Directional and Zonal Analysis of Urban Thermal Environmental Change in Fuzhou as an Indicator of Urban Landscape Transformation

Urban expansion results in landscape pattern changes and associated changes in land surface temperature (LST) intensity. Spatial patterns of urban LST are affected by urban landscape pattern changes and seasonal variations. Instead of using LST change data, this study analysed the variation of LST aggregation which was evaluated by hotspot analysis to measure the spatial dependence for each LST pixel, indicating the relative magnitudes of the LST values in the neighbourhood of the LST pixel and the area proportion of the hotspot area to gain new insights into the thermal effects of increasing impervious surface area (ISA) caused by urbanization in Fuzhou, China. The spatio-temporal relationship between urban landscape patterns, hotspot locations reflecting urban land cover change in space and the thermal environment were analysed in different sectors. The linear spectral unmixing method of fully constrained least squares (FCLS) was used to unmix the bi-temporal Landsat TM/OLI imagery to derive subpixel ISA and the accuracy of the percent ISA was assessed. Then, a minimum change threshold was chosen to remove random noise, and the change of ISA between 2000 and 2016 was analysed. The urban area was divided into three circular consecutive urban zones in the cardinal directions from the city centre and each circular zone was further divided into eight segments; thus, a total of 24 spatial sectors were derived. The LST aggregation was analysed in different directions and urban segments and hotspot density was further calculated based on area proportion of hotspot areas in each sector. Finally, variations of mean normalized LST (NLST), area proportion of ISA, area proportion of ISA with high LST, and area proportion of hotspot area were quantified for all sectors for 2000 and 2016. The four levels of hotspot density were classified for all urban sectors by proportional ranges of 0%–25%, 25%–50%, 50%–75% and 75%–100% for low-, medium-, sub-high, and high density, and the spatial dynamics of hotspot density between the two dates showed that urbanization mainly dominated in sectors south–southeast 2 (SSE2), south–southwest 2 (SSW2), west–southwest 2 (WSW2), west–northwest 2 (WNW2), north–southwest 2 (NSW2), south–southeast 3 (SSE3) and south–southwest 3 (SSW3). This paper suggests a methodology for characterizing the urban thermal environment and a scientific basis for sustainable urban development

Spatial-temporal patterns of urban anthropogenic heat discharge in Fuzhou, China, observed from sensible heat flux using Landsat TM/ETM+ data

The urban heat island (UHI) effect is the phenomenon of increased surface temperatures in urban environments compared to their surroundings. It is linked to decreased vegetation cover, high proportions of artificial impervious surfaces, and high proportions of anthropogenic heat discharge. We evaluated the surface heat balance to clarify the contribution of anthropogenic heat discharges into the urban thermal environment. We used a heat balance model and satellite images (Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images acquired in 1989 and 2001), together with meteorological station data to assess the urban thermal environment in the city of Fuzhou, China. The objective of this study was to estimate the anthropogenic heat discharge in the form of sensible heat flux in complex urban environments. In order to increase the accuracy of the anthropogenic heat flux analysis, the sub-pixel fractional vegetation cover (FVC) was calculated by linear spectral unmixing. The results were then used to estimate latent heat flux in urban areas and to separate anthropogenic heat discharge from heat radiation due to insolation. Spatial and temporal distributions of anthropogenic heat flux were analysed as a function of land-cover type, percentage of impervious surface area, and FVC. The accuracy of heat fluxes was assessed using the ratios of sensible heat flux (H), latent heat flux (L), and ground heat flux (G) to net radiation (R ), which were compared to the results from other studies. It is apparent that the contribution of anthropogenic heat is smaller in suburban areas and larger in high-density urban areas. However, seasonal disparities of anthropogenic heat discharge are small, and the variance of anthropogenic heat discharge is influenced by urban expansion, land-cover change, and increasing energy consumption. The results suggest that anthropogenic heat release probably plays a significant role in the UHI effect, and must be considered in urban climate change adaptation strategies. Remote sensing can play a role in mapping the spatial and temporal patterns of UHIs and can differentiate the anthropogenic heat from the solar radiative fluxes. The findings presented here have important implications for urban development planning

Characterizing fractional vegetation cover and land surface temperature based on sub- 1 pixel fractional impervious surfaces from Landsat TM/ETM+

Abstract 18 Estimating the distribution of impervious surfaces and vegetation is important for analyzin

Characterizing fractional vegetation cover and land surface temperature based on sub-pixel fractional impervious surfaces from Landsat TM/ETM+

Estimating the distribution of impervious surfaces and vegetation is important for analyzing urban landscapes and their thermal environment. The application of a crisp classification of land cover types to analyze urban landscape patterns and land surface temperature (LST) in land cover types to analyze urban landscape patterns and land surface temperature (LST) in detail presents a challenge, mainly due to the complex characteristics of urban landscapes. In this paper, sub-pixel percentage impervious surface area (ISA) and fractional vegetation cover (FVC) were extracted from bi-temporal TM/ETM+ data by linear spectral mixture analysis (LSMA). Their accuracy was assessed with proportional area estimates of impervious surface and vegetation extracted from high resolution data. A range approach was used to classify percentage ISA into different categories by setting thresholds of fractional values and these were compared for their LST patterns. For each ISA category, FVC, LST and percentage ISA were used to quantify the urban thermal characteristics of different developed areas in the city of Fuzhou, China. Urban LST scenarios in different seasons and ISA categories were simulated to analyze the seasonal variations and the impact of urban landscape pattern changes on the thermal environment. The results show that FVC and LST based on percentage ISA can be used to quantitatively analyse the process of urban expansion and its impacts on the spatial-temporal distribution patterns of the urban thermal environment. This analysis can support urban planning by providing knowledge on the climate adaptation potential of specific urban spatial patterns

T2MC: A Peer-to-Peer Mismatch Reduction Technique by Traceroute and 2-Means Classification Algorithm

Abstract. The Peer-to-Peer (P2P) technology has many potential advantages, including high scalability and cost-effectiveness. However, most P2P system performance suffers from the mismatch between the overlays topology and the underlying physical network topology, causing a large volume of redundant traffic in the Internet. A lot of research works have been presented to address this issue, but most results still have some drawbacks. In this paper, we propose a quite simple but efficient topology matching technique, T2MC, which uses the peers' Traceroute result to execute 2-Means Classification, thereafter lets peers to build efficient "close" cluster. By performing experiments using the measured realistic Internet data of China, we show that T2MC outperforms the well-known GNP in both aspects of accuracy and maintenance cost

Interactive effect of hot air roasting processes on the sensory property, allergenicity, and oil extraction of sesame (Sesamum indicum L.) seeds

Sesame seeds are a healthy food ingredient and an oil crop for sesame oil production; however, it has recently been recognized as an essential allergenic food by FAO/WHO. This research investigated the relationship between the hot air roasting process (at 120, 150, and 180 °C for 10, 20, and 30 min) and several quality attributes of sesame seeds since roasting is the key process for preparing sesame seeds for both consumption and oil production. The hot air process followed the central composite design. The changes of sesame in terms of color, sensory properties (odor, texture, color, and taste), allergenicity caused by oleosins (ses i 4 and ses i 5), as well as oil extraction and quality were monitored using a colorimeter, sensory evaluation panelists, ELISA, as well as oil yield and acid value, respectively. Roasting temperature influenced the product quality more than roasting time, although the two processing parameters significantly interacted with each other (P < 0.001). Sensory evaluation indicated medium roasting generated attractive flavor, order, appearance, and crispy texture. Allergenicity was high in sesame seeds after high-temperature roasting, according to IgE binding capacity test. Sesame oil extraction was favored by high-temperature roasting, which, however, adversely affected the oil quality. The optimal roasting conditions were 150.5 °C for 15 min for optimized sesame seeds quality in terms of sensory properties and allergenicity, while roasting at 158 °C for 10 min was optimal for sesame oil production. The finding will benefit the sesame seed industry

Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible

Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China&rsquo;s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible