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国土资源遥感  2018, Vol. 30 Issue (2): 186-194    DOI: 10.6046/gtzyyg.2018.02.25
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基于空间自回归模型的广州市NDVI和NDBI与气温关系研究
许剑辉1,2,3(), 赵怡4,5, 肖明虹6, 钟凯文1,2,3, 阮惠华7
1.广州地理研究所,广州 510070
2.广东省遥感与地理信息系统应用重点实验室,广州 510070
3. 广东省地理空间信息技术与应用公共实验室,广州 510070
4. 中国科学院广州地球化学研究所,广州 510640
5. 中国科学院大学,北京 100049
6.广西壮族自治区地理信息测绘院,柳州 545006
7. 广东省气象探测数据中心,广州 510080
Relationship of air temperature to NDVI and NDBI in Guangzhou City using spatial autoregressive model
Jianhui XU1,2,3(), Yi ZHAO4,5, Minghong XIAO6, Kaiwen ZHONG1,2,3, Huihua RUAN7
1. Guangzhou Institute of Geography, Guangzhou 510070, China
2. Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China
3. Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China
4. Guangzhou Institute of Geochemistry, China Academy of Sciences, Guangzhou 510640, China
5. University of Chinese Academy of Sciences, Beijing 100049, China
6. Guangxi Institute of Geographic Information Surveying and Mapping, Liuzhou 545006, China
7. Guangdong Meteorological Observation Data Center, Guangzhou 510080, China
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摘要 

为了探索城镇化地区热岛的时空变化特征,采用2015年覆盖广州市的1 km空间分辨率MOD13A3月合成归一化植被指数(normalized difference vegetation index,NDVI)数据、用Landsat8 OLI提取的归一化建筑指数(normalized difference build-up index,NDBI)数据以及不同季节的气象站点近地表气温数据,运用相关性分析方法,研究近地表气温与NDVI和NDBI的相互关系; 在此基础上,应用空间自回归方法构建不同季节的近地表气温与NDVI和NDBI的空间自回归模型,定量分析广州地区近地表气温与NDVI和NDBI的空间关系,并与普通回归模型进行比较分析。结果表明,不同季节的NDVI与近地表气温呈负相关,NDBI与近地表气温呈正相关; 与普通线性回归模型相比,空间滞后模型与空间误差模型的拟合效果最优; 通过比较分析相关系数(R 2)值、赤池信息量准则(Akaike information criterion,AIC)值及回归模型残差的莫兰指数(Moran index,Moran’s I),发现空间滞后模型的拟合效果略优于空间误差模型; 从春季到秋季,NDVI对近地表气温的影响大于NDBI对近地表气温的影响; 在空间滞后模型中,显著的、正的空间自回归系数表明,气象站点的近地表气温受到相邻气象站点的近地表气温的显著正影响。

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许剑辉
赵怡
肖明虹
钟凯文
阮惠华
关键词 空间自回归模型归一化建筑指数(NDBI)归一化植被指数(NDVI)气温    
Abstract

To study the spatio-temporal pattern of the air temperature in Guangzhou City, the authors used MODIS monthly normalized difference vegetation index (NDVI) acquired in 2015 and extracted the normalized difference built-up index (NDBI) with Landsat8 OLI data. The correlation analysis method was used to explore the relationship between air temperature and NDVI, NDBI. The experimental results show that there is a negative relation between NDVI and air temperature and a positive relation between NDBI and air temperature. On such a basis, the spatial lag model (SLM) and spatial error model (SEM) were established to discuss the spatial relations between air temperature and NDVI, NDBI in different seasons, respectively. The SLM and SEM results were compared with the ordinary least square regression (OLS) model, which shows the best performance of the SLM and SEM models. The SLM model with higher R 2 and lower AIC values performs slightly better than the SEM model. NDVI has more influence on air temperature from spring to autumn than NDBI. In the SLM model, the positive and significant spatial autoregressive coefficients indicate an active influence from neighboring meteorological stations.

Key wordsspatial autoregressive model    normalized difference built-up index(NDBI)    normalized difference vegetation index(NDVI)    air temperature
收稿日期: 2016-10-08      出版日期: 2018-05-30
:  TP79  
基金资助:广东省科学院实施创新驱动发展能力建设专项资金项目“结合地统计学与多源遥感数据时空融合的高时空分辨率城市地表温度反演”(编号: 2017GDASCX-0804);广东省引进创新创业团队项目“地理空间智能与大数据创新创业团队”(编号: 2016ZT06D336);广东省科技计划项目“基于GIS和SWAT水文模型的农业干旱实时监测与评价系统”(编号: 2016A020210059);广东省科学院平台环境与能力建设专项资金项目“广东省地理信息产业公共服务云平台”(编号: 2016GDASPT-0103)
引用本文:   
许剑辉, 赵怡, 肖明虹, 钟凯文, 阮惠华. 基于空间自回归模型的广州市NDVI和NDBI与气温关系研究[J]. 国土资源遥感, 2018, 30(2): 186-194.
Jianhui XU, Yi ZHAO, Minghong XIAO, Kaiwen ZHONG, Huihua RUAN. Relationship of air temperature to NDVI and NDBI in Guangzhou City using spatial autoregressive model. Remote Sensing for Land & Resources, 2018, 30(2): 186-194.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.02.25      或      https://www.gtzyyg.com/CN/Y2018/V30/I2/186
Fig.1  研究区及气象观测站分布
Fig.2  研究区月NDVI数据
Fig.3  研究区NDBI数据
Fig.4  不同季节近地表气温与NDVI散点图
Fig.5  不同季节的近地表气温与NDBI散点图
参数 空间自回归模型
OLS SLM SEM
(截距) 13.852(35.866)①***② 4.222(4.855)*** 13.719(23.749)***
NDVI -0.871(-0.767) -0.538 (-0.585) -1.139(-1.003)
NDBI 3.103(1.712)’ 1.792(1.224) 1.668(1.054)
ρ 0.716(12.304)***
λ 0.721(12.286)***
R2 0.026 0.310 0.309
AIC 1 177.300 1 090.200 1 090.600
N 264.000 264.000 264.000
Morans I 0.371 -0.006 -0.009
Tab.1  1月份近地表气温3种空间自回归模型参数
参数 空间自回归模型
OLS SLM SEM
(截距) 23.379(77.378)*** 8.127(5.582)*** 22.660(51.261)***
NDVI -4.661(-6.086)*** -1.783(-2.704)** -1.766(-2.198)·
NDBI -1.348(-1.060) 0.765(0.727) 2.088(1.825)’
ρ 0.665(10.687)***
λ 0.698(11.288)***
R2 0.145 0.373 0.370
AIC 1 019.200 941.600 942.700
N 264.000 264.000 264.000
Morans I 0.328 -0.017 -0.024
Tab.2  4月近地表气温3种空间自回归模型参数
参数 空间自回归模型
OLS SLM SEM
(截距) 29.588(129.219)*** 10.455(5.6350)*** 29.456(93.347)***
NDVI -2.804(-5.343)*** -1.397(-3.125)*** -1.687(-3.296)***
NDBI -1.095(-1.120) 0.265(0.328) 1.272(1.484)
ρ 0.658(10.505)***
λ 0.692(11.049)***
R2 0.117 0.356 0.365
AIC 860.500 781.300 777.400
N 264.000 264.000 264.000
Morans I 0.366 -0.006 -0.022
Tab.3  7月近地表气温3种空间自回归模型参数
参数 空间自回归模型
OLS SLM SEM
(截距) 28.086(136.991)*** 9.754(7.021)*** 26.295(79.770)***
NDVI -5.414(-10.900)*** -2.264(-5.345)*** -2.069(-4.238)***
NDBI 0.949(1.096) 1.003(1.504) 1.353(1.888)’
ρ 0.665(13.108)***
λ 0.791(16.491)***
R2 0.482 0.673 0.648
AIC 771.700 654.000 674.000
N 264.000 264.000 264.000
Morans I 0.299 -0.051 -0.045
Tab.4  10月近地表气温3种空间自回归模型参数
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