Assessment and Forecast of Shoreline Change Using Geo-Spatial Techniques in the Gulf of California
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Methodology
2.3. Remote Sensing Image Preprocessing
2.4. Shoreline Extraction
2.5. Digital Shoreline Analysis System
- (1)
- Creating a geodatabase within a Geographic Information System environment.
- (2)
- Extracting the shorelines stated in Section 2.4.
- (3)
- (4)
- Generating transects perpendicular to the baseline. Creating 33,225 transects at 100 m intervals and a smoothing distance of 3000 m for Sonora and 2000 m for the rest of the states (Figure 3).
- (5)
- Calculating the rates of change of the shoreline for which DSAS runs statistical models based on different measures between the historical positions of the shorelines for each of the transects, and generating different rates of change depending on the selected statistical methods [21].
- Net Shoreline Movement (NSM) is the distance between the first and last shoreline. A statistical parameter calculated for each transect launched perpendicularly to the coast, [28] and represented by the following equation, where is the distance in meters:
- Linear Regression Rate (LRR) comprises fitting the fewest squares regression line to multiple shoreline position points for a specific transect [28]. It is calculated by plotting the points where the coasts are intercepted by transects and calculating the linear regression, an equation:
- Weighted Linear Regression (WLR) determines a line of a better fit than the LRR, as it gives more weight to reliable data with a 95% confidence interval [54]. According to [54], the weight is defined as a function of the variance in the measurement uncertainty , and the weighting value ω is linked to the shore data before examining its rate of change [21].
- WLR considers ambiguity at each shoreline position when calculating a trend line. The weight assigned to each shore position is the inverse of the squared positional uncertainty. Therefore, the shorelines with higher uncertainty have less influence on the trend line than data points with higher uncertainty [38].
Uncertainty Estimation
2.6. Shoreline Forecast (2030, 2050) and Model Validation
3. Results and Discussions
3.1. Shoreline Change Analysis between 1981 and 2020
3.2. Analysis by State
3.3. Future Shoreline Forecast and Model Validation
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Satellite Data/Sensor | Year of Acquisition | Spatial Resolution (m) |
---|---|---|
Landsat 3-Multi-Spectral Scanner | 1981 | 60 |
Landsat 5-Multi Spectral Scanner and Thematic Mapper | 1993 | 30 |
Landsat 5-Multi Spectral Scanner and Thematic Mapper | 2004 | 30 |
Landsat 7-Enhanced Thematic Mapper | 2010 | 30 |
Landsat 8-Operational Land imager and Thermal Infrared Sensor | 2020 | 30 |
Statistics | Sinaloa | Sonora | BC | BCS | GC |
---|---|---|---|---|---|
Number of transects | 5937 | 10,906 | 6293 | 10,089 | 33,225 |
Average distance (m) | −39.61 | −176.9 | −105.46 | −4.32 | −81.57 |
Number of transects with a negative distance | 4417 | 9558 | 2749 | 5571 | 22,295 |
Maximum negative distance (m) | −515.90 | −2880.39 | −1650.21 | −176.94 | −2880.39 |
Average of all negative distances (m) | −105.48 | −210.54 | −281.73 | −30.95 | −157.17 |
Number of transects with a negative distance | 1520 | 1348 | 3544 | 4518 | 10,930 |
Maximum positive distance (m) | 1325.71 | 747.76 | 278.6 | 318.92 | 1325.71 |
Average of all positive distances (m) | 116.424 | 65.2 | 37.64 | 29 | 62.06 |
Statistics | Sinaloa | Sonora | BC | BCS | GC |
---|---|---|---|---|---|
Average rate (m/year) | −1.32 | −4.56 | −2.69 | −0.09 | 2.16 |
Maximum value erosion (m/year) | −26.54 | −118.36 | −42.83 | −29.93 | −118.36 |
Average of all erosional rates (m/year) | −3.23 | −5.44 | −7.39 | −0.76 | −4.20 |
Maximum value accretion (m/year) | 66.09 | 40.25 | 7.21 | 16.58 | 66.09 |
Average of all accretion rates (m/year) | 4.24 | 1.63 | 0.97 | 0.74 | 1.89 |
Statistics | Sinaloa | Sonora | BC | BCS | GC |
---|---|---|---|---|---|
Average rate (m/year) | −1.58 | −4.57 | −2.35 | −0.31 | −2.20 |
Percent of all transects that are erosional (%) | 78.64 | 88.05 | 47.83 | 66.14 | 72.19 |
Percent of all transects that have statistically significant erosion (%) | 33.54 | 36.36 | 3.91 | 10.14 | 21.91 |
Maximum value erosion (m/year) | −20.87 | −118.02 | −46.27 | −5.92 | −118.02 |
Average of all erosional rates (m/year) | −3.33 | −5.42 | −5.88 | −0.83 | −3.86 |
Percent of all transects that are accretion (%) | 21.36 | 11.95 | 51.17 | 33.86 | 27.62 |
Percent of all transects that have statistically significant accretion (%) | 5.12 | 1.84 | 5.13 | 3.14 | 3.46 |
Maximum value accretion (m/year) | 55.21 | 18.34 | 6.54 | 6.43 | 55.21 |
Average of all accretion rates (m/year) | 4.89 | 1.75 | 0.89 | 0.7 | 2.05 |
Statistics | Sinaloa | Sonora | BC | BCS | GC |
---|---|---|---|---|---|
Average rate (m/year) | −1.12 | −3.69 | −1.89 | −0.45 | 1.78 |
Percent of all transects that are erosional (%) | 68.87 | 77.84 | 53.54 | 69.38 | 69.06 |
Percent of all transects that have statistically significant erosion (%) | 18.41 | 22.14 | 1.57 | 7.54 | 13.21 |
Maximum value erosion (m/year) | −32.27 | −130.32 | −37.74 | −28.1 | −130.32 |
Average of all erosional rates (m/year) | −2.91 | −5.32 | −2.77 | −1.02 | −3.00 |
Percent of all transects that are accretion (%) | 31.13 | 22.16 | 46.46 | 30.62 | 30.93 |
Percent of all transects that have statistically significant accretion (%) | 5.51 | 1.12 | 2.19 | 1.71 | 2.32 |
Maximum value accretion (m/year) | 64.04 | 60.61 | 6.20 | 36.13 | 64.04 |
Average of all accretion rates (m/year) | 3.74 | 2.06 | 0.82 | 0.82 | 1.86 |
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Zambrano-Medina, Y.G.; Plata-Rocha, W.; Monjardin-Armenta, S.A.; Franco-Ochoa, C. Assessment and Forecast of Shoreline Change Using Geo-Spatial Techniques in the Gulf of California. Land 2023, 12, 782. https://doi.org/10.3390/land12040782
Zambrano-Medina YG, Plata-Rocha W, Monjardin-Armenta SA, Franco-Ochoa C. Assessment and Forecast of Shoreline Change Using Geo-Spatial Techniques in the Gulf of California. Land. 2023; 12(4):782. https://doi.org/10.3390/land12040782
Chicago/Turabian StyleZambrano-Medina, Yedid Guadalupe, Wenseslao Plata-Rocha, Sergio Alberto Monjardin-Armenta, and Cuauhtémoc Franco-Ochoa. 2023. "Assessment and Forecast of Shoreline Change Using Geo-Spatial Techniques in the Gulf of California" Land 12, no. 4: 782. https://doi.org/10.3390/land12040782
APA StyleZambrano-Medina, Y. G., Plata-Rocha, W., Monjardin-Armenta, S. A., & Franco-Ochoa, C. (2023). Assessment and Forecast of Shoreline Change Using Geo-Spatial Techniques in the Gulf of California. Land, 12(4), 782. https://doi.org/10.3390/land12040782