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Mapping without the sun
Zhang, Jixian

Figure 1 D edaon tree model for land cover m apping
In their simplest form, decision tree classifiers successively
partition the input training data into more and more
homogeneous subsets by producing optimal rules or decisions,
also called nodes, which maximize the information gained and
thus minimize the error rates in the branches of the tree
(Safavian & Landgrebe, 1991; Weiss & Kulikowski, 1991).
techniques with the data derived from the mineral resources
regulatory agency.
2.2.1 Water interpretation and classification
Before the interpretation, we first make the spectrum model
curves of all main land covers (wood field, plantation, bare
field, paddy field, water body, mine, residential area).After
much comparative analysis, we find out the
(TM2+TM3)-(TM4+TM5)>0 in paddy field, water body(Fig.2,
Fig.3), then we utilize the0 eliminate the mine and residential area by error. Considering
that the TM4 is distinct in paddy field, water body, we utilize
the condition TM4>40 to eliminate the paddy field by error. So
far, we have got the exact water location map. Before the
classification, we first select three kinds of typical water model
including the seriously contaminated water, mildly
contaminated water, uncontaminated water. According to the
spectrum model curve of the TM images (all the seven bands),
we find out the difference is discriminative in the value of the
band three (Fig.4, Fig.5). After many tests, the determinant
criteria are proved to be reliable: serious contaminated water
(TM3 (40-60)), mild contaminated water (TM3 (30-40)),
uncontaminated water (TM3 (<30)). Based on the above
analysis, we get the water quality and location map in Daye
2.2.2 Vegetation interpretation and classification
Figure 2 Spectral profile of water for TM1986
Figure 3 Spectral character profile of water for TM1986
Figure 4 Further spectral profile of water for TM1986
Figure 5 Classfication spectral profile of water for TM1986
The use of decision trees as a viable alternative to more
traditional classifiers in remote sensing research has been
explored primarily within the context of global or continental
scale land cover classifications (DeFries et al., 1998; Friedl &
Brodley, 1997; Friedl et al., 2002; Hansen et al., 1996, 2000;
Strahler et al., 1999).
In this study, we aim to capitalize on many of the ‘lessons
learned’ using decision trees for coarse-scale land cover
applications.We will examine the performance of these
According to the field investigation, the relationship among the
Normalized Difference Vegetation Index (NDVI), vegetation
types and the elevation is analyzed synthetically. Based on the
result of the water is masked, we set up the decision criteria of
the vegetation distributing in Daye area: wood field
(NDVI>0.45, DEM>100); shrub (0.350),
we get the vegetation distributing map in Daye area.