Land Use Prediction Using Land Transformation Model (LTM)
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Transcript of Land Use Prediction Using Land Transformation Model (LTM)
LAND TRANSFORMATION MODEL (LTM) FOR SEMENIYH BASIN
(MALAYSIA)
October 31st 2006
Maryam Adel SAHARKHIZ
HUMANS ARE CHANGING THE LANDSCAPE AT AN UNPRECEDENTED RATE. WHAT CAN WE EXPECT OUR FUTURE LANDSCAPES TO LOOK LIKE?
TOPIC OF TUTORIAL
Run the Land Transformation Model starting from land use maps and different drivers in GIS form.
Do a model run for Semeniyh Basin in Selangor
Predict future LandUse layout based on past land use data. (2006 and 2010)
OBJECTIVES
LTM BACKGROUND
We will model LandUse expansion in Semeniyh Basin using 2 land Use maps, one from 2006 and the other from 2010
After going through the Model we will be able to run the LTM on our study area, to forecast future land use changes in 2014.
Semeniyh Basin land use in 2006 (left) and 2010 (right)
CREATE DRIVERS = PREDICTOR VARIABLES
Driver layers represent phenomena that influence what are trying to model.
In this study, we assume that the following 6 drivers will influence urbanization an agriculture expansion in Semeniyh Basin:
Proximity to urban in 2006, to highways, to roads, to rivers, to Lake of Semeniyh and to inland lakes.
DRIVER CREATION
Drivers was created using Euclidean Distance of ArcGIS. It calculates, for each cell, the Euclidean distance to the closest source.
FORMAT LAND USE LAYERS After Diver’s creation two land use layers were
reclassified to zeros and ones, ones being the class wanted to model. In this case we are modeling urbanization and agriculture expansion so we reclass all urban and agricultures pixels to 1 rest to zero. Semeniyh
LanduseBase in 2006 (left) and LanduseFinal 2010 (right)
PREPARE EXCLUSIONARY LAYER Exclusionary cells are cells which we don’t want
to include in the analysis, i.e. cells which the LTM will never “see”.
In our dataset we excluded water pixels, Agricultures and urban in 2006 as we did not want urban and Agriculture to expand to those locations
exclusionary cells Reclassed as 4, rest of the data as 0.
All data layers need to be exported to ascii files which will be readable by the Neural Network.
EXCLUSIONARY LAYER
PREPARING THE NEURAL NETWORK (NN)
Step 1: Create inputfile.txt
Step 2: Create network file
Step 3: Create pattern file
Step 4: Batchman _ Training
Step 5: Testing
Step 6: Forecasting
STEP 1) CREATE INPUTFILE.TXT At first step we tells the NN which files it needs to get
information from for the predictor variables
STEP 2) CREATE NETWORK FILE
Gives the structure of the NN by following syntax:
Createnet 6 6 1 ltm.net
STEP 3) CREATE PATTERN FILES
Keeps track of which cell has what values in the various base and driver layers as well as the output LTM layer
Createpattern.6.5 inputfile.txt v
STEP 4) BATCHMAN _ TRAINING Different cycles are as Outputs, and learns
from the patterns in the data. It run by bellow commentBatchman –q –f train.bat > traincycles.csv The rms for each of these cycles is recorded in
the traincycles.csv file
traincycles.csv file
CREATE REAL CHANGE MAP
After running step 4 the number of new urban cells between 2006& 2010 was calculated and saved in Real Change raster layer:
Record # of 1s
STEP 5) TESTING FIRST STEP: CREATE PATTERN First RERUN createpattern Syntax this time
with inputfile-test.txt createpattern.6.5 inputfile-test.txt v
CHANGE THIS to 1 in your inputfile.txt file and save it as inputfile-test.txt
STEP 5) TESTING SECOND STEP: BATCHMAN _ TESTING
Another step in order to Testing process
Based on batchman –f batch-test.bat at the command prompt
res_10000.asc and ts_10000.asc are results of Batchman testing
CALCULATE PERCENT CORRECT METRIC
To estimate Spatial Accuracy, file0123 layer was created from ts10000 and RealChange layers as follow. The numbers 0,1,2,3 represent the following:
0 = no real change and no predicted change = True Negative
1 = no real change but change predicted by the model = False N
2 = real change but not predicted by the model = False Positive
3 = real change and predicted change = True Positive
file0123 layer
The Percent Correct Metric (PCM) is just the number of 3’s divided by the number of cells that transition (here 207551)
Sixty to 80% accuracy is
considered an exceptional model.
40% to 60% is acceptable.
CALCULATE PERCENT CORRECT METRIC
PCM = (144933/ 207551) * 100 = 69.83% spatial accuracy
LTM_stats.txt is including of PCM
for all training files.
Kappa = 0.658229
STEP 6: FORECASTING After Testing step, using inputfile-forecast.txt as well as
following comments forecast layer has been created Syntax: Createpattern.6.5 inputfile-forecast.txt Then: asciits2.3 fullreference.txt res_10000
landusefinal.asc ts_10000F.asc 1 12072
FORECASTING RESULTS
Result of forecasting saved in ts_10000F file into ArcMap
BREAKDOWN OF LTM STEPS
createnet
parameters
parameters
batchman
Train.bat or test.bat
Network file (.net)
result file (.res)
asciits
parameters
Time step
pattern files (.pat)createpat
Inputfile (.txt)
GIS driving variable layers as
ASCII grids(.asc)
Convert to
GIS
Create 0123 file Priority
2
1
3
4
5 Kappa
Step 1: Create inputfile.txt
Step 2: Create network file
Step 3: Create Pattern File
Step 4: Batchman Training
Step 5: Testing
Create Pattern
asciits2.3
Forecasting Map
Step 6: Forecasting