Tea is a characteristic cash crop native to China, mainly distributed in the south of the Yangtze River. Obtaining the planting area and spatial distribution of tea gardens is of great significance to improve the economic and ecological benefits of tea. In this paper, a method for extracting tea plantation area based on multi-source remote sensing satellite data is proposed. We collect the Landsat8 OLI′ Sentinel -2′ HJ-IA/B and GF-1 WFV data from 2017 to 2018, and then we do the pre-processing for all the remote sensing data, calculate the Normalized Difference Vegetation Index(NDVI) of the data, calculate the spectral characteristics of the data and obtain the Gabor textual characteristics after principal component analysis(PCA) of the data. In order to obtain the time-series data, all features of Sentinel-2′Y HJ-IA/B and GF-1 WFV data are relatively calibrated to Landsat8 OLI data, and finally the tea plantation area is extracted by support vector machine (SVM) classifier. We extract the area of tea garden of Huzhou City, Zhejiang Province, and the result is 235.68 km2 and the results were verified by precision. The results show that this method can obtain high precision for the extraction of tea garden area, which is of great significance for further production and application.
{"title":"Estimating Tea Plantation Area Based on Multi-source Satellite Data","authors":"Yanhong Huang, Shirui Li, Lingbo Yuang, Jiefeng Cheng, Wenjie Li, Yan Chen, Jingfeng Huang","doi":"10.1109/Agro-Geoinformatics.2019.8820716","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820716","url":null,"abstract":"Tea is a characteristic cash crop native to China, mainly distributed in the south of the Yangtze River. Obtaining the planting area and spatial distribution of tea gardens is of great significance to improve the economic and ecological benefits of tea. In this paper, a method for extracting tea plantation area based on multi-source remote sensing satellite data is proposed. We collect the Landsat8 OLI′ Sentinel -2′ HJ-IA/B and GF-1 WFV data from 2017 to 2018, and then we do the pre-processing for all the remote sensing data, calculate the Normalized Difference Vegetation Index(NDVI) of the data, calculate the spectral characteristics of the data and obtain the Gabor textual characteristics after principal component analysis(PCA) of the data. In order to obtain the time-series data, all features of Sentinel-2′Y HJ-IA/B and GF-1 WFV data are relatively calibrated to Landsat8 OLI data, and finally the tea plantation area is extracted by support vector machine (SVM) classifier. We extract the area of tea garden of Huzhou City, Zhejiang Province, and the result is 235.68 km2 and the results were verified by precision. The results show that this method can obtain high precision for the extraction of tea garden area, which is of great significance for further production and application.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121663698","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.2019.8820531
Di Geng, Liang Liang, Jiahui Wang, Ting Huang, Luo Xiang, Shuguo Wang
In order to explore the distribution and change of NPP at urban scale, and in view of the high spatial heterogeneity of cities, this paper improves the CASA model, estimates the NPP in the central urban area of Xuzhou in March 2018 based on MODIS and Landsat 8 remote sensing data, analyses the spatial distribution characteristics of NPP in the study area and compares the NPP estimates under different models. The results show that: 1) the NPP values of the eastern, southern parts of the study area are higher, while the NPP values of the western part of the central region are lower, and the NPP values of the outward parts of the central region tend to increase gradually; 2) without considering the construction land, the NPP values of cultivated land in the study area are the highest, followed by grassland, forest land and water body, and the NPP values of unused land are the lowest; 3) Compared with CASA model, the improved CASA model is better. It highlights the changes in the distribution of construction land, and reflects the impact of construction land on the results of NPP estimation at the urban scale. In addition, under this model, NPP estimation based on Landsat 8 remote sensing data is more advantageous in urban scale, and the estimation results are more accurate.
{"title":"Estimation of NPP in Xuzhou Based on Improved CASA Model and Remote Sensing Data","authors":"Di Geng, Liang Liang, Jiahui Wang, Ting Huang, Luo Xiang, Shuguo Wang","doi":"10.1109/Agro-Geoinformatics.2019.8820531","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820531","url":null,"abstract":"In order to explore the distribution and change of NPP at urban scale, and in view of the high spatial heterogeneity of cities, this paper improves the CASA model, estimates the NPP in the central urban area of Xuzhou in March 2018 based on MODIS and Landsat 8 remote sensing data, analyses the spatial distribution characteristics of NPP in the study area and compares the NPP estimates under different models. The results show that: 1) the NPP values of the eastern, southern parts of the study area are higher, while the NPP values of the western part of the central region are lower, and the NPP values of the outward parts of the central region tend to increase gradually; 2) without considering the construction land, the NPP values of cultivated land in the study area are the highest, followed by grassland, forest land and water body, and the NPP values of unused land are the lowest; 3) Compared with CASA model, the improved CASA model is better. It highlights the changes in the distribution of construction land, and reflects the impact of construction land on the results of NPP estimation at the urban scale. In addition, under this model, NPP estimation based on Landsat 8 remote sensing data is more advantageous in urban scale, and the estimation results are more accurate.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623027","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.2019.8820230
Wenjiao Shi, Yiting Liu
Climate change can affect the shifts of farming-pastoral ecotone (FPE) boundaries, but previous studies have not adequately detected the climate contributions to the FPE boundary shifts. In this study, we presented gravity center analysis, boundary shifts detected in the X- and Y-coordinate direction and the direction of transects along the boundary, and spatial analysis to detect climate contributions at a 1-km scale in different ecological functional regions from the 1970s to the 2000s.Climate and land use data were used in this study. The results showed that during the 1970s–1980s and 1990s–2000s, the northeastern and southeastern parts of the FPE in northern China had similar spatial patterns with more extensive boundary shifts. In the directions of X-, Y-coordinate and the transects along boundaries, different ecological functional regions had significant differences in climate contributions to FPE boundary shifts during the three periods. In addition, during most of the periods, the results in different directions had good agreement in most of the ecological functional regions. However, the values of contributions in the directions of transects in the X- and Y-coordinate directions (4–56%) were always larger than those in the direction of transects along boundaries (1–17%), which shows that the results in the transect directions are more reliable and stable. Thus, the method of detecting the shifts in the transect directions developed by this study is an alternative one for analyzing the climate contributions to boundary shifts. Further evidences for explanation of the driving forces of climate change were given by spatial analysis of the relationship between climate change and land use change in the context of the FPE boundary shifts in northern China. Our findings provide an improved understanding of the responses of boundary shifts in farming–pastoral ecotone of northern China to climate change, which will be important for addressing adaptation and mitigation measures to climate change and regional land use management.
{"title":"Quantitative analysis of the responses of boundary shifts in farming –pastoral ecotone of northern China to climate change","authors":"Wenjiao Shi, Yiting Liu","doi":"10.1109/Agro-Geoinformatics.2019.8820230","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820230","url":null,"abstract":"Climate change can affect the shifts of farming-pastoral ecotone (FPE) boundaries, but previous studies have not adequately detected the climate contributions to the FPE boundary shifts. In this study, we presented gravity center analysis, boundary shifts detected in the X- and Y-coordinate direction and the direction of transects along the boundary, and spatial analysis to detect climate contributions at a 1-km scale in different ecological functional regions from the 1970s to the 2000s.Climate and land use data were used in this study. The results showed that during the 1970s–1980s and 1990s–2000s, the northeastern and southeastern parts of the FPE in northern China had similar spatial patterns with more extensive boundary shifts. In the directions of X-, Y-coordinate and the transects along boundaries, different ecological functional regions had significant differences in climate contributions to FPE boundary shifts during the three periods. In addition, during most of the periods, the results in different directions had good agreement in most of the ecological functional regions. However, the values of contributions in the directions of transects in the X- and Y-coordinate directions (4–56%) were always larger than those in the direction of transects along boundaries (1–17%), which shows that the results in the transect directions are more reliable and stable. Thus, the method of detecting the shifts in the transect directions developed by this study is an alternative one for analyzing the climate contributions to boundary shifts. Further evidences for explanation of the driving forces of climate change were given by spatial analysis of the relationship between climate change and land use change in the context of the FPE boundary shifts in northern China. Our findings provide an improved understanding of the responses of boundary shifts in farming–pastoral ecotone of northern China to climate change, which will be important for addressing adaptation and mitigation measures to climate change and regional land use management.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122941887","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.2019.8820541
Lida Kouhalvandi, Ece Olcay Günes, S. Özoguz
In designing an artificial network, different parameters such as activation functions, hyper-parameters, etc. are considered. Dealing with large number of parameters and also the functions that are expensive for evalualtion are very hard tasks. In this case, it is logical to find methods that results in smaller number of evaluations and improvements in performance. There are various techniques for multiobjective Bayesian optimization in deep learning structure. S-metric selection efficient global optimization (SMS-EGO) and DIRECT are one of the many techniques for multiobjective Bayesian optimization. In this paper, SMS-EGO and DIRECT techniques are applied to deep learning model and the average number of evaluations of each objective including time and error are investigated. For training and validating the deep network, a number of images present various diseases in leaves are provided from Plant Village data set. The simulation results show that by using SMSEGO technique, performance is improved and average time per iteration is faster.
{"title":"Algorithms for Speeding-Up the Deep Neural Networks For Detecting Plant Disease","authors":"Lida Kouhalvandi, Ece Olcay Günes, S. Özoguz","doi":"10.1109/Agro-Geoinformatics.2019.8820541","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820541","url":null,"abstract":"In designing an artificial network, different parameters such as activation functions, hyper-parameters, etc. are considered. Dealing with large number of parameters and also the functions that are expensive for evalualtion are very hard tasks. In this case, it is logical to find methods that results in smaller number of evaluations and improvements in performance. There are various techniques for multiobjective Bayesian optimization in deep learning structure. S-metric selection efficient global optimization (SMS-EGO) and DIRECT are one of the many techniques for multiobjective Bayesian optimization. In this paper, SMS-EGO and DIRECT techniques are applied to deep learning model and the average number of evaluations of each objective including time and error are investigated. For training and validating the deep network, a number of images present various diseases in leaves are provided from Plant Village data set. The simulation results show that by using SMSEGO technique, performance is improved and average time per iteration is faster.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115524314","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.2019.8820526
Jiahui Sheng, Peng Rao, Hongliang Ma
Soil moisture (SM) is a key variable in the study of hydrology, the environment, meteorology, and other fields. One widely used approach to retrieve soil moisture data is based on satellite remote sensing technology. However, the spatiotemporally continuous soil moisture products retrieved from microwave remote sensing data do not meet the accuracy requirements of flood prediction and irrigation management, due to their coarse spatial resolution. China's Fengyun-3B (FY3B) microwave radiation imager (MWRI) soil moisture product is one of the relatively new passive microwave products. Remotely sensed soil moisture data retrieved by the MWRI onboard the FY3B satellite is currently provided at a 25 km grid resolution. In this study, in terms of the thermal inertia theory, the FY3B soil moisture products were downscaled from 25 km to 1 km based on the North American Land Data Assimilation System (NLDAS) grid (12.5 km). For different ranges of the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), the relationship of soil moisture and diurnal temperature change from the land surface model of NLDAS could be obtained. The 1 km soil moisture was then computed from this regression model using 1 km LST data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) (1 km), which was then bias-corrected using FY3B 25 km soil moisture data. The algorithm was applied to every FY3B pixel in the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15). The downscaling results were validated using the in-situ soil moisture from SMAPVEX15. The downscaling estimates better characterize the continuity of spatial and temporal aspects and are more consistent with the soil moisture data used for validation.
{"title":"Downscaling of FY3B Soil Moisture Based on Land Surface Temperature and Vegetation Data","authors":"Jiahui Sheng, Peng Rao, Hongliang Ma","doi":"10.1109/Agro-Geoinformatics.2019.8820526","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820526","url":null,"abstract":"Soil moisture (SM) is a key variable in the study of hydrology, the environment, meteorology, and other fields. One widely used approach to retrieve soil moisture data is based on satellite remote sensing technology. However, the spatiotemporally continuous soil moisture products retrieved from microwave remote sensing data do not meet the accuracy requirements of flood prediction and irrigation management, due to their coarse spatial resolution. China's Fengyun-3B (FY3B) microwave radiation imager (MWRI) soil moisture product is one of the relatively new passive microwave products. Remotely sensed soil moisture data retrieved by the MWRI onboard the FY3B satellite is currently provided at a 25 km grid resolution. In this study, in terms of the thermal inertia theory, the FY3B soil moisture products were downscaled from 25 km to 1 km based on the North American Land Data Assimilation System (NLDAS) grid (12.5 km). For different ranges of the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), the relationship of soil moisture and diurnal temperature change from the land surface model of NLDAS could be obtained. The 1 km soil moisture was then computed from this regression model using 1 km LST data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) (1 km), which was then bias-corrected using FY3B 25 km soil moisture data. The algorithm was applied to every FY3B pixel in the Soil Moisture Active Passive Validation Experiment 2015 (SMAPVEX15). The downscaling results were validated using the in-situ soil moisture from SMAPVEX15. The downscaling estimates better characterize the continuity of spatial and temporal aspects and are more consistent with the soil moisture data used for validation.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125670558","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.2019.8820658
A. Çilek, S. Berberoglu, C. Donmez
This study aims to evaluate CropSyst model for second crop maize in a Mediterranean Environment under different irrigation regimes including, 40% and 60% of water consumption within one-meter deep soil profile. We examined how soil influences maize yields through a process-based crop modelling called CropSyst, and climate variables observed in the Lower Seyhan Plain, Turkey. CropSyst is a process-based simulation model consist of four stages: i) database creation; ii) model calibration; iii) validation; and iv)model results to simulate the growth and development of potential maize crop. Calibration and validation procedures were implemented using climate, soil, management practices, and rotation data previously measured in the field. Daily climate data derived from 22 meteorological stations (including TARBIL Climate station), additionally, soil series, soil classification including soil profiles, profile depth, pH values, organic matter, salinity, texture, soil volume and total porosity have been transferred into GIS environment for modelling. In the world, a significant portion of the freshwater resources (72%) is used in agricultural irrigation. The rapid increase in world population and the need for more water use across sectors increase the importance of more efficient use of irrigation water. Thus, optimum strategies for management and planning of existing water resources in agriculture become a national and global strategic issue.
{"title":"Simulating Second Crop Maize Growth under Different Irrigation Regimes in Lower Seyhan Plain using CropSyst Model","authors":"A. Çilek, S. Berberoglu, C. Donmez","doi":"10.1109/Agro-Geoinformatics.2019.8820658","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820658","url":null,"abstract":"This study aims to evaluate CropSyst model for second crop maize in a Mediterranean Environment under different irrigation regimes including, 40% and 60% of water consumption within one-meter deep soil profile. We examined how soil influences maize yields through a process-based crop modelling called CropSyst, and climate variables observed in the Lower Seyhan Plain, Turkey. CropSyst is a process-based simulation model consist of four stages: i) database creation; ii) model calibration; iii) validation; and iv)model results to simulate the growth and development of potential maize crop. Calibration and validation procedures were implemented using climate, soil, management practices, and rotation data previously measured in the field. Daily climate data derived from 22 meteorological stations (including TARBIL Climate station), additionally, soil series, soil classification including soil profiles, profile depth, pH values, organic matter, salinity, texture, soil volume and total porosity have been transferred into GIS environment for modelling. In the world, a significant portion of the freshwater resources (72%) is used in agricultural irrigation. The rapid increase in world population and the need for more water use across sectors increase the importance of more efficient use of irrigation water. Thus, optimum strategies for management and planning of existing water resources in agriculture become a national and global strategic issue.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128232532","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.2019.8820623
Ömer Vanli, A. Sabuncu, Z. Avci
Accurate and timely information about crop acreage estimation is important for agricultural management. In Turkey, wheat production is very important, and it is widely planted in Anatolia and in Southeastern Turkey. In this study, four different classification types were evaluated for wheat determination. As a study area, the region of Islahiye and Nurdagi counties of Gaziantep, Turkey was chosen. As satellite data, a Landsat 8 OLI image acquired on April 10, 2017 was used. The ground-truth points that were collected in surveying, and additionally field information taken from farmer registration system provided by local administrations were used as references. The application was done by classification of the satellite image using four different methods (Maximum Likelihood, Support Vector Machine, Condition-Based and Nearest Neighbor). After the results were obtained, the wheat classes obtained were transformed to vector format to overlay on the satellite image for visual analysis. The area of wheat class obtained from each method was presented and compared. The results were also evaluated by comparing with the data taken from Turkish Statistical Institute. All of the methods provided results close to the Turkish Statistical Institute records. Even the results were not significantly different from each other, wheat area determined using Support Vector Machine classification was better than others. The accuracy assessments were performed by calculating the total accuracy and KAPPA/KIA coefficient. The accuracy assessment analysis showed that the three supervised methods were better than the unsupervised one. As a future study, evaluation of these four classification methods using a multi-temporal dataset is planned.
{"title":"Regional Classification of Winter Wheat Using Remote Sensing Data in Southeastern Turkey","authors":"Ömer Vanli, A. Sabuncu, Z. Avci","doi":"10.1109/Agro-Geoinformatics.2019.8820623","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820623","url":null,"abstract":"Accurate and timely information about crop acreage estimation is important for agricultural management. In Turkey, wheat production is very important, and it is widely planted in Anatolia and in Southeastern Turkey. In this study, four different classification types were evaluated for wheat determination. As a study area, the region of Islahiye and Nurdagi counties of Gaziantep, Turkey was chosen. As satellite data, a Landsat 8 OLI image acquired on April 10, 2017 was used. The ground-truth points that were collected in surveying, and additionally field information taken from farmer registration system provided by local administrations were used as references. The application was done by classification of the satellite image using four different methods (Maximum Likelihood, Support Vector Machine, Condition-Based and Nearest Neighbor). After the results were obtained, the wheat classes obtained were transformed to vector format to overlay on the satellite image for visual analysis. The area of wheat class obtained from each method was presented and compared. The results were also evaluated by comparing with the data taken from Turkish Statistical Institute. All of the methods provided results close to the Turkish Statistical Institute records. Even the results were not significantly different from each other, wheat area determined using Support Vector Machine classification was better than others. The accuracy assessments were performed by calculating the total accuracy and KAPPA/KIA coefficient. The accuracy assessment analysis showed that the three supervised methods were better than the unsupervised one. As a future study, evaluation of these four classification methods using a multi-temporal dataset is planned.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133811643","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.2019.8820504
Wen Dong, Yingwei Sun, Jiancheng Luo
The rapid development of industrialized agriculture has leads to the problems of soil pollution and water pollution. In order to solve these problems, precision agriculture (PA) has been applied to achieve precise management of agricultural water and fertilizer. In PA process, fine mapping of soil nutrient is an effective technology to acquire accurate water and fertilizer distribution information and make agricultural decision. A significant progress has been made in digital soil mapping (DSM) of soil nutrient content over the past 20 years. However, the accuracy of grid-based DSM cannot meet the practical application needs of PA. This paper proposed a fine DSM method of soil nutrient content using high resolution remote sensing images and multi-scale auxiliary data for PA application. Three key technologies were studied for the implementation of this method. The automatic extraction of fine mapping units was the basis of this method. We designed different automatic extraction methods based on high resolution remote sensing images for agricultural production units in plains and mountainous areas. The auxiliary variables in different scales were chosen and converted to construct fine-scale soil nutrient-environment relationship model. Finally, machine learning methods were used to map the spatial distribution of soil nutrients. We chose Zhongning County, Ningxia Province as the study area, which includes typical plain and mountainous agriculture. The proposed method and technologies were applied for typical soil nutrients mapping. A common grid-based spatial interpolation method was implemented with the same soil sample dataset to evaluate the effect of the proposed method. The result showed that this method could reduce the number of prediction units and effectively improve the prediction efficiency in both plain and mountainous areas for fine soil mapping and precision agriculture application. This study was an attempt to realize fine soil mapping based on PA application unit in different environments. The high-resolution remote sensing images provide basic data for the realization of this idea, and the conversion technology of multi-scale data provides better support for the spatial inference of fine soil attribute information. In the future, we will carry out experiments in larger areas to further improve the efficiency of application, and plan to expand this study to consider three-dimensional soil property prediction.
{"title":"Fine mapping of key soil nutrient content using high resolution remote sensing image to support precision agriculture in Northwest China","authors":"Wen Dong, Yingwei Sun, Jiancheng Luo","doi":"10.1109/Agro-Geoinformatics.2019.8820504","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820504","url":null,"abstract":"The rapid development of industrialized agriculture has leads to the problems of soil pollution and water pollution. In order to solve these problems, precision agriculture (PA) has been applied to achieve precise management of agricultural water and fertilizer. In PA process, fine mapping of soil nutrient is an effective technology to acquire accurate water and fertilizer distribution information and make agricultural decision. A significant progress has been made in digital soil mapping (DSM) of soil nutrient content over the past 20 years. However, the accuracy of grid-based DSM cannot meet the practical application needs of PA. This paper proposed a fine DSM method of soil nutrient content using high resolution remote sensing images and multi-scale auxiliary data for PA application. Three key technologies were studied for the implementation of this method. The automatic extraction of fine mapping units was the basis of this method. We designed different automatic extraction methods based on high resolution remote sensing images for agricultural production units in plains and mountainous areas. The auxiliary variables in different scales were chosen and converted to construct fine-scale soil nutrient-environment relationship model. Finally, machine learning methods were used to map the spatial distribution of soil nutrients. We chose Zhongning County, Ningxia Province as the study area, which includes typical plain and mountainous agriculture. The proposed method and technologies were applied for typical soil nutrients mapping. A common grid-based spatial interpolation method was implemented with the same soil sample dataset to evaluate the effect of the proposed method. The result showed that this method could reduce the number of prediction units and effectively improve the prediction efficiency in both plain and mountainous areas for fine soil mapping and precision agriculture application. This study was an attempt to realize fine soil mapping based on PA application unit in different environments. The high-resolution remote sensing images provide basic data for the realization of this idea, and the conversion technology of multi-scale data provides better support for the spatial inference of fine soil attribute information. In the future, we will carry out experiments in larger areas to further improve the efficiency of application, and plan to expand this study to consider three-dimensional soil property prediction.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130576383","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.2019.8820239
C. Leng, Shanzhen Yi, Wenhao Xie
Rainfall is not only an essential parameter in hydrology and in the research of water resources, but also an important consideration for the issue of flood control, disaster mitigation, runoff forecast, irrigation, etc. However, the conventional monitoring approaches of rainfall involve many disadvantages, such as limited observing range, high cost and only-one-point rainfall observation. Consequently, how to get the rainfall of any part of the valley attracts more and more attention. In this study, the main meteorological parameters which influencing the rainfall can be extracted from the MODIS satellite cloud imagery, and these meteorological parameters are combined with the actual observed rainfall data which is obtained from ground-based rainfall site correspondingly. The remote sensing retrieval model is established respectively based on the BP neural network and GA-BP neural network, and a better effect of error precision estimation is obtained. It’s also proved that the high resolution of MODIS cloud products can be used to estimate rainfall rate.
{"title":"Estimation of rainfall based on MODIS using neural networks","authors":"C. Leng, Shanzhen Yi, Wenhao Xie","doi":"10.1109/Agro-Geoinformatics.2019.8820239","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820239","url":null,"abstract":"Rainfall is not only an essential parameter in hydrology and in the research of water resources, but also an important consideration for the issue of flood control, disaster mitigation, runoff forecast, irrigation, etc. However, the conventional monitoring approaches of rainfall involve many disadvantages, such as limited observing range, high cost and only-one-point rainfall observation. Consequently, how to get the rainfall of any part of the valley attracts more and more attention. In this study, the main meteorological parameters which influencing the rainfall can be extracted from the MODIS satellite cloud imagery, and these meteorological parameters are combined with the actual observed rainfall data which is obtained from ground-based rainfall site correspondingly. The remote sensing retrieval model is established respectively based on the BP neural network and GA-BP neural network, and a better effect of error precision estimation is obtained. It’s also proved that the high resolution of MODIS cloud products can be used to estimate rainfall rate.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130459315","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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