scholarly journals Object-Based Window Strategy in Thermal Sharpening

2019 ◽  
Vol 11 (6) ◽  
pp. 634 ◽  
Author(s):  
Haiping Xia ◽  
Yunhao Chen ◽  
Jinling Quan ◽  
Jing Li
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Urban Areas ◽  
Simulated Data ◽  
Window Size ◽  
Landsat 8 ◽  
Factors Affecting ◽  
Object Based ◽  
Land Surface Temperatures ◽  
The Mean

The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregation accuracy. Global window strategies (GWSs) and local window strategies (LWSs) have been widely used and discussed, while object-based window strategies (OWSs) have rarely been considered. Therefore, this study presents an OWS based on a segmentation algorithm and provides a basis for selecting an optimal window size balancing both accuracy and efficiency. The OWS is tested with Landsat 8 data and simulated data via the “aggregation-then-disaggregation” strategy, and compared with the GWS and LWS. Results tested with the Landsat 8 data indicate that the proposed OWS can accurately and efficiently generate high-resolution LSTs. In comparison to the GWS, the OWS improves the mean accuracy by 0.19 K at different downscaling ratios, in particular by 0.30 K over urban areas; compared with the LWS, the OWS performs better in most cases but performs slightly worse due to the increasing downscaling ratio in some cases. Results tested with the simulated data indicate that the OWS is always superior to both GWS and LWS regardless of the downscaling ratios, and the OWS improves the mean accuracy by 0.44 K and 0.19 K in comparison to the GWS and LWS, respectively. These findings suggest the potential ability of the OWS to generate super-high-resolution LSTs over heterogeneous regions when the pixels within the object-based windows derived via segmentation algorithms are more homogenous.

scholarly journals SPATIOTEMPORAL FUSION OF HIGH RESOLUTION LAND SURFACE TEMPERATURE USING THERMAL SHARPENED IMAGES FROM REGRESSION-BASED URBAN INDICES

2020 ◽  
Vol V-3-2020 ◽  
pp. 247-254
Author(s):  
M. Kim ◽  
K. Cho ◽  
H. Kim ◽  
Y. Kim
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Thermal Images ◽  
Urban Regions

Abstract. Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied to enhance spatial and temporal resolutions simultaneously, but most STF approaches for the generation of Land Surface Temperature (LST) have not focused specifically on urban regions. This study therefore proposes a two-phase approach using Landsat 8 and MODIS images acquired on a study area in Beijing to first, investigate the sharpening of the fine resolution image input with urban-related spectral indices and second, to explore the potential of implementing the sharpened results into the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature Mapping (SADFAT) to generate high spatiotemporal resolution LST images in urban areas. For this test, five urban indices were selected based on their correlation with brightness temperature. In the thermal sharpening phase, the Fractional Urban Cover (FUC) index was able to delineate spatial details in urban regions whilst maintaining its correlation with the original brightness temperature image. In the STF phase however, FUC sharpened results returned relatively high levels of correlation coefficient values up to 0.689, but suffered from the highest Root Mean Squared Error (RMSE) and Average Absolute Difference (AAD) values of 4.260 K and 2.928 K, respectively. In contrast, Normalized Difference Building Index (NDBI) sharpened results recorded the lowest RMSE and AAD values of 3.126 K and 2.325 K, but also the lowest CC values. However, STF results were effective in delineating fine spatial details, ultimately demonstrating the potential of using sharpened urban or built-up indices as a means to generate sharpened thermal images for urban areas, as well as for input images in the SADFAT algorithm. The results from this study can be used to further improve STF approaches for daily and spatially continuous mapping of LST in urban areas.

scholarly journals Predicting Aedes aegypti infestation using landscape and thermal features

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Camila Lorenz ◽  
Marcia C. Castro ◽  
Patricia M. P. Trindade ◽  
Maurício L. Nogueira ◽  
Mariana de Oliveira Lage ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aedes Aegypti ◽  
Vector Control ◽  
Land Surface ◽  
Urban Areas ◽  
Negative Binomial ◽  
Control Strategies ◽  
Winter Season ◽  
Landsat 8 ◽  
Thermal Features

AbstractIdentifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.

High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images

2021 ◽  
pp. 118591
Author(s):  
Hao Lin ◽  
Siwei Li ◽  
Jia Xing ◽  
Tao He ◽  
Jie Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Urban Areas ◽  
Landsat 8 ◽  
Landsat 8 Oli

MONITORING THERMAL POLLUTION SOURCES IN VORONEZH (RUSSIA) USING REMOTE SENSING DATA

2021 ◽  
pp. 54-65
Author(s):  
Дмитрий Владимирович Сарычев ◽  
Ирина Владимировна Попова ◽  
Семен Александрович Куролап
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Heat Islands ◽  
Land Surface Temperatures ◽  
Treatment Facilities ◽  
Urban Heat ◽  
Industrial Zones

Рассмотрены вопросы мониторинга теплового загрязнения окружающей среды в городах. Представлена методика отбора спектрозональных спутниковых снимков, их обработки и интерпретации полученных результатов. Для оценки городского острова тепла были использованы снимки с космического аппарата Landsat 8 TIRS. На их основе построены карты пространственной структуры острова тепла города Воронежа за летний и зимний периоды. Определены тепловые аномалии и выявлено 11 основных техногенных источников теплового загрязнения в г. Воронеже, установлена их принадлежность к промышленным зонам предприятий, а также к очистным гидротехническим сооружениям. Поверхностные температуры данных источников в среднем были выше фоновых температур приблизительно на 6° зимой и на 15,5° С летом. Синхронно со спутниковой съемкой были проведены наземные контрольные тепловизионные измерения температур основных подстилающих поверхностей в г. Воронеже. Полученные данные показали высокую сходимость космических и наземных измерений, на основании чего сделан вывод о надежности используемых данных дистанционного зондирования Земли в мониторинговых наблюдениях теплового загрязнения городской среды. Результаты работ могут найти применение в городском планировании и медицинской экологии. The study deals with the remote sensing and monitoring of urban heat islands. We present a methodology of multispectral satellite imagery selection and processing. The study bases on the freely available Landsat 8 TIRS data. We used multitemporal thermal band combinations to make maps of the urban heat island of Voronezh (Russia) during summer and winter periods. That let us identify 11 artificial sources of heat in Voronezh. All of them turned out to be allocated within industrial zones of plants and water treatment facilities. Land surface temperatures (LST) of these sources were approximately 6° and 15.5° C above the background temperatures in winter and summer, respectively. To prove the remotely sensed temperatures we conducted ground control measurements of LST of different surface types at the satellite revisit moments. Our results showed a significant correlation between the satellite and ground-based measurements, so the maps we produced in this study should be robust. They are of use in urban planning and medical ecology studies.

scholarly journals Spatiotemporal Characterization of Land Cover Impacts on Urban Warming: A Spatial Autocorrelation Approach

2020 ◽  
Vol 12 (10) ◽  
pp. 1631 ◽  
Author(s):  
Chao Fan ◽  
Zhe Wang
Keyword(s):  
Land Cover ◽  
Spatial Autocorrelation ◽  
Land Surface ◽  
Urban Areas ◽  
Spatial Configuration ◽  
Urban Warming ◽  
Green Vegetation ◽  
Urban Rural ◽  
Land Surface Temperatures ◽  
The Cost

There has been an increasing concern of rising temperatures as cities continue to expand and intensify. Urban warming is having significant impacts on the environment that are far beyond city limits. Understanding the development pattern of the urban heat island (UHI) effect is crucial for making action plans to mitigate urban warming. In this study, we combine multitemporal satellite imagery, spatial autocorrelation indices, and statistical analysis into a spatiotemporal study of the surface UHI effect in the Boise-Meridian metropolitan area. A continuous landscape modeling perspective was taken to quantitatively depict the abundance and spatial configuration of green vegetation and built-up areas at a landscape scale. We aim to (1) evaluate the variations in the land surface temperatures (LST) along the urban–rural gradients of Boise for multiple years, (2) identify the relationships of the LST variations with the land cover variables quantified using the spatial autocorrelation indices, and (3) analyze the changing climate in Boise in conjunction with its urbanization pattern over the last two decades. Results show that the region experienced a significant increase in the LST along with a great expansion of urban areas at the cost of agriculture. The warming effect of built-up areas was greater than the cooling effect of green vegetation, suggesting an urgent need for increasing greenspace in the city. Statistical analyses show that clustered vegetation and dispersed built-up features are beneficial for reducing the LST. Our study presents a spatiotemporal framework for analyzing the surface UHI effect from multiple angles. Scientific findings from this study can help make informed policies against urban warming via optimal planning of urban land cover.

The impact of built-up surfaces on land surface temperatures in Italian urban areas

2016 ◽  
Vol 551-552 ◽  
pp. 317-326 ◽  
Author(s):  
Marco Morabito ◽  
Alfonso Crisci ◽  
Alessandro Messeri ◽  
Simone Orlandini ◽  
Antonio Raschi ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Surface Temperatures ◽  
Land Surface Temperatures ◽  
The Impact

High resolution LST climatology in an urban area using Landsat 8 thermal data

2020 ◽  
Author(s):  
Sorin Cheval ◽  
Alexandru Dumitrescu ◽  
Vlad Amihăesei
Keyword(s):  
Missing Data ◽  
High Resolution ◽  
Urban Area ◽  
Land Surface ◽  
Missing Values ◽  
Urban Climate ◽  
Landsat 8 ◽  
Data Set ◽  
Area Of Interest ◽  
Spatial Coverage

<p>The Landsat 8 satellites retrieve land surface temperature (LST) values at 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the area of interest. This paper proposes a procedure for building a long-term LST data set in an urban area using the high-resolution Landsat 8 imagery. The methodology is demonstrated on 94 images available through 2013-2018 over Bucharest (Romania). The raw images contain between 1.1% and 58.4% missing data. Based on an Empirical Orthogonal Filling (EOF) procedure, the LST missing values were reconstructed by means of the function dineof implemented in sinkr R packages. The output was used for exploring the LST climatology in the area of interest. The gap filling procedure was validated by comparing artificial gaps created in the real data sets. At the best of our knowledge, this is the first study using full spatial coverage high resolution remote sensing data for investigating the urban climate. The validation pursued the comparison between LST and Ta at 3 WMO stations monitoring the climate of Bucharest, and returned strong correlation coefficients (R2 > 0.9). Further research may be envisaged aiming to update the data set with more recent LST information and to combine data from various sources in order to build a more robust urban LST climatology.</p><p>This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI -<br>UEFISCDI, project number COFUND-SUSCROP-SUSCAP-2, within PNCDI III.</p>

Correlation of air temperature and land surface temperature of Landsat-8 in cities of South Korea

2020 ◽  
Author(s):  
Sungwon Choi ◽  
Donghyun Jin ◽  
Noh-hun Seong ◽  
Daeseong Jung ◽  
Kyung-soo Han
Keyword(s):  
Correlation Analysis ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Data ◽  
Landsat 8 ◽  
In Situ Data ◽  
Urban Greenery ◽  
Important Position

<p>Recently, there are many problems in urban area such as urban thermal island phenomenon, changes in urban green area, changes in urban weather and various urban types. And surface temperature data have been utilized in many areas to identify these phenomena. This means that surface temperatures is an important position in urban greenery and weather. High temporal and spatial resolution satellite data are needed to continuously observe the phenomenon in urban areas. In addition, the surface temperature varies from type of indicator, topography, and various factors, so there is a limit to the in-situ data for observing changes throughout the city. Therefore, various organizations around the world are currently conducting surface temperature measurements using satellites. However, the use of data in clear pixel is essential for accurate surface temperature calculations using satellites, but the accuracy of results will be reduced if the data from in the pixel which conclude clouds.</p><p>Therefore, we tried to solve these problems by analyzing the correlation between the air temperature data and the Landsat-8 LST data. The variables used in the correlation analysis are air temperature, Landsat-8 LST, NDVI and NDWI, and the study period is 2014 to 2016 and the study area is South Korea's five cities (Seoul, Busan, Daejeon, Daegu, Gwangju). For correlation analysis, the air temperature data points provided by the Korea Meteorological Administration and the Landsat-8 pixels were matched, and the correlation coefficient calculated by the correlation analysis was applied to the Landsat-8 satellite to calculate the LST. We validated by direct comparison the re-produced Landsat-8 LST with observed Landsat-8 LST. And the result of validation showed a high correlation of 0.9. It shows that compensation for the satellite's shortcomings from clouds by using the correlation between temperature and LST.</p>

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