Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images
Accurate, timely, and reliable farmland mapping is a prerequisite for agricultural management and environmental assessment in mountainous areas. However, in these areas, high spatial heterogeneity and diversified planting structures together generate various small farmland parcels with irregular sha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/22/3733 |
| _version_ | 1797547989217050624 |
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| author | Wei Liu Jian Wang Jiancheng Luo Zhifeng Wu Jingdong Chen Yanan Zhou Yingwei Sun Zhanfeng Shen Nan Xu Yingpin Yang |
| author_facet | Wei Liu Jian Wang Jiancheng Luo Zhifeng Wu Jingdong Chen Yanan Zhou Yingwei Sun Zhanfeng Shen Nan Xu Yingpin Yang |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | Accurate, timely, and reliable farmland mapping is a prerequisite for agricultural management and environmental assessment in mountainous areas. However, in these areas, high spatial heterogeneity and diversified planting structures together generate various small farmland parcels with irregular shapes that are difficult to accurately delineate. In addition, the absence of optical data caused by the cloudy and rainy climate impedes the use of time-series optical data to distinguish farmland from other land use types. Automatic delineation of farmland parcels in mountain areas is still a very difficult task. This paper proposes an innovative precise farmland parcel extraction approach supported by very high resolution(VHR) optical image and time series synthetic aperture radar(SAR) data. Firstly, Google satellite imagery with a spatial resolution of 0.55 m was used for delineating the boundaries of ground parcel objects in mountainous areas by a hierarchical extraction scheme. This scheme divides farmland into four types based on the morphological features presented in optical imagery, and designs different extraction models to produce each farmland type, respectively. The potential farmland parcel distribution map is then obtained by the layered recombination of these four farmland types. Subsequently, the time profile of each parcel in this map was constructed by five radar variables from the Sentinel-1A dataset, and the time-series classification method was used to distinguish farmland parcels from other types. An experiment was carried out in the north of Guiyang City, Guizhou Province, Southwest China. The result shows that, the producer’s accuracy of farmland parcels obtained by the hierarchical scheme is increased by 7.39% to 96.38% compared with that without this scheme, and the time-series classification method produces an accuracy of 80.83% to further obtain the final overall accuracy of 96.05% for the farmland parcel maps, showing a good performance. In addition, through visual inspection, this method has a better suppression effect on background noise in mountainous areas, and the extracted farmland parcels are closer to the actual distribution of the ground farmland. |
| first_indexed | 2024-03-10T14:52:58Z |
| format | Article |
| id | doaj.art-85acd3d5efa648999291feece6677e4e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:52:58Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-85acd3d5efa648999291feece6677e4e2023-11-20T20:51:19ZengMDPI AGRemote Sensing2072-42922020-11-011222373310.3390/rs12223733Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical ImagesWei Liu0Jian Wang1Jiancheng Luo2Zhifeng Wu3Jingdong Chen4Yanan Zhou5Yingwei Sun6Zhanfeng Shen7Nan Xu8Yingpin Yang9University of Chinese Academy of Sciences, Beijing 100049, ChinaAnt Group, Hangzhou 310013, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Geographical Sciences, Guangzhou University, Guangzhou 510006, ChinaAnt Group, Hangzhou 310013, ChinaSchool of Earth Science and Engineering, Hohai University, Nanjing 211100, ChinaInstitute of Agricultural Resources and Agricultural Regionalization, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Geographical Sciences, Guangzhou University, Guangzhou 510006, ChinaAccurate, timely, and reliable farmland mapping is a prerequisite for agricultural management and environmental assessment in mountainous areas. However, in these areas, high spatial heterogeneity and diversified planting structures together generate various small farmland parcels with irregular shapes that are difficult to accurately delineate. In addition, the absence of optical data caused by the cloudy and rainy climate impedes the use of time-series optical data to distinguish farmland from other land use types. Automatic delineation of farmland parcels in mountain areas is still a very difficult task. This paper proposes an innovative precise farmland parcel extraction approach supported by very high resolution(VHR) optical image and time series synthetic aperture radar(SAR) data. Firstly, Google satellite imagery with a spatial resolution of 0.55 m was used for delineating the boundaries of ground parcel objects in mountainous areas by a hierarchical extraction scheme. This scheme divides farmland into four types based on the morphological features presented in optical imagery, and designs different extraction models to produce each farmland type, respectively. The potential farmland parcel distribution map is then obtained by the layered recombination of these four farmland types. Subsequently, the time profile of each parcel in this map was constructed by five radar variables from the Sentinel-1A dataset, and the time-series classification method was used to distinguish farmland parcels from other types. An experiment was carried out in the north of Guiyang City, Guizhou Province, Southwest China. The result shows that, the producer’s accuracy of farmland parcels obtained by the hierarchical scheme is increased by 7.39% to 96.38% compared with that without this scheme, and the time-series classification method produces an accuracy of 80.83% to further obtain the final overall accuracy of 96.05% for the farmland parcel maps, showing a good performance. In addition, through visual inspection, this method has a better suppression effect on background noise in mountainous areas, and the extracted farmland parcels are closer to the actual distribution of the ground farmland.https://www.mdpi.com/2072-4292/12/22/3733mountainous areasprecise farmland parcelvery-high-resolution (VHR) optical imagetime-series SAR dataconvolutional Neural Networkslong and short-term memory |
| spellingShingle | Wei Liu Jian Wang Jiancheng Luo Zhifeng Wu Jingdong Chen Yanan Zhou Yingwei Sun Zhanfeng Shen Nan Xu Yingpin Yang Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images Remote Sensing mountainous areas precise farmland parcel very-high-resolution (VHR) optical image time-series SAR data convolutional Neural Networks long and short-term memory |
| title | Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images |
| title_full | Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images |
| title_fullStr | Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images |
| title_full_unstemmed | Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images |
| title_short | Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images |
| title_sort | farmland parcel mapping in mountain areas using time series sar data and vhr optical images |
| topic | mountainous areas precise farmland parcel very-high-resolution (VHR) optical image time-series SAR data convolutional Neural Networks long and short-term memory |
| url | https://www.mdpi.com/2072-4292/12/22/3733 |
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