Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT 8 has opened new possibilities for understanding the events on the Earth with remote sensing. This study presents an algorithm for the automatic mapping of land surface temperature from LANDSAT 8 data. The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 data. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, for the first case, the standard deviation was 2.4°C, and for the second case, it was 2.7°C. For future studies, the tool should be refined within situmeasurements of land surface temperature.

Water body extraction is an important part of water resource management and has been the topic of a number of research works related to remote sensing for over two decades. Extracting water bodies from satellite images with a pixel-based method or indexes cannot eliminate other objects that have a low albedo, such as shadows and built-up areas. Since their spectral differences cannot be separated, in this paper a method that combines a pixel-based index and object-based method has been used on a Sentinel-2 satellite image with a resolution of 10 m. The method uses image segmentation on a multispectral image containing 13 bands. It also uses indexes used for extracting water bodies, such as the Normalized Difference Water Index (NDWI). Two study areas with different characteristics have been chosen, one mountainous and one urban region, both of them located in Macedonia. Using object-based techniques and pixel-based indexes, such as NDWI, the results from the NDWI have been improved by a kappa value of more than 0.5.

This paper presents a Python QGIS (PyQGIS) plugin, which has been developed for the purpose of producing Land Surface Temperature (LST) maps from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS, Thermal Infrared (TIR) imagery. The plugin has been developed purposely to ease the process of LST extraction from Landsat Visible, Near Infrared (VNIR) and TIR imagery. It has the ability to estimate Land Surface Emissivity (LSE), calculating at-sensor radiance, calculating brightness temperature and performing correction of brightness temperature against atmospheric interference though the Plank function, Mono Window Algorithm (MWA), Single Channel Algorithm (SCA) and the Radiative Transfer Equation (RTE). Using the plugin, LST maps of Moncton, New Brunswick, Canada have been produced for Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS. The study put much more emphasis on the examination of LST derived from the different algorithms of LST extraction from VNIR and TIR satellite imagery. In this study, the best LST values derived from Landsat 5 TM were obtained from the RTE and the Planck function with RMSE of 2.64 °C and 1.58 °C, respectively. While the RTE and the Planck function produced the best results for Landsat 7 ETM+ with RMSE of 3.75 °C and 3.58 °C respectively and for Landsat 8 TIRS LST retrieval, the best results were obtained from the Planck function and the SCA with RMSE of 2.07 °C and 3.06 °C, respectively.

Unmanned aerial vehicles (UAV) have been used in a variety of fields in the last decade. In forestry, they have been used to estimate tree heights and crowns with different sensors. This approach, with a consumer-grade onboard system camera, is becoming popular because it is cheaper and faster than traditional photogrammetric methods and UAV-light detecting and ranging systems (UAV-LiDAR). In this study, UAV-based imagery reconstruction, processing, and local maximum filter methods are used to obtain individual tree heights from a coniferous urban forest. A low-cost onboard camera and a UAV with a 96-cm wingspan made it possible to acquire high resolution aerial images (6.41 cm average ground sampling distance), ortho-images, digital elevation models, and point clouds in one flight. Canopy height model, obtained by extracting the digital surface model from the digital terrain model, was filtered locally based on the pixel-based window size using the provided algorithm. For accuracy assessment, ground-based tree height measurements were made. There was a high 94% correlation and a root-mean-square error of 28 cm. This study highlights the accuracy of the method and compares favourably to more expensive methods.

Mapping and monitoring of wetlands as one of the world`s most valuable natural resource has gained importance with the developed of the remote sensing techniques. This paper presents the capabilities of Sentinel-2 successfully launched in June 2015 for mapping and monitoring wetlands. For this purpose, three different approaches were used, pixel-based, object-based and index-based classification. Additional, for more successful extraction of wetlands, a combination of object-based and index-based method was proposed. It was proposed the use of object-based classification for extraction of the wetlands boundaries and the use of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for classifying the contents within the wetlands boundaries. As a study area in this paper Sakarbasi spring in Eskisehir, Turkey was chosen. The results showed successful mapping and monitoring of wetlands with kappa coefficient of 0.95.