High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale u...High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese ＂High Resolution Earth Observation Systems＂, China is able to receive superb high-resolution remotely sensed images （GF series） that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales. GF-1＇s high resolution （i.e., 2 m/8 m）, high revisit cycle （i.e., 4 days）, and its visible and near-infrared （VNIR） spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring. Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system （CHARMS）. However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.展开更多
In view of the large quantities of areas, complex landform and dynamic change of resources and environment in China, China has already funded abundantly a series of macro remote sensing investigation projects in land ...In view of the large quantities of areas, complex landform and dynamic change of resources and environment in China, China has already funded abundantly a series of macro remote sensing investigation projects in land use/ cover change(LUCC) since 1990. Supported by the achievements of such projects, Chinese resources, environmental and remote sensing database (CRERS) was created. In this paper, we standardized the LUCC dataset of CRERS at scale of 1km, which facilitated the study of spatial features of LUCC in China. The analysis on the spatial features of LUCC and their causes of formation in China are based on the CRERS supported by the technologies of Geographic Information System (GIS). The whole research was based on the grade index of land use, ecological environmental index and index of population density. Based on the correlation analysis, we found that the special features of LUCC were closely related with those of ecological environment and population density, which resulted from that areas with better ecological environment and high production potential of land were easy and convenient for human being to live, which, furthermore, led to the aggravation of excessive exploitation of land resources there.展开更多
The lotus(Nelumbo nucifera Gaertn.)is an aquatic plant that grows in shallow water and has long been cultivated in South China.It can improve the incomes of farmers and plays an important role in alleviating poverty i...The lotus(Nelumbo nucifera Gaertn.)is an aquatic plant that grows in shallow water and has long been cultivated in South China.It can improve the incomes of farmers and plays an important role in alleviating poverty in rural China.However,a modern method is required to accurately estimate the area of lotus fields.Lotus has spectral characteristics similar to those of rice,grassland,and shrubs.The features surrounding areas where it is grown are complex,small,and fragmented.Few studies have examined the remote sensing extraction of lotus fields,and automatic extraction and mapping are still challenging methods.Here,we compared the spectral characteristics of lotus fields and other ground objects and devised a remote sensing method for the rapid extraction of lotus fields.Using this method,the extraction accuracy of lotus was 96.3%.The Kappa coefficient was 0.926,which is higher than those of the unsupervised K-means classification,Mahalanobis distance,and support vector machine supervised classification,and demonstrates the potential of this method for extracting and mapping lotus fields by remote sensing.展开更多
Taking Dongting Lake district as the studying area and utilizing multi-temporal MOS-lb/MESSR data as remote sensing info source, by the combination operation and ratio transform processing and the image, spectrum and ...Taking Dongting Lake district as the studying area and utilizing multi-temporal MOS-lb/MESSR data as remote sensing info source, by the combination operation and ratio transform processing and the image, spectrum and histogram comparison of the MESSR image data of all bands for the flood season and dry season with the ER-DAS IMAGINE system, a classification model was established, which can be used to acquire the spatial distributing information of water bodies. Meanwhile a water depth index model was derived and built, and then a model for detecting the depth of water body based on the non-linear recursive analysis was presented. By the overlay analysis of the classification thematic images based on the model for extracting flood information, the flooding area and distributing information were acquired.展开更多
基金financed by the National Natural Science Foundation of China (41501111 and 41271112)the National Non-profit Institute Research Grant of Chinese Academy of Agricultural Sciences (CAAS) (IARRP-2015-10)
文摘High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese ＂High Resolution Earth Observation Systems＂, China is able to receive superb high-resolution remotely sensed images （GF series） that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales. GF-1＇s high resolution （i.e., 2 m/8 m）, high revisit cycle （i.e., 4 days）, and its visible and near-infrared （VNIR） spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring. Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system （CHARMS）. However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.
文摘In view of the large quantities of areas, complex landform and dynamic change of resources and environment in China, China has already funded abundantly a series of macro remote sensing investigation projects in land use/ cover change(LUCC) since 1990. Supported by the achievements of such projects, Chinese resources, environmental and remote sensing database (CRERS) was created. In this paper, we standardized the LUCC dataset of CRERS at scale of 1km, which facilitated the study of spatial features of LUCC in China. The analysis on the spatial features of LUCC and their causes of formation in China are based on the CRERS supported by the technologies of Geographic Information System (GIS). The whole research was based on the grade index of land use, ecological environmental index and index of population density. Based on the correlation analysis, we found that the special features of LUCC were closely related with those of ecological environment and population density, which resulted from that areas with better ecological environment and high production potential of land were easy and convenient for human being to live, which, furthermore, led to the aggravation of excessive exploitation of land resources there.
基金the National Natural Science Foundation of China under Grants 31660140 and 31560150the Jiangxi Province 13th Five-Year Social Science Planning Project under Grant 17YJ11the Humanities and Social Sciences Planning Project of Colleges and Universities in Jiangxi Province under Grant GL17113.
文摘The lotus(Nelumbo nucifera Gaertn.)is an aquatic plant that grows in shallow water and has long been cultivated in South China.It can improve the incomes of farmers and plays an important role in alleviating poverty in rural China.However,a modern method is required to accurately estimate the area of lotus fields.Lotus has spectral characteristics similar to those of rice,grassland,and shrubs.The features surrounding areas where it is grown are complex,small,and fragmented.Few studies have examined the remote sensing extraction of lotus fields,and automatic extraction and mapping are still challenging methods.Here,we compared the spectral characteristics of lotus fields and other ground objects and devised a remote sensing method for the rapid extraction of lotus fields.Using this method,the extraction accuracy of lotus was 96.3%.The Kappa coefficient was 0.926,which is higher than those of the unsupervised K-means classification,Mahalanobis distance,and support vector machine supervised classification,and demonstrates the potential of this method for extracting and mapping lotus fields by remote sensing.
文摘Taking Dongting Lake district as the studying area and utilizing multi-temporal MOS-lb/MESSR data as remote sensing info source, by the combination operation and ratio transform processing and the image, spectrum and histogram comparison of the MESSR image data of all bands for the flood season and dry season with the ER-DAS IMAGINE system, a classification model was established, which can be used to acquire the spatial distributing information of water bodies. Meanwhile a water depth index model was derived and built, and then a model for detecting the depth of water body based on the non-linear recursive analysis was presented. By the overlay analysis of the classification thematic images based on the model for extracting flood information, the flooding area and distributing information were acquired.
基金National Key Research and Development Program of China(2018YFC1508301,2018YFC1508302)National Natural Science Foundation of China(31871516)Hubei Natural Science Foundation(2019CFB507)。
