INVESTIGATING WATERFOWL HABITAT-USE PATTERNS WITH MULTI-SOURCE REMOTE SENSING DATA

被引:0
|
作者
Zheng, Ruobing [1 ,2 ]
Luo, Ze [2 ]
Yan, Baoping [2 ]
机构
[1] Univ Chinese Acad Sci, Beijing, Peoples R China
[2] Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China
来源
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年
关键词
remote sensing; habitat use; Bar-headed Geese; H5N1;
D O I
暂无
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
Waterfowl habitat analysis is significant to understand species behavior and make conservation plans, especially for Bar headed Geese, which was involved in the large-scale outbreak of highly pathogenic avian influenza H5N1 in the year 2005 in China. Many studies have demonstrated there is a significant correlation between wildlife habitat and remote sensing data. The various reflectance data contain substantial ecological information that is valuable to model the habitat selection of wildlife. In this paper, we investigate the habitat use patterns of Bar-headed Geese by combining multi-source satellite images with bird GPS records, using Log-likelihood chi-square test to explore the waterfowl habitat preferences. The results show the bird's favorites are significant in various habitatcategories, which confirm previous surveys. This work helps to manage species and make disease control strategies for this sensitive waterfowl.
引用
收藏
页码:9264 / 9267
页数:4
相关论文
共 50 条
  • [21] Drought Monitoring of Spring Maize in the Songnen Plain Using Multi-Source Remote Sensing Data
    Pei, Zhifang
    Fan, Yulong
    Wu, Bin
    ATMOSPHERE, 2023, 14 (11)
  • [22] Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data
    Wei, Wei
    Wang, Jiping
    Ma, Libang
    Wang, Xufeng
    Xie, Binbin
    Zhou, Junju
    Zhang, Haoyan
    LAND, 2024, 13 (01)
  • [23] Carbonate Rocks Lithological Discrimination Using Multi-source Remote Sensing Data in Southwestern China
    Mo Yuanfu
    Xi Xiaoshuang
    ITESS: 2008 PROCEEDINGS OF INFORMATION TECHNOLOGY AND ENVIRONMENTAL SYSTEM SCIENCES, PT 2, 2008, : 623 - 629
  • [24] Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data
    Jiang, Hou
    Yao, Ling
    Lu, Ning
    Qin, Jun
    Liu, Tang
    Liu, Yujun
    Zhou, Chenghu
    ENERGY AND AI, 2022, 10
  • [25] Extraction of marsh wetland in Heilongjiang Basin based on GEE and multi-source remote sensing data
    Ning X.
    Chang W.
    Wang H.
    Zhang H.
    Zhu Q.
    National Remote Sensing Bulletin, 2022, 26 (02): : 386 - 396
  • [26] Multi-Scale PIIFD for Registration of Multi-Source Remote Sensing Images
    Gao C.
    Li W.
    Journal of Beijing Institute of Technology (English Edition), 2021, 30 (02): : 113 - 124
  • [27] IQPC 2015 TRACK: WATER DETECTION AND CLASSIFICATION ON MULTI-SOURCE REMOTE SENSING AND TERRAIN DATA
    Olasz, A.
    Kristof, D.
    Belenyesi, M.
    Bakos, K.
    Kovacs, Z.
    Balazs, B.
    Szabo, Sz.
    ISPRS GEOSPATIAL WEEK 2015, 2015, 40-3 (W3): : 583 - 588
  • [28] Floating on groundwater: Insight of multi-source remote sensing for Qaidam basin
    Liu, Xiangmei
    Chen, Jiaqi
    Zhang, Qiwen
    Zhang, Xi
    Wei, Ersa
    Wang, Nuoya
    Wang, Qingwei
    Wang, Jiahan
    Chen, Jiansheng
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 365
  • [29] Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing Data
    Yogita Rahulsing Chavan
    Brinthakumari Swamikan
    Megha Varun Gupta
    Sunil Bobade
    Anu Malhan
    Remote Sensing in Earth Systems Sciences, 2024, 7 (4) : 426 - 442
  • [30] Bathymetry-guided multi-source remote sensing image domain adaptive coral reef benthic habitat classification
    Chen, Hui
    Cheng, Liang
    Zhang, Ka
    GISCIENCE & REMOTE SENSING, 2025, 62 (01)