1. Introduction
Vast areas of land in the Great Lakes Basin (GLB) have been undergoing rapid land cover and land use (LCLU) change over the past several decades. LCLU in the watersheds that drain to the coasts have a huge impact on the inflow of nutrients and the biogeochemistry of the coastal zone and coastal wetlands. Despite their importance, more than two-thirds of the wetlands in the GLB have been drained for agriculture and other development [
1] since the turn of the last century, and the remaining wetlands are subject to several threats including land conversion, climate change, and invasion by non-native plant species. Land transformations have altered groundwater levels and stream flows, increased nutrient loadings to water bodies, and contributed to anthropogenic contamination that impacts ecosystems at a variety of scales [
2]. The Great Lakes are highly susceptible to eutrophication by both nitrogen (N) and phosphorus (P). While coastal wetlands provide a key ecosystem service of retaining inflowing nutrients [
3,
4], increases in nutrient loading are well-documented to lead to increases in wetland-invasive plants such as phragmites (
Phragmites australis) and cattail (
Typha angustifolia and
T. x glauca) [
5,
6,
7,
8,
9]. Influxes of coastal invasion are also tied to changing Great Lakes water levels, which have a historical periodicity of about 10 years, but climate change is increasing the variability over shorter time scales. For example, a record low observed in 2013 rebounded to historically high-water levels nearly 18 months later. Water level fluctuations influence coastal wetland extent and function, with implications to the source/sink nature of these invaded ecosystems. Historically, high-water periods (e.g., 1986, 1997) have been followed by waves of invasion into barren, exposed sediments after water levels declined [
10,
11,
12,
13]. These invasive species form monocultures in vast areas, reducing biodiversity, changing wildlife and fisheries habitat, and altering ecosystem functions such as nutrient retention and carbon sequestration [
8].
In real time, we are seeing changes due to both climate and land use occurring within the GLB, altering ecosystems and affecting the habitat, water quantity and quality—both surface and groundwater [
14]. Recent examples include the following: the rapid fluctuations of Great Lake water levels and erosion events [
15]; large algae blooms in Lake Erie [
16] and other freshwater lakes [
17]; contaminated drinking water in Flint and Benton Harbor, which disproportionately affect minorities [
18]; oil spills [
19]; and urban and agricultural runoff pollution [
20]. Timely data and fine resolution imagery are often needed to address the events and understand ecosystem change.
Invasive species represent one of the most significant and immediate threats to the ecosystems and economies of the GLB. Researchers have identified the Great Lakes as one of the most invaded freshwater systems across the world [
21,
22]. To date, 188 aquatic non-native species have been established in the Great Lakes, of which 34% are considered invasive [
23]. Invasive species can alter ecosystem functions including nutrient (nitrogen [N] and phosphorus [P]) retention and carbon sequestration [
8,
24]. Invasive plants such as
Phragmites often form monocultures that typically have greater biomass and productivity than the native vegetation they replace [
25], thus altering nutrient and carbon dynamics. Aquatic and terrestrial invasive species in the GLB alter food webs, degrade the habitat, and outcompete and/or cause direct mortality or injury to native species. These impacts have threatened the native biodiversity and abundance, changed the community structure and dynamics of ecosystems in the GLB, and reduced the health and resilience of many of these ecosystems. Because coastal wetlands act as a buffer to the Great Lakes, their invasion can directly influence N, P, and C loading into the Great Lakes. Increases in nutrient loading can lead to increases in wetland-invasive plants such as
Phragmites australis and
Typha spp. (cattail) [
5,
6,
8,
9], as well as harmful algal blooms (HABs) such as
Cladophora [
26].
Here, we assess the historical changes in LCLU through time via remote sensing towards a goal of better understanding the legacy effects of upland conversions on current coastal conditions in the Great Lakes watersheds of Lakes Huron, Erie and Michigan, the lakes that are most heavily infested with invasive
Phragmites. The objective of the study is to quantify basin-wide land cover changes to link these changes to the observed expansion of invasive wetland plants and better understand wetland ecosystem dynamics through process-based modeling efforts [
5]. This requires sufficient temporal resolution of change as well as a basin-wide approach. Previous assessments of LCLU change in the Great Lakes have been addressed over portions of the GLB [
27,
28] or U.S. only [
29,
30] for various ecological, economic, and social studies. Until recently, changes within the full GLB had never been evaluated [
31]. This is partially due to the time and effort required to create land change products over such a vast area from high to moderate resolution data. Cloud computing, such as the Google Earth Engine (GEE), provides the platform and datasets needed to assess inter-annual changes that we and others (e.g., [
31]) have recently leveraged for change analysis.
To assess legacy changes in land use and land cover on coastal wetland invasions, Great Lakes water level changes and coastal wetland plant invasions were mapped for three key time periods: 1985, 1995, and 2010. The c.1985 land cover map pre-dates the 1987–1990 declining Great Lake water levels [
32] and influx of wetland plant invasion (1987 onward), while c.1995 captures conditions after the 1987–1990 water level decline, during the resurgence in water levels (peak 1997). The c.2010 baseline data capture conditions near the end of a 14-year low-water-level period (1993–2012) when invasive plants rapidly expanded to their current range, and prior to large, localized efforts to control the invasive
Phragmites australis in the more southern areas of the Basin. Our goal for this study was to identify the land cover change occurring in the GLB between our three time periods of interest (1985–1995 and 1995–2010) to provide the first insights into historical changes in land cover (urban, forestry and wetlands) occurring on a bi-national watershed scale.
4. Discussion
This study builds from three previous mapping endeavors [
32,
37,
38,
39] to create continuous and consistent land cover data that crosses the international border in the Great Lakes for three time periods. Relying on existing categorical data for the specific time periods of interest as the basis for the change analysis leverages the previous efforts of the three mapping sources based on their field data and the knowledge of the region from the time periods used to create the original maps. The approach contrasts with other methods of producing new maps from various time periods (e.g., [
31]) by finding locations of current field data that have not changed radiometrically over time to use as training for all time periods.
Since the years of the original Canadian maps were not in line with the years of the C-CAP maps for the United States side of the GLB, we adjusted the products, working closely with the AAFC map producers to ensure the new maps were consistent. To address the change between the three time periods of interest, we incorporated both radiometric and categorical change analysis to reduce false change and used open-source tools LandTrendr [
33] and TimeSync [
34] for assessing time series data. The approach provides a way of assessing change across the region for all change types, providing a comprehensive dataset for use in basin-wide modeling and assessment. But there remain limitations. As with any model, the LandTrendr temporal segmentation approach that underlies the classification is an abstraction of the actual changes in the landscape. The degree to which this abstraction faithfully represents the landscape depends on the parameters and hyperparameters used to apply the algorithm; as developers of the algorithm, we made choices based on the underlying landscape types involved and the expected choices. More importantly, we recognize that our model is not perfect. Challenges include balancing signal and noise in land cover types with substantial inter-annual variability, as well as identifying real change for the first or last observations of the time series, and others. The purpose of the accuracy assessment with TimeSync is to place sideboards on the degree to which these issues introduce false change or, under capture, real change. For this study, LandTrendr was applied broadly to capture all land cover change; this caused a lack of specific change type calibration, leaving many areas denoted as change in the final change maps developed for this study without a noted change date. Because LandTrendr parameters were set to assign a default change date to all pixels, areas without a noted change date were assigned a change date either at the beginning or end of the time period. This caused a lot of the change found to be falsely assigned to either 1985, 1995, or 2010. This could be remedied by incorporating higher resolution temporal data into this research.
It should be noted that the total amount of area changed for each change period is influenced by the input classification maps, as noted in previous studies [
44]. Discrepancies in the total change area between the two change periods are partially caused by the direct comparison between optical-based land cover products (C-CAP and Landsat-adjusted AAFC) [
32,
39] and optical/SAR fusion land cover products, developed for the 2010 baseline maps [
39]. Regarding the optical/SAR approach, Bourgeau-Chavez et al. [
38] notes the inclusion of SAR for the use of land cover classification, which allows for better detection of woody and non-woody wetlands over traditional optical-based land cover products due to the sensitivity of SAR to vegetation structure and inundation. We believe this more detailed land cover information in 2010, especially that of woody and non-woody wetlands, compared to that of the 1995 product, allowed for the detection of the following: (i) more land cover change in terms of quantity; (ii) more subtle land cover changes (i.e., transitions to/from woody and non-woody wetlands); and (iii) a better understanding of how urban growth may be impacting wetlands at a more detailed level. Additionally, this difference in the earlier categorical mapping methods and the 2010 product is likely the source of the discrepancies found between wetland class changes in this study vs. the LCMAP products (
Figure 13b). Finally, it should be noted that direct comparison of land cover products from multiple sources can introduce false positive change into the analysis. Careful consideration of change detection methodology, as outlined in this study, is strongly recommended. Using hybrid approaches of categorical and radiometric change is one method for mitigating the problem.
As shown in
Table 6, while most changes had accuracies better than 84%, change from urban to another class had an accuracy of 57%, and change from any class to agriculture had an accuracy of 60%. The low accuracy of these two cases can be explained through the unique processes related to urban and agricultural change. For the case of urban change, new architectural procedures such as new roofing materials, different pavement materials, and other infrastructure updates may show as a change when they are not actually changing classes, which causes accuracy to decrease [
55]. Similarly, agriculture has a large amount of spectral variability due to differences in soil moisture and vegetation phenology depending on what has been planted at different times of the year, which complicates change accuracy [
56]. In assessing the results of this study, therefore, it is important to note that within-class change, while maybe common, is not relevant for most change analysis applications.
The temporal analysis conducted revealed that aquatic bed gain was occurring for most of the time studied. Aquatic bed includes mostly submerged vegetation, the upper portions of which may float at the surface. Harmful Algal Blooms (HABs) meet this definition. Since 1985, inland water bodies within the GLB have experienced rapid warming, providing longer growth seasons and affording the cyanobacteria that causes HABs an advantage over other taxa because of their higher temperature optima range [
57]. This process and increased phosphorus loads from agriculture both contribute to aquatic bed gain. Using LandTrendr to improve spectral information through regressing expected values between inflection years could improve predictions of surface water presence and HABs by removing inter-annual noise [
58]. We saw similar success with the LandTrendr’s ability to remove inter-annual noise to create temporally smoothed data in calculating the change vector analysis.
We found a consistent loss in the deciduous forest area for much of the time studied. Nitrogen isotope analyses of archeological fish samples show that the nitrogenous nutrient regimes of even the world’s largest freshwater ecosystems can be highly sensitive to short-term watershed forest cover disturbances [
59]. Guiry [
59] found that deforestation in the 1930s led to an elevation in aquatic nitrogen sources due to the massive levels of soil denitrification caused by deforestation. This appears to be continuing, based on the levels of forest change from 1985–2010 and the expansion of the invasive plant
Phragmites australis during this time period [
13] (because this class is only on the 2010 map, this study was not designed to map invasive expansion). Invasive phragmites has been found to outcompete native wetland plants in high N conditions [
5]. This underscores the importance of LCLU maps that allow for the quantification of change in the watersheds of the GLB, where invasive species are expanding. We anticipate that our land change maps can inform research on the role of nutrient cycling that may be affecting ecosystems basin-wide, thus allowing for the identification of potential hotspots of nutrient activity for mitigation.
Great Lakes coastal wetlands represent a vital link between land and water, providing key ecosystem services as well as habitat for a wide variety of flora and fauna. The objective of the study was to quantify basin-wide land cover changes to link these changes to the observed expansion of invasive wetland plants and to better understand wetland ecosystem dynamics through process-based modeling efforts. The creation of bi-national change maps provides a dataset that includes continuity in space and time to address this goal. The change mapping from this study is currently being used to parameterize the integration of a hydrological model (LHM, [
60]) and a wetland ecosystem model (MONDRIAN [
5]) to quantify potential impacts of anthropogenic changes on these very important yet vulnerable ecosystems. Furthermore, the data and results from this work will have value in supporting additional science inquiries and informing policy related to regional land use and climate change [
61].
The methods used in this study allow for the continued monitoring of land cover change across the GLB as new and unique land cover classification products become available. The methods used are generic (using established analysis tools) that would have value for similar assessment in other areas with appropriate land cover datasets. This study used multi-spectral sensing (Landsat), which provides a long-time record of landscapes, for radiometric change analysis. SAR, advanced multi-spectral, and imaging spectroscopy sensing systems are being planned, providing new data sources for land monitoring and change studies. As more spaceborne SAR sensors become available, detailed land cover classifications that discriminate wetland classes will improve the monitoring of dynamic wetlands. Similarly, the spectral diversity of new optical sensors, such as those planned for LandsatNEXT [
62] and the Surface Biology and Geology (SBG) [
63] mission, will allow for the discrimination of agricultural systems, such as crop and harvest patterns. Future studies on how to exploit these capabilities are needed but are optimistic with the increasing availability of small satellites and data analytical resources, such as cloud computing and machine learning, that can efficiently process large datasets.