天山北坡典型退化草地植被覆盖度监测模型构建与评价

doi:10.11849/zrzyxb.2012.08.008

自然资源学报 ›› 2012, Vol. 27 ›› Issue (8): 1340-1348.doi: 10.11849/zrzyxb.2012.08.008

天山北坡典型退化草地植被覆盖度监测模型构建与评价

杨峰^1,2, 李建龙², 钱育蓉³, 杨齐⁴, 金国平⁵

1. 四川农业大学农学院,农业部西南作物生理生态与耕作重点实验室,成都 611130;
2. 南京大学生命科学学院,南京 21009;
3. 新疆大学软件学院,乌鲁木齐 830046;
4. 中国环境监测总站,北京100012;
5. 新疆阜康市畜牧局,新疆阜康 831500

收稿日期:2011-01-18 修回日期:2012-02-09 出版日期:2012-08-20 发布日期:2012-08-20
通讯作者: 李建龙(1962- ),男,教授,主要从事草地生理生态与3S应用研究。E-mail:jlli2008@nju.edu.cn E-mail:jlli2008@nju.edu.cn
基金资助:
国家重点基础研究发展计划项目(973项目)(2010CB950702);国家高技术(863计划)专题项目(2007AA10Z231)。

Estimating Vegetation Coverage of Typical Degraded Grassland in the Northern Tianshan Mountains

YANG Feng^1,2, LI Jian-long², QIAN Yu-rong³, YAN Qi⁴, JIN Guo-ping⁵

1. College of Agronomy, Sichuan Agricultural University/Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu 611130, China;
2. School of Life Science, Nanjing University, Nanjing 21009;
3. College of Software, Xinjiang University, Urumqi 830046;
4. National Environment Monitoring Center, Beijing 100012, China;
5. Fukang Animal Husbandry Bureau, Fukang 831500, China

Received:2011-01-18 Revised:2012-02-09 Online:2012-08-20 Published:2012-08-20

摘要/Abstract

摘要： 草地退化是当今世界面临的一个极为严峻的生态问题。植被覆盖度作为草地退化监测的重要指标之一,在草地退化、荒漠化治理方面起着重要的作用。为了构建适合于天山北坡典型草地植被覆盖度监测模型,便于对草地及时、快速的监测分析,研究利用新疆阜康市2008年9月Landsat TM遥感影像数据和相应的实测数据,分别探讨5种植被指数(NDVI、RVI、GNDVI、SAVI和MSAVI)与植被覆盖度的线性和非线性(二次多项式、指数、对数以及幂函数)关系,以便获得最佳监测草地状况的植被指数和模型。研究结果表明,MSAVI和GNDVI与植被覆盖度的相关性最好(P<0.01),而NDVI和RVI较差;通过5种植被指数和植被覆盖度进行回归分析,MSAVI和GNDVI与植被覆盖度分别建立模型最佳,即:y=138.45x-1.248 2(R²=0.502 7,P<0.01)和y=2 596.66x²-561.54x+38.488(R²=0.605 3,P<0.01),精度达到90%以上。该研究结果说明不同的植被指数适用的条件不同,为今后利用3S技术深入研究荒漠退化草地植被状况的快速监测和科学管理提供支持。

关键词: 退化草地, 监测模型, 植被覆盖度, 植被指数, 新疆草地

Abstract: Grassland is one of the most widespread vegetation types worldwide, which is also an important composition of terrestrial ecosystem. Grassland degradation is a serious ecological problem in the world. Therefore, monitoring and estimating grassland degradation correctly plays an important role for managing and controlling grassland degradation and desertification. In this study, the monitoring models of grassland degradation were built for monitoring the status of grassland using remote sensing data and vegetation coverage. In addition, the relationships between five vegetation indices (Ratio Vegetation Index, RVI; Normalized Difference Vegetation Index, NDVI; Green Normalized Difference Vegetation Index, GNDVI; Soil-Adjusted Vegetation Index, SAVI; Modified Soil-Adjusted Vegetation Index, MSAVI) were discussed. The results showed that the correlations between MSAVI, GNDVI and vegetation coverage were better than other vegetation indices. Comparing the regress models between five vegetation indices and vegetation coverage, the models built by MSAVI and GNDVI appeared better effect. The optimum models for monitoring the status of grassland degradation were y=138.45x_MSAVI-1.2482 (R²=0.5027, P<0.01) and y=2596.66x²_GNDVI-561.54x_GNDVI+38.488(R²=0.6053, P<0.01)respectively.

Key words: degraded grassland, monitoring model, vegetation coverage, vegetation index, Xinjiang grassland

中图分类号:

TP79:Q948.12

杨峰, 李建龙, 钱育蓉, 杨齐, 金国平. 天山北坡典型退化草地植被覆盖度监测模型构建与评价[J]. 自然资源学报, 2012, 27(8): 1340-1348.

YANG Feng, LI Jian-long, QIAN Yu-rong, YAN Qi, JIN Guo-ping. Estimating Vegetation Coverage of Typical Degraded Grassland in the Northern Tianshan Mountains[J]. JOURNAL OF NATURAL RESOURCES, 2012, 27(8): 1340-1348.

参考文献

[1] 新疆维吾尔自治区畜牧厅.新疆草地资源及其利用[M].乌鲁木齐:新疆科技卫生出版社,1993.
[2] 董智新,刘新平.新疆草地退化现状及其原因分析[J].河北农业科学,2009,13(4):89-92.
[3] 邵景安,芦清水,张小咏.近30年青海三江源西部干旱区草地退化特征的遥感分析[J].自然资源学报,2008,23(4):643-656.
[4] 梁天刚,崔霞,冯琦胜,等.2001—2008年甘南牧区草地地上生物量与载畜量遥感动态监测[J].草业学报,2009,18(6):12-22.
[5] 钟诚,何晓蓉,李辉霞.遥感技术在西藏那曲地区草地退化评价中的应用[J].遥感技术与应用,2003,18(2):99-102.
[6] 李建龙,王建华.我国草地遥感技术应用研究进展与前景展望[J].遥感技术与应用,1998,13(2):64-67.
[7] 苏大学,刘建华,钟华平,等.中国草地资源遥感快查技术方法的研究[J].草地学报,2005,13(增刊1):4-9.
[8] Numata I, Roberts D A, Chadwick O A, et al. Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data [J]. Remote Sensing of Environment,2007,109:314-327.
[9] Röder A, Udelhoven T, del Barrio G, et al. Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece [J]. Remote Sensing of Environment,2008,112:2863-2875.
[10] Lucas K L, Carter G A. The use of hyperspectral remote sensing to assess vascular plant species richness on Horn Islant, Mississippi [J]. Remote Sensing of Environment,2008,112:3908-3915.
[11] Asner G P, Townsend A R. Pasture degradation in the central Amazon: Linking changes in carbon and nutrient cycling with remote sensing [J]. Global Change Biology,2004,10:844-862.
[12] Numata I, Roberts D A, Chadwich O A, et al. Evaluation of hyperspectral data for pasture estimate in the Brazilian Amazon using field and imaging spectrometers [J]. Remote Sensing of Environment,2008,112:1569-1583.
[13] Wang Z M, Song K S, Zhang B, et al. Shringkage and fragmentation of grasslands in the West Songnen Plain, China [J]. Agriculture, Ecosystem and Environment,2009,129:315-324.
[14] Wittich H. Area-averaged vegetative cover fraction estimated from satellite data [J]. International Journal of Biometereology, 1995,38(3):209-215.
[15] 吴云,曾源,吴炳方,等.基于MODIS数据的三北防护林工程区植被覆盖度提取与分析[J].生态学杂志,2009,28(9):1712-1718.
[16] 刘同海,吴新宏,董永平.基于TM影像的草原沙漠化植被盖度分析研究[J].干旱区资源与环境,2010,24(2):141-144.
[17] 霍艾迪,康相武,王国梁,等.基于MODIS的沙漠化地区植被覆盖度提取模型的研究[J].干旱地区农业研究,2008,26(6): 217-223.
[18] Pearson R L, Miller L D. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado // Proceedings of the 8th International Symposium on Remote Sensing of Environment. Ann Arbor, Michigan, USA, 2-6 October, 1972:1357-1381.
[19] Rouse J W, Haas R H, Schell J A, et al. Monitoring the vernal advancement of retrogradation of natural vegetation . NASA/GSFC, Type III, Final Report, Greenbelt, MD. 1974.
[20] Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS [J]. Remote Sensing of Environment,1996,58:289-298.
[21] Huete A R. A Soil-adjusted vegetation index (SAVI) [J]. Remote Sensing of Environment,1988,25:295-309.
[22] Qi J G, Chehbouni A, Huete A R, et al. A modified soil adjusted vegetation index [J]. Remote Sensing of Environment,1994, 48:119-126.
[23] 干友民,成平,周纯兵,等.若尔盖亚高山草甸地上生物量与植被指数关系研究[J].自然资源学报,2009,24(11):1963-1972.
[24] 张云霞,李晓兵,陈云浩.草地植被盖度的多尺度遥感与实地测量方法综述[J].地球科学进展,2003,18(1):85-93.
[25] 王正兴,刘闯, Huete Alfredo.植被指数研究进展:从AVHRR-NDVI到MODIS-EVI[J].生态学报,23(5):979-987.
[26] 张宏斌.基于多源遥感数据的草原植被状况变化研究——以内蒙古草原为例.北京:中国农业科学院,2007.
[27] Zeng X B, Robert E, Dickinson Alison Walker, et al. Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling [J]. Journal of Applied Meteorology,2000,39(6):826-839.
[28] Purevdor J T S, Tateishi R, Ishiyama T, et al. Relationships between percent vegetation cover and vegetation indices [J]. International Journal of Remote Sensing,1998,19(18):3519-3535.
[29] Eastwood J A, Yates M G, Thomson A G, et al. The reliability of vegetation indices for monitoring saltmarsh vegetation cover [J]. International Journal of Remote Sensing,1997,18(18):3901-3907.

天山北坡典型退化草地植被覆盖度监测模型构建与评价

Estimating Vegetation Coverage of Typical Degraded Grassland in the Northern Tianshan Mountains

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	余灏哲, 李丽娟, 李九一. 基于TRMM降尺度和MODIS数据的综合干旱监测模型构建[J]. 自然资源学报, 2020, 35(10): 2553-2568.
[2]	张文强, 孙从建, 李新功. 晋西南黄土高原区植被覆盖度变化及其生态效应评估[J]. 自然资源学报, 2019, 34(8): 1748-1758.
[3]	白雪莲, 季树新, 王理想, 陈正新, 常学礼. 鄂尔多斯十大孔兑区植被生产力变化趋势对土地利用转移的响应[J]. 自然资源学报, 2019, 34(6): 1186-1195.
[4]	邓晨晖, 白红英, 高山, 刘荣娟, 马新萍, 黄晓月, 孟清. 秦岭植被覆盖时空变化及其对气候变化与人类活动的双重响应[J]. 自然资源学报, 2018, 33(3): 425-438.
[5]	张亮, 丁明军, 张华敏, 文超. 1982—2015年长江流域植被覆盖度时空变化分析[J]. 自然资源学报, 2018, 33(12): 2084-2097.
[6]	朱林富, 谢世友, 杨华, 马明国. 基于MODIS EVI的重庆植被覆盖变化的地形效应[J]. 自然资源学报, 2017, 32(12): 2023-2033.
[7]	郭昱杉, 刘庆生, 刘高焕, 黄翀. 基于MODIS时序NDVI主要农作物种植信息提取研究[J]. 自然资源学报, 2017, 32(10): 1808-1818.
[8]	牛忠恩, 闫慧敏, 黄玫, 胡云锋, 陈静清. 基于MODIS-OLI遥感数据融合技术的农田生产力估算[J]. 自然资源学报, 2016, 31(5): 875-885.
[9]	王景旭, 丁丽霞, 程乾. 湿地植被叶面积指数对光化学指数和光能利用率关系的影响----基于实测数据和PROSPECT-SAIL模型[J]. 自然资源学报, 2016, 31(3): 514-525.
[10]	朱大运, 熊康宁, 肖华, 蓝家程. 基于植被指数的GF-1与Landsat-OLI石漠化识别能力对比评价[J]. 自然资源学报, 2016, 31(11): 1949-1957.
[11]	张灿, 徐涵秋, 张好, 唐菲, 林中立. 南方红壤典型水土流失区植被覆盖度变化及其生态效应评估——以福建省长汀县为例[J]. 自然资源学报, 2015, 30(6): 917-927.
[12]	武婕, 李增兵, 李玉环, 赵庚星, 李春光. 基于TM影像植被指数的耕地地力状况反演研究[J]. 自然资源学报, 2015, 30(6): 1035-1046.
[13]	杨存建, 周其林, 任小兰, 程武学, 王琴. 基于多时相MODIS数据的四川省森林植被类型信息提取[J]. 自然资源学报, 2014, 29(3): 507-515.
[14]	包刚, 包玉海, 覃志豪, 周义, 黄明祥, 张宏斌. 高光谱植被覆盖度遥感估算研究[J]. 自然资源学报, 2013, 28(7): 1243-1254.
[15]	唐侥, 孙睿. 基于气象和遥感数据的河南省干旱特征分析[J]. 自然资源学报, 2013, 28(4): 646-655.