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| 站点尺度的青藏高原时序NDVI重构方法比较与应用 | |
| 引用本文： | 刘建文,周玉科.站点尺度的青藏高原时序NDVI重构方法比较与应用[J].地理科学进展,2018,37(3):427-437. |
| 作者姓名： | 刘建文 周玉科 |
| 作者单位： | 1. 福州大学空间信息工程研究中心, 福州 350003 2. 数据挖掘与信息共享教育部重点实验室, 福州 350003 3. 中国科学院地理科学与资源研究所 生态系统网络观测与模拟院重点实验室, 北京 100101 |
| 基金项目： | 国家自然科学基金项目(41601478);国家重点研发计划项目(2016YFC0500103);中科院STS项目(KFJ-SW-STS-167) |
| 摘 要： | 基于卫星遥感的植被指数时序数据广泛应用于植被覆盖监测、生物量反演等多个研究领域,但由于传感器本身、大气条件、环境特征等因素引起的噪声会影响数据的应用效果,因此开展植被指数时序数据重构研究具有实际意义。本文基于2000-2015年MODIS归一化差异植被指数(NDVI)数据,采用三次样条函数法、双逻辑斯蒂函数法和奇异谱分析法3种常用方法,对青藏高原106个气象站点所在的典型覆被NDVI时序数据进行重构,并以植被物候信息提取作为应用,比较分析了3种算法的保真性、细节拟合能力及物候特征提取效果。研究表明, D-L及Spline函数分别对受冰雪及云层影响较大(荒漠、灌木、林地)及较小的覆被类型(草原、农作物)表现出较好的细节拟合能力;SSA方法拟合能力较差,易出现NDVI重构曲线整体“下移”的现象,造成峰值拟合结果偏低,并且表现出NDVI绝对值越小拟合效果越差的现象。从保持原始数据真值的能力来看,受噪声点影响较大的覆被类型(林地、灌木、草原)Spline函数略优于D-L函数法;而林地类型中SSA方法表现优于D-L函数法。从物候信息提取结果来看,D-L函数法所提取的生长季稍有提前,Spline函数及SSA方法分别表现出生长季开始点及结束点滞后的现象,灌木、林地类型表现出明显的年际波动变化的特征,荒漠类型由于NDVI绝对值偏低,3种方法物候提取结果一致性表现出锯齿状不规则波动。此外,D-L方法生长季开始期(SOS)和生长季结束期(EOS)在各覆被区均小于其他方法,波动较大;SSA方法提取的EOS在大部分覆被地区大于其他方法;Spline提取结果的年际波动与SSA高度相似。该研究可为高原植被不同覆被类型下NDVI时序数据噪声去除的方法选择提供借鉴。 |
| 关 键 词： | 时序数据重构 植被物候 双逻辑斯蒂函数 三次样条函数 奇异谱分析法 |
| 收稿时间： | 2017-04-12 |
| 修稿时间： | 2017-11-08 |
Comparison and application of NDVI time-series reconstruction methods at site scale on the Tibetan Plateau |
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| Jianwen LIU,Yuke ZHOU.Comparison and application of NDVI time-series reconstruction methods at site scale on the Tibetan Plateau[J].Progress in Geography,2018,37(3):427-437. | |
| Authors: | Jianwen LIU Yuke ZHOU |
| Institution: | 1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350003, China 2. Spatial Information Research Center of Fujian, Fuzhou 350003, China 3. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China |
| Abstract: | Vegetation index data based on satellite remote sensing have been widely applied in many fields such as vegetation monitoring and biomass estimation. However, the noise caused by the sensors, atmospheric conditions, and environmental factors affect the application of such data. Therefore, it is of practical significance to carry out research on the reconstruction of vegetation index. In this article, MODIS normalized differential vegetation index (NDVI) datasets of 106 meteorological stations on the Qinghai-Tibet Plateau with typical vegetation types, from 2000 to 2015, were reconstructed using three methods: cubic spline function (Spline), double logistic function (D-L), and singular spectrum analysis (SSA). Based on the results of phenological parameter extraction and NDVI time-series reconstruction, the ability of preserving the authenticity of the original data, detail fitting, and phenological character extraction of the three algorithms are compared and analyzed. The result suggests that there is no single method that performed the best for all types of vegetation, owing to the spatial heterogeneity of vegetation cover types and the varying denoising ability of the algorithms. D-L showed better performance for desert, shrubs, and woodland, which are heavily affected by random noise from snow and clouds, whereas for grassland and cropland, Spline had better performance. The fitting curves of SSA are below the D-L and Spline curves and the lower the NDVI value, the worse the reconstruction performance. With regard to maintaining the true values of the original data, Spline is superior to the D-L method for woodland, shrubs, and grassland, while the SSA is superior to the D-L function for woodland. The D-L method resulted in an earlier phenological period, Spline and SSA had a lagged outcome of the start of the growing season (SOS) and end of the growing season (EOS) respectively. Phenological index is unstable for shrubs and woodland and fluctuate irregularly for desert with all three methods, due to the low absolute value of NDVI. In addition, the phenology curve of spline is similar to that of SSA, and the SOS and EOS index derived from the D-L method is ahead of other methods for all vegetation cover types. The EOS extracted by the SSA method is larger than that by other methods for most of the vegetation cover types. This research could provide a reference for the selection of noise reduction methods for NDVI time-series data with different vegetation cover types in plateau vegetation. |
| Keywords: | time-series data reconstruction vegetation phenology double logistic fitting cubic spline singular spectrum analysis |
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