Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil...Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.展开更多
Most remote sensing studies assess the desertification using vegetation monitoring method. But it has the insufficient precision of vegetation monitoring for the limited vegetation cover of the desertification region....Most remote sensing studies assess the desertification using vegetation monitoring method. But it has the insufficient precision of vegetation monitoring for the limited vegetation cover of the desertification region. Therefore, it offers an alternative approach for the desertification research to assess sand dune and sandy land change using remote sensing in the desertification region. In this study, the indices derived from the well-known tasseled cap transformation(TCT), tasseled cap angle(TCA),disturbance index(DI), process indicator(PI), and topsoil grain size index(TGSI) were integrated to monitor and assess the desertification at the thirteen study sites including sand dunes and sandy lands distributed in the Mongolian Plateau(MP) from 2000 to 2015. A decision tree was used to classify the desertification on a regional scale. The average overall accuracy of 2000, 2005, 2010 and 2015 desertification classification was higher than 90%. Results from this study indicated that integration of the advantages of TCA, DI and TGSI could better assess the desertification. During the last 16 years, Badain Jaran Desert, Tengger Desert, and Ulan Buh Desert showed a relative stabilization. Otindag Sandy Land and the deserts of Khar Nuur, Ereen Nuur, Tsagan Nuur, Khongoryn Els, Hobq, and Mu Us showed a slow increasing of desertification, whereas Bayan Gobi, Horqin and Hulun Buir sandy lands showed a slow decreasing of desertification. Compared with the other 11 sites, the fine sand dunes occupied the majority of the Tengger Desert, and the coarse sandy land occupied the majority of the Horqin Sandy Land. Our findings on a three or four years' periodical fluctuated changes in the desertification may possibly reflect changing precipitation and soil moisture in the MP. Further work to link the TCA, DI,TGSI, and PI values with the desertification characteristics is recommended to set the thresholds and improve the assessment accuracy with field investigation.展开更多
Deserts and sandy land in northern China are very susceptible to sandy desertification and are the main source of sand-dust storms of Asian dust. However, because of the complex factors involved, descriptions of the r...Deserts and sandy land in northern China are very susceptible to sandy desertification and are the main source of sand-dust storms of Asian dust. However, because of the complex factors involved, descriptions of the relationship between sandy desertification and surface characteristics in these regions are lacking. We monitored the surface characteristics and their changes in time using information about soil, vegetation, and landforms in the Badain Jaran Desert(BJD), Tengger Desert(TD), and Ulan Buh Desert(UBD) in the northern China. The monitoring was done using tasseled cap angle(TCA), disturbance index(DI), and topsoil grain size index(TGSI) from Moderate Resolution Imaging Spectroradiometer(MODIS) images combined with a decision tree classification. Results showed that the TD had higher topsoil fine sand content, and the ratio of non-vegetated to vegetated areas was similar with that in the UBD. Northeast-southwest coarse sand dunes with thin interdune(NECTI) dominated the BD, fine sand dunes(FSD) dominated the TD, and a combination of northeast-southwest coarse sand dunes with wide interdune(NECWI) and northwest-southeast coarse sand dunes with wide interdune(NWCWI) dominated the UBD. From 2000 to 2015, in the BJD the area of the NECTI, non-sand dune(Non) and potential sand sources(PSS) increased, whereas the area of the NECWI, FSD and NWCWI decreased, indicating a improve process in the BJD. In the TD, the area covered by Non increased, whereas the area covered by PSS, NECWI, NECTI, FSD, and NWCWI decreased from 2000 to 2015. The area covered by the various surface characteristic types fluctuated annually in the UBD from 2000 to 2015. Changes in surface characteristics reflect the combined effects of natural conditions and human activity. The findings of our study will assist scientists and policy makers in proposing different management techniques to combat sandy desertification for the different surface characteristics of these regions.展开更多
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 d...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.展开更多
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 i...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.展开更多
基金Under the auspices of Special Fund for Ocean Public Welfare Profession Scientific Research(No.201105020)National Natural Science Foundation of China(No.41471178,41023010,41431177)National Key Technology Innovation Project for Water Pollution Control and Remediation(No.2013ZX07103006)
文摘Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe(Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer(MODIS), Normalized Difference Vegetation Indices(NDVIs) and land surface reflectance data from Landsat Thematic Mapper(TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient(CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area(with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.
基金supported by the Innovation Project of State Key of Laboratory of Resources and Environmental Information System (O88RA20CYA)the National Natural Science Foundation of China (41671422)+1 种基金the International Cooperation in Science and Technology Special Project (2013DFA91700)the National Science-Technology Support Plan Project (2013BAD05B03)
文摘Most remote sensing studies assess the desertification using vegetation monitoring method. But it has the insufficient precision of vegetation monitoring for the limited vegetation cover of the desertification region. Therefore, it offers an alternative approach for the desertification research to assess sand dune and sandy land change using remote sensing in the desertification region. In this study, the indices derived from the well-known tasseled cap transformation(TCT), tasseled cap angle(TCA),disturbance index(DI), process indicator(PI), and topsoil grain size index(TGSI) were integrated to monitor and assess the desertification at the thirteen study sites including sand dunes and sandy lands distributed in the Mongolian Plateau(MP) from 2000 to 2015. A decision tree was used to classify the desertification on a regional scale. The average overall accuracy of 2000, 2005, 2010 and 2015 desertification classification was higher than 90%. Results from this study indicated that integration of the advantages of TCA, DI and TGSI could better assess the desertification. During the last 16 years, Badain Jaran Desert, Tengger Desert, and Ulan Buh Desert showed a relative stabilization. Otindag Sandy Land and the deserts of Khar Nuur, Ereen Nuur, Tsagan Nuur, Khongoryn Els, Hobq, and Mu Us showed a slow increasing of desertification, whereas Bayan Gobi, Horqin and Hulun Buir sandy lands showed a slow decreasing of desertification. Compared with the other 11 sites, the fine sand dunes occupied the majority of the Tengger Desert, and the coarse sandy land occupied the majority of the Horqin Sandy Land. Our findings on a three or four years' periodical fluctuated changes in the desertification may possibly reflect changing precipitation and soil moisture in the MP. Further work to link the TCA, DI,TGSI, and PI values with the desertification characteristics is recommended to set the thresholds and improve the assessment accuracy with field investigation.
基金Innovation Project of LREIS(No.O88RA20CYA,08R8A010YA)National Natural Science Foundation of China(No.41671422)International Cooperation in Science and Technology Special Project(No.2013DFA91700)
文摘Deserts and sandy land in northern China are very susceptible to sandy desertification and are the main source of sand-dust storms of Asian dust. However, because of the complex factors involved, descriptions of the relationship between sandy desertification and surface characteristics in these regions are lacking. We monitored the surface characteristics and their changes in time using information about soil, vegetation, and landforms in the Badain Jaran Desert(BJD), Tengger Desert(TD), and Ulan Buh Desert(UBD) in the northern China. The monitoring was done using tasseled cap angle(TCA), disturbance index(DI), and topsoil grain size index(TGSI) from Moderate Resolution Imaging Spectroradiometer(MODIS) images combined with a decision tree classification. Results showed that the TD had higher topsoil fine sand content, and the ratio of non-vegetated to vegetated areas was similar with that in the UBD. Northeast-southwest coarse sand dunes with thin interdune(NECTI) dominated the BD, fine sand dunes(FSD) dominated the TD, and a combination of northeast-southwest coarse sand dunes with wide interdune(NECWI) and northwest-southeast coarse sand dunes with wide interdune(NWCWI) dominated the UBD. From 2000 to 2015, in the BJD the area of the NECTI, non-sand dune(Non) and potential sand sources(PSS) increased, whereas the area of the NECWI, FSD and NWCWI decreased, indicating a improve process in the BJD. In the TD, the area covered by Non increased, whereas the area covered by PSS, NECWI, NECTI, FSD, and NWCWI decreased from 2000 to 2015. The area covered by the various surface characteristic types fluctuated annually in the UBD from 2000 to 2015. Changes in surface characteristics reflect the combined effects of natural conditions and human activity. The findings of our study will assist scientists and policy makers in proposing different management techniques to combat sandy desertification for the different surface characteristics of these regions.
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.
基金Under the auspices of the Project of National Natural Science Foundation of China ( No. 41001363)Autonomous Project of State Key Laboratory of Resources and Environmental Information System,Geo-information Tupu Theory and Virtual Geoscience
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.
基金Thanks are due to Profs. Zhen Du and Li Xiaowen of the Chinese Academy of Sciences （CAS） for their instructions and encouragement. Special thanks to Profs. Yang Qinye, Wu Shaohong, Gong Peng, Liu Qinhuo, and Susan Kaspari for their great help with the research. The work is supported by the Special Funds for Major State Basic Research Project （Grant No. 2002CB412408）, by the National Natural Science Foundation of China （Grant Nos. 40371093, 40201005, 40471097）, by 0pening Fund projects of State Key Laboratory of Remote Sensing Science in the Institute of Remote Sensing Applications （Grant No. SK040006）, by 0pening Fund projects of State Key Laboratory of Estuarine and Coastal Research of East China Normal University （Grant No. 0310）.
文摘基于风景生态学 andGee 信息 Tupu 的图形、量的方法论，这篇论文学习风景黄河三角洲(YRD ) 的空间时间的进化特征，它被黄河(年) 尾部隧道的周期的物理秋千在 1855-2000 期间建立。根据关于 YRDand 年尾部隧道的空间时间的进化的研究，我们断定 Qingshuigou 隧道到达了它的限制，一条新流动隧道迟早多年应该被建立。它建议年隧道的移动应该接受它在以前的隧道之中通过消沉流动的自然规则并且能被人造的努力执行。论文在河口在 YR 流动为淤泥的高内容的处理外面讨论这些问题和交替的方法。
