Comparative Analysis of Phenological Extraction Methods for Grasslands in High-Altitude Mountainous Areas
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摘要: 准确量测高海拔山区的植物物候对理解全球变化下的敏感生态系统的响应具有重要意义。利用物候相机和遥感技术开展物候信息的提取和对比，既能准确评估物候相机在山区植物物候提取的性能，又可为山区遥感物候数据反演提供重要参考。利用中国新疆维吾尔自治区天山山区人工观测、物候相机和遥感数据，测试了5种曲线拟合方式与4种物候参数提取方法的20种组合的物候参数提取结果，对比了3种数据在物候信息提取结果的异同。结果表明：（1）植物物候相机能在天山山区草地物候观测中提供高时间分辨率的绿度变化信息，是山区开展物候观测并验证遥感物候数据的有效手段。（2）山区雨雪天气等对相对绿度指数产生较强噪声影响，需要选择合适的滤波器进行去噪。（3）曲线拟合方式和物候提取方法均对物候参数数值产生影响。而提取方法可产生更明显的差异性，其中，阈值法和导数法提取的物候数值相近，开始期与人工观测的返青期一致性较好，停止期与枯黄期一致性较好；而Klosterman方法和Gu方法提取物候数值相近，提取的开始期与人工观测的返青末期一致性较好，停止期与人工观测的枯黄末期一致性较好。（4）20种不同滤波+提取方法的组合形式在山区遥感数据物候信息提取的有效性仅为48%，中分辨率成像光谱仪数据的最有效提取方法为Beck+Derivatives组合，可见光红外成像辐射套件数据的最优提取方法为Beck+Threshold组合和Elmore+Derivatives组合。Abstract:Objectives Accurate measurement of vegetation phenology in high-altitude mountainous areas is critical in understanding the response of sensitive ecosystems to global climate change. The extraction and comparison of the phenological information using phenological cameras (PhenoCams) and remote sensing technology can help evaluate the performance of PhenoCams in vegetation phenology extraction, which provides an important reference for the accuracy of remote sensing phenological data in mountainous areas.Methods Firstly, the green chromatic coordinates (GCC) and normalization difference vegetation index (NDVI) of the vegetation are extracted for characterizing the profile of vegetation annual change based on the observed data from four PhenoCams stations and the remote sensing data in the Bayanbulak region of the Xinjiang Uygur Autonomous Region, China. Secondly, the denoising performance of seven filters for green index signals is comprehensively investigated. The phenological parameters extracted by 20 combinations of five curve fitting methods and four phenological parameter extraction methods are compared and analyzed.Results (1) Vegetation PhenoCams can accurately provide high temporal resolution variation of GCC information of grasslands (including sparse vegetation types) in Tianshan mountainous areas, China, and they are effective means to observe mountain phenology and verify remote sensing phenology data. (2) Weather conditions such as rain and snow have a strong impact on GCC, and therefore it is necessary to select appropriate filters for denoising. (3) Curve fitting methods and phenological extraction methods have an important impact on the values of phenological parameters. Moreover, obvious differences exist between the extraction methods. The phenological values extracted by Threshold and Derivatives methods are similar, and the extraction start and stop periods can well match the artificially observed periods of rejuvenation and withering respectively. The phenological values extracted by the Klosterman method and Gu method are similar, which are consistent with the observations. (4)The effectiveness of 20 combinations in extracting phenological information from remote sensing data in mountainous areas is only 48%. The most effective extraction method for moderate resolution imaging spectroradiometer (MODIS) data is the combination of Beck+Derivatives, and the best extraction methods for visible infrared imaging radiometer suite (VIIRS) data are the combination of Beck + Threshold and that of Elmore + Derivatives.Conclusions PhenoCams data and remote sensing data have obvious differences in spatial and temporal scale, and the PhenoCams data provide a higher temporal resolution than the remote sensing data. Moreover, the PhenoCams data are less affected by weather conditions, and thus the signal pollution caused by weather conditions can be reduced. Regulating the operational PhenoCams observation and expanding the spectral observation range of PhenoCams will improve the extraction of phenological information and help validate remote sensing phenological information. All of this certainly can help build a stable and long-term scientific data set for vegetation observations.
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图 2 遥感NDVI数据与物候相机GCC数据对比
Figure 2. Comparison Between Remotely Sensed NDVI Data and Phenocams GCC Data
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图 3 物候相机GCC不同曲线拟合方法与参数提取方法组合结果对比图
Figure 3. Comparison of Five Growth Curve Fitting Methods and Four Phenological Parameter Extraction Methods for Different Vegetation Types Based on Phenocams GCC
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图 4 不同曲线拟合方法的物候参数分布
Figure 4. Comparison of Phenological Parameters of Five Curve Fitting Methods
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图 5 不同参数提取方法的物候参数分布
Figure 5. Comparison of Phenological Parameters of Four Extraction Methods
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图 6 遥感NDVI数据不同曲线拟合方法与参数提取方法组合结果对比图
Figure 6. Comparison of Five Growth Curve Fitting Methods and Four Phenological Parameter Extraction Methods Based on MODIS and VIIRS Data
表 1 4个站点人工观测物候期
Table 1 In-Situ Phenological Data of Four Stations
物候期分类 站点名称 察汗乌苏 骆驼脖子 水电站 胜利道班 返青初期 05-09 04-23 05-26 05-28 返青普遍期 05-31 05-07 06-04 06-05 返青末期 06-16 05-11 06-12 06-14 枯黄初期 09-20 09-05 09-01 08-10 枯黄普遍期 09-30 09-16 09-10 08-26 枯黄末期 10-19 09-25 09-20 09-01 
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表 2 遥感与数字相机物候参数相关分析
Table 2 Correlated Analysis of Phenological Parameters Extracted from Remote Sensing Data and Phenocams
物候参数分类 开始期 停止期 MODIS VIIRS MODIS VIIRS GCC 0.628 6** 0.574** 0.063 0.444** MODIS或VIIRS 0.278 0.400* 注：*对应5%水平显著，即p < 0.05;**对应1%水平显著，即p < 0.01
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