Fine Extraction of Plateau Wetlands Based on a Combination of Object-Oriented Machine Learning and Ecological Rules: A Case Study of Dianchi Basin

被引:1
|
作者
Cai, Fangliang [1 ,2 ]
Tang, Bo-Hui [1 ,2 ,3 ]
Sima, Ouyang [1 ,2 ]
Chen, Guokun [1 ,2 ]
Zhang, Zhen [1 ,2 ]
机构
[1] Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Peoples R China
[2] Yunnan Prov Dept Educ, Key Lab Plateau Remote Sensing, Kunming 650093, Peoples R China
[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
基金
中国国家自然科学基金;
关键词
Ecological rules; Gaofen-2; object-oriented classification; random forest; wetland extraction; RANDOM FOREST; ZOIGE PLATEAU; CLASSIFICATION; CHINA; IMAGE;
D O I
10.1109/JSTARS.2024.3356656
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Wetland ecosystems are essential to the preservation of biodiversity. Plateau wetland ecosystems are vital components of wetland ecosystems, characterized by diverse wetland types and fragmented land distribution. In the extraction of plateau wetlands, there are such issues as inaccuracy in classification, inadequately fine categories, and difficulty in extracting vegetated wetlands. The aim of this study was to establish a new classification framework for extracting detailed information about plateau wetlands, and the Dianchi Basin was used as the study area. Using Gaofen-2 (GF-2) imagery from 2019 to 2021, land cover information was extracted by applying nearest neighbor classification and random forest classification. Wetlands were then extracted from the land cover data using ecological rule classification, and a detailed wetland map of the Dianchi Basin was obtained in 2020 with a 1 m resolution. Results showed that the production accuracies of forest wetlands, shrub wetlands, meadow wetlands, rivers, ponds, reservoirs, and lakes in the Dianchi Basin were 89.4%, 87.9%, 91.4%, 90.7%, 89.9%, 92.9%, and 95.9%, respectively, and the user accuracies were 94.9%, 92.4%, 92.6%, 95.4%, 94.2%, 91.0%, and 99.4%, respectively. Compared to the CAS_Wetlands, this study categorized the five categories of plateau wetlands into seven types with greater specificity and increased the spatial resolution of wetland mapping from 30 to 1 m. This study can provide a new reference framework for wetland extraction and support the conservation of plateau wetland ecosystems.
引用
收藏
页码:5364 / 5377
页数:14
相关论文
共 50 条
  • [1] Intelligent case combination of creative design based on object-oriented cases and constraint rules
    Zhang, Ning
    Information, Management and Algorithms, Vol II, 2007, : 75 - 77
  • [2] An Extraction Method of Urban Ecological Types Based on Object-oriented Classification A Case Study on Wuhan City
    Wu, Mengmeng
    Jiao, Weili
    Wang, Wei
    Liu, Huichan
    Long, Tengfei
    PROCEEDINGS OF THE 2013 THE INTERNATIONAL CONFERENCE ON REMOTE SENSING, ENVIRONMENT AND TRANSPORTATION ENGINEERING (RSETE 2013), 2013, 31 : 918 - 921
  • [3] A learning-based module extraction method for object-oriented systems
    Erdemir, Ural
    Buzluca, Feza
    JOURNAL OF SYSTEMS AND SOFTWARE, 2014, 97 : 156 - 177
  • [4] OBJECT-ORIENTED MULTI-DOMAIN MODELLING OF MACHINE TOOLS: A CASE STUDY
    Heinzl, Bernhard
    Landsiedl, Michael
    Popper, Niki
    Dimitriou, Alexandros-Athanassios
    Duer, Fabian
    Bleicher, Friedrich
    Reinisch, Christian
    Breitenecker, Felix
    24TH EUROPEAN MODELING AND SIMULATION SYMPOSIUM (EMSS 2012), 2012, : 471 - 476
  • [5] OBJECT-ORIENTED OPEN PIT EXTRACTION BASED ON CONVOLUTIONAL NEURAL NETWORK, A CASE STUDY IN YUZHOU, CHINA
    Hu Naixun
    Chen Tao
    Niu Ruiqing
    Zhen Na
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 9435 - 9438
  • [6] Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm
    Tan, Qulin
    Guo, Bin
    Hu, Jun
    Dong, Xiaofeng
    Hu, Jiping
    PATTERN RECOGNITION LETTERS, 2021, 141 : 32 - 36
  • [7] Towards digital twins through object-oriented modelling: a machine tool case study
    Scaglioni, Bruno
    Ferretti, Gianni
    IFAC PAPERSONLINE, 2018, 51 (02): : 613 - 618
  • [8] An Object-oriented Extraction Method of Arable Land Information based on a Combination of Watershed and Multi-scale
    Li, Jingwen
    Lv, Nan
    Zhou, Song
    Li, Wenqing
    Guo, Weili
    INTERNATIONAL CONFERENCE ON INTELLIGENT EARTH OBSERVING AND APPLICATIONS 2015, 2015, 9808
  • [9] Object-oriented extraction method for loess sinkholes based on deep learning and integrated terrain features
    Su X.
    Huang X.
    Wang C.
    Wu F.
    Jiang L.
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2022, 38 (10): : 102 - 110
  • [10] Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning
    Lu, Heng
    Ma, Lei
    Fu, Xiao
    Liu, Chao
    Wang, Zhi
    Tang, Min
    Li, Naiwen
    REMOTE SENSING, 2020, 12 (05)