Study On Classification Of Habitat For Oriental Migratory Locust Based On Remote Senseing

Oriental migratory locust (Locusta migratoria manilensis Meyen) is the kind of locust which has very often brought the harmful effects to many regions in China. More than 800 locust plagues have been recorded in Chinese historical documents, in which most of the plagues were caused by oriental migratory locust. In recent years, a tendency of more and more serious locust plague has emerged due to the climatic change and human activities.The key of timely and efficiently control of locust plague is conducting the monitoring of the plague. Remote sensing as the modern technology and with the advantages such as large-area and multi-temporal monitoring has a particular role in locust plague monitoring.There are close relationships between locust occurrence and outbreak and their habitas. Therefore, the monitoring of locust habitats has became a very important task in the application of remote sensing in locust plague monitoring, and habitat classification based on remotely sensed imagery has became an important foundamental task.In this paper, Huanghua city near Bohai Bay in Hebei province was taken as the study area and the locust habitat classification based on the images was conducted. First, the meanings of habitat of oriental migratory locust were explored. Second, the priciples used for the classification were determined based on the practical conditions of the study area and the used remotely sensed images, and a habitat classification system was constructed according to the classification principles. Third, the diffent combinations of the habitat classification were tested, including using the images of Landsat-5 received on August 14, 2003 and May 28 of 2004, and ASTER received at October 16 of 2003, and using the different classification methods including the maximum likehood classifier and knowleage-based layered classifier. Through the analysis and comparison of the combinations the mostly suitable one to the habitat classification in the study area was determined.Through this study the follow conclusions are drawn:1) Correctly using the knowleage-based layered classifier can much improve the classification accuracy of locust habitats. The maximum likelihood classifier is a rather good classification method and, however, much attention should be paid to selecting and purification of the training samples.2) With the aid of the spatial texture information on image, the accuracy of the habitat classification will be more or less improved.3) The accuracy of habitat classification will be particularly improved if multi-temporal remotely sensed image data are applied. The study indicated that: the overall accuracy and Kappacoefficient of the maximum likelihood are 88.8095% and 0.8462 respectively if combining of TM 5,4,3 bands of August 14, 2003 and May 28, 2004; and the overall accuracy and Kappa coefficient of the maximum likelihood will arise to 89.3188% and 0.8534 respectively if adding the spatial texture information on the images.4) Using ASTER image data can further improve the accuracy of the habitat classification. The study has shown that the overall accuracy and Kappa coefficient of the classification is 90.9739% and 0.8738 respectively if using the fused images of TM data received on August 14, 2003 and the ASTER data on October 16, 2003, and using the knowledge-based layered classification. This accuracy can basically meet the demands of the classification and mapping of the habitats for oriental migratory locust in the study area.