Pub Date : 2008-09-05DOI: 10.1109/EORSA.2008.4620355
Junming Wang, T. Sammis, V. Gutschick
International water delivery management is a difficult issue. For example, according to the US and Mexico water delivery treaty, the US needs to deliver a certain amount of water from the Elephant Butte Reservoir (in US New Mexico state) to Mexico every year. Similarly, Mexico also needs to deliver a certain amount of water to the US each year. However, Mexico had amassed a water deficit to the US since 1992. The deficit reached 1.5 million acre-feet at its highest point, costing U.S. agricultural producers in the Rio Grande Valley $1 billion. Farmers in both countries complained to their government that the water provider country did not deliver enough water. Their government explained to the farmers that although the reservoirs had a certain level of inflow, the evaporation (E) loss was large enough to decrease the outflow significantly. Reservoir evaporation measurements from an inflow-outflow water balance method, pan measurement method, or eddy covariance methods are time and labor intensive. Additionally, the accuracy of the methods may be affected by environmental factors and some of their assumptions. There is a need to create an accurate and convenient method to measure the evaporation loss that can be used internationally. The research in this paper developed a remote sensing tool to estimate evaporation loss from reservoirs to aid international water delivery management. The model uses the energy balance principle to measure evaporation depth (mm/day). The evaporation depth has a high temporal resolution (1 day) and a moderate spatial resolution (1000 m by 1000 m). The model is written in C++ as a user-friendly software package. The model uses MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data (LIB raw data) and local weather data (radiation, wind speed, and humidity). The model was calibrated and evaluated using reservoir data. The model accuracy is acceptable and is capable for aiding international water delivery management.
{"title":"A remote sensing model estimating water body evaporation","authors":"Junming Wang, T. Sammis, V. Gutschick","doi":"10.1109/EORSA.2008.4620355","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620355","url":null,"abstract":"International water delivery management is a difficult issue. For example, according to the US and Mexico water delivery treaty, the US needs to deliver a certain amount of water from the Elephant Butte Reservoir (in US New Mexico state) to Mexico every year. Similarly, Mexico also needs to deliver a certain amount of water to the US each year. However, Mexico had amassed a water deficit to the US since 1992. The deficit reached 1.5 million acre-feet at its highest point, costing U.S. agricultural producers in the Rio Grande Valley $1 billion. Farmers in both countries complained to their government that the water provider country did not deliver enough water. Their government explained to the farmers that although the reservoirs had a certain level of inflow, the evaporation (E) loss was large enough to decrease the outflow significantly. Reservoir evaporation measurements from an inflow-outflow water balance method, pan measurement method, or eddy covariance methods are time and labor intensive. Additionally, the accuracy of the methods may be affected by environmental factors and some of their assumptions. There is a need to create an accurate and convenient method to measure the evaporation loss that can be used internationally. The research in this paper developed a remote sensing tool to estimate evaporation loss from reservoirs to aid international water delivery management. The model uses the energy balance principle to measure evaporation depth (mm/day). The evaporation depth has a high temporal resolution (1 day) and a moderate spatial resolution (1000 m by 1000 m). The model is written in C++ as a user-friendly software package. The model uses MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data (LIB raw data) and local weather data (radiation, wind speed, and humidity). The model was calibrated and evaluated using reservoir data. The model accuracy is acceptable and is capable for aiding international water delivery management.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"33 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":"115633813","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.2008.4620296
Jingjing Dong, Z. Niu
In this paper, we focus on the area covered by vegetation and compare the canopy reflectance derived from MODIS images with reflectance simulated by radiation transfer model. Instead of statistic models, physical models are chosen because of the explicit physical foundation that they possess. Two sampling areas are collected in Jiangxi province in China. Detailed information of vegetation in these two sampling areas has been measured and recorded, including leaf biochemical component content, canopy structure information and some environment parameters. And the MODIS images in this period are also collected. By combining leaf model and canopy model, canopy reflectance is calculated using biochemical component content as the input parameters. Comparison between simulative canopy reflectance and reflectance derived from MODIS images shows that, considering the whole wave length region, these two kinds of reflectance match well. At 250 m spatial resolution, reflectance from MODIS band 1 (650 nm) is higher than simulative value; while reflectance from MODIS band 2 (860 nm) is lower than simulative value. These two bands focus on the vegetation status. So the experiment indicates that satellite image weakens the vegetation impact. At 500 m spatial resolution, the band 4 (560 nm) and band 3 (465 nm) both have higher value, while the ratio between them are lower. Similar problem appears in other bands. These results are useful in physical models inversion of vegetation biochemical parameters.
{"title":"Comparison of reflectance between MODIS image and simulation using radiation transfer model","authors":"Jingjing Dong, Z. Niu","doi":"10.1109/EORSA.2008.4620296","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620296","url":null,"abstract":"In this paper, we focus on the area covered by vegetation and compare the canopy reflectance derived from MODIS images with reflectance simulated by radiation transfer model. Instead of statistic models, physical models are chosen because of the explicit physical foundation that they possess. Two sampling areas are collected in Jiangxi province in China. Detailed information of vegetation in these two sampling areas has been measured and recorded, including leaf biochemical component content, canopy structure information and some environment parameters. And the MODIS images in this period are also collected. By combining leaf model and canopy model, canopy reflectance is calculated using biochemical component content as the input parameters. Comparison between simulative canopy reflectance and reflectance derived from MODIS images shows that, considering the whole wave length region, these two kinds of reflectance match well. At 250 m spatial resolution, reflectance from MODIS band 1 (650 nm) is higher than simulative value; while reflectance from MODIS band 2 (860 nm) is lower than simulative value. These two bands focus on the vegetation status. So the experiment indicates that satellite image weakens the vegetation impact. At 500 m spatial resolution, the band 4 (560 nm) and band 3 (465 nm) both have higher value, while the ratio between them are lower. Similar problem appears in other bands. These results are useful in physical models inversion of vegetation biochemical parameters.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"262 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120981489","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.2008.4620340
Wunian Yang, J. Jian, Yu-xia Li, Xin-Nan Wan, Li Peng, Hanhu Liu, H. Shao, X. Dai, Tao Zeng, Xueming Wu
Eco-water (layer) refers to the water body closely related to the ground vegetation layer. It is conserved in leaves, roots, vegetation humus layers and root soil layers, which is capable of precipitation interception and rivers and/or groundwater supplementation. As a challenging issue in the hydrological cycle field, the eco-water and its resource quantity are difficult to be quantified by ordinary methods. In this paper, experiments were performed at Maoergai area in the upper Minjiang River in China to examine properties, functions, spacial distributional characteristics and transfer rules of the eco-water (layer). Based on ecology, botany, hydrogeology, forest hydrology and genesis mechanism of remote sensing information, the information index system of the eco-water (layer) was proposed, together with conversion models between the ground parameters and the remote sensing information. The total eco-water quantity in the study area was calculated by the proposed remote sensing inversion model of the Modulus of Eco-water Conservation (MEC). Its spacial consistency with the water distributional statistics suggests a valid vegetation-centred quantitative remote sensing approach to develop hydrological cycle studies.
{"title":"Remote sensing inversion of eco-water resource quantity","authors":"Wunian Yang, J. Jian, Yu-xia Li, Xin-Nan Wan, Li Peng, Hanhu Liu, H. Shao, X. Dai, Tao Zeng, Xueming Wu","doi":"10.1109/EORSA.2008.4620340","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620340","url":null,"abstract":"Eco-water (layer) refers to the water body closely related to the ground vegetation layer. It is conserved in leaves, roots, vegetation humus layers and root soil layers, which is capable of precipitation interception and rivers and/or groundwater supplementation. As a challenging issue in the hydrological cycle field, the eco-water and its resource quantity are difficult to be quantified by ordinary methods. In this paper, experiments were performed at Maoergai area in the upper Minjiang River in China to examine properties, functions, spacial distributional characteristics and transfer rules of the eco-water (layer). Based on ecology, botany, hydrogeology, forest hydrology and genesis mechanism of remote sensing information, the information index system of the eco-water (layer) was proposed, together with conversion models between the ground parameters and the remote sensing information. The total eco-water quantity in the study area was calculated by the proposed remote sensing inversion model of the Modulus of Eco-water Conservation (MEC). Its spacial consistency with the water distributional statistics suggests a valid vegetation-centred quantitative remote sensing approach to develop hydrological cycle studies.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"32 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":"126140685","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.2008.4620351
H.T. Li, H. Gu, Y.S. Han, J.H. Yang
Multi-scale segmentation is an essential step toward higher level image processing in remote sensing. This paper presents a new multi-scale segmentation method based on statistical region merging (SRM) for initial segmentation and minimum heterogeneity rule (MHR) for merging objects where high resolution (HR) QuickBird imageries are used. It synthesized the advantages of SRM and MHR. The SRM segmentation method not only considers spectral, shape, scale information, but also has the ability to cope with significant noise corruption, handle occlusions. The MHR used for merging objects takes advantages of its spectral, shape, scale information, and the local, global information. Compared with Fractal Net Evolution Approach (FNEA) eCognition adopted and SRM methods, the results showed that the proposed method overcame the disadvantages of them and was an effective multi-scale segmentation method for HR imagery.
{"title":"An efficient multi-scale segmentation for high-resolution remote sensing imagery based on Statistical Region Merging and Minimum Heterogeneity Rule","authors":"H.T. Li, H. Gu, Y.S. Han, J.H. Yang","doi":"10.1109/EORSA.2008.4620351","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620351","url":null,"abstract":"Multi-scale segmentation is an essential step toward higher level image processing in remote sensing. This paper presents a new multi-scale segmentation method based on statistical region merging (SRM) for initial segmentation and minimum heterogeneity rule (MHR) for merging objects where high resolution (HR) QuickBird imageries are used. It synthesized the advantages of SRM and MHR. The SRM segmentation method not only considers spectral, shape, scale information, but also has the ability to cope with significant noise corruption, handle occlusions. The MHR used for merging objects takes advantages of its spectral, shape, scale information, and the local, global information. Compared with Fractal Net Evolution Approach (FNEA) eCognition adopted and SRM methods, the results showed that the proposed method overcame the disadvantages of them and was an effective multi-scale segmentation method for HR imagery.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"111 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":"114251238","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.2008.4620352
Lanwei Zhu, Huadong Guo, Changlin Wang
The land degradation alongside the Great Wall made in Ming dynasty is always the one of problems concerned by government and scientists, but all of the researches about it are focused on the whole environmental change alongside the Ming Great Wall, while they are not considered the Ming Great Wallpsilas role in the process of the environmental change. So in this paper, we will further reveal the process of the environmental change alongside the Ming Great Wall based on Landsat TM/ETM remote sensing image. Finally we found some new phenomena of the environmental change alongside the Ming Great Wall including function of the Ming Great Wall in impacting on surrounding environment.
{"title":"The research on environmental change alongside the Ming Great Wall in Ningxia and Shanxi Province by remote sensing","authors":"Lanwei Zhu, Huadong Guo, Changlin Wang","doi":"10.1109/EORSA.2008.4620352","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620352","url":null,"abstract":"The land degradation alongside the Great Wall made in Ming dynasty is always the one of problems concerned by government and scientists, but all of the researches about it are focused on the whole environmental change alongside the Ming Great Wall, while they are not considered the Ming Great Wallpsilas role in the process of the environmental change. So in this paper, we will further reveal the process of the environmental change alongside the Ming Great Wall based on Landsat TM/ETM remote sensing image. Finally we found some new phenomena of the environmental change alongside the Ming Great Wall including function of the Ming Great Wall in impacting on surrounding environment.","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":"122158682","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.2008.4620289
Breda Federico, Cremonini Marco, G. Jixi, Huguenin Robert, Martinelli Massimo, Y. Ren, Scalas Patrizia, Torriano Luigi
The present research has been carried out in the framework of the cooperation on environmental protection between the Italian and the Chinese governments. The study was aimed at connecting remote sensing and GIS applications, integrated by on-site investigations, with the estimation of greenhouse gases emissions. Remote sensing techniques, jointly with site investigation on the sites of interest, were used to perform water quality analysis in selected lakes in China and to design a preliminary model for the estimation of methane emissions from water. Three test lakes were selected according to their characteristics in methane emission, which were evaluated using satellite data from the sensor SCIAMACHY on the trend of emission of methane from the ground throughout the year. Remote sensing supported in the indirect monitoring of water quality with focus on parameters that can be correlated as chemical precursors to the production of methane. Within this research, Landsat ETM+ satellite images were used as input for a dedicated remote sensing analysis application to retrieve values of chlorophyll, SM, and colored dissolved organic carbon for three selected water bodies used as test sites. Water sampling campaigns were also carried out in the three test sites from April to September 2007. The samples were analyzed and the results, together with historical water quality data, were used to calibrate, validate and interpret the results of the remote sensing analysis. Water quality parameters measured with remote sensing analysis and validated with field and historical data were used to propose a preliminary methane emission model on one test site (Hongze Lake), based on the evaluation of field or historical data of parameters acting as precursors or products of methanogenesis processes. In this way the results of methanogenesis processes and methane production were quantitatively assessed, yielding a preliminary methane emission model that can be further applied and validated on other water bodies.
{"title":"Remote sensing methodology for the estimation of methane emissions from Chinese lakes","authors":"Breda Federico, Cremonini Marco, G. Jixi, Huguenin Robert, Martinelli Massimo, Y. Ren, Scalas Patrizia, Torriano Luigi","doi":"10.1109/EORSA.2008.4620289","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620289","url":null,"abstract":"The present research has been carried out in the framework of the cooperation on environmental protection between the Italian and the Chinese governments. The study was aimed at connecting remote sensing and GIS applications, integrated by on-site investigations, with the estimation of greenhouse gases emissions. Remote sensing techniques, jointly with site investigation on the sites of interest, were used to perform water quality analysis in selected lakes in China and to design a preliminary model for the estimation of methane emissions from water. Three test lakes were selected according to their characteristics in methane emission, which were evaluated using satellite data from the sensor SCIAMACHY on the trend of emission of methane from the ground throughout the year. Remote sensing supported in the indirect monitoring of water quality with focus on parameters that can be correlated as chemical precursors to the production of methane. Within this research, Landsat ETM+ satellite images were used as input for a dedicated remote sensing analysis application to retrieve values of chlorophyll, SM, and colored dissolved organic carbon for three selected water bodies used as test sites. Water sampling campaigns were also carried out in the three test sites from April to September 2007. The samples were analyzed and the results, together with historical water quality data, were used to calibrate, validate and interpret the results of the remote sensing analysis. Water quality parameters measured with remote sensing analysis and validated with field and historical data were used to propose a preliminary methane emission model on one test site (Hongze Lake), based on the evaluation of field or historical data of parameters acting as precursors or products of methanogenesis processes. In this way the results of methanogenesis processes and methane production were quantitatively assessed, yielding a preliminary methane emission model that can be further applied and validated on other water bodies.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"61 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":"128630911","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.2008.4620300
F. Iqbal, M. Mehdi
Site specific farming requires better understanding of variability of soil patterns. All types of soils are not suitable for zero-till wheat cultivation after rice harvesting. The normal soils with silt clay loam or lighter soil texture, well drained and with no salinity are the best suited for zero-till wheat cultivation. Moderately suitable soils are clay/silty clay or silty clay loam and lighter with drainage problem. And the unsuitable soils are saline and poorly drained with hard pan. Yet, zero-tillage is being promoted on all kinds of soils in Pakistan partly because no data are available which delineate unsuitable soils from those which are suitable. This research deals with the delineation of suitable soils for zero-till wheat cultivation in irrigated soils of Gujranwala, Pakistan. The analyses are based on remote sensing and field data using a geographical information system (GIS). We have examined how different remote sensing indices work for salinity prone lands delineation. The study has suggested new indices for assessing salinity. We have analyzed the several indices, vegetation indices, water Indices were also analyzed as concurrent indicators, especially the ratio of the signals received in the forth spectral band to thermal IR give more accurate results. Soil texture, bulk density (BD) and ground water quality data were gathered from secondary sources field measurements were interpolated to surfaces and converted in to three classes and overly analysis capability of Arc GIS were used to integrate all parameters for final map preparation.
{"title":"Detection of suitable soils for Zero-Till Wheat Cultivation in Pakistan using GITs","authors":"F. Iqbal, M. Mehdi","doi":"10.1109/EORSA.2008.4620300","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620300","url":null,"abstract":"Site specific farming requires better understanding of variability of soil patterns. All types of soils are not suitable for zero-till wheat cultivation after rice harvesting. The normal soils with silt clay loam or lighter soil texture, well drained and with no salinity are the best suited for zero-till wheat cultivation. Moderately suitable soils are clay/silty clay or silty clay loam and lighter with drainage problem. And the unsuitable soils are saline and poorly drained with hard pan. Yet, zero-tillage is being promoted on all kinds of soils in Pakistan partly because no data are available which delineate unsuitable soils from those which are suitable. This research deals with the delineation of suitable soils for zero-till wheat cultivation in irrigated soils of Gujranwala, Pakistan. The analyses are based on remote sensing and field data using a geographical information system (GIS). We have examined how different remote sensing indices work for salinity prone lands delineation. The study has suggested new indices for assessing salinity. We have analyzed the several indices, vegetation indices, water Indices were also analyzed as concurrent indicators, especially the ratio of the signals received in the forth spectral band to thermal IR give more accurate results. Soil texture, bulk density (BD) and ground water quality data were gathered from secondary sources field measurements were interpolated to surfaces and converted in to three classes and overly analysis capability of Arc GIS were used to integrate all parameters for final map preparation.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"18 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":"129072214","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.2008.4620309
J. Hu, Wei Chen, Xiaoyu Li, Xingyuan He
We put forward the spectral confusion phenomenon between vegetation and artificial objects - mostly roofs painted with "cool" blue/purple/green pigments in the urban environment. Both of them have the feature of low red and high near-infrared reflectance. For accurate vegetation extraction using high spatial resolution imagery (HSRI), we have developed a two-step threshold segmentation (TSTS) method to solve this confusion. The first step is extracting vegetation and confusing roofs together through threshold segmentation of the NDVI image, and the second step is removing roof confusion through threshold segmentation of an image generated by vegetation and achromatic objects indifferent transformation (VAOIT). VAOIT is derived from the fitting straight line of random trained vegetation and achromatic objects at either highly correlated band combinations: band1/band2 and band1/band3. Efficiency of the method is tested through producer accuracy assessment, and it is demonstrated that VAOIT using either band1/band2 or band1/band3 can remove blue and purple roofs perfectly (producer accuracy=at least 95%), while the former is powerless and the latter is goodish (producer accu- racy=approximately 90%) in removing green roofs. Since too few green roofs exist in our case, more green-roof samples are needed for further test in other cities. Our case study in Shenyang, China demonstrates that TSTS can correct overestimate of vegetation coverage by 2.14%, mostly in industrial blocks, which shows the necessity of roof confusion removal, especially for industrial cities.
{"title":"Roof confusion removal for accurate vegetation extraction in the urban environment","authors":"J. Hu, Wei Chen, Xiaoyu Li, Xingyuan He","doi":"10.1109/EORSA.2008.4620309","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620309","url":null,"abstract":"We put forward the spectral confusion phenomenon between vegetation and artificial objects - mostly roofs painted with \"cool\" blue/purple/green pigments in the urban environment. Both of them have the feature of low red and high near-infrared reflectance. For accurate vegetation extraction using high spatial resolution imagery (HSRI), we have developed a two-step threshold segmentation (TSTS) method to solve this confusion. The first step is extracting vegetation and confusing roofs together through threshold segmentation of the NDVI image, and the second step is removing roof confusion through threshold segmentation of an image generated by vegetation and achromatic objects indifferent transformation (VAOIT). VAOIT is derived from the fitting straight line of random trained vegetation and achromatic objects at either highly correlated band combinations: band1/band2 and band1/band3. Efficiency of the method is tested through producer accuracy assessment, and it is demonstrated that VAOIT using either band1/band2 or band1/band3 can remove blue and purple roofs perfectly (producer accuracy=at least 95%), while the former is powerless and the latter is goodish (producer accu- racy=approximately 90%) in removing green roofs. Since too few green roofs exist in our case, more green-roof samples are needed for further test in other cities. Our case study in Shenyang, China demonstrates that TSTS can correct overestimate of vegetation coverage by 2.14%, mostly in industrial blocks, which shows the necessity of roof confusion removal, especially for industrial cities.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"272 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":"115878742","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.2008.4620344
Jinqu Zhang, Yunpeng Wang
Ten cities with different urban sizes located in the Pearl River Delta, Guangdong Province, China were selected to study the relationships between surface urban heat island (S-UHI) intensity and land use/cover factors like urban size, development area, water proportion and mean NDVI, etc. All the cities are almost at the same latitude, show similar climate and have the same solar radiation, the influence of which shall be eliminated during our computation and comparative study. A variance-segmenting method was proposed to compute the S-UHI intensity for each city from the retrieved land surface temperature (LST). Factors like urban size, development area and water proportion were extracted directly from the classification images of the same ETM+ data. The urban mean NDVI value was used to quantify the urban vegetation abundance. Regression and correlation analyses were performed to study the relationships between the S-UHI intensity and these land use/cover factors, and the results show that S-UHI intensity is highly correlated to urban size (r = 0.83) and development area (r = 0.74). It was also proved that negative correlations existed between S-UHI intensity and urban mean NDVI (r = -0.62), and water proportion (r = -0.54). Linear and logarithm functions between S-UHI intensity and land use/cover factors were established respectively.
{"title":"Relationships between urban heat island intensity and land use/cover factors based on Landsat ETM+ in urban agglomeration of Guangdong, China","authors":"Jinqu Zhang, Yunpeng Wang","doi":"10.1109/EORSA.2008.4620344","DOIUrl":"https://doi.org/10.1109/EORSA.2008.4620344","url":null,"abstract":"Ten cities with different urban sizes located in the Pearl River Delta, Guangdong Province, China were selected to study the relationships between surface urban heat island (S-UHI) intensity and land use/cover factors like urban size, development area, water proportion and mean NDVI, etc. All the cities are almost at the same latitude, show similar climate and have the same solar radiation, the influence of which shall be eliminated during our computation and comparative study. A variance-segmenting method was proposed to compute the S-UHI intensity for each city from the retrieved land surface temperature (LST). Factors like urban size, development area and water proportion were extracted directly from the classification images of the same ETM+ data. The urban mean NDVI value was used to quantify the urban vegetation abundance. Regression and correlation analyses were performed to study the relationships between the S-UHI intensity and these land use/cover factors, and the results show that S-UHI intensity is highly correlated to urban size (r = 0.83) and development area (r = 0.74). It was also proved that negative correlations existed between S-UHI intensity and urban mean NDVI (r = -0.62), and water proportion (r = -0.54). Linear and logarithm functions between S-UHI intensity and land use/cover factors were established respectively.","PeriodicalId":142612,"journal":{"name":"2008 International Workshop on Earth Observation and Remote Sensing Applications","volume":"45 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":"131544567","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.2008.4620353
M. Wong, J. Nichol, Kwonho Lee, Zhanqing Li
Aerosol detection and monitoring by satellite observations has been substantially developed over past decades. While several state-of-art aerosol retrieval techniques provide aerosol properties in global scale, the more detail characteristics remain unknown because most of the satellite sensors are limited to 1 km resolution observations. However, a new aerosol retrieval algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m resolution data is developed to retrieve aerosol properties over land, which helps address the aerosol climatic issues on the local/urban scale. The rationale of our technique is to first estimate the aerosol reflectances by decomposing the top-of-atmosphere (TOA) reflectance from surface reflectance and Rayleigh path radiance. The modified Minimum Reflectance Technique (MRT) is adopted for the determination of the seasonal surface reflectances. A good agreement is revealed between the surface reflectances of MRT images and MODIS land surface reflectance products (MOD09), with a strong correlation of 0.9. Moreover, comprehensive look up tables (LUT) are constructed with the considerations of various aerosol optical properties and sun-viewing geometry in the radiative transfer calculations. The resulting 500 m aerosol optical thickness (AOT) data are highly correlated (r=0.94) with the AERONET sunphotometer observations in Hong Kong. This study demonstrates the applicability of aerosol retrieval at fine resolution in urban areas, which can assist the study of aerosol loading distribution and the impact of transient pollution on urban air quality. In addition, the MODIS 500 m AOT images can also be used to study the cross-boundary aerosols and feasible on locating the pollutant sources in the Pearl River Delta (PRD) region.
在过去的几十年里，卫星观测的气溶胶探测和监测已经有了很大的发展。虽然一些最先进的气溶胶检索技术提供了全球尺度的气溶胶特性，但由于大多数卫星传感器仅限于1公里分辨率的观测，因此更多的细节特征仍然未知。然而，针对中分辨率成像光谱仪(MODIS) 500 m分辨率数据，本文开发了一种新的气溶胶检索算法，用于检索陆地气溶胶特性，这有助于解决局地/城市尺度上的气溶胶气候问题。我们的技术原理是首先通过分解大气顶反射率和瑞利路径辐射来估计气溶胶反射率。采用改进的最小反射率技术(MRT)测定季节地表反射率。MRT影像的地表反射率与MODIS地表反射率产品(MOD09)具有较好的一致性，相关性为0.9。此外，在辐射传输计算中考虑了各种气溶胶光学性质和太阳观测几何形状，构建了综合查表(LUT)。所得的500米气溶胶光学厚度(AOT)资料与AERONET太阳光度计在香港的观测高度相关(r=0.94)。本研究验证了细分辨率气溶胶反演在城市地区的适用性，有助于研究气溶胶负荷分布和瞬态污染对城市空气质量的影响。此外，MODIS 500 m AOT影像也可用于研究跨境气溶胶，在珠江三角洲地区的污染源定位上是可行的。
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