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| 1 | Estimating Soil Salinity in the Yellow River Delta, Eastern China——An Integrated Approach Using Spectral and Terrain Indices with the Generalized Additive Model显示文摘Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model(DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper(TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model(GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike's information criterion(AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover.Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Visible red and near-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation. | SONG Chuangye REN Hongxu HUANG Chong | 2016 | Pedosphere2016，26，5： | 6 |
| 2 | Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models显示文摘This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision. | SONG Chuangye HUANG Chong LIU Huiming | 2013 | Chinese Geographical Science2013，23，3： | 5 |
| 3 | Simulating Potential Distribution of Tamarix chinensis in Yellow River Delta by Generalized Additive Models显示文摘There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution. | SONG Chuangye HUANG Chong LIU Gaohuan | 2010 | 湿地科学2010，8，4： | 0 |
| 4 | Solid state reaction for the formation of spinel MgFe_2O_4 across perovskite oxide interface显示文摘Solid state reaction is a conventional method to synthesize structurally stable inorganic solids by mixing powdered reactants together at high pressure(over 1×10~5 mbar(1 mbar=100 Pa))and high temperature(over 1300 K)[1-4].This method is effective and sophisticated to prepare solid materials,especially the functional complex oxides such as high temperature superconductors,piezoelectrics,dielectrics,etc.However,the chemical reactions cannot be intrinsically controlled and integrated at an atomic level in order to | Iftikhar Ahmed Malik XiaoXing Ke Xin Liu ChuanShou Wang XueYun Wang Rizwan Ullah ChuangYe Song Jing Wang JinXing Zhang | 2017 | Science China(Physics,Mechanics & Astronomy)2017，60，9： | 0 |
| 5 | Application of Remote Sensing Detection and GIS in Analysis of Vegetation Pattem Dynamics in the Yellow River Delta显示文摘Regional vegetation pattem dynamics has a great im- pact on ecosystem and climate change.Remote sensing data and geographical information system (GIS) analysis were widely used in the detection of vegetation pattern dynamics.In this study,the Yellow River Delta was selected as the study area.By using 1986, 1993,1996,1999 and 2005 remote sensing data as basic informa- tion resource,with the support of GIS,a wetland vegetation spa- tial information dataset was built up.Through selecting the land- scape metrics such as class area (CA),class percent of landscape (PL),number of patch (NP),largest patch index (LPI) and mean patch size (MPS) etc.,the dynamics of vegetation pattern was analyzed.The result showed that the change of vegetation pattern is significant from 1986 to 2005.From 1986-1999,the area of the vegetation,the percent of vegetation,LPI and MPS decreased,the NP increased,the vegetation pattern tends to be fragmental.The decrease in vegetation area may well be explained by the fact of the nature environment evolution (Climate change and decrease in Yellow River runoff) and the increase in the population in the Yellow River Delta.However,from 1999-2005,the area of the vegetation,the percent of vegetation,LPI and MPS increased, while the NP decreased.This trend of restoration may be due to the implementation of water resources regulation for the Yellow River Delta since 1999. | Song Chuangye Liu Gaohuan | 2008 | Chinese Journal of Population,Resources and Environment2008，6，2： | 0 |
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