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

Addink, E.A.; Jong, S.M. de; Davis, Stephen; Dubyanskiy, V. M.; Burdelov, L. A.; Leirs, Herwig

doi:10.1016/j.rse.2009.11.015

Cited by 51 publications

(46 citation statements)

References 22 publications

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“…Previous work has demonstrated that environmental conditions monitored by satellite imagery, including vegetation cover, landscape structure, soil moisture and water bodies [36][37][38], could be a potential information source for studying the ecology of zoonotic diseases, such as vector-borne and rodent-borne diseases [16,39,40], because the environmental conditions characterized by remote sensing images are considered to be linked to reservoir host habitats or pathogen infection risks.…”

Section: Discussionmentioning

confidence: 99%

Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China

et al. 2017

Remote Sensing

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Striped field mice (Apodemus agrarius) are the main host for the Hantaan virus (HTNV), the cause of hemorrhagic fever with renal syndrome (HFRS) in central China. It has been shown that host population density is associated with pathogen dynamics and disease risk. Thus, a higher population density of A. agrarius in an area might indicate a higher risk for an HFRS outbreak. Here, we surveyed the A. agrarius population density between 2005 and 2012 on the Weihe Plain, Shaanxi Province, China, and used this monitoring data to examine the relationships between the dynamics of A. agrarius populations and environmental conditions of crop-land, represented by remote sensing based indicators. These included the normalized difference vegetation index, leaf area index, fraction of photosynthetically active radiation absorbed by vegetation, net photosynthesis (PsnNet), gross primary productivity, and land surface temperature. Structural equation modeling (SEM) was applied to detect the possible causal relationship between PsnNet, A. agrarius population density and HFRS risk. The results showed that A. agrarius was the most frequently captured species with a capture rate of 0.9 individuals per hundred trap-nights, during 96 months of trapping in the study area. The risk of HFRS was highly associated with the abundance of A. agrarius, with a 1-5-month lag. The breeding season of A. agrarius was also found to coincide with agricultural activity and seasons with high PsnNet. The SEM indicated that PsnNet had an indirect positive effect Remote Sens. 2017, 9, 1076; doi:10.3390/rs9101076www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 1076 2 of 13 on HFRS incidence via rodents. In conclusion, the remote sensing-based environmental indicator, PsnNet, was highly correlated with HTNV reservoir population dynamics with a 3-month lag (r = 0.46, p < 0.01), and may serve as a predictor of potential HFRS outbreaks.

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Section: Discussionmentioning

confidence: 99%

Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China

et al. 2017

Remote Sensing

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“…With the development of remote sensing technology, the monitoring of rodent disaster has entered a new stage. Scholars at home and abroad used satellite images such as Landsat-8 (Li, PX., 2016), Quick Bird (Addink E.A., 2010) and SPOT-5 (Wilschut L.I., 2013) to monitor rat damage, but not as drones could obtain higher spatial resolution images. As a new type of remote sensing method, low altitude remote sensing of drones has been widely used in landscape ecology and ecology field in recent years because of its high space, time resolution, flexible operation, and low cost (Zhang ZM., 2017).…”

Section: Introductionmentioning

confidence: 99%

The Analysis of Burrows Recognition Accuracy in Xinjiang's Pasture Area Based on Uav Visible Images With Different Spatial Resolution

Sun

Zheng

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The rodent disaster is one of the main biological disasters in grassland in northern Xinjiang. The eating and digging behaviors will cause the destruction of ground vegetation, which seriously affected the development of animal husbandry and grassland ecological security. UAV low altitude remote sensing, as an emerging technique with high spatial resolution, can effectively recognize the burrows. However, how to select the appropriate spatial resolution to monitor the calamity of the rodent disaster is the first problem we need to pay attention to. The purpose of this study is to explore the optimal spatial scale on identification of the burrows by evaluating the impact of different spatial resolution for the burrows identification accuracy. In this study, we shoot burrows from different flight heights to obtain visible images of different spatial resolution. Then an object-oriented method is used to identify the caves, and we also evaluate the accuracy of the classification. We found that the highest classification accuracy of holes, the average has reached more than 80%. At the altitude of 24m and the spatial resolution of 1cm, the accuracy of the classification is the highest We have created a unique and effective way to identify burrows by using UAVs visible images. We draw the following conclusion: the best spatial resolution of burrows recognition is 1cm using DJI PHANTOM-3 UAV, and the improvement of spatial resolution does not necessarily lead to the improvement of classification accuracy. This study lays the foundation for future research and can be extended to similar studies elsewhere.

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“…Several feature selection methods have been used in conjunction with OBIA, such as the Bhattacharyya distance [29], Jeffreys-Matusita distance [30], and genetic algorithm [31]. Selection of optimal features has also been successfully applied through the decision tree analysis [32][33][34][35]. Stepwise Discriminant Analysis (SDA) effectively selects the subset of variables and has been applied for reduction of data dimensionality [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

et al. 2015

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Abstract:Cropland mapping via remote sensing can provide crucial information for agri-ecological studies. Time series of remote sensing imagery is particularly useful for agricultural land classification. This study investigated the synergistic use of feature selection, Object-Based Image Analysis (OBIA) segmentation and decision tree classification for cropland mapping using a finer temporal-resolution Landsat-MODIS Enhanced time series in 2007. The enhanced time series extracted 26 layers of Normalized Difference Vegetation Index (NDVI) and five NDVI Time Series Indices (TSI) in a subset of agricultural land of Southwest Missouri. A feature selection procedure using the Stepwise Discriminant Analysis (SDA) was performed, and 10 optimal features were selected as input data for OBIA segmentation, with an optimal scale parameter obtained by quantification assessment of topological and geometric object differences. Using the segmented metrics in a decision tree classifier, an overall classification accuracy of 90.87% was achieved. Our study highlights the advantage of OBIA segmentation and classification in reducing noise from in-field heterogeneity and spectral variation. The crop classification map produced at 30 m resolution provides spatial distributions of annual and perennial crops, which are valuable for agricultural monitoring and environmental assessment studies.

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

Cited by 51 publications

References 22 publications

Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China

Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China

The Analysis of Burrows Recognition Accuracy in Xinjiang's Pasture Area Based on Uav Visible Images With Different Spatial Resolution

Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

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