1. Introduction
Due to increased awareness of the ecological significance of vernal pools, there has been growing interest in identifying, mapping, monitoring, and protecting these valuable forested wetlands. Vernal pools are small (typically less than 1 ha), shallow, isolated, temporary wetlands that are important for maintaining healthy forest ecosystems. In addition, known as temporary or ephemeral ponds, vernal pools occur in various forms throughout the world [
1]. In most years, vernal pools are filled with water in the spring, and dry or significantly draw down in summer, exposing all or most (
i.e., >50%) of the pool bottom. As confined-basin depressions, they lack continuously flowing inlets or outlets, and they have no continuous surface-water connection with permanently flooded water bodies. Woodland vernal pools generally contain water for a minimum of two months in most years and the regular drying prevents fish from establishing populations in these wetlands. Reduced predation pressure from fish and minimum hydroperiods of two months allow the eggs and larvae of many of the amphibians and invertebrates that breed in vernal pools to successfully complete their development, metamorphose into adults, and leave the pools. Vernal pools, therefore, provide critical breeding habitats for amphibians and invertebrates, including obligate (a species that requires or is restricted to a specific habitat or environmental conditions) species that rely on vernal pools to complete their life cycle and a number of rare and declining plant and animal species [
2,
3,
4,
5,
6,
7,
8,
9,
10,
11]. Additionally, there are important ecosystem services that vernal pools provide, such as nutrient cycling, water storage, groundwater aquifer recharge, flood control, and improved water quality [
5,
11,
12,
13,
14,
15]. Due to their small size and short hydroperiods, vernal pools are vulnerable to climate change and land use development. Researchers and resource managers agree that these important ecosystems need to be protected, but conservation of these small isolated ecosystems requires knowledge of their location and distribution, which is largely unknown. Conservation also requires information about their biology and ecology, which needs to be sampled in the field.
Due to their small size, temporary and isolated nature, and association with forested ecosystems, vernal pools can be challenging to locate with conventional surveying and mapping techniques. Currently, the most common approach to inventorying and mapping vernal pools is through aerial photograph interpretation [
16,
17,
18,
19,
20,
21]. A number of state governments within the United States of America have implemented efforts to identify and map vernal pools within their jurisdictions (e.g., Massachusetts, Maine, New Hampshire, New Jersey, Rhode Island, and Vermont) [
5,
11,
19,
20]. These efforts have consisted of remotely identifying and mapping water features within forests that are labeled potential vernal pools (PVPs) and then conducting necessary field surveys to confirm that the identified water features are actual vernal pools. After confirmation, the PVPs are upgraded to a status of confirmed vernal pools (CVPs). This naming convention is followed in the research presented in this article.
While aerial photo interpretation and field surveys can be fairly effective in identifying and mapping vernal pools, these approaches are time- and labor-intensive and expensive to implement across large regions [
5]. In addition, there are limitations to optical-based imagery in detecting pools beneath tree canopies. Image interpreters typically rely on spring photos to obtain the best detection of PVPs because in leaf-off conditions the amount of foliage blocking the view of sub-canopy features is minimized. Vegetation layers within the pools are variable and may consist of trees, shrubs, submergent and floating aquatics. However, the utility of this approach is limited to deciduous forests and generally fails in the case of dense evergreen forest cover (e.g., conifers). The accuracy of aerial photograph interpretation varies depending on landscape characteristics, surrounding forest cover, pool type and size, timing of the aerial photography, photograph scale, and interpreter experience [
5,
17,
18,
19,
20,
22]. These limitations have led researchers and resource managers to investigate other image sources for detecting and mapping PVPs (e.g., radar and/or LiDAR data) [
23,
24,
25].
The low frequency radar sensors (synthetic aperture radar—SAR) are able to penetrate forest canopy cover to detect the presence of standing water at the ground surface. In addition, the all-weather capability of SAR sensors allows for timely collection of data to detect flooded conditions in the spring, irrespective of cloud cover. For example, L-band (~24 cm) wavelengths produce characteristic enhanced (bright) radar signatures from forests in a flooded condition because much of the incoming energy is reflected back to the antenna due to double-bounce scattering (dihedral effects,
Figure 1). In a non-flooded forest, more of the incoming energy is absorbed by the ground and the backscatter signatures are moderate, with gray image tones. While shorter wavelength C-band (~5.6 cm) reflects primarily off the canopy (
Figure 1), when there are gaps in the canopy or leaf-off conditions, penetration of C-band energy to the ground does occur (not shown in
Figure 1). L-band is theoretically the better SAR wavelength for PVP detection in leaf-off and leaf-on conditions, while C-band may only provide limited utility under leaf-off or low density forest conditions.
LiDAR (Light Detection and Ranging) sensors are typically used to create high resolution Digital Elevation Models (DEMs) that are useful for delineating low lying areas indicative of PVPs e.g., [
24]. The intensity of the returns has also been used to map extent of inundation of forested wetlands in leaf-off conditions [
26]. The mapping is possible because of the high absorption of incident near-infrared by water, often resulting in very low returns from inundated forests when leaves are off. While scan angle (glint), surface roughness and other variables influence whether or not the energy is absorbed, these limitations can be accounted for and using LiDAR intensity for mapping forest inundation is an active area of research. However, LiDAR data availability for the region of interest (the state of Michigan) is limited to merely a few counties. It was therefore not a main focus of study.
The goal of our research was to develop a remote sensing method for mapping vernal pools across the state of Michigan that would be efficient, cost-effective, repeatable and accurate. We evaluated satellite-based L-band radar data from the high (10 m) resolution Japanese ALOS PALSAR FBS (fine beam single) imagery (collected between 2006 and 2011) to determine if it could be used to effectively detect vernal pools. Using the definition of a vernal pool, as described above (that they are forest covered and flooded in the spring and dry in the summer), we hypothesized that two seasons of radar imagery (spring and summer) could be used to capture these hydrological differences for distinction of vernal pools from non-wetlands, as well as from permanently-flooded wetlands which should have a bright L-band signature in both spring and summer. We also evaluated LiDAR intensity data and Radarsat-2 C-band high resolution data for suitability in detection of vernal pools.
4. Discussion
It has long been known that inundation beneath a forest canopy could be detected with L band HH-polarization SAR data [
44] and in some cases with C-HH imagery [
26,
45,
46] through enhanced backscatter returns. However, detection of seasonal inundation in small isolated wetlands, such as vernal pools, which can sometimes be only 15 m across, was uncertain with an 8–10 m resolution SAR. Detection beneath a forest canopy requires not only careful timing of the seasonal imagery, but also high spatial resolution and 10 m resolution may be near the limit for a SAR for this application due to speckle.
The phase I study demonstrated the utility of L-band HH polarization 10 m resolution data for detection of cryptic vernal pools in two study areas (NLP and SLP) with overall accuracy of 48% and 62%, respectively. The SAR PVP products were compared to stratified random field sampling cells of the study areas which allowed for a fairly robust accuracy assessment of errors in seasonal SAR PVP detection. However, due to the large size of the test cells in relation to the small size of vernal pools, our validation may be biased with inflated true positives. For example, the SAR seasonal change could map a PVP polygon in the 1.0 ha cell in a different location than the field verified vernal pool, but with the large test cell size it would be marked a true positive. At the same time, due to the ephemeral nature of vernal pools, the mismatch of year and season of imagery to year and season of field sampling could also be biasing our results; thus, the number of true negatives could be overestimated.
True positives were higher at the SLP study area (49%) than the NLP study area (23%). This may be due to the timing of the imagery at each site in relation to the hydroperiod status. It may also be due to differences in vegetation structure and physiography. Average water depths were similar between the SLP and NLP (~45 cm) at the time of field sampling. However, timing of the satellite imagery was not necessarily at the peak water depth for each of the study areas. It is also interesting that more of the false positives of SLP (63%) were water bodies that were not CVPs than for the NLP (13%). The causes of these differences are in need of further investigation. However, one of the features that we have found to cause false positives is the existence of structures (homes, barns, etc.) beneath a tree canopy. Such man-made structures may cause some double bounce scattering in spring with wet soil conditions and lower return in summer when the soil is dry. The exact nature of the false positives needs further investigation with coincident SAR and field collection.
Due to the low false negative rates at both study areas (22% and 14%), SAR was deemed suitable for narrowing down the search in detecting otherwise cryptic vernal pools. Even though this was higher than the false negative error for aerial photograph interpretation (7%; [
23]), the cost-effectiveness of SAR makes it appropriate. When the location of vernal pools is largely unknown, some method to narrow down the areas to conduct further detection through field surveys or aerial photography is desirable since the latter two methods can be a formidable task. While the phase I analysis did not provide an assessment of discrete delineation of PVP boundaries, the phase II supervised classification did.
In phase II, supervised classification of the PALSAR data with derived DEM products (91%–93% overall accuracy) was implemented to improve the detection and mapping of vernal pool boundaries. Using multiple sources of imagery or ancillary information allows cross checks on the variables that define a vernal pool, thus improving map accuracy. The PALSAR RF classification that included 10 m DEM-derived isolated depressions and TPI products was demonstrated as superior to LiDAR intensity data with the same DEM products (34% overall accuracy) for detection and mapping of vernal pools in this study. Although the spring LiDAR intensity image alone (
Figure 6) appears to well delineate the vernal pools, it produces many false alarms, and when combined with the DEM product misses some CVPs (
Figure 7). Combining the spring LiDAR intensity data with the PALSAR may be a viable option.
As more LiDAR data become available for the state of Michigan, pursuing LiDAR DEM data for vernal pool mapping in combination with L-band SAR would likely improve mapping capability even further. Wu
et al. [
24] described an accurate method of using LiDAR-derived DEMs with color orthoimagery and hydrography data to identify vernal pools in Massachusetts. Their approach to using the LiDAR-derived DEMs avoided the issues of surface complexity recognized for this study, and further exploration could improve the accuracy of vernal pool detection in Michigan where LiDAR data are available. Additionally, Faccio
et al [
25] presented another LiDAR-based method used to map vernal pools in Vermont and New Jersey. Their method takes advantage of LiDAR intensity data, as well as other ancillary datasets, to detect vernal pools with an Object Based Image Analysis (OBIA) approach.
Although L-band is theoretically and practically better suited to detection of inundation beneath a forested canopy, C-band data from Radarsat-2 were preliminarily evaluated in this study. Previous work has shown 5.7 cm C-band data from Radarsat-1 as useful for detection of inundation beneath a forest canopy in Roanoke riparian wetlands [
45,
47]. Although the spring and summer C-Band SAR data were useful for detection of seasonal differences in flooding in vernal pools in the SLP, they were not able to detect sufficient variability in backscatter to detect PVPs in the WUP study area. The resolution of the USGS DEM may be too coarse to detect isolated depressions in the WUP. At the time of the WUP spring Radarsat-2 image collection, there was standing water in the vernal pools, but the leaves had fully flushed, which may be limiting the ability of the C-band SAR wavelength to reach the ground. The pools in WUP were also much smaller than in Pinckney and the canopy cover was more coniferous. This then leads to questions about the dry (summer) C-band SAR collections, and if leaves limit the penetration capability, then the methodology of differencing of spring and summer data would be of question. Leaf flush had just begun when the Pinckney spring image was collected, which may explain why it was possible to detect vernal pools there with the Spring–Summer seasonal difference image. The fact that the pools detected are smaller than those field-verified and mapped from aerial photograph interpretation or PALSAR may be due to more closed canopy conditions in the summer. Further investigation into the forest structural differences between these vernal pool locations is needed before conclusions may be drawn, but early spring collections before leaf-flush appear to have potential for detection of PVPs with C-band data, however L-band is better suited.
The only SAR sensors available for mapping at L-band at the time of this article were ALOS PALSAR (2006–2011) and ALOS-2 PALSAR-2 (2013-present). The availability of PALSAR-2 is more limited in comparison to the predecessor satellite, ALOS PALSAR. While PALSAR was available for minimal to no cost through the Alaska Satellite Facility (AFS) for North America, PALSAR-2 is available only through JAXA [
48] at high cost. Looking to the future, Argentina plans to launch an L-band satellite, SAOCOM, in 2017 and NASA is planning a mission with ISPRA called NISAR. This latter satellite has a mission plan that includes downloading and storing all data collected every 2 weeks. This will tremendously improve timely data collection of “wet”
vs. “dry” vernal pool conditions over the past satellites that downlinked and saved only some of the data due to recorder and downlink station limitations. For those pools that were missed with the seasonal change approach because they were not wet in the particular spring imagery that was available or they were still wet on the image collection date that was used as the “dry” condition, having continuous data every 2 weeks will greatly improve detection capability, but also allow for determination of hydroperiod.
5. Conclusions
The research presented has demonstrated the utility of high resolution L-band SAR for detection of potential vernal pools through a seasonal change approach to detect the seasonal hydrological change (inundated to non-inundated) that defines a vernal pool. Using an unsupervised SAR seasonal change approach allows for the area of PVP locations to be reduced dramatically with low false negatives of vernal pools, but has high false positives and true negatives (
Table 2). This may be a useful quick, cost-effective approach to narrow down a region to those areas of high likelihood of PVP locations to aid in directing follow up aerial photograph interpretation and field sampling to verify the vernal pool status. When a 10 m DEM is available, integrating that ancillary dataset with L-band SAR in a supervised classification was shown to substantially reduce the commission and omission errors of PVPs. Additionally, if leaf-off LiDAR or high resolution optical imagery were also integrated into the classification scheme with the L-band SAR, it should improve map accuracy and vernal pool delineation even more.
The novel seasonal change SAR approach to detection and mapping of woodland vernal pools (PVPs) provides an alternative to expensive airborne sensor data collection and with new sensors planned for launch in the next 1–5 years is a viable approach for detection, mapping and monitoring of PVPs. Incorporating L-band SAR and a more robust LiDAR-based topographic analysis into a classification scheme is a promising potential next step for vernal pool mapping and detection in Michigan. L-band data have the added advantage of detection of inundation beneath evergreen canopies, conditions under which LiDAR and optical data have limited capability.
Due to the archival nature of much of the imagery (PALSAR and LiDAR) used in this study, a determination of actual vernal pool extent on the ground in comparison to the supervised PVP classified polygon results was not possible. This is generally true for the use of aerial photographs as well, which are often accessed from as many years as possible to detect PVPs. Although the pools change in hydroperiod and extent from year to year, and in some years may not be wet, remote sensing provides the most viable tool to detect location of PVPs across large regions. The maps of PVPs created from a single or multiple years of imagery provide a starting point upon which to build a database of PVP locations. These locations then need to be confirmed in the field, checking for draw down in the summer and species presence. Using the new L-band satellites (PALSAR-2, SAOCOM and NISAR) with coincident field data collected in the spring when the vernal pools are inundated will allow for determination of the capability and limitations of L-band SAR–DEM methods in delineating vernal pool boundaries.