This study analyzes the change of Normalized Difference Vegetation Index (NDVI) and precipitation for forest in
different ecological zones in China and their correlation over the period of 1982-2006. The specific aim of this paper was
to identify the changing trends of NDVI and precipitation and understand their relations, especially, on which duration
the precipitation influence NDVI strongly during growing season of forest in different ecological aspects. The results
showed that 1) the break points of NDVI and precipitation appeared in different years in most ecological zones, but in
temperate continental forest and temperate mountain system, they have a high degree of consistency; 2) the NDVI in
boreal coniferous forest, temperate mountain system and tropical moist deciduous forest showed a increasing trend
during 1982-2006 and the lowest value were appeared in different time and the precipitation in boreal coniferous forest
and temperate mountain system showed a decreasing trend; 3) the forest in different ecological zones has different
patterns with different periods and lags and the peak value of pearson correlation coefficients were showed in different
duration and lag, and NDVI and precipitation generally have the negative but weak relation.
Forest cover maps are essential for current researches of biomass estimation and global change, but traditional methods
to derive forest maps are complex. These methods usually need training samples or other ancillary data as input, and are
time- and labor- consuming for large scale applications. To make the process of forest cover mapping simple and rapid,
in this paper, a simple spectral index called forest index (FI) was proposed to highlight forest land cover in Landsat
scenes. The FI is derived from three bands, green, red and near-infrared (NIR) bands and an FI image can be classified
into forest/non-forest map with a threshold. The overall accuracies of classification maps in the two study areas were
97.8% and 96.2%, respectively, which indicates that the FI is efficient at highlighting forest cover.
On May 12 in 2008, a Magnitude 8.0 earthquake hit Wenchuan in China, and the casualty shocked the whole world. The
landslide was a frequent secondary disaster in this earthquake, so to analyze the mechanism of landslides in the disaster
area is very important for post-earthquake reconstruction. The study area is located in PingWu County, which was also
hit by the earthquake severely. And the data sources are ETM+ image, DEM and interpreted ALOS image. This paper
considered four potential driving factors for landslides, and they are land cover, lineament, slope and drainage. The land
cover was classified based on the density of vegetation, and sub-pixel analysis was employed; Density of lineament was
calculated by Sobel operator and image segmentation; Slope was classified by using a threshold; Drainage was
considered without numerical analysis, because it is significant and simple in study area. To find out how they influenced
the landslides, conditional probability was utilized as a measurement. The result shows that areas in sparse vegetation,
dense lineament and steep topography were easy to meet landslides, while drainages also induced landslides.
KEYWORDS: Geographic information systems, Data mining, Information technology, Associative arrays, Roads, Geography, Data modeling, Raster graphics, Data conversion, Lithium
The paper intends to employ Geographic Information System (GIS) and Bayesian Network to discover the spatial causality between enterprises and environmental factors in Beijing Metropolis. The census data of Beijing was spatialized by means of GIS in the beginning, and then the training data was made using density mapping technique. Base on the training data, the structure of a Bayesian Network was learnt with the help of Maximum Weight Spanning
Tree. Eight direct relations were discussed in the end, of which, the most exciting discovery, "Enterprise-Run Society", as the symbol of the former planned economy, was emphasized in the spatial relations between heavy industry and schools. Though the final result is not so creative in economic perspective, it is of significance in technique view due to all discoveries were drawn from data, therefore leading to the realization of the importance of GIS and data mining to
economic geography research.
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