Dynamics of coastline changes in Mexico

Valderrama-Landeros, Luis; Martell‐Dubois, Raúl; Ressl, Rainer; Silva-Casarín, Rodolfo; Cruz-Ramírez, Cesia J.; Muñoz-Pérez, Juan J.

doi:10.1007/s11442-019-1679-x

Cited by 31 publications

(15 citation statements)

References 23 publications

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“…On the other hand, it provides a basis for land management and predicting change trends. Its application fields and research scopes are also very wide, including areas such as land cover change detection [1], forest cover detection [2], wetland change detection [3], urban area expansion [4], disaster assessment [5], and coastline changes [6].…”

Section: Introductionmentioning

confidence: 99%

Task-Related Self-Supervised Learning For Remote Sensing Image Change Detection

Cai

Jiang

Yuan

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detection methods still suffer from the problem of inadequate pseudo-changes suppression and insufficient feature representation. In this work, an unsupervised change detection method based on Task-related Self-supervised Learning Change Detection network with smooth mechanism(TSLCD) is proposed to eliminate it. The main contributions include: (1) the task-related selfsupervised learning module is introduced to extract spatial features more effectively. (2) a hard-sample-mining loss function is applied to pay more attention to the hardto-classify samples. (3) a smooth mechanism is utilized to remove some of pseudo-changes and noise. Experiments on four remote sensing change detection datasets reveal that the proposed TSLCD method achieves the state-of-the-art for change detection task.

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Section: Introductionmentioning

confidence: 99%

Task-Related Self-Supervised Learning For Remote Sensing Image Change Detection

Cai

Jiang

Yuan

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Shorelines vary constantly, under the influence of two driving forces, namely natural processes and anthropogenic activities [3,4]. Shorelines change due to natural processes, include river deposition, sediment accretion, sea-level rise, waves, tides, wind erosion, etc., and often a priori knowledge of these processes is crucial for planning anthropogenic changes to the shorelines, and for understanding both short-and long-term human impacts on the coastal environment and ecosystems [5,6].…”

Section: Introductionmentioning

confidence: 99%

Shoreline Changes Along the Coast of Mainland China—Time to Pause and Reflect?

Tian

Goés

et al. 2020

IJGI

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Shoreline changes are of great importance for evaluating the interaction between humans and ecosystems in coastal areas. They serve as a useful metric for assessing the ecological costs of socioeconomic developmental activities along the coast. In this paper, we present an assessment of shoreline changes along the eastern coast of mainland China from ~1990 to 2019 by applying a novel method recently developed by us. This method which we call the Nearest Distance Method (NDM) is used to make a detailed assessment of shorelines delineated from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images. The results indicate a dramatic decline in natural shorelines that correspond to the rapid increase in the construction of artificial shorelines, driven by China’s economic growth. Of the entire coast of mainland China, the biggest change occurred along the Bohai Sea, where artificial shorelines expanded from 42.4% in ~1990 to 81.5% in 2019. Over this period, this study indicates that China lost > 60% of its biological shorelines, a trend that is especially worrisome because these include areas that were once biologically diverse and extremely rich. As anticipated, shoreline losses were greatest where regions of low economic value had been transformed to areas of higher economic value. Overall, this influence of human activities on shorelines in China is unprecedented. The repercussions of these changes on ecosystems, and the susceptibility of new shoreline developments to population growth and sea-level rise, need to be assessed urgently before additional changes are effected.

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“…Coastal modeling is represented by digital terrain models (DTMs) of high spatial resolution. Geomorphological state can be defined as a multitemporal surface [9,10]. Depending on the beach area to be mapped (dry zone, intertidal zone, or submerged zone), various techniques and methodologies can be used [11,12].…”

Section: Introductionmentioning

confidence: 99%

Beach Leveling Using a Remotely Piloted Aircraft System (RPAS): Problems and Solutions

Contreras-de-Villar

García²,

Muñoz-Pérez

et al. 2020

JMSE

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The size and great dynamism of coastal systems require faster and more automated mapping methods like the use of a remotely piloted aircraft system (RPAS) or unmanned aerial vehicle (UAV). This method allows for shorter intervals between surveys. The main problem for surveying using low-altitude digital photogrammetry in beach areas is their visual homogeneity. Obviously, the fewer the homologous points defined by the program, the lower the accuracy. Moreover, some factors influence the error performed in photogrammetric techniques, such as flight height, flight time, percentage of frame overlap (side and forward), and the number of ground control points (GCPs). A total of 72 different cases were conducted varying these factors, and the results were analyzed. Among the conclusions, it should be highlighted that the error for noon flights is almost double that for the early morning flights. Secondly, there is no appreciable difference regarding the side overlap. But, on the other side, RMSE increased to three times (from 0.05 to 0.15 m) when forward overlap decreased from 85% to 70%. Moreover, relative accuracy is 0.05% of the flying height which means a significant increase in error (66%) between flights performed at 60 and 100 m height). Furthermore, the median of the error for noon flights (0.12 m) is almost double that for the early morning flights (0.07 m) because of the higher percentage of grids with data for early flights. Therefore, beach levelings must never be performed at noon when carried out by RPAS. Eventually, a new parameter has been considered: the relationship between the number of GCPs and the surface to be monitored. A minimum value of 7 GCP/Ha should be taken into account when designing a beach leveling campaign using RPAS.

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Dynamics of coastline changes in Mexico

Cited by 31 publications

References 23 publications

Task-Related Self-Supervised Learning For Remote Sensing Image Change Detection

Task-Related Self-Supervised Learning For Remote Sensing Image Change Detection

Shoreline Changes Along the Coast of Mainland China—Time to Pause and Reflect?

Beach Leveling Using a Remotely Piloted Aircraft System (RPAS): Problems and Solutions

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