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基于气候信息的喀斯特地区植被EVI模拟
引用本文:陈燕丽,莫伟华,罗永明,莫建飞,黄永璘,丁美花.基于气候信息的喀斯特地区植被EVI模拟[J].农业工程学报,2015,31(9):187-194.
作者姓名:陈燕丽  莫伟华  罗永明  莫建飞  黄永璘  丁美花
作者单位:1. 广西壮族自治区气象减灾研究所/国家卫星气象中心遥感应用试验基地,南宁 530022; 2. 气象GIS应用联合实验室,南宁 530022; 3. 中国农业大学资源与环境学院,北京 100193;,1. 广西壮族自治区气象减灾研究所/国家卫星气象中心遥感应用试验基地,南宁 530022; 2. 气象GIS应用联合实验室,南宁 530022;,1. 广西壮族自治区气象减灾研究所/国家卫星气象中心遥感应用试验基地,南宁 530022; 2. 气象GIS应用联合实验室,南宁 530022;,1. 广西壮族自治区气象减灾研究所/国家卫星气象中心遥感应用试验基地,南宁 530022; 2. 气象GIS应用联合实验室,南宁 530022;,1. 广西壮族自治区气象减灾研究所/国家卫星气象中心遥感应用试验基地,南宁 530022; 2. 气象GIS应用联合实验室,南宁 530022;,1. 广西壮族自治区气象减灾研究所/国家卫星气象中心遥感应用试验基地,南宁 530022; 2. 气象GIS应用联合实验室,南宁 530022;
基金项目:中国气象局气候变化专项(CCSF201308);广西自然科学基金项目(桂科自0991207);广西自然科学基金项目(2013GXNSFAA019283)。
摘    要:该研究以喀斯特地区植被为研究对象,分析各种气候因子与植被指数的相关性及作用机制,在此基础上建立基于气候因子的植被EVI拟合模型,为定量分析气候条件对植被的综合影响奠定基础。结果表明:气候因子对喀斯特地区植被EVI影响显著,植被EVI与水汽压、平均气温、露点温度、最低气温、最高气温的相关性均大于0.8且空间一致性好。除日照时数和风速外,该地区植被EVI对其他气候因子的响应均存在显著滞后性,滞后期约16 d。对植被EVI起直接作用的主要是温度类气候因子,水分类气候因子对植被EVI的直接作用不明显,但通过其他气象因子起了较强的间接作用。根据该地区植被与气候因子的关系建立了2个EVI拟合模型,其中基于同期气候因子的同期模型中入选的气候因子为水汽压(0期)、日照时数(0期)、露点温度(0期),基于同期、前期气候因子的混合模型入选气候因子为水汽压(-1期)、最高气温(-1期)、降水量(-1期)、露点温度(-1期)、日照时数(0期)。分别利用2001-2010年建模数据和2011年非建模数据对2个模型进行了单站点和片区两种尺度的精度验证。验证结果表明,2个模型对整个片区植被EVI的拟合精度高于单站点,且混合模型的拟合精度高于同期模型。2001-2010年同期模型和混合模型的片区拟合R2分别为0.843、0.892,站点拟合R2分别为0.765±0.033、0.801±0.021。2011年2个模型的片区拟合R2分别为0.797、0.873,站点拟合R2分别为0.716±0.073、0.746±0.064。对大多数站点而言,混合模型的拟合精度较高,但是由于2个模型的建模气候因子不同及各个站点植被的EVI与气候因子的综合响应也存在较大差异,同期模型对部分站点植被EVI拟合精度高于混合模型。

关 键 词:植被  模型  气候变化  喀斯特地区  增强型植被指数EVI
收稿时间:3/5/2015 12:00:00 AM
修稿时间:2015/4/23 0:00:00

EVI simulation of vegetation in Karst rocky area using climatic factors
Chen Yanli,Mo Weihu,Luo Yongming,Mo Jianfei,Huang Yonglin and Ding Meihua.EVI simulation of vegetation in Karst rocky area using climatic factors[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(9):187-194.
Authors:Chen Yanli  Mo Weihu  Luo Yongming  Mo Jianfei  Huang Yonglin and Ding Meihua
Institution:1. Guangxi Meteorological Disaster Mitigation Institute/Remote Sensing Application and Validation Base of NSMC, Nanning 530022, China; 2. Joint Laboratory for GIS Application, Nanning 530022, China; 3. College of Resources and Environment, China Agriculture University, Beijing 100193, China;,1. Guangxi Meteorological Disaster Mitigation Institute/Remote Sensing Application and Validation Base of NSMC, Nanning 530022, China; 2. Joint Laboratory for GIS Application, Nanning 530022, China;,1. Guangxi Meteorological Disaster Mitigation Institute/Remote Sensing Application and Validation Base of NSMC, Nanning 530022, China; 2. Joint Laboratory for GIS Application, Nanning 530022, China;,1. Guangxi Meteorological Disaster Mitigation Institute/Remote Sensing Application and Validation Base of NSMC, Nanning 530022, China; 2. Joint Laboratory for GIS Application, Nanning 530022, China;,1. Guangxi Meteorological Disaster Mitigation Institute/Remote Sensing Application and Validation Base of NSMC, Nanning 530022, China; 2. Joint Laboratory for GIS Application, Nanning 530022, China; and 1. Guangxi Meteorological Disaster Mitigation Institute/Remote Sensing Application and Validation Base of NSMC, Nanning 530022, China; 2. Joint Laboratory for GIS Application, Nanning 530022, China;
Abstract:Abstract: In this paper, taking the vegetation in Karst rocky area as the research subject, MODIS enhanced vegetation index (EVI) series and climatic information during 2001-2010 are used to analyze the relationship between vegetation and climate factors. Vapor pressure, precipitation, relative humidity, maximum temperature, minimum temperature, mean temperature, dew point temperature, wind speed and sunshine hours are taken as climatic variables to explore their relationships with EVI series in different stages using correlation analysis method and path analysis method. Then, climatic factors are selected to establish EVI simulation models of Karst vegetation by stepwise regression analysis method. The results show that: There are significant positive correlations between EVI of Karst vegetation and most climatic factors. The correlation coefficients between EVI and the climatic factors including vapor pressure, mean temperature, dew point temperature, minimum and maximum temperature are higher and show better consistency than other factors, and all the values are over 0.8. The response of EVI to climatic factors has obvious hysteresis nature except sunshine hours and wind speed. The lag time is about 16 days for most climatic factors. Minimum and maximum temperature and mean temperature play a most significant direct effect on vegetation EVI; vapor pressure, precipitation and relative humidity play a significant indirect effect on EVI although their direct effect are not obvious. According to the correlations between EVI and climatic factors, 2 EVI simulation models are established including the same-time model and mixed-time model. The same-time model means the stages of the climatic factor series used in the model are the same to the EVI series. But, in the mixed-time model, climatic factors and EVI series in different stages are used. Vapor pressure, sunshine hours and dew point temperature are used to build the same-time model, and vapor pressure (one stage before), maximum temperature (one stage before), precipitation (one stage before), dew point temperature (one stage before) and sunshine hours (same stage) are used to build the mixed-time model. Two models' efficiencies in total Guangxi Karst area and single station are tested using data series from 2000 to 2010 and data in 2011. The simulation precisions for total Guangxi Karst area are higher than each single station for both models. From 2000 to 2010, the R2 of the same-time model and the mixed-time model are 0.843 and 0.892, respectively, while 0.765±0.033, 0.801±0.021, respectively for single station. Meanwhile, in the year of 2011, the R2 of the same-time model and the mixed-time model are 0.797 and 0.873 while 0.716±0.073, 0.746±0.064 for single station respectively. For most stations, the efficiency of mixed-time model is higher than the same-time model. As the climatic factors used in the model are different and the relationships between climatic factors and vegetation vary among the stations, the efficiency of the same-time model for some stations is higher than the mixed-time model's.
Keywords:vegetation  models  climate change  Karst area  enhanced vegetation index (EVI)
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