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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 164-171     DOI: 10.6046/gtzyyg.2019.02.23
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Spatial variability and influencing factors of LST in plateau area: Exemplified by Sangzhuzi District
Junnan XIONG1,2, Wei LI1, Weiming CHENG2(), Chunkun FAN3, Jin LI1, Yunliang ZHAO1
1.School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500, China
2.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3.Agriculture Research Institute, Tibet Academy of Agriculture and Animal Husbandry Sciences, Lhasa 850000, China
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Abstract  

Revealing the spatial differentiation characteristics and influencing factors of land surface temperature (LST) in the plateau area is of great significance for the study of local climate change. However, the existing research merely analyzes the relationship between single factor and LST, whereas the study of the spatial differentiation characteristics and the quantitative analysis of influencing factors of LST in the plateau area are relatively insufficient. Taking the Sangzhuzi District of Xigaze City as an example, the authors used Landsat8 remote sensing data to invert the LST of the study area by using radiative transfer equation algorithm and the universal single-channel algorithm. In addition, the factor detector and interaction detector in the geodetector model were used to quantitatively detect the influence of single factor and multiple factors on LST, respectively. The results show that, in the quantifiable factors, LST increases first and then decreases with the increase in the degree of aspect, and there is a significant negative correlation between LST and other factors with a difference in the descent speed. Elevation is the most important factor affecting the spatial distribution and forming the differentiation characteristics of LST in the plateau area, followed by normalized difference vegetation index(NDVI), aspect, normalized difference moisture index(NDMI), soil type, slope, and average annual precipitation; the spatial distribution and the formation of differentiation characteristics of LST in the plateau area are the result of multiple factors, all of which have a synergistic enhancement effect under interactions, such as the interaction of elevation and aspect, elevation and NDMI, and elevation and NDVI with the most significant impact.
Keywords Landsat8      plateau region      land surface temperature(LST)      influence factors      geographical detector     
:  P237P94  
Corresponding Authors: Weiming CHENG     E-mail: chengwm@lreis.ac.cn
Issue Date: 23 May 2019
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Junnan XIONG
Wei LI
Weiming CHENG
Chunkun FAN
Jin LI
Yunliang ZHAO
Cite this article:   
Junnan XIONG,Wei LI,Weiming CHENG, et al. Spatial variability and influencing factors of LST in plateau area: Exemplified by Sangzhuzi District[J]. Remote Sensing for Land & Resources, 2019, 31(2): 164-171.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.23     OR     https://www.gtzyyg.com/EN/Y2019/V31/I2/164
Fig.1  Location and elevation of study area
时间 τ L↑/(W·m-2·
Sr-1·μm-1)		 L↓/(W·m-2·
Sr-1·μm-1)
2015-09-07
T12:35:26		 0.93 0.45 0.79
Tab.1  LST inversion parameters
Fig.2  Inversion results of LST in the study area
时间 LST反演算法 最小值 最大值 平均值 标准差
2015-09-07
T12:35:26		 RTE算法 -3.28 40.83 23.83 7.07
SC算法 -2.98 41.10 24.11 7.08
Tab.2  Comparison of LST inversion results(℃)
Fig.3  Scatter plots of quantifiable influence factors and LST
Fig.4  Influence factors and spatial distribution of LST
探测因子(X1) 解释力P 显著水平Q 排序(P值由高到低)
海拔 0.458 6 0.00 1
坡度 0.133 0 0.00 6
坡向 0.244 4 0.00 3
年均降水量 0.063 2 0.00 7
土地类型 0.037 5 0.09 8
土壤类型 0.171 3 0.00 5
NDVI 0.302 4 0.00 2
NDMI 0.196 5 0.00 4
Tab.3  Detection results of single influence factor
探测因子(X1) 1 2 3 4 5 6 7 8
1
2 N
3 Y Y
4 Y Y Y
5 N Y N N
6 Y Y N N Y
7 N Y N N N N
8 N N N N N N N
Tab.4  Multiple factors affect statistical significance of LST differences
交互因子(X1∩X2) P(X1) P(X1) P(X1∩X2) 交互结果 影响模式
土壤类型∩海拔 0.171 3 0.458 6 0.478 1 P(A∩B)>max[P(A),P(B)] 双线性增强
土壤类型∩坡度 0.171 3 0.133 0 0.239 9 P(A∩B)>max[P(A),P(B)] 双线性增强
土壤类型∩坡向 0.171 3 0.244 4 0.408 1 P(A∩B)>max[P(A),P(B)] 双线性增强
土壤类型∩年均降水量 0.171 3 0.063 2 0.233 1 P(A∩B)>max[P(A),P(B)] 双线性增强
土壤类型∩NDVI 0.171 3 0.302 4 0.407 9 P(A∩B)>max[P(A),P(B)] 双线性增强
土壤类型∩NDMI 0.171 3 0.196 5 0.392 1 P(A∩B)>P(A)+P(B) 非线性增强
NDVI∩海拔 0.302 4 0.458 6 0.573 9 P(A∩B)>max[P(A),P(B)] 双线性增强
NDVI∩坡度 0.302 4 0.133 0 0.370 4 P(A∩B)>max[P(A),P(B)] 双线性增强
NDVI∩坡向 0.302 4 0.244 4 0.478 2 P(A∩B)>max[P(A),P(B)] 双线性增强
NDVI∩年均降水量 0.302 4 0.063 2 0.341 9 P(A∩B)>max[P(A),P(B)] 双线性增强
NDVI∩NDMI 0.302 4 0.196 5 0.342 3 P(A∩B)>max[P(A),P(B)] 双线性增强
海拔∩坡度 0.458 6 0.133 0 0.482 7 P(A∩B)>max[P(A),P(B)] 双线性增强
海拔∩坡向 0.458 6 0.244 4 0.710 4 P(A∩B)>P(A)+P(B) 非线性增强
海拔∩年均降水量 0.458 6 0.063 2 0.490 8 P(A∩B)>max[P(A),P(B)] 双线性增强
海拔∩NDMI 0.458 6 0.196 5 0.633 2 P(A∩B)>max[P(A),P(B)] 双线性增强
坡度∩坡向 0.133 0 0.244 4 0.369 4 P(A∩B)>max[P(A),P(B)] 双线性增强
坡度∩NDMI 0.133 0 0.196 5 0.383 8 P(A∩B)>P(A)+P(B) 非线性增强
坡度∩年均降水量 0.133 0 0.063 2 0.184 1 P(A∩B)>max[P(A),P(B)] 双线性增强
坡向∩NDMI 0.244 4 0.196 5 0.375 0 P(A∩B)>max[P(A),P(B)] 双线性增强
坡向∩年均降水量 0.244 4 0.063 2 0.336 4 P(A∩B)>P(A)+P(B) 非线性增强
NDMI∩年均降水量 0.196 5 0.063 2 0.290 6 P(A∩B)>P(A)+P(B) 非线性增强
Tab.5  Interaction of multiple factors
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