Maximum discrimination index: a tool for land cover identification

被引:1
|
作者
Lencina, A. [1 ]
Weber, C. [2 ,3 ,4 ]
机构
[1] Univ Nacl Ctr Prov Buenos Aires, CONICET, Fac Agron, Lab Anal Suelos, Ave Republ Italia 780,POB 47, RA-7300 Buenos Aires, DF, Argentina
[2] Univ Nacl La Plata, Fac Ciencias Agr & Forestales, Casilla Correo 31, RA-1900 La Plata, Buenos Aires, Argentina
[3] UNLP, CIC, CONICET, Ctr Invest Opt, POB 3,Cno Centenario & 506, RA-1897 Buenos Aires, DF, Argentina
[4] Comis Invest Cient Prov Buenos Aires, RA-1900 Buenos Aires, DF, Argentina
关键词
Discrimination; Ryegrass; Spectral signature; Vegetation index; Wheat; VEGETATION; CLASSIFICATION; SENSITIVITY; ACCURACY;
D O I
10.1007/s13762-019-02547-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This work presents an adaptable index that is applied to a pair of covers to be discriminated. Its adaptability relies on the procedure to determine the numerical value of the wavelengths or bands involved: the maximization of an operator based on the geometric mean of squared differences. This index is applied to the particular case of discrimination of wheat from ryegrass in different phenological stages. The maximum discrimination index outperforms other indices such as the normalized difference vegetation index, advanced normalized vegetation index and normalized difference greenness index. Its efficacy of discrimination is characterized and compared with the normalized difference greenness index (the second with better performance). It is observed that the proposed index has a more predictable behavior and reaches a discrimination accuracy as high as 95.5%. The maximum discrimination index could be adjusted to different covers and employed as a tool for discrimination. Spectral signatures coming from any platform: field, aerial or satellite, can be handled.
引用
收藏
页码:1113 / 1122
页数:10
相关论文
共 50 条
  • [11] A Hierarchical Clustering Method for Land Cover Change Detection and Identification
    Hame, Tuomas
    Sirro, Laura
    Kilpi, Jorma
    Seitsonen, Lauri
    Andersson, Kaj
    Melkas, Timo
    REMOTE SENSING, 2020, 12 (11)
  • [12] LANDSAT SATELLITE IMAGES USED IN IDENTIFICATION OF LAND USE AND LAND COVER IN MOUNTAIN AREA
    Vorovencii, Iosif
    Ienciu, Ioan
    Popescu, Cosmin
    Oprea, Luciana
    GEOCONFERENCE ON INFORMATICS, GEOINFORMATICS AND REMOTE SENSING - CONFERENCE PROCEEDINGS, VOL II, 2013, : 617 - 624
  • [13] Toward Efficient Land Cover Mapping: An Overview of the National Land Representation System and Land Cover Map 2015 of Bangladesh
    Jalal, Rashed
    Iqbal, Zaheer
    Henry, Matieu
    Franceschini, Gianluca
    Islam, Mohammad S.
    Akhter, Mariam
    Khan, Zarin T.
    Hadi, Mohammad A.
    Hossain, Mohammed A.
    Mahboob, M. Golam
    Udita, Tasnuva S.
    Aziz, Tariq
    Masum, Syed M.
    Costello, Liam
    Saha, Champa R.
    Chowdhury, Abdullah A. M.
    Salam, Abdus
    Shahrin, Farzana
    Sumon, Fazle R.
    Rahman, Mahbubur
    Siddique, Mohammad A.
    Rahman, Mohammad M.
    Jahan, Md N.
    Shaunak, Mir F.
    Rahman, Mohammad S.
    Islam, Mohammad R.
    Mosca, Nicola
    D'Annunzio, Remi
    Hira, Shrabanti
    Di Gregorio, Antonio
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 12 (10) : 3852 - 3861
  • [14] A methodology to generate a synergetic land-cover map by fusion of different land-cover products
    Perez-Hoyos, A.
    Garcia-Haro, F. J.
    San-Miguel-Ayanz, J.
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2012, 19 : 72 - 87
  • [15] Assessment of land use, land cover change in the mangrove forest of Ghogha area, Gulf of Khambhat, Gujarat
    Chopade, Madhuri R.
    Mahajan, Seema
    Chaube, Nilima
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 212
  • [16] Temporal series of EVI from MODIS sensor for land use and land cover mapping of western Bahia
    Borges, Elane Fiuza
    Sano, Edson Eyji
    BOLETIM DE CIENCIAS GEODESICAS, 2014, 20 (03): : 526 - 547
  • [17] A Comparison of a Neuro-Fuzzy Method with a Maximum Likelihood Classification for Land Cover Classes
    Perakis, Konstantinos
    Lalou, A.
    Stathakis, Dimitris
    IMAGIN [E,G] EUROPE, 2010, : 338 - 345
  • [18] The evaluation of usability of VHR satellite images for land cover/land use identification in agricultural landscape
    Chmiel, J.
    Lady-Druzycka, K.
    Pluto-Kossakowska, J.
    Osinska-Skotak, K.
    Wolniewicz, W.
    Fijalkowska, A.
    Kupidura, P.
    GLOBAL DEVELOPMENTS IN ENVIRONMENTAL EARTH OBSERVATION FROM SPACE, 2006, : 427 - +
  • [19] Remote sensing methods to detect land-use/cover changes in New Zealand's 'indigenous' grasslands
    Weeks, Emily S.
    Ausseil, Anne-Gaelle E.
    Shepherd, James D.
    Dymond, John R.
    NEW ZEALAND GEOGRAPHER, 2013, 69 (01) : 1 - 13
  • [20] NDBI Based Prediction of Land Use Land Cover Change
    Kulkarni, Keerti
    Vijaya, Pa
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2021, 49 (10) : 2523 - 2537