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/ | ||||||||||||||||||||||||
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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: |
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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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