WETLAND AND WATER RESOURCE MODELING AND ASSESSMENT: A Watershed Perspective - Chapter 2 doc

Image Classification for Identifying Wetland-Related Land Cover Types Xiaobin Cai and Xiaoling Chen 2.1 INTRODUCTION Understanding wetland changes in relation to urban development using r

Image Classification

for Identifying

Wetland-Related

Land Cover Types

Xiaobin Cai and Xiaoling Chen

2.1 INTRODUCTION

Understanding wetland changes in relation to urban development using remote sens-ing techniques is critical to plannsens-ing ecosystem management and sustainable regional development The per-pixel classification methods have been used extensively in the-matic information extraction (Wang et al 2004) The spectral property of surface objects was utilized in remote sensing classification in these techniques However, the spectral property information usually is not sufficient to generate accurate clas-sification results Therefore, other types of information, such as DEM (digital eleva-tion model) and road data, have been used to improve classificaeleva-tion accuracy The intrinsic spatial distribution and relationships that exist in geographical objects enable the adoption of spatial information, such as the texture of an object, which was widely used in the process of classification at the pixel level On the other hand, to better represent spatial relationships, spatial information at the object level was more useful in identifying the distribution patterns of different classes This idea directly resulted in the invention of object-oriented classification software Ecogni-tion, a software package, was recently enhanced by integrating the object-oriented classification techniques with its classification procedures However, Ecognition’s classification procedure is too complicated (Burnett et al 2003), especially when selecting segmentation parameters This paper introduces a simple object-oriented method to improve classification accuracy

2.2 STUDY AREA AND DATA

The study area is located in northern Jiangxi Province, China, with a geographic range of E115° 24ʹ to 117° 43ʹ and N27° 57ʹ to 29° 47ʹ The area covers Poyang Lake and its surrounding wetlands, which is one of 21 wetlands in China that have been designated as having international importance under the Ramsar Convention

14 Wetland and Water Resource Modeling and Assessment

Lushan National Park, one of UNESCO’s world heritage sites, is also located in the study area

The image selected is the Landsat ETM+ image taken on October 28, 2004 The Shuttle Radar Terrain Mission (SRTM)–edited DEM was used as the comple-mentary data The SRTM DEM data has the highest resolution among datasets with

a near-global (i.e., between S 56° and N 60°) coverage It has sufficiently detailed topographic information and can be derived to fit the selected image classification Figure 2.1 shows the edited SRTM DEM in the study area As the altitude changes from high to low, a color gradient from blue to red was used in the legend to represent corresponding altitude differences The central part of the image was dominated by Poyang Lake Plain, shown as blue in the DEM

2.3 METHODOLOGY AND RESULTS

Normally it is quite difficult to acquire satisfactory classification accuracy because

of the spectral similarity of different objects By examining spectral characteris-tics, three groups of objects with spectral similarity were identified: (a) wetland and grassland/forest, (b) bare land and developing urban area, and (c) built-up area and muddy beach Figure 2.2 illustrates the special overlap between the built-up area and the muddy beach

To improve the classification accuracy, the road data and DEM (the geographic information system [GIS] data) were incorporated in the classification process The maximum likelihood classification was used to identify initial boundaries of objects, with the water bodies being extracted and masked from the image In this process, the spatial relationships among the classified objects and the GIS data were refer-enced to develop decision rules in the expert system in order to better distinguish the spectrally mixed objects

FIGURE 2.1 The edited SRTM DEM in the study area

An Expert System–Based Image Classification 15

2.3.1 WATERBODY IDENTIFICATION AND SUPERVISEDCLASSIFICATION

Generally, water bodies can be easily identified using the Normalized Difference Water Index (NDWI) as follows (McFeeters 1996, Gao 1996):

NDWI = (GREEN − NIR)/(GREEN + NIR) (2.1) where GREEN is the brightness value of the green band and NIR is the brightness value of the near-infrared band However, it is still difficult to use the NDWI (Nor-malized Difference Water Index) to separate the surface waters with plants from the plants in “wet” lands To address this problem, the Normalized Difference Vegeta-tion Index (NDVI), a good indicator of plants, was also calculated

where RED is the brightness value of the red band Then the difference between NDVI and NDWI was used to detect water bodies more accurately

Supervised classification was then employed to extract other relevant cover informa-tion from the image by masking out the water bodies Seven land cover types, includ-ing built-up area, wetland (with vegetation), grassland, forest, bare land, muddy beach, and farmland were identified

2.3.2 EXPERTKNOWLEDGE

According to spectral analysis of these land covers, the muddy beach is easily con-fused with the built-up area, while muddy beaches usually distribute along water

FIGURE 2.2 The spectral comparison of the built-up urban area with the muddy beach

16 Wetland and Water Resource Modeling and Assessment

bodies and built-up areas are close to roads and rails Thus, the spatial adjacency of these two objects to water bodies, the province-level roads, and railways were mea-sured on the relevant thematic map, and were then incorporated in the expert system Spectrally, the wetland (with vegetation) is similar to the forest and the grassland However, wetlands are usually located adjacent to waters or muddy beaches, with their elevations lower than surrounding forests and grasslands The normal water level

of Poyang Lake fluctuates between 5.9 m and 22.20 m seasonally and the height of the hygrophyte inhabiting the area ranges from 13 m to 16 m (Tan 2002) Therefore, the neighborhood relationship and the wetland classification unit’s mean elevation were used as criteria in the expert system As the spectral property of the developing urban area is similar to bare land, it was not classified as an individual land cover type

in the initial supervised classification Usually the developing urban area has more stable relationships with roads and existing urban areas than bare land; this fact was used as the decision rule in the classification to distinguish the two types of classes Figure 2.3 shows the classified results In the left picture the red part is the developing areas while the existing urban areas and roads were depicted as blue or green lines The result accords well with the color features on the original ETM+ image

2.3.3 OBJECT NEIGHBORHOOD SEARCH ANDDEM ANALYSIS

To acquire the spatial neighborhood relationship at an object level, a clump procedure was performed on the image with ERDAS Imagine The results included an object

ID image, where the pixel value stands for its object ID code A search was applied

to the reference object images, which included a water object image and a road object image at the pixel level, to produce a new neighborhood relationship image Then the neighborhood ID image was obtained with an overlay of the object ID image and the neighborhood relationship image, where the LUT (look up table) was achieved A new LUT, including only the pixels with a value larger than zero, was produced by sieving the LUT using the histogram of the neighborhood ID image The new LUT was loaded into the object ID image to produce a neighborhood object image With

FIGURE 2.3 The identified developing urban area (in the left image, the lighter area is the developing urban area identified; the right image is the Landsat ETM+ RGB image with

Bands 4, 3, and 2) (See color insert after p 162.)

An Expert System–Based Image Classification 17

the expert knowledge analysis described in section 2.3.2, five object-level search images were created through object neighborhood searching To obtain the altitude property of each classified object, the overlay analysis was utilized between the clump result of the supervised classification and the edited SRTM DEM data, which generated the mean elevation of each classified object

2.3.4 CONSTRUCTION OF THE EXPERT SYSTEM

The expert system was constructed with the ERDAS Imagine’s Knowledge Engi-neering function, where the prospective class was used as a hypothesis, the deter-mination criterion as a rule, and the parameter as a variable In the experiment, the supervised classification result, the five object-level search images, and the mean elevation in every object clump were induced as the variables The criteria were expressed as the following rules:

If (muddy-ser = T and (sc = g or sc = fr) and men-elv ≤ 20) finalc = wt;

If (muddy-ser = T and sc = u and (rod-ser = T or ral-ser = T or urb-ser = T)

and wat-ser = T) finalc = m;

If (muddy-ser = T and sc = u and (rod-ser = T or ral-ser = T or urb-ser = T)

and wat-ser = T) finalc = fm;

If (sc = bl and (rod-ser = T or ral-ser = T or urb-ser = T)) finalc = dv;

Else finalc = sc

where muddy-ser denotes the muddy beach object-searching image, rod-ser the pro-vincial road object-searching image, and ral-ser the railway object-searching image

T represents the object that was located in the searching threshold; the finalc means the final expert system classification result, and sc is the supervised classification result Grassland is denoted by g, forest fr, wetland wt, muddy beach m, built-up urban areas u, farmland fm, bare land bl and developing urban area dv.

A classified result based on the expert system was obtained, and the comparison between the expert system classification result and the supervised classification results are shown inFigures 2.4 and 2.5 The pictures illustrate that some errors generated by the per-pixel classification were corrected by the expert system–based classification For example, the previously misclassifed built-up areas were corrected

to the muddy beaches and the previously missing developing urban areas near Nan-chang City were detected by the new method

2.4 CONCLUSION

The rule-based expert system approach could improve the classification of wetland-related objects that have similar spectral characteristics The object-level spatial searching method, which was incorporated with supervised classification results and the road and elevation information, proved to be effective for deriving a set of object spatial searching images

18 Wetland and Water Resource Modeling and Assessment

ACKNOWLEDGMENTS

This study was funded by the 973 Program (2003CB415205) of the Chinese Natural Science Foundation and the Open Fund project (200401006(1) of the Key Lab of Poyang Lake Ecological Environment and Resource Development of Chinese Min-istry of Education housed in Jiangxi Normal University

REFERENCES

Burnett, Charles, Kiira Aaviksoo, and Stefan Lang 2003 An object-based methodology of mapping mires using high resolution imagery Paper presented at Ecohydrological Pro-cesses in Northern Wetlands Tallinn Estonia: Tartu University Press

Gao, B 1996 NDWI—a normalized difference water index for remote sensing of vegetation

liquid water from space Remote Sensing of Environment 58:257–266.

McFeeters, S K 1996 The use of the Normalized Difference Water Index (NDWI) in

the delineation of open water features International Journal of Remote Sensing

17:1425–1432

Water

Built-up Urban

Welland

Mud Beach

Forest

Grassland

Bare Land

Farmland

Developing Urban Area

FIGURE 2.4 The overall comparison between the per-pixel classification (a) and the expert

system–based classification (b) (See color insert after p 162.)

FIGURE 2.5 The comparison for the Poyang Lake area with the two classification meth-ods (the left image is the per-pixel classification result, the middle one the original Landsat

ETM+ image, and the right the expert system–based classification result) (See color insert

after p 162.)

An Expert System–Based Image Classification 19

Tan, Qulin 2002 Study on remote sensing change detection and its application to Poyang international importance wetland Ph.D dissertation, Institute of Remote Sensing Applications, Chinese Academy of Sciences, 48–49

Wang, Zivu, Wenxia Wei, Shuhe Zhao, and Xiuwan Chen 2004 Object-oriented classifica-tion and applicaclassifica-tion in land use classificaclassifica-tion using SPOT-5 PAN imagery Geoscience

and Remote Sensing Symposium, IGARSS ’04 Proceedings, Vol 5, 3158–3160.