Bi-temporal characterization of land surface temperature in relation to impervious surface area,NDVI and NDBI,using a sub-pixel image analysis

As more than 50% of the human population are situated in cities of the world, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect has been linked to the regional climate, environment, and socio-economic development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern China. As a key indicator for the assessment of urban environments, sub-pixel impervious surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban surface thermal patterns. In order to accurately estimate urban surface types, high-resolution imagery was utilized to generate the proportion of impervious surface areas. Urban thermal characteristics was further analysed by investigating the relationships between the land surface temperature (LST), percent impervious surface area, and two indices, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results show that correlations between NDVI and LST are rather weak, but there is a strong positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.     

综合典型地表组成的城市地表温度估算

针对单一的地表物质组成并不能充分反映城市地表热环境特点这一问题,该文基于热混合影像,利用线性光谱分解方法获取地表组成信息,然后利用光谱分解热混合、线性回归、决策树方法估算地表温度。结果表明:只研究单一地表组成对地表温度的影响,有可能扩大其环境效应;决策树模型在不同规则下能更好地模拟地表温度的空间异质性;光谱分解热混合模型只需要两组数据即可估算出不同地表覆盖下的地表温度,且估算精度较其他模型高;光谱分解热混合模型和多元回归模型结合4种地表组成监测其对地表温度的影响,决策树方法通过不透水面、水体、植被预测地表温度,前两者估算精度比后者高,因此综合考虑城市典型地表组成能更好反映其对地表温度的作用。     

Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery

Studies of urbanization and urban thermal environment are now attracting wide interests among scientists all over the world. This study investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Guangzhou, China utilizing three dates of Landsat TM/ETM+ images acquired in 1990, 2000, and 2005, respectively. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban enlargement. As a key parameter for studying urban thermal characteristics, the land surface temperature (LST) was also retrieved from thermal infrared band of each TM/ETM+ dataset. Based on these parameters, the urban expansion, urban heat island effect and the relationships of LSTs to other biophysical parameters were then analyzed. Results indicated that the area ratio of impervious surface in Guangzhou increased significantly, which grew from 20.56% in 1990, to 34.72% in 2000, and further to 41.12% in 2005, however, the intensity of urban heat island was not always enlarged in observed years. In addition, Geostatistical analyses showed that the mean-centre of the impervious surface was moving towards the northwest during 1990–2005. And correlation analyses revealed that, at the pixel-scale, the association of LSTs to other two variables (vegetation abundance and percent impervious surface) was not straightforward, while LSTs possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional-scale, respectively. This study provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.     

Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate. In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery, and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climate.     

A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis,United States

This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying urban landscape changes in Indianapolis, Indiana, the United States, and for examining the environmental impact of such changes on land surface temperatures (LST). Three dates of Landsat TM/ETM+ images, acquired in 1991, 1995, and 2000, respectively, were utilized to document the historical morphological changes in impervious surface and vegetation coverage and to analyze the relationship between these changes and those occurred in LST. Three fraction endmembers, i.e., impervious surface, green vegetation, and shade, were derived with an unconstrained least-squares solution. A hybrid classification procedure, which combined maximum-likelihood and decision-tree algorithms, was developed to classify the fraction images into land use and land cover classes. Correlation analyses were conducted to investigate the changing relationships of LST with impervious surface and vegetation coverage. Results indicate that multi-temporal fraction images were effective for quantifying the dynamics of urban morphology and for deriving a reliable measurement of environmental variables such as vegetation abundance and impervious surface coverage. Urbanization created an evolved inverse relationship between impervious and vegetation coverage, and brought about new LST patterns because of LST's correlations with both impervious and vegetation coverage. Further researches should be directed to refine spectral mixture modeling by stratification, and by the use of multiple endmembers and hyperspectral imagery.     

Landsat 8 地表温度产品降尺度深度学习方法研究

以光谱指数为趋势面因子的降尺度方法被广泛用于遥感地表温度尺度转换中,但面临构建的光谱指数难以凸显地表温度分布规律、浅层的统计模型难以精准刻画趋势面因子与地表温度之间的复杂关系的不足。为此,本文以Landsat 8 ARD 地表温度产品为降尺度对象,以Landsat 8 OLI原始数据为潜在趋势面因子,构建地表温度降尺度残差网络（LSTDRN）的深度学习模型;探索适用于Landsat 8地表温度产品空间降尺度的趋势面波段或组合,并在不同季节、不同地表类型下与经典传统方法TsHARP进行定量比较。结果表明：LSTDRN方法利用Landsat 8 OLI原始单波段作为趋势面因子就能有较好的降尺度效果,增加潜在趋势面因子的组合数量并不能提高降尺度效果。不同地表覆盖类型实验中,LSTDRN方法降尺度效果整体优于经典传统方法,且以近红外波段、红光波段和归一化植被指数为趋势面因子时,近红外波段降尺度效果定量评价表现最佳;不同地表覆盖类型的LSTDRN降尺度效果排序为：植被>建筑>水体,而经典传统方法则没有表现出明显的差异。不同季节实验中,LSTDRN方法在春夏冬3季的降尺度效果的定量评价表现明显好于经典传统方法,两类方法的秋季降尺度结果相当。因此,提出的LSTDRN对Landsat 8遥感地表温度产品具有较好的降尺度效果,整体优于经典传统方法且稳定性更强。     

Spatial-temporal impacts of urban land use land cover on land surface temperature: Case studies of two Canadian urban areas

The surface fabric of urbanized areas, (i.e. its constituent land covers and land uses) plays an essential role in the generation of the urban/rural temperature differences, i.e. the Urban Heat Island (UHI) effect. Land surface information, derived from satellite imagery, and complementary information such as demographics can be used as the basis for an understanding of the atmospheric and surface thermal variations within cities. The results of comprehensive land surface characterizations of two major Canadian urban areas, the Greater Toronto Area and Ottawa-Gatineau, are described. Spatial information, including land cover fraction maps, land use and its historic changes, population density maps are compared with intra-urban surface temperature variations derived from satellite thermal imagery. Three aspects of the impacts of land cover and land use on urban land thermal characteristics are addressed, namely, (a) the relationships between surface temperature and subpixel land cover and population density (b) intra-city seasonal temperature variations and (c) the intensification of the urban heat island effect due to urban built-up land growth.     

Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVI^THM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images.     

Analysis of land degradation processes on a tiger bush plateau in South West Niger using MODIS and LANDSAT TM/ETM+ data

Remote sensing digital image analysis has been applied to monitor land clearing and degradation processes on a plateau covered by tiger bush near Niamey in South West Niger, where signs of severe landscape degradation due to fuelwood supply have been observed in the last decades. A MODIS NDVI dataset (2000–2015) and five LANDSAT images (1986–2012) were used to identify spatial and temporal dynamics and to emphasize areas of greater degradation. The study indicates that the land clearing found by previous investigations in the second part of the 20^th century is still ongoing, with a decreasing trend of MODIS NDVI values recorded in the period 2000–2015. This trend appeared to be linked to an increase in bare soil areas that was demonstrated by analysis of LANDSAT SAVI images. The investigation also indicated that rates of degradation are stronger in more deteriorated areas like those located nearer Niamey; degradation patterns also tend to increase from the inner areas to the edges of the plateau. These results attest to the urgency to develop effective environmental preservation policies and find alternative solutions for domestic energy supply.     

Retrieval of land surface temperature from the Kalpana-1 VHRR data using a single-channel algorithm and its validation over western India

Indian geostationary satellite Kalpana-1 (K1) offers a potential to capture the diurnal cycle of land surface temperature (LST) through thermal infrared channel (10.5–12.5 μm) observations of the Very High Resolution Radiometer (VHRR) sensor. A study was carried out to retrieve LST by adapting a generalized single-channel (SC) algorithm (Jiménez-Muñoz and Sobrino, 2003) for the VHRR sensor over India. The basis of SC algorithm depends on the concept of Atmospheric Functions (AFs) that are dependent on transmissivity, upwelling and downwelling radiances of the atmosphere. In the present study AFs were computed for the VHRR sensor through the MODTRAN simulations based upon varying atmospheric and surface inputs. The AFs were fitted with the atmospheric columnar water vapour content and a set of coefficients was derived for LST retrieval. The K1-LST derived with the SC algorithm was validated with (a) in situ measurements at two sites located in western parts of India and (b) the MODIS LST products. Comparison of K1-LST with the in situ measurements demonstrated that SC algorithm was successful in capturing the prominent diurnal variations of 283–332 K in the LST at desert and agriculture experimental sites with a rmse of 1.6 K and 2.7 K, respectively. Inter comparison of K1-LST and MODIS LST showed a reasonable agreement between these two retrievals up to LST of 300 K, however a cold bias up to 7.9 K was observed in MODIS LST for higher LST values (310–330 K) over the hot desert region.     

Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method

Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and land surface temperature (LST). LST is one of the essential climate variables as well as an important parameter in the physics of land surface processes at local and global scales, while LSE is an indicator of the material composition. Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSE using remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existing approaches for calculating LSE and LST, particular attention has been paid to the normalised difference vegetation index threshold method (NDVI^THM), especially for agriculture and forest ecosystems. To apply NDVI^THM, knowledge of the proportion of vegetation cover (P_V) is essential. The objective of this study is to investigate the effect of the prediction accuracy of the P_V on the estimation of LSE and LST when using NDVI^THM. In August 2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany, coinciding with a Landsat-8 overpass. The P_V was measured in the field for 37 plots. Four different vegetation indices, as well as artificial neural network approaches, were used to estimate P_V and to compute LSE and LST. The results showed that the prediction accuracy of P_V improved using an artificial neural network (R²_CV = 0.64, RMSE_CV = 0.05) over classic vegetation indices (R²_CV = 0.42, RMSE_CV = 0.06). The results of this study also revealed that variation in the accuracy of the estimated P_V affected calculation results of the LSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be taken into account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST.     

基于地表温度—植被指数三角/梯形特征空间的地表蒸散发遥感反演综述

地表蒸散发是地表水分循环和能量平衡的重要组成成分,其准确估算对农业灌溉与干旱监测、水资源管理、气候变化预估等研究至关重要.基于地表温度—植被指数三角/梯形特征空间开展地表蒸散发遥感反演及土壤蒸发/植被蒸腾分离是地表蒸散发定量遥感研究的国际热点与前沿课题之一.本文全面、系统、深入地综述了地表温度—植被指数三角/梯形空间反...