Examining the Impact of Greenspace Patterns on Land Surface Temperature by Coupling LiDAR Data with a CFD Model

Understanding the link between greenspace patterns and land surface temperature is very important for mitigating the urban heat island (UHI) effect and is also useful for planners and decision-makers for providing a sustainable design for urban greenspace. Although coupling remote sensing data with a computational fluid dynamics (CFD) model has widely been used to examine interactions between UHI and greenspace patterns, the paper aims to examine the impact of five theoretical models of greenspace patterns on land surface temperature based on the improvement of the accuracy of CFD modeling by the combination of LiDAR data with remote sensing images to build a 3D urban model. The simulated results demonstrated that the zonal pattern always had the obvious cooling effects when there are no large buildings or terrain obstacles. For ambient environments, the building or terrain obstacles and the type of greenspace have the hugest influence on mitigating the UHI, but the greenspace area behaves as having the least cooling effect. A dotted greenspace pattern shows the best cooling effect in the central area or residential district within a city, while a radial and a wedge pattern may result in a “cold source” for the urban thermal environment

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products. In this study, LST was downscaled by a random forest model between LST and multiple remote sensing indices (such as soil-adjusted vegetation index, normalized multi-band drought index, modified normalized difference water index, and normalized difference building index) in an arid region with an oasis–desert ecotone. The proposed downscaling approach, which involves the selection of remote sensing indices, was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The spatial resolution of MODIS LST was downscaled from 1 km to 500 m. Results of visual and quantitative analyses show that the distribution of downscaled LST matched that of the oasis and desert ecosystem. The lowest (approximately 22 °C) and highest temperatures (higher than 37 °C) were detected in the middle oasis and desert regions, respectively. Furthermore, the proposed approach achieves relatively satisfactory downscaling results, with coefficient of determination and root mean square error of 0.84 and 2.42 °C, respectively. The proposed approach shows higher accuracy and minimization of the MODIS LST in the desert region compared with other methods. Optimal availability occurs in the vegetated region during summer and autumn. In addition, the approach is also efficient and reliable for LST downscaling of Landsat images. Future tasks include reliable LST downscaling in challenging regions

Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions

Spatio-Temporal Characteristics of the Evapotranspiration in the Lower Mekong River Basin during 2008–2017

Droughts and floods have occurred frequently in the Lower Mekong River Basin in recent years. Obtaining the evapotranspiration (ET) in the basin helps people to better understand water cycle and water resources. In this study, we retrieved and validated ET in the Lower Mekong Basin over multiple years (from 2008 to 2017) using remote sensing products. Based on the retrieval ET, we analyzed the spatial-temporal variation of ET and influencing factors at the monthly, seasonal, and inter-annual scale respectively. The results revealed that the overall variation trend of ET at annual scale slightly increased during 2008 to 2017, with the highest annual ET being 1198 mm/year in 2015 and the lowest annual ET being 949 mm/year in 2008. At the seasonal scale, ET in the rainy season was lower than the dry season; at the monthly scale, March had the highest monthly ET (101 mm/month) while July had the lowest monthly ET (73 mm/month). Spatial analyzing showed that ET in the margin of this region was higher (with on average about 1250 mm/year) and lower in the middle (with on average about 840 mm/year), and monthly ET changed mostly in forest areas with the difference of 60 mm/month. Influencing analyzing results showed that ET was mainly driven by solar radiation and near-surface temperature, and precipitation had an inhibitory effect on ET in the rainy season months. The study also showed that forests in the basin are very sensitive to solar radiation, with a correlation coefficient of 0.89 in March (the month with the highest ET) and 0.45 in July (the month with the lowest ET)

Spatio-Temporal Characteristics of the Evapotranspiration in the Lower Mekong River Basin during 2008–2017

Droughts and floods have occurred frequently in the Lower Mekong River Basin in recent years. Obtaining the evapotranspiration (ET) in the basin helps people to better understand water cycle and water resources. In this study, we retrieved and validated ET in the Lower Mekong Basin over multiple years (from 2008 to 2017) using remote sensing products. Based on the retrieval ET, we analyzed the spatial-temporal variation of ET and influencing factors at the monthly, seasonal, and inter-annual scale respectively. The results revealed that the overall variation trend of ET at annual scale slightly increased during 2008 to 2017, with the highest annual ET being 1198 mm/year in 2015 and the lowest annual ET being 949 mm/year in 2008. At the seasonal scale, ET in the rainy season was lower than the dry season; at the monthly scale, March had the highest monthly ET (101 mm/month) while July had the lowest monthly ET (73 mm/month). Spatial analyzing showed that ET in the margin of this region was higher (with on average about 1250 mm/year) and lower in the middle (with on average about 840 mm/year), and monthly ET changed mostly in forest areas with the difference of 60 mm/month. Influencing analyzing results showed that ET was mainly driven by solar radiation and near-surface temperature, and precipitation had an inhibitory effect on ET in the rainy season months. The study also showed that forests in the basin are very sensitive to solar radiation, with a correlation coefficient of 0.89 in March (the month with the highest ET) and 0.45 in July (the month with the lowest ET)

A whale optimization algorithm–based cellular automata model for urban expansion simulation

Cellular automata (CA) has proved to be effective and efficient in conducting urban expansion simulation. The generation of cell transition rules is a crucial step for a CA model. In this research, a whale optimization algorithm–based CA (WOA-CA) model was innovatively proposed. In the proposed model, a WOA was adapted to help mining the transition rules of the CA model, which was also evaluated and utilized in the case study of Guangzhou, simulating urban expansion from the year of 2000 to 2010. The experiment results demonstrated that the proposed model is effective and the simulation result is able to reach an overall accuracy of 92.16% with a Kappa coefficient of 0.744, and the value of Moran’s I is also quite close to that of the actual urban expansion. In addition, the proposed model has also been compared with a few representative CA models, including multi-criteria evaluation-based CA (MCE-CA), artificial neural network-based CA (ANN-CA), bat algorithm-based CA (BA-CA), convolution neural network for united mining-based CA (UMCNN-CA), and gray wolf optimizer-based CA (GWO-CA). The comparison results showd that our proposed model outperforms all these models in terms of overall accuracy and computational efficiency

Does Urbanization Exacerbate Asymmetrical Changes in Precipitation at Divergent Time Scales in China?

Abstract Urbanization alters the thermal and dynamic environment of the local climate system, resulting in significant impacts on precipitation in both urban and adjacent areas. Nevertheless, there remains a significant gap in our understanding of urbanization‐induced effects on asymmetrical, symmetrical, and other precipitation patterns in urban agglomerations (UAs) with divergent background climates and geographic regions at different timescales. Specifically, this asymmetrical change pattern is characterized by an increase in heavy (or light) rainfall and a decrease in light (or heavy) rainfall. Here, we assessed the effects of urbanization on precipitation patterns across 18 UAs situated in diverse background climates and geographical areas in China at different timescales. The results demonstrate that urbanization predominantly alters precipitation patterns in UAs located in the humid region. Specifically, urbanization amplified asymmetrical changes in Yangtze River Delta, Pearl River Delta, Beibu Gulf, Middle Yangtze River, and Guanzhong, but exacerbated symmetrical changes in precipitation in some regions such as Chengdu‐Chongqing. Notably, the urbanization effect demonstrates greater significance at the hourly scale, as exemplified in the Yangtze River Delta, Pearl River Delta, and Middle Yangtze River, where the urban impact is nearly twice as pronounced when compared to the daily scale. Moreover, urbanization had either no effect or has a negative impact on precipitation patterns in UAs located within continental and arid regions. This is related to the intensity of urbanization, background climate and complex topography. This finding implies that urban managers should consider the impact of urbanization on precipitation patterns in different contexts to provide scientific guidance for urban planning

Spatio-Temporal Variation of Cyanobacteria Blooms in Taihu Lake Using Multiple Remote Sensing Indices and Machine Learning

In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects multiple remote sensing input indices that can represent the characteristics of the primary underlying type in Taihu Lake. The proposed method performs best, with an F1 score of 0.91–0.98. Based on this method, the spatio-temporal variation of cyanobacteria blooms in the Taihu Lake complex was analyzed. During 2010–2022, the average area of cyanobacteria blooms in Taihu Lake increased slightly. Severe-scale cyanobacteria blooms occurred in 2015–2019. Cyanobacteria blooms were normally concentrated from May to November. However, the most prolonged extended duration occurred in 2017, lasting for eight months. Spatially, cyanobacteria blooms were mainly identified in the northwestern part of Taihu Lake, with an average occurrence frequency of about 10.0%. The cyanobacteria blooms often began to grow in the northwestern part of the lake and then spread to the Center of the Lake, and also dissipated earliest in the northwestern part of the lake. Our study is also beneficial for monitoring the growth of cyanobacteria blooms in other similar large lakes in long time series

The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation

Remotely sensed land surface temperature (LST) is critical for retrieving evapotranspiration (ET). However, due to cloud contamination, LST is often limited to clear-sky conditions and the differences between clear-sky and all-sky LST will lead to clear-sky biases of LST. Consequently, the accuracy of ET varies drastically under different weather conditions. To evaluate the impact of clear-sky biases on ET estimation, the monthly ET under clear-sky and all-sky conditions was estimated using a nonparametric method at several sites with different humid conditions and vegetation coverage in 2014. 35.7 % of the clear-sky LST values were acquired in the arid region (Heihe River Basin) with different vegetation coverage. As the climate became more humid, only 14.2 % of the clear-sky LST data were available (Poyang Lake Basin). The clear-sky biases of LST variation at the sites resulted in a significant reduction in the accuracy of monthly ET, with an increase in the relative error (RE) of approximately 16.6 %. The impact of clouds can be reduced by at most half by the introduction of all-sky LST products, with a significant decline (6.3 % ∼ 34.2 % of ET) in the error contribution of LST to monthly ET. The variation in vegetation coverage of land cover and the change in humidity of the climate significantly affected the influence of the clear-sky biases of LST on the ET estimation and the error contribution to ET. Replacing all-sky LST with clear-sky LST in areas with less vegetation coverage exacerbates the underestimation of ET estimation and strengthens the error contribution of LST with the decline in RE (error contribution of LST) from −7.7 % (−6.3 %) in densely vegetated areas to −29.7 % (−34.2 %) in non-vegetated areas, especially in the summer. Similarly, clear-sky biases of LST tended to have a relatively significant impact on the error contributions of LST to monthly ET estimation when the climate became humid, with a relatively significant enlarging from −6.3 % in the arid area to −10.0 % in the humid area, especially in the rainy season in the humid area (−61.3 % of ET). This study contributes to the understanding of the relationship between clear-sky biases and vegetation coverage/humidity. Quantitative analysis of the impact of clear-sky biases of LST on monthly ET estimation will be helpful for improving the accuracy of ET retrieval in the future

Effects of Building Design Elements on Residential Thermal Environment

Residential thermal environment affects the life of residents in terms of their physical and mental health. Many studies have shown that building design elements affect the urban thermal environment. In this study, Nanjing City was used as the study area. A three-dimensional microclimate model was used to simulate and analyze the effects of four main factors, namely, building height, density, layout and green ratio, on thermal environment in residential areas. Results showed that 25% building density obtained a low average air temperature (ATa) and average predicted mean vote (APMV) during 24 h. Thus, a higher building height indicates a lower ATa and APMV and better outdoor comfort level. In addition, peripheral layout had the lowest ATa and APMV, followed by the determinant and point group layouts. The green ratio increased from 0% to 50% with a 10% step and the ATa and APMV decreased gradually. However, when the green ratio increased from 30% to 40%, ATa and APMV decreased most. The effects of building height, density and green ratio on the thermal environment in residential areas were interactive. The effects of building density, green ratio and layout on hourly air temperature and hourly predicted mean vote in daytime varied from these indicators during night time. How the four building design elements interact with thermal environment were probed from two aspects of air temperature and thermal comfort based on the validated ENVI-met, which is the element of novelty in this study. However, thermal comfort has rarely been considered in the past studies about urban outdoor thermal environment