鄂尔多斯区内毛乌素沙地1987—2022年植被覆盖时空演变

doi:10.19657/j.geoscience.1000-8527.2024.066

现代地质 ›› 2024, Vol. 38 ›› Issue (03): 674-682.DOI: 10.19657/j.geoscience.1000-8527.2024.066

• 表生资源观测模拟与预测评价 • 上一篇下一篇

鄂尔多斯区内毛乌素沙地1987—2022年植被覆盖时空演变

尹永会¹^,²^,³(), 孔祥生⁴(), 吴浩然¹^,²^,³, 刘玖芬³^,⁵, 王凯¹, 陈熹卓¹, 王寒冰¹^,²^,³, 张晶¹, 王小天¹

1.中国地质调查局烟台海岸带地质调查中心,山东烟台 264000
2.自然资源要素耦合过程与效应重点实验室,北京 100055
3.内蒙古鄂尔多斯自然资源综合利用野外科学观测研究站,内蒙古鄂尔多斯 017000
4.鲁东大学,山东烟台 264025
5.中国地质调查局自然资源综合调查指挥中心,北京 100055

出版日期:2024-06-10 发布日期:2024-07-04
通讯作者: 孔祥生,男,博士,教授,1969年出生,主要从事遥感科学与技术、地理信息综合集成研究。Email: emails305@163.com。
作者简介:尹永会,男,硕士,高级工程师,1988年出生,主要从事自然资源监测、地理信息系统与遥感技术研究。Email: y2h521@163.com。
基金资助:
中国地质调查局项目“自然资源要素综合观测数据集成与应用服务”(DD20208067);“黄河流域中上游土壤植被资源调查监测与评价”(DD20220884)

Analysis of Vegetation Cover Spatio-temporal Evolution of Mu Us Sand Land of Ordos Region from 1987 to 2022

YIN Yonghui¹^,²^,³(), KONG Xiangsheng⁴(), WU Haoran¹^,²^,³, LIU Jiufen³^,⁵, WANG Kai¹, CHEN Xizhuo¹, WANG Hanbing¹^,²^,³, ZHANG Jing¹, WANG Xiaotian¹

1. Yantai Center of Coastal Zone Geological Survey, China Geological Survey, Yantai, Shandong 264000, China
2. Key Laboratory of Natural Resource Coupling Process and Effects, Beijing 100055,China
3. Inner Mongolia Ordos Natural Resources Comprehensive Utilization Field Scie.pngic Observation and Research Station, Ordos, Inner Mongolia 017000, China
4. Lu Dong University, Yantai, Shandong 264025,China
5. Command Center for Natural Resources Comprehensive Survey, China Geological Survey, Beijing 100055,China

Online:2024-06-10 Published:2024-07-04

摘要/Abstract

摘要：

毛乌素沙地作为我国四大沙地之一,是沙漠化和防沙治沙研究的重点区域。遥感已成为地表时空分析的重要手段,但当前毛乌素沙地区域基于长时间序列、中高分辨率影像研究不足。利用Google Earth Engine云平台,结合Landsat-5 TM、Landsat-7 ETM+和Landsat-8 OLI长时序归一化植被指数,采用Sen+Mann-Kendall方法探究鄂尔多斯地区毛乌素沙地1987—2022年近35年植被覆盖时空演变,并结合气象数据进行驱动力分析。结果表明:(1)鄂尔多斯地区毛乌素沙地植被不断改善,植被覆盖整体呈增长趋势, NDVI变化量为+0.0028 a^-1,NDVI增势呈先缓-后急-再缓的趋于平稳的阶段式变化特征;(2)植被改善的区域面积占比达98%以上,退化区域面积占比小于0.5%,空间上植被覆盖改善东部优于西部,南北部优于中部;(3)植被覆盖变化与自然水热变化无显著相关性。

关键词: 植被覆盖, 趋势分析, 谷歌地球引擎, 毛乌素沙地

Abstract:

The Mu Us Sand land, as one of the four major sandy areas in China, is a key region for the study and control of dese.pngication. Remote sensing has become an important tool for the analysis of spatiotemporal dynamics on the Earth’s surface. However, the Mu Us region currently lacks long-term time series and medium to high-resolution studies on the spatiotemporal evolution of vegetation cover. Based on the Google Earth Engine cloud platform and using long-term NDVI remote sensing data from Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI, the Sen+Mann-Kendall method was employed to analyze the spatiotemporal evolution of vegetation coverage in the Mu Us sand land in Ordos from 1987 to 2022, spanning nearly 35 years, and combined it with meteorological data for driving force analysis. The results indicate: (1) Vegetation has continuously improved with an overall increasing trend in vegetation cover. The NDVI change rate is +0.0028 per year, and the trend of NDVI increase shows a phased change characteristic of initially slow growth, followed by rapid growth, and then stabilization. (2) The proportion of areas with improved vegetation cover exceeds 98%, while the proportion of degraded areas is less than 0.5%. Spatially, vegetation cover improvement is better in the eastern regions compared to the western regions, and in the northern and southern regions compared to the central region. (3) There is no significant correlation between vegetation cover change in the study area and natural hydrothermal conditions.

Key words: vegetation cover, trend analysis, Google Earth Engine, Mu Us sand land

中图分类号:

P962

尹永会, 孔祥生, 吴浩然, 刘玖芬, 王凯, 陈熹卓, 王寒冰, 张晶, 王小天. 鄂尔多斯区内毛乌素沙地1987—2022年植被覆盖时空演变[J]. 现代地质, 2024, 38(03): 674-682.

YIN Yonghui, KONG Xiangsheng, WU Haoran, LIU Jiufen, WANG Kai, CHEN Xizhuo, WANG Hanbing, ZHANG Jing, WANG Xiaotian. Analysis of Vegetation Cover Spatio-temporal Evolution of Mu Us Sand Land of Ordos Region from 1987 to 2022[J]. Geoscience, 2024, 38(03): 674-682.

图/表 9

图1 毛乌素沙地(鄂尔多斯市境内)区位图

Fig.1 Map of the Mu Us sand land in Ordos

表1 数据信息表

Table 1 Data information sheet

数据集	影像行列号	年份	景数
Landsat-5 TM	127/032;127/033;127/034 128/032;128/033;128/034 129/032;129/033;129/034 130/032;130/033	1987—2011	4420
Landsat-7 ETM+		2012—2013	376
Landsat-8 OLI		2013—2022	2183
ERA5	-	1987—2022	13145

图2 毛乌素沙地年NDVI分布(1987—2022年,以5年为间隔)

Fig.2 NDVI distribution map of the Mu Us sand land (five-year interval from 1987 to 2022)

图3 1987年到2022年毛乌素沙地年NDVI均值

Fig.3 Annual average NDVI of the Mu Us sand land from 1987 to 2022

图4 毛乌素沙地植被变化趋势Sen斜率(a)和显著性检验Z值(b)

Fig.4 Sen slope of vegetation change trend (a) and Z-value of significance test (b) in Mu Us sand land

表2 Sen与Mann-Kendall法趋势分析结果

Table 2 Result of Sen and Mann-Kendall methods

Sen斜率	Z值	植被变化趋势	面积占比(%)
≥ 0.0005	≥1.96	显著改善	97.88
≥ 0.0005	-1.96~1.96	轻微改善	0.87
-0.0005~0.0005	-1.96~1.96	稳定不变	0.80
≤-0.0005	-1.96~1.96	轻微退化	0.19
≤-0.0005	≤-1.96	显著退化	0.24

图5 区域A历史影像(CIR)(区域A位置见图4)

Fig.5 History images of region A (CIR) (seeing Fig.4 for location of region A)

图6 区域B历史影像(CIR)(区域B位置见图4)

Fig.6 History images of region B (CIR) (seeing Fig.4 for location of region B)

图7 年NDVI均值与气温(a)和降水(b)相关性

Fig.7 Correlation between annual NDVI mean and temperature (a) and precipitation (b)

参考文献 40

[1]	PIAO S L, WANG X H, CIAIS P, et al. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006[J]. Global Change Biology, 2011, 17(10): 3228-3239.
[2]	WANG J, WANG K L, ZHANG M Y, et al. Impacts of climate change and human activities on vegetation cover in hilly Southern China[J]. Ecological Engineering, 2015, 81: 451-461.
[3]	WANG C, HOU P, LIU X M, et al. Spatiotemporal changes in vegetation cover of the national key ecosystem protection and restoration project areas, China[J]. Acta Ecologica Sinica, 2023, 43(21): 8903-8916.
[4]	吴薇. 毛乌素沙地沙漠化过程及其整治对策[J]. 中国生态农业学报, 2001, 9(3): 15-18.
[5]	漆喜林. 陕西毛乌素沙地沙漠化治理现状及对策[J]. 陕西林业科技, 2002(3): 61-63.
[6]	吴薇. 近50年来毛乌素沙地的沙漠化过程研究[J]. 中国沙漠, 2001, 21(2): 164-169.
[7]	房世波, 许端阳, 张新时. 毛乌素沙地沙漠化过程及其气候因子驱动分析[J]. 中国沙漠, 2009, 29(5): 796-801.
[8]	WU B, CI L J. Landscape change and dese.pngication development in the Mu Us Sandland, Northern China[J]. Journal of Arid Environments, 2002, 50(3): 429-444.
[9]	ZENG L L, WARDLOW B D, XIANG D X, et al. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data[J]. Remote Sensing of Environment, 2020, 237: 111511.
[10]	RIIHIMÄKI H, LUOTO M, HEISKANEN J. Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data[J]. Remote Sensing of Environment, 2019, 224: 119-132.
[11]	陈震, 张耘实, 陈建平, 等. 基于分形特征的高标准农田遥感分类方法研究[J]. 现代地质, 2018, 32(3): 595-601.
[12]	安国英, 郭兆成, 叶佩. 云南大理地区1989—2019年期间气候变化及对洱海水质的影响[J]. 现代地质, 2022, 36(2): 406-417.
[13]	AKRITAS M G, MURPHY S A, LAVALLEY M P. The Theil-Sen estimator with doubly censored data and applications to astronomy[J]. Journal of the American Statistical Association, 1995, 90(429): 170-177.
[14]	王欣毅, 杨洁, 林良国, 等. 基于Sen+Mann-Kendall陕西省植被覆盖度时空变化规律研究[J]. 农业与技术, 2023, 43(7): 62-66.
[15]	赵耀华, 彭小清, 金浩东, 等. 基于多源遥感数据的青藏高原植被变化特征评价[J]. 冰川冻土, 2022, 44(4): 1216-1230. DOI
[16]	汪东川, 陈星, 孙志超, 等. 格尔木长时间序列遥感生态指数变化监测[J]. 生态学报, 2022, 42(14): 5922-5933.
[17]	周紫燕, 汪小钦, 丁哲, 等. 新疆生态质量变化趋势遥感分析[J]. 生态学报, 2020, 40(9): 2907-2919.
[18]	GORELICK N, HANCHER M, DIXON M, et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202: 18-27.
[19]	国家林业和草原局. “关于毛乌素沙地绿上加绿、提质增效的建议”复文[EB]. 2021, 第3308号.
[20]	SCHMIDT G, JENKERSON C, MASEK J, et al. Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description[R]. Washington: US Geological Survey Open-File Report, 2013, 17.
[21]	DWYER J L, ROY D P, SAUER B, et al. Analysis ready data: Enabling analysis of the landsat archive[J]. Remote Sensing, 2018, 10(9): 1363.
[22]	CHEN J, ZHU X L, VOGELMANN J E, et al. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images[J]. Remote Sensing of Environment, 2011, 115(4): 1053-1064.
[23]	MUÑOZ S J. ERA5-Land monthly averaged data from 1981 to present[DB]. Copernicus Climate Change Service (C3S) Climate Data Store(CDS), 2019.
[24]	HOFFMANN L, GÜNTHER G, LI D, et al. From ERA-Interim to ERA5: The considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations[J]. Atmospheric Chemistry and Physics, 2019, 19(5): 3097-3124.
[25]	JIANG Q, LI W Y, FAN Z D, et al. Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland[J]. Journal of Hydrology, 2021, 595: 125660.
[26]	XU B, QI B, JI K, et al. Emerging hot spot analysis and the spatial-temporal trends of NDVI in the Jing River Basin of China[J]. Environmental Earth Sciences, 2022, 81(2): 55.
[27]	GAO L, WANG X F, JOHNSON B A, et al. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159: 364-377. DOI PMID
[28]	HUANG S, TANG L N, HUPY J P, et al. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing[J]. Journal of Forestry Research, 2021, 32(1): 1-6.
[29]	SUN L, LI H, WANG J, et al. Impacts of climate change and human activities on NDVI in the Qinghai-Tibet Plateau[J]. Remote Sensing, 2023, 15(3): 587.
[30]	龚斌, 万力, 胡伏生, 等. 沙漠绿洲变化的遥感监测方法[J]. 现代地质, 2005, 19(1): 152-156.
[31]	LEE T Y, KAUFMAN Y J. Non-lambertian effects on remote sensing of surface reflectance and vegetation index[J]. IEEE Transactions on Geoscience and Remote Sensing, 1986, GE-24(5): 699-708.
[32]	WANG L, JIA M M, YIN D M, et al. A review of remote sensing for mangrove forests: 1956-2018[J]. Remote Sensing of Environment, 2019, 231: 111223.
[33]	JIANG W G, YUAN L H, WANG W J, et al. Spatio-temporal analysis of vegetation variation in the Yellow River Basin[J]. Ecological Indicators, 2015, 51: 117-126.
[34]	LIU Z Z, WANG H, LI N, et al. Spatial and temporal characteristics and driving forces of vegetation changes in the Huaihe River Basin from 2003 to 2018[J]. Sustainability, 2020, 12(6): 2198.
[35]	HUSSAIN M, MAHMUD I. PyMannKendall: A python package for non parametric Mann Kendall family of trend tests[J]. Journal of Open Source Software, 2019, 4(39): 1556.
[36]	王佃来, 刘文萍, 黄心渊. 基于Sen+Mann-Kendall的北京植被变化趋势分析[J]. 计算机工程与应用, 2013, 49(5):13-17.
[37]	LEBRINI Y, BENABDELOUAHAB T, BOUDHAR A, et al. Farming systems monitoring using machine learning and trend analysis methods based on fitted NDVI time series data in a semi-arid region of Morocco[M]//Remote Sensing for Agriculture, Ecosystems, and HydrologyXXI. Strasbourg: SPLE, 2019.
[38]	HU J M, YE B Y, BAI Z K, et al. Remote sensing monitoring of vegetation reclamation in the antaibao open-pit mine[J]. Remote Sensing, 2022, 14(22): 5634.
[39]	ZHONG X Z, LI J, WANG J L, et al. Linear and nonlinear characteristics of long-term NDVI using trend analysis: A case study of lancang-mekong river basin[J]. Remote Sensing, 2022, 14(24): 6271.
[40]	中共中央国务院印发黄河流域生态保护和高质量发展规划纲要[J]. 自然资源通讯, 2021(19):17-35.

鄂尔多斯区内毛乌素沙地1987—2022年植被覆盖时空演变

Analysis of Vegetation Cover Spatio-temporal Evolution of Mu Us Sand Land of Ordos Region from 1987 to 2022

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