The impact of building height on urban thermal environment in summer: A case study of Chinese megacities.

The quantitative relationship between the spatial variation of building's height and the associated land surface temperature (LST) change in six Chinese megacities is investigated in this paper. The six cities involved are Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Shenzhen. Based on both remote sensing and building footprint data, we retrieved the LST using a single-channel (SC) algorithm and evaluate the heating/cooling effect caused by building-height difference via correlation analysis. The results show that the spatial distribution of high-rise buildings is mainly concentrated in the center business districts, riverside zones, and newly built-up areas of the six megacities. In the urban area, the number and the floor-area ratio of high to super high-rise buildings (>24m) account for over 5% and 4.74%, respectively. Being highly urbanized cities, most of urban areas in the six megacities are associated with high LST. Ninety-nine percent of the city areas of Shanghai, Beijing, Chongqing, Guangzhou, Shenzhen, and Tianjin are covered by the LST in the range of 30.2~67.8°C, 34.8~50.4°C, 25.3~48.3°C, 29.9~47.2°C, 27.4~43.4°C, and 33.0~48.0°C, respectively. Building's height and LST have a negative logarithmic correlation with the correlation coefficients ranging from -0.701 to -0.853. In the building's height within range of 0~66m, the LST will decrease significantly with the increase of building's height. This indicates that the increase of building's height will bring a significant cooling effect in this height range. When the building's height exceeds 66m, its effect on LST will be greatly weakened. This is due to the influence of building shadows, local wind disturbances, and the layout of buildings.

Lockdown effects on total suspended solids concentrations in the Lower Min River (China) during COVID-19 using time-series remote sensing images.

The COVID-19 pandemic in China in the winter-spring of 2019-2020 has decreased and even stopped many human activities. This study investigates whether there were any changes in the water quality of the Lower Min River (China) during the lockdown period. The time-series remote sensing images from November 2019 to April 2020 was used to examine the dynamics of the river's total suspended solids (TSS) concentrations in the period. A new remote sensing-based prototype was developed to recalibrate an existing algorithm for retrieving TSS concentrations in the river. The Nechad and the Novoa algorithms were used to validate the recalibrated algorithm. The results show that the recalibrated algorithm is highly consistent with the two algorithms. All of the three algorithms indicate significant fluctuation in TSS concentrations in the Lower Min River during the study period. February (COVID-19 lockdown period) has witnessed a 48% fall in TSS concentration. The TSS in March-April showed a progressive and recovery back to normal levels of pre-COVID-19. The spatiotemporal change of TSS has worked as a good indicator of human activities, which revealed that the decline of TSS in the lockdown period was due largely to the substantially-reduced discharges from industrial estates, densely-populated city center, and river's shipping. Remote sensing monitoring of the spatiotemporal changes of TSS helps understand important contributors to the water-quality changes in the river and the impacts of anthropogenic activities on river systems.

Estimating PM2.5 concentrations in Yangtze River Delta region of China using random forest model and the Top-of-Atmosphere reflectance.

Previous studies that have used remote sensing data to estimate the PM2.5 concentrations mainly focused on the retrieval of aerosol optical depth (AOD) with moderate-to-low spatial resolution. However, the complex process of retrieving AOD from satellite Top-of-Atmosphere (TOA) reflectance always generates the missingness of AOD values due to the limitation of AOD retrieval algorithms. This study validated the possibility of using satellite TOA reflectance for estimating PM2.5 concentrations, rather than using conventional AOD products retrieved from remote sensing imageries. Given that the TOA-PM2.5 relationship cannot be accurately expressed by simple linear correlation, we developed a random forest model that integrated satellite TOA reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B product, meteorological fields and land-use variables to estimate the ground-level PM2.5 concentrations. The highly-polluted Yangtze River Delta (YRD) region of eastern China was employed as our study case. The results showed that our model performed well with a site-based and a time-based CV R2 of 0.92 and 0.88, respectively. The derived annual and seasonal distributions of PM2.5 concentrations exhibited high PM2.5 values in northern YRD region (i.e., Jiangsu province) and relatively low values in southern region (i.e., Zhejiang province), which shared a similar distribution trend with the observed PM2.5 concentrations. This study demonstrated the outstanding performance of random forest model using satellite TOA reflectance, and also provided an effective method for remotely sensed PM2.5 estimation in regions where AOD retrievals are unavailable.

[Assessing fractional vegetation cover changes and ecological quality of the Wuyi Mountain National Nature Reserve based on remote sensing spatial information]. / 基于遥感空间信息的武夷山国家级自然保护区植被覆盖度变化与生态质量评估.

The Wuyi Mountain National Nature Reserve (WYS), established in 1979, is the largest and most intact subtropical forest ecosystem in southeastern China. No study has assessed the vegetation coverage change along with its ecological effect after the protection of the reserve for almost 40 years. In this study, the NDVI data of Landsat Image was corrected using the NDVI data of MODIS, the fractional vegetation cover (FVC) and the remote sensing based ecological index (RSEI) were calculated to assess the change of FVC and ecological quality in WYS with five Landsat images representing a period from 1979 to 2017. The results showed that after protection for nearly 40 years the FVC of the reserve had been significantly increased from 73.6% in 1979 to 89.5% in 2017, which consequently improved ecological quality from 0.801 in 1988 to 0.823 in 2017. In 2017, the area with the good and excellent ecological quality grades accounted for 98.7% of the total. Spatially, the ecologically-improved areas mainly distributed in the northeast core area and the center of the southwest core area. The ecologically-declined areas mostly occurred along roadsides and peaks. Vertically, the highest FVC and ecological quality areas distributed in the elevations between 1300-1900 m. In general, the improvement of FVC and ecological quality in the Wuyi Mountain National Nature Reserve was due largely to the effective policies and the successful protection by local government and people, except for some special year that may be affected mainly by climate conditions.

Spatial variability of urban climate in response to quantitative trait of land cover based on GWR model.

Land surface temperature and moisture are central components of the Earth's surface heat budget. China has experienced substantial land use/cover change that has led to deterioration of the urban microclimate, thus affecting global climate change. Understanding the spatial non-stationarity in the relationships between climate and land cover across a highly heterogeneous surface of urban landscapes is important for improving urban planning and management. This study used Landsat-8 OLI/TIRS data to explore the relationship of the three components (index-based built-up index (IBI); bare soil index (SI); and normalized difference vegetation index (NDVI)) with the urban climate (land surface temperature (LST) and land surface moisture (LSM)) using both a global model (ordinary least squares (OLS)) and a local model (geographically weighted regression (GWR)) for a megacity in Southeast China. The global regression results showed that there were significant positive correlations between the LST and the IBI and SI, while significant negative correlations were observed between the LST and the NDVI; opposite results were observed for the LSM. The IBI is the factor having the greatest impact on the LST, while the SI is among the most important factors for the LSM. The local regression results showed that the response of urban climate to land surface is affected greatly by water areas, but the role of the water areas is impacted by their size and surrounding landscape patterns. Moreover, the effects of vegetation and built-up land on the urban climate vary across locations with different wind patterns.

A new remote sensing index based on the pressure-state-response framework to assess regional ecological change.

Ecological indicators have widespread appeal to scientists, environmental managers, and the general public. Remote sensing is unique in its capability to record variety of spatio-temporal information on land surface with complete coverage, especially with regard to larger spatial scales, which has been proven to be an effective data source to create indicators to rapidly identify regional eco-environment. In this paper, a new index, remote sensing based ecological index (RSEI) based on the pressure-state-response (PSR) framework, was applied to assess regional ecological changes in Fuzhou City of Fujian Province, southeastern China, using Landsat ETM+/OLI/TIRS images. Taking the advantages of being totally free of artificial interference in the calculation using principal components analysis (PCA) to assign weights of each variable, the RSEI can assess the regional ecological status more objectively and easily. The effectivity of the new index was validated by four approaches, including point-based, classification-based, correlation-based, and urban-rural-gradient-based comparisons. The case study showed that Fuzhou has witnessed ecological improvement during the study period, with the value of RSEI increasing from 0.663 in 2000 to 0.675 in 2016. Spatial variation analysis showed that the poor level of RSEI distributed mostly in the central urban areas, and the ecological degradation was attributed to the fast expansion of the built-up area, characterized by increasing greatly in the value of the normalized differential built up and soil index (NDBSI) in such areas.

[A Comparative Study on the Calibration Accuracy of Landsat 8 Thermal Infrared Sensor Data].

The satellite thermal infrared image has been an important data source for the acquisition of the earth's surface temperature. The thermal infrared sensor (TIRS) Landsat 8 satellite newly launched onboard has added valuable data for this mission. However, the calibration parameters for the two bands of the TIRS, i.e., TIRS Bands 10 and 11, had been modified several times since its launch. This finally led the United States Geological Survey (USGS) to reprocess all achieved Landsat 8 data starting from February 2014. In order to examine the calibration accuracy of the reprocessed TIRS data, this paper crossly compares Landsat 8 TIRS data with synchronized, well-calibrated Landsat 7 ETM+thermal infrared data. A total of three date-coincident image pairs of western United States, downloaded from USGS Earth Explorer website, were used for the cross comparison. Three test sites were selected respectively from the three image pairs for the comparison, which representing moderate vegetation-cover area (test site 1), low vegetation-cover area (test site 2), and bare soil area (test site 3). The thermal infrared data of the three image pairs of both sensors had been firstly converted to at-sensor temperature. A band-by-band comparison and a regression analysis were then carried out to investigate the relationship and difference between the two sensor thermal data. The results show a very high degree of agreement between the three compared Landsat 8 TIRS and Landsat 7 ETM+thermal infrared image pairs because the correlation coefficients between the retrieved at-sensor temperature of the two sensors are generally greater than 0.95. Nevertheless, the cross comparison also reveals differences between the thermal infrared data of the two sensors. Compared with retrieved at-sensor temperature of Landsat 7 ETM+Band 6, TIRS Band 10 shows an overestimation, which can be up to 1.37 K, whereas TIRS Band 11 underestimates the temperature, with a difference reaching to -3 K. This suggests that in spite of the reprocessing of Landsat 8 thermal infrared data, the calibration parameters for the satellite's TIRS data are still unstable, especially for TIRS Band 11. It was found that the at-sensor temperature difference between ETM+Band 6 and TIRS Band 10 was enhanced with the decrease in vegetation coverage from test site 1 to test site 3. The at-sensor temperature difference of test site 1 is 0.07 K and increased to 1.37 K in test site 3, a net increase by 1.3 K. While the at-sensor temperature difference between ETM+Band 6 and TIRS Band 11 had an inverse performance. With the decrease in vegetation coverage from test site 1 to test site 3, the at-sensor temperature difference was reduced from ~-3.0 to -0.4 K. Therefore, in bare soil dominated test site 3, the temperature difference was 1.37 K for TIRS Band 10 and -0.4 K for TIRS Band 11. The RMSE of TIRS Band 11 is also much lower than that of TIRS Band 10. This suggests that TIRS Band 11 can perform batter in bare soil area than TIRS Band 10 though the latter shows an overall batter performance than TIRS Band 11. The study also found that in low vegetation cover areas like in test sites 2 and 3, taking an averaged at-sensor temperature of TIRS Bands 10 and 11, the difference between the two sensors' at-sensor temperature can be reduced to less than -0.5 K.

[Comparison of performances in retrieving impervious surface between hyperspectral (Hyperion) and multispectral (TM/ETM+) images].

The retrieval of impervious surface is a hot topic in the remote sensing field in the past decade. Nevertheless, studies on retrieving impervious surface from hyperspectral image and the comparison of the performances in retrieving impervious surface between hyperspectral and multispectral images are rarely reported. Therefore, The present paper focuses on the characteristics of hyperspectral (EO-1 Hyperion) and multispectral (Landsat TM/ETM+) images and implements a complementary study on the comparison based on the retrieved impervious surface information between Hyperion and TM/ETM+ data. For up to 242 bands of Hyperion image, a further study was carried out to select feature bands for impervious surface retrieving using stepwise discriminant analysis. As a result, 11 feature bands were selected and a new image named Hyperion' was thus composed. The new Hyperion' image was used to investigate whether this band-reduced image could obtain higher accuracy in retrieving impervious surface. The three test regions were selected from Fuzhou, Guangzhou and Hangzhou of China, with date-coincident or nearly coincident image pairs of the used sensors. The linear spectral mixture analysis (LSMA) was employed to retrieve impervious surface and the results were accessed for their accuracy. The comparison shows that the Hyperion image has higher accuracy than TM/ETM+, and the Hyperion' composed of the selected 11 feature bands has the highest accuracy. The advantages of Hyperion in spectral and radiometric resolutions over TM/ETM+ are believed to be the main factors contributing to the higher accuracy. The high spectral and radiometric resolutions of Hyperion image allow the sensor to have higher sensitivity in distinguishing subtle spectral changes of ground objects. While, the highest accuracy the 11-band Hyperion' image achieved is owing to the significant reduction of the band dimension of the image and thus the band redundancy.

[Cross comparison of ASTER and Landsat ETM+ multispectral measurements for NDVI and SAVI vegetation indices].

The present paper investigates the quantitative relationship between the NDVI and SAVI vegetation indices of Landsat and ASTER sensors based on three tandem image pairs. The study examines how well ASTER sensor vegetation observations replicate ETM+ vegetation observations, and more importantly, the difference in the vegetation observations between the two sensors. The DN values of the three image pairs were first converted to at-sensor reflectance to reduce radiometric differences between two sensors, images. The NDVI and SAVI vegetation indices of the two sensors were then calculated using the converted reflectance. The quantitative relationship was revealed through regression analysis on the scatter plots of the vegetation index values of the two sensors. The models for the conversion between the two sensors, vegetation indices were also obtained from the regression. The results show that the difference does exist between the two sensors, vegetation indices though they have a very strong positive linear relationship. The study found that the red and near infrared measurements differ between the two sensors, with ASTER generally producing higher reflectance in the red band and lower reflectance in the near infrared band than the ETM+ sensor. This results in the ASTER sensor producing lower spectral vegetation index measurements, for the same target, than ETM+. The relative spectral response function differences in the red and near infrared bands between the two sensors are believed to be the main factor contributing to their differences in vegetation index measurements, because the red and near infrared relative spectral response features of the ASTER sensor overlap the vegetation "red edge" spectral region. The obtained conversion models have high accuracy with a RMSE less than 0.04 for both sensors' inter-conversion between corresponding vegetation indices.

[Cross-comparison between ASTER and Landsat-7 ETM+ multispectral imagery].

Up to present, no study has been published with respect to the cross-comparison between ASTER and Landsat-7 ETM+ imagery. Therefore, the present paper has implemented the complementary study on the images between these two sensors. The study firstly conducted the sensors characteristics comparison, including orbit characteristic, sensor scanning mode and imagery spectral characteristic. Further comparison was implemented to get the relation equations between corresponding VNIR and SWIR bands of these two sensors based on the apparent reflectance of the three pairs of synchronization images and large common ground regions. The validation has been done to verify the effectiveness of the proposed corresponding bands relation equations and matching coefficients. The result shows that the provided relation equations have high accuracy.

[Estimation of the optically active substances in case II water of the lower Jinjiang River based on the bio-optical model].

The water's Inherent Optical Properties (IOPs), including absorption and scattering coefficients of water components, are the essential parameters for bio-optical model and retrieval of water quality using the semi-analytical method. Nevertheless, the application of the bio-optical model in river water studies is still very rare. Therefore, taking the lower Jinjiang River of Fujian, SE China as an example, this study measured and calculated the bio-optical properties of river water and concentrations of optically active substances based on in situ water samples collected from river in 2007. It shows that R(0(-))753, R(0(-))702/R(0(-))680 and R(0(-))670/R(0(-))423 can be used to estimate total suspended solids (TSS) concentration, phytoplankton pigment (PP) concentration and the CDOM absorption at 440 nm, respectively. The determination coefficients (R2) of the retrieval model of TSS, PP and CDOM are 0.953, 0.8205 and 0.6213, respectively. The corresponding relative errors of the models (RE) are 6.1%, 21.87% and 22.18%. The results show that the model for estimating TSS can achieve the highest accuracy, the PP-estimating model has the second highest accuracy and the CDOM-estimating model has the lowest. The relatively lower concentration of phytoplankton pigments, narrow characterized spectral range of CDOM and influence of CDOM's R(0(-)) by TSS and PP within this spectral range contributed to their relatively lower accuracy.

[Quantitative estimation of suspended solid concentration in the lower Min River based on multi-source synchronal data].

Three synchronal data collected on 2006-09-18 have been used in the study of the suspended solid concentration (SSC) of the lower Min River, which are in situ sampled water data, field-spectrometer measured spectral data and Landsat TM spectral data. Two models for predicting SSC have been proposed, one of which is based on field-spectrometer measured data and the other is on Landsat TM data. The statistical analysis of the field-spectroreter measured data has revealed that the reflectance of the SSC at the 690 nm has the strongest correlation with the in situ-sampled SSC data. The regression model can be expressed as SS = 116.2 (R690/R530) - 33.4. Furthermore, the model built upon the ratio of the reflectance at 690 nm to 530 nm has the best fitness with the in situ sampled SSC data. While the best predicting model for the Landsat TM data is achieved using the band combination of (TM2 + TM3)2 and is defined as SS = 3793.7 (R(TM3) + R(TM2)2 - 16.5. The assessment of the two models shows that the model on the field-spectrometer data has higher accuracy than that on the Landsat TM data but the difference is not big. This suggests that the Landsat TM data are still valuable in the prediction of the SSC if the field-spectrometer data are not available. Consequently, the predicting model based on the Landsat data has been applied in the study of the SSC of the lower Min River. The result shows that the model can efficiently reveal the SSC with its spatial distributional pattern features.

Remote sensing of the urban heat island and its changes in Xiamen City of SE China.

World-wide urbanization has significantly modified the landscape, which has important climatic implications across all scales due to the simultaneous removal of natural land cover and introduction of urban materials. This resulted in a phenomenon known as an urban heat island (UHI). A study on the UHI in Xiamen of China was carried out using remote sensing technology. Satellite thermal infrared images were used to determine surface radiant temperatures. Thermal remote sensing data were obtained from band 6 of two Landsat TM/ETM+ images of 1989 and 2000 to observe the UHI changes over 11-year period. The thermal infrared bands were processed through several image enhancement technologies. This generated two 3-dimension-perspective images of Xiamen's urban heat island in 1989 and 2000, respectively, and revealed heat characteristics and spatial distribution features of the UHI. To find out the change of the UHI between 1989 and 2000, the two thermal images were first normalized and scaled to seven grades to reduce seasonal difference and then overlaid to produce a difference image by subtracting corresponding pixels. The difference image showed an evident development of the urban heat island in the 11 years. This change was due largely to the urban expansion with a consequent alteration in the ratio of sensible heat flux to latent heat flux. To quantitatively compare UHI, an index called Urban-Heat-Island Ratio Index (URI) was created. It can reveal the intensity of the UHI within the urban area. The calculation of the index was based on the ratio of UHI area to urban area. The greater the index, the more intense the UHI was. The calculation of the index for the Xiamen City indicated that the ratio of UHI area to urban area in 2000 was less than that in 1989. High temperatures in several areas in 1989 were reduced or just disappeared, such as those in old downtown area and Gulangyu Island. For the potential mitigation of the UHI in Xiamen, a long-term heat island reduction strategy of planting shade trees and using light-colored, highly reflective roof and paving materials should be included in the plans of the city planers, environmental managers and other decision-makers to improve the overall urban environment in the future.