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          • 軟件名稱:不同數據源歸一化植被指數在中國北方草原區的應用比較
          • 軟件大小: 0.00 B
          • 軟件評級: ★★★★★★
          • 開 發 商: 陳如如, 胡中民, 李勝功, 郭群
          • 軟件來源: 《地球信息科學學報》
          • 解壓密碼:www.zgkqyl.com

          資源簡介

          摘要:

          中國北方草原區生產力在區域碳水循環、農牧業發展中舉足輕重。歸一化植被指數(Normalized Difference Vegetation Index,NDVI)廣泛應用于生產力的計算,然而目前來源眾多的NDVI數據反映中國北方草原植被時空動態的一致性仍未可知。本研究利用2000—2015年3個來源NDVI數據集(MODIS NDVI、GIMMS NDVI和SPOT NDVI)并以國際上公認的數據準確性較高的MODIS NDVI為基準對比分析了中國北方草原區NDVI時空動態的一致性,并選取適宜的NDVI產品揭示研究區NDVI長期的時空格局。結果表明:整個中國北方草原區以及部分草原類型(高寒草甸、高寒草原、高寒荒漠、溫帶荒漠草原)GIMMS NDVI和MODIS NDVI 2套數據集無論是數值范圍,還是年際波動和變化趨勢具有較高一致性(二者在高寒草甸、高寒草原、高寒荒漠、溫帶荒漠草原的相關系數分別為0.60、0.47、0.51、0.74),而SPOT NDVI數值遠高于其他2個數據集,尤其是在青藏高原草原區,SPOT NDVI數值每年較另外兩套數據集約偏高0.15,表明該區域使用SPOT數據應慎重。部分溫帶草原類型(典型草原和草甸草原)GIMMS NDVI和SPOT NDVI數據集在年際波動以及變化趨勢上具有較高的一致性(相關系數分別為0.85和0.60),但溫帶草原區3種數據集NDVI數值范圍整體相差不大,小于0.06。基于上述結果,本研究進一步采用時間序列最長且與MODIS NDVI一致性最好的GIMMS NDVI分析了研究區NDVI的時空動態,發現1982—2015年中國北方草原區NDVI整體呈增加趨勢,25%的區域達顯著水平(p<0.05),主要集中在溫帶草原區;高寒草原區NDVI大部分區域變化不顯著且有一定比例的區域NDVI呈顯著下降趨勢。本研究可以為模型數據集選擇和預測中國北方草原區植被對未來氣候變化的響應提供科學依據。

          關鍵詞: 北方草原區, 植被指數, GIMMS NDVI, MODIS NDVI, SPOT NDVI, 趨勢分析, 空間格局, 年際動態

          Abstract:

          Productivity of grasslands in northern China plays an important role in regional carbon-water cycle and the development of agriculture and husbandry. Normalized Difference Vegetation Index (NDVI) has been widely used as an indicator of net primary productivity. However, it still remains unclear about the consistency among numerous NDVI datasets in characterizing the spatial and temporal dynamics of grasslands in Northern China. In this study, taking MODIS NDVI as a benchmark dataset, three NDVI datasets (MODIS NDVI, GIMMS NDVI, and SPOT NDVI) were used to compare and analyze the spatial and temporal consistency of NDVI in the grassland of northern China from 2000 to 2015. The most suitable NDVI datasets were selected to reveal the spatial and temporal patterns of NDVI in the study area. Our results show that in terms of the inter-annual variability and changing trend, GIMMS NDVI and MODIS NDVI presented high consistency over the entire grassland area, especially in alpine grassland area including alpine meadow, alpine grassland, alpine desert, and part of temperate grassland area (i.e., desert steppe), with correlation coefficients of 0.60, 0.47, 0.51, and 0.74 respectively. While SPOT NDVI values were much higher than those of the other two datasets, especially in alpine grasslands on Qinghai-Tibet Plateau, with a higher NDVI of 0.15 on average, which implied that caution should be taken when using SPOT NDVI to analyze vegetation dynamics or model productivity in alpine grasslands. GIMMS NDVI and SPOT NDVI displayed relatively high consistency in both temporal variability and changing trend in part of typical and meadow steppes, with correlation coefficients of 0.85 and 0.60, respectively, however, all the three NDVI datasets were highly consistent in their variation ranges in this area, with differences of NDVI less than 0.06. Based on GIMMS NDVI datasets, i.e. the one with the longest time series and highest consistency with MODIS NDVI, we further analyzed the spatial and temporal patterns of NDVI in the study area. We found that NDVI increased generally from 1982 to 2015, with 25% of grassland areas (mainly in temperate grassland area) being significant (p<0.05). There was no significant change of NDVI for the entire alpine grassland area though a significant decreasing trend occurred in a small proportion of the region. Our study has implications for model communities to select datasets and provides an advanced understanding of the responses of vegetation to future climate change in the grassland of northern China.

          Key words: Grassland of northern China, Normalized Difference Vegetation Index, GIMMS NDVI, MODIS NDVI, SPOT NDVI, trend analysis, spatial pattern, temporal dynamic

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