| dc.description.abstract | Forest is a very important ecosystem and natural resource for living things. Based on forest
inventories, government is able to make decisions to converse, improve and manage forests in a
sustainable way. Field work for forestry investigation is difficult and time consuming, because it
needs intensive physical labor and costs, especially surveying in a widely and remotely
mountainous area. A reliable forest inventory can give us more accurate and timely information to
develop new and efficient approaches of forest management. The remote sensing technology has
been recently used for forest investigation for large scale. To produce an informative forest
inventory, forest attributes, including tree species are necessarily investigated.
This research focuses on the classification of forest tree species in Erdenebulgan county,
Huwsgul province, Mongolia, using Maximum entropy method. The study area covers a forest area
of 4230.1 km2 which is almost 85% of total area of Erdenebulgan county and located in a high
mountain region in northern Mongolia. For this study, Landsat 5 satellite imagery taken in July,
2011 and a 30 m DEM (Digital Elevation Model) were acquired to perform image classification.
The forest tree species inventory map collected from Forest Organization Company. Landsat
images and DEM were processed for tree species classification, and a maximum entropy model,
MaxEnt, for predicting the distribution of tree species was applied in this study. This study has
tried three different experiments: (1) spectral bands from Landsat were used for free species
classification; (2) topographical variables were used for tree species classification; and (3) tree
species classification generated from both spectral bands and topographical data. All experimental
results were compared with the tree species inventory to access the mapping accuracy.
The result shows that six different tree species were classified. The overall accuracy from only
spectral bands is 69 % and kappa coefficient is 0.35, and the result from only topographical data
shows 65 % overall accuracy and 0.28 kappa coefficient. Meanwhile, the overall accuracy from
integration of spectral bands and topographical data is 80 % with kappa coefficient of 0.48,
indicating that the integration of topographic data and image data can improve the classification of
tree species in this study area. | en_US |