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黑龙江省气象因子插值优化及与落叶松NPP相关性分析(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2018年03期
Page:
1-9
Column:
专题报道(Ⅰ)
publishdate:
2018-05-15

Article Info:/Info

Title:
Interpolation optimization of meteorological factors and its correlation analysis with the larch NPP in Heilongjiang Province, China
Article ID:
1000-2006(2018)03-0001-09
Author(s):
ZHAO Yinghui LÜ Hongchen ZHEN Zhen* LI Fengri WEI Qingbin
College of Forestry, Northeast Forestry University, Harbin 150040, China
Keywords:
Keywords:mixed interpolation growing season temperature precipitation larch net primary productivity(NPP) Heilongjiang Province
Classification number :
S758
DOI:
10.3969/j.issn.1000-2006.201709002
Document Code:
A
Abstract:
Abstract: 【Objective】In this paper, we compared different interpolation methods to estimate meteorological factors in Heilongjiang Province and investigated the relationship between meteorological factors and net primary productivity(NPP)of larch using optimized interpolation result. It provided a scientific reference for the production and management of larch in Heilongjiang Province.【Method】Based on meteorological factors(average daily temperature and precipitation)during growing season( from May to September)in 2010 in Heilongjiang Province and 1 521 fixed plots data of larch forests, this study applied inverse distance weighed(IDW), ordinary Kriging(OK), multiple linear regression(MLR)and mixed interpolation(including regression inverse distance weighed(RIDW)and regression Kriging(RK))to interpolate and explored monthly average temperature and precipitation of growing season, and obtained spatial distribution maps of average monthly temperature and precipitation during growing season in 2010 by using the best interpolation method. Then, biomass allometric models of tree species of Northeastern China were applied to calculate aboveground biomass(AGB)and NPP of larch forest and the correlation with meteorological factors was analyzed. 【Results】The RMSE of average monthly temperature and precipitation of the RK during growing season were 0.420 and 10.110 respectively, which were better than the other interpolation methods. During the growing season, the NPP decreased as average monthly temperature decreased from south to north; the NPP increased as average monthly precipitation increased from west to east. The coefficient of Pearson correlation between the average temperature and NPP, and the average precipitation and NPP during the growing season was 0.221 and 0.241, respectively. Both P values were lower than 0.01, which means that they were significantly correlated. 【Conclusion】The RK method that considered terrain factors and residuals of multiple regression model could estimate average monthly temperature and precipitation during growing season in Heilongjiang Province better than the other methods did. During growing season, the NPP of larch had the same pattern as temperature and precipitation in latitude and longitude directions. The NPP of larch had some correlation with both average monthly temperature and average monthly precipitation, in which the correlation with precipitation was more obvious than the other factor.

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Last Update: 2018-06-06