Mediterranean vegetation analysis by multi-temporal satellite sensor data

被引:48
作者
Grignetti, A [1 ]
Salvatori, R [1 ]
Casacchia, R [1 ]
Manes, F [1 ]
机构
[1] PROGRAMMA ANTARTIDE, CNR IIA, I-00159 ROME, ITALY
关键词
D O I
10.1080/014311697218430
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
A mediterranean vegetated coastal area has been classified on the basis of multi-temporal TM images and accurate field data. The latter was revealed to be particularly important due to the complexity of the area in terms of coverage degree, height structure of living vegetation and the occurrence of under-storey species. The recognition and classification of the different coenosis has taken into account that the spectral characteristics of vegetation depends on the season and on local climatic conditions. A major improvement in the spatial resolution of spectral data has been obtained by merging TM and SPOT-P by a RGB-IHS transformation that allowed an overall accuracy in the classification of 85 per cent to be achieved.
引用
收藏
页码:1307 / 1318
页数:12
相关论文
共 50 条
  • [21] Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation
    Wakulinska, Martyna
    Marcinkowska-Ochtyra, Adriana
    REMOTE SENSING, 2020, 12 (17)
  • [22] Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral-directional radiometric data
    Mousivand, Alijafar
    Menenti, Massimo
    Gorte, Ben
    Verhoef, Wout
    REMOTE SENSING OF ENVIRONMENT, 2015, 158 : 311 - 330
  • [23] Multi-temporal Satellite Image Analysis Using Unsupervised Techniques
    Arvind, C. S.
    Vanjare, Ashoka
    Omkar, S. N.
    Senthilnath, J.
    Mani, V.
    Diwakar, P. G.
    ADVANCES IN COMPUTING AND INFORMATION TECHNOLOGY, VOL 2, 2013, 177 : 757 - +
  • [24] Performance Analysis of Satellite Missions for Multi-Temporal SAR Interferometry
    Bovenga, Fabio
    Belmonte, Antonella
    Refice, Alberto
    Pasquariello, Guido
    Nutricato, Raffaele
    Nitti, Davide O.
    Chiaradia, Maria T.
    SENSORS, 2018, 18 (05)
  • [25] DOWNSCALING VEGETATION FRACTION BY FUSING MULTI-TEMPORAL MODIS AND LANDSAT DATA
    Wang, Jinying
    Wang, Hongyan
    Li, Xiaosong
    2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2014, : 757 - 760
  • [26] Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data
    Xiao, XM
    Boles, S
    Liu, JY
    Zhuang, DF
    Liu, ML
    REMOTE SENSING OF ENVIRONMENT, 2002, 82 (2-3) : 335 - 348
  • [27] Relationships analysis of land surface temperature with vegetation indicators and impervious surface fraction by fusing multi-temporal and multi-sensor remotely sensed data
    Huang, Liwen
    Shen, Huanfeng
    Wu, Penghai
    Zhang, Liangpei
    Zeng, Chao
    2015 JOINT URBAN REMOTE SENSING EVENT (JURSE), 2015,
  • [28] Forest landscape connectivity change analysis in the Yangtze River basin by multi-temporal satellite data
    Huang, Pujiang
    Du, Fengjiao
    Yang, Shuo
    Liu, Chang
    INDIAN JOURNAL OF GEO-MARINE SCIENCES, 2016, 45 (12) : 1645 - 1651
  • [29] Multi-temporal analysis of PAL images for the study of vegetation in South America
    Julien, Y.
    Sobrino, J. A.
    Morales, L.
    REVISTA DE TELEDETECCION, 2007, (27): : 17 - 26
  • [30] Vegetation structure and greenness in Central Africa from Modis multi-temporal data
    Gond, Valery
    Fayolle, Adeline
    Pennec, Alexandre
    Cornu, Guillaume
    Mayaux, Philippe
    Camberlin, Pierre
    Doumenge, Charles
    Fauvet, Nicolas
    Gourlet-Fleury, Sylvie
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2013, 368 (1625)