Rationale:The Objective of Creation of Rate of Land Cover Change in Caraga Ragion is to have a representation of how does the land cover changes at certain range of time.Methodology and ProceduresThe idea of how to make the rate of land cover change of caraga region is by Post-classification Landcover Analysis. In post-classification Landcover Analysis, land cover change is detected as a change in a Landcover label /classification between two or more images.

Procedures Download LandSat Data according to year (2014, 2002, 1996). Segmentation of the raster data according to the parameters of Layer 3, 4, and 5 as segment parameters. Manually classify the segmented data as samples. Extract the values of the manually classifed data. Classification is done, merging all classified LandSat then compute the area covered by the class (Built-up, High Density Vegetation, Low Density Vegetation). Compute the Rate of change of caraga region (1996, 2002, 2014) by using the change of the area per class.

Class DiscriptionHuman Application / ActivityThis Class has nearly same spectral values of dark brown to light brown of the color spectrumThis class is composed of the following attributes: Built-up, Settlements, Agricultural, Industrial, Mining sites, BarrenHigh Density VegetationThis Class has nearly same spectral values of dark green of the color spectrumThis class is composed of the following attributes: Rain forest, tall vegetation, untouch forest, perrenialLow Density VegetationThis Class has nearly same spectral values of light green of the color spectrumThis class is composed of the following attributes:

Rate of Land Cover Change of Caraga Region

S and T Program for Responsible Mining in Mindanao-ICT SupportTechnical Report for Rate of Land Cover Change of Caraga RegionPrepared By: James Earl D. Cubillas, ECT Science Research AnalystRESULTS:

AUGUST 22, 2014 LANDSAT IMAGEJUNE 3, 2014 LANDSAT IMAGEJUNE 19, 2014 LANDSAT IMAGE

CLASSIFIED IMAGE USING SUPPORT VECTOR MACHINE ALGORITHMNOTE: THE POSITIONOF CAPTURING THE LANDSAT IMAGE ARE BASED ON WRS_PATH = 112 WRS_ROW = 54, SPACECRAFT_ID = "LANDSAT_8 "

Merge Data of the of June 3, 9 ,and August 22, 2014.

CLASSIFIED IMAGE USING SUPPORT VECTOR MACHINE ALGORITHM YEAR 2014NOTE: THE POSITIONOF CAPTURING THE LANDSAT IMAGE ARE BASED ON WRS_PATH = 112 WRS_ROW = 53, SPACECRAFT_ID = "LANDSAT_8 "

JAN 2002MAY 2002APRIL 2002

CLASSIFIED IMAGE USING SUPPORT VECTOR MACHINE ALGORITHMNOTE: THE POSITIONOF CAPTURING THE LANDSAT IMAGE ARE BASED ON WRS_PATH = 112 WRS_ROW = 54, SPACECRAFT_ID = "LANDSAT_7"

In 1996 Human Application / Activity has total area of 379,597.0292415 HectaresHigh Density Vegetation has total area of 1,120,463.9319164 HectaresLow Density Vegetation has total area of 3,3169,316,323.83 Hectares

CLASSIFIED IMAGE USING SUPPORT VECTOR MACHINE ALGORITHM YEAR 2002NOTE: THE POSITIONOF CAPTURING THE LANDSAT IMAGE ARE BASED ON WRS_PATH = 112 WRS_ROW = 53, SPACECRAFT_ID = "LANDSAT_7 "

AUG 1996NOV 1996SEP 1996OCT 1996

MAY 1996

In 1996 Human Application / Activity has total area of 468,806.482784 HectaresHigh Density Vegetation has total area of 1,742,856.361094 HectaresLow Density Vegetation has total area of 1,990,762.756566 Hectares

CLASSIFIED IMAGE USING SUPPORT VECTOR MACHINE ALGORITHM YEAR 1996NOTE: THE POSITIONOF CAPTURING THE LANDSAT IMAGE ARE BASED ON WRS_PATH = 112 WRS_ROW = 53, SPACECRAFT_ID = "LANDSAT_7 "

Support Vector Machine algorithm This scatter plot shows the relation and the characteristics of the samples or classes (Built-up, Clouds, High density vegetation, Low density vegetation) to the feature space (Layer 3, Layer 4, Layer 5) for classification. this samples are being extracted using segmentation of the LandSat data in relation to its Layers( Layer 3, 4, and 5, which is green, red and NIR(Near-Infrared)).Support vector Machine algorithm do the process of finding separating line which is called the "Hyper Plane" for those classes(Built-up, Clouds, etc.) To find the Accuracy of classification using SVM algorithm and the best hyper plane for those classes by using "Three-Fold Cross-validation technique" . density vegetation gathered for image classisifaction to the feature space

The validation accuracy shows that using SVM in classifying the data has reach 98.57 percent and using the parameter C for making the line separator.

Support Vector Machine algorithm for LandSat 8

3 Fold Cross-Validation Techniques

Support Vector Machine algorithm for LandSat 7

The validation accuracy shows that using SVM in classifying the data has reach 99.3 percent and using the parameter C for making the line separator.

The validation accuracy shows that using SVM in classifying the data has reach 96.5 percent and using the parameter C for making the line separator.Support Vector Machine algorithm for LandSat 5