Geo-information Tupu Analysis of Land Use Change in Haihe River Basin

[Objective] This study was to provide basis for the scientific management of land use in Haihe River Basin （HRB） through the quantitative exploration of the land use conversion, changes of intensity and spatial distribution in this region. [Method] With the support of remote sensing technology and geographic information technology, the land use maps of the study area in 40 years （1970-2010） were in- terpreted and plotted. Four kinds of tupu, namely, land use change tupu, process tupu, arising tupu and evolution mode tupu were built through the spatial overlay of the land use maps to analyze the change rules of land use patterns. [Result] The conversion of arable land to construction land was the main characteristics of land use changes in HRB for the 40 years; the area of non-stable region accounted for 35% of the total, indicating that the land use changed remarkably, thus, it was nec- essary to strengthen the scientific land management in HRB; the new conversions to all land use patterns were all the lowest in 1980-1990, indicating that land use changed slowly during this period. [Conclusion] The results indicate that, compared with conventional transfer matrix method, geo-information tupu has obvious advantage in analyzing land use changes that it can demonstrate the spatial distribution of interest region, display the multi-dimensional spatial information.

Use of Intensity Analysis to Measure Land Use Changes from 1932 to 2005 in Zhenlai County, Northeast China

Analyzing spatiotemporal dynamics of land use and land cover over time is widely recognized as important to better understand and provide solutions for social, economic, and environmental problems, especially in ecologically fragile region. In this paper, a case study was taken in Zhenlai County, which is a part of farming-pastoral ecotone of Northeast China. This study seeks to use multi-temporal satellite images and other data from various sources to analyze spatiotemporal changes from 1932 to 2005, and applied a quantitative methodology named intensity analysis in the time scale of decades at three levels: time interval, category, and transition. The findings of the case study are as follows: 1) the interval level of intensity analysis revealed that the annual rate of overall change was relatively fast in 1932–1954 and 1954–1976 time intervals. 2) The category level showed that arable land experienced less intensively gains and losses if the overall change was to have been distributed uniformly across the landscape while the gains and losses of forest land, grassland, water, settlement, wetland and other unused land were not consistent and stationary across the four time intervals. 3) The transition level illustrated that arable land expanded at the expense of grassland before 2000 while it gained intensively from wetland from 2000 to 2005. Settlement targets arable land and avoids grassland, water, wetland and other unused land. Besides, the loss of grassland was intensively targeted by arable land, forest land and wetland in the study period while the loss of wetland was targeted by water except for the time interval of 1976–2000. 4) During the early reclamation period, land use change of the study area was mainly affected by the policy, institutional and political factors, followed by the natural disasters.

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.