Thematic Mapping

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PROJECT

Thematic Mapping in Lund MunicipalityImage classification

Lund UniversityRemote Sensing


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Table of Contents

1. Introduction ................................................................................................................................ 2

1.1 Aim .................................................................................................................................. 2

1.2 Study area.............................................................................................................................. 22. Methods....................................................................................................................................... 3

2.1 Image classification .............................................................................................................. 3

2.1.1 Pre-field work ............................................................................................................... 3

2.1.2 Field work ..................................................................................................................... 3

2.1.3 Post-field work .............................................................................................................. 3

2.1.4 Clouds & Shadows ......................................................................................................... 4

2.2 Accuracy assessment ............................................................................................................ 4

3. Results ......................................................................................................................................... 5

4. Discussion ................................................................................................................................... 7

5. Conclusions ................................................................................................................................. 8

References ....................................................................................................................................... 9


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1. Introduction

This project started with the geometrical correction and image enhancement of remote sensing

data with the aim to prepare the satellite imagery necessary for the last section of this project,

namely the image classification. Based on four bands satellite imagery from SPOT 5, seven landcovers were produced for Lund Municipality by applying the Fisher classification method. Fishermethod is based on a linear combination of discrimination of variable, where the different classes

are maximized and their variations are minimized within those classes. The general formula is

expressed as follows:

+ (, , )

=1

Where

= score for class im are the bands= constant for class iWj,i= weight of j class

Xi= observed value

(Klecka, 1980)

For each pixel and each class there is a computed score value.

At the end, the accuracy calculation was calculated according to the training sites and evaluationpoints acquired during the field work over the Lund municipality and then these were compared

with the predicted land covers produced using a supervised classification.

1.1 Aim

This project aims at developing a land cover map of Lund municipality with the use of Fisher

classification.

1.2 Study area

The SPOT 5 scene covers the area of southern Sweden encompassing the Lund County. It has been

taken at 10am on June, 03 2011. The study area is Lund municipality which is located in western

Scania at Coordinates: 380000, 414000 (E-W) and 6153000, 6185000 (S-N). The spectral

resolution is 4 bands: green (500-590nm), red (610-680nm), NIR (780-890nm), SWIR (1580-1750nm). The radiometric precision is 8 bits and maximum spatial resolution is 10m (nadir). The

area consists of a mixture of built-up areas, agricultural lands, forests, water and bare lands.


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2. Methods

2.1 Image classification

2.1.1 Pre-field work

Pre-fieldwork image classification (pre-classification) was primarily performed to identify thedifferent land-cover classes present in the study area. It suited well for facilitating stratified randomsampling and creating a roadmap for the fieldwork. It was also useful as a base for comparing the

post-field work classification (post-classification).

Pre-classification was performed with Fisher hard classifier method, same as for the post-classification. Five (where possible) training areas were created for each land use class with

intention to spread evenly across the study area and to be most representative. A number of sources

and techniques were used to differentiate the land-cover classes: in addition to the Spot 5 imagery

data, that was aggregated in regular false color composite (rFCC), red (R)-near infrared (NIR)composite or used individually, Google maps satellite layer was found informative, for instance to

distinguish coniferous forest stands from deciduous.

Water training areas were digitized as darkest areas with respectable shape using NIR base layer.Bare soils were differentiated in rFCC as bright turquoise patches. The brightness of these patches

was varied which, among other reasons, probably is a result of different soil moisture.

Homogeneity and spectral response signature (SRS) of urban training sites was selected with an

aim to classify urban land-cover separately from Bare Soil class, and not to mix those in developedareas, but to allow roads and squares inside towns to be classified as bare soils as well. It is worth

mentioning that the study area contains at least three significant quarries or stone-pits, which have

the same color as bare soils in rFCC, but of very high brightness. These were classified as Otherclass. Vegetation classes were distinguished using rFCC and NDVI layer. Various herbaceous

patches, which cover most of the territory, have a strong pink, or light red tint. Whereas deciduous

forests have more ruff texture and darker red color in a rFCC. Coniferous forest stands look very

dark red to maroon. Herbaceous patches also have substantially lower NDVI values than forests.Since Spot 5 imagery doesnt have a blue band, Google maps satellite view was used assupplementary true-color source to differentiate vegetation.

2.1.2 Field work

The ground control points for the training and evaluation data were taken according to the stratified

sampling methodology by dividing the land cover into categories and selecting a sample within

the desired land cover. Each land cover was defined according to the pre-classification map and aland cover map from the Swedish mapping, cadastral and land registration authority institute of

Lantmteriet. At every sample point the coordinates and dominating land cover was recorded. The

data acquisition was performed by 4 separate groups where a difference in sampling methodology

is probable. In total 154 points were recorded and divided into 77 training points and 77 evaluationpoints.

2.1.3 Post-field work

After field work all training and evaluation records were pooled into an Excel file subdivided intothree columns representing latitude, longitude and land cover type. Afterwards, these observations

were transformed from WGS84 to SWEREF 99TM.


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The gathered 154 sample points were divided in half into 77 training- and 77 evaluation-points,

where the training points were used for a final classification. The goal of this classification is to

have a more automatic approach where the training areas are defined without an interpretation ofthe background False Color Composite (FCC). Further the training sample points were converted

into 90x90 meter training polygons to match the predefined sample area. These training areas laid

the foundation for the supervised Fisher-classification.

2.1.4 Clouds & Shadows

As described earlier in the second module of the mapping of Lund municipality project, the clouds

and cloud-shadows were removed with the help of an unsupervised classification. By identifyingthe spectral identity of clouds and their corresponding shadows it was possible to eliminate the

cloud-prone areas from the final image. However, it was quite hard to make the classification

distinguish between water and cloud-shadow. Therefore it was necessary to perform a second

unsupervised classification of the cut-out clouds, cloud-shadows and water bodies.

Also worth mentioning is the use of only green, red and NIR bands for the identification, through

unsupervised classification, of clouds. This is due to the similarities between cloud and bare soilreflectances in the Short-Wave Infrared (SWIR) wavelengths.

2.2 Accuracy assessmentAfter the development of maps it was necessary to evaluate whether the products were a goodrepresentation of reality or not. This was achieved through the use of confusion matrices where

the field measured ground truth (earlier mentioned 77 evaluation points) was point wise compared

with the classified map. Here, the total accuracy then could be calculated by dividing the number

of correctly classified points with the total number of points. Also, the user and producer accuracywas calculated to analyze the classification precision within specific classes. With the same

information it was further possible to calculate the kappa () -value (Equation 1) which takes the

chance agreement into account.


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2 (1)where A is the sum of correctly

classified points, B is the sum of theproduct of the classes and N is the total

number of elements in the confusionmatrix (Lillesand, 2008).

3. Results

The result of the post-classification

showed a homogenous distribution ofland cover corresponding to a

hypothetical location of land use

classes. At first glance, the classifiedobjects seem to be well located with itsnatural hydrological distribution across

the county and the agglomeration of the

city of Lund in the north-west part of

the region.

The overall accuracy and the kappa-

value for the two classifications (Figure1) and the martckedata produced by

Lantmteriet can be seen in table 1. All

three accuracy estimations werecalculated using the earlier mentionedevaluation points from the field work.

The land cover distribution can be seenin figure 2 a, b and c from the different

maps produced and utilized in this

project. In table 1 the proportions canbe seen expressed in square-kilometers

where it is evident that the herbaceous

class is the dominant land cover.

Figure 1 Land cover classification with (a) pre-classification defined

training areas and (b) post-classification defined training areas

b)

a)

Pre-class. Post-class Marktcke

Overall acc. 66.7% 68% 81%

Kappa-value 0.57 0.61 0.75

Table 1 Statistics of the accuracy assessment for the

three maps.


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In figure 3 it is possible to study the spectral signatures

for the four bands with either the user-defined trainingareas (Figure 3a) or the fieldwork-defined training areas

(Figure 3b). In post-classification the Bare Soil SRS is

better distinguished from the Other class. Also betweenclassifications, SRS is different for the Deciduous-

Herbaceous relationship. In pre-classification Urban,

Bare Soil, Coniferous, Deciduous and Other classes are

indistinguishable in NIR. In contrary the post-classification Herbaceous class gets mixed with Bare

Soil and Deciduous classes in NIR while the rest of the

classes are quite different in this band.

gure 2 Land cover distribution of Lund municipality with the a) pre-classification Fisher classification, b) post-classification Fisher

assification and

b) c)

Class

Pre-class. Post-class. Marktcke

Water 13.2 15.1 12.8

Urban 30.2 28.2 32.3

Bare Soil 6.5 64.9 3.5

Herbecious 227.8 163.3 293.4

Coniferous 18.3 55.6 44.5

Deciduous 111.0 105.1 47.0

Other 31.6 6.5 9.0

Total 438.6 438.7 442.6

Area Km2

Table 2 Land cover per class of the pre- and post-

classification Fisher maps and the Marktcke data

roduced by Lantmteriet.

Figure 3 Signature comparison between the four utilized bands for the a) pre-classification and b) post-classification

training areas.

b)a)Pre-classification Post-classification


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4. DiscussionTwo approaches to training sites development showed very similar final classification results. Both

have sources of inaccuracy, from the field sampling strategy imperfections, or biased expertsimage analysis. The difference in overall accuracy (OA) between pre- and post-classification is

negligible, although the latter has a slight edge (1.3%), and the kappa values are comparable. A

leap in accuracy becomes obvious when comparing classifications to Marktckesdata, which has13% higher OA and 0.14 higher kappa.It is worth mentioning that the accuracy stated on the Marktckesdata map is only 75%, 6% lower

than that calculated using measured ground truth points. Therefore, supposedly, the evaluation

dataset is biased or flawed, leading to increased or biased accuracy estimates. Classification andevaluation points were collected simultaneously and by 4 group pairs, each having different

sampling approach. It is also likely that groups focused primarily on collecting classification points

rather than evaluation ones. The random or stratified random approach is essential for the latter,

and lack of it is most probably the reason of the evaluation being inaccurate.Pie charts of land-cover distribution (fig. 2) show a different perspective, especially when

comparing to the Marktckesdata. Both of the classifications underestimate the proportion of

Herbaceous class. Pre-classification is much closer to the municipal land-cover map in this respect,underestimating it by 14%, which is most likely due to largely overestimated forested areas 8%

more.

With post-classification in addition to overestimated forests, comes the Bare Soil class 15%

versus 1% on the Marktckesdata, and there are several reasons for that. First and probably mainreason is the temporal difference between the field sampling and the satellite image, resulting in

some patches of bare soil being actually croplands that havent started vegetating yet. In addition

to that, in post-classification a significant portion of urban areas ended up in Bare Soil class, and

vice versa, because sampling points were taken on the roads and in parking lots, rather thanrepresenting buildings, and the spectral response of roads is close to bare soils. For this specific

reason, in pre-classification, urban training sites were digitized on top of large warehouses and

dense built-up areas, which have a unique yellow tint in RFCC. It yielded much less confusionbetween the two classes in pre-classification, and allowed a reasonable amount of bare soil classin the city areas, representing road network, and more importantly less patches of actual bare soil,

classified as Urban (fig. 1).

According to the Marktckesdata forest types are equally distributed, while both of theclassifications give a significant edge to deciduous forests. The supposed reason is that unlike

coniferous, deciduous forests have similar SRS to Herbaceous class, especially in post-

classification (fig. 3) and may interfere under certain tree/plant type, soil type and moistureconditions.

Both of the classifications have their strengths and weaknesses, and it is arguable, which one is

better. Post-classification has slightly higher accuracy estimations and represents coniferous

forests class better. While pre-classification better represents the distribution of classes, especiallyHerbaceous, Urban and Bare Soil. Further development of the project may focus on accuracy and

class distribution improvement through training sites correction and thorough SRS analysis.

Bare soil is a class of questionable usability in a final land-cover map, being quite vague firstly,

incorporating road network, recent clear-cuts, and, most importantly, croplands that havent started

sprouting at the time of image capture and/or fieldwork. Road network can be distinguished as a

class using standard deviation filter with further refinement by mode filter of appropriate kernel


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size. Masking out roads from Bare Soil class will leave mainly not-yet-sprouting croplands in it,

as the quarries and stone-pits are easily classified as others in the pre-classification.

The classes used in this project are the most suitable for the classification of land cover of Lund

municipality. Subclasses to the existing classes could be applied like Spruce and Pine to the

Coniferous class. However, as seen in the SRS in figure 3 the distinguishability between existingclasses is already poor and would probably degrade the accuracy with additional classes. Why itis complex to add classes is probably due to the precision of the training data. The accuracy of the

spectral properties of the SPOT-images are sufficient for a more detailed classification.

Why the Herbaceous class is the most dominant land cover in Lund municipality is probably dueto the geomorphology of the region. The western part of Scania is known for its fertile soils which

explain the high amount of agriculture, which is what the Herbaceous class represents. Also, why

the deciduous vegetation is more represented than the coniferous is probably due to climate wherethe deciduous vegetation has an advantage.

5. Conclusions

The two developed maps have their strengths and weaknesses in accurately representing the land

cover of Lund municipality. Pre-classification better estimates the land cover distribution while

post-classification is more accurate spatially, according to the overall accuracy. Post-classificationprovides mixed outcomes in Urban and Bare Soil classes, resulting in built-up areas being

represented as Bare Soil and vice versa. On the other hand pre-classification, while correctly

representing Urban, Bare Soil and Herbaceous classes, is largely underestimating coniferous forestwhich is not the case for post-classification.


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References

Klecka, W. R., 1980.Discriminant analysis. Series: Quantitative applications in the social sciences

Lantmteriet, 2010.Produktbeskrivning: GSD-Marktckedata, Gvle: Lantmteriet.

Lillesand, T.M. and Kiefer, R.W., 2008.Remote Sensing and Image Interpretation, Sixth Ed., JohnWiley and Sons, Inc.: Toronto.

Thematic Mapping

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