Using satellite imagery and LIDAR data to corroborate

International Journal of Remote Sensing

ISSN: 0143-1161 (Print) 1366-5901 (Online) Journal homepage: http://www.tandfonline.com/loi/tres20

Using satellite imagery and LIDAR data to corroborate an adjudicated ordinary high water line L. Genc , S. E. Smith & B. A. Dewitt To cite this article: L. Genc , S. E. Smith & B. A. Dewitt (2005) Using satellite imagery and LIDAR data to corroborate an adjudicated ordinary high water line, International Journal of Remote Sensing, 26:17, 3683-3693, DOI: 10.1080/01431160500165922 To link to this article: http://dx.doi.org/10.1080/01431160500165922

Published online: 12 Apr 2011.

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International Journal of Remote Sensing Vol. 26, No. 17, 10 September 2005, 3683–3693

Using satellite imagery and LIDAR data to corroborate an adjudicated ordinary high water line L. GENC{, S. E. SMITH{ and B. A. DEWITT{ {Agricultural Structures and Irrigation Department, College of Agriculture, Canakkale Onsekiz Mart University, 17020 Canakkale, Turkey {Geomatics Program, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA

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(Received 13 October 2004; in final form 9 February 2005 ) Determination of the ordinary high water line (OHWL) has been and continues to be an important issue. The OHWL defines the separation of sovereignty lands and private ownership on non-tidal water bodies. Determination of OHWL is conducted on a case-by-case basis in Florida through court challenges. A judge makes the decision on where the line exists based upon several criteria—including remote sensing data. This study investigated the possibility of using various remote sensing technologies to provide an efficient and accurate means of determining OHWL for a lake in central Florida. Landsat Enhanced Thematic Mapper (ETM) satellite imagery was compared with the higher resolution imagery IKONOS and Light Detection And Ranging (LIDAR) imagery in order to determine the water’s edge and location of vegetation communities that may be correlated with OHWL. It was found that ETM imagery could be used only for mapping vegetation community transition zones and that this zone provided limited insight to OHWL. IKONOS imagery, on the other hand, was more promising for land cover mapping, but requires further study in order to draw general conclusions regarding its application to OHWL. LIDAR data provided the best results for determining OHWL, but also need further study over a larger area in order to draw final conclusions.

1.

Introduction

The ordinary high water line (OHWL) is that point on the slope or bank where the surface water from a water body ceases to exert a dominant influence on the character of the surrounding vegetation and soils (GFEII 2002). A line is found along the waterbed and banks of a stream, where the incidence of water is so common and long-standing that the soil and vegetation are distinctly different from that of the adjacent upland and it is used to define state-owned sovereignty lands (GFEII 2002). According the Guide to Florida Environmental Issues, sovereignty lands are publicly owned submerged lands beneath the state’s navigable rivers and lakes. Florida acquired title to these lands from the United States when it joined the union in 1845. State ownership extends up to the OHWL, which is the line water leaves at its ordinary high state, visible on soils and reflected by surrounding vegetation (Bay 2001, GFEII 2002). The OHWL frequently encompasses areas dominated by non-listed vegetation and non-hydric soils (GFEII 2002). For low bank lakes such as Lake Hatchineha in Florida, even a small change in water level International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2005 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/01431160500165922

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can result in a major difference in the lake’s configuration, and can affect the OHWL location between state and private lands. A common practice for determining lake boundaries is to use aerial photography in conjunction with field surveys (Davis 1973). Experts familiar with indicators of the OHWL and knowledgeable of the local field conditions interpret the photography and generally try to estimate the location of the line based on multiple indicators (FDEP 2002). They include: (1) water marks (on old dock posts, cypress trees, etc); (2) changes in vegetation; (3) soil analyses; and (4) geomorphologic analysis. The primary problem with this approach is that it is time consuming. The use of remote sensing imagery for mapping land use and land cover has become an integral component of contemporary land use studies. Multispectral reflectance data from satellite sensors serve as an alternate representation of vegetation communities and can therefore, in theory, serve as a surrogate for gathering this information on the ground (Robinove 1981). Relatively low-resolution satellite imagery such as Landsat ETM has been used in the natural science communities for vegetation mapping (Coppin and Bauer 1996, Pax-Lenney et al. 1996). Past efforts using Landsat Thematic Mapper (TM) have had success mapping to broad levels in a classification hierarchy, but have had difficulty defining fine, specific wetland vegetation classes (Mugisha and Huising 2002). The development of new instruments, such as those on IKONOS, with higher spatial resolution, offers the opportunity to improve vegetation mapping via satellite imagery. IKONOS can be used to delineate smaller groups of distinct vegetation, which are lost in imagery with large pixel sizes (Jensen 2004). While the relatively high resolution IKONOS imagery is available for most of the world, Landsat imagery is more widely used because of its relatively low cost (Mugisha and Huising 2002). In a multispectral image, each pixel has a spectral signature determined by the reflectance of that pixel in each of the spectral bands. Multi-spectral classification is an information extraction process that analyses the spectral signatures and then assigns pixels to classes based on similar signatures (ERDAS 2001). It is based on the principle that all of the pixels representing a homogeneous land cover type should have roughly the same spectral signature with relatively small differences due to variables such as shadows. The detail of the classes depends on the spectral and spatial resolution characteristics of the multispectral images. LIDAR is a relatively new technology based, essentially, on radar principles. LIDAR is primarily used for topographic mapping and so it would seem reasonable to assume it could play a role in the determination of OWHL. The drawbacks to LIDAR in this application are the facts that it (a) does not classify vegetation and (b) is much more expensive than any other type of remote sensing. LIDAR can be used in conjunction with other optical-based remote sensing systems. Frequently, for example, digital camera imagery is taken simultaneously with the LIDAR. Due to cost constraints, however, this project did not test this type of system. 2.

Methods

The study area, Lake Hatchineha, is located in south central Florida (figure 1). The test site was 12 km of shoreline along the western shore. The majority of the study area is comprised of wetland forest and marsh vegetation. The study test site was

Corroborating an adjudicated ordinary high water line

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Figure 1.

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Study area.

selected based on the fact that it is a marshy area in which the vegetation height was not more than the average tree height. A surveying team, based on the indication of the highest water level, had delineated the physical location of the OHW elevation. This level of water must have been maintained for a sufficient period of time to leave evidence of the natural vegetation changes from predominantly aquatic to predominantly terrestrial. The adjudicated OHW elevation was determined for Lake Hatchineha based on the field survey as 52.50 feet (16.00 m). 2.1

Imagery

The Landsat ETM and IKONOS images and LIDAR data were acquired when the water’s elevation was known to be near the adjudicated OHW elevation of 52.509 (16.00 m) above the mean sea level based on NGVD29. 2.1.1 Landsat. The Landsat Enhanced Thematic Mapper Plus (ETM) is a multispectral scanner having eight bands sensitive to different wavelengths of visible and infrared radiation. The nominal ground resolution cell size is 28.5 m by 28.5 m. Landsat sensor uses a ‘whisk-broom’ type scanner, which requires a larger pixel size than sensors using ‘push broom’ technology. Since the vegetation near Lake Hatchineha is ‘patchy’ in nature, it was important to use sensors with pixel size smaller than 28.5 m628.5 m in addition to the Landsat ETM. The Landsat ETM image used for this project was taken on 23 October 1999 and bands 1, 2, 3, 4, 5 and 7 were used to produce the land cover map. 2.1.2 IKONOS. IKONOS was launched in the year 2000 and is one of the latest commercial earth-looking remote sensing satellites. IKONOS has two sensors: one has four bands and a 4-m ground cell size (multi-spectral) while the other has one band (panchromatic) and a 1-m ground cell size. Bands 1–3 of the IKONOS multispectral imagery measure reflectance in the visible blue, green, and red portions of electromagnetic spectrum. Band 4, useful in classifying vegetation, measures reflectance in the near infrared portion of electromagnetic spectrum. The IKONOS image used for this project was taken on 29 March 2000. 2.1.3 LIDAR. The way LIDAR works is that a pulsed laser is optically coupled to a beam director which scans the laser pulses over a ‘swath’ of terrain, usually centred on, and co-linear with, the flight path of the aircraft in which the system is mounted. The round trip travel times of the laser pulses from the aircraft to the ground (or

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objects such as buildings, trees, and power lines) are measured with a precise interval timer and the time intervals are converted into a range of measurements by applying the velocity of light (Lefsky et al. 1999). The position of the aircraft at the epoch of each measurement is determined by Global Positioning System (GPS) in a kinematic mode (Lefsky et al. 1999, Jensen 2004, Maune 2001). Rotational positions of the beam director are combined with aircraft roll, pitch and heading values determined with an inertial navigation system, and the range measurements to obtain vectors from the aircraft to the ground points. When these vectors are added to the aircraft locations, they yield coordinates of points in the field of view. The field of view in this study was approximately 1 m. The LIDAR data used for this project was taken on 22 March 2002. The Optec ALTM 1020 system was used to acquire the LIDAR data. Both first pulse and last pulse returns coupled with first and last pulse intensity LIDAR measurements were collected. The LIDAR acquisition took approximately four hours to collect. A 50% LIDAR overlap was planned for this area to ensure adequate ground point distribution in the vegetated areas. A flying height of 300 m above ground, scanning frequency of 16 Hz and firing rate of 5000 Hz ensured high accuracy topographical results. Returns from vegetation (first pulse) produced the LIDAR_VEG (vegetation) channel. A second channel, LIDAR_BE (bare earth), was produced by last returns after processing with a vegetation removal algorithm. Waggoner Engineering, Inc. used proprietary vegetation removal programs to create the bare earth (BE) model. Last pulse data was utilized to produce the digital elevation model (DEM). Orthorectified imagery determined various terrain and vegetation types. This assisted in determining the specific data filtering techniques for unique terrain and vegetation applications. 2.2

Geometric processing

Ground truth for geometric processing was based on (1) the adjudicated OHW elevation that was determined; (2) field trips taken in March 2002 and May 2002 in order to precisely locate and identify vegetation species near the shoreline; and (3) digital ortho photo quad (DOQQ) produced from 6 January 1999 photography. To align the pixel grids and remove geometric distortions, the 1999 Landsat ETM and the IKONOS imagery were geo-referenced to a Universal Transverse Mercator (UTM) Zone 17 projection (WGS 84 datum) based upon the DOQQ image with second order polynomial transformation and the nearest neighbour resampling method. The LIDAR was acquired in ASCII (Easting, Northing, Elevation) format. The datum used was NAD 83. The horizontal coordinates were in the Florida State Plane system, East Zone and the vertical datum was NGVD 29. All coordinates are in US Survey Feet. In order to provide for a common coordinate system, LIDAR data were re-projected to UTM Zone 17 (WGS84) coordinates using ERDAS Imagine (version 8.5) software. 2.3

Creating new datasets

After geometric corrections, the imagery was subjected to several processing steps in order to produce land cover classifications. Figure 2 shows details of the image processing method for all datasets used in this study. Subsets of IKONOS and Landsat ETM images were taken to correspond to the selected LIDAR dataset area. LIDAR data were supplied as x, y and z coordinates in ASCII text files and had to

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Figure 2.

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Image processing flowchart.

be resampled to a grid in order to be subsequently processed as a raster data. The point data were resampled to 1-m grid spacing, which is consistent with the original spacing of the points. In order to overlay the LIDAR and IKONOS datasets, the four multispectral bands of IKONOS were merged with IKONOS panchromatic to correspond a 1-m pixel size that was named original IKONOS. Landsat ETM, however, was resampled to 30-m pixel size and named original ETM. A principal components analysis (PCA) was then performed, where the image was transformed into a new set of bands that more effectively convey the variation of data (Richards and Jia 1999). PCA was applied to a four-band pan-shaped IKONOS dataset and it was found that principal component 1 (PC1) of IKONOS contained nearly 97% of the original pan-shaped IKONOS dataset and PC2 contains 3%. Table 1 shows the percentage of variance based on the Eigenvalues that were calculated during the principal component transformation. LIDAR_BE and LIDAR_VEG were used to generate a new two-band LIDAR dataset, named

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Table 1. Total variance (%) in data explained by each principal component with IKONOS. Bands Eigenvalues % of variance

1

2

3

4

2139.487 96.738

66.354 3.000

5.504 0.249

0.287 0.013

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LIDAR_BE_VEG. In addition, LIDAR_BE_VEG was combined with the first two PCs of the pan-shaped IKONOS image to generate the new IKONOS, which is a four-band dataset (figure 2). For our purposes, based on the previous experience using PCA on original ETM, and LIDAR_BE_VEG, the datasets did not provide useful information for OHWL determination. 2.4

Land cover classification

Classification maps with 20 clusters were produced from original ETM, original IKONOS, and new IKONOS datasets using the ISODATA clustering method (unsupervised classification). After field trips and studying the DOQQ, these 20 clusters were condensed into the five vegetation covers named Classes 1–5. Following classification, all the datasets were smoothed using a low pass filter (363) as outlined in figure 2 in order to reduce noise in the resulting images. The LIDAR_BE_VEG image was classified based on the vegetation height to produce five vegetation height classes. These five vegetation classes had a nominal relationship with a group of vegetation species that are assigned as an indicator status for wetland vegetations and OHWL (GFEII 2002). It was assumed that Class 1 corresponded to obligate wet (OBL) species that occur almost always (.99%) under natural conditions in wetlands. Class 2 corresponds to facultative wetland (FACW) species that usually occur in wetlands (67–99%), but are occasionally found in non-wetlands. Class 3 corresponds to facultative (FAC) species, which are equally likely to occur in wetlands or non-wetlands (34–66%). Class 4 correspond to facultative upland (FACU) that rarely occur in wetlands (1–33%). In addition, Class 5 corresponds to plant species considered to assign an indicator status of upland (UPL) (GFEII 2002). Since the adjudicated OHW elevation was known to be 52.509 (16.00 m), this contour was directly generated in pixel form from LIDAR_BE. It was hypothesized that OHWL could be located at the boundary between any two vegetation classes. In order to visualize the relationship between the classified images and the OHWL, the OHWL was overlaid on the classified Landsat ETM, original IKONOS, new IKONOS and LIDAR_BE_VEG images. The particular inset was selected by closer inspection. By visualizing classified ETM, original IKONOS, new IKONOS and LIDAR_BE_VEG images, it was found that figure 3(a) shows inconsistent correspondence and figure 3(b) shows good correspondence between OHWL and land cover classes. 3.

Results

The boundary between two vegetation classes is expected to be the indicator for OHWL. Based on the ground truthing and by visual inspection, we did not find the relationship between land cover types and OHWL on classified ETM (figure 4).

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(a)

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(b)

Figure 3. Examples of (a) inconsistent indicator of OHWL overlaid to classified images (new IKONOS); and (b) good indicator of OHWL overlaid to classified images (LIDAR_BE_VEG).

However original IKONOS and new IKONOS showed a relationship between OHWL and vegetation classes (figures 5 and 6, respectively). We found the best relationship between vegetation height and OHWL on the classified LIDAR_BE_VEG image (figure 7). On classified Landsat ETM images, it was found that Class 1 corresponds to most of the OBLW species and FACW species that are mostly cypress trees and wetland indicator plants and Class 2 covers FACW species (figure 4). At the same time, both Class 1 and Class 2 on ETM also contain FAC and UPL species on ground. In some cases, the OHWL is located in both Class 1 and Class 2 (figure 4). Most of the known FAC and UPL species appears to be Class 3 vegetation cover. The OHWL

Figure 4.

Classified ETM image (OHWL outlined).

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Figure 5.

Classified original IKONOS image (OHWL outlined).

also intercepted with Class 3 in most cases. Based on the field trips, the likelihood of OHWL falling within Class 3 is minimal. Because of low spatial resolution, the classified Landsat ETM image did not provide sufficient information about the relationship between OHWL and land cover types. Visual inspection of the inset from original IKONOS verifies that some cases of Classes 3 and 4 correspond to OBL species (figure 5). However, FACW and FAC species sometimes fell into Class 2. In most cases the OHWL did intercept with these two classes (figure 5). It appeared that most of Class 4 corresponds to FAC species and the OHWL line was located inside that class. Because of spectral similarities between wetland tree and upland tree species, both wetland and upland trees were

Figure 6.

Classified New_IKONOS image (OHWL outlined).

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Figure 7.

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Classified LIDAR BE_VEG image (OHWL outlined).

classified as Classes 1 and 2 (figure 5). In many areas the OHWL occurs at the boundary between Classes 3 and 4, but this is not consistent when we examined the entire classified original IKONOS image (figure 5). The original IKONOS dataset has the potential to discriminate land cover more accurately; however, this image was unable to discern the exact location of the adjudicated OHWL that was expected to occur on the boundary between two land covers. Due to the high spatial resolution, one could expect that using the multispectral original IKONOS dataset can give a clear indication about vegetation community changes from one species to another. Because the surface is flat around Lake Hatchineha, even seasonal rainfall could dramatically change the vegetation communities around the lake. This also creates some confusion in the vegetation classification and affects the classification result of even high-resolution datasets. One promising approach for determination of OHWL location is the composite of LIDAR_BE_VEG and the PCs of IKONOS. By adding the elevation component to spectral information obtained from PCA, one is expected to be able to better understand the relationship between the plant communities and OHWL. Visual inspection of the inset on new IKONOS verifies that the dominant plant species, OBL, correspond to Classes 1 and 2 (figure 6). In addition, FACW species correspond to Class 2 more accurately than original IKONOS based on the ground observation (figure 6). It appears to be that in most cases Class 3 corresponds to FAC species. original IKONOS and new IKONOS data appear to give very similar results. FACW species and OBL species were also classified as Classes 4 and 5 (figure 6). However, Class 5 mostly appears to correspond to UPL species. Land cover classification based on the spectral information shows that there is no exact correspondence between the OHWL and land cover types using ETM, original IKONOS, and new IKONOS data. Using vegetation height classification based on LIDAR_BE_VEG shows the best results were obtained in terms of relationships between OHWL and vegetation height classes. Almost all OBL, FACW, and a small amount of FAC species were classified as Class1 and in most cases FAC species were classified as Class 2 (figure 7). The OHWL consistently falls on the boundary

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between Class 2 and Class 1 vegetation height cover. One can predict that the OHWL can be located on the boundary where land cover classes change from FACW species to FAC species.

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4.

Conclusions

Remote sensing is a promising means of determining land cover types in wetland areas, but its usefulness is related to the type of imagery used and the nature of the shoreline (Jensen 2004). In this study, conditions on the ground had almost certainly changed between the time the imagery was taken and the time field trips were made. Fortunately, the date of DOQQ imagery was close to that of the satellite images and the DOQQ could therefore be used to help validate the ground observations. Key elements were the spatial and spectral resolution and the ability to discern very small changes in vegetation. However, the Landsat ETM dataset, for example, was very limited in its usefulness due to its relatively low spatial resolution. An additional constraint on the use of the Landsat satellite image was that ground truth information about land cover was not available to directly correspond with the dates of the imagery used in the study. LIDAR yielded more accurate results in terms of determining the approximate OHWL. Because vegetation species vary widely on this lake, it was a poor indicator of OHWL for Landsat ETM and IKONOS datasets. The plant species in the vicinity of the known location of the OHWL are not classified in any single wetland designation and many are found both in wetland and upland areas, so that the identification of vegetation communities around low bank lakes will not accurately predict the OHWL. In contrast, LIDAR_BE_VEG alone accurately predicted the OHWL based on the vegetation heights classification. In non-tree areas vegetation heights could be the other indicator of OHWL. The study area covered a relatively small portion of Lake Hatchineha and so it was impossible to draw any lasting conclusions or extrapolate the results with respect to LIDAR without testing the idea on other lakes. Moreover, due to the high cost of LIDAR relative to other sensing devices, it might or might not prove to be cost-effective in OHWL determination. 5.

Further research

We recommend that further research focuses on the potential usefulness of looking at the water’s edge rather than vegetation boundaries. The waterline could provide a mask outside which to look for OHWL using the vegetation boundary. High-resolution waterlines might be detected using RadarSat’s fine beam mode SAR imagery which is an excellent means of distinguishing waterlines in tidal bodies and might also work in non-tidal situations, as in this case. RadarSat would cost less than LIDAR and might provide a clearer shoreline, since in the central Florida lakes emergent vegetation is nearly always present, thus complicating the LIDAR signal interpretation. In summary, remote sensing data can provide valuable information in determining the OHWL, and new technology such as LIDAR and RadarSat will probably have an important role. This should help bring down the high cost of establishing this line and so allow it to be determined for all lakes in Florida—not only on a case-by-case basis.

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