Reservoir water quality is traditionally monitored and evaluated based on field data. Collecting and analyzing field water quality data are costly and time consuming tasks, and whether a limited number of field data truly characterize the spatial variation of the trophic state within a vast water body is often disputed. In this study we utilize Landsat TM data to estimate the water quality and trophic state of the Te‐Chi reservoir in Central Taiwan. A modified multi‐parameter model of Carlson's trophic state index (TSI) was developed for the Te‐chi reservoir. Water quality parameters (concentration of chlorophyll‐a, total phosphorous measurement, and secchi disk depth) required by the model are found to have high correlations with combinations of TM bands. Therefore, TM data are used to map the trophic state of the reservoir. TM‐derived TSI maps of the reservoir reveal that, in summer, the trophic state in the reservoir generally improves from upstream to downstream and that zones of distinct trophic state exist. A trophic state index based on secchi disk depth may give erroneous values in the upstream section of the reservoir pool due to high sediment concentration in the reservoir inflow. We conclude that the Te‐Chi reservoir is eutrophic or worse in summer and meso‐eutrophic in winter. Implementation of best management practices to reduce nonpoint source pollution in the upstream watershed is highly recommended. This study demonstrates the capability of mapping the trophic state in impounded water bodies using the Landsat TM data.
Landslides during earthquakes have led to severe casualties and have resulted in damaged structures and facilities. The goal of the present study is to analyze the landslide problems in a remote area-Shei-Pa National Park in Taiwan. Spatial information techniques (Remote Sensing and Geographic Information System) with an innovative data mining technique, Discrete Rough Set (DRS) method, are incorporated to our study for analyzing landslides, their distribution, and classification. The present study provides how to find (1) the most representative data of landslide samples from the existing database, (2) the core attributes of the target categories: Normalized Difference Vegetation Index (NDVI) and Vegetation Index (VI), and (3) the thresholds (segment points) of each attribute on the target categories. A conventional approach, C4.5 Decision Tree Analysis, is used as a comparison. The methodology discussed in this study is of help to the analysis of landslide problems and thus facilitates the informed decision-making process.
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