Wind power in China is experiencing accelerating growth to meet the need for structural reform of the energy supply, however its impact on the ecological environment urgently needs to be taken into account. In this study, the impact of a wind farm in Gonghe County, Qinghai Province, China, on local land surface temperature (LST) was explored by removing the temporal background using year-by-year LST averaging and filtering the spatial background using pixel-by-pixel LST averaging based on Moderate Resolution Imaging Spectroradiometer (MODIS) and the European Centre for Medium-Range Weather Forecast's ERA5 data. The main findings are as follows: (1) From a long-term series perspective, the LST on the wind farm and the adjacent areas shows an increasing trend, reaching 0.060K/19a in spring; (2) the wind farm exerts a warming effect on the local LST, with a maximum of 7.04 K and a minimum of 1.63 K, and there is a significant spatial matching with the wind farm boundaries, which is more obvious in spring and winter, and less pronounced in summer and autumn; (3) the construction of wind farms also has a potential upward impact on the LST in the adjacent areas, especially in autumn and winter, and the warming effect gradually decreases with increasing distance; (4) the warming effect is most remarkable when the predominant wind directions of the upwind and downwind are close to consistent and the differences in wind volumes are significant. Research findings on the effect of wind farms on local LST are vitally important for strengthening the establishment of wind farms, site selection of wind farms, and the scientific and reasonable utilization of wind energy resources.
An urban agglomeration represents a mature stage in the development of urban areas with a highly concentrated spatial form. This research paper aimed to comprehend the spatio-temporal evolution characteristics of the Beijing-Tianjin-Hebei urban agglomeration, identify its spatial structure, and interconnections based on the NPP-VIIRS night light remote sensing data from 2012 to 2021. Additionally, the study was supplemented with statistical data from corresponding years to extract the range of urban built-up areas in the study region over the past ten years. The data were analysed using various methods such as light value statistics, urban rank-size rule, standard deviation ellipse, fractal dimension, and measure of urban association. The analysis was conducted based on three aspects: scale, morphology, and spatial structure. The findings indicated that the total amount of light in the Beijing-Tianjin-Hebei urban agglomeration had been increasing steadily, with Baoding showing the highest rate of increase, followed by Langfang. Furthermore, the urban agglomeration had a decreasing trend in fragmentation and fractal dimension, with concentrated built-up areas and regular geometry. The urban centre of gravity was shifting towards the northeast, i.e. closer to the geometric centre of the urban agglomeration. The primacy ratio of the urban agglomerations followed an undulating declining pattern, and the gap between cities in the urban agglomerations gradually narrowed. Although transport integration had led to increased urban connectivity, the northern and southern regions were still not sufficiently connected.
As the cloud covers the surface thermal radiation during the transmission process and the remote sensor in the air is difficult to detect, Thermal Infrared (TIR) remote sensing inversion of Land Surface Temperature (LST) must be performed under clear-sky and cloudless conditions. This study has established the functional relationship between LST and the vegetation index of cloudless vegetation pixels around the cloud coverage areas in the North China Plain based on the Normalized Difference Vegetation Index (NDVI) to address the issues of TIR remote sensing inversion for LST affected by cloud coverage. By acquiring NDVI and using short-term, relatively stable characteristics of vegetation, the LST of the cloud cover area is estimated. The findings show a linear negative correlation between LST and NDVI in vegetation pixels, with the vegetation type remaining essentially unchanged over time. When there are 20 pixels in each of the two thermal infrared channels of FY-3 D, the MAE value and RMSE value of the 24th thermal infrared channel are 0.77 and 0.88, respectively, and the MAE value and RMSE value of the 25th thermal infrared channel are 0.64 and 0.80, respectively. When the number of pixels is 200, the 24th thermal infrared channel’s MAE value and RMSE values are 0.96 and 0.99, respectively, while the 25th thermal infrared channel’s MAE value and RMSE values are 0.90 and 0.95, respectively. In other words, the estimation is more accurate and closer to the true value, and the land surface temperature retrieved by the 25th channel deviates from the true value to a lesser extent. The average absolute error and root mean square error are both less than 1, which may satisfy the accuracy demands of practical applications such as agricultural drought monitoring, ecological evaluation, and crop yield estimation.
Land use transformation is necessary to achieve the regional long-term socioeconomic development. The recent increase in land demand and external environmental stress in China has led to dramatic land use transformation. In the study, land use transformation in Minhou County in China was investigated from the biconditional perspective of dominant and recessive forms, which included the analysis of the quantity structure, transformation magnitude, transformation speed, spatial structure, efficiency, and function using remote sensing, geographic information system, topography, and socioeconomic data. The results revealed that, in the study of the dominant form, the construction land transformation in the industrialized town developing rapidly with a low-lying slope had the greatest speed, with an average annual growth rate of 8.74%, while the woodland was moving at the lowest rate of −3.62%. In the recessive form, the functional pattern of the construction land, cultivated land production, and the input into the woodland were revealed as the prime factors that impacted land use transformation. The linear growth of the functional index from the construction land, the increasing production index of the cultivated land, and the rebound efficiency of forestry production in Minhou County in China have been continuously influencing the variation in the land use patterns. The findings of the study would promote sustainable development and appropriate utilization of the land resources in the region by providing decision support to the government for scientific and rational planning and strengthening the efficiency of intensive land use.
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