2000~2011年毛乌素沙地植被生长状况时空变化特征

doi:10.13249/j.cnki.sgs.2013.05.602

摘要/Abstract

摘要：

采用2000~2011年MOD13Q1数据构建EVI时间序列谱,对毛乌素沙地植被生长状况的时空特征进行了研究。主要得出以下结论：① 2000~2011年毛乌素沙地EVI年最大值以轻微波动为主,植被生长状况在2001年最差,2010年最好。② 全区EVI年最大值按0.002 5/a的速度递增,植被生长状况趋于改善的面积占总面积的81.916%,主要分布在中部、东部和南部等地区;生长状况呈变差趋势的面积占总面积的18.084%,主要分布在北部、西部和中南部。③ 植被生长状况以轻度和中度波动改善为主,主要分布在西南、中部和东部;重度波动改善区主要分布在西南部和东南部,轻度和中度波动退化区主要分布在西南部、西部和中南部。

关键词: 植被指数, 生长状况, 时空特征, 毛乌素沙地

Abstract:

Mu Us Sandy Land lies in the farming-pastoral ecotone of northern China, whose ecological environment shows great sensitivity and vulnerability. In this article, temporal series of MOD13Q1 productions, which derived from moderate resolution imaging spectroradiometer (MODIS) sensor in 2000-2011, were used to establish temporal sequence of annual enhanced vegetation index (EVI) maximum. Departure, slope, standard deviation and coefficient of variance of EVI were calculated to study the spatial and temporal characteristics of vegetation growth status in Mu Us Sandy Land. Results show that: 1) in 2000-2011, annual maximum of EVI value mainly fluctuates slightly in Mu Us Sandy Land and area whose EVI departure range is -0.1≤ΔE_EVI≤0.1, occupies more than 98.467% of the total area. Area with -0.2≤ΔE_EVI<-0.1 is the largest in 2001 (1.341%) and the smallest is in 2009 (0.074%). Area with 0.1<ΔE_EVI≤0.2 is the largest in 2002 (1.050%) and is the smallest in 2001 (0.138%). In the whole region, the average EVI departure values in 2000, 2001, 2003, 2005 and 2006 are negative while the values in 2002, 2004, 2007, 2008, 2009, 2010 and 2011 are positive. Vegetation growth status is the worst in 2001 and the second worst is in 2000. Conversely, the best vegetation growth status is in 2010 and the second best in 2002. 2) Annual maximum of EVI in the whole region increases by 0.002 5/a. Area with increasing trend occupies 81.916% of the whole area, which mainly distributes in the middle, eastern and southern parts of Sandy Land. Area with decreasing trend occupies 18.084% of the whole area, which mainly distributes in the northern, western, southern and middle parts of Mu Us Sandy Land. 3) Vegetation grow status mainly improves with slight and moderate fluctuations, whose areas occupie 35.878% and 34.697% of the total area respectively, which locate in the southwest, middle and eastern parts of Mu Us Sandy Land. Area of vegetation with severe fluctuated improving growth status occupies 8.381%. Moreover, areas with slight and moderate fluctuated degraded growth status occupies 7.876% and 7.174% of the whole area, respectively. Vegetation with severe fluctuated improving growth status mainly locates in the southwest and southeast parts of Sandy Land while slight and moderate fluctuated degraded growth status locate in the southwest, western, central and southern parts of Mu Us Sandy Land.

Key words: vegetation index, growth status, spatial and temporal characteristic, Mu Us Sandy Land

中图分类号:

TP79

闫峰, 吴波, 王艳姣. 2000~2011年毛乌素沙地植被生长状况时空变化特征[J]. 地理科学, 2013, 33(5): 602-608.

Feng YAN, Bo WU, Yan-jiao WANG. Spatial and Temporal Variations of Vegetation Growth Status in Mu Us Sandy Land in 2000-2011[J]. SCIENTIA GEOGRAPHICA SINICA, 2013, 33(5): 602-608.

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参考文献 19

[1]	国家林业局.中国荒漠化和沙化状况公报[R].2011,1.
[2]	Becker F,Choudhury B J.Relative sensitivity of normalized difference vegetation Index (NDVI) and microwave polarization difference Index (MPDI) for vegetation and desertification monitoring[J].Remote Sensing of Environment,1988,24(2):297-311.
[3]	Hellden U.A coupled human-environment model for desertification simulation and impact studies[J].Global and Planetary Change,2008,64(3-4): 158-168.
[4]	Colladoa A D,Chuviecob E,Camarasab A.Satellite remote sensing analysis to monitor desertification processes in the crop-rangeland boundary of Argentina[J]. Journal of Arid Environments,2002,52(1):121-133.
[5]	Tucher C J,Dregne H E,Newcomb W W.Expansion and Contraction of the Sahara Desert from 1980 to 1990[J].Science,1991,253(5017):299-300.
[6]	Wessels K J,Prince S D,Frost P E,et al.Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series[J].Remote Sensing of Environment,2004,91(1):47-67.
[7]	Fensholt R,Rasmussen K.Analysis of trends in the Sahelian ‘rain-use efficiency’ using GIMMS NDVI,RFE and GPCP rainfall data[J].Remote Sensing of Environment,2011,115(2): 438-451.
[8]	李惠敏,刘洪斌,武伟.近10年重庆市归一化植被指数变化分析[J].地理科学,2010,30(1):119-123.
[9]	侯西勇,应兰兰,高猛.1998~2008年中国东部沿海植被覆盖变化特征[J].地理科学,2010,30(5):735-741.
[10]	甘春英,王兮之,李保生,等.连江流域近18年来植被覆盖度变化分析[J].地理科学,2011,31(8):1019-1024.
[11]	卫亚星,王莉雯,石迎春,等.青海省草地资源净初级生产力遥感监测[J].地理科学,2012,32(5):621-627.
[12]	闫峰,王艳姣,武建军,等.基于Ts-EVI时间序列谱的冬小麦面积提取[J].农业工程学报,2009, 25(4):135-140.
[13]	闫峰,王艳姣.基于Ts-EVI特征空间的土壤水分估算[J].生态学报,2009,29(9): 4884-4891.
[14]	Barnett J L,Thompson D R.Large area relation of Landsat MSS and NOAA-6 AVHRR spectral data to wheat yield[J].Remote Sensing of Environment,1983,13(2):227-290.
[15]	Singh R,Goyal R C,Saha S K,et al.Use of satellite spectral data in crop yield estimation surveys[J].International Journal of Remote Sensing,1992,13(13):2583-2592.
[16]	Huete A,Didan K,Shimabokuro Y,et al.Regional Amazon Basin and global analysis of MODIS vegetation indices: early results and comparisons with AVHRR[J].Proceeding of IGARSS,2000,6(2): 536-538.
[17]	闫峰,王艳姣.基于Ts-EVI特征空间的土壤水分估算[J].生态学报,2009,29(9): 4884-4891.
[18]	Liu H Q,Huete A R.A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J].IEEE Transactions on Geoscience and Remote Sensing, 1995,33(2):457-465.
[19]	Huete A,Didan K,Miura T,et al.Overview of the radiometric and biophysical performance of the MODIS Vegetation Indice[J].Remote Sensing of Environment,2002,83(1-3):195-213.

年代 (年)	面积占总面积百分比(%)
年代 (年)	ΔE_EVI <-0.2	-0.2≤ΔE_EVI<-0.1	-0.1≤ΔE_EVI<0	ΔE_EVI=0	0<ΔE_EVI≤0.1	0.1<ΔE_EVI≤0.2	ΔE_EVI >0.2
2000	0.023	1.198	80.507	0.006	17.954	0.272	0.040
2001	0.018	1.341	88.235	0.005	10.248	0.138	0.016
2002	0.006	0.194	39.251	0.014	59.448	1.050	0.037
2003	0.006	0.153	54.552	0.016	44.912	0.338	0.024
2004	0.001	0.112	47.656	0.017	51.618	0.516	0.079
2005	0.001	0.154	49.602	0.019	50.021	0.194	0.010
2006	0.004	0.203	53.068	0.017	46.440	0.257	0.012
2007	0.003	0.118	42.840	0.020	56.633	0.356	0.030
2008	0.003	0.146	48.959	0.018	50.540	0.322	0.012
2009	0.002	0.074	34.403	0.016	64.779	0.692	0.033
2010	0.004	0.115	23.581	0.014	75.648	0.589	0.050
2011	0.017	0.292	36.925	0.015	62.050	0.652	0.049

波动程度		变化趋势
C_CV	程度	S_slope	趋势	S_slope	趋势
0≤C_CV<0.06	正常	S_slope>0	改善	S_slope<0	退化
0.06≤C_CV<0.12	轻度
0.12≤C_CV<0.18	中度
0.18≤C_CV<0.24	重度
C_CV≥0.24	特重度

编号	类型	面积（km²）	比例（%）
-5	特重度波动退化区	276.06	0.719
-4	重度波动退化区	756.06	1.970
-3	中度波动退化区	2753.50	7.174
-2	轻度波动退化区	3022.94	7.876
-1	正常波动退化区	132.06	0.344
1	正常波动改善区	446.06	1.162
2	轻度波动改善区	13770.00	35.878
3	中度波动改善区	13316.56	34.697
4	重度波动改善区	3216.63	8.381
5	极重度波动改善区	689.69	1.797