Spatio Temporal Change Analysis of Forest Density in Doodhganga Forest Range, Jammu & Kashmir

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1 Spatio Temporal Change Analysis of Forest Density in Doodhganga Forest Range, Jammu & Kashmir ABSTRACT Majid Farooq 1, Humayun Rashid 2 1 Image Analyst, J&K State Remote Sensing Centre, Srinagar, J&K, India 2 Scientist, J&K State Remote Sensing Centre, Srinagar, J&K, India majid_nature@yahoo.com The increasing use of satellite remote sensing for civilian use has proved to be the most cost effective means of mapping and monitoring environmental changes in terms of vegetation and non renewable resources, especially in developing countries. Data can be obtained as frequently as required to provide information for determination of quantitative and qualitative changes in terrain. The ever increasing population is increasingly changing forest to the other unsuitable applications such as: agriculture, providing energy and fuel, million of hectares from this natural resource are destroyed every year and the remainder of the surfaces change quantitatively and qualitatively. For better management of the forests, the change of forest area and rate of forest density should be investigated. It is possible that there isn t any change in the area of forest during the time but the density of forest is changed. Therefore, in this research the method of Forest Density Mapping has been developed and tested in an area, which is located in the Northern part of India. For this, TM & LISS III images from different dates are used. At first, the forest density map was prepared by using Supervised Classification for two images. Then, the changing of the area and forest density during these periods was distinguished. Keywords: Spatio Temporal, Change Analysis, Recode, Supervised Classification, Change Matrix. 1. Introduction The forests in the state of Jammu and Kashmir are not only one of the important habitats for wildlife, but also the home for many other rare and endangered species. However, the vegetations, which form the home of those rare and endangered species, on the Mountains has been experiencing various extends of logging and landuse conversion for a long term. This caused the obvious decreasing of the area of wildlife s habitat and has produced strong influences on wildlife s survival and distribution. As human and natural forces modify the landscape, resource agencies find it increasingly important to monitor and assess these alterations. Changes in vegetation affect wildlife habitat, fire conditions, aesthetic and historical values and ambient air quality. These changes, in turn, influence management and policy decisions. Satellite Remote sensing play a crucial role in determining, enhancing and monitoring the overall carrying capacity. The repetitive satellite remote sensing over various spatial and temporal scales offers the most economic means of assessing the environmental parameters and 132

2 impact of the developmental processes and it has played a pivotal role in generating information about forest cover, vegetation type and landuse changes. Different conventional remote sensing method such as slicing, image arithmetic, segmentation and multispectral image classification are prepared by different authors. One of the most complete of these methods is supervised classification. Change detection through remote sensing has now been applied widely because of its quick analysis processes, accurate results and visual spatial information. Landuse/landcover classification and mapping for various period images can be used to detect the Landuse/landcover type change. Such a research has never been done in the Mountains of Jammu and Kashmir, this article applied remote sensing approach to detect landscape change (i.e. Landuse/landcover type change) based on two TM images acquired in 1994 and The aims are (1) to illustrate the quick and accurate remote sensing approach to the managers, (2) to provide the landscape change information to the managers and other groups of people Study Area Doodhganga Range is present in the Pirpanjal Forest Division. The geolocation of the study area is and North Latitude and and East Longitude. The total area of the study area is about 141 km 2. The elevation ranges from 1500m to 4800m. The tree belt occurs between 1820m to 3200m. All most all the aspects are represented, the northern being the most prevalent. The area is drained by a number of streams and nallahs namely Romshi, Doodhganga, Shaliganga, Ferozpora, and Ningli, ultimately tributing to the river Jehlum or the Wullar Lake. The Pirpanjal consists of higher mountains cut into by deep ravines and precipitous defiles. It is a singularly well defined range of mountains which may be taken as a type of mountains of middle Himalayas. The soil is comparatively shallow on steep slopes and deep slopes. The distribution of conifers in the tract depends largely on the composition depth and the porosity of the soil. The absence of deodar in Doodhganga and Raithan Ranges is largely attributed to the ill drained soils of the area. Figure 1: Location of Study Area The altitudinal variation and differing topographical features are the two main factors responsible for the variation in microclimate. It is temperate in lower elevations but very cold in 133

3 higher up with the everlasting snow. During the summer months the Kashmir valley gets very little rain due to the barrier put across the monsoon by the lofty Pirpanjal Range itself. However, these forests do get some sporadic rain every now and then when some moisture laden winds steel into the valley through the saddles of forest gullies. The main form of precipitation is snow in winter and some starry rains in other reasons particularly in spring. 1.3 Data 1. Two sets IRS 1D LISS III & LANDSAT 5 TM of 2004 and 1994 were used in this study respectively. Table 1: Specification of satellite data S.No Satellite Sensor Path/Row Spatial Resolution Date of (m) Pass 1 IRS 1D LISS 92/ III 2. LANDSAT 5 TM 149/ SRTM DATA 90m (GLCF) 3. Territorial forest boundary supplied National Geospatial Data Centre SOI, Dehradun. 4. Forest Working Plan Maps 5. Topographical Maps 6. J&K Forest Department Statistical Handbook. Besides a 12 Channel Garmin GPS was used to carry out ground truth. The application software like ERDAS IMAGINE, ArcInfo, ArcView and MS Office 2003 was used for the project work. 3. Methodology 3.1 Radiometric and Geometric Corrections When using satellite imagery to detect change, imagery must be co registered and radiometrically corrected. Image registration ensures that multidate images from the same path and row are registered to each other within one pixel by onscreen identification of common features, such as road intersections. If pixels do not correctly correspond, then changes due to misregistration will occur on the final change map. All map data have been geo referenced in order to have a same coordinate system. The images were geometrically corrected. The control points were selected from common points recognizable on the TM image and topographic map. The TM image (1994) was corrected by 30 points with RMSE=0.14 pixel. Whereas, 26 control points were selected on the LISS III (2004) image by image to image registration with RMSE = 0.24 pixel. The pixels were resampled by the nearest neighbour method to maintain their original data. Contrast and brightness enhancements were performed to get better details. 3.2 Subsetting and masking The area of interest was subsetted from the images and non forest areas were masked out with the RFPF/territorial forest boundary to extract forest areas. 134

4 3.3 Classification Supervised training requires a priori (already known) information about the data. In supervised training, we rely on our own pattern recognition skills and a priori knowledge of the data to help the system determine the statistical criteria (signatures) for data classification. Supervised classification was performed to highlight forest/vegetated areas. Training sets were developed and spectral signatures from the specified areas were generated, these signatures were then used to classify the pixels using maximum likelihood parametric decision rule. DEM was used to help identify the density type with similar spectral characteristics but different elevation information, and aspect model can correct the changed spectral information caused by the mountain shadow. Density class of forest cover and colour was accordingly allocated. The following classification system was used in the study: Table 2: Classification System S.No Category Canopy Cover 1. Very Dense Forest >70% 2. Moderately Dense 40% 70% Forest 3. Open Forest 10% 40% 4. Scrub <10% 5. Blank 0% Figure 2: Mean spectral reflectance pattern of the signatures 135

5 3.4 Ground Truth All the doubtful areas were listed for ground verification and their geographical features were recorded on ground truth proforma with the help of GPS. 3.5 Post Classification Refinement and Map Preparation Based on the groundtruth the misclassified areas were corrected with the RECODE option in ERDAS IMAGINE. Finally the Forest Density Maps for 1994 and 2004 were generated. 3.6 Accuracy Assessment The error matrix and Kappa methods were used to assess the mapping accuracy. The overall mapping accuracy only considers the correction of diagonal elements in the matrix, while the kappa method also takes the other elements in the matrix into account, which can compensate the disadvantage of the error matrix method. Overall accuracy 89% and kappa coefficient 0.86 was achieved for TM and 86% and kappa coefficient of 0.84 was achieved for LISS III image. 3.7 Detection of change and quantifying the change The forest cover density maps generated were subjected to geospatial change analysis to synthesize the resultant change analysis map depicting the change in forest cover over a period of 10 years from 1994 to The data statistical data obtained was tabulated. IRS 1D LISS III October 2004 October 1994 LANDSAT TM Radiometric and Geometric Masking of Non Forest Areas Subset of Study Territorial Forest Boundary (SOI) Forest Working Plan Maps Supervised Ground Truth Post classification A N A Accuracy Assessment Tabulation of Results Forest Density Figure 3: Approach for the Forest cover density mapping and change analysis 136

6 4. Results As per the forest cover density assessment of 1994, it is estimated that 10% of the area was under very dense category, 18% of the area under moderately dense category, 15% under open category 4% under scrub and 53% under Blank/non forest areas comprising of Alpine pastures, snow covered areas, barren rocky areas, etc. However as per the forest cover density assessment of It has been estimated that 7% of the area was under very dense category, 21% of the area under moderately dense category, 13% under open category 4% under scrub and 55% under Blank/non forest areas comprising of Alpine pastures, snow covered areas, barren rocky areas, etc. Table 3: Forest Cover Density of Doodhganga Forest Range in 1994 Forest Cover Area (km 2 ) Very Dense Moderately Dense Open Forest Blank/Non forest Scrub 5.19 Total 141 Table 4: Forest Cover Density of Doodhganga Forest Range in 2004 Forest Cover Area (km 2 ) Very Dense 8.8 Moderately Dense Open Forest Blank/Non Forest Scrub 5.9 Total 141 Figure 4: Satellite images of 1994 and 2004 showing the study area with compartments. 137

7 Figure 5: Forest Density Stratification map for the 1994 and Figure 6: Forest Change Analysis map of Doodhganga Forest Range Figure 7: Very Dense Forest Figure 8: Moderately Dense Forest 138

8 Figure 9: Blank Forest Figure 10: Degraded Forest Areas of classified change are related to cover type by GIS overlay analysis. This process intersects change classes with cover types, thus quantifying the amount of change for each cover type in the project area. Table summarizes acres and percent of classified change by cover type Assessment Table 5: Change Matrix of the Forest Cover from (Area in km 2 ) 2004 Very Dense Moderately Dense Open Forest Scrub Blank Total 1994 Very Dense Moderately Dense Open Forest Scrub Blank Total Net Change Results clearly indicate that significant loss i.e km 2 in forest cover from very dense category to moderately dense forest have taken place. Besides, loss of open forests (by 3.48 km 2 ) has resulted in increased blank and scrub forest by 3.12 km 2 and 0.71 km 2 respectively. 5. Conclusion The aim of the project was to study the dynamic changes of forest resources in terms of forest quantity and quality over a period of time. This project successfully showed that change detection techniques can be applied to forest environments. The results showed the significant change in Compartment 27 of the Doodhganga Range where major open forest has been converted to blank areas. Whereas Compartment 19 shows significant transition from very dense to moderately dense forests. Loss of forest cover can be easily attributed to the human interventions. Analysis of the change data provides information to assess landscape level changes in vegetation extent and composition. It also affords information on causal agents that 139

9 are having the greatest impact. The assessment of the forest cover density has provided an insight into the status of forest cover which is expected to be of great utility to the forest officers managing the forest at Range Level and Compartment Level. 6. References 1. Annon State of Forest Report 2005, FSI, Dehradun, India. 2. Annon Handbook of Forest Statistics 2006, DFO, Statistics Division, J&K Forest Department Srinagar. 3. Fuller, R. M., Smith, G. M., Devereux, B. J., The characterization and measurement of land cover change through remote sensing: problems in operational applications. International Journal of Applied Earth Observation and Geoinformation. 4(3), pp Gao, Z., Wei, H., Ding, F., Methods for Subtracting Vegetation Information Using Vegetation Index(VI) from TM Images. Journal of Arid Land Resources and Environment 12(3), pp Li, D., Change Detection from Remote Sensing Images. Geomatics and Information Science of Wuhan University S1, pp Mo, Y., Zhou, L., Application of TM data to land use change monitoring. Remote Sensing for Land and Resources (2), pp Singh, A., Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 10 (6), pp Yang, C., Ou, X., Dang, C., Zhang, Z., Detection the Change Information of Forest Vegetation in Lushui Country of Yunnan Province. Geo information Science (4), pp