Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620312
Huiping Liang, Xiangnan Liu
Copper is one kind of trace element in soil which is necessary for the growth and development of plants. Much more copper over the needed amount of agronomic crop is harmful to crop growth and becomes pollutants in soil. At present, there are few studies concerning the quantitative impact of heavy metal contamination on crops. This research investigates an alternative approach. Red edge parameters of rice canopy will be obtained based on the first order and second order derivative spectra, and its relationship with agricultural parameters will be analyzed. It is found that there is strong correlation between red edge position and leaf chlorophyll a / leaf chlorophyll b, red edge amplitude and carotenoid, red edge peak area and the leaf area index, margin and fresh leaves quality. There is no obvious correlation between moisture and red edge parameters. BP artificial neural network method is used to study quantitatively the inherent relation between the chlorophyll content of rice and copper contents in soil. Taking red edge parameters mentioned above which have strong correlation with agricultural parameters, as well as ph value as input, copper content as output, four layers BP neural network with five inputs, one output and two hidden layers will be established. It is tested that the network fitting accuracy reaches 98% and the model has a high fitting degree, which prediction accuracy also receives 85.4%. This study is helpful to improve the ability of monitoring the heavy metal contamination of soil and environment in agricultural region.
{"title":"Hyperspectral analysis of leaf copper accumulation in agronomic crop based on artificial neural network","authors":"Huiping Liang, Xiangnan Liu","doi":"10.1109/EORSA.2008.4620312","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620312","url":null,"abstract":"Copper is one kind of trace element in soil which is necessary for the growth and development of plants. Much more copper over the needed amount of agronomic crop is harmful to crop growth and becomes pollutants in soil. At present, there are few studies concerning the quantitative impact of heavy metal contamination on crops. This research investigates an alternative approach. Red edge parameters of rice canopy will be obtained based on the first order and second order derivative spectra, and its relationship with agricultural parameters will be analyzed. It is found that there is strong correlation between red edge position and leaf chlorophyll a / leaf chlorophyll b, red edge amplitude and carotenoid, red edge peak area and the leaf area index, margin and fresh leaves quality. There is no obvious correlation between moisture and red edge parameters. BP artificial neural network method is used to study quantitatively the inherent relation between the chlorophyll content of rice and copper contents in soil. Taking red edge parameters mentioned above which have strong correlation with agricultural parameters, as well as ph value as input, copper content as output, four layers BP neural network with five inputs, one output and two hidden layers will be established. It is tested that the network fitting accuracy reaches 98% and the model has a high fitting degree, which prediction accuracy also receives 85.4%. This study is helpful to improve the ability of monitoring the heavy metal contamination of soil and environment in agricultural region.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"1 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":"130025516","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.2008.4620314
Yong Liang, Jing Shen, Xiangguo Lin, Junfang Bi, Ying Li
Road tracking is a promising technique to increase the efficiency of road mapping. In this paper, a new semi-automatic road tracker, parallel angular texture signature (PATS) is presented. The tracker is object-oriented in some sense, because it makes best use of the texture signature of road primitives on high-resolution remotely sensed imagery. Our tracker uses parabola to model the road trajectory and predict the position of next road centreline point. It employs parallel angular texture signature (PATS) to get the moving direction of current road centreline point, and it will move on one predefined step along the direction to reach a new position, and then it uses curvature change to verify the newly added road point. We also build compactness of Angular Texture Signature polygon to check whether the parallel angular texture signature (PATS) is suitable for tracking. Extensive experiments demonstrate that the proposed tracker reliably extracts ribbon roads from high resolution optical imagery even in very complex scenes.
{"title":"Road tracking by Parallel Angular Texture Signature","authors":"Yong Liang, Jing Shen, Xiangguo Lin, Junfang Bi, Ying Li","doi":"10.1109/EORSA.2008.4620314","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620314","url":null,"abstract":"Road tracking is a promising technique to increase the efficiency of road mapping. In this paper, a new semi-automatic road tracker, parallel angular texture signature (PATS) is presented. The tracker is object-oriented in some sense, because it makes best use of the texture signature of road primitives on high-resolution remotely sensed imagery. Our tracker uses parabola to model the road trajectory and predict the position of next road centreline point. It employs parallel angular texture signature (PATS) to get the moving direction of current road centreline point, and it will move on one predefined step along the direction to reach a new position, and then it uses curvature change to verify the newly added road point. We also build compactness of Angular Texture Signature polygon to check whether the parallel angular texture signature (PATS) is suitable for tracking. Extensive experiments demonstrate that the proposed tracker reliably extracts ribbon roads from high resolution optical imagery even in very complex scenes.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"1 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":"130372921","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}
CBERDS02B satellite has been successfully launched in September 2007, the target of this paper is to get the vegetation index from visible red-band, near-infrared band and the blue-band surface reflectance data of CBERDS02B satellite, through the empirical model of the relations between the vegetation index and LAI, and combined with the classification data to integrate the appropriate model, in order to get the regional leaf area index image in Binyang County of Nanning City in Guangxi Province of China. To make the operation more rapid and feasible, I decided to use an empirical model to obtain LAI, This method is simple and easy to calculate, more realizable, and suitable for remote sensing application. In this paper I use part of the measured data to validate a wide range of VI-LAI models. In order to identify the advantages and disadvantages of the various models, different plants use different types of vegetation model, I finally choose four VIs, such as SR, NDVI, SAVI, EVI, then combine these with the classification data to get the best mixed model so as to attain the leaf area index image of the research region. Then I use the other part of the measured data to get the validation of the mixed model. Ultimately I improve the overall accuracy of the model, and gain more accurate LAI images in the region.
{"title":"Inversion and validation of leaf area index based on CBERDS02B image data in GuangXi province of China","authors":"Wu Jiali, X. Gu, Yu Tao, Qingyan Meng, Liangfu Chen, Li Li, Hailiang Gao, shangjun Wu","doi":"10.1109/EORSA.2008.4620354","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620354","url":null,"abstract":"CBERDS02B satellite has been successfully launched in September 2007, the target of this paper is to get the vegetation index from visible red-band, near-infrared band and the blue-band surface reflectance data of CBERDS02B satellite, through the empirical model of the relations between the vegetation index and LAI, and combined with the classification data to integrate the appropriate model, in order to get the regional leaf area index image in Binyang County of Nanning City in Guangxi Province of China. To make the operation more rapid and feasible, I decided to use an empirical model to obtain LAI, This method is simple and easy to calculate, more realizable, and suitable for remote sensing application. In this paper I use part of the measured data to validate a wide range of VI-LAI models. In order to identify the advantages and disadvantages of the various models, different plants use different types of vegetation model, I finally choose four VIs, such as SR, NDVI, SAVI, EVI, then combine these with the classification data to get the best mixed model so as to attain the leaf area index image of the research region. Then I use the other part of the measured data to get the validation of the mixed model. Ultimately I improve the overall accuracy of the model, and gain more accurate LAI images in the region.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"30 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":"130786382","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.2008.4620332
W. Pu, Kong Fan-ming, Ding Hui-yan, Zhao Liuhui, Nie Jianliang
Using the data of wheat spectrum and water content in 6th April and 23rd April, we figure out the values of NDVI, NDWI, GVMI, PVI and WI, which are among the most frequently used water indices, and make correlation and regression analyze between these indices and EWT and FMC, two indices indicate the water content of wheat leaves. Through analysis and comparison, we find that FMC has a better correlation with water indices than EWT in this period, that in different period the best water index to monitor the water content of wheat is different, and that along with the growth of wheat, the effect of these indices in monitoring water content of wheat becomes much better.
{"title":"A comparison between different vegetation water indices in the ability of monitoring water status of wheat in April","authors":"W. Pu, Kong Fan-ming, Ding Hui-yan, Zhao Liuhui, Nie Jianliang","doi":"10.1109/EORSA.2008.4620332","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620332","url":null,"abstract":"Using the data of wheat spectrum and water content in 6th April and 23rd April, we figure out the values of NDVI, NDWI, GVMI, PVI and WI, which are among the most frequently used water indices, and make correlation and regression analyze between these indices and EWT and FMC, two indices indicate the water content of wheat leaves. Through analysis and comparison, we find that FMC has a better correlation with water indices than EWT in this period, that in different period the best water index to monitor the water content of wheat is different, and that along with the growth of wheat, the effect of these indices in monitoring water content of wheat becomes much better.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"142 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":"123213762","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.2008.4620342
Linshan Yuan, Peijun Du, Guang-Ting Li, Huapeng Zhang
Land cover classification is conducted using the panchromatic and multi-spectral data of Beijing-1 small satellite data in the western part of Xuzhou coal mining area. Firstly, fusion images obtained from different pixel fusion methods are used to land cover classification using SVM classifier. Secondly, feature level fusion is implemented by extracting texture information from panchromatic data and NDVI from multi-spectral data, by which texture and spectral features form new vectors to SVM classifier. Finally, Decision level fusion is experimented by adopting Dempster-Shafer evidence theory for classifier combination. The experimental results show that the fusion of panchromatic and multi-spectral data of Beijing-1 small satellite is effective to land cover classification, and the decision level fusion algorithm outperforms other methods in terms of classification accuracy.
{"title":"Land cover classification in mining areas using Beijing-1 small satellite data","authors":"Linshan Yuan, Peijun Du, Guang-Ting Li, Huapeng Zhang","doi":"10.1109/EORSA.2008.4620342","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620342","url":null,"abstract":"Land cover classification is conducted using the panchromatic and multi-spectral data of Beijing-1 small satellite data in the western part of Xuzhou coal mining area. Firstly, fusion images obtained from different pixel fusion methods are used to land cover classification using SVM classifier. Secondly, feature level fusion is implemented by extracting texture information from panchromatic data and NDVI from multi-spectral data, by which texture and spectral features form new vectors to SVM classifier. Finally, Decision level fusion is experimented by adopting Dempster-Shafer evidence theory for classifier combination. The experimental results show that the fusion of panchromatic and multi-spectral data of Beijing-1 small satellite is effective to land cover classification, and the decision level fusion algorithm outperforms other methods in terms of classification accuracy.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"19 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":"134365954","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.2008.4620316
Wenpeng Lin, Ming‐yang Zhao, Yunlong Liu, Jun Gao, Chenli Wang
Terra/MODIS has spectral and spatial resolution advantage over NOAA/AVHRR. To probe into using MODIS near-infrared spectrum further, winter wheat yield estimation was taken as example in Hebei Province, China. Firstly, according to winter wheat biological characteristic, three MODIS near-infrared spectrum data were retrieved in heading stage, which central wavelength is 860 nm, 1240 nm and 1640 nm. Secondly, the normalized near-infrared spectral index (NNSI) is defined by every two near-infrared spectrum, such as (860 nm, 1240 nm), (860 nm, 1640 nm) and (1240 nm, 1640 nm). Thirdly, the statistical correlation analysis with yield were carried on and set up models for yield forecasting with NNSI. The result shows their coefficient correlations are greater than 0.77 and better than with NDVI. Especially the NNSI defined by (860 nm, 1640 nm), its coefficient correlation is 0.815. So NNSI can do well to forecast winter wheat yield. So we can conclude that normalized index in near-infrared spectrum can do better and more reliable than normalized index in visual and near-infrared spectrums for yield forecasting. And given play to the hysperspectral advantage of MODIS, it can service to crop condition monitoring and crop yield estimation of Ministry of Agriculture.
{"title":"Winter wheat yield estimation model with MODIS normalized near-infrared spectral index","authors":"Wenpeng Lin, Ming‐yang Zhao, Yunlong Liu, Jun Gao, Chenli Wang","doi":"10.1109/EORSA.2008.4620316","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620316","url":null,"abstract":"Terra/MODIS has spectral and spatial resolution advantage over NOAA/AVHRR. To probe into using MODIS near-infrared spectrum further, winter wheat yield estimation was taken as example in Hebei Province, China. Firstly, according to winter wheat biological characteristic, three MODIS near-infrared spectrum data were retrieved in heading stage, which central wavelength is 860 nm, 1240 nm and 1640 nm. Secondly, the normalized near-infrared spectral index (NNSI) is defined by every two near-infrared spectrum, such as (860 nm, 1240 nm), (860 nm, 1640 nm) and (1240 nm, 1640 nm). Thirdly, the statistical correlation analysis with yield were carried on and set up models for yield forecasting with NNSI. The result shows their coefficient correlations are greater than 0.77 and better than with NDVI. Especially the NNSI defined by (860 nm, 1640 nm), its coefficient correlation is 0.815. So NNSI can do well to forecast winter wheat yield. So we can conclude that normalized index in near-infrared spectrum can do better and more reliable than normalized index in visual and near-infrared spectrums for yield forecasting. And given play to the hysperspectral advantage of MODIS, it can service to crop condition monitoring and crop yield estimation of Ministry of Agriculture.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"150 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":"131400071","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.2008.4620291
Changyan Chi, Zhengjun Liu, Jixian Zhang
Three Gorges Reservoir area is a weak area in terms of ecological environment and an area with frequent landslide hazard disasters. These disasters will cause many negative effects to the Three Gorges Water Conservancy Project as well as the social economy in the reservoir area. In order to decrease the lives and possessions loss brought by landslide disasters, landslide hazard assessment is highly desirable in nowaday disaster prediction. This study addresses the potentials and ability for the use of high-resolution SPOT-5 remote imageries for landslide hazard detection and identification in the Three Gorges Reservoir area. At Wan County, data fusion of panchromatic and multi-spectral SPOT-5 imageries are made to generate a color image, then the fusion image draped over a DEM for 3D simulation is tailored for mapping landslide scarps. Several typical features of landslides that have actually taken place are visually recognized in combination with characteristics of landslide and remote imageries in this area. At last, results examination is necessary for landslide interpretation for precision assessment.
{"title":"Interpretation of landslide from SPOT-5 imageries in the Three Gorges Reservoir Area","authors":"Changyan Chi, Zhengjun Liu, Jixian Zhang","doi":"10.1109/EORSA.2008.4620291","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620291","url":null,"abstract":"Three Gorges Reservoir area is a weak area in terms of ecological environment and an area with frequent landslide hazard disasters. These disasters will cause many negative effects to the Three Gorges Water Conservancy Project as well as the social economy in the reservoir area. In order to decrease the lives and possessions loss brought by landslide disasters, landslide hazard assessment is highly desirable in nowaday disaster prediction. This study addresses the potentials and ability for the use of high-resolution SPOT-5 remote imageries for landslide hazard detection and identification in the Three Gorges Reservoir area. At Wan County, data fusion of panchromatic and multi-spectral SPOT-5 imageries are made to generate a color image, then the fusion image draped over a DEM for 3D simulation is tailored for mapping landslide scarps. Several typical features of landslides that have actually taken place are visually recognized in combination with characteristics of landslide and remote imageries in this area. At last, results examination is necessary for landslide interpretation for precision assessment.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"205 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":"124601174","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}
Irrigated land is the main region which produces a large amount of foodstuff. It has great meaning in the aspect of agriculture, foodstuff security and regional water resource development. Until now it has seldom researched on irrigated land by using remote sensing. This paper retrieves soil water by using crop water stress index (CWSI) during crop growth. It extracts irrigated land after removing rainfall influence. Results show that the extracted results are near the statistic data in quantity. The average deviation is 5.75%. The extracted results mainly distribute among the river, lake, reservoir, oasis and irrigated region. It verifies the results elementarily through interpreted sign. Xinjiang province is the highest while Heilongjiang province is the lowest.
{"title":"Researching on extracting irrigated land in northern China based on MODIS data","authors":"Tingting Dong, Miao Jiang, Fengkui Qian, Zengxiang Zhang","doi":"10.1109/EORSA.2008.4620297","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620297","url":null,"abstract":"Irrigated land is the main region which produces a large amount of foodstuff. It has great meaning in the aspect of agriculture, foodstuff security and regional water resource development. Until now it has seldom researched on irrigated land by using remote sensing. This paper retrieves soil water by using crop water stress index (CWSI) during crop growth. It extracts irrigated land after removing rainfall influence. Results show that the extracted results are near the statistic data in quantity. The average deviation is 5.75%. The extracted results mainly distribute among the river, lake, reservoir, oasis and irrigated region. It verifies the results elementarily through interpreted sign. Xinjiang province is the highest while Heilongjiang province is the lowest.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"66 2 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":"126975850","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.2008.4620310
Y. Ban, Qian Zhang, Yunfeng Hu, Xueyan Zhang, Jiyuan Liu, D. Zhuang
The physical geography of Mongolian Plateau plays an important role in the East Asian climate ecology system. In this research, GIMMS NDVI, the third generation of NDVI dataset, was processed using the MVC method first, then the spatial-temporal patterns of GIMMS NDVI in Mongolian Plateau during 1982-2003 was investigated, and the transect from Tariat to Xilin Gol was also selected to analyze the NDVI dynamic processes in detail. The results demonstrated that: (1) the general spatial distribution pattern of NDVI showed a clear spatial differentiation. The high value pixels were mainly distributed in the east and north of Mongolian Plateau with forest and meadow steppe land cover, while the low value pixels were mainly distributed in the west and centre part of Mongolian Plateau with desert and Gobi land cover. However, the annual NDVI variability was relative small either in the high-covered regions (i.e. forest, forest steppe, and meadow steppe) or in low-covered regions (i.e. steppe desert, desert and Gobi), while the region with typical steppe normally had higher annual NDVI variability. (2) During 1982-2003, the dynamic evolution process of NDVI in Mongolian Plateau also showed an evident spatial differentiation. About 12.4% of total area featured a significant increase, 4.8% of total area featured an increase but without significance, and 9.3% of total area featured decrease without significance. The other part, about 73.5% of total area, had no obvious change. The NDVI increased significantly in the South-East, South and of Mongolian Plateau, while it decreased in the North-East and North of Mongolian Plateau. Further, the NDVI-increased regions were those typical steppe and farming-pastoral regions before, while the NDVI-decreased regions were those well-covered forest, forest steppe and meadow steppe regions before.
{"title":"Spatial—temporal pattern of GIMMS NDVI and its dynamics in Mongolian Plateau","authors":"Y. Ban, Qian Zhang, Yunfeng Hu, Xueyan Zhang, Jiyuan Liu, D. Zhuang","doi":"10.1109/EORSA.2008.4620310","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620310","url":null,"abstract":"The physical geography of Mongolian Plateau plays an important role in the East Asian climate ecology system. In this research, GIMMS NDVI, the third generation of NDVI dataset, was processed using the MVC method first, then the spatial-temporal patterns of GIMMS NDVI in Mongolian Plateau during 1982-2003 was investigated, and the transect from Tariat to Xilin Gol was also selected to analyze the NDVI dynamic processes in detail. The results demonstrated that: (1) the general spatial distribution pattern of NDVI showed a clear spatial differentiation. The high value pixels were mainly distributed in the east and north of Mongolian Plateau with forest and meadow steppe land cover, while the low value pixels were mainly distributed in the west and centre part of Mongolian Plateau with desert and Gobi land cover. However, the annual NDVI variability was relative small either in the high-covered regions (i.e. forest, forest steppe, and meadow steppe) or in low-covered regions (i.e. steppe desert, desert and Gobi), while the region with typical steppe normally had higher annual NDVI variability. (2) During 1982-2003, the dynamic evolution process of NDVI in Mongolian Plateau also showed an evident spatial differentiation. About 12.4% of total area featured a significant increase, 4.8% of total area featured an increase but without significance, and 9.3% of total area featured decrease without significance. The other part, about 73.5% of total area, had no obvious change. The NDVI increased significantly in the South-East, South and of Mongolian Plateau, while it decreased in the North-East and North of Mongolian Plateau. Further, the NDVI-increased regions were those typical steppe and farming-pastoral regions before, while the NDVI-decreased regions were those well-covered forest, forest steppe and meadow steppe regions before.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"88 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":"125669070","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.2008.4620301
A. Fu, Guoqing Sun, Zhifeng Guo, Dianzhong Wang
Forest ecosystem in Eastern Siberia and Northeastern China (ESNC) has been undergoing dramatic changes during the last several decades due to forest fires and massive logging. These changes affect climate dynamics, economic activity and living heritage in local region, further, to the global carbon balance and climate changes. In this paper, a 2D feature space grid split (FSGS) algorithm was developed to identify forests cover region by combined TM/ ETM+ images and MODIS datasets, due to its dark object attributes. This no-parametric algorithm was based on statistical signatures in feature space and Bayesian rule. The producer accuracy of tree cover commission can be approximately 90%, comparing with local TM/ETM+ classification results. Then, forests cover was stratified into different biomes by a decision tree classifier. and Forests cover map was respectively compared with MODIS land cover products and Global land cover 2000(GLC2000) products derived from images observed by VEGETATION (VGT) sensor on both areal and per-pixel bases.
{"title":"Forest cover classification from MODIS images in Northeastern Asia","authors":"A. Fu, Guoqing Sun, Zhifeng Guo, Dianzhong Wang","doi":"10.1109/EORSA.2008.4620301","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620301","url":null,"abstract":"Forest ecosystem in Eastern Siberia and Northeastern China (ESNC) has been undergoing dramatic changes during the last several decades due to forest fires and massive logging. These changes affect climate dynamics, economic activity and living heritage in local region, further, to the global carbon balance and climate changes. In this paper, a 2D feature space grid split (FSGS) algorithm was developed to identify forests cover region by combined TM/ ETM+ images and MODIS datasets, due to its dark object attributes. This no-parametric algorithm was based on statistical signatures in feature space and Bayesian rule. The producer accuracy of tree cover commission can be approximately 90%, comparing with local TM/ETM+ classification results. Then, forests cover was stratified into different biomes by a decision tree classifier. and Forests cover map was respectively compared with MODIS land cover products and Global land cover 2000(GLC2000) products derived from images observed by VEGETATION (VGT) sensor on both areal and per-pixel bases.","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":"132582537","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：