Application of large- and medium-scale aerial photographs to forest vegetation management: A case study

被引:11
|
作者
Pitt, DG [1 ]
Runesson, U [1 ]
Belt, FW [1 ]
机构
[1] Great Lakes Forestry Ctr, Canadian Forest Serv, Sault St Marie, ON P6A 5M7, Canada
来源
FORESTRY CHRONICLE | 2000年 / 76卷 / 06期
关键词
remote sensing; digitized aerial photographs; vegetation management; forest classification;
D O I
10.5558/tfc76903-6
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Five experimental conifer release treatments applied to each of four, three- to seven-year-old spruce plantations resulted in a mosaic of woody and herbaceous vegetation complexes after two growing seasons. A combination of 1:5000-scale overview and 1:500-scale sample photographs were evaluated as a means of mapping and quantifying cover in each of eight vegetation and two non-vegetation categories. On 23-cm format, 1:5000-scale photographs, blocks were stereoscopically stratified into areas (> 25 m(2)) of uniform vegetation. A random selection of eighty 70-mm format, 1:500 photo samples were then used as "training sites" to calibrate strata assessment on the 1:5000 photographs. Remaining sample plots were used to verify the accuracy of the: final map product. Verification plots suggested that principle vegetation components such as tall, mid, and low shrub, grass, and herbaceous species were estimated to within 5-10% cover, at least 70% of the time. Errors for lesser components, such as dead shrub, conifer, bare ground and slash were 2-5% cover. Ferns could not be discerned at the 1:5000 scale and there was evidence of occasional confusion between herbaceous species and other life forms, including mid shrub, low shrub, and grass categories. Operational applications of the methodology are discussed.
引用
收藏
页码:903 / 913
页数:11
相关论文
共 50 条
  • [21] Interoperability Study of Data Preprocessing for Deep Learning and High-Resolution Aerial Photographs for Forest and Vegetation Type Identification
    Lin, Feng-Cheng
    Chuang, Yung-Chung
    [J]. REMOTE SENSING, 2021, 13 (20)
  • [22] SEASONAL CONSISTENCY OF SALT-MARSH VEGETATION CLASSES CLASSIFIED FROM LARGE-SCALE COLOR INFRARED AERIAL PHOTOGRAPHS
    DALE, PER
    HULSMAN, K
    CHANDICA, AL
    [J]. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 1986, 52 (02): : 243 - 250
  • [23] Fine-scale activity patterns of large- and medium-sized mammals in a deciduous broadleaf forest in the Qinling Mountains, China
    Zhang, Yuke
    He, Xiangbo
    Liu, Xuehua
    Songer, Melissa
    Dang, Haishan
    Zhang, Quanfa
    [J]. JOURNAL OF FORESTRY RESEARCH, 2021, 32 (06) : 2709 - 2717
  • [24] Fine-scale activity patterns of large- and medium-sized mammals in a deciduous broadleaf forest in the Qinling Mountains, China
    Yuke Zhang
    Xiangbo He
    Xuehua Liu
    Melissa Songer
    Haishan Dang
    Quanfa Zhang
    [J]. Journal of Forestry Research, 2021, 32 : 2709 - 2717
  • [25] Feasibility Assessment of Medium-Scale Hydro-Power Plant - Case Study in Kerala, India
    Johnpaul, V.
    Venkatesan, G.
    Vinoth, V.
    [J]. ASIAN JOURNAL OF WATER ENVIRONMENT AND POLLUTION, 2024, 21 (02) : 39 - 47
  • [26] Community based forest management and its impact on vegetation: a case study
    Kumar, P. G.
    Hate, S.
    Chaturvedi, A.
    [J]. IFOREST-BIOGEOSCIENCES AND FORESTRY, 2009, 2 : 93 - 98
  • [27] Medium-scale natural disaster risk scenario analysis: a case study of Pingyang County, Wenzhou, China
    Wang, Jun
    Chen, Zhenlou
    Xu, Shiyuan
    Hu, Beibei
    [J]. NATURAL HAZARDS, 2013, 66 (02) : 1205 - 1220
  • [28] Medium-scale natural disaster risk scenario analysis: a case study of Pingyang County, Wenzhou, China
    Jun Wang
    Zhenlou Chen
    Shiyuan Xu
    Beibei Hu
    [J]. Natural Hazards, 2013, 66 : 1205 - 1220
  • [29] A large scale data warehouse application case study
    Pollack, D
    [J]. PROCEEDINGS OF THE ELEVENTH SYSTEMS ADMINISTRATION CONFERENCE (LISA XI), 1997, : 59 - 63
  • [30] Streamlining urban forest monitoring based on a large-scale tree survey: a case study of highway vegetation in Hong Kong
    Lee, Louis Shing Him
    Zhang, Hao
    Ng, Kathy Tze Kwun
    Lo, Shun Cheong
    Yu, Alan Siu Lun
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (01)