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Huang J, Chen J, Mu Y, Cao C, Shen H. Remote-sensing monitoring of colored dissolved organic matter in the Arctic Ocean. MARINE POLLUTION BULLETIN 2024; 204:116529. [PMID: 38824705 DOI: 10.1016/j.marpolbul.2024.116529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 05/27/2024] [Accepted: 05/27/2024] [Indexed: 06/04/2024]
Abstract
In the Arctic Ocean, variations in the colored dissolved organic matter (CDOM) have important value and significance. This study proposed and evaluated a novel method by combining the Google Earth Engine with a multilayer back-propagation neural network to retrieve CDOM concentration. This model performed well on the testing data and independent validation data (R2 = 0.76, RMSE = 0.37 m-1, MAPD = 35.43 %), and it was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images. The CDOM distribution in the Arctic Ocean and its main sea areas was first depicted during the ice-free period from 2002 to 2021, with average CDOM concentration in the range of 0.25 and 0.31 m-1. High CDOM concentration appeared in coastal areas affected by rivers on the Siberian side. The CDOM concentration was highly correlated with salinity (r = -0.92) and discharge (r > 0.68), while melting sea ice diluted seawater and CDOM concentration.
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Affiliation(s)
- Jue Huang
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
| | - Junjie Chen
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Yulei Mu
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
| | - Chang Cao
- College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Huagang Shen
- Qingdao Topscomm Communication Co., Ltd, Qingdao 266109, China
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Feng Q, Niu B, Ren Y, Su S, Wang J, Shi H, Yang J, Han M. A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020. Sci Data 2024; 11:198. [PMID: 38351164 PMCID: PMC10864270 DOI: 10.1038/s41597-024-02994-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/25/2024] [Indexed: 02/16/2024] Open
Abstract
We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated from publicly available Sentinel-2 imagery. Meanwhile, topographic features consisting of slope and aspect that are sensitive to PV locations are also included, aiming to construct a multi-dimensional and discriminative feature space. Finally, the trained random forest model is adopted to predict PV power stations of China parallelly on GEE. Technical validation has been carefully performed across China which achieved a satisfactory accuracy over 89%. Above all, as the first publicly released 10-m national-scale distribution dataset of China's ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.
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Affiliation(s)
- Quanlong Feng
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Bowen Niu
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Yan Ren
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Shuai Su
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jiudong Wang
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Hongda Shi
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jianyu Yang
- College of Land Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Mengyao Han
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
- Centre for Environment, Energy and Natural Resource Governance (C-EENRG), University of Cambridge, Cambridge, CB2 3QZ, United Kingdom.
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Kang X, Huang C, Chen JM, Lv X, Wang J, Zhong T, Wang H, Fan X, Ma Y, Yi X, Zhang Z, Zhang L, Tong Q. The 10-m cotton maps in Xinjiang, China during 2018-2021. Sci Data 2023; 10:688. [PMID: 37816768 PMCID: PMC10564865 DOI: 10.1038/s41597-023-02584-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/21/2023] [Indexed: 10/12/2023] Open
Abstract
Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km2 with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R2 = 0.84-0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.
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Affiliation(s)
- Xiaoyan Kang
- National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
| | - Changping Huang
- National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Jing M Chen
- Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
- School of Geographical Sciences, Fujian Normal University, Fuzhou, China
| | - Xin Lv
- Xinjiang Production and Construction Corps Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, 832003, China
| | - Jin Wang
- National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
| | - Tao Zhong
- National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Huihan Wang
- Xinjiang Production and Construction Corps Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, 832003, China
| | - Xianglong Fan
- Xinjiang Production and Construction Corps Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, 832003, China
| | - Yiru Ma
- Xinjiang Production and Construction Corps Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, 832003, China
| | - Xiang Yi
- Xinjiang Production and Construction Corps Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, 832003, China
| | - Ze Zhang
- Xinjiang Production and Construction Corps Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, 832003, China.
| | - Lifu Zhang
- National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
- Xinjiang Production and Construction Corps Oasis Eco-Agriculture Key Laboratory, College of Agriculture, Shihezi University, Shihezi, 832003, China.
| | - Qingxi Tong
- National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
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Papachristoforou A, Prodromou M, Hadjimitsis D, Christoforou M. Detecting and distinguishing between apicultural plants using UAV multispectral imaging. PeerJ 2023; 11:e15065. [PMID: 37077312 PMCID: PMC10108856 DOI: 10.7717/peerj.15065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 02/23/2023] [Indexed: 04/21/2023] Open
Abstract
Detecting and distinguishing apicultural plants are important elements of the evaluation and quantification of potential honey production worldwide. Today, remote sensing can provide accurate plant distribution maps using rapid and efficient techniques. In the present study, a five-band multispectral unmanned aerial vehicle (UAV) was used in an established beekeeping area on Lemnos Island, Greece, for the collection of high-resolution images from three areas where Thymus capitatus and Sarcopoterium spinosum are present. Orthophotos of UAV bands for each area were used in combination with vegetation indices in the Google Earth Engine (GEE) platform, to classify the area occupied by the two plant species. From the five classifiers (Random Forest, RF; Gradient Tree Boost, GTB; Classification and Regression Trees, CART; Mahalanobis Minimum Distance, MMD; Support Vector Machine, SVM) in GEE, the RF gave the highest overall accuracy with a Kappa coefficient reaching 93.6%, 98.3%, 94.7%, and coefficient of 0.90, 0.97, 0.92 respectively for each case study. The training method used in the present study detected and distinguish the two plants with great accuracy and results were confirmed using 70% of the total score to train the GEE and 30% to assess the method's accuracy. Based on this study, identification and mapping of Thymus capitatus areas is possible and could help in the promotion and protection of this valuable species which, on many Greek Islands, is the sole foraging plant of honeybees.
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Affiliation(s)
- Alexandros Papachristoforou
- Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Food Science and Nutrition, School of the Environment, University of the Aegean, Myrina, Greece
| | - Maria Prodromou
- Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus
- Department of Environment and Climate, Eratosthenes Center of Excelence, Limassol, Cyprus
| | - Diofantos Hadjimitsis
- Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus
- Department of Environment and Climate, Eratosthenes Center of Excelence, Limassol, Cyprus
| | - Michalakis Christoforou
- Department of Environment and Climate, Eratosthenes Center of Excelence, Limassol, Cyprus
- Department of Agricultural Science, Biotechnology and Food Science, Cyprus University of Technology, Limassol, Cyprus
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