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In this letter, multisource earth observation (EO) data sets, including multitemporal Landsat-8, digital surface model, and spatial information, were integrated for land-cover classification by random forest (RF) and support vector machines (SVMs). We demonstrated in this letter that both RF and SVM are useful tools for classification of land cover in the local climate zones featured with highly heterogeneous landscape. Classification of land cover by RF was with an overall accuracy (OA) of 86.2%, while the OA was 85.5% for SVM. However, we found that RF was more stable than SVM for multisource EO data in classifying land cover without normalizing different feature data sets. Experiments showed that the thermal features were more important than temporal and spatial ones in discriminating impervious objects, while the temporal and spatial features were generally better than thermal ones in separating the distinct vegetation categories. Another finding was that our experiments indicated that spectral features were the most important in classification of land cover, followed by temporal, thermal, and spatial features, respectively. As to the spectral features, red channels were the most important, followed by short-wave infrared, near-infrared, and green channels. Thus, it could be concluded that the combination of spectral, thermal, spatial, and temporal information would be an optimal approach to increase the OA of land-cover classification in the zones featured with highly heterogeneous landscape. [ABSTRACT FROM PUBLISHER]

The radiometric performance of remotely-sensed images is important for the applications of such data in monitoring land surface, ocean and atmospheric status. One requirement placed on the Thermal Infrared Sensor (TIRS) onboard Landsat 8 was that the noise-equivalent change in temperature (NEΔT) should be ≤0.4 K at 300 K for its two thermal infrared bands. In order to optimize the use of TIRS data, this study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over uniform ground surfaces, including lake, deep ocean, snow, desert and Gobi, as well as dense vegetation. Results showed that the NEΔTs of the two bands were 0.051 and 0.06 K at 300 K, which exceeded the design specification by an order of magnitude. The effect of NEΔT on the land surface temperature (LST) retrieval using a split window algorithm was discussed, and the estimated NEΔT could contribute only 3.5% to the final LST error in theory, whereas the required NEΔT could contribute up to 26.4%. Low NEΔT could improve the application of TIRS images. However, efforts are needed in the future to remove the effects of unwanted stray light that appears in the current TIRS images.

Heat waves are reaching our cities more frequently nowadays, leading to the urban heat island (UHI) effect. Measured by the remotely sensed land surface temperature (LST), the effect is frequently called surface UHI (SUHI). In Estonia, the SUHI has been studied little and the LST method has not being used yet in studies of Estonian cities’ microclimate. The analysis clearly reveals that the effect of UHI in Estonian settlements is larger than ever assumed. Satellite images showed that Tallinn suffers from quite strong UHI effect. Landsat-8 thermal infrared sensor image from July 25, 2014 shows that for all settlements of densely populated Harju County, the temperature is higher than that in the surroundings by 3 °C–5 °C. To evaluate potential impact to the health of vulnerable groups of residents of Tallinn city, we applied an “exposure to risk—sensitivity—impact” method to evaluate the potential impact of the UHI effect on the health of vulnerable groups of residents of Tallinn city. Our analysis showed that the parts of Tallinn severely affected by heat are mostly the areas of apartment buildings with lower real estate prices. Thus, those areas have also a higher population density and more vulnerable people live there. Produced map of UHI impact can be successfully used for spatial planning measures for adaptation to climate change. [ABSTRACT FROM PUBLISHER]