Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820491
Yuanyuan Chen, Li Sun, Weidan Wang, Zhiyuan Pei
Drought is a major hazard that affects many different fields around the world. Among the various adverse effects of drought, its influence on agriculture is most direct and significant. The mapping and monitoring of drought have received serious attention from not only the policymakers, but also the scientific community. Over the recent years, a variety of drought monitoring models derived from remote sensing data were developed based on the change characteristics of vegetation and soil caused by drought. Perpendicular drought index (PDI), which was developed on the basis of spatial characteristics of moisture distribution in near–red reflectance space, could generally reflect drought condition and was widely used. Texas State in America is usually affected by drought. This paper evaluated the drought occurred in the west of Texas, America in 2017 using PDI calculated with Sentinel 2 data. The precipitation data was collected from the national centers for environmental information website and international soil moisture network. The precipitation anomaly index was used to determine the accuracy of PDI. The result showed that, PDI had strong correlation with the precipitation anomaly index, with the correlation coefficient of -0.66.
{"title":"Application of Sentinel 2 data for drought monitoring in Texas, America","authors":"Yuanyuan Chen, Li Sun, Weidan Wang, Zhiyuan Pei","doi":"10.1109/Agro-Geoinformatics.2019.8820491","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820491","url":null,"abstract":"Drought is a major hazard that affects many different fields around the world. Among the various adverse effects of drought, its influence on agriculture is most direct and significant. The mapping and monitoring of drought have received serious attention from not only the policymakers, but also the scientific community. Over the recent years, a variety of drought monitoring models derived from remote sensing data were developed based on the change characteristics of vegetation and soil caused by drought. Perpendicular drought index (PDI), which was developed on the basis of spatial characteristics of moisture distribution in near–red reflectance space, could generally reflect drought condition and was widely used. Texas State in America is usually affected by drought. This paper evaluated the drought occurred in the west of Texas, America in 2017 using PDI calculated with Sentinel 2 data. The precipitation data was collected from the national centers for environmental information website and international soil moisture network. The precipitation anomaly index was used to determine the accuracy of PDI. The result showed that, PDI had strong correlation with the precipitation anomaly index, with the correlation coefficient of -0.66.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"30 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":"114870446","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.2019.8820717
Jianhong Liu
Cropping patterns are closely related to food production, cropland intensification, water resource management, greenhouse gas emission and regional climate alteration. Timely and accurate mapping of cropping patterns is urgently needed in many disciplines. However, the existing cropland-related datasets are informative at the global level, but lack regional-scale details about cropland utilizations. Thus, there is a need for better information on the area and distribution of cropping patterns at regional scales. In this study, we developed a phenology-based cropping pattern (PBCP) mapping method based on remote sensing vegetation index time series. The new method first extracted vegetation phenological metrics (start of season (SOS), end of season (EOS), growing season length (GSL) and growth amplitude (GA)) from the vegetation index time series. Then, it identified crop seasons by using the minimum crop GSL, the minimum crop GA and the maximum crop GSL, which were derived from the training samples. Finally, cropping patterns were classified based on a set of decision rules. The case study in Henan province of China showed that, the results indicated that: (1) compared with cropping index derived from the supervised classification of Landsat-5 TM images, the PBCP method provided cropping index with satisfactory accuracy of 85.3%. (2) Validation sample analysis indicated that the cropping pattern mapping accuracy was 84% for the PBCP method. Different to current cropping pattern mapping methods, the PBCP method considered crop planting information in three years in deciding the cropping pattern to map the dominant cropping patterns. It can provide new insights in agriculture related land use analysis.
{"title":"A Phenology-Based Cropping Pattern (PBCP) Mapping Method Based on Remotely Sensed Time-Series Vegetation Index Data","authors":"Jianhong Liu","doi":"10.1109/Agro-Geoinformatics.2019.8820717","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820717","url":null,"abstract":"Cropping patterns are closely related to food production, cropland intensification, water resource management, greenhouse gas emission and regional climate alteration. Timely and accurate mapping of cropping patterns is urgently needed in many disciplines. However, the existing cropland-related datasets are informative at the global level, but lack regional-scale details about cropland utilizations. Thus, there is a need for better information on the area and distribution of cropping patterns at regional scales. In this study, we developed a phenology-based cropping pattern (PBCP) mapping method based on remote sensing vegetation index time series. The new method first extracted vegetation phenological metrics (start of season (SOS), end of season (EOS), growing season length (GSL) and growth amplitude (GA)) from the vegetation index time series. Then, it identified crop seasons by using the minimum crop GSL, the minimum crop GA and the maximum crop GSL, which were derived from the training samples. Finally, cropping patterns were classified based on a set of decision rules. The case study in Henan province of China showed that, the results indicated that: (1) compared with cropping index derived from the supervised classification of Landsat-5 TM images, the PBCP method provided cropping index with satisfactory accuracy of 85.3%. (2) Validation sample analysis indicated that the cropping pattern mapping accuracy was 84% for the PBCP method. Different to current cropping pattern mapping methods, the PBCP method considered crop planting information in three years in deciding the cropping pattern to map the dominant cropping patterns. It can provide new insights in agriculture related land use analysis.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"39 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":"123949861","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.2019.8820248
Ting Huang, Liang Liang, Jiahui Wang, Di Geng, X. Luo, Lijuan Wang
The vegetation indices (VIs) have been widely used to invert the leaf area index (LAI), but in the process of inversion, the accuracy of inversion is often affected by various parameters. Based on the canopy spectral reflectance simulated by the PROSAIL model, 15 vegetation indices that are commonly used for LAI inversion and have a higher mean coefficient of determination (${R}^{2}$) with LAI are screened. By analyzing the sensitivity of vegetation index to bandwidth and the relationship between R2 and bandwidths, the influence of bandwidth on the accuracy of vegetation index inversion LAI is discussed. The results show that the accuracy of estimating LAI by vegetation indices is greatly affected by bandwidth. In addition, it is found that there is some optimal bandwidth for vegetation index.
{"title":"Influence of Vegetation Index on LAI Inversion Accuracy at Different Bandwidths","authors":"Ting Huang, Liang Liang, Jiahui Wang, Di Geng, X. Luo, Lijuan Wang","doi":"10.1109/Agro-Geoinformatics.2019.8820248","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820248","url":null,"abstract":"The vegetation indices (VIs) have been widely used to invert the leaf area index (LAI), but in the process of inversion, the accuracy of inversion is often affected by various parameters. Based on the canopy spectral reflectance simulated by the PROSAIL model, 15 vegetation indices that are commonly used for LAI inversion and have a higher mean coefficient of determination (${R}^{2}$) with LAI are screened. By analyzing the sensitivity of vegetation index to bandwidth and the relationship between R2 and bandwidths, the influence of bandwidth on the accuracy of vegetation index inversion LAI is discussed. The results show that the accuracy of estimating LAI by vegetation indices is greatly affected by bandwidth. In addition, it is found that there is some optimal bandwidth for vegetation index.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"3 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":"127391741","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.2019.8820217
S. Sawant, J. Mohite, Mariappan Sakkan, S. Pappula
Natural calamities triggered by erratic weather conditions like cyclone, earthquakes, hail storms, and flood incurs substantial loss to the infrastructure and crops of the region. Countries across the globe are prone to such natural calamities. In India, specifically coastal parts are vulnerable to tropical cyclones. In 2018 east coast districts of Tamil Nadu and Andhra Pradesh, India were affected by the three cyclones namely Titli (11 Oct. 2018), Gaja (16 Nov. 2018) and Pethai (17 Dec. 2018) causing severe damage to seasonal crops such as Rice, Coconut and Areca Nut plantations. Traditional survey-based methods of crop loss assessment are time-consuming and labor-intensive.This study addresses the problem of near-real-time qualitative crop loss assessment due to tropical Gaja cyclone using the temporal data from Sentinel 1 and 2 satellites. The crop damage assessment study has been undertaken for Gaja cyclone in the affected district of Thanjavur, Tamil Nadu, India. The major crops cultivated in the district are Kharif Rice (locally called as Samba and Late Samba) and Coconut plantations. The study addresses qualitative loss assessment in terms of crop area affected. As a first step, we used time series data of Sentinel1 (VV and VH backscatter) available between Aug.-Nov. 2018 to map the Kharif rice area. Also, cloud-free Sentinel 2 scenes available during Mar.-May. 2018 have been used to map the Coconut area. Field visits were conducted to collect the geo-tagged plot boundaries for the rice crop and coconut plantations. The data collected through field visits was used both for model training and crop loss assessment. Google maps satellite layer was used as a base map for identification of other non-crop classes (i.e., forest, water, settlement, etc.). The overall accuracy of crop area classification was 87.23% for rice and 92.22% for coconut.Further, to estimate the crop loss, crop layers along with the NDVI were considered. Two crop loss scenarios, namely minimum damage and maximum damage, were identified for both the crops. The mean NDVI composite before the event, i.e., 1-15 Nov. 2018 was considered as the base. In case of maximum loss scenario, short term NDVI composite available immediately after the event, i.e., 17-25 Nov. 2018 was selected. After the cyclone, long term NDVI composite of the mean (i.e., 17 Nov.13 Dec. 2018) was used to assess the minimum loss scenario. Using field observations, the crop loss was categorized as severe loss, medium loss, low loss, and no loss. Results showed that the coconut plantations in Pattukkottai, Peravurani, and Papanasam blocks of Tanjavur are affected by the cyclone. The significant rice crop loss has been observed in Thanjavur, Orattanadu, Pattukkottai blocks. We have found the remote sensing based crop loss observations are matching with the government reports based on field observations. The remote sensing observations with human participatory sensing (i.e., field observations) has the potentia
{"title":"Near Real Time Crop Loss Estimation using Remote Sensing Observations","authors":"S. Sawant, J. Mohite, Mariappan Sakkan, S. Pappula","doi":"10.1109/Agro-Geoinformatics.2019.8820217","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820217","url":null,"abstract":"Natural calamities triggered by erratic weather conditions like cyclone, earthquakes, hail storms, and flood incurs substantial loss to the infrastructure and crops of the region. Countries across the globe are prone to such natural calamities. In India, specifically coastal parts are vulnerable to tropical cyclones. In 2018 east coast districts of Tamil Nadu and Andhra Pradesh, India were affected by the three cyclones namely Titli (11 Oct. 2018), Gaja (16 Nov. 2018) and Pethai (17 Dec. 2018) causing severe damage to seasonal crops such as Rice, Coconut and Areca Nut plantations. Traditional survey-based methods of crop loss assessment are time-consuming and labor-intensive.This study addresses the problem of near-real-time qualitative crop loss assessment due to tropical Gaja cyclone using the temporal data from Sentinel 1 and 2 satellites. The crop damage assessment study has been undertaken for Gaja cyclone in the affected district of Thanjavur, Tamil Nadu, India. The major crops cultivated in the district are Kharif Rice (locally called as Samba and Late Samba) and Coconut plantations. The study addresses qualitative loss assessment in terms of crop area affected. As a first step, we used time series data of Sentinel1 (VV and VH backscatter) available between Aug.-Nov. 2018 to map the Kharif rice area. Also, cloud-free Sentinel 2 scenes available during Mar.-May. 2018 have been used to map the Coconut area. Field visits were conducted to collect the geo-tagged plot boundaries for the rice crop and coconut plantations. The data collected through field visits was used both for model training and crop loss assessment. Google maps satellite layer was used as a base map for identification of other non-crop classes (i.e., forest, water, settlement, etc.). The overall accuracy of crop area classification was 87.23% for rice and 92.22% for coconut.Further, to estimate the crop loss, crop layers along with the NDVI were considered. Two crop loss scenarios, namely minimum damage and maximum damage, were identified for both the crops. The mean NDVI composite before the event, i.e., 1-15 Nov. 2018 was considered as the base. In case of maximum loss scenario, short term NDVI composite available immediately after the event, i.e., 17-25 Nov. 2018 was selected. After the cyclone, long term NDVI composite of the mean (i.e., 17 Nov.13 Dec. 2018) was used to assess the minimum loss scenario. Using field observations, the crop loss was categorized as severe loss, medium loss, low loss, and no loss. Results showed that the coconut plantations in Pattukkottai, Peravurani, and Papanasam blocks of Tanjavur are affected by the cyclone. The significant rice crop loss has been observed in Thanjavur, Orattanadu, Pattukkottai blocks. We have found the remote sensing based crop loss observations are matching with the government reports based on field observations. The remote sensing observations with human participatory sensing (i.e., field observations) has the potentia","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"33 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":"130496078","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.2019.8820694
Ziheng Sun, L. Di, Hui Fang, Liying Guo, E. Yu, Junmei Tang, Haoteng Zhao, Juozas Gaigalas, Chen Zhang, Li Lin, Zhiqi Yu, Shaobo Zhong, Xiaoping Wang, Xicheng Tan, Lili Jiang, Zhongxin Chen, Zhanya Xu, Jie Sun
Cyberinfrastructure plays an important role in the collection, management, and dissemination of drought information in agricultural activities, especially when the activities involve a variety of facilities, data sources, and communities. The challenge of coordinating tremendous sources of data and systems becomes paramount. Some key questions require additional attention if analyzing agricultural drought in a large social-environmental context: preprocessing observation into analysis-ready format, integrate vegetation/soil observations across platforms, and assess potential risks on the crop yield and environment. Cyberinfrastructure capable of accepting data from either research and monitoring networks or professionals in agricultural activities, must be built to achieve these goals. The cyberinfrastructure design generally consists of four components: data source, standardized web service, application service, and client interface. This study introduces a cloud-based global agricultural drought monitoring and forecasting system (GADMFS) which provides scalable vegetation-based drought indicators derived from satellite-, and model-based vegetation condition datasets. The provided datasets include global historical drought severity data from the monitoring component. The system is a significant extension to current capabilities and datasets from global drought assessment and early warning. The experiment results show that GADMFS successfully captured the major drought events in history and reflected the high-resolution spatial distribution which can specifically assist agriculture stakeholders to make informative decisions and take proactive drought management actions.
{"title":"Advanced Cyberinfrastructure for Agricultural Drought Monitoring","authors":"Ziheng Sun, L. Di, Hui Fang, Liying Guo, E. Yu, Junmei Tang, Haoteng Zhao, Juozas Gaigalas, Chen Zhang, Li Lin, Zhiqi Yu, Shaobo Zhong, Xiaoping Wang, Xicheng Tan, Lili Jiang, Zhongxin Chen, Zhanya Xu, Jie Sun","doi":"10.1109/Agro-Geoinformatics.2019.8820694","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820694","url":null,"abstract":"Cyberinfrastructure plays an important role in the collection, management, and dissemination of drought information in agricultural activities, especially when the activities involve a variety of facilities, data sources, and communities. The challenge of coordinating tremendous sources of data and systems becomes paramount. Some key questions require additional attention if analyzing agricultural drought in a large social-environmental context: preprocessing observation into analysis-ready format, integrate vegetation/soil observations across platforms, and assess potential risks on the crop yield and environment. Cyberinfrastructure capable of accepting data from either research and monitoring networks or professionals in agricultural activities, must be built to achieve these goals. The cyberinfrastructure design generally consists of four components: data source, standardized web service, application service, and client interface. This study introduces a cloud-based global agricultural drought monitoring and forecasting system (GADMFS) which provides scalable vegetation-based drought indicators derived from satellite-, and model-based vegetation condition datasets. The provided datasets include global historical drought severity data from the monitoring component. The system is a significant extension to current capabilities and datasets from global drought assessment and early warning. The experiment results show that GADMFS successfully captured the major drought events in history and reflected the high-resolution spatial distribution which can specifically assist agriculture stakeholders to make informative decisions and take proactive drought management actions.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"109 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":"122331075","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.2019.8820668
Ge-ji Zhong, Di Wang, Qingbo Zhou
Timely and accurate estimation of crop area are critical for enhancing agriculture management and ensuring national food security. Spatial sampling can take advantage of both remote sensing and field sampling, it has been widely used in large-scale crop area estimation. A large number of existing studies used a single traditional sampling method for sampling extrapolation without considering the optimization of sampling method. they are limited by the traditional sampling method and not capable to estimate the spatial distribution of crops effectively. For this reason, this paper selected Dehui County in Jilin Province as research area, and constructed the sampling frame using GF-1 PMS image at 8-m spatial resolution to extract the maize and rice area and distribution as the overall prior knowledge. Three spatial sampling methods (spatial simple random method, spatial system method and spatial stratification method) were selected for sample selection according to the same sampling ratio, and established variogram models of maize and rice based on the sample, respectively. Kriging method was used to estimate the crop area in the unsampled unit and the error between estimated and actual crop area in all sampling units (selected and unselected) was evaluated by cross validation method, to select the best sampling method suitable for estimating the spatial distribution of crop area. The experimental results demonstrate that the nugget coefficient $C_{0} /left(C+C_{0}right)$ of maize and rice variogram models established by three spatial sampling methods was less than 20%, indicating that the two kinds of crop have strong spatial variability, which is mainly structural variation (caused by natural factors such as climate and soil). Therefore, Kriging method can be used to estimate the spatial distribution of crops. Under the 3 sampling methods, the overall variation trend of kriging interpolation of maize and rice is roughly the same, but the interpolation effect of spatial system method is more consistent with the real spatial distribution trend of crops. The cross-validation results of all sample units show that the error terms ME (0.0059), MSE (0.0337) and RMSSE (0.9891) of the sample interpolation results sampled from the spatial system method are all the best, and the results from spatial random method are the worst. Considering the spatial distribution trend and accuracy of estimation, spatial system method is optimal for estimating the spatial distribution of crops. This study can provide an effective reference for improving the estimation accuracy of crop area.
{"title":"Optimization Study of Crop Area Spatial Sampling Method Based on Kriging Interpolation Estimation","authors":"Ge-ji Zhong, Di Wang, Qingbo Zhou","doi":"10.1109/Agro-Geoinformatics.2019.8820668","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820668","url":null,"abstract":"Timely and accurate estimation of crop area are critical for enhancing agriculture management and ensuring national food security. Spatial sampling can take advantage of both remote sensing and field sampling, it has been widely used in large-scale crop area estimation. A large number of existing studies used a single traditional sampling method for sampling extrapolation without considering the optimization of sampling method. they are limited by the traditional sampling method and not capable to estimate the spatial distribution of crops effectively. For this reason, this paper selected Dehui County in Jilin Province as research area, and constructed the sampling frame using GF-1 PMS image at 8-m spatial resolution to extract the maize and rice area and distribution as the overall prior knowledge. Three spatial sampling methods (spatial simple random method, spatial system method and spatial stratification method) were selected for sample selection according to the same sampling ratio, and established variogram models of maize and rice based on the sample, respectively. Kriging method was used to estimate the crop area in the unsampled unit and the error between estimated and actual crop area in all sampling units (selected and unselected) was evaluated by cross validation method, to select the best sampling method suitable for estimating the spatial distribution of crop area. The experimental results demonstrate that the nugget coefficient $C_{0} /left(C+C_{0}right)$ of maize and rice variogram models established by three spatial sampling methods was less than 20%, indicating that the two kinds of crop have strong spatial variability, which is mainly structural variation (caused by natural factors such as climate and soil). Therefore, Kriging method can be used to estimate the spatial distribution of crops. Under the 3 sampling methods, the overall variation trend of kriging interpolation of maize and rice is roughly the same, but the interpolation effect of spatial system method is more consistent with the real spatial distribution trend of crops. The cross-validation results of all sample units show that the error terms ME (0.0059), MSE (0.0337) and RMSSE (0.9891) of the sample interpolation results sampled from the spatial system method are all the best, and the results from spatial random method are the worst. Considering the spatial distribution trend and accuracy of estimation, spatial system method is optimal for estimating the spatial distribution of crops. This study can provide an effective reference for improving the estimation accuracy of crop area.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"30 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":"133877864","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}
Biomass is an important indicator of crop population characteristics and growth monitoring. Rapid and accurate monitoring of crop biomass is important for precise management of farmland. The spectral indices of the combination of any two bands of 350~2500nm were obtained that have good correlation with biomass were screened out through correlation analysis. At the same time, they were as input variables of biomass estimation models. Above-biomass of potato estimation models were established with partial least squares regression (PLSR), multiple linear regression (MLR) and random forest (RF). The result showed the potato tuber formation period and the tuber growth period, the combination index using the PLSR method to construct the potato biomass estimation model is higher, the starch accumulation period and the mature period, the combination index using MLR method to construct the biomass estimation model is high, can be better to realize the potato biomass estimation
{"title":"Estimation of aboveground biomass of potato based on ground hyperspectral","authors":"Haojie Pei, Haikuan Feng, Changchun Li, Guijun Yang, Zhichao Wu, Mingxing Liu","doi":"10.1109/Agro-Geoinformatics.2019.8820542","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820542","url":null,"abstract":"Biomass is an important indicator of crop population characteristics and growth monitoring. Rapid and accurate monitoring of crop biomass is important for precise management of farmland. The spectral indices of the combination of any two bands of 350~2500nm were obtained that have good correlation with biomass were screened out through correlation analysis. At the same time, they were as input variables of biomass estimation models. Above-biomass of potato estimation models were established with partial least squares regression (PLSR), multiple linear regression (MLR) and random forest (RF). The result showed the potato tuber formation period and the tuber growth period, the combination index using the PLSR method to construct the potato biomass estimation model is higher, the starch accumulation period and the mature period, the combination index using MLR method to construct the biomass estimation model is high, can be better to realize the potato biomass estimation","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":"28 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":"114472168","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.2019.8820424
A. Carvalho, Niall O' Mahony, L. Krpalkova, S. Campbell, Joseph Walsh, P. Doody
This research investigates how advances in Internet of Things (IoT) and availability of internet connection would enable Edge Solutions to promote smart utilization of existing machines at the edge. The presented results are based on experiments performed in real scenarios using the proposed solution. Whereas scenarios were cloned from real environments it is important to have in mind that experiments were performed with low load in terms of data and small number of devices in terms of distribution. As result of extensive architecture investigation for an optimal edge solution and its possible correlation to industrial applications, this paper will provide evidences supporting the use of edge solutions in challenging conditions which arise at the edge, including smart factories and smart agriculture. The present work assumes that the reader has some exposition to Edge computing, Cloud computing and software development. The paper will present some important findings on this area, compare main architectural aspects and will provide a broad view of how edge solutions might be built for this particular scenario. Having discussed how the ideal architecture works and having provided an overview about how it may be applied to industrial plants, the final section of this paper addresses how artificial intelligence will fit into edge solutions, forming a new source of “smart capabilities” to existing environments.
{"title":"Farming on the edge: Architectural Goals","authors":"A. Carvalho, Niall O' Mahony, L. Krpalkova, S. Campbell, Joseph Walsh, P. Doody","doi":"10.1109/Agro-Geoinformatics.2019.8820424","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820424","url":null,"abstract":"This research investigates how advances in Internet of Things (IoT) and availability of internet connection would enable Edge Solutions to promote smart utilization of existing machines at the edge. The presented results are based on experiments performed in real scenarios using the proposed solution. Whereas scenarios were cloned from real environments it is important to have in mind that experiments were performed with low load in terms of data and small number of devices in terms of distribution. As result of extensive architecture investigation for an optimal edge solution and its possible correlation to industrial applications, this paper will provide evidences supporting the use of edge solutions in challenging conditions which arise at the edge, including smart factories and smart agriculture. The present work assumes that the reader has some exposition to Edge computing, Cloud computing and software development. The paper will present some important findings on this area, compare main architectural aspects and will provide a broad view of how edge solutions might be built for this particular scenario. Having discussed how the ideal architecture works and having provided an overview about how it may be applied to industrial plants, the final section of this paper addresses how artificial intelligence will fit into edge solutions, forming a new source of “smart capabilities” to existing environments.","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":"125593377","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}
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}
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