The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050)
Abstract
:1. Introduction
2. Material and Methodology
2.1. Study Area and Datasets
2.2. Land Use/Cover Change
2.3. Calculation of Normalized Difference Vegetation Index (NDVI)
2.4. Retrieval of Land Surface Temperature (LST)
2.5. Relative LST Change Detection
2.6. Cellular Automata–Markov Chain (CA–Markov) Model Analysis
3. Results
3.1. Land Use/Cover Changes (LUCC)
3.2. Estimation of Land Surface Temperature (LST)
3.3. Relationship between LUCC and LST
3.4. Warming and Cooling Impacts of LUCC from 2004 to 2019
3.5. Cellular Automata–Markov Chain (CA–Markov) Model Analysis
4. Discussion
4.1. Implication of Land Use/Land Cover Change for LST
4.2. Land Use Conversion and Its Contribution to UHIs
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K. Global consequences of land use. Science 2005, 309, 570–574. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Beckline, M.; Yujun, S.; Yvette, B.; John, A.B.; Mor-Achankap, B.; Saeed, S.; Richard, T.; Wose, J.; Paul, C. Perspectives of remote sensing and GIS applications in tropical forest management. Am. J. Agric. For. 2017, 5, 33–39. [Google Scholar] [CrossRef] [Green Version]
- Lambin, E.F.; Geist, H.J.; Lepers, E. Dynamics of land-use and land-cover change in tropical regions. Annu. Rev. Environ. Resour. 2003, 28, 205–241. [Google Scholar] [CrossRef] [Green Version]
- Hersperger, A.M.; Gennaio, M.-P.; Verburg, P.H.; Bürgi, M. Linking land change with driving forces and actors: Four conceptual models. Ecol. Soc. 2010, 15, 1–17. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Q.; Su, S. Determinants of urban expansion and their relative importance: A comparative analysis of 30 major metropolitans in China. Habitat Int. 2016, 58, 89–107. [Google Scholar] [CrossRef]
- Huang, Y.; Qiu, Q.; Sheng, Y.; Min, X.; Cao, Y. Exploring the Relationship between Urbanization and the Eco-Environment: A Case Study of Beijing. Sustainability 2019, 11, 6298. [Google Scholar] [CrossRef] [Green Version]
- Debbage, N.; Shepherd, J.M. The urban heat island effect and city contiguity. Comput. Environ. Urban Syst. 2015, 54, 181–194. [Google Scholar] [CrossRef]
- Sexton, J.O.; Song, X.-P.; Huang, C.; Channan, S.; Baker, M.E.; Townshend, J.R. Urban growth of the Washington, DC-Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover. Remote Sens. Environ. 2013, 129, 42–53. [Google Scholar] [CrossRef]
- Buyadi, S.N.A.; Mohd, W.M.N.W.; Misni, A. Impact of land use cover changes on the surface temperature distribution of area surrounding the National Botanic Garden, Shah Alam. Procedia-Soc. Behav. Sci. 2013, 101, 516–525. [Google Scholar] [CrossRef] [Green Version]
- Chen, X.-L.; Zhao, H.-M.; Li, P.-X.; Yin, Z.-Y. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 2006, 104, 133–146. [Google Scholar] [CrossRef]
- Tonkaz, T.; Çetin, M. Effects of urbanization and land-use type on monthly extreme temperatures in a developing semi-arid region, Turkey. J. Arid Environ. 2007, 68, 143–158. [Google Scholar] [CrossRef]
- Carlson, T.N.; Arthur, S.T. The impact of land use—Land cover changes due to urbanization on surface microclimate and hydrology: A satellite perspective. Global Planet. Chang. 2000, 25, 49–65. [Google Scholar] [CrossRef]
- Huff, F.; Changnon, S., Jr. Climatological assessment of urban effects on precipitation at St. Louis. J. Appl. Meteorol. 1972, 11, 823–842. [Google Scholar] [CrossRef] [Green Version]
- Almazroui, M.; Islam, M.N.; Jones, P. Urbanization effects on the air temperature rise in Saudi Arabia. Clim. Chang. 2013, 120, 109–122. [Google Scholar] [CrossRef]
- Li, Y.; Zhu, L.; Zhao, X.; Li, S.; Yan, Y. Urbanization impact on temperature change in China with emphasis on land cover change and human activity. J. Clim. 2013, 26, 8765–8780. [Google Scholar] [CrossRef]
- Sobrino, J.A.; Jiménez-Muñoz, J.C.; Paolini, L. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens. Environ. 2004, 90, 434–440. [Google Scholar] [CrossRef]
- Feng, H.; Liu, H.; Wu, L. Monitoring the relationship between the land surface temperature change and urban growth in Beijing, China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 4010–4019. [Google Scholar] [CrossRef]
- Walawender, J.P.; Szymanowski, M.; Hajto, M.J.; Bokwa, A. Land surface temperature patterns in the urban agglomeration of Krakow (Poland) derived from Landsat-7/ETM+ data. Pure Appl. Geophys. 2014, 171, 913–940. [Google Scholar] [CrossRef] [Green Version]
- Verburg, P.H.; Veldkamp, A.; Willemen, L.; Overmars, K.P.; Castella, J.-C. Landscape level analysis of the spatial and temporal complexity of land-use change. Ecosyst. Land Use Geogr. Monogr. Ser. 2004, 153, 217–230. [Google Scholar]
- Cristóbal, J.; Jiménez-Muñoz, J.; Prakash, A.; Mattar, C.; Skoković, D.; Sobrino, J. An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band. Remote Sens. 2018, 10, 431. [Google Scholar] [CrossRef] [Green Version]
- Ding, H.; Shi, W. Land-use/land-cover change and its influence on surface temperature: A case study in Beijing City. Int. J. Remote Sens. 2013, 34, 5503–5517. [Google Scholar] [CrossRef]
- Connors, J.P.; Galletti, C.S.; Chow, W.T. Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landsc. Ecol. 2013, 28, 271–283. [Google Scholar] [CrossRef]
- Amiri, R.; Weng, Q.; Alimohammadi, A.; Alavipanah, S.K. Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sens. Environ. 2009, 113, 2606–2617. [Google Scholar] [CrossRef]
- Amir Siddique, M.; Dongyun, L.; Li, P.; Rasool, U.; Ullah Khan, T.; Javaid Aini Farooqi, T.; Wang, L.; Fan, B.; Rasool, M.A. Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China. PeerJ 2020, 8, e9115. [Google Scholar] [CrossRef]
- Santé, I.; García, A.M.; Miranda, D.; Crecente, R. Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landsc. Urban Plan. 2010, 96, 108–122. [Google Scholar] [CrossRef]
- Ullah, S.; Tahir, A.A.; Akbar, T.A.; Hassan, Q.K.; Dewan, A.; Khan, A.J.; Khan, M. Remote Sensing-Based Quantification of the Relationships between Land Use Land Cover Changes and Surface Temperature over the Lower Himalayan Region. Sustainability 2019, 11, 5492. [Google Scholar] [CrossRef] [Green Version]
- Zenil, H. Compression-based investigation of the dynamical properties of cellular automata and other systems. arXiv 2009, arXiv:0910.4042. [Google Scholar] [CrossRef]
- Civco, D.L. Artificial neural networks for land-cover classification and mapping. Int. J. Geogr. Inf. Sci. 1993, 7, 173–186. [Google Scholar] [CrossRef]
- Agarwal, C.; Green, G.M.; Grove, J.M.; Evans, T.P.; Schweik, C.M. A review and assessment of land-use change models: Dynamics of space, time, and human choice. In General Technical Report (GTR). NE-297; Department of Agriculture: Newton Square, PA, USA, 2002; p. 297. [Google Scholar]
- Artis, D.A.; Carnahan, W.H. Survey of emissivity variability in thermography of urban areas. Remote Sens. Environ. 1982, 12, 313–329. [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]
- Tian, G.; Wu, J.; Yang, Z. Spatial pattern of urban functions in the Beijing metropolitan region. Habitat Int. 2010, 34, 249–255. [Google Scholar] [CrossRef]
- Qiao, Z.; Tian, G.; Xiao, L. Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data. ISPRS J. Photogramm. Remote Sens. 2013, 85, 93–101. [Google Scholar] [CrossRef]
- Landsat. Science Data Users Handbook; Landsat: Sioux Falls, SD, USA, 2007. [Google Scholar]
- Sadiq Khan, M.; Ullah, S.; Sun, T.; Rehman, A.U.R.; Chen, L. Land-Use/Land-Cover Changes and Its Contribution to Urban Heat Island: A Case Study of Islamabad, Pakistan. Sustainability 2020, 12, 3861. [Google Scholar] [CrossRef]
- Chen, B.; Zhang, X.; Tao, J.; Wu, J.; Wang, J.; Shi, P.; Zhang, Y.; Yu, C. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agric. For. Meteorol. 2014, 189, 11–18. [Google Scholar] [CrossRef]
- Wu, L.; Sun, B.; Zhou, S.; Huang, S.-E.; Zhao, Q. A new fusion technique of remote sensing images for land use/cover. Pedosphere 2004, 14, 187–194. [Google Scholar]
- Jiang, J.; Tian, G. Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environ. Sci. 2010, 2, 571–575. [Google Scholar] [CrossRef] [Green Version]
- Sang, X.; Guo, Q.; Wu, X.; Fu, Y.; Xie, T.; He, C.; Zang, J. Intensity and Stationarity Analysis of Land use cover change based on CART Algorithm. Sci. Rep. 2019, 9, 12279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Elvidge, C.D.; Chen, Z. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sens. Environ. 1995, 54, 38–48. [Google Scholar] [CrossRef]
- Wang, R.; Hou, H.; Murayama, Y.; Derdouri, A. Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China. Remote Sens. 2020, 12, 440. [Google Scholar] [CrossRef] [Green Version]
- Sobrino, J.A.; Jiménez-Muñoz, J.C. Minimum configuration of thermal infrared bands for land surface temperature and emissivity estimation in the context of potential future missions. Remote Sens. Environ. 2014, 148, 158–167. [Google Scholar] [CrossRef]
- Snyder, W.C.; Wan, Z.; Zhang, Y.; Feng, Y.-Z. Classification-based emissivity for land surface temperature measurement from space. Int. J. Remote Sens. 1998, 19, 2753–2774. [Google Scholar] [CrossRef]
- Qiao, Z.; Liu, L.; Qin, Y.; Xu, X.; Wang, B.; Liu, Z. The Impact of Urban Renewal on Land Surface Temperature Changes: A Case Study in the Main City of Guangzhou, China. Remote Sens. 2020, 12, 794. [Google Scholar] [CrossRef] [Green Version]
- Guha, S.; Govil, H.; Dey, A.; Gill, N. Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. Eur. J. Remote Sens. 2018, 51, 667–678. [Google Scholar] [CrossRef]
- Singh, P.; Kikon, N.; Verma, P. Impact of land use cover change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustain. Cities Soc. 2017, 32, 100–114. [Google Scholar] [CrossRef]
- Zhang, Y.; Yu, T.; Gu, X.; Zhang, Y.; Chen, L. Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect. J. Remote Sens.-Beijing 2006, 10, 789. [Google Scholar]
- Tariq, A.; Riaz, I.; Ahmad, Z.; Yang, B.; Amin, M.; Kausar, R.; Andleeb, S.; Farooqi, M.A.; Rafiq, M. Land surface temperature relation with normalized satellite indices for the estimation of spatio-temporal trends in temperature among various land use land cover classes of an arid Potohar region using Landsat data. Environ. Earth Sci. 2019, 79, 1–15. [Google Scholar] [CrossRef]
- Muller, M.R.; Middleton, J. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landsc. Ecol. 1994, 9, 151–157. [Google Scholar]
- Zhou, L.; Dang, X.; Sun, Q.; Wang, S. Multi-scenario simulation of urban land change in Shanghai by random forest and CA–Markov model. Sustain. Cities Soc. 2020, 55, 102045. [Google Scholar] [CrossRef]
- Al-sharif, A.A.; Pradhan, B. Monitoring and predicting land use cover change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arab. J. Geosci. 2014, 7, 4291–4301. [Google Scholar] [CrossRef]
- Halmy, M.W.A.; Gessler, P.E.; Hicke, J.A.; Salem, B.B. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl. Geogr. 2015, 63, 101–112. [Google Scholar] [CrossRef]
- Li, X.; Li, W.; Middel, A.; Harlan, S.L.; Brazel, A.J.; Turner Ii, B. Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral-demographic-economic factors. Remote Sens. Environ. 2016, 174, 233–243. [Google Scholar] [CrossRef] [Green Version]
- Sejati, A.W.; Buchori, I.; Rudiarto, I. The spatio-temporal trends of urban growth and surface urban heat islands over two decades in the Semarang Metropolitan Region. Sustain. Cities Soc. 2019, 46, 101432. [Google Scholar] [CrossRef]
- Zhang, X.; Zhong, T.; Feng, X.; Wang, K. Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. Int. J. Remote Sens. 2009, 30, 2105–2118. [Google Scholar] [CrossRef]
- Pal, S.; Ziaul, S. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt. J. Remote Sens. Space Sci. 2017, 20, 125–145. [Google Scholar] [CrossRef] [Green Version]
- Tali, J.; Emtehani, M.; Murthy, K.; Nagendra, H. Future Threats to CBD: A Case Study of Bangalore CBD. N. Y. Sci. J. 2012, 5, 22–27. [Google Scholar]
- Turner, B.L.; Lambin, E.F.; Reenberg, A. The emergence of land change science for global environmental change and sustainability. Proc. Natl. Acad. Sci. USA 2007, 104, 20666–20671. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, W.; Zhang, Y.; Pengwang, C.; Gao, W. Evaluation of urbanization dynamics and its impacts on surface heat islands: A case study of Beijing, China. Remote Sens. 2017, 9, 453. [Google Scholar] [CrossRef] [Green Version]
- Cai, G.; Du, M.; Xue, Y. Monitoring of urban heat island effect in Beijing combining ASTER and TM data. Int. J. Remote Sens. 2011, 32, 1213–1232. [Google Scholar] [CrossRef]
- Oluseyi, I.O.; Fanan, U.; Magaji, J. An evaluation of the effect of land use/cover change on the surface temperature of Lokoja town, Nigeria. Afr. J. Environ. Sci. Technol. 2009, 3, 86–90. [Google Scholar]
- Macarof, P.; Statescu, F. Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi. Present Environ. Sustain. Dev. 2017, 11, 141. [Google Scholar] [CrossRef] [Green Version]
- Weng, Q.; Lu, D.; Schubring, J. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 2004, 89, 467–483. [Google Scholar] [CrossRef]
- Faqe Ibrahim, G. Urban land use land cover changes and their effect on land surface temperature: Case study using Dohuk City in the Kurdistan Region of Iraq. Climate 2017, 5, 13. [Google Scholar] [CrossRef] [Green Version]
- Weng, Q.; Lo, C. Spatial analysis of urban growth impacts on vegetative greenness with Landsat TM data. Geocarto Int. 2001, 16, 19–28. [Google Scholar] [CrossRef]
- Fan, J.-W.; Shao, Q.-Q.; Liu, J.-Y.; Wang, J.-B.; Harris, W.; Chen, Z.-Q.; Zhong, H.-P.; Xu, X.-L.; Liu, R.-G. Assessment of effects of climate change and grazing activity on grassland yield in the Three Rivers Headwaters Region of Qinghai-Tibet Plateau, China. Environ. Monit. Assess. 2010, 170, 571–584. [Google Scholar] [CrossRef]
- Xiong, Y.; Huang, S.; Chen, F.; Ye, H.; Wang, C.; Zhu, C. The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China. Remote Sens. 2012, 4, 2033–2056. [Google Scholar] [CrossRef] [Green Version]
- Luo, X.; Li, W. Scale effect analysis of the relationships between urban heat island and impact factors: Case study in Chongqing. J. Appl. Remote Sens. 2014, 8, 084995. [Google Scholar] [CrossRef]
- Chen, X.; Zhang, Y. Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustain. Cities Soc. 2017, 32, 87–99. [Google Scholar] [CrossRef] [Green Version]
- He, C.; Shi, P.; Xie, D.; Zhao, Y. Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens. Lett. 2010, 1, 213–221. [Google Scholar] [CrossRef] [Green Version]
- Yang, P.; Ren, G.; Liu, W. Spatial and temporal characteristics of Beijing urban heat island intensity. J. Appl. Meteorol. Climatol. 2013, 52, 1803–1816. [Google Scholar] [CrossRef]
- Quan, J.; Chen, Y.; Zhan, W.; Wang, J.; Voogt, J.; Wang, M. Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a Gaussian volume model. Remote Sens. Environ. 2014, 149, 33–46. [Google Scholar] [CrossRef]
- Xiao, R.-B.; Ouyang, Z.-Y.; Zheng, H.; Li, W.-F.; Schienke, E.W.; Wang, X.-K. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J. Environ. Sci. 2007, 19, 250–256. [Google Scholar] [CrossRef]
- Liu, W.; Ji, C.; Zhong, J.; Jiang, X.; Zheng, Z. Temporal characteristics of the Beijing urban heat island. Theor. Appl. Climatol. 2007, 87, 213–221. [Google Scholar] [CrossRef]
- Ren, G.; Chu, Z.; Chen, Z.; Ren, Y. Implications of temporal change in urban heat island intensity observed at Beijing and Wuhan stations. Geophys. Res. Lett. 2007, 34, L05711. [Google Scholar] [CrossRef]
- Pickett, S.T.; Burch, W.R.; Dalton, S.E.; Foresman, T.W.; Grove, J.M.; Rowntree, R. A conceptual framework for the study of human ecosystems in urban areas. Urban Ecosyst. 1997, 1, 185–199. [Google Scholar] [CrossRef]
- Takeuchi, W.; Hashim, N.; Thet, K.M. Application of remote sensing and GIS for monitoring urban heat island in Kuala Lumpur Metropolitan area. In Proceedings of the Map Asia 2010 and the International Symposium and Exhibition on Geoinformation, Kuala Lumpur, Malaysia, 26–28 July 2010. [Google Scholar]
- Chapa, F.; Hariharan, S.; Hack, J. A New Approach to High-Resolution Urban Land Use Classification Using Open Access Software and True Color Satellite Images. Sustainability 2019, 11, 5266. [Google Scholar] [CrossRef] [Green Version]
Acquired Date | Spacecraft ID | Resolution (m) | Cloud Cover |
---|---|---|---|
21 July 2004 | Landsat-5 TM/TIRS | 30 × 30/100 × 100 | 0.01% |
6 July 2009 | Landsat-5 TM/TIRS | 30 × 30/100 × 100 | 0.05% |
26 July 2014 | Landsat-8 ETM/TIRS | 30 × 30/120 × 120 | 0.06% |
29 July 2019 | Landsat-8 ETM/TIRS | 30 × 30/120 × 120 | 0.03% |
LUC Classes | Abbreviations | Description |
---|---|---|
Urban area | UA | Urban and rural built-up areas, roads, buildings and concrete structures |
Cropland | CL | Kharif and Rabi, agricultural plantation, bushes, etc. |
Vegetation | VA | Urban plantation, grassland |
Forest area | FA | Forest plantation, deciduous plantation |
Barren land | BL | Exposed rock, waste lands, bare soil and impervious surfaces, etc. |
Water bodies | WB | Tank, pond, lake, river, etc. |
Water | % | Vegetation | % | Urban | % | Forest | % | Cropland | % | Barren | % | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Losses | 164.90 | 27% | 755.10 | 69% | 729.97 | 23% | 1896.40 | 20% | 1431.29 | 21% | 228.71 | 43% |
Unchanged | 272.87 | 45% | 55.22 | 5% | 1436.27 | 44% | 6337.30 | 68% | 2989.21 | 44% | 67.82 | 13% |
Gains | 172.75 | 28% | 288.93 | 26% | 1072.54 | 33% | 1115.74 | 12% | 2321.39 | 34% | 234.92 | 44% |
Sr. | Ranges (°C) | Thermal Sensation |
---|---|---|
1 | <20 | Neutral |
2 | 20–25 | Slightly Warm |
3 | 25–30 | Warm |
4 | 30–35 | Hot |
5 | >35 | Very Hot |
LC | 2025 | %age | 2050 | %age | 2019–2025 | %age | 2019–2050 | %age |
---|---|---|---|---|---|---|---|---|
Water | 398.75 | 2% | 455.12 | 3% | −19.85 | 0% | 36.52 | 0% |
Vegetation | 309.93 | 2% | 355.63 | 2% | −482.27 | −3% | −436.57 | −3% |
Forest | 7494.97 | 46% | 6664.78 | 41% | −1042.79 | −6% | −1872.98 | −11% |
Urban | 2833.20 | 17% | 2808.81 | 17% | 855.17 | 5% | 830.78 | 8% |
Barren | 303.28 | 2% | 342.84 | 2% | 24.44 | 0.3% | 64.00 | 0.9% |
Cropland | 5024.82 | 31% | 5737.78 | 35% | 665.28 | 4% | 1378.24 | 5% |
Thermal Sensation | LST-2025 | %age | LST-2050 | %age | 2019–2025 | %age | 2019–2050 | %age |
---|---|---|---|---|---|---|---|---|
Slightly Warm | 4195.63 | 25.89 | 1264.00 | 7.80 | −884.36 | −3.51 | −3815.99 | −21.60 |
Warm | 7842.56 | 48.38 | 6133.90 | 37.84 | −795.26 | −1.60 | −2503.92 | −12.15 |
Hot | 4047.47 | 24.97 | 8415.76 | 51.92 | 685.20 | 5.51 | 5053.49 | 32.46 |
Very Hot | 123.05 | 0.76 | 395.06 | 2.44 | −6.83 | 0.01 | 265.18 | 1.69 |
LUCC Gain | Area (km2) | Area (10%) | RLST (oC) | RLST (10%) | UHI | UHI (10%) |
---|---|---|---|---|---|---|
Urban to Water | 35.97 | 0.10 | 0.80 | 0.18 | 0.28 | 0.18 |
Water to Cropland | 102.15 | 0.29 | 0.99 | 0.22 | 0.35 | 0.22 |
Cropland to Vegetation | 153.91 | 0.44 | 1.41 | 0.31 | 0.50 | 0.31 |
Vegetation to Cropland | 1187.49 | 3.42 | 1.73 | 0.39 | 0.62 | 0.39 |
Barren to Forest | 26.13 | 0.08 | 1.86 | 0.41 | 0.66 | 0.41 |
Urban to Forest | 75.58 | 0.22 | 2.36 | 0.53 | 0.84 | 0.53 |
Water to Urban | 130.99 | 0.38 | 2.57 | 0.57 | 0.92 | 0.57 |
Cropland to Urban | 623.62 | 1.80 | 2.71 | 0.60 | 0.97 | 0.60 |
Barren to Vegetation | 15.75 | 0.05 | 2.75 | 0.61 | 0.98 | 0.61 |
Urban to Vegetation | 26.14 | 0.08 | 2.82 | 0.63 | 1.01 | 0.63 |
Barren to Cropland | 203.44 | 0.59 | 2.84 | 0.63 | 1.01 | 0.63 |
Urban to Cropland | 433.42 | 1.25 | 2.90 | 0.65 | 1.03 | 0.65 |
Barren to Urban | 204.61 | 0.59 | 4.55 | 1.01 | 1.62 | 1.01 |
Cropland to Barren | 90.92 | 0.26 | 4.58 | 1.02 | 1.63 | 1.02 |
Vegetation to Barren | 66.49 | 0.19 | 4.92 | 1.10 | 1.76 | 1.10 |
LUCC Gain | Area (km2) | Area (10%) | RLST (°C) | RLST (10%) | UHI | UHI (10%) |
---|---|---|---|---|---|---|
Forest to Water | 78.86 | 0.06 | −3.20 | 2.69 | 2.27 | −0.23 |
Water to Forest | 68.91 | 0.05 | −2.41 | 2.03 | 1.71 | −0.17 |
Cropland to Water | 70.70 | 0.05 | −2.28 | 1.92 | 1.62 | −0.16 |
Vegetation to Water | 36.49 | 0.03 | −1.21 | 1.02 | 0.86 | −0.09 |
Cropland to Forest | 1041.16 | 0.79 | −1.05 | 0.88 | 0.74 | −0.07 |
Vegetation to Forest | 68.86 | 0.05 | −0.66 | 0.56 | 0.47 | −0.04 |
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Amir Siddique, M.; Wang, Y.; Xu, N.; Ullah, N.; Zeng, P. The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050). Remote Sens. 2021, 13, 4697. https://doi.org/10.3390/rs13224697
Amir Siddique M, Wang Y, Xu N, Ullah N, Zeng P. The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050). Remote Sensing. 2021; 13(22):4697. https://doi.org/10.3390/rs13224697
Chicago/Turabian StyleAmir Siddique, Muhammad, Yu Wang, Ninghan Xu, Nadeem Ullah, and Peng Zeng. 2021. "The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050)" Remote Sensing 13, no. 22: 4697. https://doi.org/10.3390/rs13224697