Using multi–temporal Landsat ETM+ data to monitor the plague of oriental migratory locust

This paper aims to evaluate the effectiveness of using Landsat ETM+ data to identify the extent and severity of locust damage. Two cloud‐free Landsat ETM+ images of the study area, taken before and after peak locust plague, were compared to determine the extent and severity of the 2002 locust plague according to the decrease of the Normalized Difference Vegetation Index (NDVI) derived from the two images. The results showed that the locust plague could be classified into heavy, moderate and light damage degrees based on the NDVI value decrease calculated by each pixel, which further evaluated its accuracy by extensive ground survey data. Locust plague can be identified with 98% and 92% accuracy for determining geographic extent and severity respectively using Landsat ETM+ data.

[1]  D. L. Williams,et al.  Remote detection of forest damage , 1986 .

[2]  Kim P. Bryceson,et al.  An analysis of the 1984 locust plague in Australia using multitemporal landsat multispectral data and a simulation model of locust development , 1986 .

[3]  R. Ji,et al.  Use of MODIS data to monitor the oriental migratory locust plague , 2004 .

[4]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[5]  Yuejin Zhang,et al.  Monitoring East Asian migratory locust plagues using remote sensing data and field investigations , 2005 .

[6]  P. Teillet,et al.  On the Dark Target Approach to Atmospheric Correction of Remotely Sensed Data , 1995 .

[7]  D. Hunter,et al.  Identification and monitoring of Australian plague locust habitats from landsat , 1983 .

[8]  C. Woodcock,et al.  Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland , 2004 .

[9]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[10]  Compton J. Tucker,et al.  The potential of satellite remote sensing of ecological conditions for survey and forecasting desert-locust activity , 1985 .

[11]  C. Giardino,et al.  Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images , 2001 .

[12]  R. Lunetta,et al.  A change detection experiment using vegetation indices. , 1998 .

[13]  K. Bryceson The use of Landsat MSS data to determine the locust eggbeds of locust eggbeds in the Riverina region of New South Wales, Australia , 1989 .

[14]  Darrel L. Williams,et al.  A georeferenced LANDSAT digital database for forest insect-damage assessment , 1985 .

[15]  Toshiro Sugimura,et al.  Extraction of areas infested by pine bark beetle using Landsat MSS data , 1987 .

[16]  R. Nelson Detecting forest canopy change due to insect activity using Landsat MSS , 1983 .

[17]  K. Soudani,et al.  Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands , 2006 .

[18]  James E. Vogelmann,et al.  Use of thematic mapper data for the detection of forest damage caused by the pear thrips , 1989 .