Forest Ecosystem Health Assessment And Early Warning Based On Support Vector Machines

The forest is the subject of terrestrial ecosystem, not only to the survival of humans important natural resources, but also to cope with global greenhouse effect, the loss of biodiversity, ecological balance destroyed and so on. Forest is the important basis of well-protected ecological environment and environment problem.Forest health will directly related to the global ecological security and the sustainable development of the human society. According to the ecological and statistics theory , based on the data from survey of permanent sample plots and the the latest survey data. This research constructed the health assessment and early warning system for northern mountain of Hebei.Use Matlab established based on SVM health assessment and early warning model in the typical forest ecosystem of northern mountain of Hebei. From the perspective of forest ecosystem for health assessment and early warning, aiming at provide theory basis for the forest sustainable management. The main research conclusions are as follows:This study based on Montreal process proposed indexes,reference related research achievements at home and abroad,constructed forest health evaluation index system based on support vector machine (SVM). In this study,to investigate the 21 typical forest ecosystem data as learning samples, through the SVM adaptive learning to build new classification evaluation model, and with the classification and evaluation model to evaluate the performance of the classifier, meanwhile give feedback information for learning, establishing forest health classification model. Applying this model combining the latest survey data of study area to predict the rest subcompartment,obtained the predicted classification results.In this study, SVM achieved good prediction for evaluation of forest health. For 225 predictive results of subcompartment of quality classes have 21, accounting for a total area of 5.7% of study area, Healthy forest subcompartment accounting for 61.5%; Sub-health forest subcompartment accounting for 28.1 %, Unhealthy forest subcompartment accounting for 4.7 %.This study applies to the Beijing forest health base value and warning signs index for training set and prediction set,and established early warning model.Get this research areas of the relevant early-warning index, warning the forest ecosystem health through the trained model in 2010 , and differentiate early warning results warning degrees according to the forest health condition .To predict the results of the research region:No warning forest subcmpartment accounting for 35.37%;Slight warning forest subcompartment accounting for 58.51%; Middle warning subcompartment accounting for 0.45%, Heavy warning forest subcompartment accounting for 5.66%. Make corresponding measuresa acording to the forest health assessment and warning results,Put forward the forest health management technology.