The scale-dependence of geographical pattern requires that research on the spatial heterogeneity of vegetation cover should be conducted at multi-scales. Based on the satellite-derived Normalized Difference Vegetation Index (NDVI), this paper applied Geographic Information System (GIS) and Geostatistics (GS) softwares to investigate the scale-dependence, isotropy and anisotropy of spatial heterogeneity of vegetation cover, with a case study of Guizhou Karst Plateau. The main conclusions can be drawn as follows. (1) The spatial heterogeneity of NDVI was significantly scale-dependent because scale coarsening had remarkable smoothing effects on NDVI. However, the mechanism of data coarsening was different between two kinds of resampling methods. (2) There were differences in the spatial patterns of NDVI between RS data sources. The comparative results from traditional statistics and geostatistics for three NDVI datasets were different, indicating that spatial information is very important for statistical analysis. (3) Spatial heterogeneity of NDVI was accompanied with anisotropy, which was sensitive to RS sources. The important impact of altitude, precipitation and bio-temperature on the macro spatial distribution of NDVI also changed with spatial directions. The research findings are helpful for rocky desertification controlling and ecological reconstruction.
GAO Jiangbo, WU Shaohong, CAI Yunlong
. Investigating the spatial heterogeneity of vegetation cover at multi-scales：A case study in karst Guizhou Plateau of China[J]. GEOGRAPHICAL RESEARCH, 2013
, 32(12)
: 2179
-2188
.
DOI: 10.11821/dlyj201312013
[1] 李双成, 刘逢媛, 高江波. 基于L-Z 算法的NDVI变化复杂性的空间格局及其成因: 以北京周边为例. 自然科学进展, 2008, 18(1): 68-74.
[2] Xiao J F, Moody A. Trends in vegetation activity and their climatic correlates: China 1982 to 1998. International Journal of Remote Sensing, 2004, 25(24): 5669-5689.
[3] Maselli F, Chiesi M. Integration of multi-source NDVI data for the estimation of Mediterranean forest productivity. International Journal of Remote Sensing, 2006, 27(1): 55-72.
[4] 李克让, 陈育峰, 黄玫, 等. 气候变化对土地覆被变化的影响及其反馈模型. 地理学报, 2000, 55(z1): 57-63.
[5] 方精云, 朴世龙, 贺金生, 等. 近二十年来中国植被活动在增强. 中国科学(C 辑), 2003, 33(6): 554-565.
[6] Myneni R B, Keeling C, Tucker CJ et al. Increase plant growth in the north high latitudes from 1981 to 1991. Nature, 1997, 386: 698-702.
[7] 李晓兵, 史培军. 基于NOAA/AVHRR 数据的中国主要植被类型NDVI 变化规律研究. 植物学报, 1999, 41: 314-324.
[8] Tucker C J, Slayback D A, Pinzon J E et al. Higher northern latitude NDVI and growing season trends from 1982 to 1999. International Journal of Biometeorology, 2001, 45: 184-190.
[9] 吴绍洪, 杨勤业, 郑度. 生态地理区域界线划分的指标体系. 地理科学进展, 2002, 21(4): 302-310.
[10] 张镱锂, 丁明军, 张玮, 等. 三江源地区植被指数下降趋势的空间特征及其地理背景. 地理研究, 2007, 26(3): 500-507.
[11] 张雪艳, 胡云锋, 庄大方, 等. 蒙古高原NDVI 的空间格局及空间分异. 地理研究, 2009, 28(1): 10-18.
[12] 邬建国. 景观生态学: 格局、过程、尺度与等级. 北京: 高等教育出版社, 2000.
[13] 吕一河, 傅伯杰. 生态学中的尺度及尺度转换方法. 生态学报, 2001, 21(12): 2096-2105.
[14] 李双成, 蔡运龙. 地理尺度转换若干问题的初步探讨. 地理研究, 2005, 24(1): 11-18.
[15] 蔡运龙. 贵州喀斯特高原土地系统变化空间尺度综合的一个研究方案. 地球科学进展, 2009, 24(12): 1301-1308.
[16] 高江波, 蔡运龙. 区域景观破化的多尺度空间变异研究: 以贵州省乌江流域为例. 地理科学, 2010, 30(5): 742-747.
[17] 熊康宁, 黎平, 周忠发, 等. 喀斯特石漠化的遥感-GIS 典型研究: 以贵州省为例. 北京: 地质出版社, 2002.
[18] 兰安军, 张百平, 熊康宁, 等. 黔西南脆弱喀斯特生态环境空间格局. 地理研究, 2003, 22(6): 732-740.
[19] Wang S J, Liu Q M, Zhang D F. Karst rocky desertification in southwestern China: Geomorphology, landuse, impact and rehabilitation. Land Degradation & Development, 2004, 15: 115-121.
[20] 中国科学院学部. 关于推进西南岩溶地区石漠化综合治理的若干建议. 地球科学进展, 2003, 18(4): 489-492.
[21] Herrmann S, Anyamba A, Tucker C. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Global Environmental Change, 2005, 15: 394-404.
[22] Gao J B, Li S C, Zhao Z Q. Validating the demarcation of eco-geographical regions: A geostatistical application.Environmental Earth Sciences, 2010, 59: 1327-1336.
[23] Holben B N. Characteristics of maximum-value composite images for temporal AVHRR data. International Journal of Remote Sensing, 1986, 7(11): 1435-1445.
[24] Tucker C J, Pinzon J E, Brown M E. Global Inventory Modeling and Mapping Studies (GIMMS) Satellite Drift Corrected and NOAA-16 Incorporated Normalized Difference Vegetation Index (NDVI). 1981. Version:1.0. http://glcf. umiacs.umd.edu/data/gimms/, 2004-12-12.
[25] Deutsch C V, Journel A G. GSLIB, Geostatistical Software Library and User's Guide. 2nd edn. New York: Oxford University Press, 1998.
[26] Tarnavsky E, Garrigues S, Brown M E. Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sensing Environment, 2008, 112: 535-549.
[27] Verburg P H, Chen Y Q. Multiscale characterization of land-use patterns in China. Ecosystems, 2000, 3: 369-385.
[28] Hijmans R J, Cameron S E, Parra J L et al. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 2005, 25: 1965-1978.
[29] Jarlan L, Mangiarotti S, Mougin E et al. Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model. Remote Sensing of Environment, 2008, 112: 1381-1394.
[30] Zhang G L, Zhang Y J, Dong J W et al. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. PNAS, 2013, 110(11): 4309-4314.
