Assessing the Impacts of Urbanization-Associated Land Use/Cover Change on Land Surface Temperature and Surface Moisture: A Case Study in the Midwestern United States
Abstract
:1. Introduction
2. Data and Methodology
2.1. Study Area
2.2. Methodology
2.2.1. Data Collection and Pre-processing
2.2.2. NDVI and Fractional Vegetation Cover Calculation
2.2.3. LST Computation
2.2.4. Soil Moisture Computation
2.2.5. NDWI Calculation
3. Results
3.1. Impact of Urbanization-Associated LULC Changes in Three Selected Areas
Area1 Cultivated to Residential (sample size 3264 pixels) | Area2 Forest to Commercial (sample size 192 pixels) | Area3 Open area to Commercial (sample size 225 pixels) | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2001 | 2006 | 2001 | 2006 | 2001 | 2006 | ||||||||||
mean | std | mean | std | difference | mean | std | mean | std | difference | mean | std | mean | std | difference | |
Scaled Fr | 0.508 | 0.111 | 0.465 | 0.105 | − 0.043 | 0.715 | 0.093 | 0.351 | 0.114 | − 0.364 | 0.549 | 0.083 | 0.291 | 0.052 | − 0.258 |
Scaled LST | 0.370 | 0.048 | 0.418 | 0.058 | 0.101 | 0.316 | 0.041 | 0.581 | 0.054 | 0.265 | 0.404 | 0.057 | 0.632 | 0.044 | 0.228 |
Soil moisture | 0.201 | 0.006 | 0.196 | 0.008 | − 0.005 | 0.209 | 0.005 | 0.173 | 0.008 | − 0.036 | 0.197 | 0.008 | 0.166 | 0.006 | − 0.031 |
NDWI | 0.081 | 0.176 | 0.089 | 0.145 | 0.008 | 0.397 | 0.102 | 0.040 | 0.144 | − 0.357 | 0.139 | 0.118 | -0.038 | 0.075 | − 0.177 |
Imperviousness | 0.090 | 0.181 | 0.216 | 0.240 | 0.126 | 0.067 | 0.164 | 0.561 | 0.373 | 0.494 | 0.081 | 0.138 | 0.633 | 0.337 | 0.552 |
3.2. Land Cover Types and Their Surface Characteristics
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Jackson, K.T. Crabgrass Frontier: The Suburbanization of the United States; Oxford Univervisy Press: New York, NY, USA, 1985. [Google Scholar]
- 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. Change 2000, 25, 49–65. [Google Scholar] [CrossRef]
- 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]
- 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]
- 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]
- Weng, Q.; Lu, D. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. Int. J. Appl. Earth Obs. Geoinf. 2008, 10, 68–83. [Google Scholar] [CrossRef]
- Weng, Q.; Lu, D. Landscape as a continuum: an examination of the urban landscape structures and dynamics of Indianapolis City, 1991–2000, by using satellite images. Int. J. Remote Sens. 2009, 30, 2547–2577. [Google Scholar] [CrossRef]
- Carlson, T.N. An overview of the “triangle method” for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors 2007, 7, 1612–1629. [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]
- Jiang, L.; Islam, S. Estimation of surface evaporation map over southern Great Plains using remote sensing data. Water Resour. Res. 2001, 37, 329–340. [Google Scholar] [CrossRef]
- Owen, T.W.; Carlson, T.N.; Gillies, R.R. An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. Int. J. Remote Sens. 1998, 19, 1663–1681. [Google Scholar] [CrossRef]
- Sun, D.L.; Kafatos, M. Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America. Geophys. Res. Lett. 2007, 34, L24406. [Google Scholar] [CrossRef]
- Arthur-Hartranft, S.T.; Carlson, T.N.; Clarke, K.C. Satellite and ground-based microclimate and hydrologic analyses coupled with a regional urban growth model. Remote Sens. Environ. 2003, 86, 385–400. [Google Scholar] [CrossRef]
- Goward, S.N. Thermal behavior of urban landscapes and the urban heat island. Physic. Geogr. 1981, 2, 19–33. [Google Scholar]
- Gao, B.C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Seto, K.C.; Rodriguez, R.S.; Fragkias, M. The new geography of contemporary urbanization and the environment. Ann. Rev. Environ. Resour. 2010, 35, 167–194. [Google Scholar] [CrossRef]
- Ramamurthy, P.; Bou-Zeid, E. Contribution of impervious surfaces to urban evaporation. Water Resour. Res. 2014, 50, 2889–2902. [Google Scholar] [CrossRef]
- Chander, G.; Markham, B.L.; Helder, D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113, 893–903. [Google Scholar] [CrossRef]
- Song, C.; Woodcock, C.E.; Seto, K.C.; Lenney, M.P.; Macomber, S.A. Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sens. Environ. 2001, 75, 230–244. [Google Scholar] [CrossRef]
- Johnson, P.E.; Smith, M.O.; Adams, J.B. Simple algorithms for remote determination fro mineral abundances and particles sizes from reflectance spectra. J. Geophys. Res. 1992, 97, 2649–2657. [Google Scholar] [CrossRef]
- Weng, Q.; Hu, X.; Lu, D. Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison. Int. J. Remote Sens. 2008, 29, 3209–3232. [Google Scholar] [CrossRef]
- Coll, C.; Galve, J.M.; Sanchez, J.M.; Caselles, V. Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Trans. Geosci. Remote Sens. 2010, 48, 547–555. [Google Scholar] [CrossRef]
- Barsi, J.A.; Schott, J.F.; Palluconi, F.D.; Hook, S.J. Validation of a web-based atmospheric correction tool for single thermal band instruments. Proc. SPIE 2005, 5882. [Google Scholar] [CrossRef]
- Nichol, J.E. A GIS-based approach to microclimate monitoring in singapore’s high-rise housing estates. Photogramm. Eng. Remote Sens. 1994, 60, 1225–1232. [Google Scholar]
- Sobrino, J.A.; Jiménez-Muñoz, J.C.; Sòria, G.; Romaguera, M.; Guanter, L.; Moreno, J.; Martínez, P. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Trans. Geosci. Remote Sens. 2008, 46, 316–327. [Google Scholar] [CrossRef]
- Rahimzadeh-Bajgiran, P.; Berg, A.A.; Champagne, C.; Omasa, K. Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies. ISPRS J. Photogramm. Remote Sens. 2013, 83, 94–103. [Google Scholar] [CrossRef]
- Wang, K.C.; Li, Z.Q.; Cribb, M. Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter. Remote Sens. Environ. 2006, 102, 293–305. [Google Scholar] [CrossRef]
- Lee, T.J.; Pielke, R.A. Estimating the soil surface specific-humidity. J. Appl. Meteorol. 1993, 32, 480–484. [Google Scholar]
- McFeeters, S.K. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432. [Google Scholar] [CrossRef]
- Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat satellite: The Landsat data continuity mission. Remote Sens. Environ. 2012, 122, 11–21. [Google Scholar] [CrossRef]
- Li, Z.L.; Tang, B.H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; Sobrino, J.A. Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ. 2013, 131, 14–37. [Google Scholar] [CrossRef]
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Jiang, Y.; Fu, P.; Weng, Q. Assessing the Impacts of Urbanization-Associated Land Use/Cover Change on Land Surface Temperature and Surface Moisture: A Case Study in the Midwestern United States. Remote Sens. 2015, 7, 4880-4898. https://doi.org/10.3390/rs70404880
Jiang Y, Fu P, Weng Q. Assessing the Impacts of Urbanization-Associated Land Use/Cover Change on Land Surface Temperature and Surface Moisture: A Case Study in the Midwestern United States. Remote Sensing. 2015; 7(4):4880-4898. https://doi.org/10.3390/rs70404880
Chicago/Turabian StyleJiang, Yitong, Peng Fu, and Qihao Weng. 2015. "Assessing the Impacts of Urbanization-Associated Land Use/Cover Change on Land Surface Temperature and Surface Moisture: A Case Study in the Midwestern United States" Remote Sensing 7, no. 4: 4880-4898. https://doi.org/10.3390/rs70404880