格尔木河流域植被指数时空分布及其影响因素研究

doi:10.19657/j.geoscience.1000-8527.2019.02.21

现代地质 ›› 2019, Vol. 33 ›› Issue (02): 461-468.DOI: 10.19657/j.geoscience.1000-8527.2019.02.21

• 水文地质学 • 上一篇

格尔木河流域植被指数时空分布及其影响因素研究

张绪财(), 金晓媚(), 朱晓倩, 张京

中国地质大学(北京) 水资源与环境学院,北京 100083

收稿日期:2018-02-15 修回日期:2018-10-20 出版日期:2019-05-08 发布日期:2019-05-08
通讯作者: 金晓媚
作者简介:金晓媚,女,博士,教授,1968年出生,水文地质及工程地质专业,主要从事生态水文学方面的研究。Email: jinxm@cugb.edu.cn。
张绪财,男,硕士研究生,1993年出生,环境工程专业,主要从事生态水文学方面的研究。Email: zhangxucai@cugb.edu.cn。
基金资助:
国家自然科学基金项目(41372250)

Spatial-temporal Characteristics of Vegetation Index and Its Impact Factors in the Golmud River Basin

ZHANG Xucai(), JIN Xiaomei(), ZHU Xiaoqian, ZHANG Jing

School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China

Received:2018-02-15 Revised:2018-10-20 Online:2019-05-08 Published:2019-05-08
Contact: JIN Xiaomei

摘要/Abstract

摘要：

格尔木河流域气候干旱少雨,生态环境较脆弱,植被动态对其生态环境保护具有重要意义。基于连续序列的MODIS NDVI数据,分析了格尔木河流域植被指数时空分布及其影响因素。结果表明:研究区NDVI平均值总体较小,主要在0.10~0.12间波动,但呈增大趋势。区内植被改善区分布在格尔木市东、西两侧,基本不变区为荒漠地区,植被退化区分布在北部盐湖区。区内裸土的面积逐渐减小,低覆盖率和高覆盖率植被的面积逐渐增加。研究区植被生长与气象、土壤水分和地下水位埋深都有关系。气温与植被指数相关关系较好,相关系数为0.822,而降水对植被的生长也有一定的作用。植被指数与表观热惯量是正相关关系,相关系数为0.979。区内植被的地下水位埋深范围为0~12 m,在水位埋深约为6.5 m的地方,植被长势最好。

关键词: 植被覆盖率, MODIS NDVI, 表观热惯量, 地下水位埋深, 格尔木河流域

Abstract:

Located in the south-central part of Qaidam Basin in Qinghai Province, the Golmud River Basin is arid and its ecology is fragile. Vegetation dynamics are of great significance to ecological conservation in arid regions. Based on continuous record of MODIS NDVI data, the spatial-temporal characteristics of vegetation index in the Golmud River Basin were analyzed via remote sensing, and the influencing factors were discussed. The results show that the annual average NDVI value is generally small, but increased from 0.10 to 0.12 during 2001 to 2016. Areas of increasing NDVI trend are distributed to the east and west of Golmud City, while constant and decreasing trends of NDVI occur in the desert and the northern salt pond area, respectively. Both areas of low-density and high-density vegetation show an increasing rate, whereas the bare soil shows a decreasing trend in the vegetation coverage. Generally, the vegetation variation depends on various attributes such as climate change, soil moisture and groundwater depth. The correlation between NDVI and temperature is good (correlation coefficieis=0.822), and precipitation also has a certain impact on the growth of vegetation. There is a strong correlation between NDVI value and the apparent thermal inertia (correlation coefficieis=0.979). The corresponding range of groundwater depth to vegetation in the study area is from 0 to 12 m, and the vegetation growth is the best when the groundwater depth is about 6.5 m.

Key words: vegetation coverage, MODIS NDVI, apparent thermal inertia (ATI), groundwater depth, Golmud River Basin

中图分类号:

TP79
P641

张绪财, 金晓媚, 朱晓倩, 张京. 格尔木河流域植被指数时空分布及其影响因素研究[J]. 现代地质, 2019, 33(02): 461-468.

ZHANG Xucai, JIN Xiaomei, ZHU Xiaoqian, ZHANG Jing. Spatial-temporal Characteristics of Vegetation Index and Its Impact Factors in the Golmud River Basin[J]. Geoscience, 2019, 33(02): 461-468.

图/表 9

图1 格尔木河流域地理位置图

Fig.1 Geographic map of the Golmud River Basin

图2 研究区NDVI年际变化趋势

Fig.2 Histogram showing the long-time NDVI variation in the study area

图3 研究区2016年植被指数(a)、植被覆盖率(b)及表观热惯量(c)空间分布图

Fig.3 Spatial distribution maps of NDVI (a), fc (b) and ATI (c) in 2016

图4 研究区不同地貌类型的NDVI变化

Fig.4 Diagram showing the NDVI variation between different land-cover types in the study area

图5 研究区2001—2016年植被变化趋势图

Fig.5 Variation trends of vegetation in the study area during 2001-2016

表1 2016年研究区植被覆盖率面积统计表

Table 1 Statistics of sizes of different vegetation coverage in the study area (Year 2016)

f_c/%	面积/km²	所占比例/%
<0	301.19	6.60
0~10	1 880.69	41.19
10~30	1 086.63	23.80
30~50	476.81	10.44
50~100	828.81	17.97
合计	4 566.13	100.00

图6 研究区2001—2016年不同覆盖率植被的面积变化

Fig.6 Changes of sizes of different vegetation coverage in the study area (Year 2001-2016)

图7 研究区植被指数与降水量(a)、气温(b)和ATI(c)的相关性分析

Fig.7 Correlation between NDVI and rainfall (a), temperature (b) and ATI (c) in the study area

图8 研究区植被与地下水埋深的关系

Fig.8 Correlation between groundwater depth and NDVI in the study area

参考文献 25

[1]	ZHANG X F, LIAO C H, LI J, et al. Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data[J]. International Journal of Applied Earth Observation and Geoinformation, 2013,21(1):506-512. DOI URL
[2]	XIAO J F, MOODY A. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA[J]. Remote Sensing of Environment, 2005,98(2):237-250. DOI URL
[3]	HOFFMANN W A, JACKSON R B. Vegetation-climate feedbacks in the conversion of tropical savanna to grassland[J]. Journal of Climate, 2000,13(9):1593-1602. DOI URL
[4]	KUTIEL P, COHEN O, SHOSHANY M, et al. Vegetation establishment on the southern Israeli coastal sand dunes between the years 1965 and 1999[J]. Landscape and Urban Planning, 2004,67(1/4):141-156. DOI URL
[5]	LI X B, CHEN Y H, SHI P J, et al. Detecting vegetation fractional coverage of typical steppe in Northern China based on multi-scale remotely sensed data[J]. Acta Botanica Sinica, 2003,45(10):1146-1156.
[6]	RIGGE M, WYLIE B, GU Y X, et al. Monitoring the status of forests and rangelands in the Western United States using ecosystem performance anomalies[J]. International Journal of Remote Sensing, 2013,34(11):4049-4068. DOI URL
[7]	NASH M S, BRADFORD D F, WICKHAM J D, et al. Detecting change in landscape greenness over large areas: An example for New Mexico, USA[J]. Remote Sensing of Environment, 2014,150:152-162. DOI URL
[8]	LI Z, FOX J M. Mapping rubber tree growth in mainland Southeast Asia using time series MODIS 250 m NDVI and statistical data[J]. Applied Geography, 2012,32(2):420-432. DOI URL
[9]	SPRUCE J P, SADER S, RYAN R E, et al. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks[J]. Remote Sensing of Environment, 2011,115(2):427-437. DOI URL
[10]	LUNETTAR S, KNIGHT J F, EDIRIWICKREMA J, et al. Land-cover change detection using multi-temporal MODIS NDVI data[J]. Remote Sensing of Environment, 2006,105(2):142-154. DOI URL
[11]	JIN X M, LIU J T, WANG S T, et al. Vegetation dynamics and their response to groundwater and climate variables in Qaidam Basin, China[J]. International Journal of Remote Sensing, 2016,37(3):710-728. DOI URL
[12]	宋富强, 邢开雄, 刘阳, 等. 基于MODIS/NDVI的陕北地区植被动态监测与评价[J]. 生态学报, 2011,31(2):354-363.
[13]	戴声佩, 张勃, 王海军, 等. 中国西北地区植被时空演变特征及其对气候变化的响应[J]. 遥感技术与应用, 2010,25(1):69-74.
[14]	蒲云锦, 韩春光. 新疆植被指数与气象因子关系分析[J]. 沙漠与绿洲气象, 2010,4(5):44-47.
[15]	金晓媚, 刘金韬, 夏薇. 柴达木盆地乌图美仁区植被覆盖率变化及其与地下水的关系[J]. 地学前缘, 2014,21(4):100-106.
[16]	保家有, 李晓松, 吴波. 基于沙地植被指数的荒漠化评价方法[J]. 东北林业大学学报, 2008,36(1):69-72.
[17]	STOW D, DAESCHNER S, HOPE A, et al. Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s[J]. International Journal of Remote Sensing, 2003,24(5):1111-1117. DOI URL
[18]	杨宏兵, 董霁红, 陈建清, 等. 植被覆盖度模型研究进展[J]. 安徽农业科学, 2012,40(12):7580-7585.
[19]	胡猛, 冯起, 席海洋. 基于MODIS数据的干旱区土壤水分反演[J]. 国土资源遥感, 2014,26(1):78-82. DOI URL
[20]	王凯霖, 金晓媚, 郭任宏, 等. 柴达木盆地土壤湿度的遥感反演及对蒸散发的影响[J]. 现代地质, 2016,30(4):834-841.
[21]	PRICE J C. On the analysis of thermal infrared imagery: the limited utility of apparent thermal inertia[J]. Remote Sensing of Environment, 1985,18(1):59-73. DOI URL
[22]	LIANG S L. Narrowband to broadband conversions of land surface albedo I Algorithms[J]. Remote Sensing of Environment, 2001,76(2):213-238. DOI URL
[23]	朱震达. 中国土地荒漠化的概念、成因与防治[J]. 第四纪研究, 1998,18(2):145-155.
[24]	ROBINSON D A, CAMPBELL D A, HOPMANS J W, et al. Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review[J]. Vadose Zone Journal, 2008,7(1):358-389. DOI URL
[25]	孙宪春, 金晓媚, 万力. 地下水对银川平原植被生长的影响[J]. 现代地质, 2008,22(2):321-324.

格尔木河流域植被指数时空分布及其影响因素研究

Spatial-temporal Characteristics of Vegetation Index and Its Impact Factors in the Golmud River Basin

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