This paper focuses on the relationship between remotely-sensed urban site characteristics (USCs) and land surface temperature (LST). Particular emphasis is put on an extensive comparison of ...two-dimensional (2D) and three-dimensional (3D) USCs as potential indicators of the surface urban heat island (UHI) effect and as potential predictors for thermal sharpening applications. Both widely-used as well as more recently proposed metrics of the urban remote sensing literature are investigated within a single experiment. While some of these USCs have already been used earlier, others have never been analyzed before in the context of urban temperature studies. In addition to the comparison of 2D and 3D USCs, the spatio-temporal dependencies of their relation to LST are examined. To this end, the experimental setup of this work includes two study areas, 26 USCs, and 16 LST scenes covering four seasons. Use is made of a comprehensive database compiled for the cities of Berlin and Cologne, Germany. After data preparation, very high resolution (VHR) multi-spectral and height data are employed to map fine-scale urban land cover (LC). The resulting LC maps are then used in conjunction with the height information to compute 2D and 3D USCs. Subsequently, multi-temporal LST images are retrieved from Landsat Enhanced Thematic Mapper Plus (ETM+) scenes. The spatio-temporal investigation of the USC–LST connection constitutes the final stage of the workflow and is achieved in the framework of a dedicated correlation analysis. The results of this study highlight that the linkage between USCs and LST sensed at small scan angles is not stronger when 3D parameters are considered. Even though they may offer more holistic representations of the urban landscape, 3D USCs are consistently outperformed by some of the most widely-used 2D metrics. The analysis of spatial dependencies reveals that the USC–LST interplay does not only differ between, but also within the two test sites. This is due to their distinct geographies, with urban form and compactness, green spaces and street trees, and the structural composition of LC elements being some of the determining factors. The examination of temporal dependencies yielded that the association between USCs and LST is fairly stable over time but can be subject to larger inter- and intra-season variations for different reasons, including the season of acquisition, vegetation phenology, and meteorological conditions. Since previous research was based on the analysis of a single study area, a limited number of (mainly 2D) USCs, and/or only a few LST scenes acquired in specific seasons, it is concluded that the findings of this study provide researchers and practitioners with a more complete picture of the USC–LST relationship.
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•Spatio-temporal analysis of the statistical relationship between 2D/3D USCs and LST•Detailed inspection of 2 study areas, 26 USCs, and 16 LST scenes covering 4 seasons•3D USCs are consistently outperformed by some of the most widely-used 2D indicators.•Correlations are spatially dependent due to the distinct geographies of the cities.•Larger inter-/intra-season variations are mainly driven by environmental conditions.
Each city exhibits recurring patterns consisting of similar building types, vegetation structures, and open spaces, enabling environmental and socio-economic investigations of the urban fabric. In ...this study, urban structure types (UST) of the city of Berlin are mapped on the basis of a prior land cover classification utilizing a synergistic approach of knowledge based classification and Random Forests. The results are then compared to the outcomes of a previous analysis regarding a subarea of the utilized high spatial resolution airborne data. Results show that UST classification based on a combination of prototype objects and Random Forests is suitable to generate accurate UST maps for these areas with only minor adaptations. Future analyses will focus on transferring the processes to different German cities and data of several sensors.
Dead wood such as coarse dead wood debris (CWD) is an important component in natural forests since it increases the diversity of plants, fungi, and animals. It serves as habitat, provides nutrients ...and is conducive to forest regeneration, ecosystem stabilization and soil protection. In commercially operated forests, dead wood is often unwanted as it can act as an originator of calamities. Accordingly, efficient CWD monitoring approaches are needed. However, due to the small size of CWD objects satellite data-based approaches cannot be used to gather the needed information and conventional ground-based methods are expensive. Unmanned aerial systems (UAS) are becoming increasingly important in the forestry sector since structural and spectral features of forest stands can be extracted from the high geometric resolution data they produce. As such, they have great potential in supporting regular forest monitoring and inventory. Consequently, the potential of UAS imagery to map CWD is investigated in this study. The study area is located in the center of the Hainich National Park (HNP) in the federal state of Thuringia, Germany. The HNP features natural and unmanaged forest comprising deciduous tree species such as Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), and Carpinus betulus (hornbeam). The flight campaign was controlled from the Hainich eddy covariance flux tower located at the Eastern edge of the test site. Red-green-blue (RGB) image data were captured in March 2019 during leaf-off conditions using off-the-shelf hardware. Agisoft Metashape Pro was used for the delineation of a three-dimensional (3D) point cloud, which formed the basis for creating a canopy-free RGB orthomosaic and mapping CWD. As heavily decomposed CWD hardly stands out from the ground due to its low height, it might not be detectable by means of 3D geometric information. For this reason, solely RGB data were used for the classification of CWD. The mapping task was accomplished using a line extraction approach developed within the object-based image analysis (OBIA) software eCognition. The achieved CWD detection accuracy can compete with results of studies utilizing high-density airborne light detection and ranging (LiDAR)-based point clouds. Out of 180 CWD objects, 135 objects were successfully delineated while 76 false alarms occurred. Although the developed OBIA approach only utilizes spectral information, it is important to understand that the 3D information extracted from our UAS data is a key requirement for successful CWD mapping as it provides the foundation for the canopy-free orthomosaic created in an earlier step. We conclude that UAS imagery is an alternative to laser data in particular if rapid update and quick response is required. We conclude that UAS imagery is an alternative to laser data for CWD mapping, especially when a rapid response and quick reaction, e.g., after a storm event, is required.
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