Modeling desert locust habitat using biophysical indices derived from LandSat 8 images

Subject Areas : Geospatial systems development

Sirous  Hashemi Dareh Badami ¹ (MSc. Student of Remote Sensing and Geographic Information System, University of Tehran)
Bahram  Jomezade ² (MSc. Student of Remote Sensing and Geographic Information System, University of Tehran)
Ali  Darvishi Bolourani ³ (Assis. Prof. College of Geography, University of Tehran)
Abdol-Hossein  Khakian ⁴ (MSc. Student of Environment, University of Tehran)

Received: 2015-04-18 Accepted : 2015-08-31 Published : 2016-03-20

Keywords: Locust, Biophysical indices, LANDSAT 8, Principal Component Analysis (PCA),

Abstract :

Using satellite images is a simple and inexpensive way to identify the habitats and monitor the migratory pests such as locusts. Using remote sensing technology for locust control policies has shifted from treatment methods to preventive ones. Considering the effective management of insect pest infestations based on thorough knowledge of biology and ecology, this study aimed to evaluate the use of biophysical indices derived from satellite images in order to identify and monitor the locust habitats. For this purpose, we used biophysical indicators (vegetation indices, vegetation, water content indices, drought index and land surface temperature) derived from Landsat 8 (OLI/TIRS) images coinciding with in-situ data monitoring. Then, the information of indices was summarized in one image using principal component analysis. Finally, the primary locust habitat zoning map with high risk, medium risk and low risk was developed using in-situ data obtained from the monitoring and thresholding methods. The spatial accuracy of results was evaluated by locust observed data as reference data; on the other hand, the overall accuracy and Kappa coefficient for high-risk habitat were given as 62% and 74%, respectively. For moderate-risk habitat, they were also obtained as 87% and 71%, respectively. For all of three habitats, they were estimated as 94% and 88%.

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