Monitoring and Analyzing the Seasonal Wetland Inundation Dynamics in the Everglades from 2002 to 2021 Using Google Earth Engine
2. Materials and Methods
2.1. Study Area
2.2. Data Extraction and Processing
2.3. Thresholding Method
2.4. Spatiotemporal Analysis
- Annual inundation distribution: To obtain an overview of the inundation extent, it is essential to summarize the temporal change patterns of inundated areas. The areas were quantified using the pixelArea function in GEE and organized by year and season. To better compare and extract larger or smaller areas over the 20-year time span, this product is illustrated as bar charts for both dry and wet season.
- Overlapping and inundation dynamics: To inspect the temporal dynamics of inundation, an in-depth analysis was undertaken by overlapping and comparing the maps of inundation in the subsequent years [46,47]. A total of 19 comparison results were generated from the annual maps from 2002 to 2021, where inundated pixels in the previous year appearing as non-inundated pixels in the latter year were defined as a decrease event, and as an increase event in the reverse cases. Moreover, the pixels appearing as inundated/non-inundated in two consecutive years were regarded as an unchanged event. To better understand the temporal dynamics, the pixels were converted to area in km2, and summarized as bar charts with three events (decrease, increase, and unchanged) annually and seasonally. Beyond that, this dynamic comparison was performed between the annual maps of every four years (2002–2006, 2006–2010, 2010–2014, and 2014–2018), except 2018–2021, which was conducted with a 3-year interval due to data constraints. The result of this part (focusing on spatial distribution) was visualized on maps in three events (decrease, increase, and unchanged) for wet and dry seasons. The spatial patterns illustrated by the generated maps reveal significant spatiotemporal traits, in which pixel values assigned to 1 contributed to increase, −1 contributed to decrease, and 0 contributed to unchanged inundation.
- Trend analysis: An integrated nonparametric approach was used in this study to examine and assess if there is a trend within the inundation dynamics over time. This kind of approach is widely used in the GIS community [48,49,50,51,52,53,54,55,56]. Our research used the Mann–Kendal test [57,58] to investigate whether the seasonal inundation area follows any trend that is statistically significant (when p-value is less than 0.05). If it consists of any statistically significant trend, this study used Sen’s slope estimator  to determine whether the trend runs through the downward or upward direction. To summarize the temporal trend of inundation dynamics, the annual inundation area from two seasons was applied, and the results were tabulated accordingly.
- Total inundation occurrences: An in-depth investigation was carried out based on the occurrence map by summing up the binary images from all of the 20 years. This was accomplished in GEE Code Editor using the sum function for image collection. As a result, the values of the pixels on the inundation occurrence map varied from 0 (without inundation occurrence) to 20 (always inundation occurrence). Based on the pixel value, the inundation occurrence can be categorized as very high (17–20), high (13–16), moderate (9–12), low (5–8), and very low (0–4). The spatial distributions of inundation can be obtained and compared in wet and dry season from this map.
- Inundation variance: As we had each year’s inundation map from 2002–2021, we considered measuring the seasonal inundation dispersion from its mean inundation. The important insight of this variance map is the location of the hotspot of continuous inundation deviation within the study area. As with the total inundation occurrence map, this map was also produced in GEE Code Editor with the help of the variance function. The pixel values on the inundation variance map vary from 0 (without variance) to 0.25 (maximum variance). Based on the pixel value, the inundation variance can also be categorized as high (0.19–0.25), moderate (0.13–0.18), low (0.07–0.12), and very low (0–0.06). The inundation variance map was produced for season-to-season comparison.
3. Results and Discussion
3.1. Temporal Dynamics of Inundation
3.2. Spatial Dynamics of Inundation
Data Availability Statement
Conflicts of Interest
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Hasan, I.; Liu, W.; Xu, C. Monitoring and Analyzing the Seasonal Wetland Inundation Dynamics in the Everglades from 2002 to 2021 Using Google Earth Engine. Geographies 2023, 3, 161-177. https://doi.org/10.3390/geographies3010010
Hasan I, Liu W, Xu C. Monitoring and Analyzing the Seasonal Wetland Inundation Dynamics in the Everglades from 2002 to 2021 Using Google Earth Engine. Geographies. 2023; 3(1):161-177. https://doi.org/10.3390/geographies3010010Chicago/Turabian Style
Hasan, Ikramul, Weibo Liu, and Chao Xu. 2023. "Monitoring and Analyzing the Seasonal Wetland Inundation Dynamics in the Everglades from 2002 to 2021 Using Google Earth Engine" Geographies 3, no. 1: 161-177. https://doi.org/10.3390/geographies3010010