Informativeness of the Spectral and Morphometric Characteristics of the Canopy-Gap Structure Based on Remote Sensing Data

被引:1
|
作者
Komarov, A., V [1 ]
Ershov, D., V [1 ]
Tikhonova, E., V [1 ]
机构
[1] Russian Acad Sci, Ctr Forest Ecol & Prod, Moscow 117997, Russia
基金
俄罗斯科学基金会;
关键词
canopy-gap structure; coniferous-deciduous forests; remote sensing; classification of forest communities; segmentation; random forest; CONIFEROUS FORESTS; DYNAMICS; SPRUCE; REGENERATION; REPLACEMENT;
D O I
10.1134/S1995425521070076
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
The differences in morphometric features of the canopy-gap structure of the three dominant forest types in the Valuevsky Forest Park were investigated with high-resolution and detailed-resolution remote sensing data. The forest-community groups (deciduous forest with a predominance of lime, deciduous forest with a predominance of birch or aspen, and coniferous forest with a predominance of spruce or pine) were classified according to the random forest method based on Sentinel-2/MSI multispectral satellite images. The better classificaton accuracy was 0.96 (k = 0.88). The Sentinel-2 data were used to create a layer of segments: spectrally homogeneous forest parcels. The forest gaps were obtained via cluster analysis with Resurs-P1 Geoton panchromatic data and visual interpretation of the clusters. Eight morphometric parameters were calculated for each gap. The differences were analyzed at the segment level (the Mann-Whitney U-test) and for all gap sets of each forest-community group (the Kruskal-Wallis H-test). The highest U-test values for the average values of morphometric features at the level of forest-community segments were obtained for the gap area (U = 24), gap perimeter (U = 19.3), gap-shape complexity index (U = 19.0), and the ratio of the perimeter to the gap area (U = 18.7). The highest values of the H-test at the level of individual gaps were calculated for the fractal dimension of the gap (H = 2229.2), the ratio of the perimeter to gap area (H = 2064.9), and the gap area (H = 1718.4). Analysis of the calculated and published data made it possible to find the possible reasons for the differences in the gap structure and gap parameters of coniferous, small-leaved, and lime communities of the model territory.
引用
收藏
页码:733 / 742
页数:10
相关论文
共 50 条
  • [31] Remote sensing estimation of paddy rice biomass based on microwave canopy scattering model
    Zhang Y.
    Zhang Z.
    Su S.
    Wu J.
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2011, 27 (09): : 100 - 105
  • [32] Spectral Data Augmentation Using Deep Generative Model for Remote Chemical Sensing
    Son, Jungjae
    Byun, Hyung Joon
    Park, Munyeol
    Ha, Jeongjae
    Nam, Hyunwoo
    IEEE ACCESS, 2024, 12 : 98326 - 98337
  • [33] Modelling butterfly distribution based on remote sensing data
    Luoto, M
    Kuussaari, M
    Toivonen, T
    JOURNAL OF BIOGEOGRAPHY, 2002, 29 (08) : 1027 - 1037
  • [34] ANALYSIS MODEL BASED RECOVERY OF REMOTE SENSING DATA
    Li, Xinghua
    Shen, Huanfeng
    Li, Huifang
    Zhang, Liangpei
    2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2014, : 2491 - 2494
  • [35] Evaluation of the Species Composition and the Biological Productivity of Forests Based on Remote Sensing Data with High Spatial and Spectral Resolution
    V. V. Kozoderov
    E. V. Dmitriev
    P. G. Melnik
    S. A. Donskoi
    Izvestiya, Atmospheric and Oceanic Physics, 2018, 54 : 1374 - 1380
  • [36] Spectral Characteristic Analysis and Remote Sensing Classification of Coastal Aquaculture Areas Based on GF-1 Data
    Zhu, Hongchun
    Li, Kaiqiang
    Wang, Lin
    Chu, Jialan
    Gao, Ning
    Chen, Yanlong
    JOURNAL OF COASTAL RESEARCH, 2019, : 49 - 57
  • [37] SPECTRAL DERIVATIVE FEATURES FOR SUPERVISED CLASSIFICATION OF REMOTE SENSING DATA: AN EXPERIMENTAL EVALUATION
    Bao, Jiangfeng
    Chi, Mingmin
    Benediktsson, Jon Atli
    2012 4TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING (WHISPERS), 2012,
  • [38] Experiment Design-Based Robust Spatial Spectral Analysis Techniques For Enhanced Imaging With Remote Sensing Data
    Shkvarko, Y.
    Perez Meana, H. M.
    2008 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTING SCIENCE AND AUTOMATIC CONTROL (CCE 2008), 2008, : 307 - +
  • [39] Graph-Based Interpolation for Remote Sensing Data
    Cardona, Johanna Garcia
    Ortega, Antonio
    Rodriguez-Alvarez, Nereida
    2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022), 2022, : 1791 - 1795
  • [40] Multi-Spectral Remote Sensing Image Registration Based on SURF
    Lu, Yunfei
    Zhao, Haimeng
    Li, Bo
    Yan, Lei
    2011 AASRI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRY APPLICATION (AASRI-AIIA 2011), VOL 2, 2011, : 236 - 239