Full text: Mapping without the sun

2.2.3 Non-vegetation interpretation 
The non-vegetation includes the residential area, bare field, 
meadow, and plantation in the paper. We use the similar method 
to the above paper, after careful analysis step by step, we set up 
the decision criteria of the residential area in Daye area: 
(TM4(40-80), TM5(70-100), TM4-TM3< 15),based on the 
result of the map which is masked by the water body, 
vegetation, and mine. We examine the residential area 
interpretation with the residential area information on the 
1/10000 relief map. The error of the interpretation result is less 
than 15 percent. Based on the above result, the other land cover 
decision criteria are set up as following: bare field (TM4>=12, 
TM4>TM5), plantation (TM4<120,TM4>TM5) and meadow 
(TM4<TM5).The exact non-vegetation distributing map is 
2.2.4 Mine interpretation 
Mine exploitation leads to inevitable groundsurface demolish 
and disorder. The spectrum reflection character within the 
visible light and near infrared range between the undisturbed 
field and mine field is distinct. We can use the character to 
determine the mine field by the remote sensing technique. We 
use the supervised method combined the ironoxide index (most 
of the mine field ironoxide >0.5) and NDVI (most of the mine 
field NDVI <0, the maximum value is 0.2). Before the mine 
interpretation, the water have been masked to avoid disturb. 
The final decision criteria is ironoxide >0.4 and the 
NDVI O.According to the above decision criteria, most of the 
mine field are picked up. But much residential area is picked up 
by error. In view of the fact, we pile up the all the residential 
areas in the TM image (1986), 1/10000 relief map and the 
above mine field interpretation. The reason we select the TM 
image (1986) is that the 1/10000 relief map was protracted in 
1989, which is close to the TM image (1986) acquired time. We 
first select the residential area by error and the typical mine, 
then we analysis the spectrum profile curve of the mine and 
residential area. After many test, we find out the mine spectrum 
value is larger than the residential area spectrum value in the 
arithmetic formula as following: 
2TM3-(TM2+TM4).According to the above analysis, we set up 
a new decision criteria to pick up the mine: 
2TM3-(TM2+TM4)>20.The final mine distributing map is 
achieved. We examine the interpretation result by the field 
investigation, the final map eliminate the scattered residential 
area and keep the original mine integrity. The above method is 
feasible in practice. Although the above method can pick up the 
most of mine and eliminate the most of the residential area by 
error in interpretation, there is a little residential area mix in the 
final mine map. It is very hard to eliminate the residential area 
(otherwise much mine will be eliminated) by the spectrums 
relationship. If we intend to improve the decision of the 
computer automatic interpretation, the higher spatial resolution 
image will be necessary. 
From the change detection results, it is observed that: The 
water quality of the whole waters is still serious, although the 
water quality has been improved a little in some areas. The 
area of mild contaminated water increase 11.4 km 2 .Vegetation 
shows degradation trend especially those areas close to the 
mining areas, wood field and plantations decreased 135 km 2 
and 192.5 km 2 respectively, and instead the areas of bare land 
increased 128 km 2 based on the mine database and the remote 
sensing interpretation. The reclamation percentage of the 
abandoned mining is only 20%from 1986 to 2002. The 
ecological environment in the study area is in degenerated and 
may become worse unless the efficient management of 
mining business and effective eco-environment protection in 
mining area are carried out instantly. 
The GIS is used to develop a spatially indexed database, spatial 
analysis, and as a display tool for the visualization and 
dissemination of the results of the model. The integration of 
data in a GIS was valuable for effective analyses but also 
exposed the necessity of accounting for spatial reference and 
accuracy of data from different sources. GPS technology 
proved very useful to increase the spatial accuracy of the 
various data integrated in the GIS. Such systems can be used 
in-house or deployed over the worldwide web for easy user 
An increasing number of Earth observation satellites provide 
data for monitoring mine exploitation status, each covering a 
different portion of the electromagnetic spectrum at different 
spatial, temporal and spectral resolutions. Using the 
multi-temporal data image approach described in this paper for 
the monitoring of mine exploitation status is expected to result 
in improved system reliability. 
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DeFries, R. S., Hansen, M., Townshend, J. R. G., & Sohlberg, 
R. Global land cover classifications at 8 km spatial resolution: 
the use of training data derived from Landsat imagery in 
decision tree classifiers.International Journal of Remote 
Sensing, 19, 1998,3141-3168. 
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Friedl, M. A., & Brodley, C. Decision tree classification of land 
cover from remotely sensed data. Remote Sensing of 
Environment, 61, 1997, 399-409. 
Friedl, M. A., Mclver, D. K. et al., Global land cover 
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