{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["18(2)"],"submitter":["Liang L"],"pubmed_abstract":["Tea is the most popular drink worldwide, and China is the largest producer of tea. Therefore, tea is an important commercial crop in China, playing a significant role in domestic and foreign markets. It is necessary to make accurate and timely maps of the distribution of tea plantation areas for plantation management and decision making. In the present study, we propose a novel mapping method to map tea plantation. The town of Menghai in the Xishuangbanna Dai Autonomous Prefecture, Yunnan Province, China, was chosen as the study area, andgg GF-1 remotely sensed data from 2014-2017 were chosen as the data source. Image texture, spectral and geometrical features were integrated, while feature space was built by SEparability and THresholds algorithms (SEaTH) with decorrelation. Object-Oriente"],"journal":["PloS one"],"pagination":["e0263969"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9904455"],"repository":["biostudies-literature"],"pubmed_title":["Mapping Pu'er tea plantations from GF-1 images using Object-Oriented Image Analysis (OOIA) and Support Vector Machine (SVM)."],"pmcid":["PMC9904455"],"pubmed_authors":["Liang L","Kong D","Deng F","Wang J"],"additional_accession":[]},"is_claimable":false,"name":"Mapping Pu'er tea plantations from GF-1 images using Object-Oriented Image Analysis (OOIA) and Support Vector Machine (SVM).","description":"Tea is the most popular drink worldwide, and China is the largest producer of tea. Therefore, tea is an important commercial crop in China, playing a significant role in domestic and foreign markets. It is necessary to make accurate and timely maps of the distribution of tea plantation areas for plantation management and decision making. In the present study, we propose a novel mapping method to map tea plantation. The town of Menghai in the Xishuangbanna Dai Autonomous Prefecture, Yunnan Province, China, was chosen as the study area, andgg GF-1 remotely sensed data from 2014-2017 were chosen as the data source. Image texture, spectral and geometrical features were integrated, while feature space was built by SEparability and THresholds algorithms (SEaTH) with decorrelation. Object-Oriente","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2025-04-19T13:33:01.346Z","creation":"2025-04-19T13:33:01.346Z"},"accession":"S-EPMC9904455","cross_references":{"pubmed":["36749740"],"doi":["10.1371/journal.pone.0263969"]}}