1 Introduction. ZHAO Zhilong 1,2, WU Xue 1,2, ZHANG Yili 1,2,*, GAO Jungang 1
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1 Jan., 2017 Journal of Resources and Ecology Vol. 8 No.1 J. Resour. Ecol (1) DOI: /j.issn x Assessment of Changes in the Value of Ecosystem Services in the Koshi River Basin, Central High Himalayas Based on Land Cover Changes and the CA-Markov Model ZHAO Zhilong 1,2, WU Xue 1,2, ZHANG Yili 1,2,*, GAO Jungang 1 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing , China; 2. University of the Chinese Academy of Sciences, Beijing , China Abstract: Climate warming and economic developments have created pressures on the ecological systems that human populations rely on, and this process has contributed to the degradation of ecosystems and the loss of ecosystem services. In this study, Landsat satellite data were chosen as the data source and the Koshi River Basin (KB) in the central high Himalayas as the study area. Changes in land cover and changes in the value of ecosystem services between 1990 and 2010 were analyzed and the land cover pattern of the KB in 2030 and 2050 was modeled using the CA-Markov model. Changes in land cover and in the value of ecosystem services in the KB for the period were then analyzed. The value of ecosystem services in the KB was found to decrease by USD y -1 between 1990 and Among these results, the services value of forest, snow/glacier and barren area decreased, while that of cropland increased. From 1990 to 2050, forest showed the largest reduction in ecosystem services value, as much as USD y -1, while cropland showed the greatest increase, by USD y -1. Deforestation and reclamation in Nepal contributed to a reduction in the value of ecosystem services in the KB. Barren areas that were transformed into water bodies brought about an increase in ecosystem services value in the lower reaches of the Koshi River. In general, this process is likely to be related to increasing human activity in the KB. Key words: ecosystem services; CA-Markov; land cover; Himalayas; Koshi River Basin 1 Introduction Ecosystem services are the benefits people obtain from ecosystems (Costanza et al., 1997; Daily, 1997; Millennium Ecosystem Assessment [hereafter MA], 2005). These services include provisioning services, regulating services, cultural services and supporting services (MA, 2005; Zhang et al., 2014; Fu and Zhang et al., 2014). Land use refers to the ways and situations in which humans make use of the natural attributes of land (Fu et al., 2013). Land cover types have been greatly affected by human activity, and this process has driven changes in providing increased ecosystem services in terms of numbers and capacity (MA, 2005). Global climate change is also placing mounting pressures on ecosystem services (Chapin et al., 2008). Furthermore, as a result of humans reliance on ecosystem services for livelihood and well-being, the concept has become a central issue in environmental studies (Bunker et al., 2005; Carpenter et al., 2009; Li 2010; Lafortezza et al., 2016). In this context, both the status and the changing trends of ecosystem services have become hot topics and difficult problems for ecology (Hou et al., 2015). Numerous prior studies have analyzed the status, changes Received: Accepted: Foundation: National Natural Science Foundation of China ( ); Australian Government-funded Koshi Basin Programme at the ICIMOD; International Partnership Program of Chinese Academy of Sciences (131C11KYSB ). *Corresponding author: ZHANG Yili, zhangly@igsnrr.ac.cn. Citation: ZHAO Zhilong, WU Xue, ZHANG Yili, et al Assessment of Changes in the Value of Ecosystem Services in the Koshi River Basin, Central High Himalayas Based on Land Cover Changes and the CA-Markov Model. Journal of Resources and Ecology. 8(1):
2 68 Journal of Resources and Ecology Vol. 8 No. 1, 2017 in and trends of ecosystem services on the Tibetan Plateau (Xie et al., 2003; Immerzeel et al., 2008; Gao, 2012; Wang et al., 2012; Yu et al., 2012; Zhu et al., 2015). Xie et al. (2003) estimated that the annual ecological values of the Tibetan Plateau were 151 billion USD (1 USD = ~6.2 CNY), accounting for 17.68% of China s annual ecological value and 0.61% of the global total. Yu et al. (2012) proposed that climate warming and local socioeconomic development have contributed to the degradation of the Tibetan Plateau s environmental-ecological system. Sandhu et al. (2014) analyzed the interactions between poverty and ecosystem in the eastern Himalayan region. Climate change and economic development have a deep influence on land use, and then affect the change trend of different land use types. Moreover every land use type has a corresponding ecosystem. In other words, if the land use types are changed, the corresponding ecosystems also will be changed. For example, the land use type of a plot is forest in the past, and then it was changed to farmland because of the land requirements of human. So the type of land use has been changed in this plot. Next the ecosystem of this plot also has been changed from natural ecosystem to artificial ecosystem. Furthermore, the value of different ecosystem is not same, so the ecosystem service value also has been changed. This study intends to discuss the impact of climate change and economic development on land use and the influence on the ecosystem service value caused by land use in a regional scale. In this study, the Koshi River Basin (KB) was chosen as the study area. This region is located in the central high Himalayas, and its environment and land cover are sensitive to climate change (Wu et al., 2017). The aim of our study was to analyze the changes in ecosystem services in the KB between 1990 and 2010 and then to model the changing trends in ecosystem services between 1990 and 2050 based on land cover changes and the CA-Markov model. 2 Study area, data sources and methods 2.1 Study area The KB is located between E and ~ N (Fig. 1) and covered by km 2 area of different three countries territory ( km 2 in China, km 2 in Nepal, and km 2 in India) (Zhang et al., 2013; 2016) with broad elevation range from 21 m to 8844 m. The capital of Nepal (Kathmandu) is the largest city of the basin, with a dense population (5 million) (Gao, 2012). The climatic status of the basin show that the annual average temperature is 5.1 C in the northern part of the KB (in China), 10.5 C in the central part of the KB (in Nepal) (Nie, 2010), while the monthly mean temperature is 15.4 C 41.8 C in the southern the KB (in India) (ICIMOD, 2016). In the same way, the annual precipitation is mm, mm, and above 1500mm in the northern, southern and central part of the KB, respectively (ICIMOD, 2016). 2.2 Data sources The 1990 and 2010 land cover data for the KB in China were provided by land change science and the regional adaptation research group (abrr. LCATP) of the IGSNRR, CAS. The 1990 and 2010 land cover data for the KB in Nepal and India were provided by the International Centre for Integrated Mountain Development (ICIMOD). The two datasets were then unified in a grid format and joined together using the ArcGIS Mosaic tool (Fig. 2). Objectoriented image classification was used for land cover mapping in these datasets (Nie, 2010; Gao, 2012; Zhang et al., 2016). The overall accuracy of land cover classification was 85.94% in 1990 and 92.31% in 2010 (Zhang et al., 2016; Wu et al., 2017). Fig.1 Location and elevation distribution of the study area
3 ZHAO Zhilong, et al.: Assessment of Changes in the Value of Ecosystem Services in the Koshi River Basin, Central high Himalayas based on land 69 Fig.2 Land covers of KB in 1990 and 2010 (Zhang et al., 2016) 2.3 Methods Evaluation of ecosystem service value Xie et al. (2003) has evaluated the per unit area values of ecosystem services of the different types of ecosystems in the Tibetan Plateau, by using equivalent weight factor table of ecosystem services and biomass of different ecosystem types. And this evaluation result of ecosystem services value in the Tibetan Plateau is used in this study. Besides, the classification of land covers in KB is same with Xie s (2003) study. Furthermore, we assume that climate change and economic development have little impact on the ecosystem structure CA-Markov model The CA-Markov model is an integrated model which can predict effectively both spatial and temporal changes in land use. The Markov chain can control the temporal variation by a transfer area matrix between different land use types (Wu et al., 2006). Cellular automata (CA) conversion rules can control the spatial variation. Thus the combination of CA and Markov can predict effectively regional land use and land cover changes (Kamusoko, 2009; Tang, 2010; Guan et al., 2011). The specific process is as follows (Hu et al., 2013): (a) Calculate the transition probability matrix using the Markov chain; (b) Create the atlas of land use transition probability; (c) Simulate the spatial distribution of land use changes Accuracy evaluation This paper uses the land cover map for 1990 as the basis land cover image, and uses the land use transfer matrix during as the transition rule. The land cover projection for 2010 was then obtained. Next, the real and projected land covers in 2010 were compared in order to confirm the modeling accuracy in the CA-Markov model (Fig. 3) with the classification accuracies given in Table 1. The overall accuracy in the data for projected land cover in 2010 was 88.45% and exceeded the 85% level which is referred to as the credible theoretical accuracy of this model (Eastman, 2012). Thus when using the CA-Markov model in simulating the land use spatial distribution in 2030 and 2050, this paper continued to use this model. Moreover, the basis land cover image was the real land cover in 2010, and the land use transfer matrix during was also used as the transition rule. 3 Results 3.1 Assessment of changes in the value of ecosystem services Ecosystem services refer to those life support products and services that are either directly or indirectly gained from the structure, processes and functions of an ecosystem. Natural assets contain a variety of corresponding values along with their ecological service functions. According to Xie s (2003) study of the value of ecosystem services in the Tibetan Plateau, the per unit area values of ecosystem services of the different types of ecosystems in the Tibetan Plateau are shown in Table 2. Based on this table, and combined with the transfer matrix of various land cover types (Table 3), we can estimate the changes in the value of ecosystem services in the KB between 1990 and 2010 (Table 4). (1) The total value of ecosystem services in 2010 was USD y -1. Among them, the per unit area values of ecosystem services of swamp, water bodies and forest were
4 70 Journal of Resources and Ecology Vol. 8 No. 1, 2017 Fig.3 Comparing between real and projected land covers in 2010 Table 1 Classification accuracy of different land cover types in the KB Land cover type Classification accuracy Swamp Forest Shrubland Grassland Cropland Barren area Built-up area Water bodies Snow/glacier Overall Kappa Table 2 Ecosystem services value of different ecosystem types in the Tibetan Plateau Land cover type Ecosystem services value (USD ha -2 y -1 ) Swamp Forest Shrubland Grassland Cropland Barren area Water bodies Snow/glacier highest in the KB. Moreover, the ecosystem services value of forest was USD y -1, of cropland was USD y -1, of grassland was USD y -1 and of swamp was USD y -1. The total ecosystem services value of the four land use types accounted for 91.54% of the whole watershed (Fig. 4). High value areas are swamp ecosystems distributed throughout the north of the KB, water body ecosystems distributed throughout the whole basin and forest ecosystems in the central KB, while low value areas are located in the grassland ecosystem, in barren areas and snow/glaciers in the northern KB and in cropland ecosystems in the southern KB. In comparison with the ecosystem services value of the other land cover type, the ecosystem services value of the build-up area is considered negligible. (2) The total value of ecological service functions was found to have decreased. Between 1990 and 2010, the value of ecosystem services decreased by USD y -1, accounting for 2.24% of the whole river basin ecosystem services value in This reduction occurred mainly in the forest ecosystem of Nepal. (3) Main types and means of change: Based on the above data, swamp has the highest per unit area ecosystem services value, bare land and glacier the lowest. Between 1990 and 2010, forest had the greatest loss of ecosystem services value, as much as USD y -1, followed by water bodies which lost services value of up to USD y -1. In addition, snow/glacier and bare land ecosystems also experienced services value losses. The services value of both cropland and grassland ecosystems tended to increase, by USD y -1 and USD y -1, respectively. From the viewpoint of land cover types, with respect to the transfer of ecosystem services value to other land cover types in
5 ZHAO Zhilong, et al.: Assessment of Changes in the Value of Ecosystem Services in the Koshi River Basin, Central high Himalayas based on land 71 Table 3 Transition matrix of different land cover types in Koshi River Basin during (km 2 ) Cropland; 2-Forest; 3-Water bodies; 4-Swamp; 5-Built-up area; 6-Shrubland; 7-Barren area; 8-Grassland; 9-Snow/glacier. Table 4 Ecosystem services value change of different land cover type in KB during (10 8 USD y -1 ) Land cover Value Change Value Cropland Forest Swamp Barren area Water bodies Grassland Snow/glacier Shrubland Total Fig.4 Value of ecosystem services (VES) in the KB in 2010 the period , that from forest was the highest, up to USD y -1, followed by water bodies with a loss of USD y -1. For other land cover types, with respect to the transfer of ecosystem services value to other land cover types in the same period, the cropland ecosystem gained the most ecosystem services value from other ecosystems, up to USD y -1, followed by grassland with an increase of USD y -1. The urban expansion of Kathmandu and the occupation of cropland resulted in a decrease in ecosystem services value in this region. Barren areas that were transformed into water bodies resulted in an increase in ecosystem services value in the lower reaches of the KB (Fig. 5). 3.2 Modeling changes in land cover and ecosystem services in the future The areas of different land use and land cover types in 1990 and 2010 are shown in Table 3. According to the change in the situation during this period, the CA-Markov model will simulate land use and land cover in the KB in 2030 and 2050 (Fig.6). During and , the regions of highest ecosystem services value are located in the central and northern parts of the KB, and most of them are predicted to keep shrinking (Figs. 7 and 8). In this period, the urban expansion of Kathmandu and the occupation of cropland caused a decrease in ecosystem services value in the vicinity of the capital. Barren areas transformed into water bodies caused an increase in ecosystem services value in the lower reaches of the KB (Fig. 8). Swamp: during , in the simulation the area of swamp will decrease by 1.41 km 2, accounting for 0.13% of the area of swamp in 2010; during , the area of swamp will decrease by 5.46 km 2, accounting for 0.51% of the area in 2010; and during and , the area of swamp converted into water bodies will be the greatest in the transition matrix (Tables 5 and 6). Between 2010 and 2050, the services value of the swamp ecosystem will lose up to USD y -1.
6 72 Journal of Resources and Ecology Vol. 8 No. 1, 2017 Fig.5 Change rate of Value of ecosystem services (VES) during Forest: during , the area of forest will decrease by km 2, accounting for 5.39% of the area of forest in 2010; during , the area of forest will decrease by km 2, accounting for 10.65% of the area in 2010; and during and , the area of forest converted into cropland will be the greatest in the transition matrix (Tables 5 and 6). Between 2010 and 2050, the services value of forest ecosystem will lose up to USD y -1. Shrubland: during , the area of shrubland will increase by km 2, accounting for 44.67% of the area of shrubland in 2010; during , the area of shrubland will increase by km 2, accounting for 87.55% of the area in 2010; during , the area of shrubland converted into cropland will be the greatest in the transition matrix; and during , the area of shrubland converted into water bodies will be the greatest in the transition matrix (Tables 5 and 6). Between 2010 and 2050, the services value of shrubland ecosystem will increase by up to USD y -1. Grassland: during , the area of grassland will decrease by km 2, accounting for 5.21% of the area of grassland in 2010; during , the area of grassland will decrease by km 2, accounting for 8.35% of the area in 2010; and during and , the area of grassland converted into shrubland will be the greatest in the transition matrix (Tables 5 and 6). Between 2010 and 2050, the services value of grassland ecosystem will lose up to USD y -1. Cropland: during , the area of cropland will increase by km 2, accounting for 1.25% of the area of cropland in 2010; during , the area of cropland will increase by km 2, accounting for 0.58% of the area in 2010; and during and , the area of cropland converted into built-up area will be the greatest in the transition matrix (Tables 5 and 6). Between 2010 and 2050, the services value of cropland ecosystem will increase by up to USD y -1. Fig.6 Land covers of KB in 2030 and 2050
7 ZHAO Zhilong, et al.: Assessment of Changes in the Value of Ecosystem Services in the Koshi River Basin, Central high Himalayas based on land 73 Fig.7 Value of ecosystem services (VES) in the KB in 2030(a) and 2050(b) Fig.8 Change rate of Value of ecosystem services (VES) during (a) and (b) Barren area: during , the area of barren area will decrease by 0.17 km 2 ; during , the area of barren area will decrease by km 2, accounting for 0.99% of the area in 2010; and during and , the area of barren area converted into water bodies will be the greatest in the transition matrix (Tables 5 and 6). Between 2010 and 2050, the services value of barren area ecosystem will lose up to USD y -1. Built-up area: during , the area of built-up area will increase by km 2, accounting for % of the area of built-up area in 2010; during , the area of built-up area will increase by km 2, accounting for % of the area in 2010; and during and , the area of built-up area converted into water bodies will be the greatest in the transition matrix (Tables 5 and 6).
8 74 Journal of Resources and Ecology Vol. 8 No. 1, 2017 Table 5 Transition matrix of different land cover types in Koshi River Basin during (km 2 ) Cropland; 2-Forest; 3-Water bodies; 4-Swamp; 5-Built-up area; 6-Shrubland; 7-Barren area; 8-Grassland; 9-Snow/glacier. Table 6 Transition matrix of different land cover types in Koshi River Basin during (km 2 ) Cropland; 2-Forest; 3-Water bodies; 4-Swamp; 5-Built-up area; 6-Shrubland; 7-Barren area; 8-Grassland; 9-Snow/glacier. Water bodies: during , the area of water bodies will increase by km 2, accounting for 51% of the area of water bodies in 2010; during , the area of water bodies will increase by km 2, accounting for % of the area in 2010; and during and , the area of water bodies converted into swamp will be the greatest in the transition matrix (Tables 5 and 6). Between 2010 and 2050, the services value of water bodies ecosystem will increase by up to USD y -1. Furthermore, any increase in water area in the future may be caused by the melting of glaciers. Snow/glacier: during , the area of snow/ glacier will decrease by km 2, accounting for 0.49% of the area of snow/glacier in 2010; during and , the area of snow/glacier converted into barren area will be the greatest in the transition matrix (Tables 5 and 6). Between 2010 and 2050, the services value of snow/glacier ecosystem will lose up to USD y Discussion Between 1997 and 2011, because of land use changes, the losses in the value of global ecosystem services amounted to USD y -1 (Costanza et al., 2014). During the period , the value of ecosystem services in the Tibetan Plateau increased at a rate of USD y -1 (Liu et al., 2009), while it decreased at a rate of USD y -1 between 2000 and 2010 (Liu et al., 2013). Between 2000 and 2010, urban expansion caused by rapid growth in both population and GDP led to the loss of ecosystem services on the Tibetan Plateau (Mou et al., 2016). Moreover, in the eastern Himalayas, human activity aimed at fulfilling basic needs and contributing to well-being has also resulted in destruction of ecosystems (Sandhu et al., 2014). The present study found that between 1990 and 2010 the value of ecosystem services decreased steadily in the KB of the central high Himalayas. This trend was closely related to various human activities in the region, such as urban expansion in Kathmandu, farmland reclamation and deforestation in northern Nepal, etc. Comparing our results with those of Costanza et al. (2014), the value of ecosystem services reported by Costanza increased in the KB but that reported in this study decreased. The reason for this difference is the use of updated unit ecosystem services values in Costanza s study, which leads to the value of ecosystem services in the KB increasing during the period In addition, the spatial resolution of the global land cover data used by Costanza was 1 1, but was m in this study. Also, Costanza s study used only three land cover types in the KB,
9 ZHAO Zhilong, et al.: Assessment of Changes in the Value of Ecosystem Services in the Koshi River Basin, Central high Himalayas based on land 75 while the present study uses nine. Furthermore, comparing our results with those of Liu et al. (2013), the value of ecosystem services decreased in the Chinese part of the KB between 2000 and 2010, and our results are consistent with this observation for this region. Through the parameter method presented by Xie et al (2003), we have changed the land use variation to the variation of ecosystem service value in KB in this study. This method can directly reflect the influence of land use change on the ecosystem service value. In KB, climate change may cause deglaciation, and then glacial melting induced glacial lake expansion. Human cut down forests for cropland and built-up area. These processes have changed the types of land use. Furthermore, because each type of land use has different ecosystem service value, these processes also have changed the ecosystem service value. Based on land use change between 1990 and 2010 in KB, we use parameter method and CA-Markov model to predict ecosystem service value change in KB in the future. It is an attempt for the research of ecosystem service value in this basin. We have discussed the total ecosystem service value of every land use type in this study, but we do not analyze the ecosystem service value of each land use type in different aspects, so this need further study in the future. 5 Conclusions This study has investigated the variation in land cover and the value of ecosystem services in the KB, central high Himalayas during the period The total value of ecosystem services was USD y -1 in High value area is distributed in the swamp ecosystem in the northern KB, as water body ecosystem in the entire basin and as forest ecosystem in the central KB, while low value area is located in the grassland ecosystem, as barren area and snow/glacier in the northern KB and as cropland ecosystem in the southern KB. Between 1990 and 2010, the value of ecosystem services decreased by USD y -1, and this reduction occurred mainly in the forest ecosystem of Nepal. Between 2010 and 2050, the value of ecosystem services will decrease by USD y -1, and the majority of this decrease will also be located in the forest ecosystem of Nepal. Between 1990 and 2050, forest will suffer the greatest loss in ecosystem services value, by as much as USD y -1, while cropland will see the greatest increase in value, by USD y -1. Deforestation and reclamation in Nepal contributed to a reduction in the value of ecosystem services in the KB. The urban expansion of Kathmandu and the occupation of cropland resulted in a decrease in the value of ecosystem services in the vicinity of the capital. Barren area that was converted into water bodies contributed to an increase in the value of ecosystem services in the lower reaches of the KB. The decrease in forest area and the increase in cropland in the KB may provide a case study for research into the impacts on and the adaptations of ecosystem services to global climate change and local economic development. Acknowledgement We are grateful to Dr. Basanta Shrestha, Dr. Arun Bhakta Shrestha, Dr. SM Wahid, Dr. MSR Murthy and Mr. Kabir Uddin of ICIMOD, Nepal, for sharing their valuable datasets. We are grateful to Dr. Linshan Liu and Dr. Xue Wang of IGSNRR and the anonymous reviewers for their constructive comments and suggestions regarding this paper. References Bunker, D. E., DeClerck, F., Bradford, J. C., et al Species loss and above ground carbon storage in a tropical forest. Science, 310, Carpenter, S. R., Mooney, H. A., Agard, J., et al Science for managing ecosystem services: beyond the millennium ecosystem assessment. Proceedings of the National Academy of Sciences of the United States of America, 106, Chapin III, F.S., Randerson, J.T., McGuire, A.D., et al Changing feedbacks in the earth-climate system. Frontiers in Ecology and the Environment, 6(6), Costanza, R., Groot, R. D., Sutton, P., et al Changes in the global value of ecosystem services. 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10 76 Journal of Resources and Ecology Vol. 8 No. 1, 2017 Kamusoko, C., Aniya, M., Adi, B., et al Rural sustainability under threat in Zimbabwe: Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29, Lafortezza, Raffaele., & Chen, J. Q The provision of ecosystem services in response to global change: Evidences and applications. Environmental Research, 147, Li, W. H Progresses and perspectives of ecological research in China. Journal of Resources and Ecology, 1(1), Liu, J. H., Gao, J. X., Nie, Y. H Measurement and Dynamic Changes of Ecosystem Services Value for the Tibetan Plateau Based on Remote Sensing Techniques. Geography and Geo-Information Science, 25(3), (in Chinese) Liu, X. F., Ren, Z. Y., Lin, Z. H Dynamic assessment of the values of CO 2 fixation and O 2 release in Qinghai-Tibet Plateau ecosystem. Geographical Research, 32(4), (in Chinese) Millennium Ecosystem Assessment Ecosystems and Human Well-Being: Synthesis, Island Press, Washington, DC. Mou, X. J., Zhao, X. Y., Rao, S., et al Changes of ecosystem structure in Qinghai-Tibet Plateau Ecological Barrier Area during recent ten years. Acta Scientiarum Naturalium Universitatis Pekinensis, 52(2), (in Chinese) Nie, Y Land cover changes in Mt. Qomolangma region. PhD diss., University of Chinese Academy of Sciences. (in Chinese) Sandhu, H., & Sandhu, S Linking ecosystem services with the constituents of human well-being for poverty alleviation in eastern Himalayas. Ecological Economics, 107, Tang, J., Wang, X. G., Li, Z. Y., et al The tendency forecast on land use landscape pattern change in western Jilin Province based on CA-Markov model. Journal of Jilin University (Earth Science), 40(2), (in Chinese) Wang, Q., Chen, X., Yang, D. G., et al Paying for Tibet s Environmental-Ecosystem Services. Environmental Science & Technology, 46(10), Wu, Q., Wang, R. S., Li, H. Q., et al Statistical properties of Markov chain in land use and landscape study. Chinese Journal of Applied Ecology, 17(3), (in Chinese) Wu, X., Gao, J. G., Zhang, Y. L., et al Land Cover Status in the Koshi River Basin, Central Himalayas. Journal of Resources and Ecology, 8(1): Xie, G. D., Lu, C. X., Leng, Y. F., et al Ecological assets valuation of the Tibetan plateau. Journal of Natural Resources, 18 (2), (in Chinese) Yu, C., Zhang, Y., Claus, H., et al Ecological and environmental issues faced by a developing Tibet. Environmental Science & Technology, 46 (4), Zhang, Y. L., Gao, J. G., Liu, L. S., et al NDVI-based vegetation changes and their response to climate change from 1982 to 2011: A case study in the Koshi River Basin in the middle Himalayans. Global and Planetary Change, 108, Zhang, Y. L., Yao, Z. J., Liu, L. S., et al Final report on study on land use and land cover change and erosion in the Koshi River Basin. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China. Zhang, Y., S. Zhao, R. Guo Recent advances and challenges in ecosystem service research. Journal of Resources and Ecology, 5(1), Zhu, J., Zhou, Y., Wang, S. X., et al Multicriteria decision analysis for monitoring ecosystem service function of the Three-River Headwaters region of the Qinghai-Tibet Plateau, China. Environmental Monitoring & Assessment, 187(6), CA-Markov 赵志龙 1,2, 吴雪 1,2, 张镱锂 1,2, 高俊刚 1 1. 中国科学院地理科学与资源研究所陆地表层格局与模拟院重点实验室, 北京 ; 2. 中国科学院大学, 北京 ; 摘要 : 气候变暖和经济发展正对人类赖以生存的生态系统产生巨大的压力, 这导致了生态系统的退化, 以及随之而来的生态系统服务的丧失 本文以 Landsat 遥感影像为数据源, 以喜马拉雅中部的柯西河流域为研究区, 分析了 年该区域土地覆被变化, 以及随之产生的生态系统服务价值的变化 继而运用 CA-Markov 模型模拟了未来 2030 年和 2050 年柯西河流域的土地覆被状况, 并分析了 年的土地覆被和生态系统服务的变化情况 研究结果表明 : 年, 柯西河流域的生态系统服务价值减少了 USD y -1 从土地覆被类型上看, 森林 冰川和裸地的生态系统服务价值呈持续减少态势, 而农田的生态系统服务价值则呈持续增加态势 年间, 森林生态系统服务价值减少最为显著, 高达 USD y -1, 而农田生态服务价值增加则最为显著, 多达 USD y -1 尼泊尔境内的森林砍伐 耕地开垦和加德满都等城市的扩张造成了柯西河流域生态系统服务价值的降低, 这一过程则可能与该区域日益增多的人类活动密不可分 关键词 : 喜马拉雅 ; 柯西河流域 ; 生态系统服务 ;CA-Markov; 土地覆被
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