Aerial ungulate surveys with a combination of infrared and high-resolution natural colour images

被引:1
|
作者
Franke, U. [1 ]
Goll, B. [1 ]
Hohmann, U. [2 ]
Heurich, M. [3 ]
机构
[1] Aerosense Engn, D-67280 Quirnheim, Germany
[2] Inst Forest Ecol & Forestry, Div Forest & Wildlife Ecol, D-67705 Trippstadt, Germany
[3] Bavarian Forest Natl Pk, Dept Res & Documentat, D-94481 Grafenau, Germany
关键词
Aerial survey; Infrared camera; Microlight aircraft; Ungulates; Wildlife monitoring; COST-EFFECTIVENESS; DEER; POPULATION; ABUNDANCE; DENSITY; COUNTS;
D O I
暂无
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Aerial ungulate surveys with a combination of infrared and high-resolution natural colour images.-Information on animal population sizes is crucial for wildlife management. In aerial surveys, we used a silent light aircraft (microlight) and a combination of a computer-linked thermal infrared camera (640 x 480 pixels) to detect ungulates and high-resolution visual images (5,616 x 3,744 pixels) to identify specific species. From winter 2008/2009 to winter 2010/2011, we flew 48 missions over three German national parks and a German/French biosphere reserve. Within each study area, we followed non-overlapping linear transects with a flying altitude similar to 450 m above ground level and scanned 1,500-2,000 ha every two hours of flight time. Animals best detected and identified were red deer and fallow deer. Detection rates with respect to the type and density of vegetation cover ranged from 0% (young spruce) to 75% (young defoliated beech) to 100% (open land). This non-invasive method is cost-effective and suitable for many landscapes.
引用
收藏
页码:285 / 293
页数:9
相关论文
共 50 条
  • [21] Robust approach for suburban road segmentation in high-resolution aerial images
    Guo, D.
    Weeks, A.
    Klee, H.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2007, 28 (1-2) : 307 - 318
  • [22] Disparity Estimation Networks for Aerial and High-Resolution Satellite Images: A Review
    Mari, Roger
    Ehret, Thibaud
    Facciolo, Gabriele
    IMAGE PROCESSING ON LINE, 2022, 12 : 501 - 526
  • [23] Automatic extraction of road seeds from high-resolution aerial images
    Dal-Poz, AP
    Do Vale, GM
    Zanin, RB
    ANAIS DA ACADEMIA BRASILEIRA DE CIENCIAS, 2005, 77 (03): : 509 - 520
  • [24] Robust Vehicle Detection in High-Resolution Aerial Images With Imbalanced Data
    Li X.
    Li X.
    Li Z.
    Xiong X.
    Khyam M.O.
    Sun C.
    IEEE Transactions on Artificial Intelligence, 2021, 2 (03): : 238 - 250
  • [25] Detection of Cars in High-Resolution Aerial Images of Complex Urban Environments
    ElMikaty, Mohamed
    Stathaki, Tania
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 55 (10): : 5913 - 5924
  • [26] High-resolution aerial infrared mapping of groundwater discharge to the coastal ocean
    Kelly, Jacque L.
    Glenn, Craig R.
    Lucey, Paul G.
    LIMNOLOGY AND OCEANOGRAPHY-METHODS, 2013, 11 : 262 - 277
  • [27] Study of usability of aerial images and high-resolution satellite images in cadastre renewal works in Turkey
    Nacar, Fazil
    Karabork, Hakan
    Cay, Tayfun
    SURVEY REVIEW, 2020, 52 (372) : 191 - 204
  • [28] ESFNet: Efficient Network for Building Extraction From High-Resolution Aerial images
    Lin, Jingbo
    Jing, Weipeng
    Song, Houbing
    Chen, Guangsheng
    IEEE ACCESS, 2019, 7 : 54285 - 54294
  • [29] Roads extraction through texture from aerial and high-resolution satellite images
    Malpica, JA
    Pedraza, J
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING VI, 2001, 4170 : 358 - 366
  • [30] Block-based semantic classification of high-resolution multispectral aerial images
    Avramovic, Aleksej
    Risojevic, Vladimir
    SIGNAL IMAGE AND VIDEO PROCESSING, 2016, 10 (01) : 75 - 84