Abstract
The area northwest of Beijing is one of the most important regions where many organizations invest and pay most attention. The environmental problems in this region affect not only Beijing but also the surrounding area. Based on observation of the characteristics of the change in sandy land, this study classified four types of dynamic change of sandy land, including extended sandy land, the reversely changed sandy land, the potential sandy land and no change in sandy land. Then the process and the trend of changes in sandy land and their environmental impact on the area northwest of Beijing were analyzed. The results show that the area of sandy land has increased in this region in the period of 1991 to 2002. Change between sandy land and grassland was the dominant change. It is found that the monitoring zones of Hunshandake sandy land and north of Yin Shan are regions with high ratio of extended sandy land, and are connected with widespread potential change of sandy land. This implies that these two regions have a high probability of increase in sandy land in the future. On the other hand, in the monitoring zone of Horqin sandy land and Ba Shang Plateau and its surrounding area, desertification had been controlled and the area of sandy land is expected to decrease. This indicates that the direction of the sandstorm to Beijing is expected to gradually move to the northwest. Furthermore, the decreases in sandy land and the reversing change from arable land to grassland and forests in the study region will affect the land quality and atmosphere. And the logistic multiple regression (LMR) model was employed to better understand the complexity and processes of increases in sandy land. This model predicts that there is a high probability of increases in sandy land in north of Siziwang Banner, Zhengxiangbai Banner and Zhenglan Banner. Finally, suggestions to the ecological construction of the study area have been proposed.
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References
Arbia, G.: 1986, ‘The MAUP and the spatial autocorrelation problem: Toward a joint approach’, Metron 44, 391–407.
Benoît, M. and Eric, F. Lambin: 2000, ‘Land-Cover-Change Trajectories in SouthernCameroon’, Annals of the Association of American Geographers. 90 467–494.
Brown, S. and Lugo, A.: 1990, ‘Tropical Secondary Forests’, Journal of TropicalEcology 6, 1–32.
Chavez Jr., P.S.: 1996, ‘Image Based Atmospheric Corrections Revisited andImproved’, Photogramm. Eng. Remote Sens. 62, 1025–1036.
He, H. M. and Zhou, J.: 2002, ‘Mechanisms of Block Function of Shelter-foreston Sand Storms’, China Desert 22, 197–200.
Jakubauskas, M. E., Peterson, D. L., Kastens, J. H. and Legates, D. R.: 2002, ‘Time SeriesRemote Sensing Analysis of Landscape–vegetation Interactions in the Southern GreatPlains’, Photogrammetric Engineering and Remote Sensing 68, 1021–1030.
Kalluri, S.: 2002, ‘Monitoring Ecosystems Vulnerable to Climate Change’, in: Proceedings International Geoscience and Remote Sensing Symposium (IGARSS), May, 2002, Toronto, IEEE, 2802–2804.
Liu, D. S., Iverson, L. R. and Brown, S.: 1993, ‘Rates and Patterns of Deforestation in thePhilippines: Application of Geographic Information Systems Analysis’, Forest Ecologyand Management 57, 1–16.
Liu, J. Y. and Buheaosier: 1996, ‘Developing the Land Use Change Study in China by theTechnology of Remote Sensing and Geographic Information System’, in J. Y. Liu andBuheaosier, (ed), The New Progress and the Development Stratagem of Remote Sensing, Chinese Scientific & Technological Press, Beijing, China. pp. 89–112.
Ludeke, A. K., Maggio, R. C. and Reid, L. M.: 1990, ‘An Analysis of AnthropogenicDeforestation Using Logistic Regression and GIS’, Journal of Environmental Management. 31 247–259.
Mertens, B. and Lambin, E.: 1997, ‘Spatial Modeling of Deforestation in Southern Cameroon:Spatial Disaggregation of Diverse Deforestation Processes’, Applied Geography 17, 143–162.
Press, S. J. and Wilson, S.: 1978, ‘Choosing between logisticregression and discriminant analysis’, Journal of the AmericanStatistical Association 73, 699–705.
Reng, X. and Hu, F.: 2004, ‘Assessment on PM 10 of Beijing Atmosphere Affected by Sand Storms in the Period of 2000 to 2002’, Environ. Science Research 17, 51–55.
Sader, S. A. and Joyce, A. T.: 1988, ‘Deforestation rates and Trends in Costa Rica’, Biotropica. 20, 11–19.
Shao, Y. P. and Lance M. L.: 1997, ‘Wind erosion prediction over the Australiancontinent’, J. Geophys. Res. 102, 30091–30105.
Sun, R. and Zhu, Q. J.: 2001, ‘Effect of Climate Change on Terrestrial Net PrimaryProductivity in China’, Journal of Remote Sensing. 5, 61–62.
Wang, T., Wu, W. and Wang, X. Z.: 1998, ‘Remote Sensing Monitoring and Assessing SandyDesertification: An Example from the Sandy Desertification Region of Northern China’, Quaternary Sciences. 2, 108–118.
Zhang, F. R., Wang, J. and Chen, B. M.: 2003, The Indicators System and Methods ofthe Land Sustainable Use Assessment, Chinese Agriculture Press, Beijing, pp. 1–18.
Zhu, Z. D.: 1998, ‘The Concept, Cause and Prevention of Land Desertification in China’, Quaternary Sciences. 2, 145–155.
Zhong, D. C.: 1998, ‘Desert and Classification’, in Zhong D. C. (ed.), The Dynamic Developmentof Sandy Land in China, Gansu Culture Press, Lanzhou, China. pp. 6–10.
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Wang, J., He, T., Guo, X. et al. Dynamic Changes of Sandy Land in Northwest of Beijing, China. Environ Monit Assess 121, 109–125 (2006). https://doi.org/10.1007/s10661-005-9110-8
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DOI: https://doi.org/10.1007/s10661-005-9110-8