Landform classification is commonly done using topographic altitude only. However, practice indicates that locations at a same altitude may have distinctly different landforms, depending on characteristics of soils underneath those locations. The objectives of this study were to: 1) develop a landform classification approach that is based on both altitude and soil characteristic; and 2) use this approach to determine landforms within a watershed located in northern Ordos Plateau of China. Using data collected at 134 out of 200 sampling sites, this study determined that D₁₀ (the diameter of soil particles 10% finer by weight) and long-term average soil moisture acquired in 2010, which can be estimated at reasonable accuracy from remote sensing imagery, can be used to represent soil characteristics of the study watershed. Also, the sampling data revealed that this watershed consists of nine classes of landforms, namely mobile dune (MD), mobile semi-mobile dune (SMD), rolling fixed semi-fixed dune (RFD), flat sandy land (FD), grassy sandy land (GS), bedrock (BR), flat sandy bedrock (FSB), valley agricultural land (VA), and swamp and salt lake (SW). A set of logistic regression equations were derived using data collected at the 134 sampling sites and verified using data at the remaining 66 sites. The verification indicated that these equations have moderate classification accuracy (Kappa coefficients K>43%). The results revealed that the dominant classes in the study watershed are FD (36.3%), BR (27.0%), and MD (23.5%), while the other six types of landforms (i.e., SMD, RFD, GS, FSB, VA, and SW) in combination account for 13.2%. Further, the landforms determined in this study were compared with the classes presented by a geologically-based classification map. The comparison indicated that the geologically-based classification could not identify multiple landforms within a class that are dependent upon soil characteristics.

Using ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) infrared remote sensing data we inversed the parameters of urban surface heat fluxes applying the PCACA model and theoretical position algorithm, and then we analyzed the influence of different land use types on the surface heat fluxes and energy balance. In this study, Kumagaya, a city in Saitama Prefecture, Japan, was selected as the experimental area. The result shows that the PCACA model is feasible for the surface heat fluxes estimation in urban areas because this model requires less parameters in the procedure of heat fluxes estimation in urban areas with complicated surface structure and can decrease the uncertainty. And we found that different land-use types have indicated the height heterogeneity on the surface heat fluxes significantly. The magnitudes of Bowen ratio in descending order are industrial, residential, transportation, institutional, dry farmland, green space, and water body. Under the same meteorological condition, there are distinct characteristics and regional differences in Bowen ratios among different surface covers, indicating higher sensible heat flux and lower latent heat flux in the urban construction land, while lower sensible heat flux and higher latent heat flux in the vegetation-covered area, the outskirt of the urban area. The increase of urban impervious surface area caused by the urban sprawl can enlarge the sensible heat flux and the Bowen ratio, so that it causes the increasing of urban surface temperature and air temperature, which is the mechanism of the so-called heat island effect.

Many studies on land use change (LUC), using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Case-based reasoning (CBR), as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research (Du et al. 2009), a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata (CA) to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both >70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.

Urban clusters are the expected products of high levels of industry and urbanization in a country, as well as being the basic units of participation in global competition. With respect to China, urban clusters are regarded as the dominant formation for boosting the Chinese urbanization process. However, to date, there is no coincident, efficient, and credible methodological system and set of techniques to identify Chinese urban clusters. This research investigates the potential of a computerized identification method supported by geographic information techniques to provide a better understanding of the distribution of Chinese urban clusters. The identification method is executed based on a geographic information database, a digital elevation model, and socio-economic data with the aid of ArcInfo Macro Language programming. In the method, preliminary boundaries are identified according to transportation accessibility, and final identifications are achieved from limiting city numbers, population, and GDP in a region with the aid of the rasterized socio-economic dataset. The results show that the method identifies nine Chinese urban clusters, i.e., Pearl River Delta, Lower Yangtze River Valley, Beijing-Tianjin-Hebei Region, Northeast China Plain, Middle Yangtze River Valley, Central China Plains, Western Taiwan Strait, Guanzhong and Chengdu-Chongqing urban clusters. This research represents the first study involving the computerized identification of Chinese urban clusters. Moreover, compared to other related studies, the study's approach, which combines transportation accessibility and socio-economic characteristics, is shown to be a distinct, effective and reliable way of identifying urban clusters.

Climatic conditions are difficult to obtain in high mountain regions due to few meteorological stations and, if any, their poorly representative location designed for convenient operation. Fortunately, it has been shown that remote sensing data could be used to estimate near-surface air temperature (Ta) and other climatic conditions. This paper makes use of recorded meteorological data and MODIS data on land surface temperature (Ts) to estimate monthly mean air temperatures in the southeastern Tibetan Plateau and its neighboring areas. A total of 72 weather stations and 84 MODIS images for seven years (2001 to 2007) are used for analysis. Regression analysis and spatio-temporal analysis of monthly mean Ts vs. monthly mean Ta are carried out, showing that recorded Ta is closely related to MODIS Ts in the study region. The regression analysis of monthly mean Ts vs. Ta for every month of all stations shows that monthly mean Ts can be rather accurately used to estimate monthly mean Ta (R² ranging from 0.62 to 0.90 and standard error between 2.25℃ and 3.23℃). Thirdly, the retrieved monthly mean Ta for the whole study area varies between 1.62℃ (in January, the coldest month) and 17.29 ℃ (in July, the warmest month), and for the warm season (May-September), it is from 13.1℃ to 17.29℃. Finally, the elevation of isotherms is higher in the central mountain ranges than in the outer margins; the 0℃ isotherm occurs at elevation of about 4500±500 m in October, dropping to 3500±500 m in January, and ascending back to 4500±500 m in May next year. This clearly shows that MODIS Ts data combining with observed data could be used to rather accurately estimate air temperature in mountain regions.

Rasterization is a conversion process accompanied with information loss, which includes the loss of features’ shape, structure, position, attribute and so on. Two chief factors that affect estimating attribute accuracy loss in rasterization are grid cell size and evaluating method. That is, attribute accuracy loss in rasterization has a close relationship with grid cell size; besides, it is also influenced by evaluating methods. Therefore, it is significant to analyze these two influencing factors comprehensively. Taking land cover data of Sichuan at the scale of 1:250,000 in 2005 as a case, in view of data volume and its processing time of the study region, this study selects 16 spatial scales from 600 m to 30 km, uses rasterizing method based on the Rule of Maximum Area (RMA) in ArcGIS and two evaluating methods of attribute accuracy loss, which are Normal Analysis Method (NAM) and a new Method Based on Grid Cell (MBGC), respectively, and analyzes the scale effect of attribute (it is area here) accuracy loss at 16 different scales by these two evaluating methods comparatively. The results show that: (1) At the same scale, average area accuracy loss of the entire study region evaluated by MBGC is significantly larger than the one estimated using NAM. Moreover, this discrepancy between the two is obvious in the range of 1 km to 10 km. When the grid cell is larger than 10 km, average area accuracy losses calculated by the two evaluating methods are stable, even tended to parallel. (2) MBGC can not only estimate RMA rasterization attribute accuracy loss accurately, but can express the spatial distribution of the loss objectively. (3) The suitable scale domain for RMA rasterization of land cover data of Sichuan at the scale of 1:250,000 in 2005 is better equal to or less than 800 m, in which the data volume is favorable and the processing time is not too long, as well as the area accuracy loss is less than 2.5%.

Spatial scale is a fundamental problem in Geography. Scale effect caused by fractal characteristic of coastline becomes a common focus of coastal zone managers and researchers. In this study, based on DEM and remote sensing images, multi-scale continental coastlines of China were extracted and the fractal characteristic was analyzed. The results are shown as follows. (1) The continental coastline of China fits the fractal model, and the fractal dimension is 1.195. (2) The scale effects with fractal dimensions of coastline have significant differences according to uplift and subsidence segments along the continental coastlines of China. (3) The fractal dimension of coastline has significant spatial heterogeneity according to the coastline types. The fractal dimension of sandy coastline located in Luanhe River plain is 1.109. The dimension of muddy coastline located in northern Jiangsu Plain is 1.059, while that of rocky coastline along southeastern Fujian is 1.293. (4) The length of rocky coastline is affected by scale more than that of muddy and sandy coastline. Since coastline is the conjunction of sea, land and air surface, the study of coastline scale effect is one of the scientific bases for the researches on air-sea-land interaction in multi-scales.

Specific Catchment Area (SCA) is defined as the upstream catchment area of a unit contour. As one of the key terrain parameters, it is widely used in the modeling of hydrology, soil erosion and ecological environment. However, SCA value changes significantly at different DEM resolutions, which inevitably affect terrain analysis results. SCA can be described as the ratio of Catchment Area (CA) and DEM grid length. In this paper, the scale effect of CA is firstly investigated. With Jiuyuangou Gully, a watershed about 70 km² in northern Shaanxi Province of China, as the test area, it is found that the impacts of DEM scale on CA are different in spatial distribution. CA value in upslope location becomes bigger with the decrease of the DEM resolution. When the location is close to downstream areas the impact of DEM scale on CA is gradually weakening. The scale effect of CA can be concluded as a mathematic trend of exponential decline. Then, a downscaling model of SCA is put forward by introducing the scale factor and the location factor. The scaling model can realize the conversion of SCA value from a coarse DEM resolution to a finer one at pixel level. Experiment results show that the downscaled SCA was well revised, and consistent with SCA at the target resolution with respect to the statistical indexes, histogram and spatial distribution. With the advantages of no empirical parameters, the scaling model could be considered as a simple and objective model for SCA scaling in a rugged drainage area.

The study developed a feasible method for large-area land cover mapping with combination of geographical data and phenological characteristics, taking Northeast China (NEC) as the study area. First, with the monthly average of precipitation and temperature datasets, the spatial clustering method was used to divide the NEC into four ecoclimate regions. For each ecoclimate region, geographical variables (annual mean precipitation and temperature, elevation, slope and aspect) were combined with phenological variables derived from the moderate resolution imaging spectroradiometer (MODIS) data (enhanced vegetation index (EVI) and land surface water index (LSWI)), which were taken as input variables of land cover classification. Decision Tree (DT) classifiers were then performed to produce land cover maps for each region. Finally, four resultant land cover maps were mosaicked for the entire NEC (NEC_MODIS), and the land use and land cover data of NEC (NEC_LULC) interpreted from Landsat-TM images was used to evaluate the NEC_MODIS and MODIS land cover product (MODIS_IGBP) in terms of areal and spatial agreement. The results showed that the phenological information derived from EVI and LSWI time series well discriminated land cover classes in NEC, and the overall accuracy was significantly improved by 5.29% with addition of geographical variables. Compared with NEC_LULC for seven aggregation classes, the area errors of NEC_MODIS were much smaller and more stable than that of MODIS_IGBP for most of classes, and the wall-to-wall spatial comparisons at pixel level indicated that NEC_MODIS agreed with NEC_LULC for 71.26% of the NEC, whereas only 62.16% for MODIS_IGBP. The good performance of NEC_MODIS demonstrates that the methodology developed in the study has great potential for timely and detailed land cover mapping in temperate and boreal regions.