光谱学与光谱分析 |
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Application of Hyperspectral Remote Sensing in Research on Ecological Boundary in North Farming-Pasturing Transition in China |
WANG Hong-mei1, 2, WANG Kun1*, XIE Ying-zhong2 |
1. Institute of Grassland Science, College of Animal Science and Technology, China Agricultural University, Beijing 100094, China 2. Institute of Grassland Science, Ningxia University, Yinchuan 750021, China |
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Abstract Studies of ecological boundaries are important and have become a rapidly evolving part of contemporary ecology. The ecotones are dynamic and play several functional roles in ecosystem dynamics, and the changes in their locations can be used as an indicator of environment changes, and for these reasons, ecotones have recently become a focus of investigation of landscape ecology and global climate change. As the interest in ecotone increases, there is an increased need for formal techniques to detect it. Hence, to better study and understand the functional roles and dynamics of ecotones in ecosystem, we need quantitative methods to characterize them. In the semi-arid region of northern China, there exists a farming-pasturing transition resulting from grassland reclamation and deforestation. With the fragmentation of grassland landscape, the structure and function of the grassland ecosystem are changing. Given this perspective; new-image processing approaches are needed to focus on transition themselves. Hyperspectral remote sensing data, compared with wide-band remote sensing data, has the advantage of high spectral resolution. Hyperspectral remote sensing can be used to visualize transitional zones and to detect ecotone based on surface properties (e.g. vegetation, soil type, and soil moisture etc). In this paper, the methods of hyperspectral remote sensing information processing, spectral analysis and its application in detecting the vegetation classifications, vegetation growth state, estimating the canopy biochemical characteristics,soil moisture, soil organic matter etc are reviewed in detail. Finally the paper involves further application of hyperspectral remote sensing information in research on local climate in ecological boundary in north farming-pasturing transition in China.
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Received: 2008-05-10
Accepted: 2008-08-20
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Corresponding Authors:
WANG Kun
E-mail: wangkun6060@sina.com
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[1] Fortin M J, Ferson S, Iverson L. Landscape Ecology, 2000, 15(5): 453. [2] Gose J R, Ecological Application, 1993, 3: 369. [3] Fortin M J, Jacquaz G M. Oikos, 1996, 77(1): 51. [4] Fortin M J. Ecology, 1994, 75: 956. [5] Fortin M J. Oikos, 1995, 72(3): 323. [6] DONG Dao-yi, HAN Hui(龚道溢,韩 晖). Aata Geographica Sinica(地理学报), 2004, 59: 230. [7] WANG Kun(王 堃). Acta Agrestia Sinica(草地学报), 2000, 8: 267. [8] PU Rui-liang, GONG Peng(浦瑞良,宫 鹏). Theory and Application of Hyperspectral Remote Sensing(高光谱遥感及其应用). Beijing: Higher Education Press(北京: 高等教育出版社),2000. 52. [9] GUO Shi-de(郭仕德). Science of Surveying and Mapping(测绘科学), 2005, 30: 35. [10] YANG Ke-ming, GUO Da-zhi, CHEN Yun-hao(杨可明,郭达志, 陈云浩). Computer Engineering and Application(计算机工程与应用), 2006, 42(31): 213. [11] Mutanga O, Prins H H T. Remote Sensing of Environment, 2004, 89: 393. [12] Kokaly R F. Remote Sensing of Environment, 1999, 67: 437. [13] ZHAO De-hua, GU Jian-long(赵德化,顾建龙). Advance in Earth Sciences(地球科学进展), 2003, 18: 94. [14] GAO Yong-guang, HU Zhen-qi(高永光,胡振琪). Mining Research and Development(矿业研究与开发), 2006, 26: 44. [15] Browers S A. Soil Science, 1965, 100: 130. [16] Stone E R. Soil Science Society of American Journal, 1981, 45: 1161. [17] Liu W D, Gu X F. Remote Sensing of Environment, 2002, 81: 238. [18] LIU Wei-dong, ZHANG Bing(刘伟东,张 兵). Acta Pedologica Sinica(土壤学报), 2004, 41: 700. [19] Krishnan P, Soil Science Society of American Journal, 1980, 44: 1282. [20] Ben-Dor E, Banin A. International Journal of Remote Sensing, 2002, 23: 1043. [21] Chabrillat S, Krosley L. Remote Sensing of Environment, 2002, 82: 431. [22] Yang C C, Whalen J. Biosystem Engineering, 2002, 83: 291. [23] Ben-Dor E, Braun O. International Journal of Remote Sensing, 2004, 25: 2607. [24] Bajwa S G. Transaction of the ASAE, 2005, 48: 2399. [25] Ben-Dor E, Singer A. Geoderma, 2006, 131: 1. [26] Selige T, Schmidhalter U. Geoderma, 2006, 136: 235. [27] Bruegge C J, et al. Journal of Geophysical Research-Atmospheres, 1992, 97(D17): 18759. |
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