Chapter - III LANDUSE / LANDCOVER

Transcription

1 3.1 Introduction Chapter - III LANDUSE / LANDCOVER 3.2 Brief Literature Review 3.3 Database 3.4 Landuse / Landcover Classification Scheme 3.5 Methodology 3.6 Landuse / Landcover Changes in Global Regional Level 3.7 Land use / Land cover of Base Year 3.8 Spatio-Temporal Distribution of Landuse / Landcover (1976, 1991, 2000 & 2006) 3.9 Statistical Analysis of Landuse / Landcover data 3.10 Conclusion References Web-References Appendix- 3.1 to

2 CHAPTER - III LANDUSE / LANDCOVER 3.1 Introduction The first thing people ever used to meet their basic needs was land to feed themselves, to move around and to settle. Hence, the relationship of people with land is as old as man. When the users of land decided to utilize it for different purposes, land use / land cover change occurs producing both desirable and undesirable impacts. The analysis of land use / land cover change is essentially the analysis of changing relationship between people and land. The use to which we put land could be grazing, agriculture, urban development, and mining among many others. Land use is the way in which, and the purposes for which, human beings employ the land and its resources e.g. farming, mining, lumbering, etc. Land cover describes the physical state of the land surface i.e. cropland, forests, wetland, water bodies among others. Globally, land cover today is altered principally by direct human use such as agriculture and livestock grazing, forest harvesting, urban as well as suburban construction and development activities (Meyer, 1995). The global area of land is estimated at 13,000 billion hectares. A detail analysis of global land cover reveals that slightly less than onethird is forest cover, two-thirds is farm land, the rest occupies the habitat i.e. rural and urban area along with zones unfavorable for agriculture (Datta, 2003). Comprehensive information of land use and land cover is the basic pre-requisite for land resource evaluation, management, planning and environmental assessment. Monitoring land use and land cover changes is important to check both the positive and negative impacts on the region. According to Meyer (1995) Land use and land cover are distinct yet closely linked characteristics of the earth s surface and there is no standard universally accepted set of categories for classifying land by either use or cover; rather it depends on a number

3 of multi-linked factors. The land use and land cover contains broad range whereas each region has its own characteristics and specific land use therefore number of classification systems are developed. The study of land use / land cover change will be characterized by integrated, interdisciplinary approaches. This task is expected to be facilitated greatly by further advances in the systems of data collection, compilation and management (Briassoulis, 2000). In this connection Geoinformatics can play an important role. Miraj tahsil is comparatively one of the developed tahsils of Sangli District, where the evidences of progress of urbanization, industrialization can be noticed. There are certain transformations taken place with which the process of development causing positive as well as some negative changes. Therefore, it is intended to map out the status of land use/land cover of Miraj tahsil between 1971 and The studies have proved that, use of remote sensing and Geographical Information System is an effective tool in the study of landuse and land cover. In this chapter, an attempt is made to study the landuse / landcover applying Geoinformatics technology and the grass root level realities are analyzed. 3.2 Brief Literature Review The roots of landuse study can be traced, long back in the historical era. George Perkins Marsh in the U.S.A. and J.H. Von Thunen in Germany are the most well known pioneers in the study of landuse change. They approached the issue of landuse change from different perspectives and in different continents (Briassoulis, 2000). In India, several geographers have paid attention on different aspects of land use studies at regional, district and micro levels. Some of the eminent researchers who have carried out research work on different aspects of land use studies are Chatterjee (1952), Shafi (1968), Prakasha Rao (1959), Singh (1974), Mishra (1990), Jainendra Kumar (1986) and Shinde S.D. (2002), etc

4 Nathawat and Pandey (2002), carried out a study on landuse - landcover mapping of Panchkula, Ambala and Yamunanger districts, Haryana State in India. They have observed that landuse - landcover patterns in the area are generally controlled by agro-climatic conditions, ground water potential and hosts of other factors like irrigation facilities, soil characteristics, socio-economic status and demography. Patrick, Ole and Eike (2005) studied landuse system with agricultural policy formation through GIS based modeling. According to Lillesand and Kiefer (1987) the term land use relates to human activities associated with specific piece of land, while land cover relates to the type of features present on the earth s surface. Bektas and Goksel (2006) used remote sensing and GIS in order to analyze landcover of Gökceada Island, Turkey, by using Landsat 7 ETM data with slope and aspect data. In this study use of Digital Image Processing (DIP) techniques and Digital Elevation Mode (DEM) is made for urban planning as well as to identifying problematic areas of the Gökçeada Island. Opeyemi (2006) examined the use of GIS and remote sensing in mapping Landuse / Landcover in Ilorin, (capital of Kwara State of Nigeria) between 1972 and 2001 to detect the changes that have taken place. Boriah et al., (2008) have performed a new change detection technique for the landcover change detection, especially for remote sensing data. Land is national resource in this aspect Chaudhary, Saroha and Yadav (2008) have studied human induced landuse / landcover changes in Northern part of Gurgaon District, Haryana, India. 3.3 Database To accomplish this study both primary and secondary datasets are used. The useful datasets from Regional Planning Report, Survey of India

5 Toposheets, Satellite Images, GPS readings, etc are used for base mapping. Table 3.1 Data Product Information Details Data Product Date Path / Row Source Spatial Resolution Landsat MSS 19-Jan /048 GLCF 68 m X 83 m Landsat TM 25-Jan /048 GLCF 30 m (120 m - thermal) Landsat ETM + 25-Nov /048 GLCF 30 m (60 m - thermal, 15-m pan) Landsat ETM + 22-Nov /048 GLCF 30 m (60 m - thermal, 15-m pan) Digital Globe Data 2006 Variable Digital Globe 1 Mtr Google Earth Images Variable Variable Google 1 15 Mtr (Variable) Source: Compiled together based on the data-product information manual. The utilized satellite images are the product of different satellites, sensors. The time period for the present study is from 1971 to 2006 hence as per the availability of datasets it is used in the study. 3.4 Landuse / Landcover Classification Scheme Based on the priori knowledge of the study area, a general classification scheme is developed based on the NRSA classification. Table 3.2 Landuse / Landcover Classification Scheme Code Class (Level I) Sub-Class (Level II) 1 Built-up Area 1.1 Town / Cities (Urban) 1.2 Villages (Rural) 2 Agriculture 2.1 Single Crop(Kharif or Rabbi) 2.2 Double or Triple Crops 2.3 Fallow Land (Without Crop) 3 Forest (Natural Vegetation) 4 Wastelands 4.1 Salt affected 4.2 Other (Waterlogged, Land with and without Scrub, Barren Rocky, Stony Waste, etc) 5 Water Bodies 5.1 River / Stream 5.2 Canal 5.3 Lakes / Reservoirs /Tanks Source: (LU/LC) classification scheme NRSA, 1995 with modifications In the present study the classification categorization is designed based on the classification scheme developed by National Remote Sensing Agency (NRSA) in 1995 (Table-3.2). The modification in the

6 categories is made by keeping in view about the condition of area under investigation. The Agriculture includes land under agricultural crops during Kharif, Rabi (both irrigated + un-irrigated) and the area under double / triple crop (cultivated during Kharif, Rabi as wall as in summer seasons). The fallow land is land without crop. 3.5 Methodology The landuse maps of 1971 given in Regional Planning Report are referred for mapping i.e. scanned, geo-referenced and digitized in various layers. The past land use / land cover information was gathered from available records along with informal interviews with the local people. The mapping exercise was carried out on these temporal datasets and the generated vector layer was overlaid upon each other to find the changes. The change matrix is generated to ensure and map the changed as well as unchanged areas. This technique was found to be more efficient and convenient to compute the changes and producing statistics of temporal land use changes (Table 3.3). The extensive ground truth observation is carried out, at the same time using recent satellite photos and observed ground reality an interpretation key is developed. Same key is used during mapping task. The visual interpretation technique is also adopted to understand landuse / landcover of the study area. The satellite images are captured from Digital Globe server (Pictorial dataset) and mosaic into big plates. Based on merged images, detail landuse mapping of 2006 is carried out. The comparative analysis is made in these two datasets to understand the actual change occurred in the study area. To check intermediate changes, digital landuse / landcover mapping is done with the help of Landsat satellite images. The available multiple bands of a satellite image are merged together and final image is created. Initially during pre-processing of satellite data, image enhancement is carried out. (An enhancement technique is the process to

7 increase visual distinctions between features of satellite data). After that geometric correction is carried out for the non rectified images; then radiometric correction is done on all images. (A geometric correction is a process to georeference the image with real world coordinates. Radiometric correction is used to remove the distortion and noise i.e. removing errors occurred due to intervening atmosphere, sun-sensor geometry and sensor itself.) In second stage, image classification task is performed. In this study hybrid image classification approach is adopted, in which initially both unsupervised and supervised classifications are done and then manual or visual approach is applied. (Normally manual classification is applied when analyst is well familiar with the area being classified, for that image interpretation elements are used to identify the different land use / land cover classes and its mapping). The Hybrid approach is adopted because hybrid approach combines the advantages of the automated and manual methods to produce a land use / land cover map. In hybrid approach first automated classification methods are applied to do an initial classification and then use manual methods to refine the classification and correct the occurred errors. At the last stage accuracy assessment of classification was performed. The problematic areas were verified by ground truth observation using GPS and then collected GPS points are overlaid on the imagery for refinement of the landuse / landcover map. The accuracy assessment of classification was calculated using a error matrix (Lillesand and Kiefer, 1987). for that maximum number of pixels are randomly selected and checked with real ground truth. The overall accuracy and a Kappa analysis were used to perform a classification accuracy assessment based on error matrix analysis. 3.6 Landuse / Landcover Changes in Global Regional Level Several scholars have worked on the changes occurred in landuse on global to regional scale. The statistical data on global-regional land

8 use along with population change shows the direction of transformation occurred in last few decades. As per the study made by Grubbler, et al., (1990) and Briassoulis (2000) major changes have taken place in three main land use categories i.e. forest, grassland and cropland (Appendix 3.1). The forest land has been decreasing day by day at the same time the cropland area is increasing. Continuously increasing population has resulted to urbanization and is another major reason to declining forest area and change in landuse. As per the study and statistics given by Grubbler up to 1980 forest land has decreased by 8.71 and cropland is increased by 9.26 on global scale. National Natural Resources Management System (NNRMS) of Department of Space (DOS), Government of India has done the assessment of LULC at National Level. The report of this study emphasis that, landuse / landcover system in India exhibit high degree of spatial and temporal variations due to the influence of climate and local land use practices on agriculture, reclamation activities of lands, etc. Total Net Sown Area (NSA) during different cropping seasons of is estimated as Mha i.e per cent of total geographical area (TGA) of the country. The double cropped area is estimated as 47.67Mha. The area under forest was and under fallow land. The seasonal cropping pattern contributes by rabi, by kharif, 4.40 under plantation and of double / triple crops. The study made by Jagtap, Patwardhan and Sharif (2006) highlights that the NSA and Gross Cropped Area (GCA) of Maharashtra state shows significant increasing trend. Also the average landuse of Pune division (1970 to 1998) indicates NSA is 58, forest 9, barren land 8 to total geographic area. The percentage of NSA is continuously increasing and possible causes for this are availability of black cotton soil, increasing irrigation facilities and use of modern technology in the

9 farming. Moreover population pressure is also causing a compelling situation to bring extra land under cultivation to meet the present needs. Patil Prasanna (2002) studied trends of general landuse in Sangli district. In the land under agriculture was 84.85, under nonagricultural use and still 4.51 land was containing potential for agriculture. This condition gets changed in i.e. nonagricultural land becomes (increased by 2.20) whereas agricultural land becomes (decreased by 3.87). The problem of saline land is one of the reasons to decrease land under agriculture. The ever increasing urbanization is another reason for increasing nonagriculture area. The NSA was 70.23, which decreased by 8.65 and reached to 61.58, but at the same time there is considerable rise (6.47) in double cropped area, it was 2.13 and reached to Not only this jowar has decreased by and sugarcane has increased by The local level trend in landuse / landcover change for Miraj tahsil is examined by adopting various methods. Here the attempt is made to check the grass root level changes occurred in landuse / landcover of Miraj tahsil. General Landuse / Landcover In this section an attempt is made to classify and to map out the different types of land cover and land uses along with the magnitude of changes that have taken place. To understand the rate, trend and magnitude of change in landuse / landcover of Miraj tahsil the study is carried out in three ways. The initial part deals with actual changes that have taken place in chosen time window i.e. actual two base years (1971 and 2006). Then Landsat satellite dataset is used to check the intermediate Landuse / Landcover conditions. In last phase detailed statistical database is utilized to check grass-root level variations in the classes of LU/LC

10 3.7 Land Use / Land Cover of Base Year Detection of changes in the land use / land cover involves use of at least two period data sets (Jenson, 1986). Therefore, in this section the landuse / landcover condition of 1971 and 2006 is understood Land use and land cover condition (1971) The LU/LC classification of NRSA is modified (Table 3.2) considering the need of study and study area. The mapping of landuse / landcover for the year 1971 was a critical task. It is accomplished by using various available datasets such as Regional Planning reports, Toposheets and other datasets. Table-3.3 represents the detailed statistics of land under each category. Most of the land in Miraj tahsil is under agricultural use. The western part of tahsil is benefited by the Krishna and Warana rivers. The irrigation schemes functioning in this area have supported the establishment of commercial farming. The total length of the river course in Miraj tahsil is about 94 km, out of which km is of the Krishna River and km is along the Warana River. The landuse / landcover map of 1971 for Miraj tehisl is shown in the Figure 3.1. The maximum area is under agriculture (76.20 ) out of which, the area under single crop cultivation occupies (55106 ha) (Table-3.3), wherein land under multiple crops covers (15493 ha). The land under fallow land category covers 8.24 (7637 ha) area. Around 2.68 of the total area of tahsil is under Built-up category, particularly urban built-up occupies 1.49 (1379 ha) and 1.20 (1108 ha) by rural area. Forest or Natural Vegetation occupies 1.00 (924 ha) of the total area. About (9851 ha) land is under waste land category, the problem of saline land was not prominent in 1971, hence its figures are not available. The water body class covers 1.25 (1157 ha) area out of that river and Streams cover 1.13 (1048 ha) area, while Tanks/Lake occupy 0.12 (109 ha) land of Miraj tahsil

11 ³ Miraj Tahsil Landuse / Landcover (1971) Urban (City) Rural (Village) Single Crop Multi Crops Fallow Land Legend Waste Land River / Stream Tank / Lake Natural Vegetation Scale km Fig.3.1 Source: Regional Planning Report and SOI toposheets

12 Chapter-III Land use and land cover condition (2006) ³ Urban (City) Rural (Village) Single Crop Multi Crops Fallow Land Legend Natural Vegetation Saline Land Other Waste Land River Canal Tank / Lake Group of Houses Miraj Tahsil Landuse / Landcover (2006) Scale km Fig

13 The landuse / landcover map of 2006 is prepared with the help of high resolution satellite images. Since the images are of 1 metre spatial resolution all most all features are clearly identified and mapped accordingly. The landuse / landcover map of year 2006 is shown in Fig 3.2. This map (Fig.3.2) represents eleven categories of landuse and landcover for Miraj tahsil. The total built-up in 2006 was 9.98 (9166 ha) area, in which urban area occupied 6.21 (5753 ha) and the rest 3.68 (3413 ha) covered by rural settlements. In 2006 also agriculture occupied maximum area of the tahsil i.e. out of total land (55779 ha) land was under agricultural use, in which (30326 ha) land was utilized for single cropping pattern whereas (25453 ha) used for multiple cropping (Table-3.3). About (22023 ha) land is waste, which is grouped into two categories i.e. saline waste and other waste. Out of the total area 3.88 (3595 ha) is salt affected land and (18428 ha) is other waste such as barren land, rocky, stony waste, with and without scrub, etc. The area under natural vegetation class occupies only 1.23 (1136 ha) land. The water body class occupied 1.48 (1396 ha) area out of which, river and streams are spread over 1048 ha land (1.13), while canal covers 175 ha (0.19) area, and 0.16 (146 ha) was occupied by tanks/lakes Land use and land cover change: Trend, rate and magnitude An important aspect of change detection is to determine, which is actually changing to what extent, in other words to check, which landuse class is changing. The outcome results will reveal both the desirable and undesirable changes along with relatively stable categories overtime. This information works as a vital tool in management decisions (Opeyemi, 2006). In this section the attempt is made to check the trend, rate and magnitude of changes in landuse / landcover of Miraj tahsil

14 Table 3.3 shows the area in hectares and the percentage change for year 1971 and 2006, measured against each landuse / landcover type. The percentage change is derived by applying the following formula: For obtaining annual rate of change, the percentage change is divided by 100 and multiplied by the number of study years i.e. 35 years (study period ). Table 3.3 represents a positive change especially reduction in single crop area as well as in fallow land, at the same time there is rise in double cropped area. In the time span of 35 years from 1971 to 2006 many up-and-downs have occurred, which resulted changes in the landuse and landcover of Miraj tahsil. The occurred changes with causes are discussed very precisely, as it comes in the text. Code Table 3.3 Landuse / Landcover Change of Miraj Tahsil Class 1 Built-up Area Actual Change Annual Rate of Land Land Land of Change in Ha in Ha in Ha Change (in ) 1.1 Town / Cities (Urban) Villages (Rural) Agriculture 2.1 Only Single Crop Double or Triple Crop Fallow Land Forest (Natural Vegita.) Wastelands 4.1 Salt affected, Other Water Bodies Observed Changes Percentage of Change = * 100 Sum of Change 5.1 River / Stream Canal Reservoirs / Tanks Source: Computed on the basis of the statistics derived from LU/LC classification of Miraj

15 Categories Chapter-III The built-up area has increased by an average of 2.62 per year and overall occurred change in this class was 7.48 (4374 ha). This is due to the rapid urbanization that has been taken place in Miraj tahsil and other various supporting factors such as the formation of Sangli- Miraj-Kupwad Municipal Corporation, rapid increase in industrial sector, agro-based commercial activities, etc. Along with this, one cannot ignore the ever rising population and its demand for residence. The population of Miraj tahsil has increased by persons in 35 years from 1971 to The urban population has increased by and rural population by persons. Consequently the proportion of built-up has also augmented to accommodate the population. Subsequently, agricultural land especially land under multiple cropping has increased by About 212 ha land has increased in the natural forest category and its percentage change is Considering very little proportion of forest in Miraj tahsil government has declared some parts of the tahsil as reserve forest. Also some additional plantation is carried out, because of that at least on records the proportion of forest is increased by few hectares. Tanks / Lakes Canal River / Stream Trend of Change Other Waste Land Salt Affected 6.17 Natural Vegetation 0.36 Fallow Land Double/Triple Crop Single Crop Villages (Rural) 3.95 Town/City (Urban) 7.50 Actual Change Change in Fig.3.3 Trend of change occurred in the LU/LC categories of the Miraj tahsil

16 During this period about 6.17 waste land is wasted due to saline problem and land of other waste category is increased. The reasons for rise in waste land among others are the encroachments that have been occurring in the marginal areas of urban centres. In water body class there is change of There is no change in the main river course and streams hence this category is without change. In 1971, Miraj tahsil was not having canals but after that some canal irrigation schemes have started due to which about 30 change is recorded. In general, about one per cent land per year is utilized for excavation of canals and water supply. Same time there is no change noticed in tank/lake category. 3.8 Spatio-Temporal Patterns of Landuse / Landcover (1976, 1991, 2000 & 2006) The scientific literature reveals that the direction of digital change is a complicated task to represent accurately and it is affected by spatial, spectral, temporal, and thematic constraints (Singh, 1989). In this section the study of landuse / landcover is conducted on spatiotemporal, Landsat Satellite Image datasets. The changes in land use/ land cover due to natural and human activities can be observed using current and stored (old archives) remotely sensed data (Luong, 1993). The available Landsat data (Table-3.1), which is the product of MSS (Multi Spectral Scanner), TM (Thematic Mapper) and ETM (Enhance Thematic Mapper) sensors are used for analysis. The computer aided digital analysis is essential to make full advantage of the capabilities of remote sensing data to identify and quantify the features. The image classification technique (Hybrid approach) is used for mapping landuse / landcover of the study area Landuse / Landcover Condition of Miraj Tahsil (1976) The Landsat dataset of MSS sensor (1976) is having some problem in its spectral signature therefore its classification is not performed as

17 per the expectations. Especially the built-up area, agricultural land and water body features are having problems. For comparative analysis and representation purpose the information of other sources is overlaid on 1976 data and final output (map) is prepared. This dataset is included in the study because it is one of the oldest dataset available with us hence its best possible use is made in this study. It is necessary to quantitatively assess the accuracy of classification because some of the area delineated and classified as a particular category may not be correct at all locations. Therefore, the classified map is checked by comparing classified data with actual ground data by site visits and with reference to other standard map datasets. This task is carried out with sampling method. The result of accuracy is tabulated in the form of m m matrix, where m is the number of classes under investigation. The base of this matrix is put forth by Jensen in 1986, which is generally known as error matrix or confusion matrix (Lillesand and Kiefer, 2002). The software is generating detailed statistics for each image those are presented in the form of error matrix and accuracy table (Table 3.4). e.g. in first dataset for forest class, 57 samples are chosen. Out of 57, the 54 samples belong to the forest class. The rest 3 samples which were interpreted as forest, 2 samples are belonging to agriculture and 1 in water class. In other words, 3 samples from other classes, which are misclassified and added to the wrong class (i.e. commission errors). On the other hand, ten forest samples were misclassified (to other classes i.e. omission error). Thus 3 samples are added and 10 samples are omitted, which is known as commission and omission errors respectively. Thus the proportion of vegetation classified correctly is 54 / 57. On the basis of error matrix producer and user accuracy is calculated by the software. The term producer s accuracy and user s accuracy is invented by Story and Cogalton in The producer of the classified map is interested in how well a specific area on the earth can be mapped, but at

18 the same time user is interested in how well the map represents what is really on the ground (Lillesand and Kiefer, 2002). Following are the formula to calculate the both accuracy. Number of correctly classified samples of class X PC = Total no of samples in that class seen on ground The overall apparent accuracy of classification is the sum of the diagonal elements divided by the total samples for all classes e.g. 380 / 448 = hence the percentage will be The Kappa (Khat) analysis is a discrete multi-variate technique for accuracy assessment. Kappa analysis yields a Khat statistics that is the measure of agreement of accuracy. The Khat statistics is computed as r Where, = Number of rows in the error matrix The class fallow land represents the land, which is not containing crops i.e. one kind of temporary fallow land due to any reason. The western part of tahsil is under agricultural use because of water availability and well fertile soil. Many private and co-operative irrigation schemes have started between 1960s and 70s, due to that all most entire western Miraj tahsil is under irrigation. The eastern, north-eastern and some patches of central part of waste land are hilly and barren due to topography and water scarcity. r Number of correctly classified samples of class X UC = Total number of samples classified as category X N x ii - N ( x. i+ x ) ^ i+ i=1 i=1 * K = r N 2 - N ( x i+. x i+ ) Xii = The number of observations in row i and column j (On the major diagonal) Xi+ = Total of observations in row i (shown as marginal total to right of the matrix) Xi+ = Total of observations in column i (shown as marginal total at bottom of the matrix) r i=1 N = Total number of observations included in matrix *

19 Chapter-III Class Name Legend Built Up Area Agricluture Vegetation Waste Land Harvesetd (Fallow Land) Table 3.4 Error Matrix and Accuracy Totals of Landsat-MSS (1976) Veget ation Agric ulture River Water Body Waste Land Harve sted Water Body River Built Up Row Total Commi Error Omi Error Vegetation Agriculture Waste Land (Other) Harvested (Fallow) Water Body River Built-Up Area Column Total Fig.3.4 Miraj Tahsil Landuse / Landcover Map (1976) Scale km Class Name Reference Totals Classified Totals Number Totals Producers Accuracy Users Accuracy (K^)Kappa Statistics Vegetation Agriculture Waste Land (Other) Harvested (Fallow) Water Body River Built-Up Area Total Source: Generated by Software, based on calculations done for error and accuracy assessment Overall Classification Accuracy = 84.82, Overall Kappa Statistics = The agriculture in the eastern part is depending on rainfall hence, for its irrigation Mhaisal Lift Irrigation Scheme is planned for this area

20 This classification contains vegetation as an individual class and in classified image it is seen as a major class but in reality the proportion of forest is very less. The vegetation class in classifications is healthy cluster of trees. As per the classification statistics (Table 3.8) (31.14) land is under agricultural use and (30.70) as a waste land. Overall during 1971, the land of Miraj tahsil was under agricultural use Landuse / Landcover Condition of Miraj Tahsil (1991) The second dataset of satellite image is of Thematic Mapper sensor for the year The classified image shows agricultural land in green colour. The brown patches are of harvested land (without any crop). The red patches are depicting distribution of built-up area. In the central part of Miraj tahsil there are many patches of Built-up Area (BUA) which represents the Sangli-Miraj urban area. The rest red dispersed patches are the other residential settlement areas of respective village. For checking the accuracy of the assessment of 1991 dataset about 285 sample references of eight categories are tested. The details of error matrix and accuracy totals are given in Table 3.5. The producer and user accuracies of agriculture class are and respectively. On the contrary, the producer accuracy of river class is very low because of high commission error occurred in the data i.e. 17 reference samples are falling in other class. In waste category the omission error is high i.e. 26 classified samples fall in other category. The Kappa statistics of individual class for this image is in-between 0.62 (salt affected land) to 1.00 (River). The overall Kappa statistics is and overall classification accuracy is In 1990s the proportion of fallow land, harvested and land under agricultural use is more or less same. The built-up land has been gradually increasing but at the same time land patches are converting into saline land

21 Miraj Tahsil Landuse / Landcover Map (1991) Legend Built Up Area Vegetation Agriculture Waste Land Harvested (Fallow Land) Table 3.5 Error Matrix and Accuracy Totals of Landsat-TM (1991) Class Name Class Name Veg etati on Agri cult ure Reference Totals River Water Body Saline Land Waste Land Classified Totals Number Totals Producers Accuracy Users Accuracy (K^)Kappa Statistics Vegetation Agriculture Waste Land (Other) Salt affected Land Harvested (Fallow) Water Body River Built-Up Area Totals Source: Generated by Software, based on calculations done for error and accuracy assessment Overall Classification Accuracy = 85.08, Overall Kappa Statistics = Saline Land Fig.3.5 Harve sted Water Body Scale River Built Up Row Total Commi Error Omi Error Vegetation Agriculture Waste Land (Other) Salt affected Land Harvested (Fallow) Water Body River Built-Up Area Column Total km

22 Table 3.8 shows the details for land under each class along with its percentage to total area. The built-up class has covered 6202 ha land (i.e to total area) and 2.79 per cent under salt affected land Landuse / Landcover Condition of Miraj Tahsil (2000) The huge amount of green patches in 2000 image shows that most of the land is under agriculture. At the same time, the harvested and fallow land is also in considerable proportion. The error matrix and accuracy assessment Table 3.6 of Landsat ETM for the year 2000 highlights that, the producer s accuracy for all eight categories ranging from 72 per cent to 88 per cent whereas user s accuracy is ranging from 62 per cent to 93 per cent. The commission error is varies from 3 to 14 wherein omission error is from 2 to 10. In the accuracy assessment total 257 samples are checked out of which 202 samples are correctly classified into the respective categories. It means 55 samples are placed under wrong classes. The overall classification accuracy is The Kappa statistics of all classes is ranging in-between 0.58 to 0.92 and overall Kappa statistics is The agriculture land of 2000 image is having particular continuous pattern. The land around water bodies is represented as agricultural land particularly in the proximity of river and streams. Around ha land (28.45) is under agricultural use and ha (19.87) under harvested (fallow) category. Also there is rise in built-up area in and around Sangli-Miraj-Kupwad Municipal Corporation. Around the urban built-up the proportion of waste land is increasing in other words day by day urban area is expanding very fast. Total 8732 ha land is under builtup category which becomes 9.42 per cent to total land. The proportion of saline land is also increasing very fast i.e. about 4691 ha land is salt affected, which is 5.06 per cent to total land. It is found that the salinity problems and built-up area are rising, whereas the waste land and land under agriculture remained more or less in the same proportion

23 Miraj Tahsil Landuse / Landcover Map (2000) Legend Built Up Area Agriculture Vegetation Waste Land Harvested (Fallow Land) River Water Body Saline Land Fig.3.6 Scale km Table 3.6 Error Matrix and Accuracy Totals of Landsat ETM+ (2000) Class Name Veg etat ion Agri cult ure Waste Land Saline Land Harve sted Water Body River Built Up Row Total Commi Error Omi Error Vegetation Agriculture Waste Land (Other) Salt affected Land Harvested (Fallow) Water Body River Built-Up Area Column Total Class Name Reference Totals Classified Totals Vegetation Agriculture Waste Land (Other) Salt affected Land Harvested (Fallow) Water Body River Built-Up Area Totals Source: Software Generated, based on calculations of error and accuracy assessment Overall Classification Accuracy = 78.60, Overall Kappa Statistics = Number Totals Producers Accuracy Users Accuracy (K^)Kappa Statistics

24 3.8.4 Landuse / Landcover Condition of Miraj Tahsil (2005) The Landsat dataset produced by Enhance Thematic Mapper (ETM) sensor for 2005 shows the landuse / landcover classification more precisely. The classified image shows that built-up area is increased very much around SMK Municipal Corporation. Also there are many small settlement patches developed all over the tahsil. In this image the natural vegetation (Forest) is clearly visible in the north-eastern part of tahsil; rather this is the only image which shows vegetation clearly. (In previous images the patches shown as vegetation are the comparatively healthy flora present in the tahsil which can not be considered as a part of actual forest. This is one of the limitations to the remote sensing data particularly with classification technique.) Table-3.7 shows details about error matrix and accuracy total of Landsat ETM for the year To check the accuracy assessment total 133 samples are tested out of which 115 are correctly classified. The overall accuracy of this dataset is pretty better therefore the results are also felt more satisfactory. There are many categories, which are having commission and omission error as zero. The highest commission error is five in built-up area category and four is the maximum omission error. Therefore, the producer and user accuracies of many categories become 100 per cent. The lowest producer and user accuracies are of built-up class are and per cent respectively. The overall accuracy of this classification is per cent. The Kappa statistics is between within 0.72 to 1.00 and the overall Kappa statistics is The built-up area of 2005 image is 9137 ha i.e per cent to total area. The size of villages increased enough as settlement patches are clearly seen on image in addition number of small group of settlements are appeared in the classified image. The saline patches are more prominent and clearly visible in the image, particularly the northwestern part of the tahsil is containing much saline waste land

25 Miraj Tahsil Landuse / Landcover Map (2005) Legend Built Up Area Agriculture Vegetation Waste Land Harvested (Fallow Land) River Water Body Saline Land Fig.3.7 Scale km Classes Table-3.7 Error Matrix and Accuracy Totals of Landsat ETM+ (2005) Veg etat ion Agr icul ture Waste Land Saline Land Har ves ted Water Body River Built Up Row Total Commi Error Omi Error Vegetation Agriculture Waste Land (Other) Salt affected Land Harvested (Fallow) Water Body River Built-Up Area Column Total Class Name Reference Totals Classified Totals Number Totals Producers Accuracy Users Accuracy (K^) Kappa Statistics Vegetation Agriculture Waste Land (Other) Salt affected Land Harvested (Fallow) Water Body River Built-Up Area Total Source: Generated by Software, based on calculations done for error and accuracy assessment Overall Classification Accuracy = 86.47, Overall Kappa Statistics =

26 In Miraj tahsil about 9743 ha land is more or less salt affected land and its proportion is increasing very fast. The detail discussion about saline land and related aspects is made in Chapter-6. The vegetation class contains 2382 ha land and its proportion to total land is 2.5 per cent. As per government statistics the land under natural vegetation is 1079 ha, the additional land is for healthy vegetations that contains same amount of signature value. In 1999 the execution of first two phases of Mhaisal Lift Irrigation Scheme is started on testing basis. Since then the water is releasing through natural streams due to that there is positive change in ground water level. As a final outcome, in eastern part of Miraj tahsil several lands patches area brought under irrigated agriculture Spatio-Temporal Statistics of Landuse / Landcover The spatio-temporal detail about land under each class is given in Table-3.8. It is useful to understand the precise condition of landuse / landcover for respective years. Table 3.8 Spatio-temporal Statistical details of Landuse / Landcover Code Class Land in Ha 1976 # Area in Land in Ha Area in Land in Ha Area in Land in Ha Area in 1 Built-up Area Agriculture Harvested (Fallow) Natural Vegetation Salt affected Land Other Waste Water Bodies River Source: Calculated on the basis of classified images. # Due to the problem in the spectral band of the data the figures are quite relative The comparative analysis of three datasets shows that there is continuous rise in built-up area and saline land. The proportion of other waste land, water bodies and river is constant. Since there will not be

27 Land Under Class (in ) Chapter-III any change in river course and major water tanks the figures are relatively same. Some portion of land is utilized for canal irrigation due to that there is slightly increase in water body class. The land under harvested (temporary fallow land or land, which is not containing crops) category is continuously decreasing because the land is being utilized for agriculture purposes. But the statistics of 2005 classified image shows the decrease in agricultural land class, but this change is temporary particularly for that year. Also this decrease was taken place due to increase in saline land. Otherwise, the proportion of land under agriculture class has not changed Spatio-Temporal Distribution of Landuse / Landcover Built-up Area Agriculture Harvested Natural Vegetation. Saline Land Other Waste Water Bodies River TM ETM ETM Landuse/ Landcover Category Fig.3.8 The agricultural land in western part seems decreasing but in eastern part due to Mhaisal canal lift irrigation, some part of barren land has brought under cultivation. The occupational structure of Miraj tahsil shows that persons (72.85) were engaged in agricultural sector comprising of agriculture and agricultural labourers. About per cent male and per cent females have been pushing agricultural activities. Land is the main source of income for most of the families hence agriculture in this area is quite stable

28 3.9 Statistical Analysis of Landuse / Landcover data In above section the landuse / landcover condition of Miraj Tahsil is checked for base years and four intermediate years. In this section LU / LC condition for each individual year is examined in detail Five Year Average to to c c to to c c to to c c Forest Area 3 17 Non-Agriculture Land Fallow / Unproductive Land Grazing Land Pastures Cultivable Waste Current Fallow Land Other Fallow Land Net Sown Area Double Cropped Area Fig.3.9 Five Year (Average) Landuse / Landcover

29 The classes used in this analysis are the general landuse / landcover classes developed by government department for administrative record purpose. The detail database of general landuse for the period of 1971 to 2000 is arranged systematically (Appendix 3.2) and analyzed. This data is not available up to year 2006 hence this study is carried out up to the year The average method is adopted here to understand overall generalized picture of landuse / landcover for that particular time period. There are some variations in individual years therefore, for comparative purpose an average method is much effective. The landuse statistics is grouped into six categories and each category is representing average of five individual years. Table3.9 represents the figures of average LU/LC for 30 years. The total numbers of villages are increasing as population increase i.e. in first two time periods there were total 54 villages then it becomes 67. Table 3.9 Five Year Average of General Landuse / Landcover classes Period No. of Villag es Total Geog. Area Forest Area Non- Agri. Land Fallow/ Unprod uctive Land Grazing Land Pastures Cultiva ble Waste Current Fallow Land Other Fallow Land Net Sown Area Double Cropped Area to to to to to to Source: Calculations based on Landuse Statistics of Regional Agricultural Department Pune, Tahsil Panchyat Samiti Office Miraj. The graphical presentation (Fig.3.9) shows that there is much variation in Net Sown Area, Double Cropped Area and Other Fallow Land categories of Miraj Tahsil. To understand the exact tend of change the

30 Land in Hect. Chapter-III comparative analysis is carried out (Fig3.10). The proportion of cultivable waste is very low i.e. below one per cent whereas the remaining classes do not contain much change. 25,000 Comparative Change in Landuse / Landcover 20,000 15,000 10,000 5,000 0 Forest Non-Agri. Fallow/Unproductive Categories Grazing Pastures Cultivable Waste Current Fallow Other Fallow Double Cropped to to to to to to Fig.3.10 Trend and Comparison of Landuse / Landcover in few important categories Fig 3.10 gives clear information about rise or fall in particular class. As per the records forest feature is not having change and seems constant, but real experience and field visits shows that there decrease in forest area. Due to many reasons very much deforestation occurred and land under natural vegetation is decreasing. As per records 1079 ha land is under forest and its share to total landuse is only one per cent, which is extremely poor. Considering the need of hour and importance of forest government should imply strict action plan for increasing land under forest. It is observed that under the label of social forestry and plantation, each year government is planting some trees but due to lack

31 of care and maintenance these trees have not grown properly, rather died. Ultimately the proportion of forest is not increasing. After 1995 the proportion of non-agricultural land starts increasing very fast. The major cause behind this is rapid process of urbanization. As mentioned earlier Miraj tahsil is most urbanized tahsil in Sangli district, which contains only available Municipal Corporation in the district. The rapid rate of urbanization affects the peripheral area of the urban centres. The detail discussion about urbanization and dynamics of Urban Landuse is made in Chapter-5. There is continuous development noticed in the agriculture sector of Miraj tahsil. The developing means of irrigation and advance technology supported to cropping pattern as a result the farmer in Miraj tahsil starts taking double or triple crops in a year. The trend of producing multiple crops in year has commenced after 1975 but it gets real hike after The revolution in HYV seeds is one of the major reasons behind this. The changes occurred due to irrigation and related aspects are studied thoroughly in chapter-6 i.e. Irrigation and Landuse Changes General Landuse / Landcover Analysis for Individual Year ( ) After studying average condition of landuse / landcover it is essential to understand the condition and pattern for individual year. Fig depicts the situation of each year from 1971 to In 1972 there was severe drought and as a result the land under other fallow land category has increased in Same impact can be seen in on other fallow land and current fallow land category. Due to inadequate rainfall there was shortage of water ultimately the proportion of fallow land has increased. Due to changes in cropping pattern the share of double cropped area has been gradually increasing