# On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Data and Preprocessing

#### 2.3. Method

#### 2.3.1. Land Surface Temperature Retrieval

#### 2.3.2. Landscape Metrics

#### 2.3.3. Pearson Correlation Coefficient

#### 2.3.4. Multiple Linear Regression

#### 2.3.5. Random Forest Regression

#### 2.3.6. Coefficient of Rectangle Variation

## 3. Results

#### 3.1. LST Distribution in the Second and the Fourth Ring Road of Beijing

#### 3.2. Pearson Correlation Coefficient between Landscape Metrics and LST

#### 3.2.1. Pearson Correlation Coefficient between Landscape Metrics and LST at 10 m Grain Size

#### 3.2.2. Pearson Correlation Coefficient between Landscape Metrics and LST at 30 m Grain Size

#### 3.3. Multiple Linear Regression between Landscape Metrics and LST

#### 3.3.1. Multiple Linear Regression between Landscape Metrics and LST at 10 m Grain Size

#### 3.3.2. Multiple Linear Regression between Landscape Metrics and LST at 30 m Grain Size

#### 3.4. Random Forest Regression between Landscape Metrics and LST

#### 3.5. The CORV of 3D Landscape Metrics

#### 3.5.1. The CORV of 3D Landscape Metrics at 10 m Grain Size

#### 3.5.2. The CORV of 3D Landscape Metrics at 30 m Grain Size

## 4. Discussion

#### 4.1. Multi-Scale Relationship between 3D Landscape Pattern and LST in the Fourth Ring Road of Beijing

#### 4.2. Advantages and Limitations

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## References

- Wang, S.; Hu, M.; Wang, Y.; Xia, B. Dynamics of ecosystem services in response to urbanization across temporal and spatial scales in a mega metropolitan area. Sustain. Cities Soc.
**2022**, 77, 103561. [Google Scholar] [CrossRef] - Zhao, L.; Lee, X.; Smith, R.B.; Oleson, K. Strong contributions of local background climate to urban heat islands. Nature
**2014**, 511, 216–219. [Google Scholar] [CrossRef] [PubMed] - Rongbo, X.; Zhiyun, O.; Weifeng, L.; Zhaoming, Z.; Gregory, T.J.; Xiaoke, W.; Hong, M. A review of the eco-environmental consequences of urban heat islands. Acta Ecol. Sin.
**2005**, 25, 2055–2060. [Google Scholar] - Akbari, H.; Konopacki, S. Calculating energy-saving potentials of heat-island reduction strategies. Energy Policy
**2005**, 33, 721–756. [Google Scholar] [CrossRef] - Oke, T.R. Boundary Layer Climates. Earth Sci. Rev.
**1987**, 27, 265. [Google Scholar] - Wang, H.; Zhang, Y.; Tsou, J.; Li, Y. Surface Urban Heat Island Analysis of Shanghai (China) Based on the Change of Land Use and Land Cover. Sustainability
**2017**, 9, 1538. [Google Scholar] [CrossRef] [Green Version] - Ng, E.; Chen, L.; Wang, Y.; Yuan, C. A study on the cooling effects of greening in a high-density city: An experience from Hong Kong. Build. Environ.
**2012**, 47, 256–271. [Google Scholar] [CrossRef] - Figuerola, P.I.; Mazzeo, N.S.A. Urnan-nural temperature differences in Buenos Aires. Int. J. Climatol.
**1998**, 18, 1709–1723. [Google Scholar] [CrossRef] - Tan, J.; Zheng, Y.; Tang, X.; Guo, C.; Li, L.; Song, G.; Zhen, X.; Yuan, D.; Kalkstein, A.J.; Li, F. The urban heat island and its impact on heat waves and human health in Shanghai. Int. J. Biometeorol.
**2010**, 54, 75–84. [Google Scholar] [CrossRef] [PubMed] - Guo, A.; Yang, J.; Xiao, X.; Xia, J.; Jin, C.; Li, X. Influences of urban spatial form on urban heat island effects at the community level in China. Sustain. Cities Soc.
**2020**, 53, 101972. [Google Scholar] [CrossRef] - Patz, J.A.; Campbell-Lendrum, D.; Holloway, T.; Foley, J.A. Impact of regional climate change on human health. Nature
**2005**, 438, 310–317. [Google Scholar] [CrossRef] - Sun, L.; Chen, J.; Li, Q.; Huang, D. Dramatic uneven urbanization of large cities throughout the world in recent decades. Nat. Commun.
**2020**, 11, 5366. [Google Scholar] [CrossRef] - Liu, X.; Zhou, Y.; Yue, W.; Li, X.; Liu, Y.; Lu, D. Spatiotemporal patterns of summer urban heat island in Beijing, China using an improved land surface temperature. J. Clean. Prod.
**2020**, 257, 120529. [Google Scholar] [CrossRef] - Zhao, W.; Gong, H.; Zhao, W.; Tang, T. Spatial Distribution of Urban IP Pollution and CCA Analysis between IP and Meteorological Factors: A Case Study in Beijing. Geogr. Geo-Inf. Sci.
**2009**, 25, 71–74. [Google Scholar] - He, X.; Wang, J.; Feng, J.; Yan, Z.; Miao, S.; Zhang, Y.; Xia, J. Observational and modeling study of interactions between urban heat island and heatwave in Beijing. J. Clean. Prod.
**2020**, 247, 119169. [Google Scholar] [CrossRef] - Cui, Y.; Yan, D.; Hong, T.; Ma, J. Temporal and spatial characteristics of the urban heat island in Beijing and the impact on building design and energy performance. Energy
**2017**, 130, 286–297. [Google Scholar] [CrossRef] [Green Version] - Ye, Y.; Qiu, H. Using urban landscape pattern to understand and evaluate infectious disease risk. Urban For. Urban Green.
**2021**, 62, 127126. [Google Scholar] [CrossRef] [PubMed] - Tian, Y.; Zhou, W.; Qian, Y.; Zheng, Z.; Yan, J. The effect of urban 2D and 3D morphology on air temperature in residential neighborhoods. Landsc. Ecol.
**2019**, 34, 1161–1178. [Google Scholar] [CrossRef] - Forman, R.T.T.; Godron-Wiley, M. Landscape ecology. Landsc. Arch.
**1989**, 79, 103–104. [Google Scholar] [CrossRef] - Song, J.; Du, S.; Feng, X.; Guo, L. The relationships between landscape compositions and land surface temperature: Quantifying their resolution sensitivity with spatial regression models. Landsc. Urban Plan.
**2014**, 123, 145–157. [Google Scholar] [CrossRef] - Chen, Z.; Xu, B.; Devereux, B. Urban landscape pattern analysis based on 3D landscape models. Appl. Geogr.
**2014**, 55, 82–91. [Google Scholar] [CrossRef] - Bourbia, F.; Boucheriba, F. Impact of street design on urban microclimate for semi arid climate (Constantine). Renew. Energy
**2010**, 35, 343–347. [Google Scholar] [CrossRef] - Brring, L.; Mattsson, J.O.; Lindqvist, S. Canyon geometry, street temperatures and urban heat island in Malmö, Sweden. J. Climatol.
**1985**, 5, 433–444. [Google Scholar] [CrossRef] - Lengsfeld, M.; Kaminsky, J.; Merz, B.; Franke, R.P.; Eliasson, I. Urban nocturnal temperatures, street geometry and land use. Atmos. Environ.
**1996**, 30, 379–392. [Google Scholar] - Oke, T.R. Canyon geometry and the nocturnal urban heat island: Compariso of scale model and field observations. J. Climatol.
**1981**, 1, 237–254. [Google Scholar] [CrossRef] - Chun, B.; Guldmann, J.M. Spatial statistical analysis and simulation of the urban heat island in high-density central cities. Landsc. Urban Plan.
**2014**, 125, 76–88. [Google Scholar] [CrossRef] - Wu, J.; Shen, W.; Sun, W.; Tueller, P.T. Empirical patterns of the effects of changing scale on landscape metrics. Landsc. Ecol.
**2002**, 17, 761–782. [Google Scholar] [CrossRef] - Wu, J. Landscape Ecology; Higher Education Press: Beijing, China, 2007; Volume 2. [Google Scholar]
- Yuan, S.; Xia, H.; Yang, L. How changing grain size affects the land surface temperature pattern in rapidly urbanizing area: A case study of the central urban districts of Hangzhou City, China. Environ. Sci. Pollut. Res. Int.
**2021**, 28, 40060–40074. [Google Scholar] [CrossRef] [PubMed] - Yuan, F.; Bauer, M.E. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens. Environ.
**2007**, 106, 375–386. [Google Scholar] [CrossRef] - Hoffman, S. Vegetation as a climatic component in the design of an urban street: An empirical model for predicting the cooling effect of urban green areas with trees. Energy Build.
**2000**, 31, 221–235. [Google Scholar] - Huang, X.; Wang, Y. Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China. ISPRS J. Photogramm. Remote Sens.
**2019**, 152, 119–131. [Google Scholar] [CrossRef] - Gage, E.A.; Cooper, D.J. Relationships between landscape pattern metrics, vertical structure and surface urban Heat Island formation in a Colorado suburb. Urban Ecosyst.
**2017**, 20, 1229–1238. [Google Scholar] [CrossRef] - Lai, S.; Zhao, Y.; Fan, Y.; Ge, J. Characteristics of daytime land surface temperature in wind corridor: A case study of a hot summer and warm winter city. J. Build. Eng.
**2021**, 44, 103370. [Google Scholar] [CrossRef] - Guo, F.; Wu, Q.; Schlink, U. 3D building configuration as the driver of diurnal and nocturnal land surface temperatures: Application in Beijing’s old city. Build. Environ.
**2021**, 206, 108354. [Google Scholar] [CrossRef] - Breiman, L. Bagging Predictors. Mach. Learn.
**1996**, 24, 123–140. [Google Scholar] [CrossRef] [Green Version] - Yu, S.; Chen, Z.; Yu, B.; Wang, L.; Wu, B.; Wu, J.; Zhao, F. Exploring the relationship between 2D/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China. Sci. Total Environ.
**2020**, 725, 138229. [Google Scholar] [CrossRef] - Pais, C.; Miranda, A.; Carrasco, J.; Shen, Z.-J.M. Deep fire topology: Understanding the role of landscape spatial patterns in wildfire occurrence using artificial intelligence. Environ. Model. Softw.
**2021**, 143, 105122. [Google Scholar] [CrossRef] - Lin, J.; Lu, S.; He, X.; Wang, F. Analyzing the impact of three-dimensional building structure on CO
_{2}emissions based on random forest regression. Energy**2021**, 236, 121502. [Google Scholar] [CrossRef] - Dai, T.; Qu, Z.; Shi, F. Infrastructure stock in the process of urbanization in Beijing. Alex. Eng. J.
**2021**, 61, 3277–3291. [Google Scholar] [CrossRef] - Peng, J.; Xie, P.; Liu, Y.; Jing, M. Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sens. Environ.
**2016**, 173, 145–155. [Google Scholar] [CrossRef] - Qin, Z.; Karnieli, A.; Berliner, P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sens.
**2010**, 22, 3719–3746. [Google Scholar] [CrossRef] - Qin, Z.; Dall’Olmo, G.; Karnieli, A.; Berliner, P. Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data. J. Geophys. Res. Atmos.
**2001**, 106, 22655–22670. [Google Scholar] [CrossRef] - Hanqiu, X. Retrieval of the reflectance and land surface temperature of the newly—Launched Landsat 8 satellite. Chin. J. Geophys.
**2015**, 58, 7. [Google Scholar] - Liu, L.; Zhang, Y. Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Remote Sens.
**2011**, 3, 1535–1552. [Google Scholar] [CrossRef] [Green Version] - Han, Y.; Wang, J.-l.; Li, P. Influences of landscape pattern evolution on regional crop water requirements in regions of large-scale agricultural operations. J. Clean. Prod.
**2021**, 327, 129499. [Google Scholar] [CrossRef] - Li, K.; Li, C.; Liu, M.; Hu, Y.; Wang, H.; Wu, W. Multiscale analysis of the effects of urban green infrastructure landscape patterns on PM2.5 concentrations in an area of rapid urbanization. J. Clean. Prod.
**2021**, 325, 129324. [Google Scholar] [CrossRef] - Wu, Q.; Guo, F.; Li, H.; Kang, J. Measuring landscape pattern in three dimensional space. Landsc. Urban Plan.
**2017**, 167, 49–59. [Google Scholar] [CrossRef] - Li, J.; Zhou, K.; Xie, B.; Xiao, J. Impact of landscape pattern change on water-related ecosystem services: Comprehensive analysis based on heterogeneity perspective. Ecol. Indic.
**2021**, 133, 108372. [Google Scholar] [CrossRef] - Wu, Q.; Tan, J.; Guo, F.; Li, H.; Chen, S. Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China. Remote Sens.
**2019**, 11, 3021. [Google Scholar] [CrossRef] [Green Version] - Mukaka, M.M. Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research. Malawi Med. J.
**2012**, 24, 69–71. [Google Scholar] [PubMed] - Lian, J.; Garner, G.; Muessig, D.; Lang, V. A simple method to quantify the morphological similarity between signals. Signal Process.
**2010**, 90, 684–688. [Google Scholar] [CrossRef] - Mccullagh, P. Generalized linear models. Eur. J. Oper. Res.
**1989**, 16, 285–292. [Google Scholar] [CrossRef] - Breiman, L. Random forest. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef] [Green Version] - Zhu, L.; Zhou, X.; Zhang, C. Rapid identification of high-quality marine shale gas reservoirs based on the oversampling method and random forest algorithm. Artif. Intell. Geosci.
**2021**, 2, 76–81. [Google Scholar] [CrossRef] - Xie, Y.; Li, X.; Ngai, E.W.T.; Ying, W. Customer churn prediction using improved balanced random forests. Expert Syst. Appl.
**2009**, 36, 5445–5449. [Google Scholar] [CrossRef] - Torre-Tojal, L.; Bastarrika, A.; Boyano, A.; Lopez-Guede, J.M.; Graña, M. Above-ground Biomass Estimation from LiDAR data using Random Forest algorithms. J. Comput. Sci.
**2021**, 101517. [Google Scholar] [CrossRef] - Asadi, S.; Roshan, S.; Kattan, M.W. Random forest swarm optimization-based for heart diseases diagnosis. J. Biomed. Inf.
**2021**, 115, 103690. [Google Scholar] [CrossRef] - Cho, A. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Obs. Geoinf.
**2012**, 18, 399–406. [Google Scholar] - Hutengs, C.; Vohland, M. Downscaling land surface temperatures at regional scales with random forest regression. Remote Sens. Environ.
**2016**, 178, 127–141. [Google Scholar] [CrossRef] - Dongke, L.; Shengyan, D.; Guofu, L.; Qinghe, Z.; Qian, T.; Linghua, K. Landscape heterogeneity of mountainous and hilly area in the western Henan Province based on moving window method. Acta Ecol. Sin.
**2014**, 34, 3414–3424. [Google Scholar] - Whittaker, R.H. Vegetation of the Siskiyou Mountains Oregon and California. Ecol. Monogr. Ecol. Monogr.
**1960**, 30, 279–338. [Google Scholar] [CrossRef] - Kröger, R.; Khomo, L.M.; Levick, S.; Rogers, K.H. Moving window analysis and riparian boundary delineation on the Northern Plains of Kruger National Park, South Africa. Acta Oecol.
**2009**, 35, 573–580. [Google Scholar] [CrossRef] - Logan, T.M.; Zaitchik, B.; Guikema, S.; Nisbet, A. Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature. Remote Sens. Environ.
**2020**, 247, 111861. [Google Scholar] [CrossRef] - Zhang, N.; Zhang, J.; Chen, W.; Su, J. Block-based variations in the impact of characteristics of urban functional zones on the urban heat island effect: A case study of Beijing. Sustain. Cities Soc.
**2022**, 76, 103529. [Google Scholar] [CrossRef] - Jenerette, G.D.; Harlan, S.L.; Brazel, A.; Jones, N.; Larsen, L.; Stefanov, W.L. Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landsc. Ecol.
**2007**, 22, 353–365. [Google Scholar] [CrossRef] - Li, J.; Song, C.; Cao, L.; Zhu, F.; Meng, X.; Wu, J. Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sens. Environ.
**2011**, 115, 3249–3263. [Google Scholar] [CrossRef] - Grimmond, S. Urbanization and global environmental change: Local effects of urban warming. Geogr. J.
**2007**, 173, 83–88. [Google Scholar] [CrossRef]

**Figure 2.**Landsat 8 image, land cover map and building height of two study areas. (

**a**) Landsat 8 image; (

**b**) land cover map; (

**c**) building height.

**Figure 5.**The distribution of LST and the proportion of land types at different temperature levels in the study area. (

**a**) the second ring road; (

**b**) the fourth ring road.

**Figure 6.**PCC between LST and landscape metrics at 10 m grain size. (

**a**–

**f**) PCC between LST and 3D landscape metrics at 10 m grain size; (

**g**–

**j**) PCC between LST and 2D landscape metrics at 10 m grain size.

**Figure 7.**PCC between LST and landscape metrics at 30 m grain size. (

**a**–

**f**) PCC between LST and 3D landscape metrics at 30 m grain size; (

**g**–

**j**) PCC between LST and 2D landscape metrics at 30 m grain size.

**Figure 8.**The ${R}^{2}$ of the multiple linear regression model between LST and landscape metrics at 10 m grain size.

**Figure 9.**The ${R}^{2}$ of the multiple linear regression model between LST and landscape metrics at 30 m grain size.

**Figure 11.**Feature importance of 3D landscape metrics to LST in RFR. (

**a**,

**b**) are the feature importance of each metrics at 10 m grain size; (

**c**,

**d**) are the feature importance of each metrics at 30 m grain size.

**Figure 12.**Power fitting of the normalized CORV of each 3D landscape metrics at the 10 m grain size.

**Figure 14.**Comparison of LST, land use and building height in some typical areas of the study area. (

**a**) high LST area with more vegetation; (

**b**) high LST area with less vegetation; (

**c**,

**d**) are low LST area.

Metrics | Index Calculation Formula | Description |
---|---|---|

Component Metrics | ||

Largest Patch Index (%) | $\mathrm{LPI}=\frac{max\left({a}_{ij}\right)}{A}\ast 100$ | ${a}_{ij}$ is the 2D/3D area of patch $ij$, $A$ is the total 2D/3D area of a rectangle. LPI measures the proportion of the largest patch in a rectangle. |

Edge Density (m/ha) | $\mathrm{ED}=\frac{E}{A}{10}^{6}$ | $E$ is the total 2D/3D length of all patches’ edges. ED measures the total side length of all patches divided by the 2D/3D area of a rectangle. |

Number of Patches | $\mathrm{NP}=n$ | NP measures the total number of patches in a rectangle. |

Patches Cohesion Index | $\mathrm{COHESION}=\phantom{\rule{0ex}{0ex}}\left[1-\frac{{\sum}_{j=1}^{n}{p}_{ij}}{{\sum}_{j=1}^{n}{p}_{ij}\sqrt{{a}_{ij}}}\right]{\left[1-\frac{1}{\sqrt{A}}\right]}^{-1}\ast 100$ | ${p}_{ij}$ is the 2D/3D perimeter of patch $ij$, ${a}_{ij}$ is the 2D/3D surface area of patch $ij$. COHESION measures the aggregation and dispersion of patches in a rectangle. |

Effective Mesh Size (ha) | $\mathrm{MESH}=\frac{{\sum}_{i=1}^{m}{\sum}_{j=1}^{n}{a}_{ij}^{2}}{A}$ | MESH measures the sum of squares of patch area divided by the total rectangle area. |

Configuration Metrics | ||

Landscape Shape Index | $\mathrm{LSI}=\frac{0.25E{A}_{surf}}{\sqrt{A}{A}_{prj}}$ | ${A}_{surf}$ represents the 3D area and ${A}_{prj}$ is the projected plane area of ${A}_{surf}$. $E$ is the total 3D edge length of all patches. |

Landscape Division Index | $\mathrm{DIVISION}=1-{\displaystyle {\displaystyle \sum}_{i=1}^{m}}{\displaystyle {\displaystyle \sum}_{j=1}^{n}}{\left(\frac{{a}_{ij}}{A}\right)}^{2}$ | DIVISION measures the degree of division of a rectangle. DIVISION equals 0 when the landscape consists of single patch. DIVISION achieves its maximum value (1) when the landscape is maximally subdivided. |

Euclidean Nearest-Neighbor Distance (m) | $\mathrm{ENN}-\mathrm{MN}=\frac{{\sum}_{j=1}^{n}{x}_{ij}}{{n}_{i}}$ | ${x}_{ij}$ is the 2D/3D closest distance between the same patch $ij$, and ${n}_{i}$ represents the total number of class $i$. ENN-MN measures the distance to the nearest neighboring patch of the same type. |

Shannon’s Diversity Index | $\mathrm{SHDI}=-{\displaystyle {\displaystyle \sum}_{k=1}^{n}}{P}_{k}ln\left({P}_{k}\right)$ | ${P}_{k}$ equals the 2D/3D area of class $k$, divided by the area of 2D/3D surface. SHDI measures the diversity of a rectangle. |

Shannon’s Evenness Index | $\mathrm{SHEI}=\frac{-{\sum}_{i=1}^{m}\left({P}_{i}ln{P}_{i}\right)}{lnm}$ | ${P}_{i}$ equals the 2D/3D area of class $i$, divided by the area of 2D/3D surface. SHEI measures the evenness of a rectangle. |

Roughness Metrics | ||

Root Mean Square Deviation of a Surface | $\mathrm{SQ}=\sqrt{\frac{1}{N}{{\displaystyle \sum}}_{i=1}^{N}{\left[{h}_{i}-u\right]}^{2}}$ | ${h}_{i}$ is the pixel height of class $i$, $N$ is the total number of pixels in a rectangle, $u$ is the mean height of all pixels. SQ measures the degree to the building deviates from the plane of a rectangle. |

Skewness of Surface Height Distribution | $\mathrm{SKU}=\frac{1}{N{S}_{q}^{3}}{\displaystyle {\displaystyle \sum}_{i=1}^{N}}{\left[{h}_{i}-u\right]}^{3}$ | SKU measures the skewness of the buildings in a rectangle. |

Mean Height (m) | $\mathrm{MEAN}=\frac{H}{A}$ | $H$ is the sum of all pixels in a rectangle. MEAN is the mean height of a rectangle. |

Maximum Height (m) | $\mathrm{MAX}={h}_{max}$ | MAX is the maximum height of a rectangle. |

Sky View Factor | $\mathrm{SVF}=1-\frac{{\sum}_{i=1}^{n}\mathrm{sin}{\gamma}_{i}}{n}$ | $n$ stands for the number of directions used to estimate the vertical elevation angle of the relief horizon. The vertical elevation angle ${\gamma}_{i}$ can be computed from the horizontal distance and the elevation difference between the horizon and the vantage point. SVF measures the sky visibility. |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wu, Q.; Li, Z.; Yang, C.; Li, H.; Gong, L.; Guo, F.
On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest. *Remote Sens.* **2022**, *14*, 279.
https://doi.org/10.3390/rs14020279

**AMA Style**

Wu Q, Li Z, Yang C, Li H, Gong L, Guo F.
On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest. *Remote Sensing*. 2022; 14(2):279.
https://doi.org/10.3390/rs14020279

**Chicago/Turabian Style**

Wu, Qiong, Zhaoyi Li, Changbao Yang, Hongqing Li, Liwei Gong, and Fengxiang Guo.
2022. "On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest" *Remote Sensing* 14, no. 2: 279.
https://doi.org/10.3390/rs14020279