Journal of Environmental Management xxx (2014) 1e9
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Development of 2010 national land cover database for the Nepal Kabir Uddin a, *, Him Lal Shrestha a, b, M.S.R. Murthy a, Birendra Bajracharya a, Basanta Shrestha a, Hammad Gilani a, c, Sudip Pradhan a, Bikash Dangol a a
International Centre for Integrated Mountain Development, GPO Box 3226, Kathmandu, Nepal Department of Environmental Science and Engineering, School of Science, Kathmandu University, Dhulikhel, Nepal c School of Geomatics, Liaoning Technical University, 47 Zhonghua Road, Fuxin, Liaoning Province, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 July 2013 Received in revised form 13 June 2014 Accepted 27 July 2014 Available online xxx
Land cover and its change analysis across the Hindu Kush Himalayan (HKH) region is realized as an urgent need to support diverse issues of environmental conservation. This study presents the first and most complete national land cover database of Nepal prepared using public domain Landsat TM data of 2010 and replicable methodology. The study estimated that 39.1% of Nepal is covered by forests and 29.83% by agriculture. Patch and edge forests constituting 23.4% of national forest cover revealed proximate biotic interferences over the forests. Core forests constituted 79.3% of forests of Protected areas where as 63% of area was under core forests in the outside protected area. Physiographic regions wise forest fragmentation analysis revealed specific conservation requirements for productive hill and mid mountain regions. Comparative analysis with Landsat TM based global land cover product showed difference of the order of 30e60% among different land cover classes stressing the need for significant improvements for national level adoption. The online web based land cover validation tool is developed for continual improvement of land cover product. The potential use of the data set for national and regional level sustainable land use planning strategies and meeting several global commitments also highlighted. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Remote sensing Nepal land cover map Image segmentation Landsat TM Forest fragmentation
1. Introduction In the last few decades the Hindu Kush Himalayas (HKH) has undergone rapid economic, social, and environmental changes. However, there is a lack of cohesive information on these changes and how they are impacting on land cover and land cover change. Nonetheless, it is clear that land cover change in the HKH is driving change in ecosystems and their services (Koschke et al., 2012). The HKH region extends over 3500 km encompassing all or parts of eight countries: Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan. The region contains 10 of Asia's largest river systems, which provide water and ecosystem services to the 210 million people living in mountain areas, as well as the 1.3 billion people downstream (Molden and Sharma, 2013). The region is extremely fragile in terms of land cover diversity and its association with variable terrain, climate, and socioedemographic interactions. The HKH region is significantly rich in terms of biodiversity, but is also one of the least studied in the world (Sharma and Chettri, 2005). The Intergovernmental Panel on * Corresponding author. Tel.: þ977 1 5003222. E-mail address: [email protected] (K. Uddin).
Climate Change (2007) has recognized the HKH region as a ‘datadeficit area’. Although scientists and institutions are attempting to fill some of the gaps, reasonable and reliable sources of data for the development of accurate land cover maps for the HKH are scarce. The available data in the region are sporadic, inconsistent and inaccessible (Bajracharya et al., 2010). Nepal has a high-level of diversity and complexity in terms of altitude, terrain, biodiversity, and socio-demography and is broadly representative of the land cover diversity in the HKH region (Bhattarai et al., 2009). There is a need to understand the interactions between these diversities to support land resources use, development, and conservation (Zomer and Susan, 2001). Climate Change impacts, habitat fragmentation, and high population density are changing in the way people in Nepal (and the HKH) are using land and causing land use conflicts. These multiple drivers of change and the interactions between them need to be understood so that policy makers and planners can better manage Nepal's natural resources. According to the 2011 census, Nepal has a total population of 26.5 million, with a population growth rate of 1.35% per annum. The overall literacy rate (for the population aged 5 years and above) has increased from 54.1% in 2001 to 65.9% in 2011 (National Population
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and Housing Census, 2012). Nepal's current forest policy and legislation classifies the country's forests mainly according to tenure or control over the land as government-managed, community-managed, leasehold, religious, private, and protected forest (Acharya, 2002). According to the Food and Agriculture Organization of the United Nations (2010) country report, the forest living biomass (above and below ground biomass) is 484 million metric tonnes (359 million metric tonnes above and 126 million metric tonnes below). Satellite remote sensing is as an important tool in providing reliable historical and current land cover information at the local, national, regional, and global levels (Foley et al., 2005). At the global level, numerous efforts have been made to provide satellite-based land cover and forest cover information (Reis, 2008; Schweik, 1997; Xian et al., 2009), including GLOBCOVER (Arino et al., 2007; Bontemps et al., 2011), annual MODIS land cover (Friedl et al., 2002, 2010), MODIS VCF (DiMiceli et al., 2011), the rescaling of MODIS VCF at 30 m (Sexton et al., 2013) and detection forest cover changes provided by Global Forest Watch (World Resources Institute, 2013). Public domain satellite data and online visualization tools like Google Earth and BHUVAN allow end users to assess the accuracy of land cover data based on very high resolution satellite images and observations. In Nepal, institutional-level national land cover assessments were conducted in 1963, as part of a forest resources survey (FRS) using aerial photography, and in 1986 for a land resources mapping project (LRMP) using satellite data. Since then, no national-level land cover assessments have been conducted and subsequent assessments have focused only on national forest cover mapping, for example, the multi-stakeholder forestry programme (MSFP) and national forest inventory (NFI). A number of individual researchers have also tried to fill the land cover data gap in their own capacity at various scales (Bhattarai et al., 2009; Carson B et al., 1986; Gautam, 2002; Jackson et al., 1998; Niraula et al., 2013), but none of these have produced land cover maps with national coverage using standardised classification scheme. In Nepal, landscape changes and social change patterns have been observed as a function of land use change, and these have implications for social and ecosystem functions and services (Millette et al., 1995). Nepal's community forestry programme is acknowledged to be one of the most successful forest conservation initiatives in the world (Niraula et al., 2013). However, despite the success of this programme and the importance of forests in supporting livelihoods of the people of Nepal and providing ecosystem services to those downstream, there has been little research on land cover and land cover change. A comprehensive understanding of the changing patterns of land cover over the last two decades and its drivers at the national and sub-national level is lacking. This lack of data and information has been one of the major limitations on policy and decision makers in addressing regional environmental issues including the development of greenhouse gas (GHG) inventories, the evolution of reducing emissions from deforestation and forest degradation (REDD) mechanisms, and the assessment of land degradation, as well as optimal land use planning (Dangi, 2012). This study is expected to be useful in addressing such regional issues and informing initiatives in relation to Nepal's national and global commitments, such as its communications to the United Nations Framework Convention on Climate Change (UNFCCC). The present study on land cover assessment of 2010 is taken up as part of regional initiative on developing consistent and harmonized temporal land cover databases over HKH region. At the initial level, study was conducted using public domain Landsat TM data of 2010 and 2011 by adoption of geographic object based image analysis (GEOBIA) classification technique. The land cover product
validation system is developed as part of the study using online web based tool. The assessment of land cover patterns in relation to historical trends and implications over natural resources management over different physiographic regions and potential application for different national and global commitments initiatives also described. Considering the number of global land cover datasets and studies are available, we compared our land cover product with global product of Gong et al. (2013) to explore the possible adoption of global algorithms for national monitoring systems. In this study the forest fragmentation and edge effects was calculated by dividing the land cover into forest and non-forested areas. An online crowd source-based validation tool was developed to collect and analyse feedback from voluntary participant. 2. Study area The study area covers the whole of Nepal, which falls between latitudes 26 220 N to 30 270 N and longitudes 80 040 E to 88 120 E and shares an international border with China to the north and India to the south, east, and west. With a total land area of 147,181 km2. Nepal is divided into five physiographic regions: High mountain, Middle mountain, Hill, Siwalik and Tarai (Fig. 1). Administratively, Nepal has 75 districts and 4057 village development committees (VDCs). These 75 districts are divided into 14 administrative zones, which are grouped into five development regions: far western, mid-western, western, central and eastern. Nepal is predominantly mountainous, with elevations ranging from 60 m in the southern plains to 8848 m at Mount Everest in the north, which is the highest point on the Earth. The climate and topography nurture about 118 ecosystems, 75 vegetation types, and 35 types of forest (MFSC, 2006). Four biodiversity hotspots are located within the Nepal Himalaya (Chettri et al., 2008). Average temperatures in Nepal increased at a rate of 0.06 C between 1977 and 1994. Precipitation in eastern Nepal also shows an increasing trend, whereas precipitation in the western and central parts show a negative trend of 500 acres size and 23.8% forests belong to patch and edge category forests (Table 6) (see Fig. 3). The patch forest constituted 746 km2 at national level, out of which 494 km2 of patch forests are present in hill regions. Middle mountains, Siwaliks and Terai regions have more than 70% of the forest area under core forest category >500 acres size. The edge forests constituted around 30% of forest area of High mountain and Hill regions. The forest fragmentation status within and outside protected areas has revealed that percent of area covered by core forest patches is higher in protected area than outside protected area (Table 7).
4. Results
4.3. Online validation tool
4.1. Land cover map
The land cover database developed by this study can be accessed at http://apps.geoportal.icimod.org/NepalLandCover/ index.html. This online crowd source tool provides district-wise land cover statistics and classification outputs to assess the
The study produced land cover statistics and a land cover map of Nepal showing the LCCS-based 12 classes (Table 2 and Fig. 2). The
Please cite this article in press as: Uddin, K., et al., Development of 2010 national land cover database for the Nepal, Journal of Environmental Management (2014), http://dx.doi.org/10.1016/j.jenvman.2014.07.047
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Fig. 2. Land cover map of Nepal using Landsat 30 m (2010) data.
Fig. 3. Forest fragmentation map based on Nepal land cover 2010.
reliability of the statistics and land cover maps. In addition, a user can provide feedback in terms of accuracy over a point or polygon and upload photographs or any other related text. The tool can receive multiple comments from different users over the same location. It has standard provisions for user-friendly image display
Table 4 Land cover statistics of Nepal.
Table 3 Accuracy assessment report. Land cover
tools, ancillary layers for value addition to assessment and ground control points, and a high resolution image windows for accuracy assessment. The tool has only been online since the end of 2013, but is expected to help refine the accuracy of the land cover products in the future.
Reference Classified Number Producer's User's total total correct accuracy accuracy
Needleleaved closed forest 45 Needleleaved open forest 25 Broadleaved closed forest 76 Broadleaved open forest 45 Shrubland 21 Grassland 33 Agriculture area 186 Barren area 49 Lakes 25 Rivers 12 Snow/glaciers 22 Built-up area 26 Total 565
46 23 89 35 23 27 187 49 15 14 30 27 565
38 15 71 27 16 22 174 45 15 11 22 25 481
84.44% 60.00% 93.42% 60.00% 76.19% 66.67% 93.55% 91.84% 60.00% 91.67% 100.00% 96.15%
82.61% 65.22% 79.78% 77.14% 69.57% 81.48% 93.05% 91.84% 100.00% 78.57% 73.33% 92.59%
Class name
Broadleaved closed forest Broadleaved open forest Needleleaved closed forest Needleleaved open forest Shrubland Grassland Agriculture area Barren area Lakes Rivers Snow/glaciers Built-up area Total
Area Km2
%
21,200 14,137 13,934 8267 5008 11,634 43,910 15,678 45 837 12,062 469 147,181
14.40 9.61 9.47 5.62 3.40 7.90 29.83 10.65 0.03 0.57 8.20 0.32 100.00
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Table 5 Major land cover distribution by physiographic regions. Physiographic region
Cultivated managed
Natural and semi natural vegetation
Artificial surfaces
Barren area
Natural water bodies, snow/glaciers
km2
%
km2
%
km2
%
km2
%
km2
%
High mountain Middle mountain Hill Siwalik Tarai Total
23 5286 19,783 4714 14,104 43,910
0.05 12.04 45.05 10.74 32.12 100
9667 24,148 22,621 13,464 4280 74,180
13.03 32.55 30.50 18.15 5.77 100
1 3 200 76 189 469
0.22 0.55 42.69 16.13 40.33 100
13,105 488 269 489 1326 15,678
83.59 3.12 1.72 3.12 8.46 100
12,062 178 206 230 267 12,944
93.19 1.38 1.59 1.78 2.06 100
Table 6 Forest fragmentation patterns by physiographic regions. Physiographic region
Patch
Core (500 acres)
Total
km2
%
Edge km2
%
Perforated km2
%
km2
%
km2
%
km2
%
km2
%
High mountain Middle mountain Hill Siwalik Tarai Total
27.04 168.24 494.51 43.11 13.56 746.46
3.62 22.54 66.25 5.78 1.82 100
467.93 3698.58 7077.34 1874.56 586.17 13,704.57
3.41 26.99 51.64 13.68 4.28 100
40.95 734.88 1066.29 518.99 49.22 2410.34
1.70 30.49 44.24 21.53 2.04 100
101.11 505.56 1616.51 173.29 83.65 2480.13
4.08 20.38 65.18 6.99 3.37 100
43.53 134.90 612.89 68.81 51.27 911.41
4.78 14.80 67.25 7.55 5.63 100
569.20 12,595.07 10,450.23 10,608.76 3063.29 37,286.55
1.53 33.78 28.03 28.45 8.22 100
1249.77 17,837.22 21,317.77 13,287.53 3847.17 57,539.45
2.17 31.00 37.05 23.09 6.69 100
5. Discussion The adaption of the eCognition-based classification method used in this study has helped in integrating ancillary information, developing scene-specific standards and knowledge, and achieving better classification accuracy. This study limited the classification scheme to broad land cover classes, without delineating subclasses, such as plantations, orchards, settlements, or a higher number of forest density classes, which are amenable at Landsat based TM resolution. This means that the resulting classification system could be more widely applied to a variety of data from different time periods. The use of different image metric information/indices along with individual spectral information and terrain knowledge has improved the separation of shrubland from forest. The present study represented agriculture as a merged class of currently fallow and standing crops over a given scene. The delineation of currently fallow from barren lands was not a critical issue because of their stand-alone positions in the study area. In the study area, barren land is largely found in the High mountain region, juxtaposed with grasslands and in plains as very dry non-agriculture areas. As a post-classification improvement, the currently fallow and barren areas were improved by visual inspection and editing using both Landsat TM and high resolution data. The large number of scattered trees and tree clumps in the forest fringes of the Middle mountain and High hill regions was also a challenging factor in
Table 7 Forest fragmentation status within and outside protected areas. Forest fragmentation
Patch Edge Perforated Core (500 acres) Total
Forest outside the protected area
Forest within the protected area
Km2
%
Km2
%
714.6 12905.0 2249.0 2352.3 858.8 32789.3 51869.0
1.4 24.9 4.3 4.5 1.7 63.2 100.0
31.9 799.6 161.3 127.8 52.6 4497.3 5670.5
0.6 14.1 2.8 2.3 0.9 79.3 100.0
delineating agriculture and forest areas. This resulted in a low-level of accuracy in the delineation of open forests and crops. Another critical factor affecting classification accuracy was shadows. A three-stage approach was adopted to address the classification of shadows: firstly, the classification of deep shadow areas was supplemented with additional seasonal data; secondly, high resolution satellite data and most neighbourhood non-shadow areas were used to obtain inferential information on shadow areas; and, lastly, image-based indices were used to normalize shadow effects. Despite these measures, the shadow areas affected the overall classification accuracy, especially in the high hill region. Although number of national and sub-national land and forest cover studies have been undertaken in Nepal, the datasets produced are either out-dated or were not collected using systematic approaches (Krishna et al., 2009). The importance of land cover changes to understand the dynamics of natural resources within and outside the defined conservation areas have been reported (Carvalho Ribeiro et al., 2013). Bhattarai et al. (2009) conducted a study to determine the deforestation at three-time points, 1975, 1990, and 2000. They used landsat images in central development region of Nepal's by choosing the 19 districts emphasized the importance of information on land cover dynamics. Niraula et al. (2013) have demonstrated through their studies on land cover dynamics the positive impact on forest and environment through evolution of community forestry programme in Nepal. The main value of Nepal land cover 2010 lies in its ability to provide a complete, consistent, and harmonized national land cover map and to serve as a resource for regional to national scale applications. In this context the datasets produced can be applied in numerous ways including to conservation planning, corridor mapping, agriculture monitoring, land use planning, carbon budgeting, and the estimation of fuel wood consumption, to name just a few. This study allows the classification of freely available Landsat TM images from previous years to enhance our understanding of the drivers of land cover and land use changes. At the same time, recently released public domain Landsat-8 dataset triggered globally a focus on establishing a framework for a periodic land cover monitoring system (Loveland et al., 2008). Gong et al. (2012) and World Resources Institute (2013) global land cover studies, have used the same Landsat TM dataset as used in this
Please cite this article in press as: Uddin, K., et al., Development of 2010 national land cover database for the Nepal, Journal of Environmental Management (2014), http://dx.doi.org/10.1016/j.jenvman.2014.07.047
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Table 8 Comparison of land cover statistics for Nepal between present study and global study using Landsat TM data. The grey shade indicates, no change areas within respective class. Land cover (km2)
Forest
Shrubland
Grassland
Agriculture area
Barren area
Water
Snow/glaciers
Built-up area
Total (present study)
% (present study)
Forest Shrubland Grassland Agriculture area Barren area Water Snow/glaciers Built-up areas Total (global study) % (global study)
41,445 2252 1465 11,509 436 77 77 56 57,317 38.94
3913 279 547 4524 164 20 32 18 9497 6.45
3650 879 4388 10,137 1350 106 439 116 21,065 14.31
3164 173 158 7697 294 36 2 79 11,603 7.88
1842 372 3330 8441 9239 363 3224 197 27,008 18.35
1325 695 558 236 1260 257 2069 1 6401 4.35
11 57 1156 1 2913 7 6216 0 10,361 7.04
2187 301 32 1366 22 16 3 2 3929 2.67
57,538 5008 11,634 43,910 15,678 882 12,062 469 147,181 100.00
39.1 3.40 7.90 29.83 10.65 0.60 8.20 0.32 100.00
Gong et al. (2012).
study. In this context, comparison between our study and the global study conducted by Gong et al. (2012). Table 8 revealed that the global framework of Landsat TM-based land cover monitoring needs to be complemented with local scale inputs to bring out reliable global network-based land cover monitoring systems. At the global scale, these studies have produced reasonable results and are more consistent with dense contiguous forest areas than with agriculture or other areas. The comparison revealed a significant different in forest and shrubland classification (Table 8) and scope for the improvement of global land cover products, especially in relation to the misclassification of shadow areas, grassland, agriculture area, open forest and shrubland (Fuller et al., 2003). In fact our scene specific signatures developed using local level field data would extend a good support for improvement of such misclassifications in the global products. Some of the major issues in the separation of forest and shrubland are the presence of shrubs over ecotones and overlapping physiognomy with open forests, lack of ground-based information due to inaccessibility, and the non-availability of concurrent high resolution satellite data and multi-season Landsat TM data (Wulder et al., 2008). In this context the present study has adapted the available very high resolution RapidEye 2010 satellite data and multi-season TM satellite data to locate shrubland and understand the spectral and context specific parameters. The online crowd source-based validation tool developed by this study also provides the public forum to register their feedback to improve the delineation of such overlapping land cover classes resulting in better accuracy. The land cover and forest fragmentation analysis revealed high percentage of productive lands (Cultivated/managed and natural/ semi natural vegetation) along with predominance of core and patch forest in Hill and Mid mountain regions. The national level analysis also revealed around 23.4% of patch and edge forests indicating proximate biotic interference. The land cover assessment studies of (Gautam et al., 2003; Keys, 1981; Kumar et al., 2008; Lambin et al., 2011; Laurance et al., 2002; Panta et al., 2008; Saunders et al., 1991) also reported similar such land cover dynamics in different parts of Nepal. The variability in land cover distribution captured by this study across the different elevation regions, inside and outside protected areas in Nepal will be useful in studying unique landscape structures in terms of forest fragmentation and juxtaposition to assess the potential role of land cover in affecting biodiversity. The open-ended hierarchical classification scheme used in this study will be helpful in integrating the numerous disaggregated sub-national and local-scale detailed assessments (Tabarelli and Gascon, 2005; Thompson et al., 2009; Turner, 1990; Vitousek, 1994; Vogt et al., 2007b) and up-scaling these into national assessments with the appropriate merger of land cover classes. The database could also serve as activity data for current initiatives such as Nepal's national communications to UNFCCC for assessing forest
carbon fluxes as per the Good Practice Guidelines of the Intergovernmental Panel on Climate Change (IPCC) (Penman et al., 2003). This kind of appropriate and relevant land cover information is increasingly being required for scientific, economic, and governmental applications as an essential input to analyse issues, including assessing ecosystem status and health, understanding spatial patterns of biodiversity, and developing land management policy (Homer et al., 2007). 6. Conclusion The main value of Nepal land cover 2010 is its ability to provide a complete, consistent, and harmonized national land cover map. The broad classification system used allows the classification of freely available Landsat TM images from previous years to enhance our understanding of the drivers of land cover and land use changes in Nepal. The land cover information generated can be of great use in informing policy makers and planners working in natural resource management. The study revealed two distinct findings in the context of the ecological importance of the Nepal Himalayas: firstly, 39.1% of Nepal is under national forest cover; and, secondly, there is a high level of variability of land cover classes within physiographic regions in Nepal. The heavily populated Tarai and Siwalik regions have a high-level of agriculture areas and less forest areas, indicating potential land use conflicts. The large proportion of agriculture areas and their intermixing with forests in the hill region indicates possible fringe effects on natural forests. The High mountain region has contributions from both snow/glaciers and grasslands. There is a potential role for the Global Observations of Forest and Land Cover Dynamics (GOFC-GOLD; a project of the Global Terrestrial Observing System programme) and related the Global Earth Observation System of Systems (GEOSS) based network systems in bringing about synergy in land cover classification, especially among least developed countries where global land cover dynamics play an important role in environmental management. The database could also serve as activity data to guide Nepal's national communications to the UNFCCC; the development of a national biodiversity conservation strategy; and the evolution of national REDD mechanisms. Acknowledgements This study was funded by USAID NASA (SERVIR Himalaya). The authors are grateful to NASA, USAID, and the MENRIS-Geospatial team of ICIMOD. We would also like to express our sincere gratitude to remote sensing specialist, Faisal Mueen Qamer, and Khurram Shehzad for their intermediate validation and feedback. Thanks are also due to Salman Asif Siddiqui and Manish Kokh for their contribution during the initial stages of this work. The
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Manag. 256, 1587e1595. Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, Todd, Tanabe, K., Wagner, F., 2003. Good Practice Guidance for Land Use, Land-use Change and Forestry. The Intergovernmental Panel on Climate Change (IPCC), Kanagawa, Japan. Reis, S., 2008. Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors 8, 6188e6202. Saunders, D.A., Hobbs, R.J., Margules, C.R., 1991. Biological consequences of ecosystem fragmentation: a review. Conserv. Biol. 5. Schweik, C.M., Adhikari, K., Pandit, K.N., 1997. Land cover change and forest institutions: a comparison of two sub-basins in the Southern Siwalik Hills of Nepal. Mt. Res. Dev. 17, 99e116. Sexton, J.O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K.M., Channan, S., DiMiceli, C., Townshend, J.R., 2013. 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Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Development of 2010 national land cover database for the Nepal Kabir Uddin a, *, Him Lal Shrestha a, b, M.S.R. Murthy a, Birendra Bajracharya a, Basanta Shrestha a, Hammad Gilani a, c, Sudip Pradhan a, Bikash Dangol a a
International Centre for Integrated Mountain Development, GPO Box 3226, Kathmandu, Nepal Department of Environmental Science and Engineering, School of Science, Kathmandu University, Dhulikhel, Nepal c School of Geomatics, Liaoning Technical University, 47 Zhonghua Road, Fuxin, Liaoning Province, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 July 2013 Received in revised form 13 June 2014 Accepted 27 July 2014 Available online xxx
Land cover and its change analysis across the Hindu Kush Himalayan (HKH) region is realized as an urgent need to support diverse issues of environmental conservation. This study presents the first and most complete national land cover database of Nepal prepared using public domain Landsat TM data of 2010 and replicable methodology. The study estimated that 39.1% of Nepal is covered by forests and 29.83% by agriculture. Patch and edge forests constituting 23.4% of national forest cover revealed proximate biotic interferences over the forests. Core forests constituted 79.3% of forests of Protected areas where as 63% of area was under core forests in the outside protected area. Physiographic regions wise forest fragmentation analysis revealed specific conservation requirements for productive hill and mid mountain regions. Comparative analysis with Landsat TM based global land cover product showed difference of the order of 30e60% among different land cover classes stressing the need for significant improvements for national level adoption. The online web based land cover validation tool is developed for continual improvement of land cover product. The potential use of the data set for national and regional level sustainable land use planning strategies and meeting several global commitments also highlighted. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Remote sensing Nepal land cover map Image segmentation Landsat TM Forest fragmentation
1. Introduction In the last few decades the Hindu Kush Himalayas (HKH) has undergone rapid economic, social, and environmental changes. However, there is a lack of cohesive information on these changes and how they are impacting on land cover and land cover change. Nonetheless, it is clear that land cover change in the HKH is driving change in ecosystems and their services (Koschke et al., 2012). The HKH region extends over 3500 km encompassing all or parts of eight countries: Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan. The region contains 10 of Asia's largest river systems, which provide water and ecosystem services to the 210 million people living in mountain areas, as well as the 1.3 billion people downstream (Molden and Sharma, 2013). The region is extremely fragile in terms of land cover diversity and its association with variable terrain, climate, and socioedemographic interactions. The HKH region is significantly rich in terms of biodiversity, but is also one of the least studied in the world (Sharma and Chettri, 2005). The Intergovernmental Panel on * Corresponding author. Tel.: þ977 1 5003222. E-mail address: [email protected] (K. Uddin).
Climate Change (2007) has recognized the HKH region as a ‘datadeficit area’. Although scientists and institutions are attempting to fill some of the gaps, reasonable and reliable sources of data for the development of accurate land cover maps for the HKH are scarce. The available data in the region are sporadic, inconsistent and inaccessible (Bajracharya et al., 2010). Nepal has a high-level of diversity and complexity in terms of altitude, terrain, biodiversity, and socio-demography and is broadly representative of the land cover diversity in the HKH region (Bhattarai et al., 2009). There is a need to understand the interactions between these diversities to support land resources use, development, and conservation (Zomer and Susan, 2001). Climate Change impacts, habitat fragmentation, and high population density are changing in the way people in Nepal (and the HKH) are using land and causing land use conflicts. These multiple drivers of change and the interactions between them need to be understood so that policy makers and planners can better manage Nepal's natural resources. According to the 2011 census, Nepal has a total population of 26.5 million, with a population growth rate of 1.35% per annum. The overall literacy rate (for the population aged 5 years and above) has increased from 54.1% in 2001 to 65.9% in 2011 (National Population
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and Housing Census, 2012). Nepal's current forest policy and legislation classifies the country's forests mainly according to tenure or control over the land as government-managed, community-managed, leasehold, religious, private, and protected forest (Acharya, 2002). According to the Food and Agriculture Organization of the United Nations (2010) country report, the forest living biomass (above and below ground biomass) is 484 million metric tonnes (359 million metric tonnes above and 126 million metric tonnes below). Satellite remote sensing is as an important tool in providing reliable historical and current land cover information at the local, national, regional, and global levels (Foley et al., 2005). At the global level, numerous efforts have been made to provide satellite-based land cover and forest cover information (Reis, 2008; Schweik, 1997; Xian et al., 2009), including GLOBCOVER (Arino et al., 2007; Bontemps et al., 2011), annual MODIS land cover (Friedl et al., 2002, 2010), MODIS VCF (DiMiceli et al., 2011), the rescaling of MODIS VCF at 30 m (Sexton et al., 2013) and detection forest cover changes provided by Global Forest Watch (World Resources Institute, 2013). Public domain satellite data and online visualization tools like Google Earth and BHUVAN allow end users to assess the accuracy of land cover data based on very high resolution satellite images and observations. In Nepal, institutional-level national land cover assessments were conducted in 1963, as part of a forest resources survey (FRS) using aerial photography, and in 1986 for a land resources mapping project (LRMP) using satellite data. Since then, no national-level land cover assessments have been conducted and subsequent assessments have focused only on national forest cover mapping, for example, the multi-stakeholder forestry programme (MSFP) and national forest inventory (NFI). A number of individual researchers have also tried to fill the land cover data gap in their own capacity at various scales (Bhattarai et al., 2009; Carson B et al., 1986; Gautam, 2002; Jackson et al., 1998; Niraula et al., 2013), but none of these have produced land cover maps with national coverage using standardised classification scheme. In Nepal, landscape changes and social change patterns have been observed as a function of land use change, and these have implications for social and ecosystem functions and services (Millette et al., 1995). Nepal's community forestry programme is acknowledged to be one of the most successful forest conservation initiatives in the world (Niraula et al., 2013). However, despite the success of this programme and the importance of forests in supporting livelihoods of the people of Nepal and providing ecosystem services to those downstream, there has been little research on land cover and land cover change. A comprehensive understanding of the changing patterns of land cover over the last two decades and its drivers at the national and sub-national level is lacking. This lack of data and information has been one of the major limitations on policy and decision makers in addressing regional environmental issues including the development of greenhouse gas (GHG) inventories, the evolution of reducing emissions from deforestation and forest degradation (REDD) mechanisms, and the assessment of land degradation, as well as optimal land use planning (Dangi, 2012). This study is expected to be useful in addressing such regional issues and informing initiatives in relation to Nepal's national and global commitments, such as its communications to the United Nations Framework Convention on Climate Change (UNFCCC). The present study on land cover assessment of 2010 is taken up as part of regional initiative on developing consistent and harmonized temporal land cover databases over HKH region. At the initial level, study was conducted using public domain Landsat TM data of 2010 and 2011 by adoption of geographic object based image analysis (GEOBIA) classification technique. The land cover product
validation system is developed as part of the study using online web based tool. The assessment of land cover patterns in relation to historical trends and implications over natural resources management over different physiographic regions and potential application for different national and global commitments initiatives also described. Considering the number of global land cover datasets and studies are available, we compared our land cover product with global product of Gong et al. (2013) to explore the possible adoption of global algorithms for national monitoring systems. In this study the forest fragmentation and edge effects was calculated by dividing the land cover into forest and non-forested areas. An online crowd source-based validation tool was developed to collect and analyse feedback from voluntary participant. 2. Study area The study area covers the whole of Nepal, which falls between latitudes 26 220 N to 30 270 N and longitudes 80 040 E to 88 120 E and shares an international border with China to the north and India to the south, east, and west. With a total land area of 147,181 km2. Nepal is divided into five physiographic regions: High mountain, Middle mountain, Hill, Siwalik and Tarai (Fig. 1). Administratively, Nepal has 75 districts and 4057 village development committees (VDCs). These 75 districts are divided into 14 administrative zones, which are grouped into five development regions: far western, mid-western, western, central and eastern. Nepal is predominantly mountainous, with elevations ranging from 60 m in the southern plains to 8848 m at Mount Everest in the north, which is the highest point on the Earth. The climate and topography nurture about 118 ecosystems, 75 vegetation types, and 35 types of forest (MFSC, 2006). Four biodiversity hotspots are located within the Nepal Himalaya (Chettri et al., 2008). Average temperatures in Nepal increased at a rate of 0.06 C between 1977 and 1994. Precipitation in eastern Nepal also shows an increasing trend, whereas precipitation in the western and central parts show a negative trend of 500 acres size and 23.8% forests belong to patch and edge category forests (Table 6) (see Fig. 3). The patch forest constituted 746 km2 at national level, out of which 494 km2 of patch forests are present in hill regions. Middle mountains, Siwaliks and Terai regions have more than 70% of the forest area under core forest category >500 acres size. The edge forests constituted around 30% of forest area of High mountain and Hill regions. The forest fragmentation status within and outside protected areas has revealed that percent of area covered by core forest patches is higher in protected area than outside protected area (Table 7).
4. Results
4.3. Online validation tool
4.1. Land cover map
The land cover database developed by this study can be accessed at http://apps.geoportal.icimod.org/NepalLandCover/ index.html. This online crowd source tool provides district-wise land cover statistics and classification outputs to assess the
The study produced land cover statistics and a land cover map of Nepal showing the LCCS-based 12 classes (Table 2 and Fig. 2). The
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K. Uddin et al. / Journal of Environmental Management xxx (2014) 1e9
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Fig. 2. Land cover map of Nepal using Landsat 30 m (2010) data.
Fig. 3. Forest fragmentation map based on Nepal land cover 2010.
reliability of the statistics and land cover maps. In addition, a user can provide feedback in terms of accuracy over a point or polygon and upload photographs or any other related text. The tool can receive multiple comments from different users over the same location. It has standard provisions for user-friendly image display
Table 4 Land cover statistics of Nepal.
Table 3 Accuracy assessment report. Land cover
tools, ancillary layers for value addition to assessment and ground control points, and a high resolution image windows for accuracy assessment. The tool has only been online since the end of 2013, but is expected to help refine the accuracy of the land cover products in the future.
Reference Classified Number Producer's User's total total correct accuracy accuracy
Needleleaved closed forest 45 Needleleaved open forest 25 Broadleaved closed forest 76 Broadleaved open forest 45 Shrubland 21 Grassland 33 Agriculture area 186 Barren area 49 Lakes 25 Rivers 12 Snow/glaciers 22 Built-up area 26 Total 565
46 23 89 35 23 27 187 49 15 14 30 27 565
38 15 71 27 16 22 174 45 15 11 22 25 481
84.44% 60.00% 93.42% 60.00% 76.19% 66.67% 93.55% 91.84% 60.00% 91.67% 100.00% 96.15%
82.61% 65.22% 79.78% 77.14% 69.57% 81.48% 93.05% 91.84% 100.00% 78.57% 73.33% 92.59%
Class name
Broadleaved closed forest Broadleaved open forest Needleleaved closed forest Needleleaved open forest Shrubland Grassland Agriculture area Barren area Lakes Rivers Snow/glaciers Built-up area Total
Area Km2
%
21,200 14,137 13,934 8267 5008 11,634 43,910 15,678 45 837 12,062 469 147,181
14.40 9.61 9.47 5.62 3.40 7.90 29.83 10.65 0.03 0.57 8.20 0.32 100.00
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Table 5 Major land cover distribution by physiographic regions. Physiographic region
Cultivated managed
Natural and semi natural vegetation
Artificial surfaces
Barren area
Natural water bodies, snow/glaciers
km2
%
km2
%
km2
%
km2
%
km2
%
High mountain Middle mountain Hill Siwalik Tarai Total
23 5286 19,783 4714 14,104 43,910
0.05 12.04 45.05 10.74 32.12 100
9667 24,148 22,621 13,464 4280 74,180
13.03 32.55 30.50 18.15 5.77 100
1 3 200 76 189 469
0.22 0.55 42.69 16.13 40.33 100
13,105 488 269 489 1326 15,678
83.59 3.12 1.72 3.12 8.46 100
12,062 178 206 230 267 12,944
93.19 1.38 1.59 1.78 2.06 100
Table 6 Forest fragmentation patterns by physiographic regions. Physiographic region
Patch
Core (500 acres)
Total
km2
%
Edge km2
%
Perforated km2
%
km2
%
km2
%
km2
%
km2
%
High mountain Middle mountain Hill Siwalik Tarai Total
27.04 168.24 494.51 43.11 13.56 746.46
3.62 22.54 66.25 5.78 1.82 100
467.93 3698.58 7077.34 1874.56 586.17 13,704.57
3.41 26.99 51.64 13.68 4.28 100
40.95 734.88 1066.29 518.99 49.22 2410.34
1.70 30.49 44.24 21.53 2.04 100
101.11 505.56 1616.51 173.29 83.65 2480.13
4.08 20.38 65.18 6.99 3.37 100
43.53 134.90 612.89 68.81 51.27 911.41
4.78 14.80 67.25 7.55 5.63 100
569.20 12,595.07 10,450.23 10,608.76 3063.29 37,286.55
1.53 33.78 28.03 28.45 8.22 100
1249.77 17,837.22 21,317.77 13,287.53 3847.17 57,539.45
2.17 31.00 37.05 23.09 6.69 100
5. Discussion The adaption of the eCognition-based classification method used in this study has helped in integrating ancillary information, developing scene-specific standards and knowledge, and achieving better classification accuracy. This study limited the classification scheme to broad land cover classes, without delineating subclasses, such as plantations, orchards, settlements, or a higher number of forest density classes, which are amenable at Landsat based TM resolution. This means that the resulting classification system could be more widely applied to a variety of data from different time periods. The use of different image metric information/indices along with individual spectral information and terrain knowledge has improved the separation of shrubland from forest. The present study represented agriculture as a merged class of currently fallow and standing crops over a given scene. The delineation of currently fallow from barren lands was not a critical issue because of their stand-alone positions in the study area. In the study area, barren land is largely found in the High mountain region, juxtaposed with grasslands and in plains as very dry non-agriculture areas. As a post-classification improvement, the currently fallow and barren areas were improved by visual inspection and editing using both Landsat TM and high resolution data. The large number of scattered trees and tree clumps in the forest fringes of the Middle mountain and High hill regions was also a challenging factor in
Table 7 Forest fragmentation status within and outside protected areas. Forest fragmentation
Patch Edge Perforated Core (500 acres) Total
Forest outside the protected area
Forest within the protected area
Km2
%
Km2
%
714.6 12905.0 2249.0 2352.3 858.8 32789.3 51869.0
1.4 24.9 4.3 4.5 1.7 63.2 100.0
31.9 799.6 161.3 127.8 52.6 4497.3 5670.5
0.6 14.1 2.8 2.3 0.9 79.3 100.0
delineating agriculture and forest areas. This resulted in a low-level of accuracy in the delineation of open forests and crops. Another critical factor affecting classification accuracy was shadows. A three-stage approach was adopted to address the classification of shadows: firstly, the classification of deep shadow areas was supplemented with additional seasonal data; secondly, high resolution satellite data and most neighbourhood non-shadow areas were used to obtain inferential information on shadow areas; and, lastly, image-based indices were used to normalize shadow effects. Despite these measures, the shadow areas affected the overall classification accuracy, especially in the high hill region. Although number of national and sub-national land and forest cover studies have been undertaken in Nepal, the datasets produced are either out-dated or were not collected using systematic approaches (Krishna et al., 2009). The importance of land cover changes to understand the dynamics of natural resources within and outside the defined conservation areas have been reported (Carvalho Ribeiro et al., 2013). Bhattarai et al. (2009) conducted a study to determine the deforestation at three-time points, 1975, 1990, and 2000. They used landsat images in central development region of Nepal's by choosing the 19 districts emphasized the importance of information on land cover dynamics. Niraula et al. (2013) have demonstrated through their studies on land cover dynamics the positive impact on forest and environment through evolution of community forestry programme in Nepal. The main value of Nepal land cover 2010 lies in its ability to provide a complete, consistent, and harmonized national land cover map and to serve as a resource for regional to national scale applications. In this context the datasets produced can be applied in numerous ways including to conservation planning, corridor mapping, agriculture monitoring, land use planning, carbon budgeting, and the estimation of fuel wood consumption, to name just a few. This study allows the classification of freely available Landsat TM images from previous years to enhance our understanding of the drivers of land cover and land use changes. At the same time, recently released public domain Landsat-8 dataset triggered globally a focus on establishing a framework for a periodic land cover monitoring system (Loveland et al., 2008). Gong et al. (2012) and World Resources Institute (2013) global land cover studies, have used the same Landsat TM dataset as used in this
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K. Uddin et al. / Journal of Environmental Management xxx (2014) 1e9
7
Table 8 Comparison of land cover statistics for Nepal between present study and global study using Landsat TM data. The grey shade indicates, no change areas within respective class. Land cover (km2)
Forest
Shrubland
Grassland
Agriculture area
Barren area
Water
Snow/glaciers
Built-up area
Total (present study)
% (present study)
Forest Shrubland Grassland Agriculture area Barren area Water Snow/glaciers Built-up areas Total (global study) % (global study)
41,445 2252 1465 11,509 436 77 77 56 57,317 38.94
3913 279 547 4524 164 20 32 18 9497 6.45
3650 879 4388 10,137 1350 106 439 116 21,065 14.31
3164 173 158 7697 294 36 2 79 11,603 7.88
1842 372 3330 8441 9239 363 3224 197 27,008 18.35
1325 695 558 236 1260 257 2069 1 6401 4.35
11 57 1156 1 2913 7 6216 0 10,361 7.04
2187 301 32 1366 22 16 3 2 3929 2.67
57,538 5008 11,634 43,910 15,678 882 12,062 469 147,181 100.00
39.1 3.40 7.90 29.83 10.65 0.60 8.20 0.32 100.00
Gong et al. (2012).
study. In this context, comparison between our study and the global study conducted by Gong et al. (2012). Table 8 revealed that the global framework of Landsat TM-based land cover monitoring needs to be complemented with local scale inputs to bring out reliable global network-based land cover monitoring systems. At the global scale, these studies have produced reasonable results and are more consistent with dense contiguous forest areas than with agriculture or other areas. The comparison revealed a significant different in forest and shrubland classification (Table 8) and scope for the improvement of global land cover products, especially in relation to the misclassification of shadow areas, grassland, agriculture area, open forest and shrubland (Fuller et al., 2003). In fact our scene specific signatures developed using local level field data would extend a good support for improvement of such misclassifications in the global products. Some of the major issues in the separation of forest and shrubland are the presence of shrubs over ecotones and overlapping physiognomy with open forests, lack of ground-based information due to inaccessibility, and the non-availability of concurrent high resolution satellite data and multi-season Landsat TM data (Wulder et al., 2008). In this context the present study has adapted the available very high resolution RapidEye 2010 satellite data and multi-season TM satellite data to locate shrubland and understand the spectral and context specific parameters. The online crowd source-based validation tool developed by this study also provides the public forum to register their feedback to improve the delineation of such overlapping land cover classes resulting in better accuracy. The land cover and forest fragmentation analysis revealed high percentage of productive lands (Cultivated/managed and natural/ semi natural vegetation) along with predominance of core and patch forest in Hill and Mid mountain regions. The national level analysis also revealed around 23.4% of patch and edge forests indicating proximate biotic interference. The land cover assessment studies of (Gautam et al., 2003; Keys, 1981; Kumar et al., 2008; Lambin et al., 2011; Laurance et al., 2002; Panta et al., 2008; Saunders et al., 1991) also reported similar such land cover dynamics in different parts of Nepal. The variability in land cover distribution captured by this study across the different elevation regions, inside and outside protected areas in Nepal will be useful in studying unique landscape structures in terms of forest fragmentation and juxtaposition to assess the potential role of land cover in affecting biodiversity. The open-ended hierarchical classification scheme used in this study will be helpful in integrating the numerous disaggregated sub-national and local-scale detailed assessments (Tabarelli and Gascon, 2005; Thompson et al., 2009; Turner, 1990; Vitousek, 1994; Vogt et al., 2007b) and up-scaling these into national assessments with the appropriate merger of land cover classes. The database could also serve as activity data for current initiatives such as Nepal's national communications to UNFCCC for assessing forest
carbon fluxes as per the Good Practice Guidelines of the Intergovernmental Panel on Climate Change (IPCC) (Penman et al., 2003). This kind of appropriate and relevant land cover information is increasingly being required for scientific, economic, and governmental applications as an essential input to analyse issues, including assessing ecosystem status and health, understanding spatial patterns of biodiversity, and developing land management policy (Homer et al., 2007). 6. Conclusion The main value of Nepal land cover 2010 is its ability to provide a complete, consistent, and harmonized national land cover map. The broad classification system used allows the classification of freely available Landsat TM images from previous years to enhance our understanding of the drivers of land cover and land use changes in Nepal. The land cover information generated can be of great use in informing policy makers and planners working in natural resource management. The study revealed two distinct findings in the context of the ecological importance of the Nepal Himalayas: firstly, 39.1% of Nepal is under national forest cover; and, secondly, there is a high level of variability of land cover classes within physiographic regions in Nepal. The heavily populated Tarai and Siwalik regions have a high-level of agriculture areas and less forest areas, indicating potential land use conflicts. The large proportion of agriculture areas and their intermixing with forests in the hill region indicates possible fringe effects on natural forests. The High mountain region has contributions from both snow/glaciers and grasslands. There is a potential role for the Global Observations of Forest and Land Cover Dynamics (GOFC-GOLD; a project of the Global Terrestrial Observing System programme) and related the Global Earth Observation System of Systems (GEOSS) based network systems in bringing about synergy in land cover classification, especially among least developed countries where global land cover dynamics play an important role in environmental management. The database could also serve as activity data to guide Nepal's national communications to the UNFCCC; the development of a national biodiversity conservation strategy; and the evolution of national REDD mechanisms. Acknowledgements This study was funded by USAID NASA (SERVIR Himalaya). The authors are grateful to NASA, USAID, and the MENRIS-Geospatial team of ICIMOD. We would also like to express our sincere gratitude to remote sensing specialist, Faisal Mueen Qamer, and Khurram Shehzad for their intermediate validation and feedback. Thanks are also due to Salman Asif Siddiqui and Manish Kokh for their contribution during the initial stages of this work. The
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