Mapping Coastal Vegetation Using an Expert System and ...

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Mapping Coastal Vegetation Using an Expert System and Hyperspectral Imagery K.S. Schmidt, A.K. Skidmore, E.H. Kloosterman, H. van Oosten, L. Kumar, and J.A.M. Janssen

Abstract Mapping and monitoring saltmarshes in the Netherlands are important activities of the Ministry of Public Works (Rijkswaterstaat). The Survey Department (Meetkundige Dienst) produces vegetation maps using aerial photographs. However, it is a time-consuming and expensive activity. The accuracy of the conventional vegetation map derived using aerial photograph interpretation (API) is estimated to be around 43 percent. In this study, an alternative method is demonstrated that uses an expert system to combine airborne hyperspectral imagery with terrain data derived from radar altimetry. The accuracy of the vegetation map generated by the expert system increased to 66 percent. When hyperspectral imagery alone was used to classify coastal wetlands, an accuracy of 40 percent was achieved—comparable to the accuracy of the API-derived vegetation map. An analysis of the efficiency of the proposed expert system showed that the speed of map production is increased by using the new method. This means that digital image classification using the expert system is an objective and repeatable method superior to the conventional API method.

Introduction The Dutch Ministry of Public Works (Rijkswaterstaat) incurs significant annual expenditure performing high-resolution vegetation mapping and monitoring of a number of landscapes and ecosystems within the Netherlands. Among these are the saltmarshes, which are important natural habitats for many species, including endangered birds, and which require careful management due to their sensitivity to human activity (Adam, 2002). Vegetation mapping using infrared airborne photography (aerial photograph interpretation, API) is widely used for inventory, management, and monitoring purposes with regard to nature reserves and other (semi)-natural areas. The maps can be used as indicators of the ecological properties of the areas under study and are a means of satisfying the K.S. Schmidt, A.K. Skidmore, and H. van Oosten are with the International Institute for Geo-Information Science and Earth Observation (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands ([email protected]). E.H. Kloosterman is with Meetkundige Dienst van Rijkswaterstaat, Postbus 5023, 2600 GA Delft, The Netherlands. L. Kumar was with the International Institute for GeoInformation Science and Earth Observation (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands; he is currently with the Department of Environmental Sciences & Natural Resources, Management University of New England, Armidale, NSW 2351, Australia. J.A.M. Janssen was with Meetkundige Dienst van Rijkswaterstaat, Postbus 5023, 2600 GA Delft, The Netherlands; he is currently with Alterra, Ecologie & Milieu, Postbus 47, 6700 AA Wageningen, The Netherlands. P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G

demand by managers and policy makers for environmental geoinformation. The Survey Department (Meetkundige Dienst) has produced vegetation maps on an operational basis since the early 1970s, using a land-unit approach. This approach is based on visual interpretation of stereoscopic, mainly falsecolor, aerial photographs (ranging from 1:5000 to 1:10,000 scale), and is supported by field observations. The landscape-unit approach applied within the Survey Department is based on a holistic (system) approach to “landscape” (Zonneveld, 1979; van Gils et al., 1985). Based on this concept, a landscape can be considered an integrated entity, being the result of the action and interaction of climate, rock, landform, soil, vegetation, fauna, water, and humans (Zonneveld, 1979; Schroevers, 1982), and, therefore, vegetation can be viewed as an indicator of the properties of the landscape as a whole. This definition implies that in a given place the vegetation is determined by the total complex of properties of the landscape in that area. From this, the following two deductions can be made: (1) changes in the (ecological) properties of the landscape in space or time will lead to changes in vegetation, and (2) a change in vegetation in space or time indicates a change in the (ecological) properties of the landscape. Statement (1) is the fundamental assumption of the mapping procedure applied in the Survey Department. Statement (2) forms the basis for the application of vegetation maps for management purposes. Vegetation maps based on a landscape ecological approach show the spatial distribution of, and the spatial relationships between, vegetation types. Such maps provide managers with an overview of ecologically homogeneous management units at the scale they require. Mapping as performed in the Survey Department involves the delineation of stands of vegetation or land(scape) units recognizable on remote sensing images (such as aerial photographs). Based on a number of phytosociological criteria (e.g., floristic composition and vegetation structure), a stand of vegetation (as encountered in the field) can be assigned to a particular abstract vegetation type. Thus the term “vegetation stand” (syn: phytocoenosis) is used when discussing measured field data, and the term “vegetation type” is used when discussing abstract aspects of the vegetation. If due to similarities in image characteristics or scale constraints the vegetation stands cannot be mapped separately, the final legend unit will consist of a complex of vegetation types (mosaic) in a socalled “matrix legend.” Although the approach is effective, the Ministry wished to investigate whether digital remote sensing in combination with laser altimetry would increase the objectivity and productivity of the mapping procedure. Despite the fact that high-resolution image processing based on digital remote Photogrammetric Engineering & Remote Sensing Vol. 70, No. 6, June 2004, pp. 703–715. 0099-1112/04/7006–0703/$3.00/0 © 2004 American Society for Photogrammetry and Remote Sensing June 2004

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sensing imagery is already operational within the Ministry, and the potential of digital imagery for mapping and monitoring vegetation is recognized (De Jong et al., 1997), their use for predicting vegetation resources has only been partially realized (Janssen et al., 1996). In particular, classification across ecological gradients was not improved. Consequently, more objective and cost-effective (and thus repeatable) procedures are needed to enable end users to implement digital remote sensing techniques in their daily practice. The productivity of digital remote sensing techniques can be increased if they are combined with other ancillary data. Image processing uses the spectral (and occasionally spatial) attributes of the remote sensing imagery. Additional data may be available for a particular area, such as elevation data, derived terrain variables, and geologic and climatic data. A vegetation scientist uses this information, along with experience and knowledge, to map vegetation units. Expert systems have been proposed and tested as a method for integrating data from various sources (Lee et al., 1987; Ripple and Ulshoefer, 1987; Robinson et al., 1987; Skidmore, 1989). Tools for analyzing and interpreting digital spatial information have been developed, thereby automating the currently labor-intensive manual interpretation of aerial photographs and maps (Skidmore, 1989; Skidmore et al., 1991; Skidmore et al., 1992; Skidmore et al., 1996; Skidmore et al., 1997). However, these techniques had not previously been demonstrated for monitoring the vegetation of coastal ecosystems as currently carried out by the Ministry’s Survey Department. In summary, there is a demand at the Ministry for techniques that offer higher (i.e., objective and cost-effective) productivity for vegetation mapping, as well as more accurate maps, to be embedded in operational systems for client organizations. The aim of this study was to demonstrate that the use of hyperspectral imagery in combination with laser altimetry and ecological expert knowledge of vegetation distribution relative to topographic characteristics of the landscape could increase the productivity of operational vegetation mapping of coastal ecosystems. The specific objectives were to (1) evaluate the ability to detect detailed vegetation types with hyperspectral measurements, (2) devise expert rules from the cause-effect relationship of environmental variables on the distribution of vegetation types, (3) assess the improvement when incorporating expert knowledge in the mapping process, (4) compare the results with a conventional API map in order to evaluate whether the objectivity in mapping and monitoring vegetation

can be increased, and, finally, to evaluate whether there is potential to automate vegetation mapping by introducing costsaving technologies.

Methods To meet the study objectives, an expert system for mapping vegetation units in a complex coastal wetland on the island of Schiermonnikoog, the Netherlands, was developed. Available information included elaborate field data on plant species composition, HyMap hyperspectral digital data, a digital terrain model (DTM), and ecological knowledge about the position of vegetation types in the environment. The accuracy of the vegetation map derived by the expert system was assessed in order to compare the result with that produced by an experienced vegetation scientist after considering the physical, biological, and land-use characteristics of an area. The Ecology of Schiermonnikoog Schiermonnikoog (Figure 1) is one of the Dutch barrier islands, formed by the deposition of sediments washed from the European continent by the river Rhine. The island basically extends towards the east due to the prevailing eastward direction of the sea current, while the west and northwest are artificially fortified against erosion by the sea. The northern shore of the island consists of dunes, those in the west being artificially created and maintained. From the dunes the island slopes gently down in elevation towards the south-southeast. The vegetation on the south-southeastern shore is adapted to regular inundation by seawater, forming a well-studied saltmarsh. Inundation by saltwater of the Wadden Sea is controlled by the tide, and during the winter season the prevailing west winds cause higher sea levels than in the summer. There is a gradient of succession from the west to the east of the elongated island, the pioneer vegetation establishing itself on the younger part of the island as it grows towards the east. Many factors determining the distribution of saltmarsh vegetation communities are directly (tidal regime) or indirectly (climate, geomorphology and sedimentation, soil, water composition, biotic factors) related to the elevation above mean sea level (Bakker, 1989; Adam, 1990; Bakker et al., 1993; Janssen, 2001). Therefore, one can assume that vegetation location is strongly related to position with respect to NAP (Nieuw Amsterdams Peil, Amsterdam Ordnance Datum), which cannot be directly mapped using passive remote sensing but can be accurately mapped using laser altimetry (Baltsavias, 1999).

Figure 1. Schiermonnikoog: The grey levels indicate the elevation.

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TABLE 1.

VEGETATION ZONES INFLUENCED BY ENVIRONMENTAL FACTORS (AFTER BAKKER (1989); BAKKER ET AL. (1993))

Plant Community Spartinetum townsendii Salicornietum strictae Puccinellion maritimae Puccinellietum maritimae a Halimionetum portulacoidis Plantagini-Limonietum Armerion maritimae Juncetum gerardii a Artemisietum maritimae Puccinelio-Spergularion salinae Atriplici-Agropuretum pungentis Lolio-Potentilion Agrostis stolonifera Trifolium fragiferum communities Ononis spinosa Carex distans communities Saginion maritimae Galio-Koelerion and Hippophae shrubs

Environmental Feature daily submergence in pioneer zone

–depressions –creek bank levees –intermediate positions –clay soils –sand ridges, creek banks –irregularly changing conditions, occasionally stagnant water –higher after deposition of drift material

Saltmarsh Zone tidal flats lower saltmarsh between MHW and spring tide level mid and upper saltmarsh just below spring tide and winter storm level

upper saltmarsh –slightly desalinated –adjacent to low dunes –transition wet/salt and dry/fresh

transition slightly higher dunes

a

These communities depend on livestock grazing.

Many anthropogenic factors also influence saltmarshes (Adam, 1990), but these have not been considered in this study. Table 1 summarizes some environmental factors determining the different vegetation zones. Field Data Collection In total, 384 field plots were selected and located using differential Global Positioning System (DGPS) measurements. These field plots represent two levels of detail. The first set of 208 field plots (each 2.5 by 2.5 m) were randomly selected across the saltmarsh vegetation by experienced ecologists, who were mapping the same saltmarsh on a regular basis using aerial photography, to represent the vegetation communities occurring in the saltmarsh. The ecologists recorded detailed species composition and percentage cover estimates. The second set, composed of 176 so-called “quick” samples, was taken to support image classification and accuracy assessment. The quick relevees (data collection sheets), which had been collected in the same field campaign by the ecologists, recorded cover and composition of dominant species and those contributing to the definition of the vegetation type (indicator species), together with the DGPS coordinates. For 122 of the 208 detailed plots, at least 15 field spectrometer measurements were recorded at nadir and randomly scattered over the 2.5- by 2.5-m plot. The field spectrometer measurements were not used for the current study, but were analyzed to evaluate the ability to differentiate vegetation types based on their spectral characteristics (Schmidt and Skidmore, 2003). Schmidt and Skidmore (2003) found statistically significant spectral differences between vegetation types and indicated the potential of hyperspectral remote sensing for the discrimination of vegetation types. The relevees and spectral measurements were recorded between 1000 and 1600 hours on sunny cloudless days in May and June 1999, while the rest of the field descriptions were completed by October 1999. During image processing, 27 field plots had to be removed because their spectra did not match the field description— most probably because the geometric accuracy of the imagery was lower than expected. All well-positioned field plots were used, because image spectra were used for classification and accuracy assessment. Here “well-positioned” means that the plot fell within a relatively homogeneous area of pixels (Bakker and Schmidt, 2002) and the spectrum matched the P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G

vegetation type. In other words, pixels falling on very sharp edges and thus having obviously mixed spectra, or those that had moved entirely into a neighboring patch, were discarded. The number of field DGPS points used was therefore reduced to 357 field descriptions scattered over the entire natural reserve of the island of Schiermonnikoog. They were randomly divided in half, one half being used for image classification and the derivation of the expert rule base, the other half for the accuracy assessment. The procedure followed was to sort the 357 plots by vegetation type, and then every second plot was used for classification. The remaining plots were used for the accuracy assessment. This ensured that the vegetation types were randomly divided into two equal groups. Description of the Saltmarsh Vegetation The Ministry of Public Works has prepared a vegetation classification of the saltmarsh data with the help of the SALT97 classification software (De Jong et al., 1998). This ordering software uses the plot data of species composition to group plots into vegetation types according to the similarity between the mix of species within plots. The characteristic composition of these vegetation classes was predefined from previous experience with saltmarsh vegetation composition. The SALT97 software was used for the first ordering of the saltmarsh relevees, and then the results were manually edited to finally produce 17 vegetation communities. Using the ordered results (Table 2), it became clear that SALT97 is a very useful tool for the saltmarsh vegetation types ranging from Elymus “down to” the pioneer vegetation. Vegetation characterized by Juncus maritimus and other vegetation types in the transition to the dunes and on the beach plain required manual editing. Table 2 lists how the vegetation types for the expert system relate to the SALT97 types. Some of the original SALT97 types (e.g., Glaux and Scirpus-Phragmites) were not included because they are very rare and limited in extent; some had to be excluded because the number of plot samples was insufficient to check for accuracy. The two vegetation types on the tidal flat, Spartina (1) and Salicornia (2), were very sparsely vegetated during the time of image acquisition, and consequently the clay background dominated the spectra. They were grouped to form the bare (“SpaSal”) class. Furthermore, the types Salicornia (2), with higher percentage cover, and Suaeda (3) were grouped June 2004

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TABLE 2.

VEGETATION TYPES USED IN THE STUDY, THE SALT97 CODE, THE CLASS CODE USED IN THIS PAPER, AND THE NUMBER OF SAMPLE PLOTS CONTRIBUTING TO EACH TYPE SALT97 code (De Jong et al., 1988)a

Characteristic Species

Class Code

Water Spartina t., Salicornia e. Suaeda m., Salicornia p. Salicornia e., Limonium v. Limonium v. Puccinellia m. Halimione (Atriplex) p. Festuca r., Artemisia m. Juncus g., Suaeda m., Limonium v. Juncus g., Artemisia m., Festuca r. Festuca r. Juncus m. Elymus a. Agrostis s. Agrostis s., Lotus c. Sagina m., Armeria m. Carex e., Agrostis s., Sagina n., Sedum a.b dune beach sand

Water SpaSal SuSa SaLim Lim Pucc Hal FestArte JunSuaLim

1

2

4

8 13

3

4

5

6

8

10

11

12

13

14

15

B

D

O

W

Total

4

16

4 18 32 8 6 33 9 38 16

27

27

6 14 8

5 6 33 9 38

JunAtrFes Fest JunM Ely Agros AgrosLot SagArm Dunefoot Dune Beach Total

9

7

7 16 35 21 11 12 38

16 35 21 11 12 11

27 22

4

21

22

33

6

9

48

45

16

35

32

12

11

27

4 4

22

6

4

22 4 357

(1) Ss5 Ss3, (2) Qq0 Qq3, (3) Qq3 Qu Pl3, (4) P Pp Ppl Pg, (5) Pl3, (6) Ph5 transition Ph5-Jfz, (8) Jj, (9) Pj/Jj Jf Jfz, (10) Jjm Rm, (11) Xy3 Xy5, (12) Jj-r, Jf-r Rgf,Ro, (13) Lolium perenne-Poa pratensis-type, (14) Ee, (15) Cr, (B) beach, (O) bare, (W) water. b More species were recorded. a

into one type (“SuSa”) because of their similar appearance and landscape position. In the Juncus gerardii type (8), two distinct groups can be differentiated by the abundance of Suaeda and Limonium in the “JunSuaLim” group and Artiplex postrata and Festuca rubra in the “JunAtrFes” group. Some field samples of “JunSuaLim” described in the late season (September, October) had clearly shifted to this class from the Suaeda type (3), because, in accordance with the image spectra acquired in May, they had originally been grouped with the “SuSa” class. The Festuca rubra type (9) was split into plots dominated by Artemisia maritima (“FestArte”) and those dominated by Festuca rubra (“Fest”), because the canopy structure and spectral appearance of Artemisia-dominated vegetation differ significantly from those of Festuca-dominated vegetation. The Potentilla-Odontites-Lotus type (12) was split into two groups: “Agros” and “AgrosLot,” the latter having a distinguishing abundance of Lotus Corniculatus and Poa pratensis and/or Carex distans. Because we were more interested in the saltmarsh vegetation, the Carex extensa and Agrostis stolonifera types (14 and 15) were combined to become the “Dunefoot” vegetation. This describes their position in the landscape, being the transition to dune vegetation (which does not fall within the main focus of this study). Thus, field descriptions were classified into 17 vegetation types, the addition of classes for water and beach sand making a total of 19 classes. Acquisition of Hyperspectral Imagery An Australian hyperspectral scanner called HyMap (Integrated Spectronics Pty Ltd, URL: http://www.intspec.com/ Products/HyMapProd.htm, last accessed 11 February 2004) acquired the imagery on an aircraft from the German Research Institute (Deutsches Zentrum für Luft- und Raumfahrt, DLR). The Netherlands Remote Sensing Board (Beleids Commissie Remote Sensing, BCRS) offered us the financial means for this flight over Schiermonnikoog on 27 May 1999. It was a 706

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perfectly cloudless day and DLR was able to execute the flight as planned, while two reference field spectrometer measurements (dry sand on the beach and dark water in the creek), as well as DGPS measurements, were concurrently recorded on the ground. Ground control points were collected for georeferencing the imagery, as well as for validating the georeferencing, and consisted of DGPS measurements at path and creek junctions, and other distinct features. The imagery was flown in four overlapping strips in an east-west direction, with a resulting pixel size of 3.5 by 3.5 m. Overall, the imagery was of high radiometric quality. DLR processed the HyMap imagery and undertook the geometric and radiometric corrections. The geometric accuracy has not been quantified, but the root-mean-square error (RMSE) is definitely greater than one pixel, which complicated the analysis and reduced map accuracy (this can be seen by checking the fit of the imagery onto the DTM, and comparing DGPS field descriptions with pixel spectra). Image Classification The spectral angle mapper (SAM) is one of the most widely applied supervised classification procedures for hyperspectral imagery. The SAM algorithm determines the spectral similarity between two spectra (e.g., known class spectrum and image pixel spectrum) by calculating the angle between the two spectra, and treating them as vectors in n-dimensional space, where n is the number of bands measured in the spectra. In other words, given a number of m class spectra, the result of a SAM classification is a rule image with m number of bands, where each band represents the angle of each image pixel spectrum to one of the m class spectra. Each pixel of the classified image is then assigned the class that has the minimum band value in the rule image. The training data set (comprising 179 field plots) was used to define 179 separate endmembers before combining the field plot classes to form 19 vegetation type classes (including water and beach sand). This was done by first determining P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G

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which endmember had the lowest angle to a pixel spectrum, and then looking up the field class to which this endmember belonged; this defined the class of the pixel. The spectral angles of the thus selected endmember also defines the rule weights of all classes. The rule image, consisting of 19 rule layers, was used as direct input into the expert system. The expert system inverts the spectral angle values, so that large angles represent a lower probability than do small angles. Derivation of the Digital Elevation Model (DEM) and the Digital Terrain Model (DTM) A digital elevation model (DEM) of the whole island (produced by the Ministry’s Survey Department from laser altimetry) was used to deduce height, slope, aspect, and terrain position (gully, midslope, ridge, or flat) over the whole study area. The slope gradient and slope aspect were calculated using standard GIS algorithms. Terrain position is a more complicated variable to estimate, but has been shown to be important in determining the distribution of vegetation species (Skidmore, 1989) and was derived by Skidmore (1990). An improved version of this algorithm was written in order to calculate terrain position for the saltmarsh environment because mapping terrain position in these extremely flat areas has proved difficult (O’Callaghan and Mark, 1984). The improved version of the algorithm as described in Skidmore (1990) was used to calculate the terrain position (i.e., ridge, upper midslope, midslope, lower midslope, and gully) of each cell in the regular grid. This algorithm locates ridges and stream lines from geomorphic principles, and then interpolates midslopes using a modified Euclidean distance measure. On Schiermonnikoog the variation in elevation is very low; therefore, upper midslope and lower midslope were taken as one category of almost flat terrain (category “flat”). In addition, all areas over 5 m above sea level were called “dune,” because we were not interested in distinguishing the vegetation types in the dunes, but were concentrating on the saltmarsh. The ecological position of a particular species can be estimated from these terrain variables when combined with the above elevation. The Expert System Approach The expert system approach used here was described in detail by Skidmore (1989) and the same terminology is also used. In this case, the research question to be answered by the expert system is: “what vegetation unit occurs at a given location in the wetland?” Using information from the GIS database layers, the expert system infers the most probable vegetation unit that would occur within a given grid cell. Let Sa be the saltmarsh vegetation unit class (for a 1, . . . , n classes) occurring at location Xi,j, which is at the ith row and jth column of the GIS raster database. Let Eb be an item of evidence (for b 1, . . . , k items of evidence) known at location Xi,j. Set up a hypothesis (Ha) that class Sa occurs at location Xi,j. A rule may be defined thus: Eb → Ha,

(1)

i.e., given a piece of evidence Eb, then infer Ha. The expert system then infers the most likely vegetation unit within a given cell, using Bayes’ theory to update the probability of the rule that the hypothesis (Ha) occurs at location (i,j) given a piece of evidence (Eb), i.e., P(HaEb) [P(EbHa)P(Ha)]P(Eb).

(2)

As explained by Skidmore (1989), P(Eb|Ha) is the a priori conditional probability that there is a piece of evidence Eb (e.g., a slope of less than 0.05°) given a hypothesis Ha (e.g., Festuca rubra vegetation unit) that class Sa occurs at location P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G

(i,j) (also known as the class-conditional probability; see Duda and Hart (1973)). The rules are the most subjective aspect of an expert system (Forsyth, 1984). In an ideal situation, rules may be derived statistically; often, though, this is not possible. Thus, a rule is a heuristic estimated from the “feeling” or “knowledge” of experts. Rules concerning environmental relationships cannot normally be expressed with absolute certainty (i.e., true or false). In other words, a rule lies on a continuum between true (probability of 1) and false (probability of 0), depending on how sure we are that the rule is true (or false). In Equation 2, P(Ha) is the probability for the hypothesis (Ha) that class Sa occurs at location (i,j), and is estimated by the experienced vegetation scientist from the expected extent of each vegetation unit in the area. On iterating with the b 2, . . . , k items of evidence from the GIS database, P(HaEb; b 1) (i.e., the a posteriori probability of Ha, given Eb, for b 1) replaces P(Ha) in Equation 2. P(Eb) is the “classical marginal probability,” and is the probability of the evidence alone or the probability that any cell has an item of evidence {Eb} such as a southerly aspect. Bayes’ theorem provides a formula to calculate P(Eb): i.e., n

P(Eb) P(EbHa)P(Ha),

(3)

a1

thereby allowing P(Eb) to be continually updated at runtime as P(Ha) is updated. The expert system developed for this study used forward chaining with a complete enumeration of the data (i.e., a blind search terminated by running out of evidence). The evidence {Eb} at Xi,j should be independent; otherwise, P(Eb) would become larger or smaller and perhaps cause the a posteriori probabilities to be incorrect. In this case the evidence {Eb, b 1, . . . , k} was assumed to be independent. However, Bayes’ theorem appears quite robust with respect to this problem, because we are usually interested in the relative magnitude of the probabilities (Naylor, 1984). In addition, the same number of items of evidence were used to calculate each hypothesis in this study, ensuring that the relative order of errors is the same (Naylor, 1984). The vegetation unit assigned to each cell by the expert system is that which has the highest a posteriori probability {maxP(Ha)a 1, . . . , n} at location (i,j). This expert system algorithm was programmed using Interactive Data Language (IDL; Research Systems, Inc.). The thematic map output from the various classification strategies was plotted using IDL. Derivation of the Expert Rule Base Rules provide the link between the GIS database layers described above and the knowledge of experienced vegetation scientists. The expert rule links together ecologists’ knowledge concerning individual species with the geographic data available for the study area (specifically, hyperspectal imagery and the DTM variables, i.e., elevation, slope, aspect, and terrain position). The rule base was generated from statistical analysis of the field plot data, and compared with ecological knowledge about the species occurring in the saltmarsh (see Table 1). To check the relationship between landscape position and vegetation type, the ancillary data for the field plots were used to produce expert rules. Only those plots actually used for the classification (training samples) were used. In this case, the rules were expressed as the probability of an item of evidence occurring (e.g., gradient is less than 0.05°) given a particular hypothesis (e.g., that the vegetation unit is Festuca rubra). Rules were constructed by relating the GIS data layers to the vegetation types. All 179 field plots were geographically overlaid on the ancillary data layers (height, aspect, slope, terrain position) to produce a table of four attributes for each June 2004

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Figure 2. The expert rule weights for (a) elevation above NAP in cm; (b) slope, increase in elevation per 10,000 cm; (c) aspect, where 0 degrees is north; and (d) terrain position, where 0 almost flat, 1 gully, 2 midslope, 3 ridge, and 4 more than 5 m above NAP. Each interval on the y-axis represents an interval between 0 and 1 (i.e., low to high probability) for each vegetation type as indicated.

field plot. For each attribute, the histogram distribution of the population of field plots was divided into ten equal percentiles, representing the position of the plant community in relation to, for example, sea level, which then defined the boundary layers or slices for the expert system (see intervals on the x-axis in Figure 2). Then the frequency per vegetation type of plots falling within each boundary layer defined the weight for the expert rule table. The frequency weights were normalized by fitting a normal distribution around the mean, with the standard deviation of each vegetation type’s frequency values across all boundary types. The result is a table per ancillary GIS layer, with weights representing the likelihood in favor of a hypothesis (Ha) given a piece of evidence (Eb). This was done for each ancillary data layer. In other words, P(EbHa) is a rule expressed in Figure 2a, for example. When several ancillary layers were used in the expert system, constants 1, 2, and 3 were added to all weights of aspect, terrain position, and slope, respectively. This was done in order to reduce the multiplicative increase of the Bayesian system, i.e., to ensure that the effect of the expert rules was not increased proportionally to the number of layers used. Accuracy Assessment The accuracy of all the vegetation maps resulting from the expert system was determined using the independent test data 708

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set and confusion matrices of the field observations against the classification/expert system result (Cohen, 1960; Congalton and Green, 1999). The accuracy tables were then statistically compared using the z-statistic (Cohen, 1960) to test for significant differences. In addition, a vegetation map produced in 1997 by the Ministry of Public Works using API was compared with the field samples of this 1999 campaign. This enabled direct comparison of the expert system with the API approach, and the result is presented at the end of the section on Expert System. All 357 field plots were used to assess the accuracy of the API vegetation map because none of them was used to devise the map. However, several of these plots were either outside the mapped area of the 1997 vegetation map or inside unlabeled polygons. These included the water and beach plots, some plots on the lower mudflats of the saltmarsh, as well as a few plots on the dunes and dunefoot. We therefore excluded the classes “Water,” “Beach,” “Dune,” and “Dunefoot.” The final number of usable field plots was 241. The legend classes of the API map were translated into the same vegetation classes deduced from the 1999 fieldwork by choosing the SALT97 class (corresponding to the 1999 fieldwork) constituting the maximum percentage of the API mapping unit in which a plot fell. Whenever two vegetation types had the same percentage area in one mapping unit, the API class that would be the same as the field class was chosen if P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G

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TABLE 3. THE ACCURACY ASSESSMENT OF THE VEGETATION MAP PRODUCED USING THE HYPERSPECTRAL IMAGERY WITH SAM CLASSIFIER, WITH THE KAPPA STATISTIC, OMISSION ERROR (OE), PRODUCER ACCURACY (PA), COMMISSION ERROR (CE), AND USER ACCURACY (UA) Imageclass

1

1 Water 2 2 SpaSal 3 SuSa 4 SaLim 5 Lim 6 Pucc 7 Hal 8 FestArte 9 JunSuaLim 10 JunAtrFes 11 Fest 12 JunM 13 Ely 14 Agros 15 AgrosLot 16 SagArm 17 Dunefoot 18 Dune 19 Beach % OE 0 % PA 100

2

3

5 2 1

2 6 5

1

1

4

5

2

6

7

8

9

10

11

12

13

14

15

16

1 3 1 6 2 1

1

18

19

% CE

2 0 100

0 100 29 71 50 50 100 0 50 50 67 33 71 29 68 32 70 30 67 33 100 0 67 33 68 32 60 40 100 0 0 100 40 60 40 60 0 100 % acc. 39.89 Kappa: 0.35

1

2 1 1

17

2 1

1

3 2 7 2 1

3 2 3 1 1 1

2 1

1 1

4 5 3

1

1

2 3

2 3

1

2

1

9 2 1

2 3 2 1

2 1

1

1 3

3

2 1

2 1

3

33 67

63 38

1

1 1

1 44 56

63 38

100 0

50 50

63 37

63 38

79 21

possible. By cross-tabulating the field classes with the translated API classes, we estimated the accuracy of the API map by using the same vegetation types as with the expert map.

Results and Discussion Hyperspectral Imagery The result of the SAM supervised classification of the eastern part of Schiermonnikoog is shown in Plate 1, while Table 3 shows the accuracy assessment for the SAM using the independent half of the field samples. The overall accuracy of the SAM classification for 19 vegetation type classes is 40 percent. Several vegetation types have been severely mismapped, notably “SaLim,” “Fest,” and “AgrosLot.” These types have image spectra very similar to those of the types with which they are confused. Expert System Rules The expert rules for each ancillary layer are represented as graphs in Figure 2, i.e., the probability of finding an ancillary characteristic while a vegetation type is occurring. Figure 2a clearly shows that the vegetation types as defined in this study are dependent on the elevation above NAP. The highest weights are in the lower elevation categories (to the left of the graph; the x-axis is shown as the logarithm of the elevation) and, going down the list of vegetation types (roughly arranged from low to high saltmarsh), the highest weights move towards the higher elevations (towards the right of the graph), ending on the highest weight for dune vegetation in the highest elevation, as previously found by several scientists (Bakker, 1989; Adam, 1990; Bakker et al., 1993; Janssen, 2001). In Figure 2b the slope is depicted as a logarithm, because most vegetation types (i.e., saltmarsh types such as (Halimione) Atriplex portulacoides or Juncus maritimus) are situated in the gently sloping saltmarsh with localized steep edges of the levees or pan edges. Only as the vegetation grades into dune vegetation do steeper slopes get higher weights. The first seven intervals are in fact very small, with a maximum slope of less than 1 percent (25 cm per 100 m), whereas the last three include all slopes from a 42- to 270-cm increase in elevation per 100 m. Figure 2 also represents the rules for aspect (2c) and terrain position (2d). Aspect does not exhibit a perceivable P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G

100 0

75 25

47 53

80 20

100 0

67 33

12 2

2 6

37 63

45 55

% UA

pattern, mainly because the sample plots did not randomly represent all aspect categories within each vegetation type, thereby leaving gaps in the probability rule graphs of the vegetation types. Terrain position shows some obvious relation with vegetation type distribution. The lower and middle high saltmarsh types are occurring mainly on the midslope or flat terrain. They generally have low weights for the ridge, gully, and “above 5-m” terrain positions. Only the “Dune” class has an increased weight on the ridge terrain position; others such as “Ely,” “AgrosLot,” and “SagArm” also have a probability of occurring on ridges. Apparently Halimione (“Hal”), which is known to occur on creek bank levees (see Table 1), was not assigned an increased weight for the ridge terrain position (3) because these creek levees are spatially very narrow and therefore by chance all field plots describing the “Hal” type were situated just next to the ridge on the midslope (2) or flat terrain (0). Water is heavily weighted in gullies. The local maximum at the ridge position might be caused by geographic misalignment around the narrow creeks between the image and the DEM. However, because the spectra for water are significantly different from those of all other vegetation classes, this does not influence the mapping accuracy of water. In fact, it was 100 percent accurately mapped in all confusion matrices (see Expert System section). The expert rules alone, without the hyperspectral image classification, produce the map shown in Plate 2. It shows the winning vegetation class when given the four ancillary characteristics alone, and therefore indicates the direction in which all expert rules are pushing the final decision if combined with the SAM classification. The map is overlaid with the vegetation map polygons produced in 1997 using aerial photographs. It is evident that the shapes of the vegetation units follow the shape of the terrain—most clearly seen at the dunes and creeks. Because the landscape ecological approach to stereoscopic API is the process of drawing the landscape boundaries visible on the photograph—these landscape boundaries being expected to relate significantly to the vegetation (species and structure) (Kloosterman, 1987)—the clues that the interpreter uses are greatly influenced by topography. Therefore, combining the expert rules in the expert system is comparable to the process of landscape interpretation used for API. June 2004

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Plate 1. Result of the spectral angle mapper (SAM) classification. Overall accuracy of the map is 40 percent.

Expert System The result of the SAM classification also serves as input to the expert system, which further improves the classification by using ancillary data (such as height, slope, aspect, terrain position), together with expert rules about the probability of vegetation types occurring at a certain height, slope, aspect, and terrain position. The accuracy was assessed for the expert system incorporating the SAM classification, where vegetation maps were produced for all possible combinations of ancillary layers (Figure 3).

The best combination achieved an overall accuracy of 66 percent, when using the expert rules of all ancillary information layers (Plate 3 and Table 4). The mapping accuracy of several vegetation type increased from 0 percent up to 75 percent (e.g., “SaLim” and “Fest”). However, the mapping accuracy of a few vegetation types decreased when the expert system was applied to the SAM classification (e.g., “Lim”). Not only did the overall accuracy improve significantly as compared to the SAM classification (Table 5), but also the plots have moved towards the diagonal of the confusion matrix

Plate 2. The expert system rule map using all ancillary data, i.e., elevation, slope, aspect, and terrain position. The 1997 API vegetation map is overlaid.

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Figure 3. The difference in user accuracy: The combinations of the four expert knowledge layers produced per vegetation type. The overall accuracy is shown.

which indicates the improvement is real because the vegetation types are arranged such that neighboring communities in the saltmarsh are also neighboring classes in the table (Table 4). It can be seen that the elevation above NAP is the greatest determining factor for vegetation type distribution, because the addition of the expert rules for elevation (DEM) improves the accuracy of the SAM classifier by 18 percent—from 40 per-

cent to 58 percent (see Figure 3). This increase in accuracy is statistically significant (Table 5). Elevation expert knowledge improves the user accuracy of all the vegetation types except “SaLim,” which remains at zero and still gets misclassified with “SuSa” (see Figure 3). Both vegetation types are occurring at almost the same elevation (Figure 2) and have similar spectral characteristics (Table 3). The improvement in mapping accuracy effected by the elevation rules confirms expectations,

Plate 3. The spectral angle mapper (SAM) classification in combination with the expert system using all ancillary data, with overall accuracy of 66 percent.


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TABLE 4. THE ACCURACY ASSESSMENT OF THE VEGETATION MAP OF THE EXPERT SYSTEM USING ALL ANCILLARY LAYERS, WITH THE KAPPA STATISTIC, OMISSION ERROR (OE), PRODUCER ACCURACY (PA), COMMISSION ERROR (CE), AND USER ACCURACY (UA) Imageclass

1

1 Water 2 2 SpaSal 3 SuSa 4 SaLim 5 Lim 6 Pucc 7 Hal 8 FestArte 9 JunSuaLim 10 JunAtrFes 11 Fest 12 JunM 13 Ely 14 Agros 15 AgrosLot 16 SagArm 17 Dunefoot 18 Dune 19 Beach % OE 0 % PA 100

2

3

6 2 1

1 13 1

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

% CE

2 0 100

0 100 14 86 19 81 40 60 50 50 22 78 0 100 29 71 78 22 33 67 63 38 75 25 25 75 42 58 33 67 25 75 27 73 27 73 0 100 % acc. 66.29 Kappa: 0.64

1 3 1 2

1

1 14

1 4 12 2

1 2 1

2 2 1 1 2

1 3 8

1

1 1 1

3 2

3 1

1

1 1 1 2 6 2

1 1

1 1

7

1

1 1 3

19 81

25 75

67 33

13 0 88 100

37 63

75 25

43 57

25 75

63 38

3 1

2 1

1 33 67

1

2

65 35

30 70

67 33

50 50

16 2

11

16 0 84 100

% UA

TABLE 5. THE STATISTICAL COMPARISON OF THE ACCURACY ASSESSMENTS ORDERED BY OVERALL ACCURACY (OA IN %) USING THE Z-STATISTIC. SIGNIFICANTLY DIFFERENT MAP PAIRS WITH 95% CONFIDENCE (Z 1.96) ARE BOLDFACE 1 1 SAMDEMSLOASPTER 2 SAMDEMASPTER 3 SAMDEMASP 4 SAMDEMSLOASP 5 SAMDEMTER 6 SAMDEM 7 SAMDEMSLOTER 8 SAMASPTER 9 SAMDEMSLO 10 SAMSLOASPTER 11 SAMSLOASP 12 SAMASP 13 SAMSLOTER 14 SAMTER 15 API map 16 SAMSLO 17 SAM

0.11 0.67 0.88 1.34 1.56 1.79 1.99 2.22 2.3 2.75 3.32 3.95 4.44 5.23 4.88 5.25

2

0.57 0.78 1.23 1.45 1.68 1.88 2.11 2.19 2.64 3.21 3.84 4.32 5.1 4.76 5.13

3

0.21 0.66 0.89 1.11 1.31 1.54 1.61 2.06 2.63 3.25 3.73 4.45 4.17 4.54

4

0.45 0.68 0.9 1.1 1.33 1.4 1.85 2.42 3.04 3.52 4.21 3.95 4.32

5

0.22 0.45 0.64 0.87 0.94 1.39 1.96 2.57 3.05 3.7 3.47 3.84

6

0.22 0.42 0.65 0.72 1.17 1.73 2.35 2.82 3.45 3.25 3.61

because inundation by salty seawater is the main factor determining the occupancy of the saltmarsh by halophytic vegetation, and of course all inundation characteristics are determined by the elevation above mean sea level. In combination with the SAM, the other ancillary layers have a lesser effect on the overall accuracy. Aspect (ASP) comes second with 49 percent map accuracy, next terrain position (TER) with 44 percent accuracy, and then slope (SLO) with only 42 percent accuracy. Compared with the SAM classification, however, none of these accuracies shows an improvement of statistical significance (see Table 5). The expert rules devised for slope do not significantly contribute to the increase in map accuracy (Table 5), because leaving out slope as an ancillary layer results in 65.7 percent overall accuracy. Leaving out terrain position gives 62 percent, while leaving out aspect achieves only 57 percent. These examples are not significantly lower than the best accuracy 712

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7

0.2 0.43 0.5 0.94 1.51 2.12 2.59 3.2 3.02 3.38

8

0.23 0.3 0.74 1.31 1.92 2.39 2.98 2.82 3.18

9

0.07 0.51 1.08 1.69 2.16 2.73 2.58 2.94

10

0.45 1.02 1.62 2.09 2.66 2.52 2.88

11

0.57 1.18 1.65 2.18 2.07 2.43

12

0.6 1.06 1.55 1.48 1.84

13

0.47 0.91 0.89 1.25

14

0.4 0.42 0.78

15

0.05 0.44

16

OA

0.36

66 66 63 62 60 58 57 56 55 54 52 49 46 44 43 42 40

with all the layers. This emphasizes the importance of the elevation rules in improving the mapping accuracy of the SAM classification. Comparison with API The cross-tabulation of the field classes with the translated API classes of the 1997 map is represented in Table 6. The overall accuracy of the API map is 43 percent. This is not significantly different from the SAM classifications (Table 5), but is significantly lower than that of the expert system map using all ancillary information (Table 4). As with API, the expert system uses contextual information through incorporating the expert knowledge about ancillary data layers, where the neighborhood of the DTM pixels is used. The advantage of the expert system is its objectivity. Objectivity means that the same rules applied to the same spectral and environmental characteristics will result in exactly the same map—not so for API, as P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G

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TABLE 6. THE ACCURACY ASSESSMENT OF THE API VEGETATION MAP OF 1997, USING THE FIELD CLASSES (241) OF 1999, WITH THE KAPPA STATISTIC, OMISSION ERROR (OE), PRODUCER ACCURACY (PA), COMMISSION ERROR (CE), AND USER ACCURACY (UA) API Class

2

3

4

5

2 SpaSal 3 SuSa 4 SaLim 5 Lim 6 Pucc 7 Hal 8 FestArte 9 JunSuaLim 10 JunAtrFes 11 Fest 12 JunM 13 Ely 14 Agros 15 AgrosLot 16 SagArm % OE % PA

4 6

5 13

5

2

2

6

7

8

9

10

11

14

15

16

1 5

1 1

3 3 3 6 1

13

1 3 3

1

12

2

3 1 4 10 1

26 1 1

3 8 1

2 3 3

4

1 2 19 2 1

1 1 2 2

8 2

1 2 1 1 4 18

1 1 10 2 3

1 6

100 0

100 0

5

2 67 33

58 42

100 0

50 50

82 18

50 50

24 76

81 19

recently proven by Janssen (2001) in the case of a Dutch saltmarsh. Therefore, the objectivity of the expert system makes the comparison of several maps possible. One reason for the lower accuracy of the conventional API map is that a mapping unit usually includes a mosaic of vegetation types, which makes direct comparison with the pixels of the expert map difficult. Comparing the Efficiency of the Methods A question regularly asked by (potential) end users of vegetation maps, particularly those involved in planning and decision making, concerns the relation of cost to benefits.

24 76

60 40

38 62

38 62

100 0

% CE 60 52 100 25 55 25 28 82 56 94 47 54 100 100 100 % acc.: Kappa:

% UA 40 48 0 75 45 75 72 18 44 6 53 46 0 0 0 43.15 0.37

In this study efficiency is expressed as the time needed to carry out a vegetation survey comparing different mapping methods. This is justified because the expertise required for each stage of the competing mapping methods is similar and therefore their salaries would be the same and because the aerial campaigns would cost the same order of magnitude. Figure 4 shows the time needed for implementing the two methods (API and expert system), and also gives the expected time if the proposed method is used in subsequent years for monitoring purposes. For the landscape-guided method based on aerial photographs, approximately 1400 hours were required. This equates to about 350 hours for each of the four

Figure 4. Time needed by different vegetation mapping methods in relation to the landscape-guided method based on aerial photographs.


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phases (see Figure 4, A). For the landscape-guided method based on hyperspectral imagery plus the expert system, 1270 hours were needed: 100 hours for preliminary image processing, 370 hours for fieldwork, 350 hours for vegetation classification, 250 hours for expert system design, and 200 hours for final map compilation. Note that, in compiling the above figures, time spent on developing additional algorithms for the geometric rectification of images has not been taken into account. The investigators are confident that these problems have been overcome and will not be part of future implementation costs. Also, the laser altimetry data are an available data set and were not collected specifically for this project only. Therefore, the acquisition of the laser altimetry data per se has not been taken into account in the cost-benefit analysis, whereas the preparation of any GIS layer (e.g., the terrain position) has been included in the cost estimate. The time spent on expert system design will not be part of any future costs. Hence, if this new method is used for monitoring purposes, the total time taken will be approximately 1020 hours. This leads to a total reduction of about 27 percent when compared with the landscape-guided method based on aerial photographs. Finally, one other issue needs to be addressed: how costs change in relation to scale changes when applying the two methods. For both the landscape-guided method based on aerial photographs and the landscape-guided method based on hyperspectral imagery plus expert system, there is an initial (fixed) implementation cost that is independent of the area of application, and a second component that is dependent on the area to be mapped. For the landscape-guided method based on aerial photographs, the cost of the second component will increase linearly with area, due to the manual interpretation of the aerial photographs. The first stage of the mapping work will account for most of the cost increases (see Figure 4, A). For the landscape-guided method based on hyperspectral imagery plus the expert system, the cost of the second component will be non-linear, with each additional unit area costing less than the previous unit mapped. This is because most of the classification is done digitally, and this method does not incorporate the API of the landscape-guided method based on aerial photographs. Hence, mapping progressively larger areas would cost only marginally more than the base cost.

Conclusions When hyperspectral imagery alone was used to classify coastal wetlands for 19 detailed vegetation types, an accuracy of 40 percent was achieved—comparable to the accuracy of the API-derived vegetation map (43 percent). However, ecological knowledge can be captured in a set of expert rules much like those used in API to draw lines that denote boundaries of vegetation units. It was shown that the addition of terrain variables to hyperspectral imagery increased the accuracy of the map generated by the expert system to 66 percent. Moreover, the map with 19 vegetation types produced by the expert system was more accurate (66 percent) than a conventional API vegetation map (43 percent). The proposed mapping method offers other important benefits. These are mainly in the form of objectivity and, therefore, repeatability of the new system. Apart from the apparent increase in map accuracy when mapping 19 vegetation classes with this expert system, there is also a marked reduction in time and, therefore, cost.

Acknowledgments We acknowledge the use of “International Institute for Aerospace Survey and Earth Sciences” (ITC) and “Beleids Commissie Remote Sensing” (BCRS) research funds to conduct 714

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this field campaign and the cooperation of the Survey Department (Meetkundige Dienst, Rijkswaterstaat) and the University of Groningen.

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(Received 11 December 2002; accepted 16 April 2003; revised 04 June 2003)

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