Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study

被引:0
|
作者
De Roeck, Els [1 ]
Van Coillie, Frieke [1 ]
De Wulf, Robert [1 ]
Soenen, Karen [2 ]
Charlier, Johannes [2 ]
Vercruysse, Jozef [2 ]
Hantson, Wouter [3 ]
Ducheyne, Els [3 ]
Hendrickx, Guy [3 ]
机构
[1] Univ Ghent, Fac Biosci Engn, Lab Forest Management & Spatial Informat Tech, B-9000 Ghent, Belgium
[2] Univ Ghent, Fac Vet Med, Lab Parasitol, Merelbeke, Belgium
[3] Avia GIS, Zoersel, Belgium
关键词
Fasciola hepatica; liver fluke; small water body mapping; object-based image analysis; Belgium; FASCIOLA-HEPATICA; LYMNAEA-TRUNCATULA; INFORMATION-SYSTEM; WETLAND INVENTORY; RISK; CLASSIFICATION; TRANSMISSION; CATTLE; MODEL; BORNE;
D O I
暂无
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.
引用
收藏
页码:S671 / S683
页数:13
相关论文
共 50 条
  • [21] Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery
    Whiteside, Timothy G.
    Bartolo, Renee E.
    REMOTE SENSING, 2015, 7 (09) : 11664 - 11694
  • [22] Vegetation species mapping in a coastal-dune ecosystem using high resolution satellite imagery
    Medina Machin, Anabella
    Marcello, Javier
    Hernandez-Cordero, Antonio I.
    Martin Abasolo, Javier
    Eugenio, Francisco
    GISCIENCE & REMOTE SENSING, 2019, 56 (02) : 210 - 232
  • [23] Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco
    Lin, Chenxi
    Jin, Zhenong
    Mulla, David
    Ghosh, Rahul
    Guan, Kaiyu
    Kumar, Vipin
    Cai, Yaping
    REMOTE SENSING, 2021, 13 (09)
  • [24] Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches
    Agarwal, Shivani
    Vailshery, Lionel Sujay
    Jaganmohan, Madhumitha
    Nagendra, Harini
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2013, 2 (01) : 220 - 236
  • [25] Ecosystem classification using artificial intelligence neural networks and very high spatial resolution satellite imagery
    Keramitsoglou, I
    Sarimveis, H
    Kiranoudis, CT
    Sifakis, N
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY V, 2004, 5232 : 228 - 236
  • [26] Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning
    Wei, Zhihao
    Jia, Kebin
    Jia, Xiaowei
    Liu, Pengyu
    Ma, Ying
    Chen, Ting
    Feng, Guilian
    REMOTE SENSING, 2022, 14 (02)
  • [27] Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data
    Beaumont, Benjamin
    Grippa, Tais
    Lennert, Moritz
    Vanhuysse, Sabine
    Stephenne, Nathalie
    Wolff, Eleonore
    JOURNAL OF APPLIED REMOTE SENSING, 2017, 11
  • [28] Gaussian Multiple Instance Learning Approach for Mapping the Slums of the World Using Very High Resolution Imagery
    Vatsavai, Ranga Raju
    19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), 2013, : 1419 - 1426
  • [29] Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR
    Luck-Vogel, M.
    Mbolambi, C.
    Rautenbach, K.
    Adams, J.
    van Niekerk, L.
    SOUTH AFRICAN JOURNAL OF BOTANY, 2016, 107 : 188 - 199
  • [30] Seagrass mapping using high resolution multispectral satellite imagery: A comparison of water column correction models
    Mederos-Barrera, A.
    Marcello, J.
    Eugenio, F.
    Hernandez, E.
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2022, 113