This is thegeneral guidefor landuse
mapping using
mid-resolution
remotesensing data

February 11

2015This document has been prepared for “Training workshop on REDD+
Research Project in Peninsular Malaysia” based on Multispec &QuantumGIS Open Source Software applications

GENERAL GUIDE FOR LANDUSE MAPPING USING MID-RESOLUTION REMOTESENSING DATA
1Classification system and map legend

The general methodology of landuse mapping consists of six steps: (1) Determination ofclassification system; (2) Preparation of ground truth data based on the predeterminedclassification system; (3) Preprocessing of satellite data; (4) Classification by satellite
data; (5) Correction and modification; (6) Validation using ground truth data. The firstone of all steps is about the classification system; it means the classification system isquite important. Land cover classification system is decided considering the purpose of
the land cover mapping project. Usually classification systems of different projects vary.
Based on this project mapping purpose, we need to define land cover classification
system. The detailed definition of each type sees Table 1.
Table1No

2

Landuse classification system

Landuse Type

Landuse Definition

1

Inland Forest

Natural forests with > 30% canopy cover, the canopy
being > 75% of dipterocarps forest.

2

Cropland

Cultivated, pasture land, rubber and oil palm plantation

3

Mangrove

Natural forests with > 30% canopy cover, composed of
species of mangrove tree, generally along coasts in ornear brackish or salt water.

4

Urban & Built-up

Includes residential, commercial and industrial,
transportation, sport facilities

5

Shrub land

Woody vegetation < 3m in height, with at least 10%
ground cover

6

Grassland

Upland herbaceous grasses >10% ground cover

7

Water body

Permanent open water bodies

8

Clouds/Shadows/No
data

Areas where land cover interpretation was not possible

Data preparation

2.1 Input Remote sensing data
Medium resolution multi-spectral remote sensing data were chosen for landuse
mapping in this project, taking into account the price and time limits for the production.
The spatial resolution of the remote sensing image ranges from 10 to 30m, while the2band number ranges from 3 to 10. In this project we use Landsat multi-spectral data.
Table 2 The key parameters of the multi-spectral remote sensing data to be used in thisproject
Data sources

Imaging years

Resolution (m)Bands to be
used

Availability

Landsat TM

1982 to present

30 m formulti-spectral
bands, 120 m forthermal infrared
band; Each scene
covers an area of
185 3 185 km.
Temporal resolution
is 16 days

7

Free, fullcoveragedifficultLandsat ETM+

15 m forpanchromatic band,
60m for thermal
infrared and 30m
1999 to present
for multi-spectral
bands; Temporal
resolution is 16
days

7

Free, no fullcoverage

2.2 Reference data
Collect any archived data useful for defining the location and extent of training samples
for classification. For examples: history landuse map, dominate forest types map, forest
coverage map etc. High resolution (1-5m) remote sensing data can also be used forclassification reference such as by using Google Earth. The user should know themapping or imaging date of these reference data very well. Don’t use data sources
without any metadata information as reference data for landuse mapping.

3

Image Classification

Image classification, in a broad sense, is defined as the process of extracting
differentiated classes or themes (e.g. land use categories, vegetation species) from raw
remotely sensed satellite data. Obviously this definition includes the preprocessing ofimages. We here simply refer to the process following the image preprocessing as
image classification.
33.1 Supervised versus unsupervised classification
Supervised classification is most appearing if there is much training data available tosupport training the classifier. However, for classifications in an area without extensive
training data, unsupervised classification may be faster and easier.
Unsupervised classification tends to be most useful when the desired map classes arespectrally complex, that is, they are not well characterized by a single multi-variate
distribution function. Unsupervised classification allows multiple spectral classes within
individual map classes. The best approach when using supervised classification in thissituation is to employ a non-parameterc classifier such as neural networks or decision
trees or to develop multiple spectral classes for each map class through the training andclassification steps.
It should be noticed that the direct results from unsupervised classification can only tell
you the distinct spectral classes, however, what we expect from one classification are
specific information classes, so the spectral classes should be reclassified or combined
into some information classes as in the final landuse classes, this needs the knowledge
learned from the ground true data.
3.2 Image classification methods
Image classification method, as one kind of multivariate methods, can be grouped into
parametric, non-parametric, spatial-spectral and spectral-temporal classification asshown in table 3. The classification method most suitable to your project is mapping site,
project objective, data sources used dependant. It is upon the user to choose one ofthem according to the specific mapping activities.

4

Table 2 Summary of classification methods, and their advantages and availability forforest attribute mapping
Classifier

Comments

ParametricMaximum likelihood

Works for “well separated” classes (eg. Land cover, forestclasses with different physiognomy): requires relatively few
training sites but subclasses must be characterized; results
improve using iterative “cluster busting”

Minimum distance

Fast; Works for “well separated” classes with similarvariability

Boxclassifieror Fast; Works for “well separated”

parallelepiped (meanandstandard

deviations)
Generalized

Linear Useful if “response function” between dependent andModel (GLM)

independent variables well known, or of interest (eg. Treespecies distributions)NonparametricBoxclassifieror Fast; Works for “well separated” classes; useful for “quickparallelepipedlook” or to apply before MLC(minimum, maximum)
Artificial

Neural Good results for forest classification; requires extensive

Networks

training data

Decision

Useful for combing remote sensing and other variables;(Classification) Trees

decision rules are explicit; classification accuracies may not
be much greater than MLC

5

4.2 Procedures for supervised landuse classification
The general procedures for supervised classification can be summarized as that shownin Fig. 1. A brief description of the major steps is as follows.

Ground truth &Remotesensing data

reference data forclassifier training

Statistic & Report
Class delineation & Training sample designation
Final map
Features extraction & Preliminary classification
Manual editing
Final classification description definition

Classification

Class speckle filtering

No

Yes

Accuracy satisfied

Accuracy assessment

Ground true & reference data for
accuracy assessment

Fig. 1 General procedures for supervised classification

Class Delineation. The analysis process begins with a general overview of the data set
to be analyzed, often by viewing a 3-color simulated color infrared presentation of thedata in image space. The intent is to create a list of classes which is suitably exhaustive,
and which includes the classes of user interest. To the extent possible at this point andfrom such an image presentation, consideration should be given to picking classes insuch a way as to provide for a set that are separable from a spectral standpoint.
Training Sample Designation. Following, or as a part of listing the desired classes, thespectral description of the classes must be designated. How this is done varies widelywith the particular data set and the information about the scene that is available to the6analyst. Examples of some of the ways this might come about are as follows.
Observations taken from a portion of the ground scene at the time of the datacollection;
Observations from aerial photographs from which examples of each class can belabeled;Conclusions that can be drawn directly from the image space, itself. For example,
an urban mapping problem where the spatial resolution was great enough to makeobjects of human interest recognizable in image space.
Conclusions that can be drawn about individual pixels by observing a spectral
space representation of a pixel. The use of “imaging spectroscopy” characteristics,
where specific absorption bands of individual molecules are used to identify specificminerals, are an example of this.
Optimal class definition requires that the classes defined must be
Exhaustive. There must be a logical class to assign each pixel in the scene to.
Separable. The classes must be separable to an adequate degree in terms of thespectral features available.
Of informational value. The classes must be ones that meet the user needs.
These three conditions on the list of classes must be met simultaneously. Note that theexhaustive condition and separability are properties of the data set, while the user
imposes the informational value condition. It is the bringing together of thesecircumstances, those imposed by the data with those imposed by the user’s desires thatis the challenge to the analyst. It is further noted that the classes are defined by thetraining samples selected. That is to say that the definition of classes is a quantitativeand objective one, not a semantic one. One has not really defined a class one mightwish to call “forest” until one has labeled the training samples to be associated with thatclass name, thus documenting quantitatively what is meant (and what is not meant) by
the word “forest”.
Feature Extraction and Preliminary Classification. At this point one can expect tohave training sets defined for each class, but they may be small. There would thus bevalue in eliminating features that are not effective for the particular set of classes athand, so as to reduce the dimensionality without loss of information. A feature extraction
algorithm would be used for this purpose, followed by a preliminary classification. From

7the preliminary classification, one can determine if the class list is suitably exhaustive,
or if there have been classes of land cover of significant size that have been overlooked.
One can also determine if the desired classes are adequately separable. If not, theclassification can be used to increase the selection of training samples, so that a more
precise and detailed set of quantitative class descriptions are determined.
Final Class Description Determination. With the new augmented training set, in
terms of either additional classes having been defined or more samples labeled for theclasses or both, any of the following several steps may be taken to achieve the finalclass descriptions in terms of class statistics.
It may be appropriate to re-apply a feature extraction algorithm, given the improved
class descriptions. In this way, a more optimal subspace may be found.
The Statistics Enhancement algorithm may be applied. This algorithm is known tobe sensitive to outliers, and thus would not be expected to perform well until it is
known that the list of classes is indeed exhaustive, as classes not previously
identified would function as outliers to the defined classes. The intended result ofapplying this algorithm at this point is to increase the accuracy performance of thefollowing classification and to improve the generalization capabilities of theclassifier from the training areas to the rest of the data set.
Post-Classification Filtering. Resulting classification map may be difficult to interpret,
because it has a salt-and-pepper appearance due to inherent variability of the per-pixel
classifier. So post-classification filtering is needed to remove pixels and pixel groups notsatisfying a minimum requirement. It usually can enhance interpretability and increases
classification accuracy. A final classification of adequate quality should be possible atthis point.
Classification Accuracy Assessment. If the accuracy is not good enough, dependingon the nature of the further improvement needed, a return to any of the above steps
may be used.

8

4.3 Classification Accuracy Assessment
4.3.1 Accuracy assessment stages
Four significant stages have been witnessed in accuracy assessment methods.
Accuracy assessment in the first stage was done by visual inspection of derived maps.
This method is deemed to be highly subjective and often not accurate.
The second stage used a more objective method by which comparisons of the area
extents of the classes in the derived thematic maps (e.g. the percentage of a specificvegetation group in area) were made with the corresponding extents on ground or inother reference dataset. However, there is a major problem with this non-site-specificapproach since the correct proportions of vegetation groups do not necessarily mean
the correct locations at which they locate.
In the third stage, the accuracy metrics were built on a comparison of the class labels in
the thematic map with the ground data for the same locations. Measures such as thepercentages of cases correctly (and wrongly) classified were used to evaluate theclassification accuracy.
The accuracy assessment at the fourth stage made further refinements on the basis of
the third stage. The obvious characteristic of this stage is the wide use of the confusion
or error matrix, which describes the fitness between the derived classes and thereference data through using the measures like overall accuracy and kappa coefficient.
Additionally, a variety of other measures is also available or can be derived from theerror matrix. For example, the accuracy of individual classes can be derived if the user
is interested in specific vegetation groups.
4.3.2 Ground truth data collection for accuracy assessment
Generally, there are three major ground truth data collection methods by different
reference data sources:
1) Record the landuse type and its location using GPS in the field through groundsurvey work.
2) Through aerial photo interpretation when the aerial photos is available and timely
consistent with the remote sensing data used for classification.
3) Using GIS layer from other sources.

9

The area standing for by the ground survey point (sample unit) should be consistent
with the resolution of the remote sensing image used for classification. It is suggested touse Systematic Non-Aligned Sampling (SNAS) method to determine the sampling
location in the field. Figure 2 shows the concept of this sampling method. The whole
mapping region is divided into sub-regions, and in each sub-region equal number ofpoints are sampled, whose location can be randomly distributed.

Figure 2 Systematic Non-Aligned Sampling

5. Landuse classification example: Supervised classification using Multispec
software.
Exercise
1
2
3
4
5
6
7

Title
Display and Inspection of Image Data
Image Enhancement
Unsupervised Classification (Cluster Analysis)
Supervised Classification - Select Training Fields
Overlay Shape Files on Image Window
Selecting Areas and the Coordinate View
Creating Vegetation Indices Images

6. Landuse change analysis example: Change detection using QuantumGIS
Exercise
1
2
3
4

Title
Vector data preparation
Using Analysis Tool for changes analysis
Analysis of landuse changes result
Producing Map

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 training.img raining  
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Klang, Selangor   4 January 2015        
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

 0



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

                   raining         


       
             
   
               
               
        









                    





 2 5



             eight            

          

               





    .



                 



7 North


 





 






             










 2 5 2
training.img               




15











                




 5



     
               

   
            

           





5






                         
                               



               

                              


            







15 3 
training.img
   


training.img
              

            






15














               


                




                 
             



 215



                 





              
raining_clMask.tif         
  












 15



 

            
aterbody
    







            
             
             
            



 5 4
training.img
               


               


    

    
          
       
       


    
           













5



 
    


         
 
       

         
   
         

Waterbody
        our Urban & built-up, Horticulture, Oil
Palm, Bareland

 5



            

  





               
      




5 



 

 



              






         
          







 5





                 
 1007   153)          



            


 
      




   
                 


5 



 



                      
            

              

                 
  

             







 5




              
              
 


             
               
             

5

 5
rainingimgdistrict_boundary
                


 raining.img           raining 


5  training.img         
           


5
district_boundaryTraining                          
      

5

5



         


            

                 
               

5                

                          


5



           





 5 6
training


          
          
          

6 




               

 

6           


                

25

6                        

 training   and training_wgs  
                          


               




 05 7
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               
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              
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               
training.img  
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 
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            
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            

 
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           
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           
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

            

                

             
           









             


              



               



 2015 












































,159





0














0





 


         
            






0

Quantum GIS (QGIS) Exercise : Working with vector and change detection
What is QGISQGIS is a user friendly Open Source Geographic Information System (GIS) licensed under the
GNU General Public License that you helps you visualize, manage, edit, analyze, and compose
maps with geographic data. QGIS is an official project of the Open Source Geospatial Foundation(OSGeo). It runs on Linux, Unix, Mac OSX, Windows and Android and supports numerous
vector, raster, and database formats and functionalities. QGIS provides a continously growing
number of capabilities provided by core functions and plugins
QGIS GUI
1
2

3

4

5

QGIS GUI can be divided into 6 main components:-

1. Menu Bar
2. Tool Bar
3. Map Legend
4. Map View
5. Status Bar

1. Working with vector data
Vector data is arguably the most common kind of data you will find in the daily use of GIS. It
describes geographic data in terms of points, that may be connected into lines and polygons.
Every object in a vector dataset is called a feature, and is associated with data that describes thatfeature. Vector data can be change in term of size, position and colors.
1.1

Start QGIS using the icon on the desktop or from QGIS Desktop from Startup Menu.
1.2
Select menu Layer and choose Add Vector Layer
1.3
At the dialog box vector layer, browse to
c:\latihan\selangor_utm47.shp and road_selangor_utm47.shp
1.4
Try to change the color of the polygon and line

2. Working with raster data
Raster data in GIS are matrices of discrete cells that represent features on, above or below theearth’s surface. Each cell in the raster grid is the same size, and cells are usually rectangular (inQGIS they will always be rectangular). Typical raster datasets include remote sensing data, suchas aerial photography, or satellite imagery and modelled data, such as an elevation matrix.
Unlike vector data, raster data typically do not have an associated database record for each cell.
They are geocoded by pixel resolution and the x/y coordinate of a corner pixel of the raster layer.
This allows QGIS to position the data correctly in the map canvas.
QGIS makes use of georeference information inside the raster layer (e.g., GeoTiff) or in an
appropriate world file to properly display the data
2.1
2.2

Select menu Layer and choose Add Raster Layer
At the dialog box raster layer browse to
c:\latihan\training_clmask.tif

3. Raster to vector
Converting between raster and vector formats allows you to make use of both raster and vector
data when solving a GIS problem, as well as using the various analysis methods unique to these
two forms of geographic data. This increases the flexibility you have when considering data
sources and processing methods for solving a GIS problem.
To combine a raster and vector analysis, you need to convert the one type of data to the other.
Let’s convert the raster result of the previous lesson to a vector.
The goal for this lesson: To get the raster result into a vector that can be used to complete theanalysis.
3.1
3.2
3.3
3.4
3.5

Select menu Raster > Conversion and choose Polygonize (Raster to Vector)
At the raster to vector dialog box, browse the input file to folder
c:\latihan\training_clmask.tif
At the output file, save the output to c:\latihan\training_clmask_vector.shp
Type Code to the field name
Click close when finish

4.
Working with Attributes (Open Attribute Table)
GIS data has two parts - features and attributes. Attributes are structured data about each feature.
Here you can make query, add another column, calculate area etc..
4.1

Right click on the layer and choose Open Attribute Table

4.2

Area Calculation in hectare
4.1
Click Start editing
4.2
Click Open Field Calculator

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4

5

4.3

Save the changes

5. Layer Symbology
The symbology of a layer is its visual appearance on the map. The basic strength of GIS overother ways of representing data with spatial aspects is that with GIS, you have a dynamic
visual representation of the data you’re working with.
Therefore, the visual appearance of the map (which depends on the symbology of theindividual layers) is very important. The end user of the maps you produce will need to beable to easily see what the map represents. Equally as important, you need to be able toexplore the data as you’re working with it, and good symbology helps a lot.
In other words, having proper symbology is not a luxury or just nice to have. In fact, it’s
essential for you to use a GIS properly and produce maps and information that people will beable to use.
The goal for this lesson: To be able to create any symbology you want for any vector layer.
5.1
52

Right click on the layer and choose Properties
Layer Properties dialog box will appear and follow the step below.

1
2

3
4

6.

Vector Analysis (Change Detection using Intersect processing)

Vector data can also be analyzed to reveal how different features interact with each other inspace. There are many different analysis-related functions in GIS, such as union, intersect, merge
etc..
6.1

Select Menu Vector and choose Geoprocessing Tools and click Intersect

6.2

The Intersect dialog box will appear and follow the step below

1
2

3
4

Reference:
1. http://www.qgisworkshop.org/html/workshop/qgis.html
2. http://docs.qgis.org/2.2/en/docs/user_manual/
3. http://manual.linfiniti.com/