DIEST

Description

DIEST combines multiple layers of spatial data to derive a favorability model for predicting a geological event (mineral potential, landslide, etc.). Available algorithms include probability estimation, certainty factor estimation, and fuzzy membership estimation. DIGRP must be run before DIEST.

Parameters

Name	Type	Length	Value range
Input: Uni-condition region IDs channel *	Raster port	1 - 1
ASCII Data File *	String	1 -
Estimation Algorithm	String	1 - 1	See Description Default: CF
Output: Output estimated favorability map channel *	Raster port	1 - 1
Report	String	0 - 192	See parameter description

* Required parameter

Parameter descriptions

Input: Uni-condition region IDs channel

Specifies the channel containing the uni-condition region ID(s), produced by DIGRP.

ASCII Data File

Specifies the name of the ASCII data file that contains the data layer statistics, as produced by DIGRP.

Estimation Algorithm

Specifies the estimation algorithm to be used.

Supported algorithms include:

CF: Certainty Factor interpretation / estimation
P-DIR: Probability direct estimation
P-BAY: Probability Bayesian estimation
P-REG: Probability Regression estimation
PF-GAM0.4: Fuzzy Logic Gamma operator with specified Gamma value
F-GAM: Fuzzy Logic Gamma operator with default Gamma value
F-SUM: Fuzzy Logic algebraic sum operator
F-AVE: Fuzzy Logic average operator
F-MAX: Fuzzy Logic maximum operator
F-MIN: Fuzzy Logic minimum operator

Output: Output estimated favorability map channel

Specifies the output channel to receive the estimated favorability map (which is scaled to [0, 255]).

Report

Specifies where to direct the generated report.

Available options are:

LOG: generates a report on the terminal (default)
<modulename.RPT>: appends a report to the module's RPT file
DISK: generates a report on file "IMPRPT.LST"

Details

DIEST provides various algorithms for estimating the favorability of occurrence, by combining evidence from multiple input layers. All the statistics of input layers are gathered and saved in an ASCII data file by DIGRP in a preprocessing step. DIEST does not need to read data from the original input channels.

The favorability measure is estimated for each unique condition region rather than for each pixel. Because the number of unique condition regions is much smaller than the number of pixels in the image, this process significantly reduces computation.

The computed favorabilities for all the uni-condition regions are saved in a file specified by REPORT.

In addition, a raster image of favorabilities is generated where each pixel contains the favorability measure mapped to the range of 0-255 (the lowest favorability is mapped to 0 and the highest is mapped to 255). The mapping function is similar to histogram equalization; you may expect approximately the same number of pixels at each gray level. For example, to obtain the top 10% most favorable areas, threshold the output image at 255*90%. The pixels above this threshold should fall in the top 10% category.

Algorithm