Pub Date : 2019-07-16DOI: 10.1109/Agro-Geoinformatics.2019.8820236
Chen Zhang, L. Di, Li Lin, Liying Guo
It is still a challenge to generate the timely crop cover map at large geographic area due to the lack of reliable ground truths at early growing season. This paper introduces an efficient method to extract “trusted pixels” from the historical Cropland Data Layer (CDL) data using crop rotation patterns, which can be used to replace the actual ground truth in the crop mapping and other agricultural applications. A case study in the Nebraska state of USA is demonstrated. The common crop rotation patterns of four major crop types, corn, soybeans, winter wheat, and alfalfa, are compared and analyzed. The experiment results show a considerable number of pixels in CDL following the certain crop sequence during the past decade. Each observed crop type has at least one reliable crop rotation pattern. Based on the reliable crop rotation patterns, a great proportion of pixels can be correctly mapped a year ahead of the release of current-year CDL product. These trusted pixels can be potentially used to label training samples for crop type classification at early growing season.
{"title":"Extracting Trusted Pixels from Historical Cropland Data Layer Using Crop Rotation Patterns: A Case Study in Nebraska, USA","authors":"Chen Zhang, L. Di, Li Lin, Liying Guo","doi":"10.1109/Agro-Geoinformatics.2019.8820236","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820236","url":null,"abstract":"It is still a challenge to generate the timely crop cover map at large geographic area due to the lack of reliable ground truths at early growing season. This paper introduces an efficient method to extract “trusted pixels” from the historical Cropland Data Layer (CDL) data using crop rotation patterns, which can be used to replace the actual ground truth in the crop mapping and other agricultural applications. A case study in the Nebraska state of USA is demonstrated. The common crop rotation patterns of four major crop types, corn, soybeans, winter wheat, and alfalfa, are compared and analyzed. The experiment results show a considerable number of pixels in CDL following the certain crop sequence during the past decade. Each observed crop type has at least one reliable crop rotation pattern. Based on the reliable crop rotation patterns, a great proportion of pixels can be correctly mapped a year ahead of the release of current-year CDL product. These trusted pixels can be potentially used to label training samples for crop type classification at early growing season.","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":"115406245","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.2019.8820524
Yan Wang, Hongshuo Wang, Weizhong Yang, Yuan Li
Based on MODIS sensor-based vegetation index (MODI 3A3) and surface temperature(MODllA2) product and Henan summer maize yield data, comparing the fitting results of Ordinary Least Square (OLS) and the Geographically Weighted Regression model (GWR), Studied the spatial heterogeneity of drought monitoring index affect on summer maize yield during summer maize growth in Henan Province. The results showed that in Henan Province, the impact of drought on summer maize yield was significantly spatially heterogeneous, and the drought reflected by VCI had a greater impact on summer maize yield than TCI. On the whole, there is a trend of weakening from north to south, and human activities such as fertilization and irrigation will reduce the impact of drought on summer maize yield.
{"title":"Study on Summer Maize Yield Responses to Remote Sensing Drought Indices in Henan Province with GWR Model","authors":"Yan Wang, Hongshuo Wang, Weizhong Yang, Yuan Li","doi":"10.1109/Agro-Geoinformatics.2019.8820524","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820524","url":null,"abstract":"Based on MODIS sensor-based vegetation index (MODI 3A3) and surface temperature(MODllA2) product and Henan summer maize yield data, comparing the fitting results of Ordinary Least Square (OLS) and the Geographically Weighted Regression model (GWR), Studied the spatial heterogeneity of drought monitoring index affect on summer maize yield during summer maize growth in Henan Province. The results showed that in Henan Province, the impact of drought on summer maize yield was significantly spatially heterogeneous, and the drought reflected by VCI had a greater impact on summer maize yield than TCI. On the whole, there is a trend of weakening from north to south, and human activities such as fertilization and irrigation will reduce the impact of drought on summer maize yield.","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":"122639686","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.2019.8820238
J. Shan, Zhiming Wang, Ling Sun, Lin Qiu, Kun Yu, Jingjing Wang
Three GF-1 WFV images on March 16, 2014, April 9, 2014, and April 30, 2014 were selected to extract the planting area of winter wheat in Jianhu county of Jiangsu province. Vegetation indexes were extracted from the original spectrum data in order to extract winter wheat area with Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Classification and Regression Trees (CART). The extraction accuracy of wheat was verified through on-site GPS measurement of 5 ground samples area with the scale of 1km $times$ 1km. The extraction accuracy of winter wheat area with SVM reached 84.138% on April 9 was the highest among three phases image. It indicated that the image on 9 April (booting stage) was the most suitable temporal for wheat identification. The GF-1 satellite image can be used for monitoring the cultivated area of wheat and it has higher accuracy and broad application prospects in the field of agriculture remote sensing monitoring.
{"title":"Study on Extraction Methods of Winter Wheat Area Based on GF-1 Satellite Images","authors":"J. Shan, Zhiming Wang, Ling Sun, Lin Qiu, Kun Yu, Jingjing Wang","doi":"10.1109/Agro-Geoinformatics.2019.8820238","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820238","url":null,"abstract":"Three GF-1 WFV images on March 16, 2014, April 9, 2014, and April 30, 2014 were selected to extract the planting area of winter wheat in Jianhu county of Jiangsu province. Vegetation indexes were extracted from the original spectrum data in order to extract winter wheat area with Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Classification and Regression Trees (CART). The extraction accuracy of wheat was verified through on-site GPS measurement of 5 ground samples area with the scale of 1km $times$ 1km. The extraction accuracy of winter wheat area with SVM reached 84.138% on April 9 was the highest among three phases image. It indicated that the image on 9 April (booting stage) was the most suitable temporal for wheat identification. The GF-1 satellite image can be used for monitoring the cultivated area of wheat and it has higher accuracy and broad application prospects in the field of agriculture remote sensing monitoring.","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":"121754976","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.2019.8820692
Lizhen Lu, Chuan Wang, Xiao Yin
Super-pixel methods cluster spatially connected similar pixels into perceptually meaningful regions, which are generally used as basic units instead of the original pixels in pre-processing and segmentation of high spatial resolution images for the object-oriented image classification. Among a number of super-pixel methods, the simple linear iterative clustering (SLIC) has been widely applied due to its simplicity, efficiency, and ability to adhere to image boundaries. SLIC itself, however, was originally designed to group black-white or three-color common images rather than multi-spectral/ hyperspectral remote sensing ones into super-pixels. In order to better apply SLIC to segmenting remote sensing images at high spatial resolution, the SLIC algorithm was modified by incorporating grey-level co-occurrence matrix texture with color features and expanding measure approach for weighted distance of texture and color similarity and spatial proximity between super-pixel center and neighboring pixels. Gaofen-2 panchromatic, multispectral and fused images were used to valid the modified SLIC (MSLIC) algorithm. Both completeness (CPS) and correctness (CRS) were used to quantitatively evaluate both MSLIC and SLIC algorithms. Visually interpreting approach was also applied to compare the segmentation and classification maps from the two algorithms. The experimental results indicate MSLIC achieves higher CPS and CRS than SLIC.
{"title":"Incorporating Texture into SLIC Super-pixels Method for High Spatial Resolution Remote Sensing Image Segmentation","authors":"Lizhen Lu, Chuan Wang, Xiao Yin","doi":"10.1109/Agro-Geoinformatics.2019.8820692","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820692","url":null,"abstract":"Super-pixel methods cluster spatially connected similar pixels into perceptually meaningful regions, which are generally used as basic units instead of the original pixels in pre-processing and segmentation of high spatial resolution images for the object-oriented image classification. Among a number of super-pixel methods, the simple linear iterative clustering (SLIC) has been widely applied due to its simplicity, efficiency, and ability to adhere to image boundaries. SLIC itself, however, was originally designed to group black-white or three-color common images rather than multi-spectral/ hyperspectral remote sensing ones into super-pixels. In order to better apply SLIC to segmenting remote sensing images at high spatial resolution, the SLIC algorithm was modified by incorporating grey-level co-occurrence matrix texture with color features and expanding measure approach for weighted distance of texture and color similarity and spatial proximity between super-pixel center and neighboring pixels. Gaofen-2 panchromatic, multispectral and fused images were used to valid the modified SLIC (MSLIC) algorithm. Both completeness (CPS) and correctness (CRS) were used to quantitatively evaluate both MSLIC and SLIC algorithms. Visually interpreting approach was also applied to compare the segmentation and classification maps from the two algorithms. The experimental results indicate MSLIC achieves higher CPS and CRS than SLIC.","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":"134074242","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.2019.8820698
Mustafa Ustuner, F. B. Sanli, S. Abdikan, G. Bilgin, C. Goksel
This study evaluates the impacts of three booster types (two tree-based and one linear model) in extreme gradient boosting (XGBoost) for crop classification using multi-temporal PolSAR (Polarimetric Synthetic Aperture Radar) images. Ensemble learning algorithms have received great attention in remote sensing for classification due to their greater performance compared to single classifiers in terms of accuracy. Extreme gradient boosting is the regularized extension of traditional boosting techniques and could overcome the overfitting constrain of gradient boosting (a.k.a gradient boosting machine). Three types of booster which are linear booster, tree booster and DART (Dropouts meet Multiple Additive Regression Trees) booster were tested on XGBoost for crop classification. From the multi-temporal PolSAR data, two types of polarimetric dataset (linear backscatter coefficients and Cloude–Pottier decomposed parameters) were extracted and incorporated into the classification step. The impacts of polarimetric features for crop classification were also analyzed in detailed besides exploring the boosting types of XGBoost. Our experimental results demonstrated that tree booster and DART booster were found to be superior compared the linear booster in terms of overall classification accuracy for both polarimetric dataset. The highest classification accuracy (87.97%) was achieved by tree booster with linear backscatter coefficients. Furthermore, linear backscatter coefficients achieved higher performance with respect to Cloude–Pottier decomposition in terms of classification accuracy.
{"title":"A Booster Analysis of Extreme Gradient Boosting for Crop Classification using PolSAR Imagery","authors":"Mustafa Ustuner, F. B. Sanli, S. Abdikan, G. Bilgin, C. Goksel","doi":"10.1109/Agro-Geoinformatics.2019.8820698","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820698","url":null,"abstract":"This study evaluates the impacts of three booster types (two tree-based and one linear model) in extreme gradient boosting (XGBoost) for crop classification using multi-temporal PolSAR (Polarimetric Synthetic Aperture Radar) images. Ensemble learning algorithms have received great attention in remote sensing for classification due to their greater performance compared to single classifiers in terms of accuracy. Extreme gradient boosting is the regularized extension of traditional boosting techniques and could overcome the overfitting constrain of gradient boosting (a.k.a gradient boosting machine). Three types of booster which are linear booster, tree booster and DART (Dropouts meet Multiple Additive Regression Trees) booster were tested on XGBoost for crop classification. From the multi-temporal PolSAR data, two types of polarimetric dataset (linear backscatter coefficients and Cloude–Pottier decomposed parameters) were extracted and incorporated into the classification step. The impacts of polarimetric features for crop classification were also analyzed in detailed besides exploring the boosting types of XGBoost. Our experimental results demonstrated that tree booster and DART booster were found to be superior compared the linear booster in terms of overall classification accuracy for both polarimetric dataset. The highest classification accuracy (87.97%) was achieved by tree booster with linear backscatter coefficients. Furthermore, linear backscatter coefficients achieved higher performance with respect to Cloude–Pottier decomposition in terms of classification accuracy.","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":"123926969","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.2019.8820437
Boyi Shangguan, P. Yue, Zhipeng Cao, Bo Wang
In the big data era, there are various geospatial data and processing algorithms that can be available and accessible on the Web. It is often necessary to access all these resources using standardized interfaces and services, and build an integrated platform with capabilities of geospatial big data management and processing. The work in this paper is an infrastructural design and software implementation towards a geospatial big data platform for geospatial information services. The infrastructure of the platform is designed with 4 subsystems: Geospatial Data Store, Geospatial Computing Store, Application Store and Management Center. Each of them is developed using latest Web and cloud computing technologies. Based on the infrastructural design and implementation software, several use cases running on the platform are presented to demonstrate the applicability and promise of the platform.
{"title":"Towards a Geospatial Big Data Platform for Geospatial Information Services","authors":"Boyi Shangguan, P. Yue, Zhipeng Cao, Bo Wang","doi":"10.1109/Agro-Geoinformatics.2019.8820437","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820437","url":null,"abstract":"In the big data era, there are various geospatial data and processing algorithms that can be available and accessible on the Web. It is often necessary to access all these resources using standardized interfaces and services, and build an integrated platform with capabilities of geospatial big data management and processing. The work in this paper is an infrastructural design and software implementation towards a geospatial big data platform for geospatial information services. The infrastructure of the platform is designed with 4 subsystems: Geospatial Data Store, Geospatial Computing Store, Application Store and Management Center. Each of them is developed using latest Web and cloud computing technologies. Based on the infrastructural design and implementation software, several use cases running on the platform are presented to demonstrate the applicability and promise of the platform.","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":"125659094","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.2019.8820649
Y. Kurucu, M. Esetlili, Gizem Çiçek, Özge Demirtaş
In order to manage the drought caused by changing climatic conditions, the reactions of the soils against the water scarcity must be known. In this research, drought-sensitive soil map was made in Izmir province by using soil properties which are effective in the resistance of soils to drought. These properties were examined in a GIS based model under 4 different headings as soil taxon, geomorphologic units, topography and physicochemical parameters of soils. The water holding capacity of soil taxon varies according to their genetic structure and horizon characteristics. The soil taxon was grouped according to their horizon depth, genetic origin of the parent materials, water holding capacity and they were graded in terms of resistance to drought. The topographic structure of the land was taken into consideration especially in terms of the degree and the shape of the slope such as concave-convex-linear straight etc. Geomorphological units are considered as another important parameter. Soil water budgets can also differ in terms of formation characteristics and location of each geomorphological unit. For example, lands have poor drainage condition, marshes, land around the lagoonary system, old lacustrine flat lands have a location close to surface and subsurface water. In addition, independent of the above mentioned parameters, the physical and chemical properties of soils such as texture, organic material and lime content effect soil water holding capacity. In this study, in order to determine the coexistence of the parameters effecting the soil water budget, a query model which is compatible with Analytic Hierarchy Process method has been formed in GIS. For this purpose, 22 soil great group and 9 soil parameters for each soil great groups were used as sub variable parameters. As a result of the research, a 4-graded drought-resist soil map was created as a base map for drought management projects.
{"title":"Building Drought-Resistant Soil Map by Using GIS","authors":"Y. Kurucu, M. Esetlili, Gizem Çiçek, Özge Demirtaş","doi":"10.1109/Agro-Geoinformatics.2019.8820649","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820649","url":null,"abstract":"In order to manage the drought caused by changing climatic conditions, the reactions of the soils against the water scarcity must be known. In this research, drought-sensitive soil map was made in Izmir province by using soil properties which are effective in the resistance of soils to drought. These properties were examined in a GIS based model under 4 different headings as soil taxon, geomorphologic units, topography and physicochemical parameters of soils. The water holding capacity of soil taxon varies according to their genetic structure and horizon characteristics. The soil taxon was grouped according to their horizon depth, genetic origin of the parent materials, water holding capacity and they were graded in terms of resistance to drought. The topographic structure of the land was taken into consideration especially in terms of the degree and the shape of the slope such as concave-convex-linear straight etc. Geomorphological units are considered as another important parameter. Soil water budgets can also differ in terms of formation characteristics and location of each geomorphological unit. For example, lands have poor drainage condition, marshes, land around the lagoonary system, old lacustrine flat lands have a location close to surface and subsurface water. In addition, independent of the above mentioned parameters, the physical and chemical properties of soils such as texture, organic material and lime content effect soil water holding capacity. In this study, in order to determine the coexistence of the parameters effecting the soil water budget, a query model which is compatible with Analytic Hierarchy Process method has been formed in GIS. For this purpose, 22 soil great group and 9 soil parameters for each soil great groups were used as sub variable parameters. As a result of the research, a 4-graded drought-resist soil map was created as a base map for drought management projects.","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":"128842429","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.2019.8820249
Yingpin Yang, Qiting Huang, Jiancheng Luo, Wei Wu, Yingwei Sun
Sugarcane crop, cultivated in subtropical and tropical regions, provides major sugar supply, and makes great contributions to human life and economic development. The sugarcane leaf area index (LAI) is highly related to the production. Our research aims at estimating sugarcane LAI through remote sensing observations. The physically-based radiative transfer model (RTM) inversion methods are widely applied in vegetation variable estimation. However, ill-posedness problem widely exists in the model inversion processes. Therefore, the study develops a spatial constraint method to regularize the RTM inversion, and LAI variable is estimated on object-level. The estimated object-level LAI variable is compared with the pixel-level, and validated using the SNAP biophysical processor. The results shows that the object-level LAI estimates show great performance.
{"title":"Improved sugarcane LAI estimation using radiative transfer models with spatial constraint","authors":"Yingpin Yang, Qiting Huang, Jiancheng Luo, Wei Wu, Yingwei Sun","doi":"10.1109/Agro-Geoinformatics.2019.8820249","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820249","url":null,"abstract":"Sugarcane crop, cultivated in subtropical and tropical regions, provides major sugar supply, and makes great contributions to human life and economic development. The sugarcane leaf area index (LAI) is highly related to the production. Our research aims at estimating sugarcane LAI through remote sensing observations. The physically-based radiative transfer model (RTM) inversion methods are widely applied in vegetation variable estimation. However, ill-posedness problem widely exists in the model inversion processes. Therefore, the study develops a spatial constraint method to regularize the RTM inversion, and LAI variable is estimated on object-level. The estimated object-level LAI variable is compared with the pixel-level, and validated using the SNAP biophysical processor. The results shows that the object-level LAI estimates show great performance.","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":"131115285","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.2019.8820547
Wen-Tong Zhu, Hongzhong Li, Jinsong Chen, Tinggang Zhou, Yu Han
Due to the very fragmentation of cultivated land in Guangdong Province, there are few studies on the cultivated land multiple cropping index (MCI), which can not meet the needs of agricultural production and policy. Therefore, this paper selects the 2015 paddy field data of Guangdong Province and uses the 16-day synthetic MODIS-EVI data from 2014 to 2016, Savitzky-Golay and Asymmetric Gaussian methods are used to reconstruct multi-temporal remote sensing data, and quadratic difference algorithm is used to extract the paddy field MCF. A comparative analysis of the two methods shows that the Asymmetric Gaussian function fitting is more suitable for a single season. The Savitzky-Golay filtering is more sensitive, and there are many pseudo-peaks in the fitted curve, resulting in a large extraction result compared with verification data. The twice Savitzky-Golay filtering further smooths the curve and removes a large number of false peaks, which is more suitable for the vegetation characteristics of paddy fields in Guangdong Province; The paddy field planting area is highly correlated with the rice planting area, but there are rice-peanut, rice-sweet potato and rice-sweet sugarcane planting patterns in the paddy field. In addition, the classification accuracy of paddy fields is one of the main influencing factors, so it is difficult to extract rice planting area accurately; During 2014-2016, the paddy field in Guangdong Province is dominated by double cropping system. The area of single cropping system is increased and then decreased, and the area of the double cropping system is reduced and then increased. The fallow paddy fields are mainly distributed around the construction land, especially in the Guangdong-Hong Kong-Macao Greater Bay Area.
{"title":"Dynamic monitoring of multiple cropping index of paddy field based on MODIS-EVI data in Guangdong province","authors":"Wen-Tong Zhu, Hongzhong Li, Jinsong Chen, Tinggang Zhou, Yu Han","doi":"10.1109/Agro-Geoinformatics.2019.8820547","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820547","url":null,"abstract":"Due to the very fragmentation of cultivated land in Guangdong Province, there are few studies on the cultivated land multiple cropping index (MCI), which can not meet the needs of agricultural production and policy. Therefore, this paper selects the 2015 paddy field data of Guangdong Province and uses the 16-day synthetic MODIS-EVI data from 2014 to 2016, Savitzky-Golay and Asymmetric Gaussian methods are used to reconstruct multi-temporal remote sensing data, and quadratic difference algorithm is used to extract the paddy field MCF. A comparative analysis of the two methods shows that the Asymmetric Gaussian function fitting is more suitable for a single season. The Savitzky-Golay filtering is more sensitive, and there are many pseudo-peaks in the fitted curve, resulting in a large extraction result compared with verification data. The twice Savitzky-Golay filtering further smooths the curve and removes a large number of false peaks, which is more suitable for the vegetation characteristics of paddy fields in Guangdong Province; The paddy field planting area is highly correlated with the rice planting area, but there are rice-peanut, rice-sweet potato and rice-sweet sugarcane planting patterns in the paddy field. In addition, the classification accuracy of paddy fields is one of the main influencing factors, so it is difficult to extract rice planting area accurately; During 2014-2016, the paddy field in Guangdong Province is dominated by double cropping system. The area of single cropping system is increased and then decreased, and the area of the double cropping system is reduced and then increased. The fallow paddy fields are mainly distributed around the construction land, especially in the Guangdong-Hong Kong-Macao Greater Bay Area.","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":"123663936","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.2019.8820476
Fei Zhang, N. Cao
The application of Geographic Information System (GIS) in agriculture is a new and important research field in agricultural science. This paper first introduces the evolution of GIS technology. As the third generation language of geography, GIS is a comprehensive application system developed in the last 50 years. Internationally, GIS began to be used in agriculture in the 1970s. It has been applied in land resource survey, land resource evaluation, and management analysis of agricultural resource information. After the 1990s, the application of GIS in the field of agriculture has been deepened and popularized. It is mainly used in the followings, such as the regional agricultural sustainable development research, land crop suitability evaluation, agricultural production information management, farmland soil erosion and protection research, land agricultural productive potential research, agricultural system imitation and simulation research, integrated of modern high-tech “precision agriculture” research and application, agro-ecosystem monitoring and quantitative research, investigation, planning and management of farmland and agricultural input-output benefits and environmental protection research, forest pest control, etc. In China, in the mid-1980s, GIS began to be applied to the agricultural field including land and resources decision management, agricultural resource information, regional agricultural planning, grain distribution management and food production assisted decision-making, agricultural production potential research, crop yield estimation research, regional agricultural sustainable development research, agricultural land suitability evaluation, agro-ecological environment monitoring, and research on precision agricultural information processing systems based on GPS and GIS, which have made great achievements. Some research results have been directly applied to agricultural production and great economic benefits have been obtained. This paper focuses on the specific application research of GIS technology on agricultural resource information management, agroclimatic zoning, agricultural disaster prevention, agro-ecological environment management, precision agriculture, crop yield estimation and monitoring, soil erosion, ecological sensitivity and non-point source pollution, etc. The research believes that the application of “3S” integration technology, the integration of integrated agricultural expert system (ES) combined with GIS, and the application of portable mobile GIS are the main trend of modern agriculture development of GIS technology under the background of today’s informatization and networking.
{"title":"Application and Research Progress of Geographic Information System (GIS) in Agriculture","authors":"Fei Zhang, N. Cao","doi":"10.1109/Agro-Geoinformatics.2019.8820476","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820476","url":null,"abstract":"The application of Geographic Information System (GIS) in agriculture is a new and important research field in agricultural science. This paper first introduces the evolution of GIS technology. As the third generation language of geography, GIS is a comprehensive application system developed in the last 50 years. Internationally, GIS began to be used in agriculture in the 1970s. It has been applied in land resource survey, land resource evaluation, and management analysis of agricultural resource information. After the 1990s, the application of GIS in the field of agriculture has been deepened and popularized. It is mainly used in the followings, such as the regional agricultural sustainable development research, land crop suitability evaluation, agricultural production information management, farmland soil erosion and protection research, land agricultural productive potential research, agricultural system imitation and simulation research, integrated of modern high-tech “precision agriculture” research and application, agro-ecosystem monitoring and quantitative research, investigation, planning and management of farmland and agricultural input-output benefits and environmental protection research, forest pest control, etc. In China, in the mid-1980s, GIS began to be applied to the agricultural field including land and resources decision management, agricultural resource information, regional agricultural planning, grain distribution management and food production assisted decision-making, agricultural production potential research, crop yield estimation research, regional agricultural sustainable development research, agricultural land suitability evaluation, agro-ecological environment monitoring, and research on precision agricultural information processing systems based on GPS and GIS, which have made great achievements. Some research results have been directly applied to agricultural production and great economic benefits have been obtained. This paper focuses on the specific application research of GIS technology on agricultural resource information management, agroclimatic zoning, agricultural disaster prevention, agro-ecological environment management, precision agriculture, crop yield estimation and monitoring, soil erosion, ecological sensitivity and non-point source pollution, etc. The research believes that the application of “3S” integration technology, the integration of integrated agricultural expert system (ES) combined with GIS, and the application of portable mobile GIS are the main trend of modern agriculture development of GIS technology under the background of today’s informatization and networking.","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":"130012651","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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