LITERATURE ....................................................................................7

2.1 Indicators Used to Assess Desertification Risk .........................................................8CHAPTER-III................................................................................................................... 12

3.0 STUDY AREA ........................................................................................................12

3.1 Location and Extent .................................................................................................12

3.2 Climate.....................................................................................................................12

3.3 Soils..........................................................................................................................14

3.4 Geology and Geomorphology..................................................................................15

3.5 Agriculture and Land Use........................................................................................15

3.6 Relief, Elevation, Slope and Drainage .....................................................................16

3.7 Socio-Economic Characteristics ..............................................................................18

CHAPTER-IV................................................................................................................... 19

4.0 DATA USED...........................................................................................................194.1 Remote Sensing Data...............................................................................................19

4.2 Meteorological Data.................................................................................................19

4.3 Agricultural Data .....................................................................................................19

4.4 Collateral Data .........................................................................................................19

4.5 Softwares Used ........................................................................................................20

CHAPTER-V.................................................................................................................... 21

5.0 METHODOLOGY ................................................................................................. 21

5.1 Crop Inventory, Land use / Land cover and Cropping Pattern Mapping.................21

5.2 Desertification Status Mapping................................................................................21

5.3 Characterization of Desertification Status Using Satellite Derived Biophysical

Parameters......................................................................................................................245.4 MODIS Data Algorithm ..........................................................................................25

5.4.1 ALBEDO ..............................................................................................................25

5.4.2 LAND SURFACE TEMPERATURE (LST)........................................................26

5.4.3 LEAF AREA INDEX (LAI) .................................................................................27

5.4.3 NDVI and EVI ......................................................................................................27

CHAPTER-IV................................................................................................................... 30

6.0 RESULTS AND DISCUSSIONS............................................................................30


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6.1 Crop and Land Use Inventory..................................................................................30

6.2 Cropping pattern ......................................................................................................34

6.3 Desertification Status Mapping................................................................................42

6.4 Characterization of present biophysical conditions of the study area......................48

CHAPTER-VII ................................................................................................................. 69

7.0 SUMMARY AND CONCLUSIONS......................................................................69

7.1 CONCLUSIONS......................................................................................................71

GROUND PHOTOS LU /LC CLASSES (1).................................................................... 72



REFERENCES ................................................................................................................. 75

APPENDIX....................................................................................................................... 78

Key for decoding different parameters ............................................................................. 85


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FIGURE LIST

Fig. 3.1 Location map of study area ..................................................................................12

Fig.3.2 DEM and Terrain slope of study area....................................................................17

Fig. 5.1 Flow diagram of methodology of crop & land use inventory and cropping pattern

mapping......................................................................................................................22

Fig. 5.2: Schematic diagram showing methodology of desertification status mapping by

integrated use of Satellite data and GIS.....................................................................23

Fig. 5.3: Methodology for computation of district and desertification status zone wise area

weighted values of biophysical parameters. ..............................................................24

Fig. 6.1 FCC of study area (Kharif season) ......................................................................31

Fig. 6.2 Classified image showing crop and land use classes of Kharif season...............31

Fig. 6.3 FCC of study area (Rabi) season ..........................................................................35

Fig. 6.4 Classified image showing crop and land use classes of Rabi season ...................35

Fig. 6.5. Cropping pattern map of study area ....................................................................39

Fig. 6.6: (a) Rainfall pattern of study area, (b) Water Surplus of study area.....................42

Fig. 6.7: Soil taxonomic association map of the study area...............................................43

Fig. 6.8 (a) Surface Texture ...............................................................................................44

Fig. 6.8 (b) Erosion class ...................................................................................................44

Fig. 6.8 (c) Salinity/Sodicity class .....................................................................................44

Fig. 6.8 (d) Soil moisture availability ................................................................................44

Fig. 6.9: Desertification status map of the study area........................................................45

Fig. 6.10. Monthly NDVI variation of the study area........................................................50

Fig. 6.11 District and desertification zone-wise average monthly NDVI and EVI

variations....................................................................................................................52

Fig. 6.12 Monthly EVI variations of the study area ..........................................................53

Fig. 6.13 Monthly Albedo variations of the study area .....................................................55

Fig. 6.14 District and Desertification zone-wise average monthly Albedo and LAI

variations....................................................................................................................57

Fig. 6.15 Monthly LAI variations of the study area ..........................................................58

Fig. 6.16 Monthly LST variations of the study area ..........................................................60

Fig. 6.17 District and Desertification zone-wise average monthly LST variations...........62

Fig.6.18 District-wise NDVI, EVI, LST, LAI, Albedo correlation September 2004 64

Fig.6.19 District-wise NDVI, EVI, LST, LAI, Albedo correlation February 2005...67


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TABLE LIST

Table 1.1. Aridity zones defined by P/PE ratios (UNDP 1992a.b)......................................2

Table 3.1 Rainfall characteristics of the study area (District-wise mean monthly rainfall) ...13

Table 3.2 Air temperature of characteristics of the study area 14Table 6.1(a): Area statistics of land use / land cover classes of Kharif season (sq.km.) ...32

Table 6.1(b): Area statistics of land use / land cover classes of Kharif season (sq.km.)...33

Table 6.2(a): Area statistics of land use / land cover classes of Rabi season (sq.km.) ......36

Table 6.2(b): Area statistics of land use / land cover classes of Rabi season (sq.km.)......37

Table 6.3(a). Area statistics of Cropping Pattern (sq.km.) ................................................40

Table 6.3(b). Area statistics of Cropping Pattern (sq.km.) ................................................41

Table 6.4 Statistics of Desertification status mapping.......................................................47

Table 6.5 District - wise average monthly NDVI variations .............................................51

Table 6.6 Desertification zone-wise average monthly NDVI variations...........................51

Table 6.7 District - wise average monthly EVI variations.................................................54

Table 6.8 Desertification zone-wise average monthly EVI variations ..............................54

Table 6.9 District - wise average monthly Albedo variations ...........................................56

Table 6.10 Desertification zone-wise average monthly Albedo variations .......................56

Table 6.11 District - wise average monthly LAI variations...............................................59

Table 6.12 Desertification zone-wise average monthly LAI variations ...........................59

Table 6.13 District - wise average monthly LST variations ..............................................61Table 6.14 Desertification zone-wise average monthly LST variations............................61


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CHAPTER-I

1.0 INTRODUCTION

Desertification has long been recognized as a major environmental problem

affecting the living conditions of the people in the affected regions in many countries of

the world. In 1977, a United Nations Conference on Desertification (UNCOD) was

convened in Nairobi, Kenya to produce an effective, comprehensive and coordinated

program for addressing the problem of land degradation. The various assessments by

UNEP continued to point out that desertification results from complex interactions among

physical, chemical, biological, socio-economic, and political problems that were local,

national, and global in nature. In 1992, UNEP produced a World Atlas of Desertification

(UNEP 1992b). The studies indicated that over the preceding 25 years, the problem of

desertification and land degradation had continued to worsen. Many nations of the worldare facing the problem of rapidly growing populations and lack of food supply. In many

cases, the main reason for lack of food supply is land degradation and desertification.

1.1 Definitions and Impact of Desertification

Desertification can be defined as: Land degradation in arid, semi-arid, and dry

sub-humid areas resulting from various factors, including climatic variation and human

activities [United Nations Convention to Combat Desertification, UNCCD (1994)]

Desertification is now a direct threat to over 250 million people around the world,

and an indirect threat to further 750 million people. Over the last twenty years,

desertification has become increasingly apparent in the dry sub-humid regions of the

world, where mean annual rainfall ranges from 750-1500mm, and where the majority of

the human inhabitants of the dry lands now live. Dry land refers to the arid (excluding

the polar and sub-polar regions), semi-arid and dry sub-humid areas in which the ratio of

annual precipitation to potential evapo-transpiration falls within the range from 0.05 to

0.65.

The arid areas cover 12.5 % of the earths land area, the semi-arid areas 17.5%

and dry sub humid areas cover a further 9.9%. These are the areas most vulnerable to

desertification and together they occupy nearly 40% of the total earths land area. The

hyper-arid areas cover 7.5% of the total land area, and very poorly vegetated and sparsely

populated due to desertification processes. The dry lands cover 5.2 billion hectares, or a


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third of the land area of the globe (UNEP 1992a). Roughly one-firth of the worlds people

live in these dry lands. The exclusion of the hyper-arid regions of the world, such as the

Sahara, Atacama and Arabian deserts, which together occupy about 0.9 billion hectares

(UNEP 1992a).

French forester Aubrevelle employed the term desertification in 1949. Aubrevelle

used this term to describe the process of land degradation initiated by deforestation and

resulting in land being turned into desert. (Aubrevelle, 1949).

Land degradation means reduction or loss in arid, semi-arid and dry sub-humid

areas of the biological or economic productivity and complexity of rain fed cropland,

irrigated cropland, or range, pasture, forest and woodlands resulting from land uses or

from a process or combination of processes, including processes arising from human

activities and habitation patterns such as:

(i) Soil erosion caused by wind and or water;

(ii) Deterioration of the physical, chemical, and biological properties of the soil;

(iii) Long-term loss of natural vegetation.

Aridity of a region is categorized by the ratio of P = Mean Annual Precipitation to PE =

Mean Annual Potential Evapotranspiration, using modified Thornthwaite formula. As per

this, the aridity zones are classifieds (Table: 1.1).

Table 1.1. Aridity zones defined by P/PE ratios (UNDP 1992a.b)

Climate Zone P/PE ratio % of world covered

Hyper-arid

Arid

Semi-arid

Dry sub-humid

Humid

Cold

0.65

>0.65

7.5

12.5

17.5

9.9

39.2

13.6

Desertification together with deforestation, accelerated soil erosion, salinization,

water pollution, and reduced species diversity are now environmental problems of global

concern, since their indirect effects have worldwide economic and political repercussions

while their direct effects adversely influence the health and well-being of an ever-

increasing world population.

Due to global climate changes and the over-exploitation of ecosystems by the


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increased human economic activities, desertification is accelerated in many parts of the

world. It is not only deteriorates the productivity of the fragile ecosystems but also causes

serious environmental and social problems. The problems of combating desertification

are facing by many countries. Around 70% all agriculturally used dry lands are some

degree degraded, especially in terms of soils and plant cover. (UNEP, 1992a.b). The total

area concerned 3.6 billion hectare and over 100 countries are now suffering from the

adverse and economic impact of dry land degradation. (UNEP, 1992a & 1992b).

The extent and impact of desertification on the utilization of natural resources,

environmental deterioration, as well as the production of agriculture, forest, and animal

husbandry are now much more than before.

Manifestation of desertification include accelerated soil erosion by wind and

water, increasing salinisation of soils and near-surface groundwater supplies, a reduction

in species diversity and plant biomass, and reduction in the overall productivity of dry

land ecosystems, with an attendant improvement of the human communities dependent on

these ecosystems. A combination of climatic stress and dry land degradation can lead in

turn to extreme social disruption, migrations, and famine.

Combating desertification has become the top priority for governments around the

world, international organizations, and the United Nations. Combating desertification

includes activities, which are part of the integrated development of land in arid, semi-arid,

and dry sub-humid areas for sustainable development which are aimed at:

(i) Prevention and/or reduction of land degradation;

(ii) Rehabilitation of partly degraded land; and

(iii) Reclamation of desertified land.

Desertification produces a number of adverse conditions:

Deterioration of the natural resources adversely affecting the socio-economic condition

in addition, livelihood support systems;

Reduction of irrigation potential;

Diminishing of the food security base of human beings and livestock;

Scarcity of drinking water;

Health and nutrition status of the population;

Reduced availability of biomass for fuel;

Loss of bio-diversity; and

Impoverishment, indebtedness and distress sale of assets of production.


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Principal processes of desertification are vegetative degradation, water erosion, wind

erosion, salinization and water logging, soil crusting and compaction.

For stopping/minimizing desertification process, need to consider the following important

factors:

-Climatic monitoring and forecasting

-Genetic diversity and its erosion

-Land occupation and use

-Drainage, salinization and alkalinization of soils

-Vegetation development

-Relationships between animal and plant resources

-Population dynamics

-Ways of managing natural resources-The impact of natural resource management

policies on these resources

1.2 Desertification Status in India

Desertification is not confined to the desert areas or to the arid region, but relates

to land degradation in about two-thirds of countrys geographical area falling within the

arid, semiarid, and dry sub-humid regions. Land degradation has a direct impact on land

and other natural resources which results in reduced agricultural productivity, loss of bio-

diversity and vegetative cover, decline in groundwater and availability of water in the

affected regions. All these lead to a decline in the quality of life, eventually affecting the

socio-economic status of the region.

In India about 107.43 m ha, or 32.75 percent of the total geographical area is affected by

various forms and degree of desertification. (UNCCD, National Report on

Implementation of United Nations Convention to Combat Desertification, 2000, Ministry

of Environment and Forests, Government of India). Particularly the arid, semi-arid, and

sub-humid regions, commonly called dry land, represent fragile ecosystems that are

susceptible to desertification. These regions are also susceptible to frequent droughts that

accelerate the process of desertification and exacerbate its impact. Aridity is severe in

western part of Rajasthan, which is an eastern extension of the much larger arid areas of

the Middle East.

The major causes of desertification in the country are:

(i) Unsustainable - Extensive and frequent cropping of agricultural areas.


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Agricultural practices - Excessive use of fertilizers.

- Shifting cultivation without allowing adequate period of recovery.

(2) Unsustainable - Poor & Inefficient Irrigation Practices.

Water Management - Over abstraction of ground water, particularly in the coastal regions

resulting in saline intrusion into aquifers.

(3) Conversion of land - Prime forest into agricultural land for other uses - Agricultural

land for other uses.

-Encroachment of cities and towns into agricultural land.

1.3 Role of Remote Sensing and GIS in Desertification Study

Computers and satellites have brought development of two new technologies that

are especially valuable in combating desertification. One is Remote Sensing. Remote

Sensing is the only method of choice for monitoring desertification over large areas

because of its capability of collecting data frequently, synoptically and objectively over

such areas. Information derived from remote sensing data has been widely used in

modeling and prediction of desertification. It has also been used in supporting decision

making for combating desertification. Satellite imagery holds great-unrealized promise in

inventorying environmental conditions, especially land degradation features. In the recent

years, there are two significant advances in the remote sensing infrastructure for

facilitating and enhancing the desertification research. The first one is the enhanced

remote sensing capabilities for producing a new suite of remote sensing data and products

important to the desertification research. The second one is the web-based data discovery

and access technology enabling desertification researchers to easily access vast amount of

remote sensing data from multiple sources. Many new satellite remote sensing systems

have been commissioned to monitor earths climate and environment.

Geographic Information System popularly abbreviated as GIS is defined as an

automated tool to capture, store, retrieve, manipulate, display and querying of both spatial

and non spatial data to generate various planning scenarios for decision making

Assessment of desertification risk is the major contribution of GIS to combating

desertification. The advent of satellite imagery, coupled with the collection of spatial

data, has helped demonstrate the impact of desertification and provide the data needed for

improving the situation. GIS allows researchers to view and manage land cover, natural

vegetation, soil types, climate, topography, and socioeconomic data and to analyze it all


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within one framework. GIS is proving a most effective tool for studying this complex

phenomenon. GIS technology is applied as essential tools to address important aspects of

environmental monitoring.

Several advanced satellite sensor systems e.g. Hyper spectral, Multi-angle sensors,

have recently been launched and show great promise in characterizing and monitoring

soil surface.

1.4 Objectives

To assess and map spatial desertification status by GIS aided integration ofsatellite derived desertification indicators, soils and climatic conditions

To characterize present biophysical conditions of desertification induced degradedzones using satellite derived temporal biophysical parameters.


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CHAPTER-II

2.0 REVIEW OF LITERATURE

Desertification is a complex, evolutionary process resulting from several factors

with implications in all fields, including human behavior, and having a continuous effect

on all elements of the ecosystem. There are so many an interpretation of the concept of

desertification is that it is contextual. Although most desertification definitions include

both human and natural factors, researchers tend to emphasize one aspect more than the

order. This could be because each researcher brings his own expertise and looks for

evidence of expertise and looks for evidence of desertification-based of their experience

of knowledge. It could be also be that the differences in the physical, social, and cultural

attributes of each area studied contribute to a unique set of circumstances. Some

researchers has been done under the assumption that land degradation is caused by human

actions alone, entirely disregarding the climate factors and focusing only on the social,

economic and political factors (Andrew, 2002). Many authors have identified drought as a

contributing factor to desertification (Charney, 1975). Although there are many context

specific definitions of drought, it can generally be defined as deficient rainfall for the

needs of vegetation. Drought is seen as a relatively short-term cyclic phenomenon

whereas desertification occurs over a longer time scale. Other environmental conditions

such as topography, soil types, and vegetation cover also play a role in the susceptibility

of an area to desertification.

Some researchers and politicians view desertification as a social problem, where

people are the initiators and the subsequent victims. Under this point of view, the process

maybe exacerbated by prolonged drought and desertification but desertification is the

consequence of resource management failure resulting in excessive pressures on

ecosystem. Examples of human induced factors that exacerbate desertification include

deforestation, water resource diversion, agricultural practices, and overgrazing.

Desertification not only threats the ecosystem health and human living within theregion, but also affects areas far away from deserts. For example, dust storms from the

Gobi desert from Mongolia have caused significant air quality and traffic problems in

Beijing China even reached as far as the east coast of Northern America. One of the

significant features of desertification is the loss of surface vegetation. As result, soil

erosion caused by winds has become a prominent problem in desert and semi-desert

areas. Dust storm (weather phenomena that makes the horizontal visibility lower than 1


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km, caused by dust particles elevated by strong winds.) not only erodes the topsoil of arid

and semi-arid regions, further deteriorating the environment there and far away.

Climate change and desertification are both global process that lead to

environmental change. Specially, climate change refers to global warming of the

atmosphere due to emissions of greenhouse gases. Various international organizations

and researchers studied interactions of desertification and climate.

2.1 Indicators Used to Assess Desertification Risk

The European Environmental agency (EEA) has considered that an indicator can

be defined as a parameter or value derived from parameters, which provides information

about phenomena. In this sense, indicators should not be confused with raw data from

which they are derived. Indicators are quantified information, which help to explain how

things are changing over time and space. Environmental indicators are playing an

increasingly important role in supporting development polices. Single indicator is

generally not sufficient, several indicators are would necessary, even if not many, but

organized into a precise set for characterizing desertification status. It is rather difficult to

identify perfect indicators describing desertification risk. It is preferable to work with a

set of indicators informing about different aspects and condition.

Environmental indicators can facilitate the assessment and monitoring of

desertification at regional and local level, as they provide synthetic information on status

and trends of environmental processes leading to desertification.

The indicators used for Desertification Monitoring and Assessment can be

categorized into four types.

a). Pressure Indicators characterize driving forces both natural and man-made, affecting

the status of natural resources and leading to desertification. Pressure indicators are used

to assess desertification trends and make an early warning for desertification. Natural

indicators describe natural factors, mainly climatic conditions, natural disasters, which

promote the occurrence and development of desertification. Non-natural indicators

describe the pressure on land leading to land degradation from human activities.

b). State indicators characterize the status of natural resource including land. The

physical and biological features pf desertified land ecosystem is the main factors to be

considered. Physical indicators describe the land characteristics, physical and chemical


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properties of soil and hydrological features of the land ecosystem. Biological indicators

are used to describe biological characteristics of the land ecosystem.

c). Desertification impact indicators are used to evaluate the effects of desertification

on human beings and environment.

d). Implementation indicators are used to assess the action taken for combating

desertification and to assess its impact on natural resources and human beings. Such

impacts refer to improvements of socio-economic and natural conditions.

Satellite data are increasingly utilized to monitor the albedo of arid and semi-arid

lands, given the importance of albedo as an indicator of soil degradation and

desertification. (Hute 2004.) Soil colour, moisture, structure, all affects albedo and

structure less soils may increase albedo by 15-20% (Post et.al., 2000).

Saha and Pande (1995) used Landsat-TM optical bands data for computation of

regional surface albedo following the approach suggested by Goita and Royer (1992).

Ghosh and Tripathy (1994) investigated soil degradation due to desertification

processes in the arid and semi-arid regions of Gulbarga district in India using IRS and

Landsat MSS imagery (1984-1991). They analyzed multi-temporal albedo and NDVI

(Normalized Difference Vegetation Index) and generated albedo change images. They

found that albedo correlated well with factors such as reduced soil moisture conditions

and potential soil erosion.

S.O.Mohamed and A.Farshad et.al., (1994) described vulnerability to desert

conditions over northwestern Nigeria using remote sensing coupled with other ancillary

data (erosion, sealing, crusting, compaction, cover change, organic matter monitoring,

salinity and aridification) within a GIS environment. They show how assessment of land

degradation can be used to determine degrees of vulnerability of that land to desertic

conditions.

Eriksen (2003) considered the biophysical and social linkages between climate

change and desertification or dry land desertification. He suggests that underlying causes

of vulnerability to both climate change, and desertification include the political ecology

of resource control, urbanization, and economic globalization affecting domestic markets

and agricultural specialization.

Two leading scientists (Martin A J Williams and Robert C Balling Jr.), jointly

commissioned by the United Nations Environment Program (UNDP) and the World

Meteorological Organization (WMO), have produced a referenced report on current

knowledge of the interactions of desertification and climate in the dry lands (excluding


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hyper-arid regions) of the world. This report tells us how climate influences the

hydrologic cycle, vegetation, and soil, and how in turn these factors affected by human

actions lead to qualitative changes in soil and vegetation.

Semi-Arid ecosystems can show distinct vegetation alternative states. In many

regions, excessive biomass removal like wood harvesting, overgrazing has resulted in

depletion of vegetation biomass and soil erosion. These changes are very difficult to

reverse due to the positive feedbacks that stabilize the degraded situation. Holmgren

(2003) presented a restoration hypothesis suggesting climatic oscillations such as El Nino

Southern Oscillation (ENSO) could be used combination with controlled grazing to

restore degraded arid ecosystems.

Interactions between human societies and the environment, of which they are an

integral part, are complex and hard to unravel. Williams (1994) published paper about

relative influence of climatic variation and human activities when assessing the causes of

desertification.

Symeonakis and Drake (2002) did research on monitoring desertification and land

degradation over sub-Saharan Africa and developed a desertification monitoring system

that uses four indicators derived using continental-scale remotely data: vegetation cover

(NDVI), rain use efficiency (NDVI and Rainfall from Meteosat cold cloud duration data),

surface run-off [SCS (Soil Conservation Service) model] and soil erosion. Soil erosion,

one of the most indicative parameters of the desertification process was estimated using

model parameterized by overland flow, vegetation cover, the digital soil maps, and DEM.

Another important contributing factor to the desertification process is wind

erosion in many dry land environments and can be a major mechanism for soil

degradation. Brown and Nickling (2002) have been used multiple approaches to assess

and monitor the severity and extent of wind erosion including visual indicators, direct

measurement, remote sensing and modeling.

Kosmas et.al. (2003) analyzed using simple indicators related to the physical

environment such as soil depth, slope gradient, slope exposure, parent material, rock

fragment content, annual rainfall, aridity index, type of vegetation, plant cover percent

and land management characteristics such as tillage operations, tillage depth, controlled

grazing, period of exiting land use type, erosion control measures, etc., used for defining

desertification risk.

Soil erosion is one of the most important processes contributing to land

degradation over large areas of terrestrial Earth. Remote sensing data are often used for


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direct identification of eroded areas on un vegetated soils. Direct spectral measures

indicative for soil erosion include changes in organic matter content, mineral

composition, albedo, roughness, and soil structure (Hute, 2004). Gully and rill erosion in

un vegetated / sparse vegetated landscape can be identified directly using remote sensing

data. Remote sensing can effectively provides temporal and spatial information that can

be coupled with soil erosion models, such as vegetation cover, soil moisture, land use,

digital elevation, and sediment transport.

Soil erosion prediction and assessment has been challenge to researchers since the

1930s and several models have been developed. (Lal, 2001).

Salinization is another important process promoting desertification. In many

cases, the rapid development resulted in the over exploitation of the aquifer systems for a

variety of uses, such as agricultural, industrial and domestic. Irrigation using water with

high salt concentrations increased the salinity of soil, causing unproductive decertified

land. (Convention Project to Combat Desertification (CCD Project). Soil degradation

related to salinization and alkalization represents an increasing environmental hazard to

natural and agricultural ecosystems. Salinization involves the accumulation of salts

(chlorides, sulfates, carbonates) of sodium, magnesium, or calcium in root sons, as salts

move upwards in the soil and are left at the surface as water evaporates.

Salt-affected soils reveal presence of salts in two different ways in remotely

sensed data a.) directly on bare with efforescence and salt crust; b.) indirectly by affecting

condition/type of vegetation or soil moisture condition. Numerous remote sensing studies

have involved the mapping and monitoring of salt-affected soils with variety of satellite

data (Saha et.al., 1990; Metternicht and Zink, 2003; Hute, 2004). Dwivedi (1992) used

post-monsoon (October) and pre-monsoon (April/May) Landsat-TM data for delineating

various categories of sodic in parts of Gangetic alluvium plains or northern India. Verma

and Singh (1999) used temporal optical satellite data and GIS tool to monitor changes in

status of sodic land in part of Uttar Pradesh.

Csillag et.al. (1993) suggested that potential exists of spectral recognition of

salinity status with Hyperspectral remore sensing data.

The increased availability of remote sensing time series data in recent years makes

it possible to analyze desertification at regional, continental, and global scales. For

continental and global desertification studies, time series data from AVHRR and MODIS

are widely used, for the local or regional studies, times series data from Landsat TM and

other high-resolution data are used.


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CHAPTER-III

3.0 STUDY AREA

3.1 Location and Extent

Rajasthan is the second largest state of India situated in the northwestern part of

the Indian Union. The study area is Part of Eastern Rajasthan State and falls in geographic

coordinates of Latitude 23 03N to 28 13N and Longitude 72 14 to 78 16 E (Fig.1).

The study area covers 21 districts of Eastern Rajasthan State.

3.2 Climate

The climate of study area is semiarid to sub humid in the east of Aravalli range,

characterized extreme in temperatures. The annual rainfall ranges between 550mm (in

Ajmer) to 1640mm (Mount AbuSirohi districts) (Table 3.1).

Fig. 3.1 Location map of study area

The month of March marks the beginning of summer and the temperature starts rising

progressively through April, May and June (Table 3.2). The temperature rise


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Table 3.1 Rainfall characteristics of the study area (District-wise mean monthly rainfall)

during this period is almost uniform all over the state. The minimum daily temperatures

drops down at night around 26C and daily maximum temperature reach in summer 40-

45C. At Udaipur and Mount Abu, temperature, however is relatively lower and the mean

daily maximum temperature in summer reaches 38C and 31.5C respectively. Daily

minimum temperature for these two stations 25C and 22

C, respectively. January is the

coldest month of the year (Table 3.2).

On the basis of climatic conditions and terrain characteristics, study area is

divided into 5 Agro-climatic zones :

Zone 1:Semi-Arid Eastern Plain (Ajmer, Jaipur, Dausa, Tonk districts)

Zone 2:Flood Prone Eastern Plains (Alwar, Bharatpur, Dhaulpur, Karauli districts)

Zone3: Sub-humid Southern Plains and Aravalli Hills (Udaipur, Rajsamand, Bhilwara,

Chittaurgarh, Sirohi districts)

Zone 4:Humid Southern Plains (Dungarpur and Banswara districts)

Zone 5: Humid South-Eastern Plains (Bundi, Sawai Madhopur, Kota, Baran, Jhalwar)

No District Name/Month Jan Feb Mar April May June July Aug Sep Oct Nov Dec Annual

1 AJMER 0.51 0 .56 0.44 0.30 1.07 4.99 16.20 16.35 7.30 1.01 0.28 0.41 49.42

2 ALWAR 1.25 1.09 0.92 0.57 1.26 4.90 17.99 12.77 10.37 1.29 0.24 0.33 52.98

3 BANSWARA 0.32 0.19 0.14 0.08 0.42 10.97 32.22 29.35 16.13 1.76 0.57 0.62 92.77

4 BHARATPUR 1.26 1.00 0.74 0.54 1.00 5.11 20.48 20.85 12.10 1.81 0.30 0.42 65.61

5 BHILWARA 0.51 0 .22 0.36 0.28 0.68 5.93 25.67 25.30 9.56 0.62 0.19 0.59 69.91

6 BUNDI 0.54 0.34 0.32 0.25 0.72 6.76 28.10 27.35 10.62 0.78 0.21 0.09 76.08

7 CHITTAURGARH 0.60 0.23 0.23 0.15 0.55 8.55 29.49 30.91 12.50 0.99 0.63 0.58 85.41

8 DUNGARPUR 0.21 0.19 0.13 0.11 0.72 9.89 28.67 23.32 11.30 1.10 0.43 0.38 76.45

9 JAIPUR & DAUSA 1.12 0 .90 0.59 0.36 0.99 5.13 18.21 18.07 8.50 0.99 0.19 0.10 55.15

10 JHALAWAR 1.05 0.54 0.35 0.33 0.92 10.09 33.45 30.01 15.17 1.35 1.29 0.57 95.12

12 KOTA & BARAN 1.01 0.54 0.34 0.31 0.84 8.34 31.96 28.59 13.48 1.46 0.84 0.57 88.28

13 PALI 0.35 0 .47 0.21 0.20 1.09 4.16 14.48 18.24 7.09 0.65 0.15 0.13 47.22

14 SAWAI MADHOPUR 1.04 0.63 0.59 0.38 0.80 5.75 23.39 23.60 10.35 1.19 0.27 0.50 68.49

15 SIROHI 0.42 0 .52 0.17 0.25 1.20 5.77 23.81 22.63 7.63 0.92 0.30 0.22 63.84

16 TONK 0.80 0 .47 0.36 0.33 0.77 5.75 21.23 20.75 9.44 0.81 0.21 0.44 61.36

17 UDAIPUR & Rajsamand 0.80 0.32 0.29 0.17 0.92 6.80 22.89 20.49 10.79 1.00 0.45 0.20 65.12


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Table 3.2: Air temperature characteristics of the study area (District-wise mean daily

maximum, minimum temperature (Centigrade)).

No District Name/Month January February March April May June

Max Min Max Min Max Min Max Min Max Min Max Min

1 AJMER 22.2 7.3 25.3 9.9 30.7 15.7 35.9 21.9 39.5 27.3 38.1 27.7

2 BHARATPUR 22.7 7.1 26.7 9.8 32.7 15.4 38.6 21.5 42.2 26.4 41.9 30.1

3 CHITTAURGARH 25.2 7.8 28.9 10.2 34.0 16.4 38.5 22.1 41.5 26.8 39.5 27.4

4 JAIPUR 22.0 8.3 25.4 10.7 30.9 15.5 36.5 21.0 40.6 25.8 39.2 27.3

5 JHALAWAR 25.1 9.4 28.4 11.4 33.9 16.4 38.6 22.0 42.0 27.3 39.1 27.5

6 KOTA 24.5 10.6 28.5 13.1 34.1 18.5 39.0 24.4 42.6 29.7 40.3 29.5

7 PALI 25.3 0.5 28.4 11.9 37.7 19.3 37.5 23.7 40.2 26.2 38.2 27.3

8 SIROHI 19.3 9.3 21.2 11.5 35.3 15.9 29.4 20.0 31.5 22.3 29.1 20.5

9 UDAIPUR 24.2 7.8 27.6 9.7 32.3 15.1 36.0 20.2 38.6 2.9 35.9 25.3

No District Name/Month July August September October November December

Max Min Max Min Max Min Max Min Max Min Max Min

1 AJMER 33.3 25.6 30.9 24.3 32.1 23.7 32.9 17.8 28.9 10.9 24.4 7.7

2 BHARATPUR 35.0 27.1 33.1 25.8 33.3 24.1 33.3 18.5 29.5 11.6 24.4 7.4

3 CHITTAURGARH 33.4 24.0 31.1 29.2 32.1 23.0 33.1 17.9 30.2 11.9 26.7 8.3

4 JAIPUR 34.1 25.6 32.9 24.3 33.2 23.0 33.2 18.3 29.0 12.0 24.4 9.1

5 JHALAWAR 32.3 24.9 30.6 24.1 31.9 23.2 33.5 18.3 29.8 12.2 26.5 9.6

6 KOTA 33.3 26.4 31.7 25.4 33.1 24.7 34.5 21.0 30.8 14.8 26.7 11.3

7 PALI 32.9 25.5 31.3 24.8 31.7 23.9 33.5 21.0 30.7 14.6 24.4 10.9

8 SIROHI 24.3 19.3 22.5 18.3 24.0 18.4 26.6 17.4 24.1 13.5 21.2 11.2

9 UDAIPUR 30.7 23.9 29.3 22.9 30.9 22.1 32.0 18.9 29.1 11.0 26.5 8.3

3.3 Soils

Rajasthan, being geographically the second largest state in India, has

proportionately the greater soil recourse. When seen in detail landscape levels, the soils of

Rajasthan are complex, and highly variable, reflecting a variety of differing parent

materials, physiographic land features, range of distribution of rainfall, and its effects.

Soil characteristics of selected soil properties of the study area are presented in

Annexure-1. Dominant soil great groups found in the study area are - Chromusterts


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Pellusterts, Haplustalfs, Chromusterts, Pellusterts, Haplustalfs, Ustifluvents,

Qartzipsamments, Torripsamments, Ustochrepts, and rock out crops.

3.4 Geology and Geomorphology

Rajasthan is endowed with a continuous geological sequence of rocks from the

oldest Archaean Metamorphites represented by Bhilwara Super-Group (more than 2500

million years old) to sub recent alluvium and wind blown sand. The south Eastern

extremity of the Rajasthan is occupied by a pile of basaltic flows of Deccan Traps of

Cretaceous age. The Deccan traps found in Southern and South Eastern Rajasthan and

extends over a vast area in southern Jhalawar and in the eastern parts of Chittaurgarh and

Banswara districts, are notable formations of Upper Cretaceous to Lower Eocene age

when large area of peninsular India was also covered with fissure eruptions of black lava.

Pleistocene sandy alluvium, blown sand, kankar (calcium nodules), carbonate beds are

found over a large area of Eastern Part of Rajasthan. The Great Boundary Fault, through

which the River Champal has carved its course, passes through southeastern parts of

Rajasthan. This fault is visible in Begun (Chittaurgarh district) and northern parts of Kota.

It reappears again in Sawai Madhopur and Dhaulpur districts. Besides this, several mega

lineaments also traverse in the state.

The geological sequence of the state is highly varied and compex, revealing the

co-existence of the most ancient rocks of Pre-Cambrian age and most recent alluvium as

wind blown sand. The Aravallis, one of the most ancient mountains in the world, have the

oldest granitic and gneissic rocks at their base, overlain by the rocks of the Aravalli Super

group, Delhi Super Group, the Vindhyan Super Group and younger rocks. These rocks

are highly metamorphosed at certain places and show rich occurrences of minerals of

great commercial importance.

3.5 Agriculture and Land Use

Rajasthan's economy is mainly agriculture-based. About 80 percent of the

population lives in rural areas and is dependent on farming. In the total gross cultivated

area over the study area, Bajra (pearl millet), jowar (sorghum), maize, guar, sesamum (oil

seeds), soybean and groundnut, pulses are grown in the Kharif (Rainy) season. Wheat,

barley, gram, mustard, are grown in the Rabi (Winter) season. Cotton and sugarcane are

the chief cash crops grown in the black soil some region. Cereal crops such as bajra,


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wheat and mustard cover the largest cultivated area. Bajra (pearl millet) is the major crop

of Kharif in Eastern part of Rajasthan. This millet can be grown in sandy soil under rain

fed conditions. Jowar, known for its drought tolerance, is one of the important food and

fodder crops of Rajasthan. Jowar (sorghum) can be grown on loam to clay loam soils

without irrigation and hence has importance in dry land agriculture. Jowar can grown in

Rabi season too. One of important crop in Kharif season is Guar. This is fodder as well as

its gum, extracted from its seed, which has an industrial importance. This is rain fed crop

and depending on rainfall pattern. Other crop groundnut, pulses, sesamum can be grown

in kharif season over study area. Wheat, mustard and rapeseeds are rabi season crop.

Wheat is grown from December to February in Rabi (Winter season) loamy or loamy-

sandy soils which can retain moisture and are rich in nutrients. Mustard and rapeseeds

requires cool, dry weather and bright sunshine. These crops may be grown in rain fed

conditions but higher yields are obtained under irrigated conditions. These crops grow

well in sandy loam to loam.

3.6 Relief, Elevation, Slope and Drainage

Eastern Part of Rajasthan lay approximately below Aravalli hill ranges, which is

called eastern semi-arid regions. Here area is well drained by several integrated drainage

systems. Aravalli hills ranges are the most prominent hill features extending from Sirohi,

Udaipur and Dungarpur districts in the South-west to Jaipur and Alwar districts northeast.

They rise to their highest summit at Mount Abu (1722 m above MSL) in Sirohi district.

These ranges from a Lbyrinth of low hills in Udaipur, Dungarpur, and Banswara districts,

and stretch North Eastwards in the form of undulating low hills through parts of Ajmer ,

Tonk, Sawai Madhopur, Jaipur and Alwar disricts. Coverning most parts of Alwar ,

Bharatpur, Jaipur, Dhaulpur, Tonk, Sawai Madhopur, Bundi and Kota districts, the

eastern plains have rich alluvial soil drained by seasonal rivers.

The DEM (Digital Elevation Model) of the study area is derived from SRTM

(Shuttle RADAR Topographic Mission) elevation model on 90m spatial resolution. The

elevation of the study area varies from 100m to 1698m. The slope map is generated by

processing of elevation values in SRTM DEM. The slope of the area ranges from less

than 1% to more than 30%. The DEM and slope map of the study area is shown in Fig.

3.2 and 3.3 respectively


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Fig.3.2 and Fig.3.3 DEM and Terrain slope of study area

0 40 80 120 16020

Kilometers

LEGEND

N

0 40 80 120 16020

Kilometers

N

LEGEND


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3.7 Socio-Economic Characteristics

Most populous districts are Bhatpur, Jaipur, Alwar, Dhaulpur and Kota lie on the

eastern fringe of Rajasthan. The fertile plains of the east, drained by several ephemeral

rivers and streams, which have deposited fertile alluvial soil over the years, provide rich

arable soils to sustain the people. Coupled with this, are the climatic factors-the moderate

climate in the Eastern part, providing a comfortable zone of temperature, humidity and

precipitation, and accentuating better living conditions compare to western part of

Rajasthan. Regional disparities are markedly discernible amongst various districts of

Rajasthan; Jaipur and Dausa are the most thickly populated district with population of

335 persons/sq.km followed by Bharatpur and Alwar. In eastern part of Rajasthan there

has been preponderance of males over females.

In Rajasthan, urbanization is at a slow pace. Only about 23% of the totalpopulation of the state lives in towns and cities. Jaipur, Kota and Ajmer districts have a

higher percentage of urban population. Other districts which have medium sized urban

population are Bharatpur, Udaipur, Pali, Tonk, Bhilwara, Sirohi, Bundi, Dhaulpur,

Jhalawar, Chittaurgarh, Sawai Madhopur, and Alwar. Considerably low urban population

districts are Dungarpur, Banswara. Large proportion leaves in rural area. Agricultural

occupation forms the main stay of employment in the state.


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CHAPTER-IV

4.0 DATA USED

The varieties of data used in this study are described below:

4.1 Remote Sensing Data

Digital satellite data: IRS-1D:

Wide Field Sensor (WiFS) data with a spatial resolution of 188m, two spectral bandsin the visible and near infra-red regions, with a swath of 810 Km.

Data acquisition: 17 October 2004 and 18 February 2005. Path/ Row: 96/56; 96/57

MODIS Data:

Monthly composite Normalized Different Vegetation Index (NDVI) 1 km Monthly composite Enhanced Vegetation index (EVI) with spatial resolution 1km. Leaf Area Index (LAI) , 8 day composite BRDF/Albedo,16 day composite with spatial resolution 1km Land Surface Temperature 8 day composite with spatial resolution 1 km4.2 Meteorological Data

Rainfall data (past 25 years average monthly rainfall data), Air temperature (past 25 years average maximum and minimum monthly temperature) Climatic water balance water surplus / water deficit

(Source: Recourse Atlas of Rajasthan. Department of Science and Technology

Government of Rajasthan. Jaipur, 1994).

4.3 Agricultural Data

Agricultural data of study area viz., cropping pattern, crop calendar, and crop phenology,

historical crop yield etc. were collected in this study during field ground truth collection.

4.4 Collateral Data

Topographical Maps: Survey of India (SoI) topographical maps Sheets No

54E, 45L, 45M, 54D, 45O, 54C, 45K, 54B, 45H, 45D, 45P, 45G, 54H, 54F, 54G, 54J,

45N, 45C, 54A, and 45J at 1: 250,000 scales.


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Soil Map: Soil map in 1: 250,000 scale prepared by National Bureau of Soil Survey

and Land Use Planning (NBSS & LUP), Govt. of India, was utilized in the present study.

4.5 Softwares Used

Digital image processing and GIS analysis were carried out by using following

softwares:

ERDAS IMAGINE 8.7 ILWIS 3.2 (Integrated Land and Water Information System) ARCGIS 8.3 ARC VIEW 3.2a


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CHAPTER-V

5.0 METHODOLOGY

5.1 Crop Inventory, Land use / Land cover and Cropping Pattern Mapping

The methodology adopted for crop and land use and cropping pattern inventories

is depicted in Fig. 5.1. Crop inventory and land use/land cover maps of Rabi and Kharif

crop season were derived from IRS WiFS satellite data. Ground truth was collected

through integrated use of same year IRS 1D WiFS hard copy image (1:250,000 scale),

Survey of India (SoI) toposheets and handheld Global positioning System (GPS).

Combination of satellite data acquired during Kharif (17 October 2005) and Rabi (18

February 2005) images were digitally classified to land use/land cover information

classes for Kharif and Rabi, respectively using MXL classifier. Agricultural land useclasses in Rabi season were refined with respect to land use information in Kharif season

and crop calendar of major crops cultivated in the region. Finally, digitally classified land

use /land cover maps of Kharif and Rabi seasons were logically integrated in GIS for

deriving cropping pattern / cropping system.

5.2 Desertification Status Mapping

Desertification status map of the study area showing spatial variation of varying

degree of ecosystem degradation was generated by GIS aided integration of land use /

land cover; cropping pattern, annual climatic water balance derived water surplus / deficit

maps and soil characteristics affecting desertification processes viz. surface soil texture,

soil erosion, salinity / sodicity and profile plant, available soil moisture content (Fig. 5.2).

Annual climatic soil moisture surplus / deficit map was prepared by computing monthly

Potential Evapotranspiration (PET); Actual Evapotranspiration (AET), rainfall and soil

moisture storage following Thornthwaite Climatic Water Balance approach (1948).

Monthly rainfall and air temperature data were used for this purpose. Soil characteristics

maps of four parameters affecting desertification processes were prepared by linking

digitized soil coverage and soil attribute table in GIS environment. Finally Desertification

Status Index (DSI) was computed in GIS environment using following relationship


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Fig. 5.1 Flow diagram of methodology of crop & land use inventory and cropping

pattern mapping

DSI = CPLU + WS/WD + SE + SS + ST + SMC. (i)

SATELLITE DATA IRS 1D WiFS

Rabi image

(17 Oct 2005)

Download MODIS image

of study area

Crop Inventory

Rabi

Import to ERDAS

Image Re-projection

Extraction of study area

Image from MODIS data

EXTRACTION OF STUDY AREA IMAGES OF

KHARIF AND RABI

Crop Inventory

Kharif

SATELLITE DATA IRS 1D WiFS

Kharif image

(18 Feb 2005)

Image Rectification and transformation using

MODIS data

Subset study area

Digital classification

of Rabi and Kharif images

GENERATION OF CROPPING PATTERN MAP

Mosaic images

GIS aided spatial integration

GCP IDENTIFICATION OF MAP TO IMAGE

TRANSFORMATION MODEL


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Where, CPLU Cropping pattern and Land use; WS/WD Climatic soil moisture

surplus / deficit; SE Soil Erosion status; SS Soil salinity / sodicity; ST Surface soil

texture; and SMC Profile plant available soil moisture content

Mapping classes in each parameter in the relationship (i) were assigned value 1 to

10 depending on degree of desertification risk involved (e.g. 1 very low risk; 10 very

high risk). The assign values ranges vary from parameter to parameter depending on

number of classes present in each parameter. Finally DSI was grouped into 5 classes such

as Very low; Low; Moderate; Moderately high; High; Very High,

Fig. 5.2: Schematic diagram showing methodology of desertification status mapping

by integrated use of Satellite data and GIS.

TEMPORAL

SATELLITE DATA

OF KHARIF AND RABI

DIGITAL

CLASSIFICATION AND

INTEGRATION

METEOROLOGICAL

DATA

RAINFALL, AIR

TEMPERATURE AND

SOIL MOISTURE

STORAGE

DIGITIZED SOIL

MAP

AND SOIL

ATTRIBUTE TABLE

LAND USE AND

CROPPING

PATTERN MAPS

WATER SURPLUS/

DEFICIT MAP

Maps of soil parameters

affecting desertification

processes such as soil

texture, salinity, erosion,

available soil moisture

Assigning numeric values

to Thematic classes based

on desertification risk







DESERTIFICATION

STATUS INDEX

DESERTIFICATION

STATUS MAP


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5.3 Characterization of Desertification Status Using Satellite Derived Biophysical

Parameters

In this study various MODIS biophysical parameters derived from May, 2004 to

April, 2005 data products viz. albedo, vegetation indices (NDVI, EVI), land surface

temperature (LST), LAI (Leaf Area Index), NPP (Net Primary Productivity) etc. are used

for characterizing district wise and desertification status zone wise bio-physical

conditions for the current crop seasons. The methodology of this analysis is shown in

Fig. 5.3: Methodology for computation of district and desertification status zone

wise area weighted values of biophysical parameters.

DOWNLOADING MODIS DATA

MAY 2004-APRIL 2005

IMPORT TO ERDAS

Layer Stack,

Max. Computation,

Image re-projection,

Extraction of study area,

Multiplying with scale factor

LAI/FPAR

8 day

com osite

LST

8 day

com osite

Surface

Reflectance

8 day composite

NDVI/EV

I

Monthly

BRDF/Albedo

16 day composite

Digitized district map

of the study area

Monthly images of biophysical

Parameters of the study area

Desertification status

Zones map

Computation of district wise and

Desertification zones wise

Weighted average values of

bio-physical parameters


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5.4 MODIS Data Algorithm

MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument

on-board the Terra (EOS AM) and Aqua (EOS PM) satellites. MODIS has a viewing

swath width of 2,330 km and views the entire surface of the Earth every one to two days.

Its detectors measure 36 spectral bands between 0.405 and 14.385 m, and it acquires

data at three spatial resolutions -- 250m, 500m, and 1,000m. There are 44 standard

MODIS data products that scientists are using to study global change.

5.4.1 ALBEDO

The amount of solar radiation (0.4 4.0m) reflected by a surface is characterized

by its hemispherical albedo, which may be defined as the reflected radiative flux per unit

incident flux. Surface albedo is an important parameter used in global climatic models to

specify the amount of solar radiation absorbed at the surface. Moreover, variations in

surface albedo can serve as diagnostic of land surface changes and their impact on the

physical climatic system can be assessed when routinely monitored surface albedo is used

in climatic model. Albedo also has potential utility for land surface changes, climate

model, also monitoring crop growth, prediction of crop yield, and monitoring

desertification.

Due to its three-dimensional structure, the Earth's surface scatters radiation

anisotropically, especially at the shorter wavelengths that characterize solar irradiance.

The Bidirectional Reflectance Distribution Function (BRDF) specifies the behavior of

surface scattering as a function of illumination and view angles at a particular

wavelength. The albedo of a surface describes the ratio of radiant energy scattered

upward and away from the surface in all directions to the downwelling irradiance incident

upon the surface. The completely diffuse bihemispherical (or white-sky) albedo can be

derived through integration of the BRDF for the entire solar and viewing hemisphere,

while the direct beam directional hemispherical (or black-sky) albedo can be calculated

through integration of the BRDF for a particular illumination geometry. Actual albedos

under particular atmospheric and illumination conditions can be estimated as a function of

the diffuse skylight and a proportion between the black-sky and white-sky albedos.


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Every 16 days, the MODIS BRDF/Albedo Product algorithm relies on multidate,

atmospherically corrected, cloud-cleared data and a semiempirical kernel-driven

bidirectional reflectance model to determine a global set of parameters describing the

Bidirectional Reflectance Distribution Function (BRDF) of the land surface. These one-

kilometer gridded parameters are then used to determine directional hemispherical

reflectance ("black-sky albedo"), bihemispherical reflectance ("white-sky albedo"), and

nadir BRDF-adjusted reflectance (NBAR) for seven narrow spectral bands and (in the

case of albedo) three broad bands (MODIS channels 1-7) and three broad bands (0.3-

0.7m, 0.7-5.0m, and 0.3-5.0m) at the mean solar zenith of local solar noon). Since the

parameters of the simple kernel-based BRDF model Ross Thick Li SparseR are also

provided, along with extensive quality information, the MODIS BRDF/Albedo Product

offers members of the global remote sensing and modeling community the additional

flexibility to derive reflectance and albedo measures particularly suited to their specific

applications.

5.4.2 LAND SURFACE TEMPERATURE (LST)

It is the skin temperature of the land surface i.e. kinetic temperature of the soil

plus the canopy surface (or in the absence of vegetation, the temperature of the soil

surface). Surface temperature can be used for various agro-meteorological applications

Surface heat energy balance study

Characterization of local climate in relation with topography and land use

Mapping of low temperature for frost conditions (night-time) or winter cold episodes

(day/night)

MODIS Land Surface Temperature (LST) products provide per-pixel temperature

values. Temperatures are extracted in Kelvin with a view-angle dependent algorithm

applied to direct observations. The view angle information is included in each LST

product. The LST algorithms use MODIS data as input, including geolocation, radiance,

cloud masking, atmospheric temperature, water vapor, snow, and land cover. The

temperature products in turn are key inputs to many of the high-level MODIS products

and provide data for global temperature mapping and change observation. On land, soil

and canopy temperature are among the main determinants of the rate of growth of

vegetation and they govern seasonal start and termination of growth. Hydrologic

processes such as evapotranspiration and snow and ice melt are highly sensitive to surface


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temperature fluctuation, which is also an important discriminating factor in classification

of land surface types.

5.4.3 LEAF AREA INDEX (LAI)

Leaf area index (LAI) is ratio of the total area of all leaves on a plant to the area of

ground covered by the plant and is viewed as an important variable of vegetation

function. The LAI measures the surface involved in radiation absorption and turbulent

transfers between vegetation and the atmosphere. The LAI is a controlling parameter for

air-surface exchange processes associated with the canopies. This variable is a key

variable for models of evapotranspiration and photosynthesis at the regional and global

levels. The LAI and surface optical properties such as soil and leaf reflectances are also

important variables in the study of radiation processes involving the surface albedo andradiation budget. As the LAI shows high variability even within a vegetation type, it is

therefore difficult to prescribe a priori values for the different biomass.

MODIS LAI product is 1 km global data products updated once each 8-day period

throughout each calendar year. LAI defines an important structural property of a plant

canopy as the one sided leaf area per unit ground area. These products are derived from

the atmosphere corrected surface reflectance product, land cover product and ancillary

information on surface characteristics using a 3D radiative transfer model. LAI and FPAR

are biophysical variables which describe canopy structure and are related to functional

process rates of energy and mass exchange. Both LAI have been used extensively as

satellite derived parameters for calculation of surface photosynthesis, evapotranspiration,

and annual net primary production. These products are essential in calculating terrestrial

energy, carbon, water cycle processes, and biogeochemistry of vegetation.

5.4.3 NDVI and EVI

Several indices, which could be used, amongst the others, for desertificationmonitoring, have been developed over the past few decades using remote sensing data.

They are calculated from the reflectance in different bands and may be obtained for each

pixel (the size of a pixel depends upon the resolution of a sensor). These indices have a

few advantages over conventional climate-data related indices, as they "cover" large areas

and may show how desertification process is progressing over the area.


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An improved, or enhanced vegetation index (EVI) gives complementary

information on the spatial and temporal variations of vegetation, while minimizing much

of the contamination problems present in the NDVI, such as those associated with canopy

background and residual aerosol influences. Whereas the NDVI is chlorophyll sensitive

and responds mostly to red band variations, the EVI is more NIR sensitive and responsive

to canopy structural variations, including leaf area index, canopy type, and canopy

architecture. NDVI and EVI are calculated as

)/()( REDNIRREDNIRNDVI =

Where is the reflectance in the near infra-red & red bands respectively.

NDVI ranges form -1 to 1. (Jordan, 1969; Deering, 1978;Tucker,1979).

LCCGEVI

BLUEREDNIR

REDNIR

++

=

***

21

MODIS Vegetation Index product uses, as input, the 16 days MODIS Vegetation

Index and composited surface reflectance product. All available 16 days MODIS VI

products (a maximum of 3) that overlap the calendar month are used. A temporal

averaging scheme is used to generate the monthly product. Each 16-day product is

weighted by the number of actual days that overlap the month being processed. Two

vegetation index (VI) algorithms are produced globally for land. One is the standard

normalized difference vegetation index (NDVI), which is referred to as the "continuity

index" to the existing NOAA-AVHRR derived NDVI. The other is an 'enhanced'

vegetation index with improved sensitivity into high biomass regions and improved

vegetation monitoring through a de-coupling of the canopy background signal and a

reduction in atmosphere influences. The two VIs compliment each other in global

vegetation studies and improve upon the extraction of canopy biophysical parameters.

The compositing method used is a simple temporal averaging scheme adjusted for

temporal overlap. The algorithm will produce the monthly surface reflectance first from

NIR -NIR Reflectance; 1C -Atmosphere Resistance Red Correction Coefficient;

RED -Red Reflectance; 2C - Atmosphere Resistance Blue Correction Coefficient;

BLUE

-Blue Reflectance; L-Canopy Background Brightness Correction Factor;

G-Gain factor;


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the sixteen-day composite surface reflectance (red, NIR, blue and MIR) in the MOD13A2

product, then derives the VI (NDVI/EVI) products. No sixteen-day VI data is used in this

product, only the surface reflectance. A worst-case scenario is used to generate the per-

pixel quality information. The gridded vegetation indices will include quality assurance

(QA) flags with statistical data that indicate the quality of the VI product and input data.

Due to their simplicity, ease of application, and widespread familiarity, vegetation indices

have a wide range of use within the user community. Some of the more common

applications may include global biogeochemical and hydrologic modeling, agricultural

monitoring and forecasting, land-use planning, land cover characterization, and land

cover change detection.


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CHAPTER-IV

6.0 RESUTS AND DISCUSSIONS

6.1 Crop and Land Use Inventory

Crop and land use inventory of Kharif and Rabi crop seasons were prepared by

digital classification of temporal satellite data. The FCC and classified image of Kharif

season of the study area are presented in Fig. 6.1 and Fig. 6.2, respectively. 14 types of

crop and land use / land cover classes were identified by digital classification of WiFS

data. The classification accuracies obtained for dominant land cover classes are Bajra

(pearl millet) 85.4%; Other crops (soybean, guar etc.) 78.5%; Current Fallow Land

85.7%; Sandy / Sand Dunes 88.35%; Other wasteland (Undulating uplands, Gullied /

Ravinous & Hills (Barren /Rocky) 80%; Forest Dense 88.7%; Forest Open 93.1% and

Water Body / River 96.8%. The district wise areas under various land use / land cover

classes of Kharif crop season are presented in Table 6.1(a) and 6.1(b)

Bhilwara district has highest area (5496 sq. km and 14.6% of 37590.19 sq.km the

total area) under Bajra crop followed by Udaipur district. Baran and Jhalawar districts

have less area 15.59 sq.km. (0.04%) and 15.24 sq. km (0.04%) respectively, under Bajra.

Banswara district has highest area 517 sq.km. (27.5%) under other crops (soybean, guar

etc.). Bhilwara, Dausa, Sawai Madhopur districts have very less area 0.71 sq. km.; 0.78 sq

km. and 0.18 sq. km. Respectively, under other crops. Alwar, Bharatpur , Jaipur and Tonk

districts have large area 5182.1 sq.km., 4022.2 sq.km, 5019.1 sq.km and 4783.25 sq.km

respectively, under current fallow condition. Banswara, Chittaurgarh, Rajsamand, Shirohi

and Udaipur districts have relatively large area under dense forest. Ajmer, Dausa, Karauli

Tonk districts have very little or nil area under dense forest.

Alwar, Baran, Chittaurgarh, Jhalawar, Kota districts have relatively large area

under open forest. Udaipur (1555.24 sq.km), Chittorgarh (969.87 sq.km), Rajsamand

(599.19 sq.km), Bhilwara (499.27 sq.km) districts have large land under gullied/ravenousland. Respectively Dhaulpur (0.07 sq.km) and Karauli (1.98 sq.km) district have very less

area under gullied land. Baran (1232.52 sq.km), Karauli (1079.87 sq.km) and Alwar

(928.03 sq.km) districts have large area under hills and barren/rocky area. Pali (671.15

sq.km) and Sirohi (334.99 sq.km) have area under the salt-affected land. Ajmer (1232.02

sq.km) and Rajsamand (1145.39 sq.km) districts


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Fig. 6.1 FCC of study area (Kharif season)

Fig. 6.2 Classified image showing crop and land use classes of Kharif season

LEGEND

0 40 80 120 16020Kilometers

NCLASSIFIED IMAGE OF KHARIF SEASON


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Table 6.1(a): Area statistics of land use / land cover classes of Kharif season (s

1 AJMER 2528.83 6.73 1147.97 2.43 0.00 128.23 1.97

2 ALWAR 369.84 0.98 5182.14 10.98 28.81 0.99 1094.71 16.78

3 BANSWARA 1263.16 3.36 761.13 1.61 107.59 3.71 500.44 7.67

4 BARAN 15.59 0.04 2615.20 5.54 81.82 2.82 708.15 10.85

5 BHARATPUR 232.35 0.62 4022.22 8.52 2.40 0.08 74.33 1.14

6 BHILWARA 5496.06 14.62 1367.22 2.90 0.78 0.03 397.51 6.09

7 BUNDI 1079.76 2.87 2273.46 4.82 25.52 0.88 164.17 2.52

8 CHITTAURGARH 2378.05 6.33 2480.34 5.25 457.10 15.76 1108.53 16.99

9 DAUSA 708.40 1.88 1628.97 3.45 0.00 73.02 1.12

10 DHAULPUR 776.37 2.07 1260.33 2.67 3.29 0.11 156.82 2.40

11 DUNGARPUR 2784.33 7.41 139.01 0.29 30.89 1.06 35.27 0.54

12 JAIPUR 3296.43 8.77 5019.09 10.63 15.34 0.53 224.36 3.44

13 JHALAWAR 15.24 0.04 3530.94 7.48 35.56 1.23 680.09 10.42

14 KARAULI 573.49 1.53 2136.08 4.52 0.00 113.42 1.74

15 KOTA 352.21 0.94 3783.33 8.01 35.87 1.24 381.86 5.85

16 PALI 4326.21 11.51 883.53 1.87 113.98 3.93 23.01 0.3517 RAJSAMAND 2457.96 6.54 72.10 0.15 311.49 10.74 59.55 0.91

18 SAWAI MADHOPUR 361.39 0.96 3309.97 7.01 0.28 0.01 228.15 3.50

19 SIROHI 2784.79 7.41 231.53 0.49 146.75 5.06 42.09 0.65

20 TONK 651.14 1.73 4783.25 10.13 0.00 171.88 2.63

21 UDAIPUR 5138.59 13.67 580.88 1.23 1503.78 51.83 159.68 2.45 1

Sum 37590 100 47209 100 2901 100 6525 100

Bajra(%)

C.Fallow(%)

Forest(Dense)(%)

Forest(Open)(%)

Forest(Dense)

Forest(Open)

No

District Name

C.Fallow

Bajra


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have highest undulating upland (barren scrub) area. Chittorgarh (36.58 sq.km) and

Alwar (61.32 sq.km) districts have very less undulating upland. Pali district has highest

sandy and sand dunes (barren) area 4092.98 sq.km. and 43.3% of 9452.51sq.km (the total

area), followed by Ajmer (1850.01 sq.km), Bhilwara (622.23 sq.km), Banswara (0.28

sq.km) and Alwar (1.98sq.km) which have very less sandy area.

The FCC and classified image of Rabi crop season are presented in Fig. 6.3 and Fig

6.4, respectively.16 number of land use / land cover classes were delineated by digital

classification of Rabi season data. The district wise areas under various land use / land

cover classes of Rabi crop season are presented in Table 6.2(a) and 6.2(b).

The classification accuracies of Rabi season obtained for dominant land cover

classes are Wheat 95%; Mustard 97%; Current Fallow Land 80%; Sandy / Sand Dunes

(Barren) 88.35%; Other wasteland (Undulating uplands, Gullied / Ravinous & Hills(Barren /Rocky) 80%; Forest Dense 88.7%; Forest Open 93.1% and Water Body / River

94%. Alwar district has highest area under Wheat crop 2063.17 sq. km and 23.3% of

8836.02 sq.km the total area, followed by Jaipur district (1396.3sq.km). Rajsamand and

Sirohi districts have less area 10.6 sq.km and 20.04 sq.km respectively, under wheat,

respectively. Bharatpur district has highest area under mustard crops 3246.56 sq.km. and

13% of 24575.58sq.km the total area, followed by Alwar 2705.30sq.km, Kota-

2326.62sq.km, Jaipur-2176.52sq.km. Pali, Rajsamand, Sawai Madhopur, districts have

very less area under mustard crops 1.91 sq. km.; 5.23 sq km. and 6.57 sq. km,respectively. Udaipur, Jaipur, Tonk, Chittorgarh districts have large area under current

fallow condition 4759.49 sq.km, 4667.21 sq.km, 528.43 sq.km, and 3157.50 sq. km.,

respectively. Pali district has highest sandy and sand dunes (barren) area 6533.44 and

34.3% of 19021.97sq.km the total area, followed by Ajmer (3165.3 sq.km), Bhilwara

(2226.28 sq.km), Alwar (2.23 sq.km) and Bharatpur (1.94sq.km).

6.2 Cropping pattern

Cropping pattern map was prepared by GIS aided integration of digitally

classified Kharif and Rabi crop inventories maps.. Digitally classified land use /land

cover maps of Kharif and Rabi seasons were logically integrated in GIS for deriving


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Fig. 6.3 FCC of study area (Rabi) season

LEGEND

0 40 80 120 16020

Kilometers

N

Fig. 6.4 Classified image showing crop and land use classes of Rabi season

CLASSIFIED IMAGE OF RABI SEASON


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Table 6.2(a): Area statistics of land use / land cover classes of Rabi season (sq

1 AJMER 2072.57 4.76 0.00 128.23 1.97 241.29 4.93

2 ALWAR 785.38 1.80 28.81 0.99 1094.71 16.78 3.78 0.08

3 BANS WARA 1313.55 3.02 107.59 3.71 500.44 7.67 95.25 1.94

4 BARAN 1071.72 2.46 81.82 2.82 708.15 10.85 90.55 1.85

5 BHARATPUR 526.85 1.21 2.40 0.08 74.33 1.14 0.39 0.01 6 BHILWARA 3946.97 9.06 0.78 0.03 397.51 6.09 497.25 10.15

7 BUNDI 1517.01 3.48 25.52 0.88 164.17 2.52 108.12 2.21

8 CHITTAURGARH 3150.25 7.23 457.10 15.76 1108.53 16.99 987.33 20.16

9 DAUSA 794.12 1.82 0.00 73.02 1.12 8.98 0.18

10 DHAULPUR 838.29 1.92 3.29 0.11 156.82 2.40 0.07 0.00

11 DUNGARPUR 2279.09 5.23 30.89 1.06 35.27 0.54 117.20 2.39

12 JAIPUR 4645.32 10.66 15.34 0.53 224.36 3.44 55.81 1.14

13 JHALAWAR 2384.92 5.47 35.56 1.23 680.09 10.42 12.51 0.26

14 KARAULI 1012.14 2.32 0.00 113.42 1.74 2.72 0.06

15 KOTA 924.80 2.12 35.87 1.24 381.86 5.85 156.43 3.19

16 PALI 2720.50 6.24 113.98 3.93 23.01 0.35 95.08 1.94

17 RAJSAMAND 1511.95 3.47 311.49 10.74 59.55 0.91 592.19 12.09

18 SAWAI MADHOPUR 2021.86 4.64 0.28 0.01 228.15 3.50 8.02 0.16

19 SIROHI 1785.57 4.10 146.75 5.06 42.09 0.65 152.33 3.11

20 TONK 3498.90 8.03 0.00 171.88 2.63 120.66 2.46

21 UDAIPUR 4761.22 10.93 1503.78 51.83 159.68 2.45 1552.63 31.70

SUM 43563 100 2901 100 6525 100 4899 100

No

District Name

C.Fallow

C.Fallow(%)

Forest(Dense)(%)

Forest(Dense

)

Forest(Open)

Gullied/RavinousLand

Forest(Open)(%

)

Gullied/RavinousL

and(%)


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Table 6.2(b): Area statistics of land use / land cover classes of R

j02 tuul beny friend project report

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