Pub Date : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820255
Bilgi Görkem Yazgaç, Halil Durmus, M. Kirci, Ece Olcay Günes, Hakan Burak Karli
In this work an IoT urban agriculture monitoring system for smart urban agriculture design procedure is examined. The designed system gathers multiple sensor information such as temperature, moisture etc. and process the information relative to extreme conditions and communicate with the end user through online applications. Additionally, the system integrated to a greater agriculture monitoring system as a static sensor node. Hardware/software partitioning of the sensor node is decided by analyzing Petri Net model.
{"title":"Petri nets based procedure of hardware/software codesign of an urban agriculture monitoring system","authors":"Bilgi Görkem Yazgaç, Halil Durmus, M. Kirci, Ece Olcay Günes, Hakan Burak Karli","doi":"10.1109/Agro-Geoinformatics.2019.8820255","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820255","url":null,"abstract":"In this work an IoT urban agriculture monitoring system for smart urban agriculture design procedure is examined. The designed system gathers multiple sensor information such as temperature, moisture etc. and process the information relative to extreme conditions and communicate with the end user through online applications. Additionally, the system integrated to a greater agriculture monitoring system as a static sensor node. Hardware/software partitioning of the sensor node is decided by analyzing Petri Net model.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"219 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120870207","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820256
Peijun Sun, R. Congalton
Upscaling techniques have been extensively used to produce upscaled maps to fill data gaps serving various Earth observation models by providing area and landscape pattern information. Base maps as input for upscaling techniques inevitably have mapping errors that greatly impact the upscaling performance. However, the influence of mapping error on the representation of landscape pattern of upscaled maps has rarely been explored. To address this issue, the Crop Data Layer (CDL) data for two study sites were first used to generate agricultural maps as the base maps. A probability-based Monte Carlo algorithm was then used to simulate different error levels for the base maps. Two upscaling techniques, Fusing class Membership probability and Confidence level probability (FMC) and a conventional upscaling method (i.e., Majority Rule Based, MRB), were conducted. The results highlight that higher mapping error results in higher change of landscape pattern for upscaled maps. Overall, this work extends our understanding of the influence of mapping error on the upscaling performance. Also, it suggests that next generation upscaling techniques should greatly consider the mapping error and how to accurately present landscape pattern.
升比例尺技术已被广泛用于制作升比例尺地图，通过提供面积和景观格局信息来填补各种地球观测模型的数据空白。基本映射作为升级技术的输入不可避免地会产生映射错误，从而极大地影响升级性能。然而，地图绘制误差对景观格局呈现的影响却鲜有研究。为了解决这一问题，首先使用两个研究地点的作物数据层(Crop Data Layer, CDL)数据生成农业地图作为基础地图。然后使用基于概率的蒙特卡罗算法来模拟基本图的不同误差水平。采用融合类隶属概率和置信水平概率(FMC)和基于多数决定规则(MRB)的常规升级方法进行升级。结果表明，地图绘制误差越大，景观格局变化越大。总的来说，这项工作扩展了我们对映射误差对升级性能影响的理解。同时，建议下一代升级技术应充分考虑映射误差和如何准确呈现景观格局。
{"title":"Comparing the impact of mapping error on the representation of landscape pattern on upscaled agricultural maps","authors":"Peijun Sun, R. Congalton","doi":"10.1109/Agro-Geoinformatics.2019.8820256","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820256","url":null,"abstract":"Upscaling techniques have been extensively used to produce upscaled maps to fill data gaps serving various Earth observation models by providing area and landscape pattern information. Base maps as input for upscaling techniques inevitably have mapping errors that greatly impact the upscaling performance. However, the influence of mapping error on the representation of landscape pattern of upscaled maps has rarely been explored. To address this issue, the Crop Data Layer (CDL) data for two study sites were first used to generate agricultural maps as the base maps. A probability-based Monte Carlo algorithm was then used to simulate different error levels for the base maps. Two upscaling techniques, Fusing class Membership probability and Confidence level probability (FMC) and a conventional upscaling method (i.e., Majority Rule Based, MRB), were conducted. The results highlight that higher mapping error results in higher change of landscape pattern for upscaled maps. Overall, this work extends our understanding of the influence of mapping error on the upscaling performance. Also, it suggests that next generation upscaling techniques should greatly consider the mapping error and how to accurately present landscape pattern.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125854619","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820606
Yuanyuan Chen, Li Sun, Kai Liu, Zhiyuan Pei
Drought is one of the most complex natural hazards that can produce devastating impacts on many aspects, especially on agricultural production. Insufficient site observation data for drought monitoring makes remote sensing technique a key issue for global and regional drought assessment with high spatial and temporal resolutions. Some agricultural drought indexes have been developed during the last decade. Perpendicular drought index (PDI) and modified perpendicular drought index (MPDI) have received considerable attention because of their simplicity and efficacy. In this paper, PDI and MPDI were calculated using MODIS data and applied to assess the 2018 spring drought under dense vegetation cover condition in the south of Hebei Province. The validation was carried out using in situ relative soil water content from 0 to 10 cm and the consistency between perpendicular drought indexes, including PDI and MPDI, and in situ soil water content was analyzed. Result shows a good agreement between the drought information extracted by PDI and MPDI and the field measurement of soil water content, with the correlation coefficients of –0.62 and –0.74.
{"title":"Drought monitoring using MODIS derived perpendicular drought indexes","authors":"Yuanyuan Chen, Li Sun, Kai Liu, Zhiyuan Pei","doi":"10.1109/Agro-Geoinformatics.2019.8820606","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820606","url":null,"abstract":"Drought is one of the most complex natural hazards that can produce devastating impacts on many aspects, especially on agricultural production. Insufficient site observation data for drought monitoring makes remote sensing technique a key issue for global and regional drought assessment with high spatial and temporal resolutions. Some agricultural drought indexes have been developed during the last decade. Perpendicular drought index (PDI) and modified perpendicular drought index (MPDI) have received considerable attention because of their simplicity and efficacy. In this paper, PDI and MPDI were calculated using MODIS data and applied to assess the 2018 spring drought under dense vegetation cover condition in the south of Hebei Province. The validation was carried out using in situ relative soil water content from 0 to 10 cm and the consistency between perpendicular drought indexes, including PDI and MPDI, and in situ soil water content was analyzed. Result shows a good agreement between the drought information extracted by PDI and MPDI and the field measurement of soil water content, with the correlation coefficients of –0.62 and –0.74.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132706767","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820551
E. Yu, L. Di, Li Lin, Haoteng Zhao, M. S. Rahman, Chen Zhang, Junmei Tang
Decisions on efficient irrigation in agriculture depend on sufficient information from different sources. The source of information may come in different form and different scale. Properly delivering them to farmers in field is required to make the decisions on spot where internet access may be intermittent. Requirements are of supporting diverse data products, reactive, responsive, and progressive. The design of a geospatially capable service system needs to adapt fit technologies in both server and client. This study reviews relevant geospatial information technologies to meet the requirements: data integration, responsive Web design, and progressive Web application. A full stack design of Web geospatial information service system is developed to efficiently serve farmers in irrigated agriculture up to fields.
{"title":"Full Stack Web Development of a Geospatial Information Service System for Intelligently Irrigated Agriculture","authors":"E. Yu, L. Di, Li Lin, Haoteng Zhao, M. S. Rahman, Chen Zhang, Junmei Tang","doi":"10.1109/Agro-Geoinformatics.2019.8820551","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820551","url":null,"abstract":"Decisions on efficient irrigation in agriculture depend on sufficient information from different sources. The source of information may come in different form and different scale. Properly delivering them to farmers in field is required to make the decisions on spot where internet access may be intermittent. Requirements are of supporting diverse data products, reactive, responsive, and progressive. The design of a geospatially capable service system needs to adapt fit technologies in both server and client. This study reviews relevant geospatial information technologies to meet the requirements: data integration, responsive Web design, and progressive Web application. A full stack design of Web geospatial information service system is developed to efficiently serve farmers in irrigated agriculture up to fields.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128762708","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820589
B. Lu, Kun Yu, Zhiming Wang, Jing Wang, Liangjun Mao
The ORYZA2000 rice model is currently the most versatile rice growth simulation model jointly developed by the International Rice Research Institute (IRRI) and the Wageningen University. In this paper, Jiangsu province, well known as land of plenty, was chosen as a study area to evaluate ORYZA2000 using datasets where the same (similar) varieties were grown under both direct sowing and transplanting conditions, in order to verify the capability and adaptability of model to reproduce the effect of different establishing methods on single-cropping rice growth and development in the middle region of Jiangsu province with the same calibrated parameter set. The field experiments were carried out in 2016. All the available observation data, collected at nine field experimental sites located in the middle region of Jiangsu province, was split into calibration and validation datasets. In the general, it is concluded that the ORYZA2000 model was adaptable and could be applied in scenarios analysis in Jiangsu province with the parameters adjustment and the model localization.
{"title":"Preliminary Approach on Adaptability of Oryza2000 Model for Single Cropping Rice in Jiangsu Province (China)","authors":"B. Lu, Kun Yu, Zhiming Wang, Jing Wang, Liangjun Mao","doi":"10.1109/Agro-Geoinformatics.2019.8820589","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820589","url":null,"abstract":"The ORYZA2000 rice model is currently the most versatile rice growth simulation model jointly developed by the International Rice Research Institute (IRRI) and the Wageningen University. In this paper, Jiangsu province, well known as land of plenty, was chosen as a study area to evaluate ORYZA2000 using datasets where the same (similar) varieties were grown under both direct sowing and transplanting conditions, in order to verify the capability and adaptability of model to reproduce the effect of different establishing methods on single-cropping rice growth and development in the middle region of Jiangsu province with the same calibrated parameter set. The field experiments were carried out in 2016. All the available observation data, collected at nine field experimental sites located in the middle region of Jiangsu province, was split into calibration and validation datasets. In the general, it is concluded that the ORYZA2000 model was adaptable and could be applied in scenarios analysis in Jiangsu province with the parameters adjustment and the model localization.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116033654","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820229
Li Lin, L. Di, Chen Zhang, Liying Guo, Junmei Tang, E. Yu, M. S. Rahman, Haoteng Zhao, Zhiqi Yu, Ziheng Sun, Juozas Gaigalas
WaterSmart project is an NSF funded projected seeks water consumption reduction using satellite observations. In order to fit the fine temporal resolution requirement, satellites are required to have a high revisit cycle. MODIS is an ideal platform for monitoring the ground thanks to its daily coverage while the spatial resolution is too coarse. Research has demonstrated the possibility to improve the spatial resolution of MODIS using the Landsat 8 images. This research is aimed to establish a workflow to adapt the data fusion algorithm to achieve automatically processing at real-time in order to support short-term decision making.
{"title":"Building Near-Real-Time MODIS Data Fusion Workflow to Support Agricultural Decision-making Applications","authors":"Li Lin, L. Di, Chen Zhang, Liying Guo, Junmei Tang, E. Yu, M. S. Rahman, Haoteng Zhao, Zhiqi Yu, Ziheng Sun, Juozas Gaigalas","doi":"10.1109/Agro-Geoinformatics.2019.8820229","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820229","url":null,"abstract":"WaterSmart project is an NSF funded projected seeks water consumption reduction using satellite observations. In order to fit the fine temporal resolution requirement, satellites are required to have a high revisit cycle. MODIS is an ideal platform for monitoring the ground thanks to its daily coverage while the spatial resolution is too coarse. Research has demonstrated the possibility to improve the spatial resolution of MODIS using the Landsat 8 images. This research is aimed to establish a workflow to adapt the data fusion algorithm to achieve automatically processing at real-time in order to support short-term decision making.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117223297","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820620
Yingying Dong, Fang Xu, Linyi Liu, Xiaoping Du, H. Ye, Wenjiang Huang, Yining Zhu
Infected areas and damage levels due to pest and disease have been growing seriously according to the global climate changes. The government department of plant protection normally collects the crop pest and disease information by manual inspection, which is unable to provide timely and spatially continuous crop growth status and pest/ disease development over large areas. The manuscript aims to bring together and produce cutting edge research to provide crop pest and disease monitoring and forecasting information, integrating multi-source (Earth Observation-EO, meteorological, entomological and plant pathological, etc.) to support decision making in sustainable management of pest and disease. Taking national disease –a fungal disease of wheat rust and national pest – a serious insect pest locust as the experimental object, we conducted the following research: 1) the sensitive spectral features of rust and locust with capability of stresses differentiation would be identified or formed based on field hyperspectral data and UAV hyperspectral images for detection, 2) multi-sources of data that are composed by remote sensing images (eg. GF, Landsat and Sentinel) and meteorological observations would be integrated to evaluating habitat of rust and locust in farmland level, 3) multi-temporal remote sensing images and vegetation ecological dataset are combined to provide environmental information of rust and locust dispersion, and forecast the damaged areas and levels in regional scales. Moreover, an automatic system is developed to do the disease and pest timeseries monitoring and forecasting, also the visual display of the thematic maps and analysis reports. The system is constructed based on WebGIS platform. It selected Browser/Server (B/S) architecture, access the system interface through a web browser, simple, quick and easy to operate. And, it could timely, efficiently, quantitatively do the extraction and analysis of crop pest and disease occurrence and developing information, also produce pest and disease thematic maps and scientific report. The crop pest and disease monitoring and forecasting system, can provide effective information of pest and disease developing for our agricultural sector, provide a scientific basis to formulate pest and disease prevention and control measures, and also provide data basis and technical support for the crop network management. Based on the system, we analysed the damaged situation and changes of national wheat rust in 2019, and locust in Tianjing 2019. The results would not only promote efficacy of pest and disease management and prevention by improving the accuracy of monitoring and forecasting, but also help to reduce the amount of chemical pesticides, which could thus guarantee the food security and sustainable development of agriculture in China.
{"title":"Monitoring and forecasting for disease and pest in crop based on WebGIS system","authors":"Yingying Dong, Fang Xu, Linyi Liu, Xiaoping Du, H. Ye, Wenjiang Huang, Yining Zhu","doi":"10.1109/Agro-Geoinformatics.2019.8820620","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820620","url":null,"abstract":"Infected areas and damage levels due to pest and disease have been growing seriously according to the global climate changes. The government department of plant protection normally collects the crop pest and disease information by manual inspection, which is unable to provide timely and spatially continuous crop growth status and pest/ disease development over large areas. The manuscript aims to bring together and produce cutting edge research to provide crop pest and disease monitoring and forecasting information, integrating multi-source (Earth Observation-EO, meteorological, entomological and plant pathological, etc.) to support decision making in sustainable management of pest and disease. Taking national disease –a fungal disease of wheat rust and national pest – a serious insect pest locust as the experimental object, we conducted the following research: 1) the sensitive spectral features of rust and locust with capability of stresses differentiation would be identified or formed based on field hyperspectral data and UAV hyperspectral images for detection, 2) multi-sources of data that are composed by remote sensing images (eg. GF, Landsat and Sentinel) and meteorological observations would be integrated to evaluating habitat of rust and locust in farmland level, 3) multi-temporal remote sensing images and vegetation ecological dataset are combined to provide environmental information of rust and locust dispersion, and forecast the damaged areas and levels in regional scales. Moreover, an automatic system is developed to do the disease and pest timeseries monitoring and forecasting, also the visual display of the thematic maps and analysis reports. The system is constructed based on WebGIS platform. It selected Browser/Server (B/S) architecture, access the system interface through a web browser, simple, quick and easy to operate. And, it could timely, efficiently, quantitatively do the extraction and analysis of crop pest and disease occurrence and developing information, also produce pest and disease thematic maps and scientific report. The crop pest and disease monitoring and forecasting system, can provide effective information of pest and disease developing for our agricultural sector, provide a scientific basis to formulate pest and disease prevention and control measures, and also provide data basis and technical support for the crop network management. Based on the system, we analysed the damaged situation and changes of national wheat rust in 2019, and locust in Tianjing 2019. The results would not only promote efficacy of pest and disease management and prevention by improving the accuracy of monitoring and forecasting, but also help to reduce the amount of chemical pesticides, which could thus guarantee the food security and sustainable development of agriculture in China.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117239230","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820465
Chen Zhang, L. Di, Zhengwei Yang, Li Lin, E. Yu, Zhiqi Yu, M. S. Rahman, Haoteng Zhao
Cropland Data Layer (CDL) is an annual crop-specific land use map produced by the U.S. Department of Agricultural (USDA) National Agricultural Statistics Service (NASS). The CDL products are officially hosted on CropScape website which provides capabilities of geospatial data visualization, retrieval, processing, and statistics based on the open geospatial Web services. This study utilizes cloud computing technology to improve the performance of CropScape application and Web services. A cloud-based prototype of CropScape is implemented and tested. The experiment results show the performance of CropScape is significantly improved in the cloud environment. Comparing with the original system architecture of CropScape, the cloud-based architecture provides a more flexible and effective environment for the dissemination of CDL data.
{"title":"Cloud Environment for Disseminating NASS Cropland Data Layer","authors":"Chen Zhang, L. Di, Zhengwei Yang, Li Lin, E. Yu, Zhiqi Yu, M. S. Rahman, Haoteng Zhao","doi":"10.1109/Agro-Geoinformatics.2019.8820465","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820465","url":null,"abstract":"Cropland Data Layer (CDL) is an annual crop-specific land use map produced by the U.S. Department of Agricultural (USDA) National Agricultural Statistics Service (NASS). The CDL products are officially hosted on CropScape website which provides capabilities of geospatial data visualization, retrieval, processing, and statistics based on the open geospatial Web services. This study utilizes cloud computing technology to improve the performance of CropScape application and Web services. A cloud-based prototype of CropScape is implemented and tested. The experiment results show the performance of CropScape is significantly improved in the cloud environment. Comparing with the original system architecture of CropScape, the cloud-based architecture provides a more flexible and effective environment for the dissemination of CDL data.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127397205","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820219
Shaobo Zhong, Zhanya Xu, Ziheng Sun, E. Yu, Liying Guo, L. Di
With several decadal accumulations of remotely sensed data and products and advances in satellite based vegetative drought detection methods, the global and regional characteristics of drought are expected be discovered from those long-term historical inventory data. In this study, we investigate the trend and variability of global vegetative drought using bf 1981-2017 NOVAA/AVHRR weekly VHI products. We proposed a methodological framework to perform trend and variability analysis from overall trend test to trend location detection to trend magnitude estimate. Accounting for the effect of the global geographical heterogeneity on trend analysis, we aggregated the VHI dataset on designated zones in view of latitude ranges and climate zones. We found that: (1) although the overall trends are not obvious for some cases, the local trends are significant in some specific periods, and (2) the trends of vegetative drought in the north hemisphere is better than that in the south hemisphere.
{"title":"Global vegetative drought trend and variability analysis from long-term remotely sensed data","authors":"Shaobo Zhong, Zhanya Xu, Ziheng Sun, E. Yu, Liying Guo, L. Di","doi":"10.1109/Agro-Geoinformatics.2019.8820219","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820219","url":null,"abstract":"With several decadal accumulations of remotely sensed data and products and advances in satellite based vegetative drought detection methods, the global and regional characteristics of drought are expected be discovered from those long-term historical inventory data. In this study, we investigate the trend and variability of global vegetative drought using bf 1981-2017 NOVAA/AVHRR weekly VHI products. We proposed a methodological framework to perform trend and variability analysis from overall trend test to trend location detection to trend magnitude estimate. Accounting for the effect of the global geographical heterogeneity on trend analysis, we aggregated the VHI dataset on designated zones in view of latitude ranges and climate zones. We found that: (1) although the overall trends are not obvious for some cases, the local trends are significant in some specific periods, and (2) the trends of vegetative drought in the north hemisphere is better than that in the south hemisphere.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128247339","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 : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820223
Lin Guo, Zhiyuan Pei, Yin Zhang, Chunmei Zhao
The system for the contracted management of rural lands is one of the basic systems of rural lands, and farmers and contracted land are the two major variables in the land contract relationship. It is one of the key technologies for the contracted management right of rural land ownership registration certification work to overcome the contradiction between the land centered data model in traditional land management and farmers, as the basic unit in the management of land contract relations. Thus, a complete and efficient data model was established to match the diversity of change of land contract relationship. For this issue, the present study established a spatial data model named, Active-Active mode of Contract farmer and Contracted land from two perspectives of farmers and contracted land. The elements in the contract relationship of geographic objects, events and states are integrated into the data model. A database system is developed based on this model, and the present took a variety of business events as an example, and verified the feasibility of the model.
{"title":"Active-Active Mode of Contract Farmer and Contracted Land for Rural Land Contractual Management Data","authors":"Lin Guo, Zhiyuan Pei, Yin Zhang, Chunmei Zhao","doi":"10.1109/Agro-Geoinformatics.2019.8820223","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820223","url":null,"abstract":"The system for the contracted management of rural lands is one of the basic systems of rural lands, and farmers and contracted land are the two major variables in the land contract relationship. It is one of the key technologies for the contracted management right of rural land ownership registration certification work to overcome the contradiction between the land centered data model in traditional land management and farmers, as the basic unit in the management of land contract relations. Thus, a complete and efficient data model was established to match the diversity of change of land contract relationship. For this issue, the present study established a spatial data model named, Active-Active mode of Contract farmer and Contracted land from two perspectives of farmers and contracted land. The elements in the contract relationship of geographic objects, events and states are integrated into the data model. A database system is developed based on this model, and the present took a variety of business events as an example, and verified the feasibility of the model.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129880276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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