Journal of Geo-information Science >
The Remote Sensing Monitoring Model of the Typical Vegegtation Phenology in the Qinghai-Tibetan Plateau
Received date: 2013-10-11
Request revised date: 2013-12-04
Online published: 2014-09-04
Copyright
Vegetation phenology changes is one of the most direct and sensitive indicators of global climate change and it has become the focus problem of the word studies. The Qinghai-Tibetan Plateau is a unique geographical unit covered by alpine vegetation types so that it is very important to study the remote sensing monitoring model of these vegetation types’ phenology. Firstly, Based on MODIS Normalized Difference Vegetation Index (NDVI) data from 2003 to 2012, we reconstructed the long-term time-series datasets through the combination of Inverse Distance Weighted Interpolation and Savitzky-Golay fitting method. After filtering, the obvious noise is removed but the detail information of vegetation growth is kept well so that the time-series curve is definitely suitable for the extraction of phenology paramethers. Then, we studied the extraction models of the typical vegetation phenology in the Qinghai-Tibetan Plteau with dynamic threshold value method, biggest change slope method and logistic curve fitting method. We compared and analyzed the monitoring results based on the nearly ten years NDVI dataset using the relationship between vegetation growing characteristics and daily mean temperature and then selected the dynamic threshold value method as the best model for typical vegetation phenology extraction in the Qinghai-Tibetan Plateau. Finally, we extracted the phenology information of grassland in the plateau with dynamic threshold value model. After the analysis of nearly ten years vegetation phenology, the results showed that the alpine grassland in the plateau experienced the trend of start of season (SOS) advancing (the ratio is 0.248d/a) as the end of season (EOS) following a more complex rule. The andvanced SOS manily caused by the rise of spring temperature and the influence of precipation is not significant. What’s more, the vegetation phenology and variation trends in the plateau showed obvious spatial distribution rule from the southeast to the northwest.
CHANG Qing , WANG Siyuan , SUN Yunxiao , YIN Hui , YIN Hang . The Remote Sensing Monitoring Model of the Typical Vegegtation Phenology in the Qinghai-Tibetan Plateau[J]. Journal of Geo-information Science, 2014 , 16(5) : 823 -816 . DOI: 10.3724/SP.J.1047.2014.00815
Fig.1 Vegetation types of the Qinghai-Tibetan Plateau图1 青藏高原植被分布图(数据来源于中国科学院地理科学与资源研究所2000年1:100万植被类型图) |
Fig.2 Flowchart of the NDVI time-series resconstruction method图2 NDVI时序数据重建流程 |
Fig.3 Results of NDVI time-series reconstructing图3 长时间序列数据重构结果 |
Fig.4 Monitoring results of the typical vegetation phenology in the Qinghai-Tibetan Plateau based on three different models图4 基于3种模型的青藏高原典型植被物候期遥感提取结果 |
Fig.5 The start data when mean daily temperature of alpine meadow exceed 0℃ (2003-2012)图5 高寒草甸日均温≥0℃的初始日期(2003-2012年) |
Fig.6 Spatial distribution and temporal trends of start of season (SOS), end of season (EOS) and length of season (LOS) for the period of 2003-2012; ten years averaged SOS(a), EOS(b) and LOS(c); ten years trends of SOS(d), EOS(e) and LOS(f)图6 2003-2012年间青藏高原典型高寒植被生长季时空动态变化 |
Tab.1 The correlational analysis between the SOS in Qinghai-Tibetan Plateau and meteorological data表1 青藏高原植被SOS与气象数据的相关性 |
春季温度(℃) | 春季降水(mm) | 秋季温度(℃) | 秋季降水(mm) | ||
---|---|---|---|---|---|
高寒草原的SOS | -0.32 | –0.32 | 高寒草原的EOS | 0.19 | 0.11 |
高寒草甸和苔原的SOS | -0.67 | –0.11 | 高寒草甸和苔原的EOS | 0.24 | -0.02 |
高寒草地的SOS | -0.66 | –0.24 | 高寒草地的EOS | 0.37 | 0.28 |
Fig.7 The response of phenological phases (SOS, EOS) to temperature and precipitation changes in the Qinghai-Tibetan Plateau图7 青藏高原典型植被物候期(SOS、EOS)对温度、降水变化的响应 |
The authors have declared that no competing interests exist.
[1] |
|
[2] |
|
[3] |
|
[4] |
|
[5] |
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
/
〈 | 〉 |