Surface roughness classification

The capability of remote sensing data to provide spatial information on surface roughness classes makes it an important tool for this research. The chosen classification technique and its accuracy are the core of the methodology. This sub-chapter explains step by step the surface roughness classification using remote sensing data, from pre-processing to classification of surface roughness and determination of coefficients. Additionally, the accuracy assessment to check the quality of the result is explained.

SPOT 5 multi-spectral and panchromatic 2.5 m imagery were used in this research to investigate the surface roughness conditions in the study area. The steps to perform the roughness coefficient classification are as follows:

 Pre-processing of satellite images including spatial enhancement, image variable calculation and textural analysis.

 Analysis of relevance of variables to identify effective parameters to be used in the remote sensing classification.

 Classification of main land use types as basis of surface roughness classification.

single steps of pre-processing, analyzing the relevance of variables, main land use classification, and density and neighborhood classification.

Figure 4.1 The steps of surface roughness classification using optical satellite imagery

Pre-processing

First, the satellite imagery, in this case SPOT 5 imagery (multi-spectral and panchromatic), must be pre-processed. The pre-processing consists of:

- spatial enhancement; - image variables calculation; - and textural analysis.

The spatial enhancement combines the high resolution panchromatic image with the lower resolution multi-spectral image to produce high spatial resolution multi-spectral imagery (pan-sharpening). This method applies a multiplicative algorithm that integrates two raster images, e.g. SPOT 5 multi-spectral image (10 m) and SPOT 5 panchromatic (2.5 m) images, to increase the intensity of the image and the spatial resolution of the multi-spectral data.

Additionally, several indices such as the Normalized Differenced Vegetation Index (NDVI), the sigmoid NDVI (SNDVI) and exponential NDVI (ENDVI) are calculated from the multi-spectral image. The following are the formulas of NDVI, SNDVI and ENDVI:

NDVI = (NIR-RED)/ (NIR + RED) (4.1) SNDVI = 1/(1 + e –NDVI) (4.2) ENDVI= EXP (NDVI) (4.3)

Where:

NDVI is the Normalized Differenced Vegetation Index

SNDVI is the Sigmoid Normalized Differenced Vegetation Index ENDVI is the Exponential Normalized Differenced Vegetation Index RED is the sensor’s red band (e.g. SPOT 5 band 2 (610-680 nm)

NIR is the sensor’s near-infrared band (e.g. SPOT 5 band 3 (780-890 nm).







n i i

x

N

x

1

1

(4.4)









n i i

x

x

N

1 2

)

(

1



(4.5)

x

c

v





(4.6) Where:

s

is the standard deviation of the digital numbers in a kernel

N

is the total of pixels in a kernel

i

x

is the digital number of each pixel in a kernel

x

is the average digital number in a kernel

v

c

is the variation coefficient of digital numbers in a kernel.

These results of the pre-processing (spatial enhancement, image variable calculation, and the texture) are then stacked to a single file, which is used as input for analyzing relevant variables for the land use classification.

Analysis of image variables

After the pre-processing, the relevance of variables for the classification process is analyzed to find the most effective combination of parameters. The decision tree method is used to decide which image variables are the most appropriate for the classification. Therefore, training samples with land use known from field surveys and visual interpretation of satellite data are analyzed. For every sample of land use class, the layer stack created during the pre-processing is used, which contains the four spatially enhanced spectral bands of SPOT 5, NDVI, SNDVI, ENDVI, and the texture of the panchromatic image. Diverse parameter combinations are then examined and tested. The image variable combination with the highest classification accuracy of the training areas is chosen for the classification of the complete remote sensing imagery. Table 4.2 shows the analyzed parameter combinations.

Table 4.2 Analysis of image variables

No. Image variable combinations

1. Only NDVI 2. Only ENDVI 3. Only SNDVI 4. Only Texture 5. NDVI and Texture

6. Four spatially enhanced spectral bands of SPOT 5

7. Four spatially enhanced spectral bands of SPOT 5 and NDVI

8. Four spatially enhanced spectral bands of SPOT 5, NDVI and SNDVI 9. Four spatially enhanced spectral bands of SPOT 5, ENDVI and SNDVI 10. Four spatially enhanced spectral bands of SPOT 5, NDVI and texture 11. Four spatially enhanced spectral bands of SPOT 5, NDVI, SNDVI, ENDVI

and texture.

Main land use classification

Having identified the most effective image variable combination for a remote sensing data set, the surface roughness classification is performed in two steps: main land use classification and density and neighborhood classification.

For the classification of main land use classes, the non-parametric decision tree method is implemented here. The thresholding method of the decision tree analysis facilitates the classification process and is well integrated in modeler software to create an automatic classification procedure.

Decision tree classifiers belong to the group of supervised and hierarchical classifiers where subsets of classes are processed at multiple stages. The internal node includes more than one class, while the leaf node includes only one class. The decision tree has a number of advantages over the maximum likelihood (ML) and artificial neural network (ANN) algorithms, being computationally fast and easy to apply (Chen and Wang, 2007).

Similar to supervised classifiers, training samples of land use are required to then develop the decision tree rules. The number of main land use classes influences the result of the determination of surface roughness coefficients. After setting up the decision tree rules for every main land use class, the main land use classification can be performed, which is then followed by density and

Density and neighborhood analyses

The density of land use features was calculated by using a kernel size of 9 x 9 pixels (~23 m spatial resolution) based on Koshimura (2009). It resulted in three categories of densities — rare, medium and high density. These categories were classified as follows (Murashima et al., 2008):

- Rare density : 0–20 % - Medium density : 20–50 %

- High density : more than 50%.

The result is used for the neighborhood classification. A class of rarely dense residential area may contain a combination of trees and residential area, or vice versa. Hence, the classification of neighborhood is performed as follows particularly as an example for residential area with trees:

- Selected main land use classes such as the class of residential and tree are grouped into one class (grouping).

- The focal density analysis is performed and is categorized into two classes, high (> 80%) and low (< 80%); this is called the “density class” (after grouping).

- The result of the density class (before grouping) is then overlaid with the new density class (after grouping).

- The class of residential with trees can be identified where low or medium density residential/tree class (before grouping) meet the high density class (after grouping).

- Where low or medium density residential/tree class (before grouping) meets the low-density class (after grouping), the original class is not changed.

The same process is also performed for classifying the neighborhood of trees and shrubs, and other neighborhood classes.

Neighborhood analysis

The processes of the main land use classification by decision tree and the density and neighborhood analyses are packed into a GIS procedure. After manually selecting training samples, this package can be applied and the

classification process will be performed automatically. The processing steps of the semi-automatic model are shown in Table 4.3.

Table 4.3 Steps of the semi-automatic classification

No. The process Type of process

1. Training sample selection Manual 2. Decision tree modeling Automatic 3. Transfer of decision tree model to image

processing software

Manual 4. Process of main land use classification Automatic 5. Process of density and neighborhood

classification

Automatic

In document Khomarudin, Muhammad Rokhis (2010): Tsunami Risk and Vulnerability: Remote Sensing and GIS Approaches for Surface Roughness Determination, Settlement Mapping and Population Distribution Modeling. Dissertation, LMU München: Fakultät für Geowissens (Page 87-93)