13A high geometric precision method for automated shoreline detection from Landsat TM and ETM+ 14 imagery is presented. The methodology is based on the application of an algorithm that ensures accurate 15 image geometric registration, and a new algorithm for sub-pixel shoreline extraction, both at sub-pixel 16 level. The analysis of the initial errors shows the influence of the differences in reflectance of land cover 17 types over the shoreline detection, allowing us to create a model to substantially reduce these errors. 18Three correction models were defined attending to the type of gain used in the acquisition of the original 19Landsat images. Error assessment tests were applied on three straight coast segments artificially 20 stabilized, all of them located in microtidal coastal areas. A testing set of 45 images (28 TM, 10 ETM 21 high-gain and 7 ETM low-gain) was used. The mean error obtained in shoreline location ranges from 1.22 22 to 1.63 m, and the RMSE from 4.69 to 5.47 m. Since the errors follow a normal distribution, then the 23 maximum error at a given probability can be estimated. The results obtained show the possibility to apply 24 this methodology over large coastal sectors in order to determine and analyse the evolution trend of these 25 dynamic areas. 26 27

This paper evaluates the accuracy of shoreline positions obtained from the infrared (IR) bands of Landsat 7, Landsat 8, and Sentinel-2 imagery on natural beaches. A workflow for sub-pixel shoreline extraction, already tested on seawalls, is used. The present work analyzes the behavior of that workflow and resultant shorelines on a micro-tidal (<20 cm) sandy beach and makes a comparison with other more accurate sets of shorelines. These other sets were obtained using differential GNSS surveys and terrestrial photogrammetry techniques through the C-Pro monitoring system. 21 sub-pixel shorelines and their respective high-precision lines served for the evaluation. The results prove that NIR bands can easily confuse the shoreline with whitewater, whereas SWIR bands are more reliable in this respect. Moreover, it verifies that shorelines obtained from bands 11 and 12 of Sentinel-2 are very similar to those obtained with bands 6 and 7 of Landsat 8 (−0.75 ± 2.5 m; negative sign indicates landward bias). The variability of the brightness in the terrestrial zone influences shoreline detection: brighter zones cause a small landward bias. A relation between the swell and shoreline accuracy is found, mainly identified in images obtained from Landsat 8 and Sentinel-2. On natural beaches, the mean shoreline error varies with the type of image used. After analyzing the whole set of shorelines detected from Landsat 7, we conclude that the mean horizontal error is 4.63 m (±6.55 m) and 5.50 m (±4.86 m), respectively, for high and low gain images. For the Landsat 8 and Sentinel-2 shorelines, the mean error reaches 3.06 m (±5.79 m).

No abstract

The urban spatial structure reflexes the local particularities produced during the historical 25 development of a city. Currently high spatial resolution imagery and LiDAR data are used to 26 derive numerical attributes to characterize the intra-urban structure and morphology. The urban-27 block boundaries have been frequently used to define the units to extract metrics from the 28 remotely sensed data. In this paper, we propose to complement those metrics with a set of 29 descriptors of the streets surrounding the urban blocks that numerically characterize the 30 geometry, presence of vegetation, and relationship with buildings. To carry out this purpose we 31 also introduce a methodology to define the street area related with an urban block from which 32 derive the urban metrics referred to the street. The assessment of these metrics is fulfilled using 33 one-way ANOVA procedure and decision trees classifier. These results reveal that street 34 metrics, and particularly those describing the street geometry, are suitable to enhance the 35 2 discrimination of complex urban typologies. Thus, the overall classification accuracy increases 36 from 72.7% to 81.1% when adding the street descriptors. The results of this study demonstrate 37 the usefulness of the metrics describing the street properties to complement the information 38 derived from the urban blocks and to improve the characterization of urban areas. 39 40 Highlights 41We propose a set of urban metrics to describe the streets with remotely sensed data 42 A methodology to relate the street space to urban blocks is defined 43Results show that street metrics are useful to improve the characterization of cities 44 45

No abstract