USE OF AVHRR IMAGERY FOR LARGE-SCALE FOREST INVENTORIES

被引:14
|
作者
TEUBER, KB
机构
[1] USDA Forest Service, Southern Forest Experiment Station, Starkville, MS 39759
关键词
D O I
10.1016/0378-1127(90)90223-X
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Satellite-based remote-sensing observations provide a continuous source of data useful for evaluating forest vegetation over large areas. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration's (NOAA's) series of polar-orbiting environmental satellites is well suited for large-scale studies, providing worldwide coverage daily in five spectral bands with a spatial resolution of 1.1 km. Research is being conducted by the Forest Inventory and Analysis unit of the Southern Forest Experiment Station, USDA Forest Service, on the use of AVHRR data for forest-inventory applications. Digital image data from the AVHRR sensor were used in an unsupervised clustering procedure to produce generalized land-cover classifications of three states in the southern United States. Statewide estimates of forest area were generated for Arkansas, Louisiana, and Mississippi and were compared with recent forest-survey estimates. All AVHRR-based estimates were within 5% of the ground-based forest-survey estimates, and the estimate for Louisiana was within 1%. A more detailed analysis of Louisiana revealed a very high correlation between AVHRR and forest-survey estimates at the parish (county) level. Though not appropriate for detailed forest cover-type mapping, the frequent repeat cycle, large area coverage, spectral characteristics, and relatively low cost of AVHRR data make it attractive as a potential component of a forest inventory-update model, especially when combined with higher-resolution imagery such as from Landsat. This type of data could also be useful for developing countries, which do not yet have a complete inventory of their forest resources, and for monitoring tropical deforestation. © 1990.
引用
收藏
页码:621 / 631
页数:11
相关论文
共 50 条
  • [21] Detecting large-scale diversity patterns in tropical trees: Can we trust commercial forest inventories?
    Rejou-Mechain, Maxime
    Fayolle, Adeline
    Nasi, Robert
    Gourlet-Fleury, Sylvie
    Doucet, Jean-Louis
    Gally, Michel
    Hubert, Didier
    Pasquier, Alexandra
    Billand, Alain
    FOREST ECOLOGY AND MANAGEMENT, 2011, 261 (02) : 187 - 194
  • [22] RANCHING IN THE AMAZON BASIN - LARGE-SCALE CHANGES OBSERVED BY AVHRR
    MALINGREAU, JP
    TUCKER, CJ
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 1990, 11 (02) : 187 - 189
  • [23] The use of Landsat imagery to assess large-scale forest cover changes in space and time, minimizing false-positive changes
    Borrelli, Pasquale
    Sandia Rondon, Luis Alfonso
    Schuett, Brigitta
    APPLIED GEOGRAPHY, 2013, 41 : 147 - 157
  • [24] The urbanized forest and large-scale disturbances
    Omi, PN
    MEETING IN THE MIDDLE, PROCEEDINGS, 1997, : 86 - 92
  • [25] Large-scale forest bioenergy not sustainable
    Faden, Mike
    FRONTIERS IN ECOLOGY AND THE ENVIRONMENT, 2012, 10 (05) : 229 - 229
  • [26] A method for detecting large-scale forest cover change using coarse spatial resolution imagery
    Fraser, RH
    Abuelgasim, A
    Latifovic, R
    REMOTE SENSING OF ENVIRONMENT, 2005, 95 (04) : 414 - 427
  • [27] Derivation of a dryness index from NOAA-AVHRR data for use in large-scale hydrological modelling
    Sandholt, I
    Rasmussen, K
    Andersen, J
    REMOTE SENSING AND HYDROLOGY 2000, 2001, (267): : 212 - 216
  • [28] Derivation of a dryness index from NOAA-AVHRR data for use in large-scale hydrological modelling
    Sandholt, I.
    Rasmussen, K.
    Andersen, J.
    IAHS-AISH Publication, 2000, (267): : 212 - 216
  • [29] Regression estimation using a cluster design in large scale forest inventories
    Dees, M
    ALLGEMEINE FORST UND JAGDZEITUNG, 1998, 169 (10-11): : 177 - 185
  • [30] Large-Scale Damage Detection Using Satellite Imagery
    Gueguen, Llonel
    Hamid, Ralfa. Y.
    2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2015, : 1321 - 1328