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

{"title":"Analysis of impacts of drought on GPP in Yunnan province based on MODIS products","authors":"Gu Xiaobin, Shen Huanfeng, Gan Wenxia, Zhang Liangpei","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910652","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910652","url":null,"abstract":"Drought has profound impacts on the balance of regional ecosystem. Recently, Yunnan province in China experiences more and more frequent and severe droughts. It is significant to study the effect of drought on the carbon cycle of Yunnan province. Gross primary productivity (GPP) could intuitively reflect the growth status of vegetation. In this paper, we analyzed the response of GPP to droughts in Yunnan province, and evaluated the impact of meteorological factor on the carbon cycle. The results indicated that droughts could apparently lead to reduction of GPP respectively on annual, seasonal and monthly scale. And for most vegetation types, the GPP showed good correlation with both temperature and precipitation, excepting the evergreen forests. What's more, croplands were more sensitive to the droughts than other vegetation types, with the highest correlation coefficient to the precipitation and no lag existed.","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":"124389927","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":"Soybean yield estimation using HJ-1 CCD data in Northeast China","authors":"Xin Du, Fang-yun Song, Hongyan Wang, Huanxue Zhang, J. Meng, Qiangzi Li, Jilei Liu, Leilong Ding, Yewei Lu","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910627","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910627","url":null,"abstract":"In this study, soybean biomass was estimated using HJ-1 CCD data, combined with a radiation use efficiency model. HJ-1 CCD data provide time-serial parameters, with which we can get the information that describe the growth process of soybean, so inputted these information into a biomass model with the real time meteorological data, and then we could get the biomass. And then a fix value of harvest index (HI) was used to calculate the soybean yield. The results were validated with ground measure data. The coefficients of determination R2 is 0.425, and the average fractional differences between estimated and observed yield of soybean is 29.73%. We concluded that estimation with remote-sensing data, such as the HJ CCD data that have a high spatial resolution and a shorter revisit cycle, can show more detail in its spatial patterns and improve the application of remote sensing on a local scale. The potential also exists for applying the approach to many other studies, including agricultural production estimation, crop growth monitoring, and agricultural ecosystem carbon cycle studies.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"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":"126319968","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":"Assessment of agricultural drought indicators impact on soybean crop yield: A case study in Iowa, USA","authors":"Youxin Huang, Xiuguo Liu, Yonglin Shen, Jian Jin","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910573","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910573","url":null,"abstract":"Agricultural drought is a condition of insufficient soil moisture caused by a deficit in precipitation over some time period. Soil moisture drops to a certain extent, adverse to the crop yield, and then reduces the production of crops. Soybean is one of the most important sources of oil and protein in the world. It is vulnerable to recurrent drought condition in the U.S. state of Iowa. This study was conducted to identify agricultural drought indicator that strongly correlated with soybean crop yield. Detail crop data (e.g., soybean crop yield, etc.) were collected from the USDA's National Agricultural Statistics Service (NASS). A region of interests is defined based on the MODIS 16-days 250m resolution vegetation index synthetic products (MOD13Q1) and daily land surface Temperature/Emissivity 1km resolution products (MOD11A1) from 2000 to 2013 in Iowa, which were used to compare three kinds of remote sensing derived agricultural drought monitoring indicator of crop water demand status: (i) Crop morphological indices (e.g., NDVI/VCI); (ii) Crop physiological indices (canopy temperature, e.g., TCI); and (iii) Crop comprehensive indices (e.g., VSWI). Drought cumulative effects were considered according to the specific soybean crop growth stages including from planted to emerged, vegetative period (from emerged to blooming), reproductive period (from blooming to setting pods), and growing season (from emerged to dropping leaves). The impacts of drought duration on the soybean crop yield by both of indices were analyzed. These results imply that physiological indices and comprehensive index were more correlated to assess the effect of drought on soybean yield. Drought indices accumulated in reproductive period (from blooming to setting pods) are highly superior to other accumulated for impacting on soybean yield, while over the growth season (from emerged to dropping leaves) is also highly correlated with the total yield assessment. The results can aid on evaluating the effects of drought on soybean yield in different growth stage. This could be very useful to providing auxiliary decision-making information for drought relief, agricultural manager and grain merchant to plan solutions and prepare for potential drought in advance.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"3 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":"126884660","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 based winter wheat growth dynamic changes and the spatial-temporal relationship with meteorological factor","authors":"H. Qing, Wu Wenbin, Zhou Qingbo, L. Dandan","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910643","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910643","url":null,"abstract":"Based on MODIS-NDVI data, taking Shaaxi and Gansu provinces as examples, this paper explored the correlation between different grades of winter wheat growth and main meteorological factors at 7 different phenophases. The growth condition of winter wheat from sowing to maturity stages from 2011-2012 were assessed based on MODIS-NDVI data, then, the lag correlation between different grades of winter wheat growth in each phenophase and the meteorological factors with corresponding phenophases (temperature, precipitation) were analyzed by using correlation analysis, and GIS spatial analysis methods. The results showed that the winter wheat growth varied across time and space in the study area. And no matter what grades of winter wheat growth, the correlation coefficients between winter wheat growth condition and accumulated precipitation were higher than synchronous precipitation and pre-phenophase precipitation in terms of the average value in 7 phenophases. The influences of temperature on winter wheat were different according to different grades of winter wheat growth. The study showed that winter wheat with better growth condition was more sensitive to precipitation; whereas winter wheat with normal growth condition was largely influenced by temperature in the whole. This study is of significant to comprehend the quantitative relationship between meteorological factors and crop growth.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125334607","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":"Object-oriented classification of rubber plantations from Landsat satellite imagery","authors":"Shengpei Dai, Hailiang Li, Hongxia Luo, Mao-fen Li, Jihua Fang, Lingling Wang, Jianhua Cao, Wei Luo","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910635","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910635","url":null,"abstract":"Due to increasing global demand for natural rubber products, rubber (Hevea brasiliensis) plantation expansion has occurred in many regions where it was originally considered unsuitable. However, accurate maps of rubber plantations are not available, which substantially constrain our understanding of the environmental and socio-economic impacts of rubber plantation expansion. In this study, the rubber plantations was accurate mapped from Landsat satellite imagery based on object-oriented classification method in Yangjiang State Farm in Hainan Island in 2010. The results show that: (1) The rubber plantation area in Yangjiang State Farm was estimated at 5866 hm2 in 2010, which was slightly higher than the stand inventory data (5190 hm2) in 2009. (2) The resulting rubber plantation map has a high accuracy according to the confusion matrix by using the ground truth ROIs. The overall accuracy is 90% and the kappa coefficient is 0.9. It showed that object-oriented classification method is suitable for mapping rubber plantation from Landsat satellite imagery.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131283169","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":"Changing grain production in China: Perspective on changing grain acreage","authors":"J. Tao, F. Zhou","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910663","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910663","url":null,"abstract":"The consecutive growth in China's grain output over the past decade has partly depended on the expansion in grain sown area. The paper examines spatiotemporal patterns of China's grain sown area by using a complete decomposition approach. The results show that changing grain sown area during 1978-2012 is the product of augmenting multiple cropping index (MCI), shrinking share of grain crops and loss of cultivated land on the whole. Owing much to augmenting MCI, grain acreage has expanded mainly in the high latitude areas since the mid-1990s, while it has shrunk in the eastern and central parts of China. A shift in growing grain crops from south to north can thus be discerned, affecting China's grain production pattern. Pattern change in grain sown area, associated with declining cultivated land and regional variability in changing MCI, is then discussed for the sake of China's grain supply security.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126272939","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":"Grain consumption forecasting in China for 2030 and 2050: Volume and varieties","authors":"Mingjie Gao, Qiyou Luo, Yang Liu, Jian Mi","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910669","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910669","url":null,"abstract":"Grain consumption projections are necessary inputs for developments in infrastructure as a means to ensure national food security. In this paper, firstly, we use time series analysis to forecast the key parameters affecting grain consumption in China in 2030 and 2050, and then we employ panel data analysis to estimate the long-run demand functions for feed grain in China across a range of scenarios. Our main findings are as follows. First, we expect that per capita grain consumption (in kilograms) for urban residents increase to 355kg in 2030 and 387kg in 2050, while that of rural residents will at first decrease to 248kg in 2030, and then increase to 262kg in 2050. Second, the gross volume of grain ration and feed grain for urban grain consumption in China will be 349 million tons in 2030 rising to 416 million tons in 2050, while that for rural grain consumption will fall to 113 million tons in 2030 and 77 million tons in 2050. Third, we anticipate that other grain consumptions in China, including food processing, seed, and waste, will increase to 108 million tons in 2030 and 131 million tons in 2050. Fourth, on this basis, total grain consumption in China will be about 571 million tons in 2030 and 624 million tons in 2050. Finally, we forecast the consumption of maize, rice, and wheat in China to be 262, 154, and 114 million tons in 2030, and 318, 156 and 100 million tons in 2050, respectively.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122493564","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":"A new approach for measuring 3D digitalized rape leaf parameters based on images","authors":"Fang Yihang, Lin Chengda, Zhai Ruifang, Tang Yao, W. Xingyu","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910633","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910633","url":null,"abstract":"At present, measurement method of crop leaf area mainly proceeds in two-dimensional way, which may lead to certain degree of damage. Therefore, it will be of great significant to propose a non-contact method for the measurement of leaf area. In this paper, pictures of rape are obtained from different viewangles with mobile camera, and then three-dimensional digitization of rape is realized by SFM(Structure From Motion) and MVS(Multiple View Stereo) technologies. We conduct a case study and establish a three-dimensional modeling of rape leaf by applying the NURBS surface fitting. Finally, surface area of rape leaf is automatically calculated, and then compared to the area which is obtained by traditional digital image measurement methodology, in this way it realizes non-destructive detection of rapte growth modeling for precision agriculture. According to the experimental results, the the method of 3D digitalized rape leaf measurement based on images is supported.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"57 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116384159","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":"A web-based semi-automated method for semantic annotation of high schools in remote sensing images","authors":"M. You, Ziheng Sun, L. Di, Zhe Guo","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910672","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910672","url":null,"abstract":"The overwhelming volume of routine image acquisition requires automated methods or systems for feature discovery instead of manual image interpretation. While most existing researches focus on extracting elementary features such as basic terrains and individual objects, the detection of compound feature is still a challenge. This paper proposes a semi-automated approach integrating supervised image classification and geo-processing workflow to discover and annotate compound objects within RS images. Taking the high school in U.S. as an example, we developed a web-based prototype system to detect compound objects. Experimental results by the prototype show that the approach is capable of annotating high schools with an acceptable accuracy. This paper demonstrates a novel way to leverage existing technologies in completing the semantic annotation of RS images.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132218881","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":"Crop identification by means of seasonal statistics of RapidEye time series","authors":"E. Zillmann, H. Weichelt","doi":"10.1109/AGRO-GEOINFORMATICS.2014.6910572","DOIUrl":"https://doi.org/10.1109/AGRO-GEOINFORMATICS.2014.6910572","url":null,"abstract":"Crop classification greatly benefits from the analysis of multi-temporal Earth Observation (EO) data within a growing season utilizing the distinct phenological behavior of each crop. RapidEye's high repetition rate increases the chances of providing sufficient high resolution image time series offering new ways of classifying crops. This study introduces a supervised decision tree (DT) classification approach using image objects in combination with seasonal statistics of various vegetation indices (VI) for crop identification. The aim of this study is, first, to show the potential of VI seasonal statistics for crop identification, and secondly, to evaluate the relative contribution of each variable to the overall classification accuracy. The results presented in this paper correspond to an area of 625 km2 in Saxony-Anhalt, Germany. The cultivated landscape is characterized by large agricultural fields, with winter wheat, canola, corn and winter barley as the main crops. Crop identification accuracies were assessed on the basis of reference fields and the importance of each employed variable is assessed by rule set analysis. The classification accuracy for the test area demonstrates that the proposed approach of multi-temporal image analysis provides spatially detailed and thematically accurate information on the crop type distribution.","PeriodicalId":161866,"journal":{"name":"2014 The Third International Conference on Agro-Geoinformatics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115518276","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}