The use of high-resolution remote sensing for plague surveillance in Kazakhstan

被引:40
|
作者
Addink, E. A. [1 ]
De Jong, S. M. [1 ]
Davis, S. A. [2 ,3 ]
Dubyanskiy, V. [4 ]
Burdelov, L. A. [4 ]
Leirs, H. [5 ,6 ]
机构
[1] Univ Utrecht, Dept Phys Geog, NL-3508 TC Utrecht, Netherlands
[2] Yale Univ, Sch Med Epidemiol & Publ Hlth, New Haven, CT 06520 USA
[3] Univ Utrecht, Dept Vet Med, NL-3508 TD Utrecht, Netherlands
[4] M Aikimbayevs Kazakh Sci Ctr Quarantine & Zoonot, Anti Plague Inst, Alma Ata 050074, Kazakhstan
[5] Univ Antwerp, Dept Biol, B-2020 Antwerp, Belgium
[6] Univ Aarhus, Dept Integrated Pest Management, Danish Pest Infestat Lab, DK-2800 Lyngby, Denmark
关键词
Bubonic plague; Object-based image analysis; Quickbird; LAND-COVER; CLASSIFICATION; MOUNDS; SCALE;
D O I
10.1016/j.rse.2009.11.015
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Bubonic plague, caused by the bacteria Yersinia pestis, persists as a public health problem in many parts of the world, including central Kazakhstan. Bubonic plague occurs most often in humans through a flea bite, when a questing flea fails to find a rodent host. For many of the plague foci in Kazakhstan the great gerbil is the major host of plague, a social rodent well-adapted to desert environments. Intensive monitoring and prevention of plague in gerbils started in 1947, reducing the number of human cases and mortalities drastically. However, the monitoring is labour-intensive and hence expensive and is now under threat due to financial constraints. Previous research showed that the occupancy rate of the burrow systems of the great gerbil is a strong indicator for the probability of a plague outbreak. The burrow systems are around 30 m in diameter with a bare surface. This paper aims to demonstrate the automatic classification of burrow systems in satellite images using object-oriented analysis. We performed field campaigns in September 2007 and May and September 2008 and acquired corresponding QuickBird images of the first two periods. User's and producer's accuracy values of the classification reached 60 and 86%, respectively, providing proof of concept that automatic mapping of burrow systems using high-resolution satellite images is possible. Such maps, by better defining great gerbil foci, locating new or expanding foci and measuring the density of great gerbil burrow systems could play a major role in a renewed monitoring system by better directing surveillance and control efforts. Furthermore, if similar analyses can separate occupied burrow systems from empty ones, then very-high-resolution images stand to play a crucial role in plague surveillance throughout central Asia. (C) 2009 Elsevier Inc. All rights reserved.
引用
下载
收藏
页码:674 / 681
页数:8
相关论文
共 50 条
  • [21] HIGH-RESOLUTION REMOTE SENSING IMAGE SCENE UNDERSTANDING: A REVIEW
    Zhu, Qiqi
    Sun, Xiongli
    Zhong, Yanfei
    Zhang, Liangpei
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 3061 - 3064
  • [22] Learning to Semantically Segment High-Resolution Remote Sensing Images
    Nogueira, Keiller
    Dalla Mura, Mauro
    Chanussot, Jocelyn
    Schwartz, William Robson
    dos Santos, Jefersson A.
    2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2016, : 3566 - 3571
  • [23] Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review
    Neyns, Robbe
    Canters, Frank
    REMOTE SENSING, 2022, 14 (04)
  • [24] High-resolution remote sensing mapping of global land water
    LIAO AnPing
    CHEN LiJun
    CHEN Jun
    HE ChaoYing
    CAO Xin
    CHEN Jin
    PENG Shu
    SUN FangDi
    GONG Peng
    Science China Earth Sciences, 2014, 57 (10) : 2305 - 2316
  • [25] Alignment and Parallelism for the Description of High-Resolution Remote Sensing Images
    Vanegas, Maria Carolina
    Bloch, Isabelle
    Inglada, Jordi
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (06): : 3542 - 3557
  • [26] Optimisation of global grids for high-resolution remote sensing data
    Bauer-Marschallinger, Bernhard, 1600, Elsevier Ltd (72):
  • [27] Interactive Multiscale Classification of High-Resolution Remote Sensing Images
    dos Santos, Jefersson Alex
    Gosselin, Philippe-Henri
    Philipp-Foliguet, Sylvie
    Torres, Ricardo da S.
    Falcao, Alexandre Xavier
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2013, 6 (04) : 2020 - 2034
  • [28] Accuracy assessment of coastal zone remote sensing survey based on high-resolution remote sensing image
    Zhang, Huaguo
    Huang, Weigen
    EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING-GIS APPLICATIONS, 2010, 7831
  • [29] Multi-resolution classification network for high-resolution UAV remote sensing images
    Cong, Ming
    Xi, Jiangbo
    Han, Ling
    Gu, Junkai
    Yang, Ligong
    Tao, Yiting
    Xu, Miaozhong
    GEOCARTO INTERNATIONAL, 2022, 37 (11) : 3116 - 3140
  • [30] Identification of shelterbelt width from high-resolution remote sensing imagery
    Deng, Rongxin
    Yang, Gao
    Li, Ying
    Xu, Zhengran
    Zhang, Xing
    Zhang, Lu
    Li, Chunjing
    AGROFORESTRY SYSTEMS, 2022, 96 (08) : 1091 - 1101