*3.2. Post-Classification Change Detection*

The random forest classifier produced four landcover maps for CSMR (Figure 4). Each map shows the extent of the five landcover classes—bare soil, high marsh, mid marsh, senesced, and subtidal—and represents different states of disturbance and recovery. The high marsh landcover had the most area in November 2017 and January 2018, and mid marsh vegetation was the largest landcover class in November 2018 and 2020 (Figures 5

and 6). Senesced vegetation and subtidal landcover experienced little change compared to bare soil, high marsh, and mid marsh vegetation (Figures 5 and 6).

**Figure 4.** Maps produced by the random forest classification. Maps depict the extent of bare soil, high marsh, mid marsh, senesced vegetation, and subtidal/water landcover. Top to bottom, left to right: (**a**) November 2017, (**b**) January 2018, (**c**) November 2018, and (**d**) November 2020. (Projection: NAD 84 UTM Zone 11).

**Figure 5.** Percent cover for each landcover class in CSMR stacked by date.

**Figure 6.** Difference of landcover class area (ha) compared to pre-flow conditions (November 2017). Bars are clustered by date.

The post-classification change detection showed a 19.1 ha increase in bare soil coverage between November 2017 and January 2018 (Figure 6). This amounted to 27.69 ha (~29%) of the marsh being covered in bare soil immediately following the debris flow (Figure 5). In November 2020, bare soil coverage decreased by 15.52 ha when compared to January 2018, a decrease of bare soil coverage to 12.17 ha (~16%) of total marsh area (Figure 5). Between November 2017 and November 2020, there was a 2.66 ha (~31%) net increase in bare soil coverage in the marsh (Figure 6).

On the other hand, overall marsh vegetation (high marsh + mid marsh) coverage experienced little change, with only a 0.1 ha (0.15%) net decrease in total vegetation coverage between November 2017 and November 2020 (Figure 5). However, when split into the two respective vegetation landcover classes, we find that high marsh vegetation coverage decreased as mid marsh vegetation increased (Figure 6). There were a few areas where change in landcover was prominently seen in the landscape, especially areas that were high marsh vegetation and/or near areas covered by bare soil that changed to mid marsh vegetation, such as near the salt pan in the northeast (Figure 7c) and some of the mudflat region in the western portion of the marsh (Figure 7a,b).

**Figure 7.** Maps highlighting areas where landcover change is most prominent. (**a**) High marsh to mid marsh conversion, (**b**) difference in mudflat extent, and (**c**) difference in salt pan (soil) and vegetated (mid marsh) perimeter. (Projection: NAD 84 UTM Zone 11, converted to lat/long).

#### **4. Discussion**

#### *4.1. Model Accuracy*

The accuracy metrics (mtry accuracy, kappa, producer's and user's error) suggested that landcover was accurately mapped by the random forest classifier and that the produced maps were reliable for use in change detection. The accuracy of the random forest classification is comparable to that of other wetland classifications. For example, Wu et al. (2020) also performed a random forest classification for a subtropical wetland that had a similar overall accuracy value of 92.96% compared to this study's average of 96.3% [17]. The model also performed as well as or better than classifications done using other methods such as maximum likelihood classification, iso-cluster unsupervised classification, or reclassification/recoding of vegetation indices [14,16,19]. The random forest classification done here was more accurate than the maximum likelihood classification done by Parihar et al. (2012), with an average accuracy of 96.3% vs. 76.5%, respectively [14]. When compared to Tuxen et al. (2007), the random forest did approximately the same or slightly better than reclassification, with reclassification having accuracy values of 81.4% and 96.3% compared to our average accuracy of 96.3% [19]. Iso-cluster classification on NDVI did somewhat better than the random forest, with accuracy values of 97.3%, 97.5%, 97.6%, and 98.0% for the respective dates [16].

High marsh had the highest accuracy, while the mid marsh class had high user's and producer's errors. As mid marsh is one of the classes that experienced the most change following the debris flow, any error present in its classification presents a problem; however, this error only exceeds 10% in January 2018 (user's: 17.1%, producer's: 10.5%) and is within acceptable margins for all other dates. Possible sources for the error include: (1) training data may have included misclassified pixels and introduced error to the corresponding landcover class, (2) pixels may have had values similar to that of multiple landcover classes, (3) resampled 20 m resolution Sentinel-2 bands may have still been too coarse to assess changes in the marsh, and (4) the use of a different spectral library for January 2018 may have led to lower accuracies for this date. To remedy this, the use of data from higher spatial resolution sensors may be useful in reducing the frequency of mixed pixels and the need for fractional cover. Additionally, higher spectral resolution may improve the building

of spectral libraries that can better differentiate between endmember classes, which then improves inputs into the random forest model.

#### *4.2. Landcover Change and Ecological Implications*

A majority of the landcover change occurred in bare soil, high marsh, and mid marsh vegetation. Bare soil area increased by 222% following the debris flows and dropped considerably in area by November 2018, likely due to the mechanical clean-up effort and king tides which removed a large amount of the sediment. Bare soil continued to decrease until there was only a net 31% increase in bare soil by November 2020. This may indicate that the marsh was still recovering from the debris flows and would continue to change over time.

Total vegetated area in the marsh showed little change over the 3 years, with only a 0.15% decrease in total marsh vegetation between November 2017 and November 2020. However, change was occurring, which is apparent when total vegetation is broken down into community types (high vs. mid marsh) and compared. High marsh (a mixed community of *Salicornia pacifica*, *Arthrocnemum subterminale*, *Frankenia salina*, and *Distichlis spicata*) area decreased by about the same amount that mid marsh (primarily only *S. pacifica*) area increased, creating the illusion of little change in vegetated area. The post-classification change detection showed that this shift from high to mid marsh community primarily occurred near areas that had been covered by bare soil following the debris flow.

The conversion to mid marsh vegetation from high marsh vegetation signifies a decrease in plant biodiversity as the community shifts from a mixed community to one that is largely composed solely of *S. pacifica*. This change in diversity poses some ecological challenges important to long-term wetland management. Studies have shown that a less diverse community is less resilient to the effects of disturbance, and spatial heterogeneity is important in the enhancement of the resilience of ecosystem functions [45]. Less resilience may dictate a need for more management intervention following disturbances, especially as the frequency of disturbances, such as wildfire, sea level rise and flooding, and landslide and mudslide damage, are predicted to increase with global climate change [7]. Studies have found that the addition of sediment via depositional events can promote plant growth by the delivery of mineral nutrients [5]. These nutrients may promote increased primary productivity by providing limiting nutrients. However, biodiversity has also been found to be positively linked to primary productivity and its temporal stability [46]. A trend of conversion from a mixed community of several plant species to one made of primarily only one plant species may have harmful repercussions for marsh productivity and other ecosystem services and functions. Determining whether this change to a less diverse community is a permanent change or only a short-term condition as the marsh recovers from the debris flow would require analysis of a longer time series of imagery over several years following the debris flows.

Sea level rise (SLR) is a challenge for the conservation of coastal wetlands, especially in developed regions, as rising sea levels contribute to coastal squeeze, leading to landcover change, fragmentation, and eventual loss of coastal marshes [47]. Sediment deposition and soil accretion are viewed as important processes for the offsetting of SLR [5,48]. However, our results imply that debris flow deposition is also leading to landcover and plant community change. While there are not clear policy implications from this work, beyond possibly assisted restoration, landcover change may become an important consideration when planning for the management of coastal wetlands that can be prone to depositional events; this study is an important example of how to inform those plans in the absence of field data.

#### *4.3. Limitations and Challenges*

As discussed above, the resolution of remotely sensed data is important in the assessment of the fine scale changes that occur in marsh ecosystems. Some Sentinel-2 bands do not have a native 10 m resolution and, therefore, have pixels that represent an average

of a larger mix of landcover types. Resampling, as conducted in this study, only splits this coarser data into smaller pixels and not into its disaggregated components. Therefore, landcover classification would benefit from a sensor where all bands have the same fine spatial resolution, such as unmanned aerial vehicles. High density LiDAR for more dates would also help in the assessment of biomass and vertical landcover differences such as water in channels vs. plants in upland regions. In addition, the baseline landcover prior to the debris flow was limited to a single date due to cloud cover, tide, and the length of historical record. Baseline assessments could be improved by using a sensor with a longer history or by using multiple dates per year. Ground reference data were also scarcely available due to the lack of prior field data to compare against classification of historical imagery and due to the COVID-19 pandemic limiting ability to go into the field to collect such data, hence the emphasis on other accuracy metrics. The results are also limited in their predictive power. For example, the rate at which sediment is being removed from the system or identification of whether the recently mapped sediment was the same sediment that had been deposited during the debris flow cannot be properly ascertained from these data. The processes leading to the conversion of high marsh to mid marsh vegetation also cannot be directly detected from these data.

#### **5. Conclusions**

Post-classification change detection tracked change in the five different landcover types in CSMR and found that mid marsh, high marsh, and bare soil landcover changed most dramatically in the dates studied. Total marsh vegetation (high marsh + mid marsh) cover returned to similar levels to those before the debris flows; however, assessing change as total marsh vegetation, as was the initial frame of the research question, does not lead to a robust conclusion. Areas that were covered in debris transitioned from high marsh vegetation to mid marsh vegetation despite total vegetated area remaining relatively unchanged. This transition has important ecological implications for marsh productivity and resilience to disturbance that continue after the debris is removed from the system.

The method used here shows promise in being applied to other depositional disturbances to wetland systems. For example, the random forest model identified important classification variables that can be used to classify marsh landcover without field-based data. The method can also serve as an important first step in the identification of regions of interest that can be used to inform field campaigns to address further questions that arise from the use of remote sensing (e.g., a field campaign to assess the factors that are leading to the transition from high marsh to mid marsh vegetation).

The Montecito Debris Flows provided a unique opportunity to study debris interactions with marshes in a context different than what is known from previous studies which more commonly focused on hurricane deposition. Data and information are an important part of making informed management decisions, and this study provides a successful demonstration of the use of post-classification change detection to assess wetland landcover response to an episodic event and the data that can be expected from such an assessment.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/rs14122819/s1, Table S1. Descriptive Statistics for Training Data Parameters, including min. and max. values, mean, and standard deviation for all variables used.

**Author Contributions:** G.D.S., D.A.R., J.P.M., and J.Y.K. contributed to study conception and design. Material preparation, data collection, and analyses were performed by G.D.S. The first draft of the manuscript was written by G.D.S., and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Science Foundation Graduate Research Fellowship to GDS (Grant number: 2139319).

**Data Availability Statement:** Original Sentinel-2 data obtained from USGS Earth Explorer, available here: https://earthexplorer.usgs.gov/ (accessed on 2 February 2021). Code and end products

generated during this study, such as layered images, classified maps, etc., available here: https: //github.com/German-Sil/carpinteria\_debris\_thesis (accessed on 10 November 2021).

**Acknowledgments:** The authors would like to acknowledge a few individuals and organizations: First, Kristin Morell for providing the FEMA LiDAR data set used in the January 2018 assessment and Andy Brooks for providing helpful insight into the landscape and work being done at Carpinteria Salt Marsh Reserve. Second, Alex Feldwinn for his computer support through the duration of the pandemic. And last, but certainly not least, the National Science Foundation Graduate Research Fellowship Program for funding the graduate work of G.D.S. that this manuscript is derived from.

**Conflicts of Interest:** The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
