Using least squares support vector machines to estimate time series leaf area index
-
摘要: 遥感反演的叶面积指数（LAI）时间序列被广泛应用于气候模拟、作物长势监测等研究。但遥感数据受天气等因素影响，时间序列的LAI 数据存在缺失。支持向量机（SVM）是一种有效的数据分类和回归预测工具，而最小二乘支持向量机（LS-SVM）是对SVM 的有效改进。以西藏那曲县为例，使用2003-2011 年MODIS LAI 产品，分别用LS-SVM 和SVM 两种方法对研究区域2011 年LAI 时间序列进行预测，并用MODIS 原始LAI 以及部分地面实验样点值进行验证。结果表明，基于LS-SVM 的LAI 时间序列预测算法的精度比基于SVM 的算法高，从而证明LS-SVM 方法能够弥补遥感反演时间序列LAI 数据的缺失问题，对提高时间序列的LAI 遥感产品质量具有重要意义。
-
关键词:
- 最小二乘支持向量机(LS-SVM) /
- 支持向量机(SVM) /
- 叶面积指数 /
- 时间序列 /
- MODIS
Abstract: The multi-temporal leaf area index (LAI) data retrieved from remote sensing images have been widely used in climate simulation, crop growth monitoring and etc. However,there might be some missing data owing to temporal resolution, weather and some other factors. The support vector machine (SVM) is a kind of machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. In this paper, the LS-SVM and SVM models were used to predict the LAI time series products of MODIS data of Naqu in year 2011, based on The multi-temporal leaf area index (LAI) data retrieved from remote sensing images have been widely used in climate simulation, crop growth monitoring and etc. However,there might be some missing data owing to temporal resolution, weather and some other factors. The support vector machine (SVM) is a kind of machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. In this paper, the LS-SVM and SVM models were used to predict the LAI time series products of MODIS data of Naqu in year 2011, based on the MODIS LAI from 2003 to 2011. The results show that LS-SVM method performs better than SVM method. Therefore the predicted LAI data is proved to be very supportive for making up for the loss of remote sensing LAI time-series data, the LS-SVM method proposed in this study is significant to improve the quality of the LAI time series remote sensing products. -
[1] [2] My neni R B, Hoffman S, Knyazikhin Y, et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data [J]. Remote Sensing of Environment, 2002, 83(1-2): 214-231. [3] Peng D L, Zhang B, Liu L L, et al. Characteristics and drivers of global NDVI-based FPAR from 1982 to 2006[S]. Global Biogeochemical Cycles, 2012, 26,GB3015. [4] [5] Httich C, Herold M, Schmullius C, et al. Indicator of Northern Eurasia's land -cover change trends from SPOT-Vegetation time-series analysis 1998-2005 [J]. International Journal of Remote Sensing, 2007, 28(18): 4199-4206. [6] [7] [8] R觟der A, Hill J, Duguy B, et al. Using long time series of Landsat data to monitor fire events and post -fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain)[J]. Remote Sensing of Environment, 2008, 112(1): 259-273. [9] [10] Kuusk A. A two -layer canopy reflectance model [J]. Journal of Quantitative Spectroscopy Radiative Transfer, 2001, 71: 1-9. [11] Alhamad M N, Stuth J, Vannucci M. Biophysical modeling and NDVI time series to project near -term forage supply: spectral analysis aided by wavelet denoising and ARIMA modelling [J]. International Journal of Remote Sensing, 2007, 28: 2513-2548. [12] [13] [14] Xin Zhiyun, Gu Ming. Complicated financial data time series forecasting analysis based on least square support vector machine [J]. Journal of Tsinghua University (Science Technology), 2008, 48(7): 1147-1149. (in Chinese) 辛治运, 顾明. 基于最小二乘支持向量机的复杂金融时间序 列预测[J]. 清华大学学报(自然科学版)), 2008, 48(7): 1147- 1149. [15] Hou Qingjian, Wang Hongli. Random error coefficient prediction of laser gyro based on LSSVM [J]. Infrared and Laser Engineering, 2008, 37(5): 802-805. (in Chinese) 侯青剑,王宏力. 基于LSSVM 的激光陀螺随机误差系数预 测[J]. 红外与激光工程, 2008, 37(5): 802-805. [16] [17] [18] Ling Xufeng, Yang Jie, Ye Chenzhou. Support vector machinebased human face recognition method[J]. Infrared and Laser Engineering, 2001, 30(5): 318-327. (in Chinese) 凌旭峰, 杨杰, 叶晨洲. 基于支撑向量机的人脸识别技术[J].红外与激光工程, 2001, 30(5): 318-327. [19] [20] Wang Qunming, Wang Liguo, Liu Danfeng, et al. Sub -pixel mapping for land class with linear feat least square support vector machine [J]. Infrared and Laser Engineering, 2012, 41(6): 1669-1675. [21] [22] Wang Peng, Wang Zhicheng, Zhang Jun, et al. Infrared weak small target detection based on multi -scale wavelet kernel LS-SVM [J]. Infrared and Laser Engineering, 2006, 35(S): 251-257. (in Chinese) 王鹏, 王志成, 张钧, 等. 基于多尺度小波核LS-SVM 的红外 弱小目标检测[J]. 红外与激光工程, 2006, 35(S):251-257. [23] Peng D L, Alfredo R. Huete, Huang J F, et al. Detection and estimation of mixed paddy rice cropping patterns with MODIS data [J]. International Journal of Applied Earth Observation and Geoinformation, 2001, 13(1): 13-23. -
点击查看大图
计量
- 文章访问数: 433
- HTML全文浏览量: 85
- PDF下载量: 162
- 被引次数: 0
下载:
