English  |  正體中文  |  简体中文  |  Items with full text/Total items : 73032/73032 (100%)
Visitors : 23196682      Online Users : 527
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68552

    Title: 應用最大熵法於蒙古山區進行森林樹種分類;The Classification of Forest Tree Species Using Maximum Entropy Method in Mongolia
    Authors: 杜姝任;Dolgorsuren
    Contributors: 國際永續發展碩士在職專班
    Keywords: 衛星影像;地理資料;森林樹種;最大熵法;蒙古;Satellite imagery;Topographical data;Forest tree species;MaxEnt;Mongolia
    Date: 2015-07-29
    Issue Date: 2015-09-23 12:20:44 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 森林是重要的生態系統,提供生物圈豐富的自然資源。森林調查能持續協助政府對
    本研究使用最大熵法(Maximum Entropy Method, MaxEnt)分類庫蘇古爾省(Huwsgul)
    額爾德尼布勒甘縣的森林樹種。研究區位於蒙古北部高山地區,森林面積共4,230.1 平方
    公里,占額爾德尼布勒甘縣總面積約85%,本研究測試資料為2011 年7 月的Landsat-5 衛
    星影像、空間解析度30 公尺的數值高程模型(Digital Elevation Model, DEM),以及森林組
    織公司提供的森林樹種調查圖。本研究使用Landsat-5 衛星影像和數值高程模型,以最大
    熵法分類森林樹種和其分布,並進行三項測試:(1)以Landsat 多波段影像分類森林樹種;
    (2)以數值高程模型衍生的地理變數分類森林樹種;(3)整合Landsat 多波段影像和地理變數
    研究成果顯示,僅用多波段影像分類六個樹種的總體精度為69%,Kappa 值為0.35,
    僅用地理資料分類六個樹種的總體精度為65%,Kappa 值為0.28,而整合兩種資料後的分
    類總體精度為80%,Kappa 值為0.48。根據上述成果比較,整合後的多波段影像資料和地
    理資料,能有效提升森林樹種分類的精度。;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.
    Appears in Collections:[國際永續發展在職專班] 博碩士論文

    Files in This Item:

    File Description SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明