Keywords:Thresholds, Automatic selection, Mangrove area, TM data of Landsat
Abstract
We had suggested a mask process that separates three categories of land area, mangrove
forest and waters from a mangrove area composed with the categories. And the procedure and a practical
way had been examined to decide threshold in order to avoid the misclassification and to prepare data
sets for quantitative estimation of stand parameters on mangrove forest with TM data of Landsat 5.
In this paper, an automatic determination method of the threshold was examined to produce masks for
extraction of individual categories. In the first step, on the histogram of CCT count for band 4 and 5, the
provisional range of threshold selection was decided and the pixels corresponded to each CCT count
values in the rage were displayed with white on a black background. After the locational distribution of
those pixels was observed and the meaning of pixel distribution was checked, the ranges for searching
threshold were decided.
In the second step, the Otsu’s automatic threshold selection method was applied to the ranges as an
automatic searching threshold and the validity of the results was verified.
1. Introduction
One of the authors and other researchers had shown it is possible to estimate stand parameters
on mangrove forest such as mean of tree height, diameter at breast height, trunk volume and so
on, with TM data of Landsat 5 and several indexes expressed combination of the data (Dwi et al.,
1997). It must be prepared a data set on mangrove forest without surroundings for application of
the estimation equations. On the other hand, although many and good training areas are selected,
misclassification occurs to some extent in case of classification of mangrove area by any
classifiers. Because there are pixels having similar spectral reflectance in the categorized area as
land area, mangrove forest and waters. Small ponds, swamps and channels in land area can be
classified as a category of mangrove forest or waters, and cutovers and new planted areas on the
high level sedimentary areas in mangrove forest can be classified as land area. Although the
result was statistically classified by rational classifier, it should include misclassification to
some extent in several categories set to put in our circumstances and convenience. A mask
procedure was suggested to classify three categories of land area, mangrove forest and waters
separately. It is possible to avoid the interference from other quite different category by the
classification after the mask procedure as a pre-processing. Three classification imageries can be
overlaid (Sato et al., 1997,1998). It was tried to express simultaneously qualitative and
quantitative information for example mangrove species and stand stock (Kanetomi et al., 1998).
In this paper, a selection method of threshold for binary value imagery had been examined to
build up a stand analysis system on mangrove forest. The selection method was composed of
two steps. In the first step, the provisional range including threshold was searched on the
histograms of CCT count for band 4 and 5, and in the second step threshold was automatically
searched with applying Otsu’s method to slightly wide range selected in the first step.
