Analysis of Land cover changes in Dzongu, Sikkim

Transcription

1 Saori Ogura MLA-EP LA221 Final Paper May 12 th 2014 Analysis of Land cover changes in Dzongu, Sikkim Identify how land cover has changed in accordance with social changes I. Problem Statement Sikkim is one of the biodiversity-hotspots in the world. The indigenous Lepcha people have lived in the area for well over eight centuries. They have developed extensive knowledge on flora and fauna of the biodiverse forests. This knowledge began disappearing in the 1950s with the introduction of a cash economy. Livelihoods changed from diverse agricultural systems with close relationships to nature to a mono-cultural cardamom cash crop system. Increasing concern about loss of traditional landraces and biodiversity, makes this an ideal site to conduct research. I worked in Darjeeling and Sikkim Himalayas with the Indian Environmental Research Institute, Ashoka Trust for Research and the Environment (ATREE) for thirteen months from 2011 to I investigated changes in agricultural patterns and livelihoods over the past century in an indigenous Lepcha village. This project for LA221 will build on my Figure 1: Map of Dzongu, in Sikkim, ethnographical fieldwork during that time, providing spatial information on changes in socio-ecological systems of the Lepchas. The question of this paper is, how has land cover changed in Dzongu, between 1977 and 2009? The outcome of the paper will be sent to ATREE, my client, which is aiming to understand land use changes and identify underlying spatial drivers of the change in the region. It will also be included in my Master s thesis, which will focus on changes in the landscape of the socio-ecological systems of Dzongu, Sikkim. Source:

2 II. Site Context and Livelihoods Changes of Dzongu Sikkim is one of the smallest states of India with total geographical area of 7,096 sq km, which merged into India most recently in According to Census, 2001, the total population of the state is The geographical position of the state is 27º 00' 46" to 28º 07' 48" N latitude and 88º 00' 58" to 88º 55' 25" E longitude. The Eastern Himalayas receive a great deal of rainfall from the Bay of Bengal during the monsoon season, which enriches its biodiversity. Therefore it is recognized as one of the biodiverse hotspots of the world, including hundreds of butterfly and bird species with elevation ranges from 500m to 8500m. It is surrounded by Nepal in the west, by Darjeeling district in the south, by Chumbi Valley of Bhutan in the southeast and by Tibet in the north and northeast. The region consists of mountains and valleys; especially regions that have human inhabitants. There is hardly a flat place. The Himalayan Mountains are particularly vulnerable to impacts of a rapidly changing climate often coupled with anthropogenic alteration of mountain landscapes because of their young and fragile nature coupled with sharp gradients. The Third Assessment Report of the IPCC (2001) pointed out the critical role of Himalayas in the provision of water to continental monsoon Asia, and also clearly revealed their vulnerability to climate change in terms of their hydrology and water resources. Dzongu is a protected area of indigenous Lepcha people, located in North Sikkim. The area is about 15,846ha, with a population of around One hundred and fifty years ago, Lepcha people were hunters and gathers who also practiced shifting agriculture. The area experienced the introduction of cash economy and cash crop in around 1950s. This change influenced the way people live, landscapes of villages and relationships to the forests, and people s values. Nomadic lifestyles shifted to become settled agriculturalists. The self-sufficient lifestyles with barter system has transformed to cash economy and market dependent lifestyles. The mono-cultural cardamom cash crop system increased concerns on loss of traditional landraces and biodiversity. The Indo-China War, annexation to India, and introduction of modern social systems encouraged road construction in the second half of 20 th century. In Upper Dzongu, road construction resulted in settlement changes from upper altitudes to the roadside. Better accessibility to the outside world encouraged people to adapt to modern ways of lifestyles, hence their relationships with the forest

3 have decreased, and also encouraged youth to migrate to towns and cities. The nearest town, Mangan, which is located on the other side of the hill from Dzongu across the Teesta, has developed over the past few decades. The shopping street was a street in 1960s, but now there are three streets. The road construction resulted in deforestation and small-scale urbanization. While the increase in Cardamom fields may have decreased forest cover, unfortunately, Cardamom production got damaged by disease, resulting in abandoned forests. This has put a lot of pressure on the Lepcha people (Bhasin 2011). According to a local documentary film Director, this event influenced local people to sell their lands to the dam projects, which came in to Dzongu in early 2000s. If, for instance, Teesta Stage IV, one of the proposed hydropower projects is constructed, around 135ha of forests will be impacted (Environmental Impact Assessment for Teesta state IV). Figure 2: Conceptual Model on Drivers of Land Cover Changes in Dzongu

4 In summary, the key changes affecting livelihoods in Dzongu are: 1950s~: Increase in cardamom fields. 2000~: Decrease in cardamom due to disease. Based on those facts, I expect to see the forest cover decrease from 1977 to 2000, and increase from 2000 to 2009, after cardamom fields were abandoned. If the dams I mentioned above would be built, the forest cover of Dzongu will most likely decrease. III. Literature review of Image Classification In this section, I will write a general overview of the methods I am going to use: Image classification and Fragstats. More details are found in the method section. Image Classification Remote sensing technique and associated spatial analysis tools are useful in conservation planning, landscape ecology, and assessing the impacts of climate change (Tamang 2011). One of the main purposes of satellite remote sensing is to interpret the observed data and classify features (Liu). These tools are particularly useful for the Himalayas, where field sampling is often difficult due to the rugged terrain (Tamang 2011). There are two classification procedures: supervised classification and unsupervised classification. The supervised classification requires assigning points or polygons to known pixels to represent a certain category. This process is called training. Unsupervised classification on the other hand does not require people to have known pixels, and mainly using some clustering algorithm to classify an image data (Richards 1993). For those processes, Accuracy Assessment is useful, which can assess what accuracy percentage is about the classified image comparing to an image which is assumed to be correct. According to Parece and Campbell (2013), This task is accomplished by compiling an error matrix. An error matrix is a table of values that compares the value assigned during the classification process to the actual value from an aerial photo. These are compared on a point-by-point basis. A random set of points is generated for the region covered by the area. Then using the aerial photos, the value for each point is identified. Then, these same random points are used to identify each point s known value in the

5 classified image. The error matrix table is completed by comparing these two values. The accuracy assessment process will become clearer as we proceed step-by-step. The best way is to collect the ground reference as close to the date of remote sensing data acquisition as possible ( In order to increase accuracy of detection of remotely sensed imageries, shadow correction methods have been explored, particularly de-shadowing and shadow detection (Shahtahmassebi et al. 2013). Fragstats Fragstats is a useful tool for Identification of fragmentation using the results of image classification, which Landscape Metrics, Patch Metrics, and/or Class Metrics can be used. It was developed in the Landscape Ecology Lab at U Mass Amarst. According to the user guide: FRAGSTATS is a spatial pattern analysis program for quantifying the structure (i.e., composition and configuration) of landscapes. The landscape subject to analysis is user-defined and can represent any spatial phenomenon. FRAGSTATS simply quantifies the spatial heterogeneity of the landscape as represented in either a categorical map (i.e., landscape mosaic) or continuous surface (i.e., landscape gradient, expected in version 4.4); it is incumbent upon the user to establish a sound basis for defining and scaling the landscape in terms of thematic content and resolution and spatial grain and grain. (McGarigal 2014) IV. Method This study is two fold. First, ArcGIS Supervised image classification was done using satellite data in three time periods: 1977, 2000, and For image analysis, a field survey with GPS is often essential (For instance, Tamang 2011) in order to increase the accuracy of the classification. The classification was therefore backed by my field survey during 2011 to 2012 (without GPS), as well as Google Earth Pro. Then, using the classified images, I ran Fragstats to see fragmentation of forest covers. For this method section, I will present (1) data discovery, (2) image classification, and (3) fragstats analysis.

6 Figure 3: Flow Chart of the Whole Process (i) Data discovery I have gained satellite data from USGS Earth Explorer. I also gained a shape file of the Dzongu boundary from a researcher in Sikkim. Below are the data I got: Data Source: USGS Earth Explorer 1977 Janyary 23 rd Landsat 1 60m Resolution 2000 December 26 th Landsat 5 30m Resolution 2009 February 10 th Landsat 5 30m Resolution Projection: WGS 1984 UTM zone 45N For image classification, I aggregated the two Landsat 5 images of 30m resolutions to 60m resolutions to match up with the Landsat 1 imagery. (ii) Processing the data: Supervised Classification ArcGIS Image Classification has two ways: Unsupervised Classification and Supervised Classification. I chose to use supervised classification, since I know the area and it was more accurate for me to assign points by myself. In order to do Supervised Classification, both points and polygons can be used. I first chose assigning polygons for 1977 image, and points for 2009 image, but because the process needed to be the same in order to compare results of fragstats, I made all the images classified with points. If points are chosen, the below two tools are used to classify images: -Create Signature -Maximum Likelihood Classification.

7 Below is the process of ArcGIS Supervised Classification I examined (Figure 3). Orange colors are ArcGIS tools. (1) First I degraded the 2000 and 2009 images from 30m resolutions to 60m resolutions, using aggregate tool. Then, I tried different band combinations for each image, using composite band tool, as well as using raster calculator to create NDVI (figure 5). Composite bands NDVI (Normalized Difference Vegetation Index) Raster calculator After exploration, I found that combinations of band1, 4, and 7 for landsat 5 images, band6 and band5 combinations for Landsat1 images were the best to see the difference in land covers (see table 2). (2) I created a shapefile for assigning points in ArcCatalog. (3) I assigned 50 points for each category: Forest, Agricultural fields, and River. As it was not possible to see the roads and to distinguish houses and agricultural fields, the Agricultural category includes houses and commercial areas, indicating developed areas (Figure 6). (4) I edited Attribute tables and create new fields of Class, which is the category. I assigned River as 1, Agricultural areas as 2, and Forest as 3. (5) I used Create Signatures tool, using the Class I created in (4). (6) The file created in (5) was used for the image classification, using Maximum Likelihood Classification tool. (7) Finally I clipped the images with the Dzongu boundary. Figure 4: Flow Chart of the process of ArcGIS Supervised Classification The clipped image data was then exported as ESRI BIL file, in order to run Fragstats.

8 Table 1: Bands used for image classification (orange color) Multispectral Scanner (MSS) Landsat 1-3 Landsat 4-5 Wavelength (micrometers) Resolution (meters) Band 4(Green) Band Band 5 (Red) Band Band 6(Near IR) Band Band 7(Near IR) Band Thematic Mapper (TM) Landsat 4-5 Wavelength (micrometers) Resolution (meters) Band 1 (Blue) Band 2 (Green) Band 3 (Red) Band 4 (Near IR) Band 5 (Mid IR) Band 6 (Thermal) * (30) Band 7 (Mid IR) Figure 5: Various band combinations and NDVI tried to find what works the best for Landsat 1 image band 46 band 64 band 47 band 74 band 56 band 65 6_5ratio NDVI float Figure 6: Assign 50 points for each Class

9 (iii) Fragstats In order to identify fragmentations of forest covers for each image, I ran Flagstats using the bil files of the results of the Supervised Classification. Within the three Metrics, Landscape, Patch, and Class, I chose to use Class Metrics, as I wanted to see the changes in the three different classes (Forest, Agriculture and River). I chose Total Area (CA), Total Edge (TA), Edge Density (ED), Area Mean (AREA_MN), and Perimeter-Area Ratio and Mean (PARA_MN) for the run (Figure 8), but I focused on the results of PARA_MN to identify the fragmentation of the forest covers. Figure 7: Fragstats Figure 8: Results of running the 2009 image (Fragtats)

10 V. Results Left: Image classification and attribute table. Right:Fragstats Images and Tables show each Class: 1.River (blue) 2.Agricultural/Developed areas (yellow) 3. Forest (green)

11 As a result, Mean Perimeter-Area Ratio (PARA_MN) of Forest were: 571 (1977), 587 (2000), 524 (2009). Forest Mean Perimeter-Area Ratio Cover (PARA_MN) Reading the results, fragmentation of the forest covers increased from 1977 to 2000, and from 2000 to 2009 it decreased. The result is the same as I assumed based on the changes of Cardamom fields, which increased from 1950s to 2000, and decreased from 2000 because of the disease. In addition, attribute tables in the Image Classification also show the same results; the forest cover in 1977 was 37154, in 2000 it decreased to 32314, and increased to in Looking at the supervised images, too, it is clear that Agricultural/Developed areas (yellow color) increased from 1977 to 2000, and decreased from 2000 to Notes on Errors Although I did not do Accuracy Assessment this time, areas that I assume where errors exist are: 1) Results of Image Classification, as I see rivers in the areas that should not be there, especially in the 1977 image. 2) 60 m Resolutions were not as accurate as 30m resolutions. Comparing those two 2000 satellite images below, the 30m resolution shows less Agricultural/Developed areas but more fragmentation of forests, compared to the one with 60m.

12 60m Resolution (2000) 30m Resolution (2000) VI. Conclusion Image Classification and Fragstats showed the increase in fragmentation of the forest cover from 1977 to 2000, and decrease in its fragmentation from 2000 to This coincides with the agricultural change to Cardamom, which Cardamom fields increased from 1950s to 2000, and the fields decreased after 2000 due to the disease. This result indicates that Cardamom plays a big role not only in livelihoods but also in forest covers. For the next step, I will learn accuracy assessment and de-shadowing method for image analysis. Also, I will conduct a survey with GPS in the field in the summer, so that I can do in-depth analysis on both land cover and land use changes, including roads, houses, commercial areas, and different agricultural field types. After this analysis will be done, I will deliver the results to my client, ATREE. They will also be included in my Master s thesis.

13 References Bhasin, V., Settlements and Land-Use Patterns in the Lepcha Reserve-Dzongu Zone in the Sikkim Himalaya, India, New Delhi: J Biodiversity, 2011 "Classification Accuracy Assessment." N.p., n.d. Web. 13 May < Centre for Intern-Disciplinary Studies of Mountain & Hill Environment. (2012). Environemntal Impact Assessment and Management Plan for Teesta Stage-IV H.E. Project Sikkim. "IPCC Third Assessment Report." IPCC. N.p., Web. 13 May < Liu, X. "Supervised Classification and Unsupervised Classification." N.p., n.d. Web. 13 May < Malczewski, J., GIS-based land-use suitability analysis: a critical overview, Department of Geography, Progress in Planning, McGarigal, Kevin. "Fragstats Help." UMASS Landscape Ecology Lab. N.p., 16 Jan Web. 13 May < Parece T., and Campbell J. (2013). Remote Sensing Analysis in an ArcMap Environment 20. Accuracy Assessment. Unknown publisher. Richards, J. A. (1993). Remote sensing digital image analysis: an introduction (second edition). Shahtahmassebi A., Ninng Y., Ke W., Moore N., Zhangquan S. (2013). Review of Shadow Detection and De-Shadowing Methods in Remote Sensing. Chin.Geogra.Sci. Vol.23. No.4. pp

14 Tambe, S. and Naroati, S. (2011). Assessing the Priorities for Sustainable Forest Management in the Sikkim Himalayas, India: A Remote Sensing Based Approach. Journal of the Indian Society of Remote Sensing. December 2011, Volume 39, Issue 4, pp