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# Land Use Prediction Using Land Transformation Model (LTM)

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### Transcript of "Land Use Prediction Using Land Transformation Model (LTM)"

1. 1. Maryam Adel SAHARKHIZ October 31st 2006 LAND TRANSFORMATION MODEL (LTM) FOR SEMENIYH BASIN (MALAYSIA)
2. 2. HUMANS ARE CHANGING THE LANDSCAPE AT AN UNPRECEDENTED RATE. WHAT CAN WE EXPECT OUR FUTURE LANDSCAPES TO LOOK LIKE?
3. 3. TOPIC OF TUTORIAL OBJECTIVES  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)
4. 4. LTM BACKGROUND Semeniyh Basin land use in 2006 (left) and 2010 (right) 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. 
5. 5. 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.
6. 6. DRIVER CREATION  Drivers was created using Euclidean Distance of ArcGIS. It calculates, for each cell, the Euclidean distance to the closest source.
7. 7. 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)
8. 8. 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. 
9. 9. EXCLUSIONARY LAYER
10. 10. 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
11. 11. STEP 1) CREATE INPUTFILE.TXT  At first step we tells the NN which files it needs to get information from for the predictor variables
12. 12. STEP 2) CREATE NETWORK FILE  Gives the structure of the NN by following syntax: Createnet 6 6 1 ltm.net
13. 13. 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 
14. 14. STEP 4) BATCHMAN _ TRAINING Different cycles are as Outputs, and learns from the patterns in the data.  It run by bellow comment Batchman –q –f train.bat > traincycles.csv  The rms for each of these cycles is recorded in the traincycles.csv file  traincycles.csv file
15. 15. 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
16. 16. 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
17. 17. 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
18. 18. 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: file0123 layer 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
19. 19. CALCULATE PERCENT CORRECT METRIC The Percent Correct Metric (PCM) is just the number of 3’s divided by the number of cells that transition (here 207551) PCM = (144933/ 207551) * 100 = 69.83% spatial accuracy Kappa = 0.658229 Sixty to 80% accuracy is considered an exceptional model. 40% to 60% is acceptable. LTM_stats.txt is including of PCM for all training files.
20. 20. 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
21. 21. FORECASTING RESULTS  Result of forecasting saved in ts_10000F file into ArcMap
22. 22. BREAKDOWN OF LTM STEPS Step 1: Create inputfile.txt Step 3: Create Pattern File Step 2: Create network file 2 3 Train.bat or test.bat 1 parameters createnet parameters GIS driving variable layers as ASCII grids (.asc) createpat pattern files (.pat) batchman Inputfile (.txt) result file (.res) 4 Step 4: Batchman Training Network file (.net) asciits Time step parameters Step 6: Forecasting Convert to GIS 5 Kappa Create Pattern Create 0123 file Step 5: Testing Priority asciits2.3 Forecasting Map
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