Method of calculating land surface temperatures based on the low-altitude UAV thermal infrared remote sensing data and the near-ground meteorological data

Wu, Yafei; Shan, Yao; Lai, Yuanming; Zhou, Shunhua

doi:10.1016/j.scs.2021.103615

Cited by 24 publications

(21 citation statements)

References 54 publications

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“…According to previous studies, urbanization would inevitably lead to the change of land use type, structure and pattern, the destruction of the balance between surface radiation and energy, the excessive release of consumed energy, and the change of near surface atmospheric structure [4,5,22,32], which would eventually lead to the change of temporal and spatial pattern of surface temperature. Moreover, for rapidly expanding urban areas, the replacement of vegetation, the changes in building materials and surface reflectivity will be very rapid [30,41,45], which will lead to rapid evolution of the spatial distribution of LST.…”

Section: Discussionmentioning

confidence: 99%

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Temporal and Spatial Variation of Land Surface Temperature in Recent 20 Years and Analysis of the Effect of Land Use in Jiangxi Province, China

et al. 2022

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Under the background of global warming, it is of great significance to study the temporal and spatial evolution of land surface temperature (LST) on long-time scale and the impacts of land use in the fields of urban thermal environment and regional climate change. Based on MODIS LST long time series remote sensing data, the temporal and spatial evolution characteristics of pixel-wise LST in Jiangxi Province, the middle inland province of China from 2000 to 2020 were analyzed by using Theil-Sen + Mann-Kendall, coefficient of variation and Hurst index, and the response of LST to land use was identified by combining the contribution and diversity index. The results showed as follows: (1) LST was generally distributed as "high in Middle-East-West-South and low in North-northwest-southeast direction". LST showed an overall downward trend, indicating a weakening of the warming trend. The dynamic trend of LST was characterized by more descending than ascending tendency. The dynamic stability showed a coexistence of high and low fluctuation tendency, with a higher proportion of medium and low fluctuation areas having obvious spatial differences. The overall dynamic sustainability was characterized by uncertainty of future change trend. (2) The LST were strongly affected by land use in the past 20 years. Firstly, the areas of high LST were mostly located in construction land and unused land, while the areas of low LST were mostly in water area and forest land. However, forest land and water area of high temperature were gradually turned to construction land later on. Secondly, the land use structure and pattern had an strong effects on LST. With the increase of the area proportion of different land use, the LST showed significant differences. The more complex the spatial pattern of land use, the more obvious its impact on LST. The research results will provide some reference for the regions with the same characteristics as Jiangxi Province to deal with LST under the background of global climate change.

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Section: Discussionmentioning

confidence: 99%

“…Compared with traditional meteorological observation methods, LST remote sensing monitoring has the advantages of wide observation range and strong spatial continuity [21,22]. Moreover, remote sensing monitoring data provide a good visual effect for the extraction of LST [5,23].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variation of Land Surface Temperature in Recent 20 Years and Analysis of the Effect of Land Use in Jiangxi Province, China

et al. 2022

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“…The segmentation results of this method are better. With the deepening of research, Huang et al proposed a nonlinear optimization Otsu segmentation algorithm for remote sensing information with different granularities [24]. Firstly, the PCA algorithm is used to reduce the dimension of the remote sensing image of the UAV, and then based on the minimum value judgment, the end condition of the segmentation algorithm is added, and finally the optimal value is output.…”

Section: Uav Remote Sensing Image Segmentationmentioning

confidence: 99%

Research on segmentation algorithm of UAV remote sensing image based on deep learning

Zhou

Sui²,

Ding³

et al. 2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

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With the rapid development of UAV technology, the research topic of remote sensing image segmentation has gradually attracted more and more attention. Whether the image can be accurately segmented is a measure of the goodness of the algorithm. In recent years, machine learning methods have been applied to a large number of fields, and deep neural network technology has also been widely used in the field of UAV remote sensing image segmentation. This paper introduces the specific applications of various deep learning methods in remote sensing image segmentation, and briefly analyzes the development of neural networks in this problem according to the research status of several typical deep neural networks in UAV remote sensing image segmentation. Research shows that on the basis of proper adjustment of optimizer, learning rate and loss function, using deep learning method to segment UAV remote sensing images, the accuracy rate can reach more than 96%.

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“…The near ground temperature is a popular and practical temperature indicator, which can reflect the temperature that the human body can perceive on the ground [17][18][19]. Usually, meteorological station data is the first-hand data to obtain the near ground temperature distribution [20,21].…”

Section: Introductionmentioning

confidence: 99%

Dense Temperature Mapping and Heat Wave Risk Analysis Based on Multisource Remote Sensing Data

Liu

Chai

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As high temperature and heat wave have become great threats to human survival, social stability, and ecological safety, it is of great significance to master the spatial and temporal dynamic changes of temperature to prevent high temperature and heat wave risks. The meteorological station can provide accurate near ground temperature, but only within a specific space and time. In order to meet the needs of large-scale research, spatial interpolation methods were widely used to obtain spatially continuous temperature maps. However, these methods often ignore the influence of external factors on temperature, such as land cover, height, etc., and neglect to supplement temporal-wise information. To deal with these issues, a joint spatio-temporal method is proposed to obtain dense temperature mapping from multi-source remote sensing data, which combining a geographically weighted regression (GWR) model and a polynomial fitting model. Besides, a heat wave risk model is also built based on the dense temperature maps and population data, in order to evaluate the heat wave risk of different areas. Accuracy evaluations and experiments have verified the effectiveness of the proposed methods. Case study on the four cities of Zhejiang Province, China have demonstrated that areas with higher degree of urbanization are often accompanied by higher heat wave risks, such as the northern part of the study area. The study also found that the heat wave risks have presented a centralized distribution and spatial autocorrelation characteristics in the study area.

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Method of calculating land surface temperatures based on the low-altitude UAV thermal infrared remote sensing data and the near-ground meteorological data

Cited by 24 publications

References 54 publications

Temporal and Spatial Variation of Land Surface Temperature in Recent 20 Years and Analysis of the Effect of Land Use in Jiangxi Province, China

Temporal and Spatial Variation of Land Surface Temperature in Recent 20 Years and Analysis of the Effect of Land Use in Jiangxi Province, China

Research on segmentation algorithm of UAV remote sensing image based on deep learning

Dense Temperature Mapping and Heat Wave Risk Analysis Based on Multisource Remote Sensing Data

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