NDVI、NDMI与地表温度关系的对比研究

doi:10.18306/dlkxjz.2017.05.006

地理科学进展 ›› 2017, Vol. 36 ›› Issue (5): 585-596.doi: 10.18306/dlkxjz.2017.05.006

NDVI、NDMI与地表温度关系的对比研究

李斌^1,²(), 王慧敏³, 秦明周^2,^4,^*(), 张鹏岩^2,⁴

1. 黄河中下游数字地理技术教育部重点实验室,河南开封 475004
2. 河南大学环境与规划学院,河南开封 475004
3. 新乡市规划设计研究院,河南新乡 453000
4. 河南大学环境与健康工程技术中心,河南开封 475004

收稿日期:2016-06-01 出版日期:2017-05-20 发布日期:2017-05-20
通讯作者: 秦明周
作者简介:
作者简介：李斌(1974-),河南汝州人,高级实验师,博士研究生,主要从事GIS与环境地理研究,E-mail:libin@henu.edu.cn。
基金资助:
国家自然科学基金项目(41171439,41401457)

Comparative study on the correlations between NDVI, NDMI and LST

Bin LI^1,²(), Huimin WANG³, Mingzhou QIN^2,^4,^*(), Pengyan ZHANG^2,⁴

1. Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, Henan, China
2. College of Environment and Planning, Henan University, Kaifeng 475004, Henan, China
3. Xinxiang Institute of Planning and Design, Xinxiang 453003, Henan, China
4. Environmental and Health Engineering Center, Henan University, Kaifeng 475004, Henan, China

Received:2016-06-01 Online:2017-05-20 Published:2017-05-20
Contact: Mingzhou QIN
Supported by:
National Natural Science Foundation of China, No.41171439, 41401457

摘要/Abstract

摘要：

通过研究归一化植被指数(NDVI)、归一化水汽指数(NDMI)与地表温度(LST)的相关关系,对比NDVI和NDMI定量分析LST的适宜程度。以Landsat 8遥感影像为数据源,以郑汴都市区为例,反演LST,计算NDVI和NDMI。从整体、分区、像元等不同层面分析NDVI和NDMI与LST的相关关系,并利用GEO-Da软件,分析150 m、300 m、450 m三种不同采样间隔数据的NDVI、NDMI与LST的空间相关性。主要结论为：①NDVI与LST的线性拟合度较差,而NDMI与LST具有很强的线性关系,剖面分析显示NDMI与LST呈显著负相关关系。②缓冲区分析结果表明,随着距城市中心距离的增加,用地类型增多,LST和NDMI之间的相关性逐渐增强。③在Moran's I空间相关性分析中,不同采样间隔下两指数与LST的负相关关系均比较明显,但由于水体在2个指数中数值的差异,使NDMI与LST表现出更强的空间负相关性,而NDVI与LST空间相关性则相对较弱。因此,总的来说NDMI是热环境研究的有效指标,与NDVI相比,NDMI与LST的负相关关系更强、更稳定,也更适宜于对LST的定量分析。

Abstract:

Through the comparative study of the correlations between the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST), the suitability to research LST using NDVI and NDMI was verified. Based on Landsat 8 remote sensing imagery in the Zhengzhou-Kaifeng metropolitan area, LST was retrieved, and both NDVI and NDMI were calculated. At the overall, regional, and pixel levels, the correlations between LST and NDVI, NDMI were analyzed. Using GEO-Da, the spatial correlations between LST and NDVI, NDMI were simulated with the data of three sampling intervals of 150 m, 300 m and 450 m. The conclusions are as follows. First, there is a stronger linear negative correlation between LST and NDMI, and sectional analysis shows that NDMI resembles a mirror image of LST, whereas the correlation between LST and NDVI is much weaker. Second, buffer analysis shows that with the increase of distance from the down town and increase of the number of land use types, the correlation between NDMI and LST also increases gradually. Third, in the analysis of Moran's I spatial correlation, the negative correlations between LST and the two indices were significant, but because of the great difference between the values of water body in the two indices, NDMI and LST show stronger negative correlation, while the correlation between NDVI and LST is relatively weak. In general, compared with NDVI, NDMI is more suitable as an effective indicator for quantitative analysis of LST.

Key words: spatial correlation, normalized difference vegetation index (NDVI), normalized difference moistureindex (NDMI), land surface temperature (LST), Zhengzhou-Kaifeng metropolitan area

李斌, 王慧敏, 秦明周, 张鹏岩. NDVI、NDMI与地表温度关系的对比研究[J]. 地理科学进展, 2017, 36(5): 585-596.

Bin LI, Huimin WANG, Mingzhou QIN, Pengyan ZHANG. Comparative study on the correlations between NDVI, NDMI and LST[J]. PROGRESS IN GEOGRAPHY, 2017, 36(5): 585-596.

图/表 11

图1

图2

图3

图4

表1

表2

缓冲区NDMI-LST的线性回归分析"

区域	缓冲区半径/km	R²	回归方程	自由度	区域	缓冲区半径/km	R²	回归方程	自由度
郑州市	0.5	0.243	y = -20.849x + 49.209	928	中牟县	0.5	0.033	y = -4.084x + 35.724	925
	1.0	0.209	y = -19.815x + 48.136	2664		1.0	0.178	y = -8.180x + 37.840	2661
	1.5	0.311	y = -22.342x + 49.152	4396		1.5	0.401	y = -17.166x + 43.225	4408
	2.0	0.417	y = -29.190x + 53.907	6160		2.0	0.301	y = -18.166x + 44.008	6145
	2.5	0.298	y = -28.433x + 53.888	7910		2.5	0.461	y = -25.607x + 48.666	7925
	3.0	0.354	y = -25.252x + 51.525	9647		3.0	0.474	y = -29.608x + 50.836	9645
	3.5	0.234	y = -22.235x + 49.628	11407		3.5	0.505	y = -25.396x + 47.723	11426
	4.0	0.270	y = -26.600x + 52.205	13168		4.0	0.546	y = -26.417x + 48.295	13097
	4.5	0.171	y = -20.423x + 48.116	14882		5.0	0.579	y = -26.752x + 48.306	16654
	5.0	0.195	y = -23.178x + 49.911	16636		5.5	0.628	y = -30.059x + 50.703	18346
	5.5	0.326	y = -30.130x + 54.403	18369		6.0	0.607	y = -28.380x + 49.686	20138
	6.0	0.350	y = -29.325x + 53.308	20129		6.5	0.623	y = -29.759x + 50.838	21815
	6.5	0.399	y = -29.839x + 53.812	21835		7.0	0.568	y = -26.586x + 48.513	23608
	7.0	0.366	y = -29.070x + 53.401	23616		7.5	0.576	y = -25.094x + 47.324	25370
	7.5	0.367	y = -27.477x + 52.335	25287		8.0	0.584	y = -28.452x + 49.986	27039
	8.0	0.356	y = -25.619x + 51.066	27104		8.5	0.610	y = -30.197x + 51.334	28823
	8.5	0.407	y = -29.332x + 53.511	28842		9.0	0.630	y = -31.872x + 52.685	30638
	9.0	0.398	y = -32.580x + 55.542	30525	开封市	0.5	0.009	y = -2.313x + 33.926	925
	9.5	0.413	y = -35.210x + 57.236	32347		1.0	0.260	y = -17.877x + 42.875	2667
	10.0	0.402	y = -32.520x + 55.566	34074		1.5	0.446	y = -47.803x + 60.906	4391
	10.5	0.399	y = -30.274x + 54.014	35794		2.0	0.319	y = -40.069x + 56.520	6154
	11.0	0.323	y = -30.914x + 54.005	37546		2.5	0.362	y = -34.108x + 52.790	7918
	11.5	0.318	y = -27.295x + 51.801	39631		3.0	0.316	y = -21.770x + 45.261	9646
	12.0	0.372	y = -27.668x + 52.344	41009		3.5	0.295	y = -22.519x + 45.753	11421
	12.5	0.429	y = -29.299x + 53.172	42844		4.0	0.459	y = -24.834x + 47.320	13125
	13.0	0.496	y = -32.015x + 54.758	44149		4.5	0.547	y = -25.370x + 47.443	14885
	13.5	0.470	y = -29.711x + 53.287	43346		5.0	0.566	y = -25.419x + 47.248	16651
	14.0	0.528	y = -33.138x + 55.629	43118		5.5	0.522	y = -25.170x + 47.163	18374
	14.5	0.551	y = -35.129x + 56.732	43307		6.0	0.609	y = -27.567x + 48.530	20084
	15.0	0.466	y = -30.534x + 53.455	43318		6.5	0.642	y = -28.850x + 49.537	20669
	15.5	0.534	y = -30.042x + 52.985	43960		7.0	0.681	y = -30.975x + 50.972	21169
	16.0	0.531	y = -34.612x + 55.691	42313
	16.5	0.500	y = -30.932x + 53.374	42435
	16.5	0.500	y = -30.932x + 53.374	42435		17.0	0.532	y = -34.884x + 55.999	39269

表2

图5

图6

图7

图8

表3

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	NDMI-LST	NDVI-LST	NDMI-NDVI
横剖面	-0.7677	-0.5619	0.8523
纵剖面	-0.7903	-0.5540	0.8400

采样间隔/m	变量	不显著点数 (占比/%)	第一象限High-High 点数(占比/%)	第三象限Low-Low 点数(占比/%)	第二象限Low-High 点数(占比/%)	第四象限High-Low 点数(占比/%)
150	NDVI-LST	82199(61.8)	3483(2.6)	6068(4.6)	23635(17.8)	17671(13.3)
150	NDMI-LST	82189(61.8)	2522(1.9)	2496(1.9)	24612(18.5)	21237(16.0)
300	NDVI-LST	21630(65.0)	952(2.9)	1578(4.7)	5239(15.7)	3874(11.6)
300	NDMI-LST	21621(65.0)	815(2.4)	830(2.5)	5381(16.2)	4626(13.9)
450	NDVI-LST	9901(67.0)	457(3.1)	728(4.9)	2124(14.4)	1572(10.6)
450	NDMI-LST	9944(67.3)	410(2.8)	419(2.8)	2134(14.4)	1875(12.7)

NDVI、NDMI与地表温度关系的对比研究

Comparative study on the correlations between NDVI, NDMI and LST

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