Relationship between oriental migratory locust plague and soil moisture extracted from MODIS data

Liu, Zhenbo; Shi, Xuezheng; Warner, Eric; Ge, Yao; Yu, Dongsheng; Song, Ning; Wang, Hongjie

doi:10.1016/j.jag.2007.09.001

Cited by 24 publications

(19 citation statements)

References 14 publications

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“…Also, the status of the vegetation cover is a function of soil water content. Thus, the curve relating ST and NDVI, commonly known as the ST/NDVI space, allows the assessment of the moisture conditions of the surface and the estimation of soil water status [24,34,56,[101][102][103][104][105]. For a given site, the point cloud of the relationship TS/ NDVI defines a trapezoidal space.…”

Section: Surface Humiditymentioning

confidence: 99%

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Kotchi¹,

Barrette²,

Viau³

et al. 2016

Geospatial Technology - Environmental and Social Applications

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A precise estimation and the characterization of the spatial variability of microclimate conditions (MCCs) are essential for risk assessment and site-specific management of vector-borne diseases and crop pests. The objective of this study was to estimate at local scale, and assess the uncertainties of Surface Microclimate Indicators (SMIs) derived from airborne infrared thermography and multispectral imaging. SMIs including Surface Temperature (ST) were estimated in southern Quebec, Canada. The formulation of their uncertainties was based on in-situ observations and the law of propagation of uncertainty. SMIs showed strong local variability and intra-plot variability of MCCs in the study area. The ST values ranged from 290 K to 331 K. They varied more than 17 K on vegetable crop fields. The correlation between ST and in-situ observations was very high (r = 0.99, p = 0.010). The uncertainty and the bias of ST compared to in-situ observations were 0.73 K and ±1.42 K respectively. This study demonstrated that very high spatial resolution multispectral imaging and infrared thermography present a good potential for the characterization of the MCCs that govern the abundance and the behavior of disease vectors and crop pests in a given area.

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Section: Surface Humiditymentioning

confidence: 99%

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Kotchi¹,

Barrette²,

Viau³

et al. 2016

Geospatial Technology - Environmental and Social Applications

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show abstract

“…21,22 Among a number of various satellitebased vegetation indices, routinely produced from AVHRR, SPOT, and MODIS sensors, the normalized difference vegetation index (NDVI) is the most used product for such studies. [23][24][25][26] The NDVI serves as a general indicator of vegetation greening and is derived from the red and near-infrared portions of reflected radiation. 27 Fig.…”

Section: Introductionmentioning

confidence: 99%

Satellite-based monitoring system for assessment of vegetation vulnerability to locust hazard in the River Ili delta (Lake Balkhash, Kazakhstan)

Propastin

2013

J. Appl. Remote Sens

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“…This work indicated that the frequency of locust outbreaks may be altered by changes in climatic patterns. Among many environmental factors that may affect locusts, SM is the variable that mostly influences egg-laying location, egg-survival, and egg-hatching rate, 19 in addition to temperature. 20 In general, female locusts prefer open and warm sites of dry, soft, and sandy soils in which over 6 cm of depth have enough moist soil conditions.…”

mentioning

confidence: 99%

“…37 In spite of being very promising, very few studies have addressed the link between SM remote sensing and desert locusts. 19 Traditional SM measures are ground based so that survey areas are usually limited for being an expensive and time consuming activity. 38,39 Laboratory and ground-based experiments have demonstrated that SM intervenes in egg development and interruption under particular conditions of humidity.…”

mentioning

confidence: 99%

Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

Gómez

Salvador

Sanz

et al. 2018

J. Appl. Rem. Sens.

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Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture," J. Appl.Abstract. Desert locusts have attacked crops since antiquity. To prevent or mitigate its effects on local communities, it is necessary to precisely locate its breeding areas. Previous works have relied on precipitation and vegetation index datasets obtained by satellite remote sensing. However, these products present some limitations in arid or semiarid environments. We have explored a parameter: soil moisture (SM); and examined its influence on the desert locust wingless juveniles. We have used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM conditions under different time scenarios. RF obtained the best model performance with very good validation results according to the true skill statistic and receiver operating characteristic curve statistics. It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07 m 3 ∕m 3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA CCI SM product to complement or substitute current monitoring techniques based on precipitation datasets. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Relationship between oriental migratory locust plague and soil moisture extracted from MODIS data

Cited by 24 publications

References 14 publications

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Satellite-based monitoring system for assessment of vegetation vulnerability to locust hazard in the River Ili delta (Lake Balkhash, Kazakhstan)

Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

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