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).
Elsevier Duque, JC.; Patiño Quinchía, JE.; Ruiz Fernández, LÁ.; Pardo Pascual, JE. (2015). Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data. Landscape and Urban Planning. 135:11-21.
AbstractThis paper contributes empirical evidence about the usefulness of remote sensing imagery to quantify the degree of poverty at the intra-urban scale. This concept is based on two premises: first, that the physical appearance of an urban settlement is a reflection of the society; and second, that the people who reside in urban areas with similar physical housing conditions have similar social and demographic characteristics. We use a very high spatial resolution (VHR) image from one of the most socioeconomically divergent cities in the world, Medellin (Colombia), to extract information on land cover composition using per-pixel classification and on urban texture and structure using an automated tool for texture and structure feature extraction at object level. We evaluate the potential of these descriptors to explain a measure of poverty known as the Slum Index. We found that these variables explain up to 59% of the variability in the Slum Index. Similar approaches could be used to lower the cost of socioeconomic surveys by developing an econometric model from a sample and applying that model to the rest of the city and to perform intercensal or intersurvey estimates of intra-urban Slum Index maps.
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