Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning
Accurately estimating vegetation productivity in the forest areas is important for studying the terrestrial ecosystem and carbon cycles. Global LAnd Surface Satellite (GLASS) vegetation production datasets provide new long-term basic products of gross primary production (GPP) and net primary product...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9904574/ |
| _version_ | 1797996674842361856 |
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| author | Tao Yu Yong Pang Rui Sun Xiaodong Niu |
| author_facet | Tao Yu Yong Pang Rui Sun Xiaodong Niu |
| author_sort | Tao Yu |
| collection | DOAJ |
| description | Accurately estimating vegetation productivity in the forest areas is important for studying the terrestrial ecosystem and carbon cycles. Global LAnd Surface Satellite (GLASS) vegetation production datasets provide new long-term basic products of gross primary production (GPP) and net primary production (NPP) for monitoring the issues related with carbon exchange and carbon storage. But the coarse spatial resolution of the GLASS GPP/NPP products have limited their application in ecosystem service assessment in regional scales. In this paper, a spatial downscaling method based on GLASS vegetation production datasets and four typical deep learning methods (deep neural network, convolutional neural network, back propagation neural network and recurrent neural network) was proposed to generate high resolution GPP/NPP in the forest areas in the upper Luanhe River basin in the north of Hebei Province in China. Then the downscaled GPP/NPP were validated with ground measurement data and reference high resolution GPP/NPP data, and the accuracy of downscaled GPP/NPP from different deep learning methods was compared. Results of this paper indicated the applicability and feasibility of deep learning methods in downscaling GPP/NPP. Direct validation and cross validation demonstrated that downscaled GPP/NPP using convolutional neural network obtained the highest accuracy. |
| first_indexed | 2024-04-11T10:20:02Z |
| format | Article |
| id | doaj.art-f9491a3d0b8d44f39bcb28794c2c4fb9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T10:20:02Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f9491a3d0b8d44f39bcb28794c2c4fb92022-12-22T04:29:47ZengIEEEIEEE Access2169-35362022-01-011010444910446010.1109/ACCESS.2022.32102189904574Spatial Downscaling of Vegetation Productivity in the Forest From Deep LearningTao Yu0https://orcid.org/0000-0002-1589-7928Yong Pang1https://orcid.org/0000-0002-9760-6580Rui Sun2https://orcid.org/0000-0002-2070-3278Xiaodong Niu3Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, ChinaAccurately estimating vegetation productivity in the forest areas is important for studying the terrestrial ecosystem and carbon cycles. Global LAnd Surface Satellite (GLASS) vegetation production datasets provide new long-term basic products of gross primary production (GPP) and net primary production (NPP) for monitoring the issues related with carbon exchange and carbon storage. But the coarse spatial resolution of the GLASS GPP/NPP products have limited their application in ecosystem service assessment in regional scales. In this paper, a spatial downscaling method based on GLASS vegetation production datasets and four typical deep learning methods (deep neural network, convolutional neural network, back propagation neural network and recurrent neural network) was proposed to generate high resolution GPP/NPP in the forest areas in the upper Luanhe River basin in the north of Hebei Province in China. Then the downscaled GPP/NPP were validated with ground measurement data and reference high resolution GPP/NPP data, and the accuracy of downscaled GPP/NPP from different deep learning methods was compared. Results of this paper indicated the applicability and feasibility of deep learning methods in downscaling GPP/NPP. Direct validation and cross validation demonstrated that downscaled GPP/NPP using convolutional neural network obtained the highest accuracy.https://ieeexplore.ieee.org/document/9904574/Downscalingvegetation productivitydeep learningGLASSvalidation |
| spellingShingle | Tao Yu Yong Pang Rui Sun Xiaodong Niu Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning IEEE Access Downscaling vegetation productivity deep learning GLASS validation |
| title | Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning |
| title_full | Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning |
| title_fullStr | Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning |
| title_full_unstemmed | Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning |
| title_short | Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning |
| title_sort | spatial downscaling of vegetation productivity in the forest from deep learning |
| topic | Downscaling vegetation productivity deep learning GLASS validation |
| url | https://ieeexplore.ieee.org/document/9904574/ |
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