Detecting plague-host abundance from space: Using a spectral vegetation index to identify occupancy of great gerbil burrows

Wilschut, Liesbeth I, Heesterbeek, Johan AP, Begon, Mike ORCID: 0000-0003-1715-5327, de Jong, Steven M, Ageyev, Vladimir, Laudisoit, Anne and Addink, Elisabeth A
(2018) Detecting plague-host abundance from space: Using a spectral vegetation index to identify occupancy of great gerbil burrows. International Journal of Applied Earth Observation and Geoinformation, 64. pp. 249-255.

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In Kazakhstan, plague outbreaks occur when its main host, the great gerbil, exceeds an abundance threshold. These live in family groups in burrows, which can be mapped using remote sensing. Occupancy (percentage of burrows occupied) is a good proxy for abundance and hence the possibility of an outbreak. Here we use time series of satellite images to estimate occupancy remotely. In April and September 2013, 872 burrows were identified in the field as either occupied or empty. For satellite images acquired between April and August, 'burrow objects' were identified and matched to the field burrows. The burrow objects were represented by 25 different polygon types, then classified (using a majority vote from 10 Random Forests) as occupied or empty, using Normalized Difference Vegetation Indices (NDVI) calculated for all images. Throughout the season NDVI values were higher for empty than for occupied burrows. Occupancy status of individual burrows that were continuously occupied or empty, was classified with producer's and user's accuracy values of 63 and 64% for the optimum polygon. Occupancy level was predicted very well and differed 2% from the observed occupancy. This establishes firmly the principle that occupancy can be estimated using satellite images with the potential to predict plague outbreaks over extensive areas with much greater ease and accuracy than previously.

Item Type: Article
Uncontrolled Keywords: Plague, Yersinia pestis, Infectious disease, Great gerbil, Population abundance, NDVI, Object-based image analysis, Segmentation, Random Forest
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User: Symplectic Admin
Date Deposited: 19 May 2021 08:56
Last Modified: 18 Jan 2023 22:46
DOI: 10.1016/j.jag.2017.09.013
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