Mapping Pu’er tea plantations from GF-1 images using Object-Oriented Image Analysis (OOIA) and Support Vector Machine (SVM)

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 pla...

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Vydané v:PloS one Ročník 18; číslo 2; s. e0263969
Hlavní autori: Liang, Lei, Wang, Jinliang, Deng, Fei, Kong, Deyang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 07.02.2023
Public Library of Science (PLoS)
Predmet:
Accuracy
Algorithms
Biology and Life Sciences
China
Computer and Information Sciences
Decision making
Decision trees
Earth Sciences
Engineering and Technology
Image analysis
Image processing
Mapping
Medicine and Health Sciences
Neural networks
Physical Sciences
Plantations
Remote sensing
Research and Analysis Methods
Social Sciences
Source studies
Support Vector Machine
Support vector machines
Tea
Unmanned aerial vehicles
Vegetation
China
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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-Oriented Image Analysis (OOIA) with a Support Vector Machine (SVM) algorithm was utilized to map tea plantation areas. The overall accuracy and Kappa coefficient ofh the proposed method were 93.14% and 0.81, respectively, 3.61% and 0.05, 6.99% and 0.14, 6.44% and 0.16 better than the results of CART method, Maximum likelihood method and CNN based method. The tea plantation area increased by 4,095.36 acre from 2014 to 2017, while the fastest-growing period is 2015 to 2016.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0263969