The Loess positive and negative terrains （P-N terrains）, which are widely distributed on the Loess Plateau, are discussed for the first time by introducing its characteristic, demarcation as well as extraction metho...The Loess positive and negative terrains （P-N terrains）, which are widely distributed on the Loess Plateau, are discussed for the first time by introducing its characteristic, demarcation as well as extraction method from high-resolution Digital Elevation Models. Using 5 m-resolution DEMs as original test data, P-N terrains of 48 geomorphological units in different parts of Shaanxi Loess Plateau are extracted accurately. Then six indicators for depicting the geomorphologic landscape and spatial configuration characteristic of P-N terrains are proposed. The spatial distribution rules of these indicators and the relationship between the P-N terrains and Loess relief are discussed for further understanding of Loess landforms. Finally, with the integration of P-N terrains and traditional terrain indices, a series of un-supervised classification methods are applied to make a proper landform classification in northern Shaanxi. Results show that P-N terrains are an effect clue to reveal energy and substance distribution rules on the Loess Plateau. A continuous change of P-N terrains from south to north in Shaanxi Loess Plateau shows an obvious spatial difference of Loess land-forms and the positive terrain area only accounted for 60.5% in this region. The P-N terrains participant landform classification method increases validity of the result, especially in the Loess tableland, Loess tableland-ridge and the Loess low-hill area. This research is significant on the study of Loess landforms with the Digital Terrains Analysis methods.展开更多
In mountainous area, spatial interpolation is the traditional method to calculate air temperature by use of observed temperature data. Due to lack of sufficient observation data in mountainous areas many precise inter...In mountainous area, spatial interpolation is the traditional method to calculate air temperature by use of observed temperature data. Due to lack of sufficient observation data in mountainous areas many precise interpolation methods could give only coarse result which could not meet the demand of precision agriculture and local climate exploration. Based on DEMs of 25 m resolution, a reversed model is constructed, with which temperature is simulated to the corresponding slope unit from the solar radiation. Taking Yaoxian county as a test area, and mean monthly temperature data as basic information sources, which are collected from 15 weather stations around Yaoxian county in Shaanxi province from the year of 1970 to 2000, a simulation for the solar radiation cell by cell is completed. By simulating solar radiation at each slope and flat cell unit, the terrain revised temperature model could be realized. A comparison between the simulated temperature and the radiation temperature from TM6 thermal infrared image shows that the terrain improved model gets a finer temperature distribution at local level. The accuracy of simulated temperature in mountainous area is higher than it is in flat area.展开更多
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 environmen...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.展开更多
Landslide susceptibility mapping is a typical two-class classification problem where generating pseudo absence (non-slide) data plays an important role.In this paper,a new method,target space exteriorization sampling ...Landslide susceptibility mapping is a typical two-class classification problem where generating pseudo absence (non-slide) data plays an important role.In this paper,a new method,target space exteriorization sampling method (TSES),is presented to generate pseudo absence data based on presence data directly in feature space.TSES exteriorizes a presence sample to become a pseudo absence one by replacing the value of one of its features with a new one outside the value range of this feature of all presence data.This method is compared with two existing methods,buffer controlled sampling (BCS) and iteratively refined sampling (IRS),in a study area of Shenzhen city.The pseudo absence data generated by each of these three methods are organized into 20 subsets with increasing data sizes to study the effects of the proportion of pseudo absence data to presence data.The landslide susceptibility maps of the study area are calculated with all these datasets by general additive model (GAM).It can be concluded that,through a 10-fold validation,TSES and IRS-based models have similar AUC values that are both greater than that of BCS,but TSES outperforms BCS and IRS in prediction efficiency.TSES results also have more reasonable spatial and histogram distributions than BCS and IRS,which can support categorization of an area into more susceptibility ranks,while IRS shows a tendency to separate the whole study area into two susceptibility extremes.It can be also concluded that when using BCS,the pseudo absence data proportion to the presence data would be about 50% to get a considerable result,while for IRS or TSES the minimum proportion is 40%.展开更多
基金Key Project of National Natural Science Foundation of China, No.40930531 National Youth Science Foundation of China, No.40801148 Anhui Provincial Natural Science Foundation. No. 090412062
文摘The Loess positive and negative terrains （P-N terrains）, which are widely distributed on the Loess Plateau, are discussed for the first time by introducing its characteristic, demarcation as well as extraction method from high-resolution Digital Elevation Models. Using 5 m-resolution DEMs as original test data, P-N terrains of 48 geomorphological units in different parts of Shaanxi Loess Plateau are extracted accurately. Then six indicators for depicting the geomorphologic landscape and spatial configuration characteristic of P-N terrains are proposed. The spatial distribution rules of these indicators and the relationship between the P-N terrains and Loess relief are discussed for further understanding of Loess landforms. Finally, with the integration of P-N terrains and traditional terrain indices, a series of un-supervised classification methods are applied to make a proper landform classification in northern Shaanxi. Results show that P-N terrains are an effect clue to reveal energy and substance distribution rules on the Loess Plateau. A continuous change of P-N terrains from south to north in Shaanxi Loess Plateau shows an obvious spatial difference of Loess land-forms and the positive terrain area only accounted for 60.5% in this region. The P-N terrains participant landform classification method increases validity of the result, especially in the Loess tableland, Loess tableland-ridge and the Loess low-hill area. This research is significant on the study of Loess landforms with the Digital Terrains Analysis methods.
基金National Natural Science Foundation of China, No.40671148, No.40571120 Specialized Research Fund for the Doctoral Program of Higher Education, No.20050319006 Chair Professor Foundation of Nanjing Normal University
文摘In mountainous area, spatial interpolation is the traditional method to calculate air temperature by use of observed temperature data. Due to lack of sufficient observation data in mountainous areas many precise interpolation methods could give only coarse result which could not meet the demand of precision agriculture and local climate exploration. Based on DEMs of 25 m resolution, a reversed model is constructed, with which temperature is simulated to the corresponding slope unit from the solar radiation. Taking Yaoxian county as a test area, and mean monthly temperature data as basic information sources, which are collected from 15 weather stations around Yaoxian county in Shaanxi province from the year of 1970 to 2000, a simulation for the solar radiation cell by cell is completed. By simulating solar radiation at each slope and flat cell unit, the terrain revised temperature model could be realized. A comparison between the simulated temperature and the radiation temperature from TM6 thermal infrared image shows that the terrain improved model gets a finer temperature distribution at local level. The accuracy of simulated temperature in mountainous area is higher than it is in flat area.
基金Key Project of National Natural Science Foundation of China No.40930531 National Youth Science Foun-dation of China No.40901185 Specialized Research Fund for the Doctoral Program of Higher Education No.20093207120008
文摘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.
基金supported by the Research Fund from Hong Kong Polytechnic University(Grant Nos.G-U632,G-YF24)National Key Technologies Research and Development Program of China(Grant Nos.2008BAJ11B04,2006BAJ14B04)+1 种基金National Natural Science Foundation of China(Grant Nos.40928001,40701134,40771171)National High technology Research and Development Program of China("863"Program)(Grant No.2007AA120502)
文摘Landslide susceptibility mapping is a typical two-class classification problem where generating pseudo absence (non-slide) data plays an important role.In this paper,a new method,target space exteriorization sampling method (TSES),is presented to generate pseudo absence data based on presence data directly in feature space.TSES exteriorizes a presence sample to become a pseudo absence one by replacing the value of one of its features with a new one outside the value range of this feature of all presence data.This method is compared with two existing methods,buffer controlled sampling (BCS) and iteratively refined sampling (IRS),in a study area of Shenzhen city.The pseudo absence data generated by each of these three methods are organized into 20 subsets with increasing data sizes to study the effects of the proportion of pseudo absence data to presence data.The landslide susceptibility maps of the study area are calculated with all these datasets by general additive model (GAM).It can be concluded that,through a 10-fold validation,TSES and IRS-based models have similar AUC values that are both greater than that of BCS,but TSES outperforms BCS and IRS in prediction efficiency.TSES results also have more reasonable spatial and histogram distributions than BCS and IRS,which can support categorization of an area into more susceptibility ranks,while IRS shows a tendency to separate the whole study area into two susceptibility extremes.It can be also concluded that when using BCS,the pseudo absence data proportion to the presence data would be about 50% to get a considerable result,while for IRS or TSES the minimum proportion is 40%.
