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Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index

Löw, Fabian and Waldner, François and Latchininsky, Alexandre and Biradar, Chandreshekhar and Bolkart, Maximilian (2016) Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index. Journal of Environmental Management, 3, pp. 562-575. Elsevier. doi: 10.1016/j.jenvman.2016.09.001. ISSN 0301-4797.

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Official URL: http://www.sciencedirect.com/science/article/pii/S0301479716306508

Abstract

"The Asian Migratory locust (Locusta migratoria migratoria L.) is a pest that continuously threatens crops in the Amudarya River delta near the Aral Sea in Uzbekistan, Central Asia. Its development coincides with the growing period of its main food plant, a tall reed grass (Phragmites australis), which represents the predominant vegetation in the delta and which cover vast areas of the former Aral Sea, which is desiccating since the 1960s. Current locust survey methods and control practices would tremendously benefit from accurate and timely spatially explicit information on the potential locust habitat distribution. To that aim, satellite observation from the MODIS Terra/Aqua satellites and in-situ observations were combined to monitor potential locust habitats according to their corresponding risk of infestations along the growing season. A Random Forest (RF) algorithm was applied for classifying time series of MODIS enhanced vegetation index (EVI) from 2003 to 2014 at an 8-day interval. Based on an independent ground truth data set, classification accuracies of reeds posing a medium or high risk of locust infestation exceeded 89% on average. For the 12-year period covered in this study, an average of 7504 km2 (28% of the observed area) was flagged as potential locust habitat and 5% represents a permanent high risk of locust infestation. Results are instrumental for predicting potential locust outbreaks and developing well-targeted management plans. The method offers positive perspectives for locust management and treatment of infested sites because it is able to deliver risk maps in near real time, with an accuracy of 80% in April-May which coincides with both locust hatching and the first control surveys. Such maps could help in rapid decision-making regarding control interventions against the initial locust congregations, and thus the efficiency of survey teams and the chemical treatments could be increased, thus potentially reducing environmental pollution while avoiding areas where treatments are most likely to cause environmental degradation."

Item URL in elib:https://elib.dlr.de/109356/
Document Type:Article
Title:Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Löw, FabianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Waldner, Françoisearth and life institute, université catholique de louvainUNSPECIFIEDUNSPECIFIED
Latchininsky, AlexandreUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Biradar, ChandreshekharUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bolkart, MaximilianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:1 December 2016
Journal or Publication Title:Journal of Environmental Management
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:3
DOI:10.1016/j.jenvman.2016.09.001
Page Range:pp. 562-575
Publisher:Elsevier
ISSN:0301-4797
Status:Published
Keywords:Aral Sea Land cover Change Locust Management MODIS Random forest Reeds Satellite earth observation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Geoscientific remote sensing and GIS methods
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:09 Jan 2017 14:17
Last Modified:09 Jan 2017 14:17

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