Canopy spectral characteristics of broadleaved Korean pine forest in different successional stages and its relation with temperature in Changbai Mountain of northeastern China
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摘要:目的 通过遥感数据分析长白山阔叶红松林不同演替阶段冠层光谱变化特征，为揭示长白山群落内部种间变化以及植被生产力对气候因子的响应机制提供理论依据。方法 通过Google Earth Engine平台提取1984—2019年长白山原始阔叶红松林与次生白桦林Landsat和Sentinel多年冠层光谱数据并计算植被绿度参数，分析二者冠层光谱特征季节变化、植被绿度的季节与年际变化，计算植被年际绿度变化与同期月均温的Pearson相关系数。结果 （1）原始林与次生林冠层可见光反射率在非生长季较高，生长季下降，而近红外光变化趋势则与此相反。在生长旺盛季节（5—10月底）原始林与次生林可见光波段冠层反射率相近，近红外波段差异明显，次生林冠层反射率更高。二者都具有明显的“红谷”、 “绿峰”、 “蓝谷”和“红边”现象，原始林冠层光谱反射率年变化幅度小于次生林。（2）原始林与次生林的绿度表现为相同的变化趋势，即春季展叶期间增长、秋季落叶期衰减。非生长季，原始林植被指数变化较为稳定且大于次生林，次生林林下透光度高。生长季，次生林增强植被指数（EVI）和哨兵二号红边位置（S2REP）均大于原始林，植被冠层生理活动更为旺盛，不同的卫星影像数据表现一致，且次生林的EVI峰值比原始林出现得略早。（3）1985—2019年的35年期间，长白山气温呈上升趋势，植被绿度也随之变化，即：二者EVI在增加，且夏季（生长季）增长幅度大于其他季节，春、秋季的年际差异较大。（4）与原始林相比，次生林EVI年际变化受春季气温影响较大，在生长季初期，二者的EVI与气温呈显著正相关；在整个生长季期间，当气温增加达到一定阈值后，EVI增长显著。结论 长时间的连续冠层光谱变化监测与分析，可有效反映原始林与次生林植被物候变化差异。气温上升可能是引起长白山阔叶红松林绿度变化的重要因素之一。Abstract:Objective Based on remote sensing data, the characteristics of canopy spectral changes in different succession stages of broadleaved Korean pine forest in Changbai Mountain of northeastern China were analyzed to provide theoretical basis for revealing the interspecies change and the response mechanism of vegetation productivity to climate factors in Changbai Mountain.Method Through the Google Earth Engine platform, Landsat and Sentinel series of remote sensing images were used to extract multi-temporal canopy spectrum data for the broadleaved Korean pine forest (primary forest) and birch-aspen forest (secondary forest), both were in a same succession series in Changbai Mountain. Also we analyzed the seasonal variations of the canopy spectrum characteristics of the two, the seasonal and inter-annual variation of vegetation greenness, and calculated the Pearson correlation coefficient between the inter-annual vegetation greenness variation and the monthly average temperature of the same period from 1985 to 2019.Result (1) For canopy spectral reflectance of the primary forest, the visible light was higher in leaf-off season than in growing season, while the near-infrared reflectance showed an opposite pattern. In the vigorous growth season (from the end of May to the end of October), the canopy reflectivity of the primary forest and the secondary forest was similar in the visible light band, but the near-infrared band was significantly different, and the secondary forest canopy reflectivity was higher. The phenomenon of “red valley”, “green peak”, “blue valley” and “red edge”, the curve form of spectral reflectance in the two vegetation types were evident, and the interannual fluctuation was weaker than that of the secondary forest. (2) The greenness of primary forest and secondary forest showed the same changing trend. It exhibited growth during leaf development in spring and attenuation during leaf fall in autumn. In the non-growing season, the degree of change in vegetation index of the primary forest was relatively stable and greater than that of the secondary forest, indicating that the understory of the secondary forest had high light transmittance. In vigorous growing season, the EVI and S2REP of the secondary forest were larger than those of the original forest, and the physiological activities of the vegetation canopy were more vigorous. Different satellite image data showed consistent performance, and the EVI peak of the secondary forest appeared slightly earlier than the original forest. (3) During the 35-year period from 1985 to 2019, the temperature in the study region had been on the rise, resulting in the increase in both vegetation greenness and the length of growing season; EVI of the primary forest was increasing, with the rate greater in summer than in other seasons. The interannual difference between spring and autumn for enhanced vegetation index was significant. (4) Compared with the primary forest, the interannual variation in EVI of the secondary forest was more correlated with spring temperature. At the beginning of growing season, both forests presented the same pattern that EVI and temperature were positively correlated. During the entire growing season, EVI increased steadily prior to the period when temperature reached a high level.Conclusion Long-term continuous monitoring and analysis of canopy spectrum changes can effectively reflect the difference in vegetation phenology between the primary forest and the secondary forest. Temperature rise may be one of the important factors causing the greenness of the broadleaved Korean pine forest in Changbai Mountain of northeastern China.
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图 1 植被遥感光谱信息提取位点
Figure 1. Sampling points for extracting spectral information from satellite images
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图 2 长白山两种森林植被冠层光谱8 d合成的反射率季节进程(2013—2019年Landsat 8数据)
Figure 2. Eight-day-composite seasonal pattern of canopy spectral reflectance of two forest vegetation types in Changbai Mountain (Landsat 8 data in 2013−2019)
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图 3 高分辨率影像提取的林冠层光谱反射率季节变化(2019年)
Figure 3. Seasonal pattern of forest canopy spectral reflectance from high-resolution images (2019)
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图 4 2019年长白山两种森林植被的绿度(a、b)与红边位置季节变化(c)
Figure 4. Seasonal changes of greenness (a, b) and red-edge position of two vegetation types in Changbai Mountain (c)
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图 5 长白山两种森林植被归一化植被指数逻辑斯蒂曲线
Figure 5. Logistic curves of NDVI of two vegetation types in Changbai Mountain
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图 6 长白山两种森林植被EVI季节变化(1985—2019年)
Figure 6. Seasonal pattern of EVI of two vegetation types in Changbai Mountain (1985−2019)
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图 7 长白山两种森林植被生长季EVI(a)和气温(b)的关系(1985—2018年)
Figure 7. Relationship between EVI (a) and temperature (b) of two forest vegetation types during growing season in Changbai Mountain (1985−2018)
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图 8 长白山两种森林植被EVI多年月均值与标准差(1985—2018年)
Figure 8. Multi-year monthly mean and standard deviation of EVI of two forest vegetation types during growing season in Changbai Mountain (1985−2018)
表 1 长白山两种森林植被月均EVI年际变化与同期气温的相关系数(1985—2018年)
Table 1 Correlation coefficients between month-specific average EVI and temperature of two vegetation types in Changbai Mountain (1985−2018)
月份 Month 原始林 Primary forest 次生林 Secondary forest 平均气温
Mean temperature最高气温
Maximum temperature最低气温
Minimum temperature平均气温
Mean temperature最高气温
Maximum temperature最低气温
Minimum temperature1月 January 0.007 1 0.163 9 −0.079 1 −0.118 5 −0.022 1 −0.237 3 2月 February −0.079 5 −0.009 5 −0.094 9 −0.126 5 0.161 5 −0.006 2 3月 March −0.099 8 0.097 7 0.070 8 −0.456 4** −0.445 1** −0.158 0 4月 April −0.229 8 −0.158 2 −0.260 6 −0.367 1* −0.266 7 −0.283 1 5月 May 0.524 4** 0.476 4** 0.171 6 0.548 2** 0.399 2* 0.114 4 6月 June 0.375 3* 0.378 6* 0.102 0 0.230 6 0.378 5* 0.001 6 7月 July 0.218 2 0.314 5 0.057 2 0.091 6 0.200 6 0.041 0 8月 August −0.035 4 0.259 0 −0.383 9* −0.030 0 0.176 5 −0.368 3* 9月 September 0.053 0 0.059 5 0.007 0 0.017 2 −0.023 6 −0.158 9 10月 October 0.266 9 0.377 2* −0.024 2 0.232 7 0.241 1 0.014 3 11月 November −0.116 8 −0.176 8 −0.088 4 0.1375 0.107 8 0.108 7 12月 December 0.087 4 0.005 2 −0.209 7 −0.073 0 −0.390 9* −0.029 8 注：*表示在P < 0.05水平上显著相关，**表示在P < 0.01水平上显著相关。Notes: * means a significant correlation at the P < 0.05 level, ** means a significant correlation at the P < 0.01 level. 
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