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Mapping Lorey's height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images

Pourrahmati Manizheh Rajab, Baghdadi Nicolas, Darvishsefat Ali Asghar, Namiranian Manouchehr, Gond Valéry, Bailly Jean Stéphane, Zargham Nosratollah. 2018. Mapping Lorey's height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images. European Journal of Remote Sensing, 51 (1) : pp. 100-115.

Journal article ; Article de recherche ; Article de revue à facteur d'impact Revue en libre accès total
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Quartile : Q3, Sujet : REMOTE SENSING

Abstract : Lorey's height, representative of mean height in uneven-aged forest stands, is a valuable parameter for forest ecosystem management. While in situ measures provide the most precise information, remote-sensing techniques may provide less expensive but denser and more operational alternative of Lorey's height estimation over highly mountainous areas. This research aims first to evaluate the performances of two nonparametric data mining methods, random forest (RF) and artificial neural network (ANN), for estimation of Lorey's height using ice, cloud and land elevation satellite/geoscience laser altimeter system (ICESat/GLAS) in Hyrcanian forests of Iran and then to provide Lorey's height map using a synergy of ICESat/GLAS and optical images (TM and SPOT). RF and ANN GLAS height models were developed using waveform deterministic metrics, principal components (PCs) from principal component analysis (PCA) and terrain index (TI) extracted from a digital elevation model (DEM). The best result was obtained using an ANN combining first three PCs of PCA and waveform extent ʺWextʺ (RMSE = 3.4 m, RMSE% = 12.4). In order to map Lorey's height, GLAS-estimated heights were regressed against indices derived from optical images and also topographic information. The best model (RF regression with RMSE = 5.5 m and = 0.59) was applied on the entire study area, and a wall-to-wall height map was generated. This map showed relatively good compatibility with in situ measurements collected in part of the study area. (Résumé d'auteur)

Mots-clés Agrovoc : Forêt, Télédétection, Satellite radar, Landsat, Dynamique des populations, Croissance, Hauteur, Écosystème forestier, Aménagement forestier

Mots-clés géographiques Agrovoc : Iran République islamique

Mots-clés libres : Lorey’s height, ICESat/GLAS, Optical images, Artificial, Neural network (ANN), Random forest, Iran

Classification Agris : K01 - Forestry - General aspects
U30 - Research methods

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

Auteurs et affiliations

  • Pourrahmati Manizheh Rajab, Université de Téhéran (IRN) - auteur correspondant
  • Baghdadi Nicolas, IRSTEA (FRA)
  • Darvishsefat Ali Asghar, Université de Téhéran (IRN)
  • Namiranian Manouchehr, Université de Téhéran (IRN)
  • Gond Valéry, CIRAD-ES-UPR BSef (FRA) ORCID: 0000-0002-0080-3140
  • Bailly Jean Stéphane, AgroParisTech (FRA)
  • Zargham Nosratollah, Université de Téhéran (IRN)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/586253/)

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