Remote Sensing Monitoring of Vegetation Coverage in Southern China Based on Pixel Unmixing: A Case Study of Guangzhou City

doi:10.6046/gtzyyg.2011.03.16

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Abstract

Based on the measurement of the ground spectral reflectance of basal land covers and the accurate atmospheric correction for Landsat TM data,the authors improved the linear spectral mixture model (LSU)and developed a vegetation coverage retrieval model suitable for southern China. The effects of the atmospheric environment and the imaging time of remote sensing data were both reduced,contributing to the multi-temporal comparison,by the utilization of the ground spectral reflectance from field survey. The soil moisture factor was considered to eliminate its remarkable spatial differentiation error in southern China. The vegetation coverage retrieval model was proved to be efficient with high precision over the in situ field verification and was applied to extract the vegetation coverage information in Guangzhou from 1998 to 2009. It is inferred that the urbanization, the large-scale architectural engineering and the reclamation activities constitute the main factors responsible for the formation of the spatio-temporal vegetation change in this area.

Keywords Polarization Multi-angle Oil pollution monitoring Object identification

TP 79

Issue Date: 07 September 2011

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	LUO Yang-jie
	ZHU Jun

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LUO Yang-jie,ZHU Jun. Remote Sensing Monitoring of Vegetation Coverage in Southern China Based on Pixel Unmixing: A Case Study of Guangzhou City[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(3): 88-94.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.03.16 OR https://www.gtzyyg.com/EN/Y2011/V23/I3/88

[1] 程红芳,章文波,陈锋.植被覆盖度遥感估算方法研究进展
[J].国土资源遥感,2008(1):13-18.

[2] 秦伟,朱清科,张学霞,等.植被覆盖度及其测算方法研究进展
[J].西北农林科技大学学报:自然科学版,2006,34(9):163-170.

[3] 但尚铭,但玻.若尔盖地区典型湿地NDVI动态特点分析
[J].四川气象,2006,26(3):115-117.

[4] Busetto L,Meroni M,Colombo R.Combining Medium and Coarse Spatial Resolution Satellite Data to Improve the Estimation of Sub-pixel NDVI Time Series
[J].Remote Sensing of Environment,2008,112(1):118-131.

[5] 阳小琼,朱文泉,潘耀忠,等.基于修正的亚像元模型的植被覆盖度估算
[J].应用生态学报,2008,19(8):1860-1864.

[6] 陈效逑,王恒.1982~2003年内蒙古植被带和植被覆盖度的时空变化
[J].地理学报,2009,64(1):84-94.

[7] 董印,焦黎,杨光华,等.基于SPOT-VGT数据的新疆1998~2007年植被覆盖变化监测
[J].水土保持通报,2009,29(2):125-129.

[8] 李娟.兰州市南北两山植被覆盖度动态变化遥感监测
[J].矿山测量,2010(1):34-38.

[9] 田庆久,闵祥军.植被指数研究进展
[J].地球科学进展,1998,13(4):327-333.

[10] Iehoku C,Karnieli A.A Review of Mixture Modeling Techniques for Sub-pixel Land Cover Estimation
[J].Remote Sensing Reviews,1996,13(3-4):161-186.

[11] Gutman G,Ignatov A.The Derivation of the Green Vegetation Fraction from NOAA/AVHRR Data for Use in Numerical Weather Prediction Models
[J].International Journal of Remote Sensing,1998,19(8):1533-1543.

[12] Qi J,Marsett R C,Moran M S,et al.Spatial and Temporal Dynamics of Vegetation in the San Pedro River Basin Area
[J].Agricultural and Forest Meteororology,2000,105(1-3):55-68.

[13] 陈晋,陈云浩,何春阳,等.基于土地覆盖分类的植被覆盖率估算亚像元模型与应用
[J].遥感学报,2001,5(6):416-422.

[14] 唐世浩,朱启疆,王锦地,等.三波段梯度差植被指数的理论基础及其应用
[J].中国科学(D辑),2003,33(11):1094-1102.

[15] 阳小琼,朱文泉,潘耀忠,等.基于修正的亚像元模型的植被覆盖度估算
[J].应用生态学报,2008,19(8):1860-1864.

[16] Jasinski M F.Estimation of Subpixel Vegetation Density of Natural Regions Using Satellite Multispectral Imagery
[J].IEEE Transaction on Genscience and Remote Sensing,1996,34(3):804-813.

[17] Gilabert M A,García-Haro F J,Meli J.A Mixture Modeling Approach to Estimate Vegetation Parameters for Heterogeneous Canopies in Remote Sensing
[J].Remote Sens Environ,2000,72(3):328-345.

[18] Paringit E C,Nadaoka K.Using Spectral Mixture Modeling Techniques to Derive Land-cover Parameters for Distributed Ediment Yield Estimation
[C]//Proceedings of the 21st Asian Conference on Remote Sensing.Taipei,Taiwan,2000:52-57.

[19] 王天星,陈松林,马娅.基于改进线性光谱分离模型的植被覆盖度反演
[J].地球信息科学,2008,10(1):114-120.

[20] Metternicht G I,Fermont A.Estimating Erosion Surface Features by Linear Mixture Modeling
[J].Remote Sensing of Environment,1998,64(3):254-265.

[21] Lobell D B,Asner G P.Cropland Distributions from Temporal Unmixing of MODIS Data
[J].Remote Sensing of Environment,2004,93(3):412-422.

[22] Bannari A,Pacheco A,Staenz K,et al.Estimating and Mapping Crop Residues Cover on Agricultural Lands Using Hyperspectral and IKONOS Data
[J].Remote Sensing of Environment,2006,104(4):447-459.

[23] 赵英时,杨立明,陈冬梅,等.遥感应用分析原理与方法
[M].北京:科学出版社,2003.

[24] 蔡薇,郭洪海,隋学艳,等.基于混合像元分解提取小麦种植面积的技术与方法研究
[J].山东农业科学,2009(5):12-16.

[25] 陶雪涛.线性模型下多通道遥感图像混合像元分解方法研究
[D].上海:复旦大学,2008:10.

[26] 邓孺孺.青藏高原地表反照率反演及冷热源分析
[D].北京:中国科学院研究生院遥感应用研究所,2002:16-19.

[27] 邓孺孺,田国良,柳钦火,等.粗糙地表土壤含水量遥感模型研究
[J].遥感学报,2004,8(1):75-80.

[28] 齐志新,邓孺孺.多暗像元大气校正方法
[J].国土资源遥感,2007(2):16-19.

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