Effect of Jhum Cultivation on Biodiversity of NE India With special reference to Tripura

Jhum cultivation or slash-and-burn agricultural system, also known as shifting cultivation is an age-old agricultural practice by the ethnic tribal communities of uplands and it is still prevalent in all the northeastern states. The existing practice of jhum cultivation in Tripura and other north-eastern states of India has been identified as one of the anthropogenic and unscientific form of land use which is influencing the biodiversity to impede the ecological balance of the region. Over the last few years emergence of a new class of shifting cultivators who reduced the earlier 15–20 year cycle of shifting cultivation on a particular land to 2–3 years now resulted in large-scale deforestation, soil and nutrient loss, and by the way affecting the indigenous biodiversity to a large extent. 

For shifting cultivation farmers generally select a forest patch and clear fell the vegetation normally in December and January. Thereafter, they burn the vegetation. During this practice, small, cut-trunks portion and roots are normally not removed. The herbs, shrubs and twigs and branches (slashed vegetation) are burnt in February and March. Sowing of seeds is followed during April and May. They will continue the cultivation for a few years and abandon the cultivated site and shift to other forest sites. After that they will return to the former site, and once again practice shifting cultivation on it. From the erosion point of view, the second year of jhumming cycle is more hazardous than the first year

Shifting cultivation is prevalent in all the northeastern states. Chart 1  present the results of successive studies carried out by the Forest Survey of India, i.e. State of Forest Report, 1995 and 1997. It was noted that loss in forest cover in the northeastern states was mainly due to the shifting cultivation.Chart 2 presents total area affected by the shifting cultivation in the region, a study carried out by the RTFSC .

 

Chart-1: Loss of forest cover in NE India Due to shifting cultivation (in Sq. km)  

 Source: State of Forest Report (1995, 1997)

 Chart 2: Annual area under shifting cultivation (in Sq. Km)

Source: RTFSC (1983), Basic Statistics of NER, 2002, Government of India, North Eastern Secretariat, Shillong. p. 42.

A comparison between the forest cover since 1987 onwards till 2003 is depicted in Table 1 below. Although it is improper to make a comparison in different assessments due to change in technology and scale of interpretation, however, it may still be observed that the forest cover of the country has remained between 19.5% to 20.5% in the last two decades.

Table 1: Forest Cover in Different Assessments (1987 to 2003) (Area in square km)

Year of Assessment	1987	1989	1991	1993	1995	1997	1999	2001	2003
Forest Cover in India	640,819	638,804	639,364	639,386	638,879	633,397	637,293	675,538	678,333
Percent	19.49	19.43	19.45	19.45	19.43	19.27	19.39	20.55	20.64

Source: FSI Reports, 1987-2003

Cumulative area affected by shifting cultivation during 10 years was found to be 1.73 m ha. State-wise details are given in Table 2.

Table 2: Area affected by the shifting cultivation (Area in ‘m ha’)

States	Cumulative area of Shifting Cultivation (1987-89 to 1995-97)
Arunachal Pradesh (AP)	0.23
Manipur (MN)	0.36
Mizoram (MZ)	0.38
Tripura (TR)	0.06
Assam (AS)	0.13
Meghalaya (MG)	0.18
Nagaland (NG)	0.39
Total	1.73

 

Source: FSI report on shifting cultivation (Between 1987 to 1997)

The extent of area under shifting cultivation is maximum (0.39 m ha) in Nagaland followed by Mizoram (0.38 m ha) and Manipur (0.36 m ha). These states together account for about 65% of the total area under shifting cultivation in the N-E.

Impact of shifting cultivation

          Deforestation: Shifting cultivation was assessed by the FAO to be one of the causes of deforestation  In Tripura, comparison of the current forest cover (Satellite data of Nov 2006-Jan 207) with the previous assessment (satellite data of Nov 2004) shows a loss of 100 sq. km of forest cover.  The change matrix given in the table-3 reveals that there has been a decrease of 2 sq. km in the very dense forest, 46 sq. km in moderately dense forest and 52 sq. km in open forest..

Table 3: Forest cover change matrix (area in sq. km) in Tripura

2005 assessment (data of Nov-Dec 2004)	2007 (data of Nov 2006-Jan 2007)					Total of 2005
	VDF	MDF	OF	Scrub	NF
Very Dense Forest (VDF)	110	1	1	0	1	113
Moderately Dense Forest (MDF)	0	4,754	9	9	43	4,816
Open Forest (OF)	0	13	3,152	25	54	3,244
Scrub	0	0	13	40	1	54
Non-Forest (NF)	0	2	17	1	2,244	2,264
Total of 2007	111	4,770	3,192	75	2,343	10,491
Net Change	-2	-46	-52	21	79

Source: FSI Report (2009) on Forest and Tree Resources in States and Union Territories; Tripura. 

On the basis of ground truthing by the official of FSI, main reasons for decrease of forest cover is shifting cultivation in all districts. Degradation of forest causes ecological imbalance, rapid drying up of small water sources, and loss of productivity of land causing reduction in family income and enhancement of poverty in absence of any subsidiary income.

Loss of nutrients and top soil: With reduction in jhum cycle from 20–30 years to 2–3 years, the land under shifting cultivation looses its nutrients and the top soil. With reduction in crop yield, the families start moving to other virgin areas. Now, a stage has come that it has already affected 2.7 million ha of land, and each year 0.45 ha of land fall under shifting cultivation, in northeast India. So long as the jhum cycle has duration of 10 years or more this type of cultivation did not pose any threat to the ecological stability and soils of the largely forested hill area. While studying jhum ecology in Meghalaya, it was reported that water and nutrient losses in shifting-cultivation areas were far greater than in the virgin areas, and areas left for 50 years after jhuming. Thus, reduction in the cycles of jhuming, adversely affects the recovery of soil fertility, and the nutrient conservation by the ecosystem. Repeated short-cycle jhuming has created forest-canopy gaps which are evident from the barren hills.
 

Other ecological consequences: Frequent shifting from one land to the other has affected the ecology of these regions. The area under natural forest has declined; the fragmentation of habitat, local disappearance of native species and invasion by exotic weeds and other plants are some of the other ecological consequences of shifting agriculture. The area having jhum cycle of 5 and 10 years is more vulnerable to weed invasion compared to jhum cycle of 15 years. The area with fifteen-year jhum cycle has more soil nutrients, larger number of species, and higher agronomic yield with ratio of energy output to input as 25.6 compared to jhum cycle of 10 and 5 years (4.6–9.8) .

Although jhuming has many benefits from livelihood point of view, but in long run it destroys the ecosystem balance because one inch soil formation in nature takes about 1000 years. But several inches of soil are washed out each year due to jhuming. Heavy siltation of Brahmaputra River from its tributaries and frequent breaking of embankments are caused by heavy soil erosion from hills of North East India. A study by ICAR institute of NEH region, Barapani, reported that the shifting cultivation on steep slopes (44-53%) has soil loss value of about 50 tones/ ha/ year with corresponding nutrient losses of 703 kg of organic carbon, 144 kg of phosphorus and 7 kg of potash annually. Major adverse effect of shifting cultivation apart from soil and nutrient losses are rapid siltation of river beds causing floods, denudation of forests which hardly gets regeneration time because after 3 to 5 years they have to be burnt for another jhuming due to population pressure. Previously this period was for 7 to 10 years.

GIS Technology in Biodiversity

Authors:Sanjoy Deka and Bhairab Sarma

Introduction: 

Biodiversity refers to the variety of life forms at all levels of organization, from gene through species to higher taxonomic forms and also includes the variety of ecosystems and habitats as well the processes occurring therein. Given the increasing demand for information on the status of biological diversity, many are realizing the need for improved information systems (Davis et al, 1990). A diversity information system should support the assessment and monitoring processes by providing the data needed to describe current environmental baseline conditions identify the species and habitats at greatest risk, guide land management decisions, and model the effects of alternative conservation policies (Davis et al., 1990). An important tool for monitoring biodiversity is a geographic information system (GIS), since GIS plays an important role as a tool for environmental management, with the current greater concern for sustainable use of resources, and conservation and monitoring of biodiversity. Remote sensing and GIS is a new technology used to sense, collect, store, assemble, manipulate and display geographic information’s.

A geographic information system (GIS) is a computer-based expert system tool for mapping, monitoring and analyzing geographic information. GIS technology integrates a number of modern techniques to visualize the major challenges regarding biological diversion, overpopulation, pollution, deforestation, natural disaster etc. GIS mainly have the ability to create map metaphor, integrate information, visualize scenarios and provide powerful ideas to develop solution. GIS technologies have many applications in the field of research and development including biodiversity.

Habitat loss, global climate change, and human disruptions, such as pollution and deforestation, are threats to wildlife biodiversity and can cause fragmentation and extinction. GIS technology is an effective tool for managing, analyzing, and visualizing wildlife data in order to target areas where conservation practices are needed.

Protected area mapping is an important aspect of protected area management. It serves as baseline for ecological modeling and future monitoring and assessment. Advances in geospatial techniques have further improved the efficiency of mapping land use/ land cover (LULC) types at landscape level. Remote sensing data helps in acquiring temporal and spatial data which can be assessed and analyzed using Geographic Information System (GIS). Thus integration of these two techniques can form a potential tool for landscape mapping and baseline survey (Lillesand et al., 2007).

ROLE OF GIS TECHNOLOGY IN BIODIVERSITY

An important tool for monitoring biodiversity is a geographic information system (GIS), which accommodates large varieties of spatial and aspatial (attribute) data. The information embedded in a GIS is used to target surveys and monitoring schemes. Data on species and habitat distribution from different dates allow monitoring of the location and the extent of change. Spatial analytical capabilities of GIS allow quantifying all above parameters with the remote sensing based vegetation type map alone. Roy et al, 1996 have used GIS to characterize habitat of endangered animal, Mountain Goral, using GIS for evaluating principles of landscape ecology. Ravan and Roy, 1998 have again proved potential of GIS in landscape ecology by mapping disturbance zones in natural ecosystem and quantifying its impact on the biodiversity and biomass accumulation along the disturbance gradient. GIS was used in this study for quantifying patch sizes, shapes, porosity and patchiness of vegetation types. GIS was also used to extrapolate results of ground based estimations such as species richness, diversity index and biomass values.

McCullok (1995) identify some of the fields where GIS are used as outlined below:

1. Land map generation: With the help of remote sensing satellite image data, GIS enable to create map. The various land cover map may be of the types as vegetation, forest allocation band, sand, black soil, water body and hill allocation.
2. Flood monitoring and control: Most of the river of our country is flood prone. Thousands of kilometers are in a dangerous situation over a period of time. By continuously monitoring the satellite picture we can identify the river basin and changes of their course, can predict the situation before and after the flood.
3. Monitoring Environmental Changes: GIS technology can be used to monitor the ecological and environmental changes caused by industrialization and civilization. Industrial ash and residues create a major effect on the inhabitant that forced to migrate from one place to another.
4. Urban planning: By using map metaphor GIS technology being used for urban planning. The planner uses this system to accurately plan and placement of water channel, bridges, roads, railways, tunnels, hospitals and residential areas.

NGO sectors such as World Wide Fund for Nature-India (WWF-India) and Tata Energy Research Institute (TERI) have also stepped in the biodiversity conservation efforts using GIS. WWF-India has already computerised third edition of forest cover maps of FSI in GIS environment. In addition, baseline database on important national parks/sanctuaries are also developed. The attempts have also been made to link taxonomic details of rare and endangered species to GIS database. All these NGOs need the support from the custodians (generally govt. organisations) of primary data on biodiversity.

Many Research Institutes working in the area of biodiversity conservation have started use of GIS technology. Prominent among them is Wildlife Institute of India. Other institutions are G.B. Pant Institute of Himalayan Environment and Development, Centre for Ecological Sciences (Indian Institute of Science), Kerala Forest Research Institute, Gujrat Institute of Desert Ecology etc.

Technologies used in GIS

GIS is a collection of multiple technologies. GIS technology integrates five key components: Hardware, Software, Data, People and Methods. Modern computer, Remote sensing Devices, High resolution Cameras, CCDs are include as GIS hard ware. A number of vendors develop GIS software to automated cartography, global positioning and to analyze data. Data in GIS include photographic information that collected by remote sensor devices. Data in GIS consider as a thematic layer, thematic map (base map, business map and data, environmental map and data, general reference map), that represent different information with different shape and resolutions. People or manpower is one of the key components of GIS. GIS utilized many methods for data acquisition, data analysis and for data visualization. In Modern GIS technology, innovative methods includes for handling uncertainty, Fuzzy logic is the example.

We can broadly classify the entire GIS system into three main technologies:

• Data Acquisition or Data Collection
• Data Processing and analysis
• Data Visualization and Display Technique

Data Acquisition Techniques

Remote sensing is a technology used to sense, collect, stored, assemble and manipulate geographic information. NASA defined remote sensing as “ Remote Sensing in the most generally accepted meaning refers to instrument-based techniques employed in the acquisition and measurement of spatially organized (most commonly, geographically distributed) data/information on some property(ies) (spectral; spatial; physical) of an array of target points (pixels) within the sensed scene that correspond to features, objects, and materials, doing this by applying one or more recording devices not in physical, intimate contact with the item(s) under surveillance (thus at a finite distance from the observed target, in which the spatial arrangement is preserved); techniques involve amassing knowledge pertinent to the sensed scene (target) by utilizing electromagnetic radiation, force fields, or acoustic energy sensed by recording cameras, radiometers and scanners, lasers, radio frequency receivers, radar systems, sonar, thermal devices, sound detectors, seismographs, magnetometers, gravimeters, scintillometers, and other instruments.”

Remote sensing is the art of GIS technology that sense the object on earth surface remotely, using visible, infrared, thermal and also microwave imaging techniques. Principle behind remote sensing is that radiation energy incident on the earth surface gets reflected and the reflected electromagnetic wave is captured by a sensor that carried by aircraft or a satellite. These captured image information are transmitted to the earth observation station where the images are processed digitally. In practice, remote sensing is the stand-off collection through the use of a variety of devices for gathering information on a given object or area. For example Earth observation or weather satellite collection platforms, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), X-ray and space probes etc are all example of remote sensing.  

There are two main types of remote sensing: passive remote sensing and active remote sensing. In Passive sensors, reflected sunlight is used for radiation. Natural light reflected by the object or surrounding area being observed. Examples of passive remote sensors include photographic film, infrared, CCD, and radiometers. Active type of remote sensor emits electromagnetic energy in order to scan objects and areas whereupon a sensor exist then detects and measures the radiation that is reflected or backscattered from the target. RADAR is an example of active remote sensing device where the time difference between emitted and reflected energy is measured in order to measure the location, distance, shape, speed and direction of the target object. An advanced technology used in remote sensing is the Multispectral images where the image subdividing spectral ranges of radiation into bands and allow to produces several bands of differing wavelengths to form multispectral image. Different colors can be separated by different color filter based on wavelength.The wavelengths are approximate and exact values depend on the instruments used by the satellite. Some colour of light with their wavelength and characteristics are given below:

Table 1: Multispectral Color used for Radiation

Color	Wave Length (in nm)	Characteristics and Application
Blue	450-515..520	Used for atmospheric and deep water imaging. Can reach within 150 feet (46 m) deep in clear water
Green	515..520-590..600	Used for imaging of vegetation and deep water structures, up to 90 feet (27 m) in clear water.
Red	600..630-680..690	Used for imaging of man-made objects, water up to 30 feet (9.1 m) deep, soil, and vegetation.
Near infrared	750-900	Primarily for imaging of vegetation
Mid-infrared	1550-1750	Used for imaging vegetation and soil moisture content, and some forest-fire.
Mid-infrared	2080-2350	Used for imaging soil, moisture, geological features, silicates, clays, and fires.
Thermal infrared	10400-12500	Uses emitted radiation instead of reflected, for imaging of geological structures, thermal differences in water currents, fires, and for night studies

Modern GIS used following techniques for data acquisition purposes:

• RADAR (Radio Detection and Ranging) and related technologies, useful for mapping terrain and for detecting various objects.
• LIDAR (Light Detection and Ranging) is used to detect and measure the concentration of various chemicals in the atmosphere. Airborne LIDAR can be used to measure heights of objects and features on the ground more accurately than with radar technology. Vegetation remote sensing is a principal application of LIDAR.
• Radiometers and photometers are the most common instrument used for collecting reflected and emitted radiation in a wide range of frequencies. The most common are visible and infrared, microwave, gamma ray, ultraviolet sensors. They are used to detect the emission spectra of various chemicals, providing data on chemical concentrations exist in the atmosphere.
• Stereographic pairs of aerial photographs have been used to make topographic maps by imagery and terrain analysts.
• Landsat have been in use since the 70's as multispectral image. These thematic mappers take images in multiple wavelengths of electro-magnetic radiation (multi-spectral) and are usually found on earth observation satellites for example the Landsat program or the IKONOS satellite. Land cover and land use map from thematic mapping can be used to prospect for minerals, detect or monitor land usage, deforestation, and examine the health and growth of plants and crops, in entire farming regions or forests land.

Data Processing techniques

After capturing the image, information is processed based on of different level of resolution of the image. Lesser the resolution result is the less detail and larger coverage and higher the resolution, result is more detail, less coverage. Two types of film are normally used for data analysis: Panchromatic and Orthochromatic film. Panchromatic film is a type of black-and-white film that is sensitive to all wavelength of visible light. Almost all modern photographic film is panchromatic that produces a realistic image of a scene. Orthochromatic film is used for specific wavelength that proved troublesome for motion pictures. Quick bird and IKONOS (a commercial earth observation satellite) are two example of digital panchromatic imagery. The quality of data output of remote sensing data depends on its spatial, spectral, radiometric and temporal resolutions.

ü  Spatial resolution: The size of a pixel that is recorded in a raster image - typically pixels may correspond to square areas ranging in side length from 1 to 1,000 meters (3.3 to 3,300 ft).

ü  Spectral resolution: This is related to the number of frequency bands recorded by the platform. Current Land sat collection is that of seven bands, including several in the infra-red spectrum, ranging from a spectral resolution of 0.07 to 2.1 μm.

ü  Radiometric resolution: This is the intensity level of radiation that a sensor can distinguish. Typically, this ranges from 8 to 14 bits, corresponding to 256 levels of the gray scale and up to 16,384 intensities or "shades" of colour, in each band.

ü  Temporal resolution: Flyover frequency of the plane or satellite used to monitor deforestation, cloud cover over a region.

Georeferencing is a computer based techniques used to measure the distances between known points on the ground to create sensor-based maps.  In addition, images may need to be radiometrically and atmospherically corrected. Radiometric correction gives a scale to the pixel values, e.g. the monochromatic scale of 0 to 255 will be converted to actual radiance values. Atmospheric correction eliminates atmospheric haze by rescaling each frequency band so that its minimum value corresponds to a pixel value of 0. The digitizing of data also make possible to manipulate the data by changing gray-scale values. Data interpretation in GIS is a critical process. Computer based automated system, for example CAD, CAM, Desktop mapping etc. are used to process data in GIS. Recently, many of image analysis techniques are developed that automated computer-aided application. Object-Based Image Analysis (OBIA) is a sub-discipline of GIS Science developed to partition remote sensing imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale.

Remote sensing and data analysis software

Software used to process and analyze remote sensing data is known as a remote sensing application. A large number of proprietary and open source applications exist to process remote sensing data. According to an NOAA Sponsored Research by Global Marketing Insights Inc., the most used applications among Asian academic groups involved in remote sensing are as follows:

 

Table 2: GIS Software and Utilities

Software	Utilities and Vendors
ERDAS	ERDAS IMAGINE is a remote sensing application with raster graphics editor capabilities designed by ERDAS, Inc for geospatial applications. The latest version is 2010, version 10.1.
ESRI	Esri was founded as Environmental Systems Research Institute in 1969 as a land-use consulting firm.
ENVI	ITT Visual Information Solutions (ITT VIS), announced a new release of ENVI, software for processing geospatial images
GRASS- GIS	Geographic Resources Analysis Support System is a free, open source geographical information system  capable of handling raster, topological vector, image processing, and graphic data.
QGIS	Quantum GIS (QGIS) is a free software desktop Geographic Information Systems  application that provides data viewing, editing, and analysis capabilities.
OSSIM	Open Source Security Information Management, is a collection of tools designed to aid network administrators in computer security, intrusion detection and prevention. Opticks is the software and Orfeo is the toolbox.

Accuracy of GIS

GIS accuracy depends upon source and methods used for data collection. In developing a Digital Topographic Data Base for a GIS, topographical maps are the main source of data. Uncertainty is a significant problem in designing a GIS because spatial data tend to be used for purposes for which they were never intended. A quantitative analysis of maps used to accuracy issues. The electronic instrument and other equipment used to make measurements for GIS is also take an important role in accurate data processing. In fact all geographical data are inherently inaccurate, and these inaccuracies will provide unpredictable result. For example, Accuracy Standards for 1:24000 Scales Map: 1:24,000 ± 40.00 feet means that when we see a point or attribute on a map, its probable location is within a +/- 40 feet area of its rendered reference, according to area representations and scale.

A GIS can also convert existing digital information, which may not yet be in map form, into forms it can recognize, employ for its data analysis processes, and use in forming mapping output.  GTGN (Getty Thesaurus of Geographic Names) is a new technology, proposed by J. Paul Getty which is a structured vocabulary containing about 1,000,000 names and other information about places. Digital satellite images generated through remote sensor can be analyzed to create map of digital information about land, forest, vegetation, rivers.  Fuzzy logic, introduced by Zadeh (1965) provides some useful concept in overcoming the expressive inadequacy and permits pixel belonging to more than one category with graded membership. Fuzzy classification of multispectral remotely sensed data was applied to estimated sub-pixel component.

Data Visualization Techniques

GIS data represents real objects (such as roads, land use, elevation, trees, waterways, etc.) with digital data determining the mix. Real objects can be considered of two abstractions: discrete objects (e.g., a house, tree) and continuous fields (such as mountain, rainfall amount, or elevations). Two methods are used to store data in a GIS for both kinds of abstractions mapping references: Raster method and Vector method. Another new hybrid method of storing data is developed that of identifying point clouds, which combine three-dimensional points with RGB information at each point and returning a 3D color image. GIS thematic maps then are becoming more and more realistically visually descriptive of what they set out to show or determine.

Raster Method

A raster data type is the collection of vertical and horizontal grid lines. In raster representation the map is considered over a rectangular set of grid of cells where each grid is known as pixel. A combination of the pixels making up an image color formation scheme will compose details of an image, as is distinct from the commonly used points, lines, and polygon area location symbols as in the vector model of area attribute. Additional raster data sets used by a GIS that contain information regarding elevation. Examples are a digital elevation model, or reflectance of a particular wavelength of light, Landsat, or other electromagnetic spectrum indicators.

 

Vector Method

 Different geographical features are expressed by different types of geometry shapes: such as points, lines or plotlines, polygons etc. Zero-dimensional points are used for geographical features that can best be expressed by a single point reference — in other words, by simple location like wells, peaks, features of interest, and trailheads. One-dimensional lines or pollylines are used for linear features such as rivers, roads, railroads, trails, and topographic lines. Again, as with point features, linear features displayed at a small scale will be represented as linear features rather than as a polygon. Line features can measure distance. Two-dimensional polygons are used for geographical features that cover a particular area of the earth's surface. Such features may include lakes, park boundaries, buildings, city boundaries, or land uses. Polygons convey the most amount of information of the file types. Measuring perimeter and area of polygon features, size and shape of the real object can be estimated. Attributes consider during data analysis are: Spatial analysis, Slop analysis and Aspect analysis. Slope, aspect and surface curvature in terrain analysis are all derived from neighborhood operations using elevation values of a cell’s adjacent neighbors (Longley et al., 2005).

Topological modeling

A GIS can recognize and analyze the spatial relationships that exist within digitally stored spatial data. These topological relationships allow complex spatial modeling and analysis to be performed. Spatial Relational Databases are used to store and analyze topological relationships between geometric entities.

Cartographic modeling

Cartographic is the term used to refer a process where several thematic layers of the same area are produced, processed, and analyzed. Cartography is the design and production of maps, or visual representations of spatial data. The vast majority of modern cartography is done with the help of computers, usually using a GIS but production quality cartography is also achieved by importing layers into a design program to refine it. Most GIS software gives the user substantial control over the appearance of the data. Operations on map layers can be combined into algorithms, and eventually into simulation or optimization models. Interpolation is the process by which a surface is created, usually a raster dataset, through the input of data collected at a number of sample points. There are several forms of interpolation, each which treats the data differently, depending on the properties of the data set. Digital elevation models (DEM), triangulated irregular network (TIN), edge finding algorithms, Thiessen polygons, Fourier analysis, spine and surface representation are the example of interpolation method.

Cartographic work serves two major functions: First, it produces graphics on the screen or on paper that convey the results of analysis to the people who make decisions about resources. Second, other database information can be generated for further analysis or use. Cartography is primarily used to study global change, climate history program and prediction of its impact on biodiversity. GIS technology, as an expansion of cartographic science, has enhances the efficiency and analytic power of traditional mapping.