Pub Date : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820703
Jianhong Liu, Xin Huang
Crop phenological information is an important parameter for crop growth monitoring, grain yield prediction, crop model simulation and crop’s response to climate change. Improving the accuracy of the retrieved crop phenology parameters contributes to researches about climate change, global carbon balance, etc. This paper focuses on assessing the retrieval accuracy of crop SOS and EOS by remote sensing based on the dynamic threshold model. Ground observations of crop growth and development records from China Meteorological Administration (CMA) and Chinese Ecosystem Research Network (CERN) in 2015 and 2016 were used as reference data. Firstly, we improved the dynamic threshold model to ensure the 100% retrieval rate for detecting SOS and EOS. Then, we retrieved the SOS and EOS of different crops under different thresholds by the improved dynamic threshold model from the Normalized Difference Vegetation Index (NDVI) time series derived from MODerate-resolution Imaging Spectroradiometer (MODIS). Accuracy assessment indicated that the mostly used 20% or 50% threshold is not the optimal threshold for retrieving all crops’ SOS and EOS. In additional, it is inappropriate to use the same threshold to retrieve SOS and EOS. There is a big difference between the optimal thresholds for retrieving SOS and EOS of different crops.
{"title":"Evaluating crop phenology retrieving accuracies based on ground observations","authors":"Jianhong Liu, Xin Huang","doi":"10.1109/Agro-Geoinformatics.2019.8820703","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820703","url":null,"abstract":"Crop phenological information is an important parameter for crop growth monitoring, grain yield prediction, crop model simulation and crop’s response to climate change. Improving the accuracy of the retrieved crop phenology parameters contributes to researches about climate change, global carbon balance, etc. This paper focuses on assessing the retrieval accuracy of crop SOS and EOS by remote sensing based on the dynamic threshold model. Ground observations of crop growth and development records from China Meteorological Administration (CMA) and Chinese Ecosystem Research Network (CERN) in 2015 and 2016 were used as reference data. Firstly, we improved the dynamic threshold model to ensure the 100% retrieval rate for detecting SOS and EOS. Then, we retrieved the SOS and EOS of different crops under different thresholds by the improved dynamic threshold model from the Normalized Difference Vegetation Index (NDVI) time series derived from MODerate-resolution Imaging Spectroradiometer (MODIS). Accuracy assessment indicated that the mostly used 20% or 50% threshold is not the optimal threshold for retrieving all crops’ SOS and EOS. In additional, it is inappropriate to use the same threshold to retrieve SOS and EOS. There is a big difference between the optimal thresholds for retrieving SOS and EOS of different crops.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130111335","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.2019.8820578
Halil Durmus, Ece Olcay Günes
Robotics and Internet of Things (IoT) are two hot topics in the research area. There are studies in the literature that combine these two topics. In this study, robotics and IoT are used for the agricultural fields. Because, with the technology becoming more available on agriculture; food security, crop yield will be increased and the environmental hazards will be decreased. But this can be achieved by strictly monitoring the agricultural fields and greenhouses. For these purposes, static sensors or sensor networks, and mobile agents are used. IoT forms the backbone of such systems because there are too many units in different places and a lot of data is coming out from these units. Also, processing this data reveals the big data problem. Purpose of this work is integrating the mobile robot platform to gather data from the agricultural fields or greenhouses and then post this gathered data to the web application. So that, data can be stored, processed, and classified on the web application or cloud. This study proposes a design scheme for the mobile internet of things concept where the client or the agent is the mobile robot whether it is autonomous or not. Furthermore, the design structure is not limited to the mobile ground vehicle. Any type of unmanned vehicle or static sensor can be integrated into the system. Internet connection is not limited to only Wi-Fi, there is also a cellular connection in the system. With this study, mobile data acquisition and transferring this acquired data to the web application can be done. Also, the infrastructure of the mobile agent-based internet of things system is shown. On the robot side, autonomy can be added to the system.
{"title":"Integration of the Mobile Robot and Internet of Things to Collect Data from the Agricultural Fields","authors":"Halil Durmus, Ece Olcay Günes","doi":"10.1109/Agro-Geoinformatics.2019.8820578","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820578","url":null,"abstract":"Robotics and Internet of Things (IoT) are two hot topics in the research area. There are studies in the literature that combine these two topics. In this study, robotics and IoT are used for the agricultural fields. Because, with the technology becoming more available on agriculture; food security, crop yield will be increased and the environmental hazards will be decreased. But this can be achieved by strictly monitoring the agricultural fields and greenhouses. For these purposes, static sensors or sensor networks, and mobile agents are used. IoT forms the backbone of such systems because there are too many units in different places and a lot of data is coming out from these units. Also, processing this data reveals the big data problem. Purpose of this work is integrating the mobile robot platform to gather data from the agricultural fields or greenhouses and then post this gathered data to the web application. So that, data can be stored, processed, and classified on the web application or cloud. This study proposes a design scheme for the mobile internet of things concept where the client or the agent is the mobile robot whether it is autonomous or not. Furthermore, the design structure is not limited to the mobile ground vehicle. Any type of unmanned vehicle or static sensor can be integrated into the system. Internet connection is not limited to only Wi-Fi, there is also a cellular connection in the system. With this study, mobile data acquisition and transferring this acquired data to the web application can be done. Also, the infrastructure of the mobile agent-based internet of things system is shown. On the robot side, autonomy can be added to the system.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127713457","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 : 2019-07-14DOI: 10.1109/Agro-Geoinformatics.2019.8820655
F. Muthoni
Quantifying the magnitude and significance of climate change variables over space and time in Africa is challenging due to sparse distribution of weather stations and poor quality of existing data. Time series climate data generated from remote sensing platforms could provide plausible alternative for measuring the trends of climate change in data limiting context. This study utilise time series remote sensing data for rainfall, maximum temperature and minimum temperature to investigate the magnitude and significance of spatial-temporal trends over six countries in West Africa. A modified Mann-Kendall test and Theil-Sen’s slope are utilised to test the significance and the magnitude of trends respectively for period between 1981 and 2017. June to September rainfall along the Sahel, Sudan and northern Guinea savanna agro-ecological zones revealed a significant increase (0.1 – 3 mm yr $^{-1}$) that peaked in August. Extreme temperatures for period between August and October remained stable while significant positive trend (0.005 – 0.07°C yr $^{-1}$) was observed in rest of months. Areas experiencing significant drying and warming trends are earmarked as priority for targeting appropriate climate smart agricultural technologies. The widespread significant increase of extreme temperatures justifies increased investments in measures to cope with heat stress.
{"title":"Tracking the magnitude of climate change and variability with remote sensing data to improve targeting of climate smart agricultural technologies","authors":"F. Muthoni","doi":"10.1109/Agro-Geoinformatics.2019.8820655","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820655","url":null,"abstract":"Quantifying the magnitude and significance of climate change variables over space and time in Africa is challenging due to sparse distribution of weather stations and poor quality of existing data. Time series climate data generated from remote sensing platforms could provide plausible alternative for measuring the trends of climate change in data limiting context. This study utilise time series remote sensing data for rainfall, maximum temperature and minimum temperature to investigate the magnitude and significance of spatial-temporal trends over six countries in West Africa. A modified Mann-Kendall test and Theil-Sen’s slope are utilised to test the significance and the magnitude of trends respectively for period between 1981 and 2017. June to September rainfall along the Sahel, Sudan and northern Guinea savanna agro-ecological zones revealed a significant increase (0.1 – 3 mm yr $^{-1}$) that peaked in August. Extreme temperatures for period between August and October remained stable while significant positive trend (0.005 – 0.07°C yr $^{-1}$) was observed in rest of months. Areas experiencing significant drying and warming trends are earmarked as priority for targeting appropriate climate smart agricultural technologies. The widespread significant increase of extreme temperatures justifies increased investments in measures to cope with heat stress.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121078675","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 : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820712
Haydar Akcay, S. Kaya, Elif Sertel, U. Alganci
Global warming, which triggers climatic changes, has direct effects on the phenology of plants. For a sustainable agricultural production, continuous monitoring of crops and trees is critical to have updated information and producing effective agricultural plans. Remote sensing is an efficient option for this purpose and is a very popular technique. Olive is an essential agricultural product for the economy of Mediterranean countries such as Turkey. Determination of olive trees, which are expanded all around Aegean and}{Mediterranean regions of the country, is critical to assess the production capacity and the quality of products. In this study, combinations of time series of Sentinel-1 satellite images, Sentinel-2 satellite images and NDVI products obtained from Sentinel-2 satellite images are used to investigate the classification accuracy of olive trees. According to analysis results, a significant correlation with R2 = 0.67 found between NDVI and SAR data (sigma nought VH/VV in decibel scale). This result pointed out probable accuracy improvement in classification of fused data from different sensors. In the next step, supervised random forest classification was applied on the fused data combinations and results showed that Sentinel-1 – Sentinel-2, Sentinel-1 – NDVI and Sentinel-2 – NDVI combinations achieved the highest overall accuracy with 73 %, while standalone Sentinel-1 and Sentinel-2 image time series classification accuracies are 48 % and 68 % respectively.
{"title":"Determination of Olive Trees with Multi-sensor Data Fusion","authors":"Haydar Akcay, S. Kaya, Elif Sertel, U. Alganci","doi":"10.1109/Agro-Geoinformatics.2019.8820712","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820712","url":null,"abstract":"Global warming, which triggers climatic changes, has direct effects on the phenology of plants. For a sustainable agricultural production, continuous monitoring of crops and trees is critical to have updated information and producing effective agricultural plans. Remote sensing is an efficient option for this purpose and is a very popular technique. Olive is an essential agricultural product for the economy of Mediterranean countries such as Turkey. Determination of olive trees, which are expanded all around Aegean and}{Mediterranean regions of the country, is critical to assess the production capacity and the quality of products. In this study, combinations of time series of Sentinel-1 satellite images, Sentinel-2 satellite images and NDVI products obtained from Sentinel-2 satellite images are used to investigate the classification accuracy of olive trees. According to analysis results, a significant correlation with R2 = 0.67 found between NDVI and SAR data (sigma nought VH/VV in decibel scale). This result pointed out probable accuracy improvement in classification of fused data from different sensors. In the next step, supervised random forest classification was applied on the fused data combinations and results showed that Sentinel-1 – Sentinel-2, Sentinel-1 – NDVI and Sentinel-2 – NDVI combinations achieved the highest overall accuracy with 73 %, while standalone Sentinel-1 and Sentinel-2 image time series classification accuracies are 48 % and 68 % respectively.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116416272","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 : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820689
Dennis Lee
In this paper, an efficient classifier based on extreme learning machine (ELM) is proposed to use for mapping agricultural tillage practices from hyperspectral remote sensing imagery. The kernel version, called kernel ELM (KELM), is implemented due to its powerfulness. To utilize spatial information of an image, a spatial convolution filter is adopted to generate spatial-spectral features of a hyperspectral pixel by incorporating its surrounding pixels, which are the actual inputs to the KELM. Experimental results using airborne hyperspectral images demonstrate that the KELM can outperform other classic methods, such as support vector machine and random forest.
{"title":"Mapping Agricultural Tillage Practices Using Extreme Learning Machine","authors":"Dennis Lee","doi":"10.1109/Agro-Geoinformatics.2019.8820689","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820689","url":null,"abstract":"In this paper, an efficient classifier based on extreme learning machine (ELM) is proposed to use for mapping agricultural tillage practices from hyperspectral remote sensing imagery. The kernel version, called kernel ELM (KELM), is implemented due to its powerfulness. To utilize spatial information of an image, a spatial convolution filter is adopted to generate spatial-spectral features of a hyperspectral pixel by incorporating its surrounding pixels, which are the actual inputs to the KELM. Experimental results using airborne hyperspectral images demonstrate that the KELM can outperform other classic methods, such as support vector machine and random forest.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123029297","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 : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820461
Emrullah Acar, M. S. Özerdem, B. Üstündağ
The soil surface humidity parameter over vegetated fields is of great importance for controlling water consumption; prevention of salinity caused by over-irrigation; efficient use of irrigation system and improving the yield and quality of the cultivated crop. However, determination of the soil surface humidity is very difficult on vegetated fields. In order to overcome this problem, polarimetric decomposition models and machine learning based regression model were implemented. The main purpose of this study is to predict soil surface humidity on moderately vegetated fields. Thus, the study is conducted in agricultural fields of Dicle University and it consists of several stages. In the first stage, a Radarsat-2 data was obtained in 3 March 2016 and the local humidity samples were measured simultaneously with the Radarsat-2 acquisition. In the second stage, 10 polarimetric features were obtained from each cell (2x2 pixels) of ground sample by utilizing standard ıntensity-phase technique as well as Freeman-Durden and H/A/$alpha$ polarimetric decomposition models. This step is repeated for all ground samples and as a result, a dataset with 156x10 lengths is formed. In the next stage, Extreme Learning Machine based Regression (ELM-R) model was used for predicting the soil surface humidity with the aid of polarimetric SAR features. For the validation of the proposed system, leave-one-out cross-validation method was applied and finally, 2.19% Root Mean Square Error (RMSE) were computed.
植被地土壤表面湿度参数对控制水分消耗具有重要意义;防止过度灌溉造成的盐碱化;有效利用灌溉系统，提高栽培作物的产量和品质。然而，在植被覆盖的农田中，土壤表面湿度的测定是非常困难的。为了克服这一问题，实现了极化分解模型和基于机器学习的回归模型。本研究的主要目的是预测中等植被田的土壤表面湿度。因此，该研究是在Dicle大学的农业领域进行的，它包括几个阶段。在第一阶段，2016年3月3日获得了Radarsat-2数据，并在获取Radarsat-2数据的同时测量了当地的湿度样本。在第二阶段，利用标准ıntensity-phase技术以及Freeman-Durden和H/A/$alpha$极化分解模型，从地面样品的每个单元(2x2像素)中获得10个极化特征。对所有地面样本重复此步骤，结果形成一个长度为156x10的数据集。第二阶段，利用极端学习机回归模型(Extreme Learning Machine based Regression, ELM-R)，结合极化SAR特征对土壤表面湿度进行预测。采用留一交叉验证法对系统进行验证，最终计算出2.19%的均方根误差(RMSE)。
{"title":"Machine Learning based Regression Model for Prediction of Soil Surface Humidity over Moderately Vegetated Fields","authors":"Emrullah Acar, M. S. Özerdem, B. Üstündağ","doi":"10.1109/Agro-Geoinformatics.2019.8820461","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820461","url":null,"abstract":"The soil surface humidity parameter over vegetated fields is of great importance for controlling water consumption; prevention of salinity caused by over-irrigation; efficient use of irrigation system and improving the yield and quality of the cultivated crop. However, determination of the soil surface humidity is very difficult on vegetated fields. In order to overcome this problem, polarimetric decomposition models and machine learning based regression model were implemented. The main purpose of this study is to predict soil surface humidity on moderately vegetated fields. Thus, the study is conducted in agricultural fields of Dicle University and it consists of several stages. In the first stage, a Radarsat-2 data was obtained in 3 March 2016 and the local humidity samples were measured simultaneously with the Radarsat-2 acquisition. In the second stage, 10 polarimetric features were obtained from each cell (2x2 pixels) of ground sample by utilizing standard ıntensity-phase technique as well as Freeman-Durden and H/A/$alpha$ polarimetric decomposition models. This step is repeated for all ground samples and as a result, a dataset with 156x10 lengths is formed. In the next stage, Extreme Learning Machine based Regression (ELM-R) model was used for predicting the soil surface humidity with the aid of polarimetric SAR features. For the validation of the proposed system, leave-one-out cross-validation method was applied and finally, 2.19% Root Mean Square Error (RMSE) were computed.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116586897","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 : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820503
Ling Sun, Zesheng Zhu
The goal of this paper was to investigate the strength of key spectral vegetation indices for the rice rotation effect related to the rice yield increment. Six widely used spectral indices were investigated in a study of the rice rotation effect in cotton-rice rotation and rice-rice monoculture in Xinghua, China. These six indices related closely with rice yield were investigated for cotton and rice during 2 years (2001 and 2002) on LANDSAT 7 ETM+images. Six rice vegetation indices of cotton-rice rotation were increased by an average of 6.28% (NDVI), 11.28% (GVI), 6.28% (SAVI), 1.25% (IPVI), 3.40% (RVI), and 7.66% (DVI) compared with that of rice-rice monoculture, respectively.
{"title":"Assessing Effects of Cotton-Rice Rotation on Rice Yield Using Different Remote Sensing Vegetation Indices","authors":"Ling Sun, Zesheng Zhu","doi":"10.1109/Agro-Geoinformatics.2019.8820503","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820503","url":null,"abstract":"The goal of this paper was to investigate the strength of key spectral vegetation indices for the rice rotation effect related to the rice yield increment. Six widely used spectral indices were investigated in a study of the rice rotation effect in cotton-rice rotation and rice-rice monoculture in Xinghua, China. These six indices related closely with rice yield were investigated for cotton and rice during 2 years (2001 and 2002) on LANDSAT 7 ETM+images. Six rice vegetation indices of cotton-rice rotation were increased by an average of 6.28% (NDVI), 11.28% (GVI), 6.28% (SAVI), 1.25% (IPVI), 3.40% (RVI), and 7.66% (DVI) compared with that of rice-rice monoculture, respectively.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116604477","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 : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820225
Abdurrahman Gonenc, M. S. Özerdem, Emrullah Acar
Remote Sensing is the acquisition of information about its physical properties without direct contact with an object. This information is obtained through sensors. These sensors do not come into contact with objects. There are two different systems for remote sensing. These are Active and Passive Sensor Systems. Passive Sensor Systems measure the energy of the rays reflected from the objects by the rays sent by the sun. On the other hand, Active Sensor Systems measure the energy reflected from the objects by transmitting their rays to the object. Passive Sensor Systems can be shown as an example of optical sensor systems. The Landsat-8 satellite works with an optical sensor system. Synthetic Aperture Radar (SAR) systems are examples of active sensor systems. SAR systems have a wide range of usage in all weather conditions and they are a radar system that displays the earth in high resolution. Radarsat-2 satellite has SAR sensor systems. The aim of this study is to compare each of the vegetation indices by using Landsat-8 and Radarsat-2 satellite images with two different types of sensors. In this study, Radar Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) were investigated. For the calculation of the RVI index, the back-scattering coefficient of the four different bands (HH, HV, VH, VV) of the multi-time full-polarimetric Radarsat-2 FQ satellite image dated 8 April 2015 was used. In the calculation of NDVI index, Band 5 (Near Infrared) and Band 4 (Red) of the Landsat-8 satellite image of May 25, 2015 were used. Dicle University agricultural areas were chosen as the study area. 100 different GPS points belonging to this agricultural area were determined and RVI and NDVI values of these points were calculated. A good correlation was observed between RVI and NDVI indices with the aid of statistically approach.
{"title":"Comparison of NDVI and RVI Vegetation Indices Using Satellite Images","authors":"Abdurrahman Gonenc, M. S. Özerdem, Emrullah Acar","doi":"10.1109/Agro-Geoinformatics.2019.8820225","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820225","url":null,"abstract":"Remote Sensing is the acquisition of information about its physical properties without direct contact with an object. This information is obtained through sensors. These sensors do not come into contact with objects. There are two different systems for remote sensing. These are Active and Passive Sensor Systems. Passive Sensor Systems measure the energy of the rays reflected from the objects by the rays sent by the sun. On the other hand, Active Sensor Systems measure the energy reflected from the objects by transmitting their rays to the object. Passive Sensor Systems can be shown as an example of optical sensor systems. The Landsat-8 satellite works with an optical sensor system. Synthetic Aperture Radar (SAR) systems are examples of active sensor systems. SAR systems have a wide range of usage in all weather conditions and they are a radar system that displays the earth in high resolution. Radarsat-2 satellite has SAR sensor systems. The aim of this study is to compare each of the vegetation indices by using Landsat-8 and Radarsat-2 satellite images with two different types of sensors. In this study, Radar Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) were investigated. For the calculation of the RVI index, the back-scattering coefficient of the four different bands (HH, HV, VH, VV) of the multi-time full-polarimetric Radarsat-2 FQ satellite image dated 8 April 2015 was used. In the calculation of NDVI index, Band 5 (Near Infrared) and Band 4 (Red) of the Landsat-8 satellite image of May 25, 2015 were used. Dicle University agricultural areas were chosen as the study area. 100 different GPS points belonging to this agricultural area were determined and RVI and NDVI values of these points were calculated. A good correlation was observed between RVI and NDVI indices with the aid of statistically approach.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129609653","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}
A critical nitrogen (Nc) concentration, defined as the minimum nitrogen (N) concentration required for maximum plant growth, could be used as an intermediate variable between remote sensing data and recommendation for N fertilizer. In this study, the critical N concentration dilution curve was established based on aboveground biomass (AGB) from data at jointing, booting, anthesis and filling stages. The quantitative correlations between normalized difference vegetation (NDVI) and nitrogen nutrition index (NNI) were established. Finally, an N recommendation model combined with the N fertilizer effect function and NDVI was established and verified by field test data. Results showed that the N concentration of winter wheat decreased gradually during the reproductive growth period, and The plant N concentration could be described by $N _{mathrm{c}} =$ 6.27*AGB-0.54, with the R2 value of 0.80. Moreover, the thresholds of NDVI were 0.87, 0.91, 0.91 and 0.81 at jointing, booting, anthesis and filling stages, respectively, and the amounts of recommending nitrogen fertilizer were 2.63, 10.00, 11.11 and 10.00 kg N/hm2 when NNI value lowed 1% of corresponding period’s thresholds. Field experiments illustrated that jointing and booting stage could be used as the N fertilizer periods to guarantee winter wheat yield, and integrating Nc curve and hyperspectral data had advantages in N fertilizer recommendations.
临界氮(Nc)浓度定义为植物最大生长所需的最小氮(N)浓度，可作为遥感数据与氮肥推荐用量之间的中间变量。本研究以拔节、孕穗期、开花期和灌浆期的地上生物量(AGB)数据为基础，建立了临界氮浓度稀释曲线。建立了归一化植被差异(NDVI)与氮营养指数(NNI)之间的定量相关性。最后，建立了氮肥效应函数与NDVI相结合的氮素推荐模型，并通过田间试验数据进行了验证。结果表明，在繁殖生长期，冬小麦氮素浓度逐渐降低，植株氮素浓度可描述为$N _{ mathm {c}} =$ 6.27*AGB-0.54, R2值为0.80。拔节期、孕穗期、开花期和灌浆期NDVI阈值分别为0.87、0.91、0.91和0.81，当NNI值低于相应阈值的1%时，推荐施氮量分别为2.63、10.00、11.11和10.00 kg N/hm2。田间试验结果表明，拔节期和孕穗期可作为冬小麦产量的氮肥施用期，且综合Nc曲线和高光谱数据在氮肥推荐方面具有优势。
{"title":"Recommendations for Nitrogen Fertilizer in Winter wheat Based on Nitrogen Nutrition Index","authors":"Yu Zhao, Zhenhai Li, Jianwen Wang, Wude Yang, Dandan Duan, Xiaobin Xu","doi":"10.1109/Agro-Geoinformatics.2019.8820439","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820439","url":null,"abstract":"A critical nitrogen (Nc) concentration, defined as the minimum nitrogen (N) concentration required for maximum plant growth, could be used as an intermediate variable between remote sensing data and recommendation for N fertilizer. In this study, the critical N concentration dilution curve was established based on aboveground biomass (AGB) from data at jointing, booting, anthesis and filling stages. The quantitative correlations between normalized difference vegetation (NDVI) and nitrogen nutrition index (NNI) were established. Finally, an N recommendation model combined with the N fertilizer effect function and NDVI was established and verified by field test data. Results showed that the N concentration of winter wheat decreased gradually during the reproductive growth period, and The plant N concentration could be described by $N _{mathrm{c}} =$ 6.27*AGB-0.54, with the R2 value of 0.80. Moreover, the thresholds of NDVI were 0.87, 0.91, 0.91 and 0.81 at jointing, booting, anthesis and filling stages, respectively, and the amounts of recommending nitrogen fertilizer were 2.63, 10.00, 11.11 and 10.00 kg N/hm2 when NNI value lowed 1% of corresponding period’s thresholds. Field experiments illustrated that jointing and booting stage could be used as the N fertilizer periods to guarantee winter wheat yield, and integrating Nc curve and hyperspectral data had advantages in N fertilizer recommendations.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129393064","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 : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820220
Jiayuan Lin, Xingxia Zhou, Shunjie Deng, Xiaolin Du, Meimei Wang, Xinjuan Li
The irrigation system of Dujiangyan takes charge of irrigating about 1 million hectare farmland in the Midwest of Sichuan Basin. It costs huge manpower and financial resources to periodically conduct manual inspection on the full range of irrigation canals, especially in mountainous area. With the advantages of low cost, flexible taking-off and landing, and hyperspatial image resolution, Unmanned Aerial Vehicles (UAVs) are very suitable for obtaining photographs along the irrigation canals. In this paper, two test sites of the Renmin Canal were chosen for UAV operations. The UAV system and its major components were introduced along with the planned flight routes, acquired UAV images, and photographing parameters. Aerial triangulation, generation of DSM and DOM, and textured 3D scenery were described followed by confidence-based edge detection and mean shift image segmentation on DOM. Then the ancillary buildings of the two test sites were identified and the current status of the irrigation canal was assessed on the resulting DOMs and DSMs. Results proved the feasibility and potential of applying the UAV system to rapidly inspecting unattended irrigation canals in mountainous area.
{"title":"Inspecting Unattended Irrigation Canals of Dujiangyan in Mountainous Area with UAV Remote Sensing Technology","authors":"Jiayuan Lin, Xingxia Zhou, Shunjie Deng, Xiaolin Du, Meimei Wang, Xinjuan Li","doi":"10.1109/Agro-Geoinformatics.2019.8820220","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820220","url":null,"abstract":"The irrigation system of Dujiangyan takes charge of irrigating about 1 million hectare farmland in the Midwest of Sichuan Basin. It costs huge manpower and financial resources to periodically conduct manual inspection on the full range of irrigation canals, especially in mountainous area. With the advantages of low cost, flexible taking-off and landing, and hyperspatial image resolution, Unmanned Aerial Vehicles (UAVs) are very suitable for obtaining photographs along the irrigation canals. In this paper, two test sites of the Renmin Canal were chosen for UAV operations. The UAV system and its major components were introduced along with the planned flight routes, acquired UAV images, and photographing parameters. Aerial triangulation, generation of DSM and DOM, and textured 3D scenery were described followed by confidence-based edge detection and mean shift image segmentation on DOM. Then the ancillary buildings of the two test sites were identified and the current status of the irrigation canal was assessed on the resulting DOMs and DSMs. Results proved the feasibility and potential of applying the UAV system to rapidly inspecting unattended irrigation canals in mountainous area.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123913871","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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