Metrics of scale in remote sensing and GIS
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Scale in a digital geographic world
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2024, International Journal of Applied Earth Observation and GeoinformationIdentifying important at-sea areas for seabirds using species distribution models and hotspot mapping
2020, Biological ConservationCitation Excerpt :Maximum Curvature, which does not involve local smoothing, resulted in hotspots with greater similarity to estimated UD contours in Shags, but even in this case similarity was much lower than the corresponding similarity indices observed between Maximum Curvature hotspots and utilisation distributions in the other three species. One potential explanation is that because the maximum foraging range observed in Shags during the study was 35 km compared to 300 km in Kittiwakes, 340 km in Guillemots and 305 km in Razorbills the spatial scale (sensu Goodchild, 2001) of our analyses differs between the species. Specifically, due to the interaction between relatively limited foraging range of Shags and the grid resolution used for analysis, hotspots defined using Maximum Curvature or Getis-Ord hotspot boundaries tend to be coarser or blockier than the underlying utilisation distributions resulting in relatively low similarity even when high density areas are successfully identified.
Evaluation of climate and human effects on the hydrology and water quality of Burdur Lake, Turkey
2019, Journal of African Earth SciencesCitation Excerpt :Using multitemporal satellite data aligned and registered properly, comparisons can be made between and among years for specific months (Pozdnyakov et al., 2005; Durduran, 2009). Integration of the Remote Sensing (RS) with Geographic Information Systems (GIS) are an essential tool for analyzing and extracting more reliable and consistent information by using satellite image as a base data (Bausmith and Leinhardth, 1997; Goodchild, 2001; Jaiswal et al., 2002; Durduran, 2009). This technique is quite useful monitoring of water bodies, long-term climate change and water pollution.
Sensitivity of multiresolution segmentation to spatial extent
2019, International Journal of Applied Earth Observation and GeoinformationCitation Excerpt :A scene can therefore be segmented at a variety of scale levels, ideally emulating the scale levels of geographical features distinguishable in that scene (Costa et al., 2018). However, the scale of segmentation outputs depends on the scale characteristics of the input imagery, namely spatial resolution and spatial extent as the most important components of the geospatial data (Goodchild, 2001). While the relationship between segmentation scale and spatial resolution has been relatively well addressed, the impact of spatial extent on segmentation results has been basically ignored.
A LiDAR-based decision-tree classification of open water surfaces in an Arctic delta
2015, Remote Sensing of EnvironmentCitation Excerpt :Selecting a boundary, whether in the field with a GPS receiver or during the digitization process, is susceptible to operator-based subjective decisions over boundary locations. Jahn and Dunne (1997) outline several criteria that influence subjective interpretations, which can occur in remote sensing feature detection; including biases resulting from previous experience or due to the level of vector generalisation during the digitization process (Goodchild, 2001; Sarti, Malladi, & Sethian, 2000). In most channel/bank situations, the boundary between land and water is easy to interpret.
Remote sensing and GIS analysis for mapping spatio-temporalchanges of erosion and deposition of two Mediterranean river deltas: The case of the Axios and Aliakmonas rivers, Greece
2015, International Journal of Applied Earth Observation and GeoinformationCitation Excerpt :Indeed, the detection of coastline changes using EO data has gained high importance over recent decades, since satellites are able to provide digital imagery in infrared spectral bands where the land–water interface can be well-defined (Ekercin, 2007; Durduran, 2010). Furthermore, EO data can be combined with geographic information systems (GIS) to provide an effective set of tools for analysing and extracting spatial information to support reliable and consistent decision making (Bausmith and Leinhardth, 1997; Goodchild, 2001; Jaiswal et al., 2002; Chen et al., 2005; Durduran, 2010; Gens, 2010). This integration with EO datasets provides an excellent framework for data capture, storage, synthesis measurements and analysis, all of which are essential in coastline changes investigations.