Remote Sensing Monitoring of Phragmites Treatment and Fish Habitat Restoration in Long Point, Lake Erie, Canada

Highlights

What are the main findings?

• Random forest classification of high-resolution satellite images was used to track herbicide treatment in wetlands over eight years between 2016 and 2024.
• Efforts on Phragmites Best Management Practices and fish habitat restoration was successful in the Great Lakes wetlands.

What are the implications of the main findings?

• Evaluation of the long-term efficacy of herbicide treatment in wetlands using remote sensing is possible.
• Wetland management resulted in a decrease in Phragmites cover; however, declines in non-target plant species caused by the impact on non-target vegetation need to be considered.

Abstract

The invasive expansion of Phragmites australis in coastal wetlands, including the Long Point wetland complex in Ontario, has led to significant declines in plant and wildlife diversity, impacting ecosystem functions. Despite ongoing management efforts, the long-term ecological outcomes of Phragmites control remain poorly understood. This study developed a framework to evaluate the long-term efficacy of herbicide treatment by tracking changes in target and non-target plant species and fish habitats in Long Point, Ontario, over an eight-year period (2016–2024). High-resolution satellite imagery from WorldView sensors was classified using a random forest algorithm, achieving over 94% mapping accuracy. Results showed a decrease in Phragmites cover (3–21%) and an increase in fish habitat area (7–58%) within treatment areas. However, some sites also experienced increases in Dead Vegetation (up to 23.6%) and declines in Grass/Herbaceous and Typha (up to 20.5% and 32%, respectively). These findings highlight both the success of Phragmites Best Management Practices and the temporary non-target effects on wetland vegetation.

1. Introduction

Phragmites australis (common reed, hereafter referred to as Phragmites), a non-native Eurasian lineage, is considered an invasive species in the Great Lakes region. Unlike the native Phragmites americanus, its capacity for rapid expansion, aggressive colonization, dense monoculture formation, and tolerance of variable hydrological conditions and disturbances confers a competitive advantage over other emergent vegetation for resources such as nutrients, light, and space [1,2,3]. Although Phragmites has been present in North America since before the 1900s, it was first recognized as invasive in the Great Lakes region in the 1980s. Formal acknowledgment and scientific confirmation of its non-native invasive status followed in the early 2000s, after genetic studies distinguished it from the native lineage [1,2,3,4,5]. The rapid spread of Phragmites since 1995 may be due to water level changes and/or increasing anthropogenic and natural disturbances in surrounding areas [1,6]. Due to its rapid growth and dispersal capability, Phragmites impedes the growth of native wetland vegetation and disrupts overall ecosystem functioning, biodiversity, and habitat for native species [1,2,4,7]. Recently, invasive Phragmites has become a concern throughout the coastal wetlands of the Great Lakes. Based on the mapping data collected between 2008 and 2010 [8], it is estimated that 25.7% of the coastal zone in the Great Lakes could possibly be invaded by Phragmites [9]. The Great Lakes region holds 20 percent of the Earth’s fresh water, and the wetlands in the Great Lakes provide important ecosystem services as well as habitat for many wildlife species, including those designated as Species at Risk (SAR) (endangered, threatened, or special concern). To reduce negative environmental impacts associated with the spread of Phragmites, measures have been taken to control Phragmites throughout the lower Great Lakes wetlands [1,4,8,10]; however, their long-term effectiveness remains poorly understood. The majority of studies occur within 1–2 growing seasons of treatment at the plot scale [11]. Typically, the effect of treatment is monitored using vegetation sampling with quadrats along transects and relies on field assessments of patch cover [12,13,14]. Without a framework to quantify the biological outcomes for Phragmites and non-target species, long-term impacts cannot be reliably studied. While traditional surveys are effective in identifying biodiversity and assessing plot-scale impacts [11], they do not evaluate the total impact on the landscape. Long-term monitoring of Phragmites treatment that provides information before, during, and after treatment, and spans the wetland complex, is fundamental for understanding the overall ecosystem.

Long-term monitoring of Phragmites treatment has been facilitated by the use of aerial and satellite imagery, which serves as a cost-effective tool for mapping and monitoring large-scale ecosystems such as the coastal wetlands of the Great Lakes. Given the availability of new sensors and data sources, various remote sensing techniques and applications have been developed in recent years. Multispectral optical and synthetic aperture radar satellite data are frequently used for generating and updating wetland maps [15,16,17,18] and for monitoring wetland water levels in Phragmites marsh [19,20]. Recently, various studies have shown promising results in identifying and mapping Phragmites using medium-resolution satellite imagery [8,21], high-resolution satellite imagery [22,23], airborne hyperspectral imagery [24], and drone imagery [25,26,27].

Medium-resolution satellite imagery (≥10 m, e.g., Landsat 8, Sentinel-2, ALOS-2) offers frequent, multi-season coverage; however, its effectiveness in mapping Phragmites is limited. Previous studies report reduced accuracies (<80%) for stands smaller than 100 m², with <50% cover density or plant heights below 1 m [8,21]. Optical sensors with resolutions coarser than 10 m often fail to distinguish Phragmites from other wetland vegetation such as Typha spp. (cattail, hereafter Typha) and meadow marsh due to spectral confusion [21,28]. Radar sensors, while advantageous for weather-independent data acquisition and vegetation structure detection, are also limited by speckle noise and restricted signal penetration in dense marshes [8,16,19,20]. These issues hinder their ability to delineate small patches or detect treatment effects. Consequently, the coarse spatial resolution of medium-resolution imagery constrains its use in evaluating management outcomes or identifying early regrowth following Phragmites control. Airborne and unmanned aerial vehicle (UAV) imagery achieve high classification accuracy [25,26,27,29] but are restricted by limited spatial coverage, making them impractical for landscape-scale assessments.

With the increasing availability of high-resolution optical satellite imagery, Phragmites mapping using such data presents a promising option for operational-scale monitoring and management. High-resolution imagery has demonstrated strong performance in distinguishing Phragmites stands [22,23] due to its ability to capture fine spatial details of vegetation composition, canopy density, and aquatic vegetation structure. For instance, WorldView (WV) imagery, with submeter panchromatic and eight multispectral bands at 2 m resolution, enhances the detection of subtle differences in vegetation and habitat conditions. Although mapping submerged aquatic vegetation (SAV) is influenced by factors such as water depth, light attenuation, and reflectance contrast, previous studies have reported encouraging results even in optically complex waters [30,31,32]. These characteristics make WV imagery particularly well suited for large-scale monitoring of Phragmites management and habitat restoration efforts.

Accurate wetland mapping can be achieved using field-verified reference data combined with robust supervised classification methods. Previous studies have employed algorithms such as maximum likelihood [8,20,21,22,23], support vector machines, random forest, and neural networks for Phragmites detection [15,26,27,28]. Beyond spectral information, incorporating auxiliary data, such as vegetation height and textural measures, can further enhance classification accuracy, as the tall, dense stems and feather-like seed heads of Phragmites are distinct from typical wetland vegetation [23,24,25,26,27].

Wetland species classification using high-resolution imagery can be implemented through pixel-based or object-based approaches. While most Phragmites mapping studies have relied on pixel-based methods, numerous studies have demonstrated that object-based image analysis, through segmentation of high-resolution imagery, can improve classification accuracy in shrub [33], forest [34], coastal [35], and Phragmites-dominated environments [23,25,26]. Although several studies have assessed short-term effects of Phragmites treatments at small spatial scales using high-resolution drone imagery [21,25], large-scale evaluations of treatment outcomes, particularly the responses of non-target species and fish habitat restoration across extensive wetlands, remain scarce. While some studies proposed their methods for long-term monitoring, most were limited to single-year observations (e.g., [25]) or focused on multitemporal mapping (e.g., [15,28]) without tracking treatment progress over time.

The Long Point coastal wetland complex is located on the northern shore of Lake Erie and extends along the peninsula and inner bay of the Long Point Bay sandspit. The Long Point wetland complex was recognized as a wetland of international significance under the Ramsar Convention in 1982. Fifty SAR species listed under the federal Species at Risk Act (SARA) have been identified within the Big Creek and Long Point National Wildlife Area, as well as a number of provincially listed Species at Risk on neighboring lands [5]. While the use of glyphosate-based herbicides for the management of Phragmites in aquatic habitats and coastal wetlands in the United States and elsewhere began in the 1980s [4,5,11,12,14], a pilot herbicide program initiated in Long Point in 2016 marked the first instance of such work being conducted in Canada [13,36]. Under this management program, Best Management Practices for Phragmites were implemented, which included herbicide (RoundUp Custom (a.i. glyphosate)) and a water-safe surfactant (AquaSurf Non-Ionic Spray Adjuvant) application on selected dates between August and October. Helicopters were used to apply herbicide to large, dense stands of Phragmites, whereas hydraulic sprayers affixed to Marsh Masters and backpacks were used to treat small and sparse stands, as well as Phragmites adjacent to sensitive features and SAR plants. The dead Phragmites was then cut or rolled and burned, where ecologically appropriate, during the winter months to speed decomposition. This Best Management Practice aims to facilitate native wetland plant regeneration and restore the wetland to a diverse and healthy ecosystem, thus re-establishing critical habitat for fish and wildlife within Long Point. Habitat availability is a critical component in the restoration of aquatic ecosystems [37]. Given that the rapid spread of Phragmites results in the degradation of fish habitats, detecting the extent and types of fish habitat within treatment areas following implementation of Best Management Practices is crucial for evaluating the habitat restoration process.

The goal of this research was to develop a remote sensing-based framework for evaluating the long-term efficacy of Phragmites management and fish habitat restoration. As the first study of its kind, it provides important insights into the effectiveness of Phragmites management throughout the Great Lakes coastal wetlands. By mapping spatial changes in Phragmites, wetland vegetation classes, and fish habitat over time, impacts of herbicide treatment on targeted and non-targeted vegetation are quantified. Using high-resolution WV satellite imagery acquired during the growing season over multiple years, including before and after Phragmites management efforts, and supported by drone imagery and ground survey data for training and validation, the analysis focus are as follows: (1) mapping and quantifying changes in available fish habitat through the conversion of emergent vegetation cover to open water, shallow water, and floating and submerged aquatic vegetation; (2) assessing the extent to which management, including herbicide application and mechanical treatment, reduced Phragmites cover; and (3) evaluating associated impacts on other wetland vegetation. Remote sensing monitoring was conducted at the Long Point wetland complex, Canada, using WV imagery from 2016, 2018, 2020, 2022, and 2024.

2. Study Area and Data

The Long Point study area in Ontario, Canada (referred to as Long Point) includes the Long Point Provincial Park, private properties, and federally and provincially protected areas, and comprises minimally developed coastal wetlands, sand dunes, and forests, spanning a total of 122.33 km² (Figure 1). The protected areas within the study site include Big Creek National Wildlife Area (BCNWA), Long Point National Wildlife Area (LPNWA), and Long Point Crown Marsh. BCNWA is sub-divided into the Hahn Marsh Unit (NWA-HU) and the Big Creek Unit (NWA-BC). LPNWA includes the Thoroughfare Unit (NWA-TH) and the Long Point Unit (NWA-LP). Both BCNWA and LPNWA are managed by the federal government. Long Point Provincial Park and Crown Marsh are managed by the Province of Ontario. The land between NWA-TH and NWA-LP is privately owned by the Long Point Company, a local hunt club, for its conservation value. Long Point provides breeding and foraging habitats to many wildlife including birds, fish, amphibians, and reptiles [38,39].

The primary Land Use/Land Cover (LULC) types in the study area include forests (including coniferous and deciduous trees and shrubs), sedge/grass meadows, marsh, barren (sand dunes and beaches), and built-up (houses and roads). The marsh wetlands are characterized by emergent vegetation, mainly Phragmites, Typha, grass, and other herbaceous plants. A study of Phragmites distribution using aerial photos from 1945 to 2013 indicated that Phragmites increased 14–37% annually in certain areas of Long Point [29], outcompeting other native communities. The most frequent plant communities that were replaced by invasive Phragmites include marsh meadow, Typha, other mixed emergent vegetation, grass/sedge, and native Phragmites [2]. Phragmites also affected the distribution and abundance of SAV. Phragmites management has been conducted on non-federal lands (e.g., Long Point Crown Marsh, Long Point Provincial Park, and Long Point Company Land) by the Ontario Ministry of Natural Resources and Forestry (OMNRF) and Ontario Ministry of Environment, Conservation and Parks (MECP) since 2016, and on federal lands (e.g., BCNWA and LPNWA) by the Environment and Climate Change Canada—Canadian Wildlife Service (ECCC-CWS) since 2019.

In this study, 11 classes were adopted to generate the LULC map (

Table 1. LULC classes defined in this study.

LULC Type	Class ID	Description
Bare Ground	1	Exposed soil, sand, rocks, dirt trails, and roads.
Built-up	2	Residential and commercial buildings, paved roads, and wharfs.
Dead Vegetation	3	Exterminated or naturally dead vegetation of all types.
Floating Vegetation	4	Pond-lily (Nuphar variegata) and white-water lily (Nymphaea odorata).
Grass/Herbaceous	5	Grass and sedge on the land and in the marsh, swamp loosestrife (Decodon verticillatus), marsh fern (Thelypteris palustris), and other short vegetation species.
Open Water	6	Clear, vegetation-free water area.
Phragmites	7	Dominated by invasive Phragmites, may contain some native Phragmites, sedge (Cyperaceae), wild rice (Zizania palustris), and Typha.
SAV	8	Canada waterweed (Elodea canadensis), sago pondweed (Stuckenia pectinata), American eelgrass (Vallisneria americana), muskgrass (Chara), Eurasian milfoil (Myriophyllum spicatum), naiad (Najas spp.), slender pondweed (Potamogeton pusillus), Richardson’s pondweed (Potamogeton richardsonii), elodea (Elodea canadensis), coontail (Ceratophyllum demersum), and water-stargrass (Heteranthera dubia).
Shallow Water	9	Water depths under two meters, sometimes containing sparse vegetation.
Tree/Shrub	10	Dominated by trees including paper birch (Betula papyrifera), eastern cottonwood (Populus deltoides), eastern white pine (Pinus strobus), eastern red cedar (Juniperus virginiana), northern red oak (Quercus rubra), basswood (Tilia americana), and some shrub and understory layer.
Typha	11	Dominated by cattail, may contain some sedge, wild rice, and Phragmites.

), they are as follows: Bare Ground, Built-up, Dead Vegetation, Tree/Shrub, Floating Vegetation, Grass/Herbaceous, Phragmites, Typha, SAV, Shallow Water, and Open Water. Open Water, SAV, Shallow Water, and Floating Vegetation are considered fish habitats. Preliminary image analysis indicated that meadow marsh, consisting of grasses, sedges, and emergent shrubs, were often confused with upland meadows due to spectral similarity in single-date imagery. To improve mapping accuracy, these two categories were combined into a general class called Grass/Herbaceous. The Dead Vegetation class was included to capture the extensive areas of dead Phragmites and other vegetation that appeared throughout the study area following treatment. In preliminary processing where this class was not used, dead vegetation was grouped with other low near-infrared reflection vegetation, such as SAV, and with non-vegetation classes. Tracking changes in Dead Vegetation also helped to understand wetland vegetation conversion processes and the impact on non-target plants during the treatment period. The Phragmites class included both the invasive and native subspecies. Although native Phragmites exist in Long Point, their coverage is known to be limited and sporadically mixed with invasive species; therefore, no effort was made to separate native from invasive Phragmites in this study.

To study the effectiveness of Phragmites management and fish habitat restoration, high-resolution optical satellite images from WV sensors were acquired in 2016, 2018, 2020, 2022, and 2024. WV tasking imagery from 2018, 2020, 2022, and 2024, acquired during the peak growing season (July–August), was prioritized to monitor wetland dynamics and vegetation conditions; however, persistent cloud cover in the Great Lakes region limited data availability. Consequently, a June 11 image was used in 2016 to supplement the missing July–August data. In total, three WV images were acquired for 2016, two each for 2018, 2020, and 2024, and one for 2022. Additional PlanetScope images from 2022 and 2024 were included to provide LULC information for the cloud-covered areas in the corresponding WV images. Together, these images provided complete coverage of the study area across all time periods. The characteristics of the images are summarized in

Table 2. Satellite images used in this study.

Year	Sensor	Spatial and Spectral Resolutions	Acquisition Dates	Coverage of Long Point
2016	WV-2, WV-3	1.6 m, 8 multispectral bands 0.3 m, Panchromatic band	2016-06-11, 2016-07-02, 2016-07-12	Eastern section Central section Western section
2018	WV-3	1.2 m, 8 multispectral bands 0.3 m Panchromatic band	2018-07-04, 2018-08-09	Western section Central and Eastern section
2020	WV-3	1.2 m, 8 multispectral bands 0.3 m Panchromatic band	2020-07-08, 2020-07-15,	Western and Central section Eastern section
2022	WV-3	1.2 m, 8 multispectral bands 0.3 m Panchromatic band	2022-07-25,	Whole section
	PlanetScop	3 m, 8 multispectral bands	2022-07-19	Western and Central section
2024	WV-3PlanetScope	1.2 m, 8 multispectral bands	2024-08-25 2024-08-30	Western and Central section Eastern section
		0.3 m Panchromatic band 3 m, 8 multispectral bands	2024-08-25	Eastern section

, and LULC classification was performed for the whole study area to support overall change detection.

Five subareas in which Phragmites was managed during 2016–2024 were selected to evaluate treatment effectiveness. These five subareas account for 54% of the total study area (

Table 3. Area of five subareas in the study area.

Subarea Name	Area (km2)
Subarea 1—NWA-BC	6.1
Subarea 2—Old Cut	3.62
Subarea 3—NWA-TH	4.26
Subarea 4—LPCL	24.85
Subarea 5—NWA-LP	28.35

) and include three subareas within federal lands (Subarea 1: NWA-BC, Subarea 3: NWA-TH, and Subarea 5: NWA-LP), one subarea within Long Point Crown Marsh on provincial lands (Subarea 2: Old Cut), and one subarea within private land—Long Point Company Land (Subarea 4: LPCL) (Figure 1). In Subareas 2 and 4, aerial and ground spraying were primarily conducted between 2016 and 2019, with additional small-scale ground spraying in 2023. However, no detailed records of treatment timing and coverage are available for these subareas. Management in the three national wildlife areas (Subareas 1, 3, and 5) began later, in 2019. Ground spraying was first applied to a small section of northern Subarea 1 and the eastern tip of Subarea 5 in 2019. Details on the timing and coverage of aerial and ground spraying in the three NWA subareas are summarized in

Table 4. Phragmites treatment timing and coverage in three NWAs (ASA: Air Spraying Area, GSA: Ground Spraying Area).

Year	NWA-BC		NWA-TH		NWA-LP
	ASA (km2)	GSA (km2)	ASA (km2)	GSA (km2)	ASA (km2)	GSA (km2)
2019	0	0.02	0	0	0	0.01
2020	0.42	0.19	0.53	0.07	0	0
2021	0.73	0.28	0.50	0.09	0.65	0.74
2022	0	0	0	0	0.84	1.75
2023	0	0.56	0	0.10	0	0.78

. In this paper, the term ‘Phragmites treatment’ refers to management using both herbicide and mechanical methods, unless otherwise specified. Notably, no burning occurred on federal lands, including the three NWAs.

From April to October during 2016–2024, multiple field visits were conducted to collect ground-truth data for the 11 LULC classes. This included vegetation surveys in various locations that were easily accessible either by road, all-terrain vehicle, or foot (Figure 2). Drone images were collected for larger areas that were not accessible (Figure 3). UAV data were collected using a fixed-wing eBee X equipped with real-time kinematic (RTK) capabilities and connected to the virtual reference system. Flights were conducted in a cross-grid pattern with 80% lateral and 60% longitudinal overlap at an altitude of 120 m above ground, producing orthomosaic images with a final resolution of 10 cm. Detailed information on UAV image acquisition, processing, and orthomosaic generation is provided in [27]. Based on these field visits and drone imagery, reference polygons were created for training and validation purposes, to assist in the classification of the 2016, 2018, 2020, 2022, and 2024 imagery.

3. Methodology

Remote sensing monitoring of Phragmites management and fish habitat restoration in Long Point includes image pre-processing, analysis, change detection, and accuracy assessment. An overview of the WV-based workflow for monitoring Phragmites is shown in Figure 4. Decision rules were applied to improve classification performance. Image pre-processing and analysis, including texture analysis, segmentation, and classification, were conducted at the individual image level to reduce computational demands of high-resolution imagery and to address challenges in mosaicking images acquired on different dates. The classification outputs from each section of the study area of the same year were mosaicked to generate the final LULC maps, which were then independently assessed for accuracy. Finally, post-classification change detection was applied to identify changes using the final maps.

Image pre-processing steps, including atmospheric correction, pan-sharpening, orthorectification, and image co-registration steps, were conducted to prepare images for the comparison, classification, and creation of final map products. All image pre-processing steps were completed using functions available in PCI Catalyst software (Version-Professional 2024). Atmospheric correction was applied to minimize radiometric and atmospheric effects and convert digital numbers from original images into surface reflectance values. This step was essential because images from multiple dates were used for classification and comparison. The correction standardized reflectance values, reduced variations from changing atmospheric conditions, and ensured consistent NDVI and NDWI calculations for classification. The higher-resolution panchromatic band of WV images was used to upscale the resolution of eight multispectral bands (Coastal, Blue, Green, Yellow, Red, RedEdge, NIR1, and NIR2) during pan-sharpening. All the subsequent analyses were performed with pan-sharpened imagery. Orthorectification was then applied to correct the terrain distortion with a digital elevation model (DEM) with a 30 m spatial resolution from the Shuttle Radar Topography Mission (SRTM). Image co-registration was performed to improve alignment between images. Without image registration, images from different years (e.g., 2016, 2018, 2020, 2022, and 2024) might not align well, potentially introducing errors in change detection. The PlanetScope sensor does not have panchromatic bands; therefore, no pan-sharpening was conducted for the 2022 and 2024 PlanetScope images. Following pre-processing, all WV images were resampled to an output cell resolution of 0.3 m by 0.3 m, and PlanetScope images to 3 m × 3 m.

Although multitemporal WV images in 2016, 2018, 2020, and 2024 were used for classification, each image covered a different section of the study area. Consequently, classification was performed on each area using a single-date image. To enhance the accuracy of these single-date classifications, texture measures and spectral indices were extracted. Texture measures based on second-order statistics derived from the Gray Level Co-Occurrence Matrix (GLCM) [40] have proven effective in distinguishing vegetation and improving classification accuracy in previous work [35]. GLCM texture measures were calculated from eight spectral bands using window sizes ranging from 5 × 5 to 45 × 45 to assess their contribution to classification accuracy. The evaluated texture measures included Homogeneity, Contrast, Dissimilarity, Mean, Standard Deviation, Entropy, Angular Second Moment, and Correlation. Texture measures were generated in PCI Catalyst with 128 values. Their relative importance for improving classification accuracy was subsequently assessed based on the widely used Boruta algorithms. The Boruta feature selection worked as a wrapper around random forest (RF) classification and was implemented using the Scikit-Image module in Python [41,42]. During the Boruta selection process, all texture measures were included in the statistical ranking, and only the top predictors, identified after 1000 iterations, were retained for classification. This selection was necessary to reduce both data dimensionality and the training time of classification models. Additionally, the importance of textural features, as well as the type of features characterized by different window sizes, was assessed in the classification. In addition to texture measures, spectral indices of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were also calculated to improve the classification accuracy. NDVI was calculated as the ratio between the red band and NIR1 band, and NDWI as the ratio between the green band and NIR1 band. All eight spectral bands, NDVI, NDWI, and the bands containing the selected texture measures were then included in the image classification process (

Table 5. RF classification parameters.

Parameters Name
Coastal Blue
Blue
Green
Yellow
Red
RedEdge
NIR1
NIR2
NDVI
NDWI
GLCM-Derived Texture Feature: Homogeneity
GLCM-Derived Texture Feature: Contrast
GLCM-Derived Texture Feature: Dissimilarity
GLCM-Derived Texture Feature: Mean
GLCM-Derived Texture Feature: Standard Deviation
GLCM-Derived Texture Feature: Entropy
GLCM-Derived Texture Feature: Angular Second Moment
GLCM-Derived Texture Feature: Correlation

).

Segmentation is the process of grouping neighboring pixels into meaningful objects in an image by considering properties such as shape, spatial arrangements, and spectral similarities. Through image segmentation, each segment can be treated as a homogeneous object by averaging the spectral information or other attributes of its pixels, which reduces heterogeneity and enables object-based image analysis. Object-based image analysis is particularly useful for extracting information from high-resolution imagery [35]. During segmentation, parameters related to object size, shape, and compactness were used to control segment size and boundaries. Only eight original spectral bands were used to limit computational demands and the complexity of segment properties. Segments were generated using PCI Catalyst software, and afterward, pixel values within each segment were averaged.

RF classification is an ensemble of decision tree classifiers that classify the image according to the majority vote of decision trees [43]. Compared to other methods, RF has lower sensitivity to training samples, easy parameter tuning, and high performance in classification accuracy in our preliminary tests and previous studies. It was therefore selected for use in this work [16,27,35]. RF classifications were performed using a Python script (Version-Python 3.9). Pixel-based RF classification was also performed on the 2016 images, and the results were compared with those of the object-based RF classification to evaluate performance. Both classification approaches used the same input data as follows: the eight spectral bands, NDVI, NDWI, and selected texture measures. A total of 200 decision trees were constructed, with the square root of the total number of image bands considered for each tree split. This configuration of trees and variable splits was identified in previous work as optimal for minimizing generalization error and maximizing model accuracy [16,27,35].

In the RF classification, training polygons play a critical role in determining classification accuracy. They were primarily delineated using field survey data and visual interpretation of UAV imagery in this study. In areas lacking field or UAV data, WV imagery was interpreted to enhance polygon coverage. For each class, training polygons were generated to ensure homogeneity, spatial representativeness, and sufficient sample size to capture variations in phenology, canopy density, and water turbidity (

Table 6. Summary of training polygons by LULC class.

Class	2016	2018	2020	2022	2024
Bare Ground	112	113	91	135	109
Built-up	85	89	63	70	72
Dead Vegetation	58	84	110	83	79
Floating Vegetation	141	126	153	155	240
Grass/ Herbaceous	226	240	195	156	224
Open Water	10	12	7	8	10
Phragmites	245	248	196	136	133
SAV	77	59	73	77	104
Shallow Water	72	87	77	70	51
Tree/Shrub	94	94	103	91	100
Typha	213	210	179	159	173

). For relatively homogeneous classes, such as Open Water, fewer training polygons were required. In contrast, more complex and heterogeneous classes, such as Phragmites, Grass/Herbaceous, and Typha required a greater number of smaller polygons to capture variability within the class. There were 1333 training polygons collected for the 2016 image classifications, 1362 polygons for 2018, 1272 polygons for 2020, 1140 polygons for 2022, and 1295 polygons for 2024. Fewer polygons were needed for 2022 because more homogeneous regions appeared after treatments conducted in previous years. Stratified random sampling was then used to select the reference points from reference polygons, maintaining a minimum distance of 10 m between points to avoid overrepresenting any single area. Reference samples from 2016, 2018, 2020, 2022, and 2024 were randomly divided into two sets as follows: a training dataset and a validation dataset. For each image classification, bootstrap sampling of two-thirds of the training dataset was used to select training samples, while the remaining points were used as testing samples. After classifying all images for a given year, the results were mosaicked to produce an LULC map for the entire study area. During the classification, it was found that shadows from trees and buildings were misclassified as Typha or Phragmites, and some bare soil areas were misclassified as Dead Vegetation. Therefore, the following decision rules were applied to improve the separation of non-wetland classes from wetland classes due to the mis-classification of shadow from trees and buildings as Typha or Phragmites, and mis-classification of some bare soil area as Dead Vegetation.

(1) If a pixel was identified as Typha or Phragmites and located within 5 m of Forest/Shrub and within 5 m of either Built-up or Bare Ground, then the pixel belonged to the Forest/Shrub class.

(2) If a pixel was identified as Dead Vegetation and located within 5 m of either Built-up or Bare Ground, then the pixel belonged to Bare Ground.

(3) If an object size was smaller than 30 pixels (8-connected neighbors), then it was merged into the neighboring classes.

Classification performance was evaluated using an independent validation dataset. A standard error matrix approach was used to calculate producer’s, user’s, and overall accuracy. The F1-score, defined as the harmonic mean of user’s accuracy and producer’s accuracy, was also computed to provide a balanced measure of the RF model’s performance. The 2022 and 2024 PlanetScope images were also classified using the same methodology, and the results were used to fill in the gaps for the cloud-covered areas in the corresponding WV images. After classification of images for each year, a post-classification change detection approach was applied. Change maps indicating areas of change in classes were then generated by comparing the classification maps from different dates. The classes of Open Water, SAV, Shallow Water, and Floating Vegetation were commonly recognized as important fish habitats. Although the Long Point wetland serves as an important fish habitat that supports numerous species and functions as a critical nursery and feeding ground, the lack of data on fish presence, hydrological connectivity, and other favorable conditions during the Phragmites management period necessitated defining Open Water, Shallow Water, SAV, and Floating Vegetation as potential fish habitats. The changes from non-fish habitat to potential fish habitat, from Phragmites to potential fish habitat, and from others to Phragmites were detected within the following time periods: 2016–2018, 2018–2020, 2020–2022, and 2022–2024.

4. Results

4.1. Results of RF Classification

Boruta selection of texture measures was based on the classification of the 12 July 2016 WV image because this image had good coverage of all the classes in the study area. Only the top eight most important texture features were selected to be included in the final image classification for computational efficiency. The texture features included are as follows: mean (7 × 7) from band NIR1, mean (17 × 17) from bands RedEdge and NIR1, contrast and mean (25 × 25) from bands RedEdge and NIR1, and contrast (25 × 25) from band NIR2. NIR1, NIR2, and RedEdge bands were found to be sensitive to vegetation and helpful in separating vegetation such as Phragmites and trees from the non-vegetation neighboring pixels. In particular, the NIR1 band was effective in capturing the unique texture patterns of buildings with a 7 × 7 window size, while the NIR1, NIR2, and RedEdge bands performed well in characterizing Phragmites patches with a 17 × 17 window size and trees with a 25 × 25 window size.

After testing different segmentation sizes with the parameters of scale, shape, and compactness, the scale of 25, shape of 0.7, and compactness of 0.3 were found to best keep the feature boundary and produced reliable classification results. Therefore, all subsequent classifications used these segmentation settings. Comparison between the pixel-based and object-based classification results of the 12 July 2016 image indicated that the latter method achieved 4% higher overall accuracy (

Table A1. Accuracy for pixel-based RF classification of the 2016 WV image (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T; User’s accuracy (%)—UA; Producer’s accuracy (%)—PA; Overall accuracy (%)—OA; F1 score—F1).

Validation Label	Classification Class											UA	PA	OA	F1
	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T
BG	34	2	1	0	0	0	0	0	0	0	0	79	92	91	0.85
B	3	15	0	0	0	0	0	0	0	0	0	79	83		0.81
DV	4	1	13	0	0	0	0	0	0	0	1	93	68		0.79
FV	2	0	0	29	1	0	0	0	0	0	0	97	91		0.94
GH	0	0	0	1	78	0	5	0	0	2	2	90	89		0.89
OW	0	1	0	0	0	174	0	0	1	0	0	97	99		0.98
PH	0	0	0	0	1	0	111	0	0	0	4	88	96		0.92
SAV	0	0	0	0	0	0	0	37	0	0	0	86	100		0.93
SW	0	0	0	0	0	6	0	4	37	0	0	97	79		0.87
TS	0	0	0	0	7	0	8	0	0	42	6	95	67		0.79
T	0	0	0	0	0	0	2	2	0	0	108	89	96		0.93

and

Table A2. Accuracy for segment-based RF classification of the 2016 WV image (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T; User’s accuracy (%)—UA; Producer’s accuracy (%)—PA; Overall accuracy (%)—OA; F1 score—F1).

Validation Label	Classification Class											UA	PA	OA	F1
	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T
BG	34	2	1	0	0	0	0	0	0	0	0	92	92	95	0.92
B	1	17	0	0	0	0	0	0	0	0	0	77	94		0.85
DV	2	0	16	1	0	0	0	0	0	0	0	94	84		0.89
FV	0	0	0	31	0	0	0	1	0	0	0	94	97		0.95
GH	0	0	0	0	84	0	1	0	0	3	0	95	95		0.95
OW	0	3	0	0	0	173	0	0	0	0	0	99	98		0.99
PH	0	0	0	1	2	0	109	0	0	1	3	93	94		0.94
SAV	0	0	0	0	0	0	0	36	1	0	0	90	97		0.94
SW	0	0	0	0	0	2	0	2	43	0	0	98	91		0.95
TS	0	0	0	0	2	0	7	0	0	54	0	92	86		0.89
T	0	0	0	0	0	0	0	1	0	1	110	97	98		0.98

). As such, the object-based classification using RF classifier was adopted for all following images. Accuracy of classification results is reported in Appendix A, Appendix B and Appendix C.

The final maps generated for the entire study area for the years of 2016, 2018, 2020, 2022, and 2024 were mosaics of the classification results from individual images of corresponding years (Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9). For example, the 12 July 2016 WV image covering the western area, the 2 July 2016 WV image covering the central area, and the 11 June 2016 WV image covering the eastern area were combined to encompass the whole study area for 2016. The final maps were evaluated using the independent validation datasets previously generated. The object-based classification produced maps with overall accuracy ranging from 94% to 96% for all years (Table A2,

Table A3. Accuracy for segment-based RF classification of the 2018 WV image (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T; User’s accuracy (%)—UA; Producer’s accuracy (%)—PA; Overall accuracy (%)—OA; F1 score—F1).

Validation Label	Classification Class											UA	PA	OA	F1
	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T
BG	25	4	2	1	0	0	0	0	0	0	0	89	78	94	0.83
B	1	19	0	0	0	0	0	0	0	0	0	83	95		0.88
DV	1	0	30	0	0	0	0	0	0	0	0	88	97		0.92
FV	0	0	0	31	0	0	0	0	0	0	0	94	100		0.97
GH	0	0	1	1	120	0	2	2	0	4	2	89	91		0.9
OW	0	0	0	0	0	125	0	0	1	0	0	100	99		1
PH	0	0	0	0	3	0	119	0	0	2	3	96	94		0.95
SAV	0	0	0	0	0	0	0	23	0	0	0	88	100		0.94
SW	0	0	1	0	0	0	0	1	78	0	0	99	97		0.98
TS	1	0	0	0	12	0	3	0	0	57	0	88	78		0.83
T	0	0	0	0	0	0	0	0	0	2	122	96	98		0.97

,

Table A4. Accuracy for segment-based RF classification of the 2020 WV image (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T; User’s accuracy (%)—UA; Producer’s accuracy (%)—PA; Overall accuracy (%)—OA; F1 score—F1).

Validation Label	Classification Class											UA	PA	OA	F1
	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T
BG	50	1	1	0	0	0	0	0	0	0	0	98	96	94	0.97
B	0	27	1	0	0	0	0	0	0	0	0	96	96		0.96
DV	1	0	66	0	0	0	0	0	0	0	0	81	99		0.89
FV	0	0	3	36	0	0	0	0	0	0	0	100	92		0.96
GH	0	0	2	0	76	0	2	0	0	2	0	95	93		0.94
OW	0	0	0	0	0	138	0	0	1	0	0	96	99		0.98
PH	0	0	2	0	0	0	86	0	0	2	2	90	93		0.91
SAV	0	0	1	0	0	0	0	44	1	0	0	98	96		0.97
SW	0	0	0	0	0	6	0	1	61	0	0	97	90		0.93
TS	0	0	0	0	3	0	5	0	0	53	1	91	85		0.88
T	0	0	5	0	1	0	3	0	0	1	85	97	89		0.93

,

Table A5. Accuracy for segment-based RF classification of the 2022 WV image (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T; User’s accuracy (%)—UA; Producer’s accuracy (%)—PA; Overall accuracy (%)—OA; F1 score—F1).

Validation Label	Classification Class											UA	PA	OA	F1
	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T
BG	53	2	3	0	0	8	0	0	0	0	0	96	80	94	0.88
B	0	21	0	0	0	1	0	2	0	0	0	91	88		0.89
DV	1	0	45	0	0	0	0	0	0	0	0	94	98		0.96
FV	0	0	0	37	0	0	0	0	0	0	0	97	100		0.99
GH	0	0	0	1	79	0	1	0	0	0	0	96	98		0.97
OW	1	0	0	0	0	115	0	0	0	0	0	89	99		0.94
PH	0	0	0	0	1	0	54	0	0	2	0	95	95		0.95
SAV	0	0	0	0	0	0	0	56	1	0	0	97	98		0.97
SW	0	0	0	0	0	5	0	0	82	0	0	99	94		0.96
TS	0	0	0	0	1	0	2	0	0	50	6	96	85		0.9
T	0	0	0	0	1	0	0	0	0	0	69	92	99		0.95

and

Table A6. Accuracy for segment-based RF classification of the 2024 WV image (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T; User’s accuracy (%)—UA; Producer’s accuracy (%)—PA; Overall accuracy (%)—OA; F1 score—F1).

Validation Label	Classification Class											UA	PA	OA	F1
	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T
BG	108	5	2	0	0	0	0	0	0	0	0	97	94	96	0.96
B	2	62	2	0	0	0	0	0	0	0	0	91	94		0.93
DV	0	0	71	1	4	0	3	0	0	0	0	92	90		0.91
FV	0	0	1	231	0	0	0	0	0	0	1	99	99		0.99
GH	1	0	1	1	214	0	1	0	0	4	1	97	96		0.96
OW	0	0	0	0	0	15	0	0	0	0	0	94	100		0.97
PH	0	0	0	0	3	0	126	0	0	2	1	91	95		0.93
SAV	0	1	0	0	0	0	2	105	1	1	0	100	95		0.98
SW	0	0	0	0	0	1	0	0	54	0	0	98	98		0.98
TS	0	0	0	1	0	0	5	0	0	99	1	92	93		0.93
T	0	0	0	0	0	0	2	0	0	2	173	98	98		0.98

). The narrow range of overall accuracy percentages in the final maps indicated that the overall accuracy achieved with RF was consistent and representative of the final maps. The producer’s accuracy ranged from 78% to 100%, and the user’s accuracy ranged from 77% to 100%. The lowest producer’s accuracy was from the Bare Ground and Tree/Shrub classes in the 2018 image, and the lowest user’s accuracy was from the Built-up class in the 2016 image. Based on the F1-score, Bare Ground, Built-up, and Tree/Shrub classes produced relatively lower accuracy than other classes.

4.2. LULC and Temporal Changes Across the Entire Study Area

The area and percentage of each LULC class were calculated based on the classification results (

Table 7. The cover area of each LULC class in the study area (total area: 122.33 km²).

Class		2016	2018	2020	2022	2024
Bare Ground	Area (km2)	4.69	4.49	4.4	4.87	4.02
	Percent (%)	3.83	3.67	3.55	3.98	3.29
Built-up	Area (km2)	1.05	0.57	0.4	0.66	0.79
	Percent (%)	0.86	0.47	0.33	0.54	0.65
Dead Vegetation	Area (km2)	1.75	2.46	9.79	3.8	1.28
	Percent (%)	1.43	2.01	8	3.1	1.04
Floating Vegetation	Area (km2)	2.58	1.89	3.35	8.58	5.03
	Percent (%)	2.11	1.55	2.73	7.01	4.11
Grass/Herbaceous	Area (km2)	14.23	15.11	11.52	11.12	15.41
	Percent (%)	11.64	12.35	9.41	9.09	12.59
Open Water	Area (km2)	26.2	31.42	24.48	27.52	27.73
	Percent (%)	21.42	25.69	20.01	22.49	22.66
Phragmites	Area (km2)	8.65	7.55	7.95	3.98	6.26
	Percent (%)	7.07	6.18	6.5	3.25	5.12
SAV	Area (km2)	8.25	10.44	20.2	17.32	15.87
	Percent (%)	6.75	8.54	16.51	14.16	12.97
Shallow Water	Area (km2)	24.17	17.68	21.26	23.49	22.01
	Percent (%)	19.76	14.45	17.38	19.2	17.99
Tree/Shrub	Area (km2)	8.9	9.26	7.5	8.38	8.73
	Percent (%)	7.27	7.57	6.13	6.85	7.13
Typha	Area (km2)	21.86	21.45	11.55	12.62	15.2
	Percent (%)	17.87	17.53	9.44	10.31	12.43

). Within the study area, the percent area of the combined Bare Ground and Built-up classes ranged from 3.9% to 4.7%, and Tree/Shrub ranged from 6.1% to 7.6% during the period between 2016 and 2024. With less than 1.5% of variation in area, Bare Ground, Built-up, and Tree/Shrub classes were considered stable. Among all classes, the largest increase in area occurred in the SAV class with an increase of 9.7%, and the largest decrease in area occurred in the Typha class with a decrease of 8.5% of the total area between 2016 and 2020. No class showed a consistent trend of either increase or decrease across the years. Dead Vegetation and SAV increased steadily from 2016 to 2020 and then declined between 2020 and 2024, whereas Typha showed the opposite trend, decreasing from 2016 to 2020 and increasing from 2020 to 2024. The coverage of Phragmites varied over the study period, ranging from a maximum of 7.1% in 2016 to a minimum of 3.3% in 2022. Dead Vegetation and SAV increased by 6.6% and 9.7%, respectively, between 2016 and 2020, and then decreased by 7% and 3.5%, respectively, between 2020 and 2024. The coverage of Shallow Water and Open Water varied during the study period, the combined coverage of both classes ranging from a maximum of 41.7% in 2022 to a minimum of 37.4% in 2020. The coverage of Floating Vegetation reached a maximum of 7% in 2022 and a minimum of 1.6% in 2018. The potential fish habitat area was created by combining the area extent of Open Water, SAV, Shallow Water, and Floating Vegetation. Potential fish habitat area increased steadily from 2016 to 2022, accounting for 50%, 50.5%, 56.7%, and 62.9% of the total study area in 2016, 2018, 2020, and 2022, respectively, before declining to 57.7% in 2024.

The rate of change from non-Phragmites to Phragmites varied across periods, ranging from a minimum of 2% during 2020–2022 to a maximum of 4.6% during 2022–2024 (

Table 8. Conversion of non-Phragmites to Phragmites between 2016 and 2024.

Period	Area (km2)	Percentage of Total Area (%)
2016–2018	3.69	3.02
2018–2020	4.38	3.58
2020–2022	2.49	2.04
2022–2024	5.58	4.56

). Typha, Grass/Herbaceous, Tree/Shrub, and Dead Vegetation were the main land cover classes that contributed to the increase in Phragmites across the whole study area. Between 2016 and 2018 and between 2018 and 2020, over 90% of the newly grown Phragmites originated from areas previously occupied by Typha, Grass/Herbaceous, and Tree/Shrub vegetation. In particular, Typha accounted for 35.8%, 43.3%, 18.1%, and 12.7% of the area that converted to Phragmites during 2016–2018, 2018–2020, 2020–2022, and 2022–2024, respectively. Many changes were also observed between the three emergent vegetation cover types of Phragmites, Typha, and Grass/Herbaceous.

Dead Vegetation accounted for 16.2% and 14.4% of new Phragmites between 2020 and 2022 and 2022–2024, respectively, indicating regrowth from previously treated areas. In contrast, less than 5% of new Phragmites originated from Floating Vegetation and SAV before 2022, whereas 14.4% and 21.5% of new growth from these sources during 2022–2024, suggesting that Phragmites expansion increasingly occurred in non-emergent vegetation areas. Changes in SAV, Floating Vegetation and Shallow Water between years were also observed, as well as changes between non-vegetation class and vegetated classes in some locations. In addition, Dead Vegetation, Typha, and Phragmites were the primary land cover types that converted into potential fish habitat. Between 2016 and 2020, over 35% of the increase in potential fish habitat originated from the Typha class, while between 2020 and 2022, 53% of increase came from Dead Vegetation.

Assuming that the total water area, including both Shallow Water and Open Water, is primarily influenced by natural water level fluctuations rather than management activities, its extent should reflect water level variation rather than Phragmites management. The combined Shallow Water and Open Water extent in 2016 was used as the baseline reference prior to large-scale management, and subsequent changes were examined within the same area. Results showed that the total water area decreased by approximately 8–17% between 2018 and 2024, with most of the reduced water area transitioning into SAV (

Table 9. Changes occurred to the water area in 2016.

	Reference (2016)	2018	2020	2022	2024
Combined water area (km2)	50.37	46.28	41.84	45.14	45.54
Percentage (compared to 2016)	100	91.87	83.07	89.62	90.41
Area changed into SAV (km2)	0	3.2	7.77	4.32	3.84
Percentage changed into SAV	0	6.35	15.43	8.57	7.62

). However, when comparing the combined water area within the baseline reference area (46.28 km² in 2018, 41.84 km² in 2020, 45.14 km² in 2022, and 45.54 km² in 2024) to that within the entire study area (49.10 km² in 2018, 45.74 km² in 2020, 51.01 km² in 2022, and 49.74 km² in 2024), we found that new water areas emerged in the managed zones that did not exist before 2018. These newly formed water areas resulted from the conversion of previous non-water areas due to Phragmites management.

4.3. Phragmites Management Effects and Changes Across Subareas

Although Phragmites accounted for only about 7% of the total study area in 2016, its density was higher in the subareas undergoing Phragmites treatment (NWA-BC, NWA-TH, NWA-LP, Old Cut, and LPCL) (Figure A1, Figure A2, Figure A3, Figure A4, Figure A5, Figure A6, Figure A7, Figure A8, Figure A9 and Figure A10). For example, before treatment, Phragmites cover reached 21.5% in NWA-BC and 17% in NWA-TH (Figure A1 and Figure A3,

Table 10. Phragmites distribution in the five subareas.

Year	NWA-BC		Old Cut		NWA-TH		LPCL		NWA-LP
	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)
2016	1.31	21.45	0.59	16.42	0.72	16.97	1.43	5.77	3.35	11.82
2018	1.14	18.67	0.11	3.04	0.77	18.12	0.61	2.44	3.77	13.29
2020	0.81	13.29	0.03	0.73	0.95	22.41	1.35	5.42	3.93	13.85
2022	0.18	2.95	0.01	0.39	0.03	0.71	0.68	2.72	2.67	9.43
2024	0.09	1.41	0.03	0.89	0.73	17.15	2.89	11.64	1.86	6.57

). The classification results indicated that the conversion rate from non-Phragmites to Phragmites between 2016–2018, 2018–2020, 2020–2022, and 2022–2024 varied by subarea (

Table 11. Conversion of non-Phragmites to Phragmites in the five subareas.

Year	NWA-BC		Old Cut		NWA-TH		LPCL		NWA-LP
	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)
2016–2018	0.33	5.48	0.07	2.01	0.16	3.83	0.39	1.56	2.02	7.13
2018–2020	0.25	4.13	0.03	0.7	0.37	8.59	1.17	4.71	2.01	7.08
2020–2022	0.09	1.41	0.01	0.37	0.02	0.48	0.45	1.83	1.64	5.77
2022–2024	0.06	1.06	0.03	0.88	0.72	16.94	2.65	10.66	1.51	5.33

). The Old Cut subarea had the lowest new Phragmites growth during 2016–2024 because the Phragmites treatment was applied between 2016 and 2019. After three years of Phragmites treatment, both the distribution and transformation from non-Phragmites to Phragmites were the lowest in 2022 and continued to remain at low levels in 2024. As such, less than 1% of the area converted from non-Phragmites to Phragmites during 2018–2020, 2020–2022, and 2022–2024. Although the LPCL subarea was treated between 2016 and 2019, Phragmites cover fluctuated throughout the study period. For instance, both Phragmites coverage and the conversion from other wetland classes declined in 2018, increased in 2020, decreased again in 2022, and then rose sharply in 2024. For the three NWA subareas, changes in Phragmites cover reflected the treatments applied between 2019 and 2023. Most treatments in NWA-BC and NWA-TH occurred during 2020–2021, while treatments for NWA-LP were mainly conducted between 2021 and 2023 (Table 4). The largest decrease in Phragmites cover across NWA-BC, NWA-TH, and NWA-LP were observed in 2022 (Figure A6, Figure A8 and Figure A10). Declines continued in NWA-BC and NWA-LP through 2024, whereas Phragmites cover increased again in NWA-TH in 2024. Despite these changes, NWA-TH still had the highest Phragmites coverage among the five treated subareas in 2024.

Across all subareas, Phragmites most commonly replaced Typha. For example, between 2018 and 2020, Typha contributed to the conversion into Phragmites in 48% of the change area in NWA-BC, 71.3% in Old Cut, 65.7% in NWA-TH, 62.5% in LPCL, and 33% in NWA-LP. Phragmites growth was observed in both treated areas and untreated areas. To understand the efficacy of Phragmites management efforts, we also calculated the remaining Phragmites coverage in 2024 and compared it to the coverage prior to any treatment. In three NWAs (NWA-BC, NWA-TH, and NWA-LP), peak Phragmites cover reached 1.31 km², 0.95 km², and 3.93 km², respectively. Between 2019 and 2023, the total treated areas were 2.21 km² in NWA-BC, 1.2 km² in NWA-TH, and 4.76 km² in NWA-LP, with some locations receiving multiple treatments during the study period. In those directly treated areas, only 0.06 km² in NWA-BC, 0.39 km² in NWA-TH, and 0.57 km² in NWA-LP still contained Phragmites.

Unlike the decrease observed in Phragmites across five subareas, potential fish habitat expanded to its maximum coverage in 2022 before slightly declining in 2024. The Old Cut subarea had the highest potential fish habitat coverage among the five subareas, increasing from 18.7% in 2016 to 64.8% in 2024. The other four subareas also showed a 5–18% increase in potential fish habitat over the study period (

Table 12. Potential fish habitat distribution in five subareas.

Year	NWA-BC		Old Cut		NWA-TH		LPCL		NWA-LP
	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)
2016	1	16.43	0.68	18.72	1.14	26.69	11.71	47.11	4.89	17.26
2018	0.91	14.9	1	27.71	1.19	27.88	11.6	45.72	4.56	16.08
2020	1.44	23.56	2.29	63.33	1.43	33.58	14.34	57.72	5.48	19.33
2022	1.8	29.98	2.79	77.04	2.38	55.9	18.76	75.5	6.88	24.27
2024	1.64	26.86	2.34	64.77	1.91	44.86	14.54	58.5	6.25	22.05

). All five subareas experienced high rates of conversion from non-fish habitat to potential fish habitat between 2018 and 2020 and between 2020 and 2022. The rate of change from non-fish habitat to potential fish habitat varied in each area, with Old Cut showing the greatest change in 2018–2020, where 37.2% of its area converted, increasing potential fish habitat coverage to 63.3% (

Table 13. Conversion of non-fish habitat to potential fish habitat in five subareas.

Year	NWA-BC		Old Cut		NWA-TH		LPCL		NWA-LP
	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)	Area (km2)	Percent (%)
2016–2018	0.11	1.83	0.41	11.32	0.09	2.11	1.64	6.59	0.35	1.24
2018–2020	0.66	10.75	1.35	37.2	0.28	6.56	3.8	15.31	1.28	4.51
2020–2022	0.55	8.96	0.71	19.71	1	23.53	4.9	19.73	1.76	6.22
2022–2024	0.17	2.83	0.09	2.62	0.1	2.39	0.32	1.28	0.73	2.58

).

The Phragmites management also resulted in changes in Dead Vegetation, Grass/Herbaceous, and Typha directly. Before the treatment, the percent cover of Dead Vegetation in the three NWAs was less than 2% between 2016 and 2018 (

Table 14. Change in percent cover (%) of Dead Vegetation, Grass/Herbaceous, and Typha in three NWA subareas.

Year	NWA-BC			NWA-TH			NWA-LP
	Dead Vegetation	Grass/ Herbaceous	Typha	Dead Vegetation	Grass/ Herbaceous	Typha	Dead Vegetation	Grass/ Herbaceous	Typha
2016	0.68	13.66	40.56	0.4	6.69	44.98	0.8	29.42	12.44
2018	0.34	24.58	38.67	0.37	8.12	44.43	1.91	21.08	16.25
2020	4.6	13.78	41.42	20.41	3.26	19.86	7.68	23.97	7.37
2022	7.18	23.08	33.1	23.56	6.18	12.11	4.59	20.52	10.39
2024	1.76	18.65	46.14	2.8	10.04	22.36	2.69	29.95	10.83

). During the same period, Typha cover was 38.7%, 44.4%, and 16.25% in NWA-BC, NWA-TH, and NWA-LP, respectively. After the treatment, the maximum percent cover of Dead Vegetation increased to 7.2%, 23.6%, and 7.7% in the corresponding NWAs, while Typha cover declined to a minimum of 33.1%, 12.11%, and 7.4%, respectively. The percent cover of Grass/Herbaceous also declined following the treatment. A similar pattern was observed in the suareas of Old Cut and LPCL, where treatment began earlier than in the three NWAs (

Table 15. Change in percent cover (%) of Dead Vegetation, Grass/Herbaceous, and Typha in subareas of Old Cut and LPCL.

Year	Old Cut			LPCL
	Dead Vegetation	Grass/Herbaceous	Typha	Dead Vegetation	Grass/Herbaceous	Typha
2016	4.56	5.12	48.65	3.17	3.9	36.33
2018	7.97	21.38	37.26	3.58	10.47	33.91
2020	16.2	0.89	17.57	19.54	2.76	12.79
2022	1.26	3.05	16.57	2.57	3.71	13.03
2024	0.63	9.26	20.08	0.43	5.03	21.34

). Among the five subareas, the greatest change in Grass/Herbaceous cover occurred in Old Cut, which showed a 20.5% decline from its peak (Figure A2). The most significant decline in Typha cover was recorded in NWA-TH, with a 32% reduction from its peak value (Figure A3). When the treatment was eased in 2024, both Grass/Herbaceous and Typha cover expanded, while Dead Vegetation cover decreased across all five subareas.

4.4. Effects of Phragmites Management in NWA-BC

The impact of Phragmites management on both target and non-target vegetation can be illustrated by changes among Phragmites, wetland classes, and Dead Vegetation in subarea NWA-BC (Figure A1). Prior to management, the total cover was 0.04 km² of Dead Vegetation, 0.36 km² of Floating Vegetation, 0.83 km² of Grass/Herbaceous, 1.31 km² of Phragmites, 0.34 km² of SAV, and 2.48 km² of Typha. Before management began in 2019, changes between classes from 2016 to 2018 were considered natural processes. During this period, only 0.02 km² of other classes converted into Dead Vegetation, while about 0.03 km² of Dead Vegetation transitioned into Grass/Herbaceous, SAV, or Typha. By 2018, the coverage of Phragmites, Grass/Herbaceous, and Typha was 1.14 km², 1.50 km², and 2.36 km², respectively (

Table A7. Area change between classes from 2016 to 2018 (km²) in subarea—NWA-BC (columns = 2016 classes, rows = 2018 classes) (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T).

Class Name	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T	2018 Subtotal
BG	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
B	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
DV	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.02
FV	0.00	0.01	0.00	0.21	0.02	0.00	0.01	0.02	0.01	0.02	0.01	0.31
GH	0.00	0.00	0.01	0.10	0.64	0.00	0.24	0.03	0.00	0.13	0.34	1.50
OW	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
PH	0.00	0.00	0.00	0.01	0.07	0.00	0.81	0.00	0.00	0.09	0.16	1.14
SAV	0.00	0.00	0.01	0.01	0.00	0.00	0.00	0.26	0.22	0.01	0.01	0.53
SW	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.06	0.00	0.00	0.07
TS	0.00	0.00	0.00	0.01	0.01	0.00	0.03	0.01	0.00	0.07	0.03	0.17
T	0.00	0.00	0.01	0.01	0.09	0.00	0.22	0.01	0.00	0.10	1.92	2.36
2016 subtotal	0.00	0.02	0.04	0.36	0.83	0.00	1.31	0.34	0.30	0.42	2.48	6.1

).

When approximately 0.02 km² of Phragmites was treated in 2019 (Table 4), 0.06 km² of Grass/Herbaceous, 0.04 km² of Phragmites, and 0.15 km² of Typha transitioned into Dead Vegetation, while conversions from Dead Vegetation remained low (0.01 km² from 2018 to 2020). By 2020, Phragmites, Grass/Herbaceous, and Typha covered 0.81 km², 0.80 km², and 2.53 km², respectively (

Table A8. Area change between classes from 2018 to 2020 (km²) in subarea—NWA-BC (columns = 2018 classes, rows = 2020 classes) (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T).

Class Name	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T	2020 Subtotal
BG	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01
B	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
DV	0.00	0.00	0.00	0.01	0.06	0.00	0.04	0.01	0.00	0.01	0.15	0.28
FV	0.00	0.00	0.00	0.20	0.11	0.00	0.04	0.03	0.00	0.03	0.08	0.50
GH	0.00	0.00	0.00	0.02	0.57	0.00	0.08	0.00	0.00	0.01	0.15	0.84
OW	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
PH	0.00	0.00	0.00	0.00	0.12	0.00	0.56	0.00	0.00	0.01	0.12	0.81
SAV	0.00	0.00	0.01	0.04	0.17	0.00	0.04	0.43	0.04	0.02	0.13	0.89
SW	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.03	0.00	0.00	0.04
TS	0.00	0.00	0.00	0.01	0.07	0.00	0.04	0.00	0.00	0.04	0.03	0.19
T	0.00	0.00	0.00	0.02	0.40	0.00	0.34	0.04	0.00	0.03	1.70	2.53
2018 subtotal	0.00	0.00	0.02	0.31	1.50	0.00	1.14	0.53	0.07	0.17	2.36	6.1

). Following larger treatments—0.61 km² in 2020 and 1.0 km² in 2021—further changes were observed from 2020 to 2022. About 0.01 km² of Grass/Herbaceous, 0.16 km² of Phragmites, and 0.24 km² of Typha transitioned into Dead Vegetation. Meanwhile, Dead Vegetation converted into 0.04 km² of Floating Vegetation, 0.05 km² of Grass/Herbaceous, 0.06 km² of SAV, and 0.10 km² of Typha. By 2022, Phragmites, Grass/Herbaceous, and Typha covered 0.18 km², 1.41 km², and 2.02 km², respectively (

Table A9. Area change between classes from 2020 to 2022 (km²) in subarea—NWA-BC (columns = 2020 classes, rows = 2022 classes) (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T).

Class Name	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T	2022 Subtotal
BG	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01
B	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.03
DV	0.00	0.00	0.02	0.00	0.01	0.00	0.16	0.00	0.00	0.00	0.24	0.44
FV	0.00	0.00	0.04	0.26	0.04	0.00	0.05	0.09	0.00	0.02	0.17	0.68
GH	0.00	0.00	0.05	0.04	0.48	0.00	0.29	0.02	0.00	0.05	0.48	1.41
OW	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
PH	0.00	0.00	0.00	0.01	0.02	0.00	0.09	0.00	0.00	0.02	0.04	0.18
SAV	0.00	0.00	0.06	0.15	0.02	0.00	0.01	0.67	0.00	0.00	0.12	1.04
SW	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.07	0.04	0.00	0.00	0.11
TS	0.00	0.00	0.00	0.01	0.03	0.00	0.03	0.00	0.00	0.05	0.05	0.18
T	0.00	0.00	0.10	0.03	0.23	0.00	0.17	0.03	0.00	0.04	1.41	2.02
2020 subtotal	0.01	0.00	0.28	0.50	0.84	0.00	0.81	0.89	0.04	0.19	2.53	6.1

). In 2023, about 0.56 km² of Phragmites was treated. Between 2022 and 2024, 0.01 km² of Grass/Herbaceous, 0.07 km² of Phragmites, and 0.02 km² of Typha converted into Dead Vegetation, while Dead Vegetation itself transitioned into 0.06 km² of Grass/Herbaceous, 0.01 km² of SAV, 0.02 km² of Tree/Shrub, and 0.35 km² of Typha. By 2024, the cover of Phragmites, Grass/Herbaceous, and Typha was reduced to 0.09 km², 1.14 km², and 2.82 km², respectively (

Table A10. Area change between classes from 2022 to 2024 (km²) in subarea—NWA-BC (columns = 2022 classes, rows = 2024 classes) (Bare Ground—BG; Built-up—B; Dead Vegetation—DV; Floating Vegetation—FV; Grass/Herbaceous—GH; Open Water—OW; Phragmites—PH; Submerged Aquatic Vegetation—SAV; Shallow Water—SW; Tree/Shrub—TS; Typha—T).

Class Name	BG	B	DV	FV	GH	OW	PH	SAV	SW	TS	T	2024 Subtotal
BG	0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01
B	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.01	0.00	0.00	0.01	0.02
DV	0.00	0.00	0.00	0.00	0.01	0.00	0.07	0.00	0.00	0.01	0.02	0.11
FV	0.00	0.00	0.00	0.28	0.04	0.00	0.00	0.07	0.00	0.01	0.03	0.45
GH	0.00	0.00	0.06	0.08	0.59	0.00	0.03	0.03	0.00	0.04	0.31	1.14
OW	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
PH	0.00	0.00	0.00	0.01	0.02	0.00	0.02	0.00	0.00	0.01	0.02	0.09
SAV	0.00	0.00	0.01	0.13	0.03	0.00	0.00	0.74	0.05	0.00	0.04	0.99
SW	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.12	0.06	0.00	0.00	0.20
TS	0.00	0.00	0.02	0.04	0.06	0.00	0.02	0.02	0.00	0.05	0.08	0.28
T	0.00	0.02	0.35	0.12	0.65	0.00	0.04	0.05	0.00	0.05	1.53	2.82
2022 subtotal	0.01	0.03	0.44	0.68	1.41	0.00	0.18	1.04	0.11	0.18	2.02	6.1

). Based on these observations, it is believed that changes in Dead Vegetation resulted from herbicide and mechanical treatments. After treatment, some areas of former emergent vegetation initially transitioned into Dead Vegetation and later to SAV, with new emergent vegetation regrowing after a few years.

5. Discussion

Research on the effects of herbicide treatment for Phragmites management in wetlands is limited, particularly regarding its impact on both targeted and non-targeted species and the resulting habitat assemblages. Quantifying long-term outcomes is essential for assessing the effectiveness of herbicide treatment, which is currently considered the best management practice [11,44]. In this study, as the initial attempt of its type, we used high-resolution optical satellite imagery from WV sensors to monitor wetland changes in a marsh ecosystem during and after herbicide treatment. Supported by field observations and UAV imagery, satellite image analysis enabled successful RF classification using spectral bands, vegetation and water indices, and texture measures. This approach effectively evaluated treatment outcomes by quantifying spatial and temporal changes in potential fish habitat, Phragmites, Typha, and Grass/Herbaceous within the Long Point wetland complex, Canada, between 2016–2024.

Comparison of classification results for the Subarea NWA-BC using Sentinel-2 imagery (16 July 2006) from [28] with those derived from WV imagery (12 July 2006) in this study showed that the WV-based classification improved user’s and producer’s accuracies for the Phragmites class by 10% and the overall accuracy by 8%. The WV-derived maps also captured smaller Phragmites patches that were not detected by Sentinel-2 data due to its coarser spatial resolution. These findings are consistent with those of [21], confirming that WV imagery provides more reliable results than medium-resolution datasets (e.g., Sentinel-2, Landsat) for Phragmites mapping.

In contrast to the annual expansion rate of 14–37% reported from historical aerial photo interpretation (1945–2013) and the projected expansion to 2022 from spatial modeling in a previous study [29], this study observed only a 1–1.5% expansion in NWA-TH and NWA-LP between 2016 and 2018, prior to Phragmites management. However, despite intensive Phragmites management, substantial expansions of 16.4% in NWA-TH and 8.9% in the LPCL subarea between 2022 and 2024 indicate that Phragmites expansion rate remains highly variable. Consistent with the findings of [29], this study observed that between 2016 and 2024, Phragmites replaced Typha, Grass/Herbaceous, Floating Vegetation, SAV, and Shallow Water in the three NWAs and encroached into Forest/Shrub areas. Similar patterns were reported by [2] for Phragmites distribution between 1995 and 1999. In NWA-TH, NWA-LP, and the LPCL subarea, large patches of Phragmites expanded into higher elevation areas near sand dunes. Over the study period, Phragmites patch sizes also increased within tree stands in NWA-LP and the LPCL subarea.

The analysis showed the progression of changes caused by Phragmites management including herbicide and mechanical treatment, from living Phragmites to dead vegetation after spraying, followed by a transition from dead vegetation to SAV, and ultimately from SAV to new emergent vegetation, including Phragmites. While treatment in provincially and privately managed areas began as early as 2016, most applications in federally managed areas started after 2019, suggesting that this cycle may take only 3–4 years. It should be noted that Phragmites management did not begin in the NWA-BC and NWA-LP subareas until 2019, and in NWA-TH until 2020. Therefore, wetland cover changes observed from 2016 to 2018 in NWA-BC and NWA-LP, and from 2016 to 2020 in NWA-TH, were due to natural processes rather than management interventions. This study also found that some Phragmites persisted or regrew directly from areas of dead vegetation 1–2 years after herbicide application, suggesting that the treatment did not completely eliminate the underground root system. In addition to the reduction in Phragmites, decreases in Typha and Grass/Herbaceous were observed, indicating non-target impacts of the treatment. These findings regarding herbicide effects have not been previously reported.

Another focus of this study was detecting changes in potential fish habitat extent, a key indicator of aquatic ecosystems restoration following herbicide treatment. Habitat changes were assessed by mapping cover shifts in Open Water, SAV, Shallow Water, and Floating Vegetation. A substantial increase in potential fish habitat observed 1–2 years after treatment suggests that the Phragmites Best Management Practices contributed to effective ecosystem restoration. Notably, these effects on potential fish habitat restoration in response to herbicide treatment have not been previously documented. While this approach successfully detected changes within the study area, continued monitoring is recommended due to ongoing treatment and wetland recovery efforts. For future consideration, there are several concerns associated with the monitoring and assessment of Phragmites management using the methods applied in this study that need to be addressed:

(1) The detected wetland changes resulted from both herbicide and mechanical treatments. However, the treatment areas in Table 4 included only those subjected to herbicide application. Some of these areas also underwent mechanical treatments, such as rolling, cutting, or burning. Consequently, the impact of herbicide-only treatment may not be fully captured.

(2) Mis-classifications among wetland classes occurred due to spectral confusion and the heterogeneous mixture of vegetation. Reference samples for Phragmites, Typha, and Grass/Herbaceous were selected from relatively homogeneous patches. However, in the marsh environment, the greatest confusion was observed among emergent wetland vegetation classes, particularly Phragmites, Typha, and Grass/Herbaceous. In general, different wetland vegetation classes naturally intermingle, with the dominant cover shifting each year in response to fluctuating water levels, salinity, disturbances, soil conditions, and nutrient availability [2,21,26]. Spectral distinction among the three classes was only reliable for the dominant cover types and was difficult in transition areas. This confusion contributed to mis-classifications, potentially leading to errors in estimating Phragmites cover. Similar issues have been reported in other studies [21,26]. Since Phragmites surpass surrounding vegetation in height during the late season (August–September), using late-season imagery for classification can improve the accuracy of Phragmites cover estimates. In addition, the spread of Phragmites and the regrowth of grasses in open areas between trees and shrubs, along with shadows cast by trees and shrubs, contributed to confusion among the Phragmites, Grass/Herbaceous, and Tree/Shrub classes. During this study, frequent field visits and UAV flights allowed the collection of a large set of training samples which improved the classification accuracy. High confusion also occurred between the two non-wetland classes, Bare Ground and Built-up, due to their similar reflectance values in non-urban environments. Most classification errors between these classes were found in sand beach areas and were attributed to spectral similarity. Since non-wetland classes were not a major focus for wetland monitoring, no additional effort was made to improve the classification accuracy for Bare Ground and Built-up in this study.

(3) Detected changes may be temporary and continued monitoring is needed. New Phragmites were detected in previous Grass/Herbaceous, Typha, and Floating Vegetation areas. As transitions from one type of wetland vegetation to another occurs naturally due to mixed living conditions, treatment in certain localized areas may not be sufficient to alter the environments that favor Phragmites growth. The expansion rate and extent of Phragmites in small patches among trees or shrubs should be closely monitored, as identification remains challenging due to spectral confusion and the limited size of these patches. This study examined the impacts of treatment between 2016 and 2024. During this period, we detected regrowth of Phragmites from Dead Vegetation, confirming that Phragmites can regrow and expand after treatment [11]. As the percent cover of Dead Vegetation peaked in 2020, the observed increase in conversion from Dead Vegetation to Phragmites during 2020–2022 and 2022–2024 suggests that Phragmites regrowth or expansion may extend beyond the timeframe of this study. The increase in potential fish habitat associated with Dead Vegetation was likely due to the eventual decay of vegetation following treatment. No assessments were made regarding the suitability of habitat components for fish survival (e.g., spawning places, food, shelter, and cover), indicating that additional studies on fish habitat suitability may be needed. Owing to the absence of data on fish presence, Phragmites, Typha, and grass/herbaceous wetlands were delineated as distinct wetland classes and classified as non-fish habitats in this study. Nonetheless, in natural settings, when these vegetation types occur in a mosaic with open water and SAV rather than as continuous monocultures, they may provide shelter and refuge for certain fish species. Consequently, continued monitoring of this study area remains important.

(4) The post-classification change detection method adopted in this study requires that images from each date to be classified and compared using a consistent classification scheme with a set of discrete land cover types. Classes were defined based on dominant cover type, vegetation height, and reflectance differences relative to Phragmites and potential fish habitat, and on detectability in the available imagery. Dominant vegetation, such as Phragmites and Typha, were kept as separate cover types, while all low vegetation was grouped as Grass/Herbaceous. No attempts were made to differentiate (a) native versus invasive Phragmites, or (b) various SAV and Floating Vegetation types due to challenges in image classification.

(5) Image characteristics such as acquisition dates, atmospheric conditions, spatial resolution, and monitoring duration are critical for wetland monitoring. Accurate mapping of Phragmites and other marsh vegetation is best conducted during the peak growing season (July–August), avoiding periods directly influenced by herbicide application (September–October) or mechanical ground treatment carried out in winter (November–March). In this study, most images were tasked and acquired in July and early August. Only one scene, covering the eastern part of the study area, was acquired outside of this timeframe (June 2016) due to persistent cloud cover. This June image was deemed suitable for image classification because no treatment occurred in that area during 2016. Most acquired images were cloud-free, except for a few from 2022 and 2024. PlanetScope imagery was used to fill in areas affected by cloud cover. All images were atmospherically corrected to minimize haze and sun angle effects. The contrast between SAV and surrounding features was sufficient for accurate SAV classification in the Long Point wetland environment; therefore, no additional corrections such as water depth estimation using Secchi depth or water column correction, as suggested by other studies [30] were applied beyond standard atmospheric correction. However, future studies may consider incorporating these additional corrections to further improve SAV mapping accuracy. Data from WV2 and WV3 sensors showed minimal differences in both spectral and spatial resolution, ensuring consistency for change detection. Although PlanetScope data had a different resolution, its use was restricted to cloud-covered areas. Herbicide treatment affected vegetation health, altering the spectral separability of different wetland species in image classification. For instance, treated Phragmites, along with nearby Grass/Herbaceous and Typha, exhibited similar spectral reflectance values that were lower than those in untreated areas. While most treatments occurred between 2016 and 2023, 2016 imagery indicated that a large portion of Old Cut had already been treated prior to 2016. Without prior validation samples, we could not determine the actual Phragmites cover in Old Cut area before treatment; thus, our results for this area only reflect post-treatment conditions. Overall, conducting change detection at two-year intervals effectively captured the changes across the study area over time.

(6) Wetland cover is strongly influenced by water levels. Some of the changes observed among non-vegetated areas, vegetated areas, and wetland classes in the study area were primarily driven by fluctuating water levels and beach erosion. Rising water levels converted former Bare Ground areas into Shallow Water, which is considered potential fish habitat. Frequent shoreline erosion also transformed former Tree/Shrub areas into Shallow Water. Water levels in 2019 and 2020 were the highest recorded in the past 20 years [45]. In mid-July of both years, water levels at the nearest water level station at Port Dover were approximately 30 cm higher than at the same time in 2016, 2018, and 2022 [45]. Consequently, some changes between SAV and Shallow Water may have resulted from these water level fluctuations. The percent cover of SAV reached its peak in 2020, while the combined area of Shallow Water and Open Water was at its lowest. The reduced cover of rooted vegetation, particularly Typha, Phragmites, and Grass/Herbaceous, and especially Phragmites and Typha in 2020, likely reflects the combined effects of Phragmites treatment and rising water levels. Previous studies have suggested that high water levels negatively affect Phragmites abundance by constraining the growth of rooted vegetation [2,46]. This observation aligns with findings by [46] in Lake Ontario, where rising water in 2019 corresponded with increased SAV and decreased Typha cover. However, ongoing Phragmites management in Long Point may complicate such patterns.

6. Conclusions

In this study, we developed a framework to evaluate the long-term effects of Phragmites management, including both herbicide and mechanical treatment, by quantifying changes in wetland vegetation (Phragmites, Typha, Grass/Herbaceous) and potential fish habitat using high-resolution optical satellite imagery from WV sensors. Leveraging the temporal and spatial coverage of an eight-year monitoring period (2016–2024), this study revealed unprecedented wetland dynamics following Phragmites management in the Long Point wetland complex. An RF classification method was applied to images acquired in 2016, 2018, 2020, 2022, and 2024. With a mapping accuracy of over 94%, we detected spatial and temporal changes in potential fish habitat, and both target and non-target vegetation. Potential fish habitat area increased over the study period, covering 50%, 50.2%, 56.6%, 62.9%, and 57.7% of the total study area in 2016, 2018, 2020, 2022, and 2024, respectively. Among the potential fish habitat types, combined area of Open Water and Shallow Water remained relatively stable, with some spatial variations, while SAV and Floating Vegetation increased by 6% and 2%, respectively. Phragmites was the only class observed with a consistent decline, decreasing from 7.1% of the study area in 2016 to 5.1% in 2024.

Typha, Grass/Herbaceous, Tree/Shrub, and Dead Vegetation were the primary classes displaced by Phragmites expansion. Between 2016 and 2018 and between 2018 and 2020, over 90% of new Phragmites growth came from these classes. Following herbicide treatment, in some treatment areas, Phragmites cover dropped from 16 to 21% before treatment to 0.9–1.4% afterward. Dead Vegetation increased to 23.6%, while Grass/Herbaceous and Typha declined by 20.5% and 32%, respectively, likely due to non-target effects. However, these losses are minor compared to the 20% reduction in Phragmites cover and the 46% increase in potential fish habitat, making them an acceptable trade-off given the ecological restoration achieved. A detected increase in conversion from Dead Vegetation to Phragmites between 2020 and 2024 likely reflects regrowth in previously treated areas. Recent expansion of 16.4% in NWA-TH and 8.9% in the LPCL subarea between 2022 and 2024 indicate that Phragmites expansion rate remains highly variable. Continued remote sensing monitoring and repeated treatments over several years are recommended for complete Phragmites removal. Although this study covered only eight years, the results confirmed the effectiveness of Phragmites management and fish habitat restoration, highlighting the importance of ongoing management in Great Lakes coastal wetlands.