Introduction Forests and biodiversity is the very important key of all living phenomena

Introduction
Forests and biodiversity is the very important key of all living phenomena. The good quality of forest are the batter opportunity for socio-economic development and adjusting response to the challenge of climate change persuaded disasters Geographical Information System (GIS) and Remote Sensing are the well Establishment technologies, which applications in land and water resource management are widely recognized. Generally we say that forest it the green blankets on the environment which is conserving the natural resources and naturally protecting and balancing the environment. In Our country the total geographical area forest is covered by 67.83 m.ha. which is 19.39% including dense forest, open forest, as well as mangroves forest. Forests are the vast social and biological units of living communities. The forests communities are play a vital role for balancing and maintaining the eco-system all over the world. The measure of change detection data modeling is well defined and the thematic changing information can be guidance to more tangible insights into the underlying process involving land cover and land use changes than the information obtained from continuous changes. The change detection modeling is the process which helps to determine the changing associate with land use and land cover properties with reference to the geo-spatial multi-resolution optical remote sensing data. It is help to identify the changing between the two periods or more than two periods those are uncharacteristic variation. The application of changes detection techniques are use the various field such as land use and land cover changes, forest cover changes, coastal changes, habitat fragmentation, urban sprawl and other geomorphological changes through the spatial and temporal analysis techniques such as GIS (Geographic Information System) and Remote Sensing along with digital image processing techniques. India has been well-endowed with a huge variety of forest resources. However the population pressure are continuing exploding and the subsequent growing to needs of food, fodder, small timber, fuel wood, industries etc., depletion and degradation of forests and subsequent adverse changes in ecosystem are taking place (Hoffer, 1986). Remote sensing imagery is a precious tool for rapid mapping applications. The very high resolution remote sensing data are helps to analyzing the rate of changes as well as the casual factors of changes between the different time interval periods. This along with the spatial and temporal analysis technologies namely Geographic Information System (GIS) and Global Positioning System (GPS) help in maintaining up-to date land-use dynamics information for a sound planning and a cost-effective decision. GIS is the most important systematic technological introduction of numerous interdisciplinary spatial and statistical data that can be used in inventorying and analyzing of the environment, changing observation and prediction for current management plans. Remote Sensing helps to collecting data in different spatial, spectral, and temporal resolution from space borne sensors. And image processing techniques is the image correction technique which helps to atmospheric and radiometric correction and analyzing the dynamic changing with the natural resources such as land and water using Remote Sensing data. For understanding the land ecosystem is dynamics the spatial and temporal analyzing technologies are most important and useful for generating scientifically based on statistical data. Remotely sensed data are successfully utilization for land use and land cover change detection needs a careful selection of appropriate datasets. The present study is based on remote sensing & GIS techniques supplemented with ground truth information from site specific interviews. Forest vegetation changes have been defined as the spatial and structural changes in the form of forest degradation and depletion, reducing the forest density and species diversity and the extension of arable land and other land use types inside the natural cover after its disturbance (Sader, et.al, 1985). Most of these studies have analyzed spectral signatures or simple indices (calculated from reflectance data) such as the Normalized Difference Vegetation Index (NDVI). The main reasons for this unfortunate development include climate change, air pollution and increased human activities. There is a need to monitor and quantitatively measure the change in forest.
Aim
Forest Cover Change Detection using Remote Sensing and GIS techniques.

Objective
? To assess the spatial and structural changes in forest vegetation along with the changes in land use/land cover types.
? To find out the impact of forest cover change.
? To prepare the various thematic and Normalized Difference VegetationIndex (NDVI) maps of the area using GIS technology.
? Forest cover change mapping of the year 1990 and 2017.
Location of the Study
The area lies between 22? 23? to 22? 49? North Latitude and 87? 00? to 87? 30? East Longitudes and the area bounded on the North and West by Rupnarayan river and Kangshabati river. The study area is covering between two districts Paschim Medinipur and Jhargram. Three blocks are covering under Paschim Medinipur district i.e. GarhbetaII, Salboni and Midnapore Sadar. And eight blocks are covering under Jhargram district i.e. Binpur-I, Binpur-II, Jamboni, Jhargram, Gopiballavpur-I, Gopiballavpur-II, Sankrail and Nayagram. (Figure No. 1)
Methodology
The NDVI modeling was carried to analyse the forest cover change in study area. The most commonly methods are used to analyzed the dynamics forest cover change in the study area. It involves modeling and rectification each remotely sensed image. After the extraction NDVI, it compared the resulting maps on a pixel-by-pixel basis using a change detection matrix. The flowing steps were carried out in procedure of image processing: 1) data collection, 2) data preparation, 3) NDVI modeling between two periods, 4) overlay analysis and 5) preparation of change detection maps. These applications were carried out using ERDAS imagine 9.2 and Arc GIS 10.3software. (Table No.1 Flow Chart of the Work)
Change Detection Methods
The objectives of change detection are comparing of spatial representation of two time periods by control all the variance caused by differences in variables that are not of interest and the changing measurement is caused by difference in the variables of interest (Green, et.al, 1994). There are many types of change detection methods of multi-spectral image data. They can be used NDVI modeling as two categories: characteristic analysis of spectral type, raster analysis of spectral changes and time series analysis. The aim of characteristic analysis of spectral type method is to make sure the distribution and characteristic of changes based on NDVI modeling and calculation of different phases remote sensing images. The methods are multi-temporal images stacking, algebraic change detection algorithm of image, and change detection of the main components of the image and change detection after extract the NDVI model. The techniques of change detection have been divided into two categories one is pre-classification and another post-classification change detection (Pilon, et.al, 1998).
NDVI
The Normalized Difference Vegetation Index (NDVI) was introduced by (Rouse, et.al, 1974) in order to produce a spectral VI that separates green vegetation from its background soil brightness using Landsat TM digital data. It is expressed as the difference between the near infrared and red bands normalized by the sum of those bands. The expression of NDVI Index is written as follows:
(NIR-RED) / (NIR+RED). (Figure No. 2 & 3)
VI is the most commonly used as it maintain the ability to reduce the topography effects while producing a linear measurement scale. In addition, division by zero error is significantly reduced. Where the measurement scale or ranging the NDVI value is always from -1 to 1 and lower value or negative values are represent the approximate value of non-vegetation and the near 1 or higher values are represent the healthy vegetation on the earth surface.
NDWI
The Normalized Difference Water Index (NDWI) was firstly introduced by McFeeters in 1996 to identify or extract the surface water, wetland and easily measurement the area coverage of the surface water. Mainly the models are used for Landsat multispectral optical sensor image data and it is successfully used to other sensor applications for the calculating the extant of open surface water is needed. The calculated using equation of NDWI is mentioned bellow
(Green-NIR) / (Green+NIR)
Where Band 2 or Green is use for the green light reflectance and the Band 4 or Near Infrared (NIR) is sue for NIR reflectance. According to McFeetrs greater than zero Normalized Difference Water Index (NDWI) values are represent the water bodies or wetland on the earth surface, while the lower values less than or equal to zero values are assumed to be non-water bodies on the earth surface. (McFeeters, 2013)
Environmental Impact of Change
The present and past conditions, the positive and negative trends experienced by the people of the research study area is traditionally primary knowledge of information and the secondary information indicates that the impact on the environment decreasing in descending order of magnitude of rainfall, wildlife in forest area, yields and ground water and surface water.
Management Plan
The growth of settlements and the other activities of economic conditions are includes the conservation of any kind of land use and land cover types in to settlements, mining, agriculture construction etc. the major source of income of the study area is orchard generating. The assessment of forest damages, suppression and management of bush fire, succession, regeneration, new plantation. The abounded agriculture fields are considered the positively changes the environment and are categorized separately.
Result and Discussion
The forest vegetation Normalized Difference Vegetation Index maps reflect the changes of the forest coverage area during the period of 1990 and 2017. The analysis (Table No. 2) shows that the forest area of the study area has been maximum change of density of the forest are reduction after heavy biotic influence. In the study area the habitation has mostly been expended a very short time periods. Thus, the forest area also been converted into agriculture land which is extent to exceeding the other forest types. The remote sensing and GIS technologies helps to find out the monitoring of forest cover change as well as settlement and agriculture land of the study area. Increasing population, their habitation and the economic condition for earning money those are find out for the major causes of forest degradation and negative impact on the surrounding area. The present and past conditions, the negative and the positives trends are experienced by the local people of the study area, traditionally primary knowledge of information and the secondary information indicates that the impact on the environment decreasing in descending order of magnitude of rainfall, wildlife in forest area, yields and ground water and surface water. (Table No. 2 Block Wise Forest Area Distribution), (Figure No. 6, 7 & 8)
Conclusion
The remote sensing data are used for analyzing and detecting the area of forest cover change in the study area. For detecting the change of forest area here analyzing some modeling and the analysis modeling helps to take a proper step for management the land cover area, proper planning for conserve affective biodiversity, planning for afforestation and also proper planning for the local people or surrounding specific development. The overlay analysis and NDVI modeling is has been successfully applied in the remote sensing and GIS environments. And prepare so many maps; the maps are most important to identifying the vegetation conditions in the study area time to time. With the help of NDVI difference images, calculated between a long-term average NDVI in the year of 1990 and 2017, the spatial distribution of vegetation anomalies could be detected and vulnerable areas highlighted. The change detection techniques also applied for extraction changing forest cover area. It also clearly shows that the total forest area is continuously degrading and transforming into various land use/land cover categories. The remarkable decrease forest area is 81.37 sq. km. within periods of 27 years between 1990 and 2017.