Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620331
Ping Wang, Jixian Zhang, Weidong Jia, Zongjian Lin
Adopting the decision tree technology, utilizing its process pattern that imitates human judgment and thinking and fault-tolerance features, the authors developed a decision tree classification method. Initially utilizing SPOT and TM, the work effectively enhanced LULC information and established the synthetic database; then, combining geoscience synthetic analysis with ground spectral feature information, utilizing the CART system; the authors built the decision tree model that is based on the decision rules. At last, we discussed the wild use of LULC decision tree classified and stratified extractive technology. Taking three counties in Hebei province as examples, we divided the research area to classify each unit (county area) by ecological division, utilized multiple data resources and geoscience rules to build the decision tree model and test and verify the method. The results demonstrated that the method improves the speed and precision of classification.
{"title":"A study on decision tree classification method of land use/land cover -Taking tree counties in Hebei Province as an example","authors":"Ping Wang, Jixian Zhang, Weidong Jia, Zongjian Lin","doi":"10.1109/EORSA.2008.4620331","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620331","url":null,"abstract":"Adopting the decision tree technology, utilizing its process pattern that imitates human judgment and thinking and fault-tolerance features, the authors developed a decision tree classification method. Initially utilizing SPOT and TM, the work effectively enhanced LULC information and established the synthetic database; then, combining geoscience synthetic analysis with ground spectral feature information, utilizing the CART system; the authors built the decision tree model that is based on the decision rules. At last, we discussed the wild use of LULC decision tree classified and stratified extractive technology. Taking three counties in Hebei province as examples, we divided the research area to classify each unit (county area) by ecological division, utilized multiple data resources and geoscience rules to build the decision tree model and test and verify the method. The results demonstrated that the method improves the speed and precision of classification.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620336
Wenbin Wu, Ryosuke Shibasaki, P. Yang, Qingbo Zhou, Huajun Tang
This study used time-series of NDVI datasets at a spatial resolution of 8 km and 15-day interval to identify the spatial patterns of cropland phenology in China. To do so, a smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent data processing for detecting cropping systems and phonological parameters was based on the smoothed NVDI time-series datasets. The results show that the cropping system in Chinapsilas cropland becomes complex as moving toward to the south from the north China. Under this cropping system, the starting date (SGS) and ending date (EGS) for the first growing season vary over space, and those regions with multiple cropping systems present a significant advanced SGS and EGS than the regions with a single cropping. On the contrary, the phenology of the second growing season including both the SGS and EGS show little difference between regions. This study concludes that spatial patterns of cropping system and phenology in Chinapsilas cropland are highly related to the geophysical environmental factors in China. In addition, several anthropogenic factors, such as crop variety, cultivation levels, irrigation and fertilizers, can profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.
{"title":"Characterizing spatial patterns of phenology in China’s cropland based on remotely sensed data","authors":"Wenbin Wu, Ryosuke Shibasaki, P. Yang, Qingbo Zhou, Huajun Tang","doi":"10.1109/EORSA.2008.4620336","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620336","url":null,"abstract":"This study used time-series of NDVI datasets at a spatial resolution of 8 km and 15-day interval to identify the spatial patterns of cropland phenology in China. To do so, a smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent data processing for detecting cropping systems and phonological parameters was based on the smoothed NVDI time-series datasets. The results show that the cropping system in Chinapsilas cropland becomes complex as moving toward to the south from the north China. Under this cropping system, the starting date (SGS) and ending date (EGS) for the first growing season vary over space, and those regions with multiple cropping systems present a significant advanced SGS and EGS than the regions with a single cropping. On the contrary, the phenology of the second growing season including both the SGS and EGS show little difference between regions. This study concludes that spatial patterns of cropping system and phenology in Chinapsilas cropland are highly related to the geophysical environmental factors in China. In addition, several anthropogenic factors, such as crop variety, cultivation levels, irrigation and fertilizers, can profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116164404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620323
Shen Qian, Chuanqing Wu, Z. Bing, Junsheng Li
The problem of water source pollution has become more and more serious in Wuxi and Suzhou district. It is urgent to monitor water quality widely and rapidly, which is the advantage of remote sensing. However, water sources around cities are inland waters in which chlorophyll-a and suspended matter concentrations are hard to retrieve accurately from remote sensing just by using empirical methods. To overcome this problem, this study has developed an analytical method based on inherent optical parameters to retrieve chlorophyll-a and suspended matter concentrations. To validate this method, we have collected a dataset as follows: CBERS CCD image in Taihu Lake around Wuxi and Suzhou, in situ measured water reflectance spectra,and inherent optical parameters, and the simultaneously measured aerosol data from Wuxi. Based on two approximate premises, we apply the red and the near infrared images to get total suspended matter concentration and chlorophyll-a concentration. The retrieved concentrations of total suspended matter and chlorophyll-a are close with in situ measured ones. This study is helpful for monitoring water quality of water supply sources from multi-spectral remote sensing images.
{"title":"Retrieval of chlorophyll-a and suspended matter concentration in water supply sources of Wuxi and Suzhou using multi-spectral remote sensing images","authors":"Shen Qian, Chuanqing Wu, Z. Bing, Junsheng Li","doi":"10.1109/EORSA.2008.4620323","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620323","url":null,"abstract":"The problem of water source pollution has become more and more serious in Wuxi and Suzhou district. It is urgent to monitor water quality widely and rapidly, which is the advantage of remote sensing. However, water sources around cities are inland waters in which chlorophyll-a and suspended matter concentrations are hard to retrieve accurately from remote sensing just by using empirical methods. To overcome this problem, this study has developed an analytical method based on inherent optical parameters to retrieve chlorophyll-a and suspended matter concentrations. To validate this method, we have collected a dataset as follows: CBERS CCD image in Taihu Lake around Wuxi and Suzhou, in situ measured water reflectance spectra,and inherent optical parameters, and the simultaneously measured aerosol data from Wuxi. Based on two approximate premises, we apply the red and the near infrared images to get total suspended matter concentration and chlorophyll-a concentration. The retrieved concentrations of total suspended matter and chlorophyll-a are close with in situ measured ones. This study is helpful for monitoring water quality of water supply sources from multi-spectral remote sensing images.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127125703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620347
Nannan Zhang, Yi-Bo Luo, Chongchang Wang
Crops and spatial distribution of their planting area are two important factors for agricultural water management. Remote sensing has been proved an effective technique in agricultural monitoring. Crop recognition and sown area monitoring are important topics in agricultural remote sensing using data sources from variety of sensors. This paper made efforts in extracting winter wheat and its sown area in an irrigation district along the lower Yellow River stream using the newly launched CBERS-02B sensor. Based on selection of the winter wheat training samples, spectral features, NDVI, MSAVI, spatial information of soils and texture analysis, a rule sets were developed for extracting winter wheat and its sown area. Google Earth was also employed to identify specific ground truth at a high resolution. It is tentatively concluded that the newly launched CBERS-02B CCD data is a reliable source for remote sensing monitoring for agriculture. The rule-based method proposed in this paper has improved the accuracy of crop monitoring. Integration of the spectral information, texture information, information of soils and land use/cover into the rule sets has strengthened identification capacity of the rule-based method. Compared to the unsupervised classification result, the rule-based crop recognition method achieved a better accuracy. Google Earth is a powerful tool which can be employed in sample selection and accuracy assessment. Its high resolution makes lots of small objects identifiable and identification mixed classes possible.
{"title":"Identification of winter wheat and its distribution using the CBERS-02B images in an irrigation district along the lower Yellow River, China","authors":"Nannan Zhang, Yi-Bo Luo, Chongchang Wang","doi":"10.1109/EORSA.2008.4620347","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620347","url":null,"abstract":"Crops and spatial distribution of their planting area are two important factors for agricultural water management. Remote sensing has been proved an effective technique in agricultural monitoring. Crop recognition and sown area monitoring are important topics in agricultural remote sensing using data sources from variety of sensors. This paper made efforts in extracting winter wheat and its sown area in an irrigation district along the lower Yellow River stream using the newly launched CBERS-02B sensor. Based on selection of the winter wheat training samples, spectral features, NDVI, MSAVI, spatial information of soils and texture analysis, a rule sets were developed for extracting winter wheat and its sown area. Google Earth was also employed to identify specific ground truth at a high resolution. It is tentatively concluded that the newly launched CBERS-02B CCD data is a reliable source for remote sensing monitoring for agriculture. The rule-based method proposed in this paper has improved the accuracy of crop monitoring. Integration of the spectral information, texture information, information of soils and land use/cover into the rule sets has strengthened identification capacity of the rule-based method. Compared to the unsupervised classification result, the rule-based crop recognition method achieved a better accuracy. Google Earth is a powerful tool which can be employed in sample selection and accuracy assessment. Its high resolution makes lots of small objects identifiable and identification mixed classes possible.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127026714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620329
Jinshu Wang, Guicai Li, Liu Yujie, Guangzhen Cao
Thermal-infrared images of Landsat Enhanced Thematic Mapper Plus (ETM+) in 1999 was used to retrieve the land surface temperature (LST) of urban and surrounding area of Beijing, The urban heat island ratio index (URI) was selected as the indicator to estimate spatial characteristic of land surface temperature. The paper quantitatively analyzed the spatial characteristic of LST by calculating URI of Beijing area, each zone between ring road and each profile buffer. The results show that difference of LST is significant in Bejing. LST of urban area (including East district, West district, Chongwen district and Xuanwu district) is higher than other regions. UHI show multi-center distribution pattern. Among all administrative. districts, the URI of Shunyi district and its mean LST is the highest, and the value is 0.6210, the mean LST is 38.08degC. The URI of urban area is 0.6118, its mean LST is 37.18degC. The mean LST is 36.97degC in the fifth ring road zone, the mean LST is 35.18degC beyond of the fifth ring road zone. Among all zones between ring roads, the highest URI is in the zone between the fifth ring road and the fourth ring road, the value is 0.6327, and the mean LST is 37.48degC. Among eight profile buffers, the URI of east profile and south profile buffer are higher than others, and the URI of west profile and north profile buffer are lower.
利用1999年Landsat Enhanced Thematic Mapper Plus (ETM+)的热红外影像检索了北京城区及周边地区的地表温度(LST)，选取城市热岛比指数(URI)作为估算地表温度空间特征的指标。通过计算北京地区、环线与各剖面缓冲区之间各区域的地表温度URI，定量分析了地表温度的空间特征。结果表明，北京地区地表温度差异显著。城区(包括东区、西区、崇文区和宣武区)的LST高于其他地区。城市热岛呈多中心分布格局。在所有的行政。其中，顺义区的URI及其平均地表温度最高，为0.6210，平均地表温度为38.08℃。市区URI为0.6118，平均地表温度为37.18℃。五环内平均地表温度为36.97°c，五环外平均地表温度为35.18°c。在所有环路之间的区域中，URI最高的区域在五环和四环之间，其值为0.6327，平均LST为37.48℃。8个剖面缓冲带中，东剖面和南剖面缓冲带的URI较高，西剖面和北剖面缓冲带的URI较低。
{"title":"The spatial characteristic of land surface temperature in Beijing area","authors":"Jinshu Wang, Guicai Li, Liu Yujie, Guangzhen Cao","doi":"10.1109/EORSA.2008.4620329","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620329","url":null,"abstract":"Thermal-infrared images of Landsat Enhanced Thematic Mapper Plus (ETM+) in 1999 was used to retrieve the land surface temperature (LST) of urban and surrounding area of Beijing, The urban heat island ratio index (URI) was selected as the indicator to estimate spatial characteristic of land surface temperature. The paper quantitatively analyzed the spatial characteristic of LST by calculating URI of Beijing area, each zone between ring road and each profile buffer. The results show that difference of LST is significant in Bejing. LST of urban area (including East district, West district, Chongwen district and Xuanwu district) is higher than other regions. UHI show multi-center distribution pattern. Among all administrative. districts, the URI of Shunyi district and its mean LST is the highest, and the value is 0.6210, the mean LST is 38.08degC. The URI of urban area is 0.6118, its mean LST is 37.18degC. The mean LST is 36.97degC in the fifth ring road zone, the mean LST is 35.18degC beyond of the fifth ring road zone. Among all zones between ring roads, the highest URI is in the zone between the fifth ring road and the fourth ring road, the value is 0.6327, and the mean LST is 37.48degC. Among eight profile buffers, the URI of east profile and south profile buffer are higher than others, and the URI of west profile and north profile buffer are lower.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134237111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620350
L. Zuo, Zengxiang Zhang, Fuxing Zhang
Farmland is a significant form of land use; it plays an important part in the food security and the stability of ecosystem environment. Fallow is a part of cultivated land, but it is somewhere no crops planting on for the consideration about the soil fertility protection. This paper had done some experiments to extract fallow land from the cultivated land by means of GIS and RS. The test area in this paper was Akesu area, which is located at the west part of Xinjiang Province. The data used in this paper are MODIS EVI, whose temporal resolution is 16 day and spatial resolution is 250 m. The extracting of the fallow is based on the theory of density slicing, and the method can divided into the following steps. Firstly, the land use map of Akesu area was gained by visual interpretation. It is based on the TM images whose spatial resolution is 30 m. Secondly, grid with spatial resolution of 250 m was drawn In ArcGIS. Then the land use map and the grid were overlaid, and the percentage of each land use type in each grid was calculated. Thirdly, we pick out the pixels in which agriculture land account for more than 90% and then some analysis was made on the relation between the coverage of the agriculture land and the maximum EVI in the crop growing season. As a result, when the threshold of the maximum EVI was set as 0.25, fallow could be discriminated form the cultivated land. Thist was validated by the result from CBERS images which has higher spatial resolution, and it is indicated that the method had a good performance on the extracting. By analyzing the result, we can conclude that fallow land generally distributes far away from the river and in the Akesu area, the Wei Gan River-Ku Che River delta oasis has the largest area of fallow land which mostly results from soil salinization.
{"title":"A research on the fallow land of Akesu area based on GIS and RS","authors":"L. Zuo, Zengxiang Zhang, Fuxing Zhang","doi":"10.1109/EORSA.2008.4620350","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620350","url":null,"abstract":"Farmland is a significant form of land use; it plays an important part in the food security and the stability of ecosystem environment. Fallow is a part of cultivated land, but it is somewhere no crops planting on for the consideration about the soil fertility protection. This paper had done some experiments to extract fallow land from the cultivated land by means of GIS and RS. The test area in this paper was Akesu area, which is located at the west part of Xinjiang Province. The data used in this paper are MODIS EVI, whose temporal resolution is 16 day and spatial resolution is 250 m. The extracting of the fallow is based on the theory of density slicing, and the method can divided into the following steps. Firstly, the land use map of Akesu area was gained by visual interpretation. It is based on the TM images whose spatial resolution is 30 m. Secondly, grid with spatial resolution of 250 m was drawn In ArcGIS. Then the land use map and the grid were overlaid, and the percentage of each land use type in each grid was calculated. Thirdly, we pick out the pixels in which agriculture land account for more than 90% and then some analysis was made on the relation between the coverage of the agriculture land and the maximum EVI in the crop growing season. As a result, when the threshold of the maximum EVI was set as 0.25, fallow could be discriminated form the cultivated land. Thist was validated by the result from CBERS images which has higher spatial resolution, and it is indicated that the method had a good performance on the extracting. By analyzing the result, we can conclude that fallow land generally distributes far away from the river and in the Akesu area, the Wei Gan River-Ku Che River delta oasis has the largest area of fallow land which mostly results from soil salinization.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132910479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620321
Zhengjun Liu, S. Cui, Q. Yan
In this paper, we established a new general semiautomatic building rooftop extraction method applied for high resolution satellite imagery. Based on investigation of the current existed methods for building extraction and its feature extraction, a general framework of building rooftop extraction is proposed. To extract the precise building roof boundary, an seeded region growth segmentation or localized multi-scale object oriented segmentation is applied to extract small and simple rectilinear rooftops from its background; to delineate the precise position of complex rooftop, the pose clustering is applied for building locating, and model matching techniques based on node graph search is used for finding the correct building rooftop shape. Integration of these two methods makes extraction of buildings from simple rectangle rooftop to complicated building more practical. Preliminary experimental results on QuickBird imagery show that the proposed method can successfully extract about 75% of the regular building rooftops.
{"title":"Building extraction from high resolution satellite imagery based on multi-scale image segmentation and model matching","authors":"Zhengjun Liu, S. Cui, Q. Yan","doi":"10.1109/EORSA.2008.4620321","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620321","url":null,"abstract":"In this paper, we established a new general semiautomatic building rooftop extraction method applied for high resolution satellite imagery. Based on investigation of the current existed methods for building extraction and its feature extraction, a general framework of building rooftop extraction is proposed. To extract the precise building roof boundary, an seeded region growth segmentation or localized multi-scale object oriented segmentation is applied to extract small and simple rectilinear rooftops from its background; to delineate the precise position of complex rooftop, the pose clustering is applied for building locating, and model matching techniques based on node graph search is used for finding the correct building rooftop shape. Integration of these two methods makes extraction of buildings from simple rectangle rooftop to complicated building more practical. Preliminary experimental results on QuickBird imagery show that the proposed method can successfully extract about 75% of the regular building rooftops.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"392 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122722191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620328
Jing Wang, Yuhuan Li, Yongqi Chen, Ting He, C. Lv
Land degradation is a major problem world-wide. Soil degradation is the core of land degradation. Soil degradation at regional scale is related to susceptibility to erosion, soil suitability, and soil characteristics. There is a pressing need for an objective measure on land degradation at regional scale. The study area is in Hengshan county in ShanXi province, where is in the agriculture-pasture mixed area in Northern China with complex physical geographical situation. On the basis of discussing the spectrum feature of soils and soil line parameters and analyzing the spectrum feature parameters responding to land degradation, the study is to develop and evaluate a spectral approach for soil features for degraded soil using Hyperion by exploiting the potential of its red bands and near infrared bands. This index called Degraded Soil Line Index (DSLI) is based on SAM classification approach and the soil line concept. The potential of Hyperion image data for mapping soil degradation through the application of this spectral index and the spectral angle mapping (SAM) approaches in semi-arid area of North China will also be compared and investigated. It was indicated that results showed that both the SAM and DSLI approaches have the ability to map soil degradation and clearly show the degraded soil class. It also showed the difference between the DSLI and SAM methods is significant, and showed the interest and the contribution of the DSLI index in the study of land degradation. The validation of the results obtained with the DSLI index and soil samples served as an additional tool showed that that the map of distribution of land degradation types using DSLI method depends on the physico-chemical characteristics of the different classes. It indicated that the highly degraded soils are associated with low soil organic matter and available phosphorus, high slope gradient and sand particle percentage and total phosphorus.
{"title":"Hyperspectral degraded soil line index and soil degradation mapping in agriculture-pasture mixed area in Northern China","authors":"Jing Wang, Yuhuan Li, Yongqi Chen, Ting He, C. Lv","doi":"10.1109/EORSA.2008.4620328","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620328","url":null,"abstract":"Land degradation is a major problem world-wide. Soil degradation is the core of land degradation. Soil degradation at regional scale is related to susceptibility to erosion, soil suitability, and soil characteristics. There is a pressing need for an objective measure on land degradation at regional scale. The study area is in Hengshan county in ShanXi province, where is in the agriculture-pasture mixed area in Northern China with complex physical geographical situation. On the basis of discussing the spectrum feature of soils and soil line parameters and analyzing the spectrum feature parameters responding to land degradation, the study is to develop and evaluate a spectral approach for soil features for degraded soil using Hyperion by exploiting the potential of its red bands and near infrared bands. This index called Degraded Soil Line Index (DSLI) is based on SAM classification approach and the soil line concept. The potential of Hyperion image data for mapping soil degradation through the application of this spectral index and the spectral angle mapping (SAM) approaches in semi-arid area of North China will also be compared and investigated. It was indicated that results showed that both the SAM and DSLI approaches have the ability to map soil degradation and clearly show the degraded soil class. It also showed the difference between the DSLI and SAM methods is significant, and showed the interest and the contribution of the DSLI index in the study of land degradation. The validation of the results obtained with the DSLI index and soil samples served as an additional tool showed that that the map of distribution of land degradation types using DSLI method depends on the physico-chemical characteristics of the different classes. It indicated that the highly degraded soils are associated with low soil organic matter and available phosphorus, high slope gradient and sand particle percentage and total phosphorus.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114439819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620298
H.Q. Du, H. Ge, E. Liu, W. Xu, W. Jin, W. Fan
This study has presented a new classifier - the Partial Least Squares (PLS) classifier including linear and nonlinear based on the Partial Least-Squares Regression theory, then explained the classification algorithm and process of this new classifier, and finally, them have been applied to classify Landsat TM remote sensing data. Results of PLS linear classifier showed that there exist many classify mistake among six kinds of land use types. On the contrary, the nonlinear classifier based on Gaussian kernel function got better classification result, the overall classification accuracy is 79.297% and overall Kappa statistics is 0.74213. So, to remote sensing classification, the nonlinear PLS classifier is basic feasible, however, it is necessary for us to improve its algorithms or learning process further.
{"title":"A new classifier for remote sensing data classification : Partial Least-Squares","authors":"H.Q. Du, H. Ge, E. Liu, W. Xu, W. Jin, W. Fan","doi":"10.1109/EORSA.2008.4620298","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620298","url":null,"abstract":"This study has presented a new classifier - the Partial Least Squares (PLS) classifier including linear and nonlinear based on the Partial Least-Squares Regression theory, then explained the classification algorithm and process of this new classifier, and finally, them have been applied to classify Landsat TM remote sensing data. Results of PLS linear classifier showed that there exist many classify mistake among six kinds of land use types. On the contrary, the nonlinear classifier based on Gaussian kernel function got better classification result, the overall classification accuracy is 79.297% and overall Kappa statistics is 0.74213. So, to remote sensing classification, the nonlinear PLS classifier is basic feasible, however, it is necessary for us to improve its algorithms or learning process further.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127080366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620293
Liang Chen, Jiancheng Shi, Lingmei Jiang
Soil moisture, as the fundamental parameters for land surface water resource formation, it plays an important role in climate change. The goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land is to infer surface soil moisture from L-band, dual-polarization radiometric measurements under a range of viewing angles. Previous research has shown that L-band passive microwave remote sensing sensors can be better used to monitor soil moisture over land surface. However, the effects of soil surface roughness play a significant role in the microwave emission from the surface. Therefore, a good parameterization of the effects is a prerequisite for retrieving surface soil moisture information. There are two types of approaches - the physical modeling and semi-empirical approaches that are commonly used in modeling the surface emission. The model parameters used in semi-empirical approaches are often derived from limited field observations and always need to be evaluated when applying to other datasets or application purposes. In recent theoretical model developments, advanced integral equation model (AIEM) has demonstrated a much wider application range for surface roughness conditions than that from conventional models. In this study, we generate a simulated database with a wide range of the surface roughness and soil moisture conditions under SMOS sensor configurations using AIEM model. A simple and accurate surface emission model is developed based on the simulated database that can be easily used as forward model in the passive microwave remote sensing applications. An inversion procedure is set up in terms of dual-polarization microwave brightness temperatures available from the forward model to retrieve soil moisture with a minimum of auxiliary information about the ground. The inversion technique is validated with microwave radiometer experimental data at Beltsville, MD. The results reveal that the use of dual-polarization and multi-angular inversion technique to estimate soil moisture from radiometric measurements decrease the perturbing effects of surface roughness on the soil moisture estimation.
{"title":"Physically based estimation Soil Moisture from L-band radiometer","authors":"Liang Chen, Jiancheng Shi, Lingmei Jiang","doi":"10.1109/EORSA.2008.4620293","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620293","url":null,"abstract":"Soil moisture, as the fundamental parameters for land surface water resource formation, it plays an important role in climate change. The goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land is to infer surface soil moisture from L-band, dual-polarization radiometric measurements under a range of viewing angles. Previous research has shown that L-band passive microwave remote sensing sensors can be better used to monitor soil moisture over land surface. However, the effects of soil surface roughness play a significant role in the microwave emission from the surface. Therefore, a good parameterization of the effects is a prerequisite for retrieving surface soil moisture information. There are two types of approaches - the physical modeling and semi-empirical approaches that are commonly used in modeling the surface emission. The model parameters used in semi-empirical approaches are often derived from limited field observations and always need to be evaluated when applying to other datasets or application purposes. In recent theoretical model developments, advanced integral equation model (AIEM) has demonstrated a much wider application range for surface roughness conditions than that from conventional models. In this study, we generate a simulated database with a wide range of the surface roughness and soil moisture conditions under SMOS sensor configurations using AIEM model. A simple and accurate surface emission model is developed based on the simulated database that can be easily used as forward model in the passive microwave remote sensing applications. An inversion procedure is set up in terms of dual-polarization microwave brightness temperatures available from the forward model to retrieve soil moisture with a minimum of auxiliary information about the ground. The inversion technique is validated with microwave radiometer experimental data at Beltsville, MD. The results reveal that the use of dual-polarization and multi-angular inversion technique to estimate soil moisture from radiometric measurements decrease the perturbing effects of surface roughness on the soil moisture estimation.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134032079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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