Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia

被引:8
|
作者
Potic, Ivan [1 ]
Srdic, Zoran [1 ]
Vakanjac, Boris [1 ]
Bakrac, Sasa [1 ,2 ]
Dordevic, Dejan [1 ,2 ]
Bankovic, Radoje [1 ,2 ]
Jovanovic, Jasmina M. [3 ]
机构
[1] Mil Geog Inst Gen Stevan Boskovic, Belgrade 11000, Serbia
[2] Univ Def, Mil Acad, Belgrade 11000, Serbia
[3] Univ Belgrade, Fac Geog, Belgrade 11000, Serbia
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 14期
关键词
vegetation detection; remote sensing; !text type='Python']Python[!/text; machine learning; classification accuracy; Sentinel-2; GOOGLE EARTH ENGINE; GLOBAL VEGETATION; SENTINEL-2; BANDS; INDEX; CLASSIFICATION; COLOR; LEAF; LAI;
D O I
10.3390/app13148289
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Featured Application
引用
收藏
页数:24
相关论文
共 50 条
  • [31] Remote sensing and machine learning for tree detection and classification in forestry applications
    Mosin, Vasilii
    Aguilar, Roberto
    Platonov, Alexander
    Vasiliev, Albert
    Kedrov, Alexander
    Ivanov, Anton
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXV, 2019, 11155
  • [32] Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning
    Ogungbuyi, Michael Gbenga
    Guerschman, Juan P. P.
    Fischer, Andrew M. M.
    Crabbe, Richard Azu
    Mohammed, Caroline
    Scarth, Peter
    Tickle, Phil
    Whitehead, Jason
    Harrison, Matthew Tom
    LAND, 2023, 12 (06)
  • [33] Multisource Remote Sensing Data Visualization Using Machine Learning
    Plajer, Ioana Cristina
    Baicoianu, Alexandra
    Majercsik, Luciana
    Ivanovici, Mihai
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 12
  • [34] Improving forest age prediction performance using ensemble learning algorithms base on satellite remote sensing data
    Chen, Jinjin
    Du, Huaqiang
    Mao, Fangjie
    Huang, Zihao
    Chen, Chao
    Hu, Mengchen
    Li, Xuejian
    ECOLOGICAL INDICATORS, 2024, 166
  • [35] Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)
    Mansourmoghaddam, Mohammad
    Rousta, Iman
    Ghafarian Malamiri, Hamidreza
    Sadeghnejad, Mostafa
    Krzyszczak, Jaromir
    Ferreira, Carla Sofia Santos
    REMOTE SENSING, 2024, 16 (03)
  • [36] Retrieval of suspended sediment concentrations using remote sensing and machine learning methods: A case study of the lower Yellow River
    Hu, Jinlong
    Miao, Chiyuan
    Zhang, Xiangping
    Kong, Dongxian
    JOURNAL OF HYDROLOGY, 2023, 627
  • [37] Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam
    Nguyen, Huu Duy
    Nguyen, Quoc Huy
    Du, Quan Vu Viet
    Pham, Viet Thanh
    Pham, Le Tuan
    Hoang, Thanh Van
    Truong, Quang-Hai
    Bui, Quang-Thanh
    Petrisor, Alexandru-Ionut
    GEOLOGICAL JOURNAL, 2024, 59 (02) : 636 - 658
  • [38] Using Machine Learning and Aggregated Remote Sensing Data for Wildfire Occurrence Prediction and Feature Selection: A Case Study in California
    Gao, Timothy
    Wang, Lufan
    Gao, Xiang
    COMPUTING IN CIVIL ENGINEERING 2023-RESILIENCE, SAFETY, AND SUSTAINABILITY, 2024, : 52 - 59
  • [39] Drone remote sensing in urban forest management: A case study
    Wavrek, Mia T.
    Carr, Eric
    Jean-Philippe, Sharon
    McKinney, Michael L.
    URBAN FORESTRY & URBAN GREENING, 2023, 86
  • [40] Anomaly Detection in IIoT: A Case Study using Machine Learning
    Shah, Gauri
    Tiwari, Aashis
    PROCEEDINGS OF THE ACM INDIA JOINT INTERNATIONAL CONFERENCE ON DATA SCIENCE AND MANAGEMENT OF DATA (CODS-COMAD'18), 2018, : 295 - 300