Assessing the effect of landcover changes on air temperatu×re using remote sensing images—A pilot study in northern Taiwan

Cheng, Kit‐Yan; Su, Yuan; Kuo, Fang-Tzu; Hung, Wei‐Chun; Chiang, J. L.

doi:10.1016/j.landurbplan.2007.09.014

Cited by 57 publications

(38 citation statements)

References 21 publications

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“…Studies on statistical relationships between measured atmospheric temperature and LULC using RS are limited (e.g., [22][23][24][25][26]). The most obvious and latest results that were obtained in [27,28] indicated the relationship between air temperature and LULC detection.…”

Section: Introductionmentioning

confidence: 95%

Assessing the Correlation between Land Cover Conversion and Temporal Climate Change—A Pilot Study in Coastal Mediterranean City, Fethiye, Turkey

Çinar

2015

Atmosphere

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Abstract:The rapid growth and expansion of urbanized landscapes in cities has resulted in an increase in air temperature and has lowered the bioclimatic comfort levels in urban landscapes. Recent studies to estimate the climatic response of urban landscape conversion have mostly examined the relationship between land use/land cover (LULC) change and land surface temperature (LST) data collected using advanced remote sensing (RS) techniques instead of atmospheric temperature. In this respect, four decadal Landsat images from the 1980s were used to investigate the impact of landscape transformation on atmospheric temperature. The mean and average minimum and maximum monthly air temperature datasets were used in the analysis. The CORINE (Coordination of Information on Environment) index was used to determine LULC diversity in an urban development boundary and urban periphery. Consequently, clustered LULC change values for the last three decades were integrated with decadal air temperature anomalies. The findings revealed an important relationship between monthly mean air temperature and land changes over recent decades, which resulted in an increase in urban fabric land use, deforestation land cover changes and conversion of permanent crop fields to artificial green houses for earlier vegetable production; the R-sqr values for these regressions were 97.7%, 88.5% and 90.6% respectively. On the other hand, the most important increasing temperature trends were obtained for the average monthly minimum air temperature, which supports the global warming concerns of the IPCC (Intergovernmental Panel on Climate Change) and related studies, which have concluded that an increased nighttime temperature results in urban heat islands (UHIs). The results should be used to support better urban landscape plans and architectural designs to improve human thermal comfort for sustainable urban life in Mediterranean cities. Street geometry and orientation to wind

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Section: Introductionmentioning

confidence: 95%

Assessing the Correlation between Land Cover Conversion and Temporal Climate Change—A Pilot Study in Coastal Mediterranean City, Fethiye, Turkey

Çinar

2015

Atmosphere

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“…Applying this scheme, thermal endmembers derived from ground station TIR measurements are used to simulate LST for the whole study area, even during days with comprehensive cloud cover [60]. Other studies used empirical measurements to convert land-cover-specific surface temperatures to land-cover-specific air temperatures applied on mixed pixels to predict the pixel average air temperature [61]. In this study, a related approach was developed where the abovementioned spectral unmixing is replaced by mixing various surface characteristics (i.e., land cover fractions, vegetation cover, albedo, and solar irradiance sum) to a coarser resolution (100-m cells).…”

Section: Research Questionsmentioning

confidence: 99%

“…Future improvements could be the use of more L8 data due to the growing archive, the application of a geographically weighted regression [90], and the combination with ambient temperature measurements [61].…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Wicki

Parlow

2017

Remote Sensing

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Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data.

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“…Remote sensing images have been widely used for earth surface monitoring [1][2][3][4][5][6][7][8], environmental change detection [9][10][11][12][13][14], and water resource management [15][16][17][18][19][20][21][22]. Many of these applications require land-use/land-cover (LULC) classifications derived from multispectral images.…”

Section: Introductionmentioning

confidence: 99%

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

Hsiao¹,

Cheng

2016

Remote Sensing

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Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the uncertainty in classification results of the bootstrap-training data sets, but also the classification uncertainty of individual pixels in the study area. Two measures of pixel-specific classification uncertainty, namely the maximum class probability and Shannon entropy, were derived from the class probability vector of individual pixels and used for the identification of unclassified pixels. Unclassified pixels that are identified using the traditional chi-square threshold technique represent outliers of individual LULC classes, but they are not necessarily associated with higher classification uncertainty. By contrast, unclassified pixels identified using the equal-likelihood technique are associated with higher classification uncertainty and they mostly occur on or near the borders of different land-cover.

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Assessing the effect of landcover changes on air temperatu×re using remote sensing images—A pilot study in northern Taiwan

Cited by 57 publications

References 21 publications

Assessing the Correlation between Land Cover Conversion and Temporal Climate Change—A Pilot Study in Coastal Mediterranean City, Fethiye, Turkey

Assessing the Correlation between Land Cover Conversion and Temporal Climate Change—A Pilot Study in Coastal Mediterranean City, Fethiye, Turkey

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Assessing Uncertainty in LULC Classification Accuracy by Using Bootstrap Resampling

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