2014 The Third International Conference on Agro-Geoinformatics最新文献

{"title":"Estimation of regional evapotranspiration over the Southern Great Plains based on Penman-Monteith theory and the soil moisture estimates","authors":"S. Liang, Chen Zhongxin, Jiang Zhiwei","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910680","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910680","url":null,"abstract":"Penman-Monteith (PM) theory is a classic method to calculate evapotranspiration (ET) of land surfaces. However, soil resistance, related to soil moisture, is always difficult to determine over a large region. In this study, we developed an ET estimation algorithm by incorporating a soil moisture index (SMI) derived from the improved surface temperature-vegetation cover feature space, denoted as the PM-SMI algorithm. The PM-SMI algorithm was compared with the triangle algorithm and another Penman-Monteith based algorithm (PM-Yuan) that calculated soil evaporation using relative humidity. The three ET algorithms are compared and validated by Bowen Ratio measurements at 12 sites in the Southern Great Plain (SGP) that were mainly covered by grassland and cropland with low vegetation cover. The results showed that the PM-SMI algorithm performs the best among the three ET algorithms both on the instantaneous scale with R2 of 0.86, RMSE of 53.67 W/m2, bias of 6.83 W/m2 and the daily scale with R2 of 0.87, RMSE of 39.07 W/m2, and bias of -4.04 W/m2. PM-Yuan algorithm significantly underestimates ET resulting from soil evaporation and compared to triangle ET algorithm, PM-SMI is more reliable for estimation of ET over regional scale.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"60 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127463554","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}

{"title":"Design, development and application of a satellite-based field monitoring system to support precision farming","authors":"Meng Jihua, Liu Zhongyuan, Wu Bingfang, Xu Jin","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910626","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910626","url":null,"abstract":"The factual base of precision agriculture (PA) - the spatial and temporal variability of soil and crop factors within or between different fields has been recognized for centuries. Field information on seeding suitability, soil & crop nutrition status and crop mature date is needed to optimize field management. How to acquire the spatially and temporally varied field parameters accurately, efficiently and at affordable cost has always been the focus of the researches in the field. Satellite remote sensing has held out much promise for within & between-field monitoring, along with the promising development regarding spatial, temporal and spectral resolution in the last decade. Scientists from all over the world have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods to detect spatially and temporally varied environmental stresses which limit crop productivity. This can make significant contribution in optimizing crop management as sowing, irrigation, fertilization and harvest. However, gathering, accessing, and processing of remote sensing images from different satellites require high technical skills, not mention the time consumed in processing large amount of images. The lack of comprehensive software platforms to extract useful spatially and temporally varied information from satellite image hindered the wide application of satellite image to support PF. With this back ground, an integrated satellite-based field monitoring system was designed and developed with .Net and IDL (Interactive Data Language). The system consists of 4 primary functional models: 1) satellite image pre-processing model; 2) field seeding suitability evaluating model; 3) soil & crop nutrition status monitoring model and 4) crop mature date predicting model. In the first model, remote sensing images from different sensors can be pre-processed with format conversion, radiation calibration, atmospheric correction and geometric correction. BRDF correction was also provided for images with wide swath. Fusion of images from different sensors can also be implemented in this model to provide images with both high spatial and high temporal resolutions. In the second model, soil moisture and surface temperature will be acquired from satellite images. Together with the information on needs of different crops in seeding, seeding suitability of different fields can be evaluated. In the third model, the soil and crop nutrition status (nitrogen and chlorophyll concentration for crop; available nitrogen and organic matter content for soil) will be mapped with satellite image, and then been transferred to field/pixel scale fertilization prescriptions. In the fourth model, crop canopy/leaf water and chlorophyll content will be quantitatively mapped, along with the digital e","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125409047","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}

{"title":"Retrieval Of canopy chlorophyll content for spring corn using multispectral remote sensing data","authors":"Xu Jin, Meng Jihua","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910668","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910668","url":null,"abstract":"Nitrogen is an important organic element during the growth of the crop, the accuracy of estimation for the crop N status may improve fertilizer N use efficiency. The chlorophyll content has a close relationship with the Nitrogen content. The multispectral remote sensing data may be used to assess crop N status by estimating chlorophyll content. This paper used the statistical model and the physical model to estimate the canopy chlorophyll content. As the statistical model, a few typical VI(vegetation index), Normalized Difference Vegetation Index (NDVI), Green chlorophyll index (CIgreen), Triangular greenness index(TGI), Enhanced vegetation index(EVI), Optimized Soil-Adjusted Vegetation Index(OSAVI) were used to assess the canopy chlorophyll content. For the physical model, the PROSAIL radiative transfer model and the lookup-table(LUT) method were used. The result showed these two methods have advantages and disadvantages respectively. In terms of the estimation accuracy for the chlorophyll content, the physical model is a better choice.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115104799","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}

{"title":"Evaluating a new leaf fluorescence sensor for estimating rice nitrogen status","authors":"Weifeng Yu, Y. Miao, Shanshan Hu, Jianning Shen, Hongye Wang","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910600","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910600","url":null,"abstract":"Real-time non-destructive diagnosis of crop nitrogen (N) status is crucially important for the success of in-season site-specific N management. Chlorophyll meter (CM) has been commonly used to non-destructively estimate crop leaf chlorophyll concentration, and indirectly estimate crop N status. Dualex 4 is a newly developed leaf fluorescence sensor that can estimate both leaf chlorophyll concentration and polyphenolics, especially flavonoids. The ratio of chlorophyll and flavonoid concentration, which is termed N balance index (NBI), has been reported to be more sensitive to crop N status than SPAD meter readings. So far, no studies have been reported to evaluate this new sensor for estimating rice N status. Therefore, the objective of this study was to compare the accuracy of Dualex 4 sensor and chlorophyll meter SPAD-502 for estimating the N nutritional status of rice in Northeast China. A field experiment involving five N application rates (0, 70, 100, 130, 160 kg N ha-1), two cultivars (Longjing 21 and Kongyu 131) and three replications was conducted in Jiansanjiang, Heilongjiang Province, China in 2013. Leaf and plant samples were collected at three key growth stages and analyzed for chlorophyll and N concentrations. The preliminary results indicated that both instruments could explain over 80% of variation of leaf chlorophyll concentration. Their ability to estimate leaf N concentration was influenced by rice cultivars and growth stages. Across cultivars and growth stages, Dualex 4 and SPAD meter-based indices only explained about 30% of variation of leaf N concentration. Although flavonoid was negatively correlated with leaf N concentration (R2=0.172, p=0.0002), the new index NBI (Chlorophyll/Flavonoid) did not significantly improve the accuracy of estimating leaf N concentration as compared with SPAD meter. More studies are needed to further evaluate the Dualex 4 leaf fluorescence sensor for estimating rice N status and determine its potential benefits over SPAD meter.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122114901","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}

{"title":"The Correction ground spectral model for estimating above-ground net primary productivity at the peak of growing season on Meadow Steppe, Hulunbeier, Inner Mongolia, China","authors":"Zhaoyan Diao, Shi-hai Lv, Chaoyang Feng, D. Su, Liang Sun, Xue Tian","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910596","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910596","url":null,"abstract":"On the surface of hyperspectral data of remote sensing to estimate grassland biomass, the soil water content, organic matter content important impact on the model application. An ASD Fieldspec 3 spectroradiometer was used for spectral measurements of the Hulunbeier Meadow Steppe, Inner Mongolia, China, in late July, 2013. Ground spectral models were built to estimate the Above-ground Net Primary Productivity (ANPP) at the peak of the growing season from the Correction Normalized Difference Vegetation Index (C_NDVI) measured in the field. The SPSS software were used to assess relationships between ANPP and C_NDVI. Based on the coefficient of determination (R²), quadratic model (R²=0.808) and exponential model (R²=0.717) were batter than others. Error analysis shows that the linear equation has the biggest standard error of the prediction (SE = 82.42g/m²), the logarithmic curve equation has the smallest SE (SE=18.59g/m²). After considering all factors, a quadratic equation between ANPP and C_NDVI (ANPP=3738.048 NDVI_MODIS²-259.1588NDVI_MODIS+1309.847, R2=0.808, SE=66.25g/m², MEC=0.820, P <; 0.001) was selected and used for the study area at the peak of the growing season.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129224365","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}

{"title":"Ecological security evaluation in farming-pastoral ecotone using TM data","authors":"Haijian Ma, Xiaoxuan Li, D. Hu, Hu Zhao","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910622","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910622","url":null,"abstract":"Farming-pastoral zone is one of typical ecological transition zone in China. To monitor and evaluate the ecological security in farming-pastoral zone is of great significance for regional environmental protection and rational utilization. This paper chooses Guyuan County as the research area, which is in typical farming-pastoral zone area named Bashang in north China. Then, in the administrative divisions of villages and towns, a comprehensive evaluation index of ecological security at small scale in Guyuan County is set up through the following steps. Firstly, based on the \"Pressure - State - Response\" (PSR) framework and according to the relationship between each component object, a multi-level evaluation index system is constructed. Then, TM remote sensing images, digital elevation data and statistical data are comprehensive utilized to calculate every evaluation index. Next, index weight is obtained by using the Analytic Hierarchy Process (AHP). Results show that the range of comprehensive evaluation index of ecological security in Guyuan County was 0.28-0.64. At most areas, the ecological security was at the \"warning\" level. At the minority areas, the ecological security even reaches \"dangerous\" level, such as Huanggaizhuo and Gaoshanbao. The worst town is Guyuan pasture. This study analyzed the ecological security in the region, provided a decision-making basis for the ecological protection.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121722253","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}

{"title":"Land use change on sloping areas in Phuket Province, Thailand","authors":"W. Pantanahiran","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910644","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910644","url":null,"abstract":"The impact of continuous land use change on the slope areas in Phuket, Thailand was investigated in the present study. Landslide disasters may occur due to heavy rain storms in the area affected by global climate changes, causing an enormous impact on the tourism industry, especially life and property. Three time periods were focused on using remote sensing data, including 2001, 2006, and 2011. The Normalized Difference Vegetation Index (NDVI) was used to calculate and compere the land use and/or land cover on the steep areas and seven classes of vegetation change were proposed: High Vegetation Increase, Medium Vegetation Increase, Low Vegetation Increase, No Vegetation Change, Severe Vegetation Decrease, Moderate Vegetation Decrease, and Low Vegetation Decrease. Land use change using the NDVI comparison during the years 2001 to 2006 showed that the areas of High Vegetation Increase were highest (79.51%), followed by the areas of Low Vegetation Increase (11.05%), No Vegetation Change (3.16%), Medium Vegetation Increase (0.79%), Low Vegetation Decrease (5.33%), and Moderate Vegetation Decease (0.16%). In addition, the land use change using the NDVI comparison during the years 2006 to 2011 showed the areas of Low Vegetation Increase (40.81%), followed by the areas of Low Vegetation Decease (32.41%), Medium Vegetation Increase (10.79%), No Vegetation Change (6.77%), Moderate Vegetation Decrease (4.98%), High Vegetation Increase (2.95%), and Severe Vegetation Decrease (1.29%). A study of the difference between vegetation changes between the years 2001-2006 and the years 2006-2011 found that vegetation the during the first period increased, but the second period showed a reduction in vegetation, which might have resulted from the urbanization of those areas. It was also found that the vegetation change in Amphoe Kathu was more severe than in other areas, and that the areas of vegetation reduction in Amphoe Kathu increased from 3.30% to 72.92%, where the areas of the vegetation reduction showed the Low Vegetation Decease (59.09%), followed by the Moderate Vegetation Decease (11.61%) and Severe Vegetation Decease (2.22%). It is possible that the areas of natural forest or other plant cover have been changed to other usages, such as urban development, because Patong beach has high tourism activity. It can be implied that the slope areas of Amphoe Kathu are probably vulnerable to disaster after the heavy rain storms.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114810994","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}

{"title":"Remote sensing assisted multi-level crop acreage estimation — An application of small area estimation in Heilongjiang province","authors":"W. Zhou, Jinshui Zhang, Yaozhong Pan, R. Zhu","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910602","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910602","url":null,"abstract":"The planted acreage estimation for major crops by using remote sensing is typically combine the sample data from ground survey with information derived from image classification, and the common applied approaches are regression estimator by using linear model and calibration estimator by using confusion matrix. In general, the crop acreage estimation for provincial level in China only satisfied the precision for the target population and could not disaggregate to small areas, such as county and town level statistics. In recent years, the small area estimation is extended its application to agricultural statistics, so that by building a small area model it is applicable to estimate the sub-population or domains. This paper is adopted small area estimation approach to estimate crop acreage at county level in Heilongjiang province, China by combining image classification and ground survey data of year 2011. First, the scheme of sample selection for ground survey and method of remote sensing classification for crops in Heilongjiang province is introduced. Historical Landsat TM images in recent years are used to extract cropland to construct area frame, and then a stratified two stage sampling is adopted to select samples for ground survey. Some real Landsat TM images in early August of year 2011 as a key phenological period are used to discriminate major crops(corn, rice and soybean) by ML for entire province. Second, for the purpose of multi-level crop acreage estimation, we expect that the aggregation of county level estimates are equal to the estimate for entire province based on regression estimation. To meet this constraint, this paper is adopted a basic level small area model with fixed effects so that the sum of county estimates could be added up to the estimate of provincial total. The fitted model in the case of Heilongjiang province is in the form of a multi-response multiple regression. Third, the precision in terms of MSE of the estimates for corn, rice and soybean derived from small area model are illustrated, the coefficient of variation of estimates for these three major crops on county level average are relatively small and acceptable in practice. Finally, we discuss the model fitness of small area model, which are relevant to sample design for ground survey and form of model setting. In conclusion, it is efficient and robust to estimate sub-provincial crop acreage by adopting small area model if model itself is statistical sounding.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114874701","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}

{"title":"Modeling crop spatial patterns and their phenological calendars using geographical factors — A case study in Northwest China","authors":"Zhuokun Pan, Jingfeng Huang, F. Mao, Xiuzhen Wang, Zhewen Zhao","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910571","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910571","url":null,"abstract":"Crop growth has strong regional and phenological properties. This study was aiming to test the capability of modeling crops spatial pattern and their phenological calendars based on geographical factors. Study area was located in a mountainous middle to north-western transition zone of China. Agrometeorological data was critical research material which provided local actual cropping system and their phenologies records. We focused on the temperature response of crop regional patterns and their phenological phases; multiple regression models were established based on agrometeorological records against geographical factors (latitude, longitude, and altitude). Geographical information system (GIS) was employed to execute the regression models to simulate a raster surface. Result showed that goodness-of-fit of prediction are satisfactory except for minor circumstances, which demonstrates that the feasibility and deficiency when using regression model-based statistical methodology. Further study should be focus on crop growth simulation model and remote sensing to advance the research in crop phenology.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114972972","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}

{"title":"Research on image mosaic method of UAV image of earthquake emergency","authors":"J. Bi, W. Mao, Yuejian Gong","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910665","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910665","url":null,"abstract":"In recent years, remote sensing image of Unmanned Aerial Vehicle (UAV) is widely used in preventing and fighting calamities, searching and rescuing, territorial resources monitoring, forest fire prevention and so on. However, the development of UAV image processing has its own disadvantages, including time-consuming process and low level of automation. This paper extracts the scale invariant feature points of remote sensing image which is based on Scale Invariant Feature Transform (SIFT). Then utilize k-d tree on the feature points to create index. The next step is to further precise with RANSAC algorithm to correct the mismatching feature points to work out the accurate transformation matrix and achieve the image mosaic effect with image fusion technology. On this basis, compare with existing software of image mosaic to investigate its advantages and disadvantages.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127052832","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}