Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery

By François Kayitakire, C. Hamel, Pierre Defourny. 2006

372 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/a994558d75b4a3f6cb5d7a9fdb82f72724558db0

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In Ecology

In Environmental Engineering

Citations in the corpus

Estimation of tropical forest structure from SPOT-5 satellite images (2010, methodology): “…of near-infrared) was the most important predictor; combining textural and spectral variables, we obtained R2s similar to studies with the high-resolution images of Ikonos-2 (Kayitakire et al. 2006) or airborne imagery (Hyyppä et al. 2000), despite the more complex forest structure in our case.”
Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data (2020, methodology): “Kayitakire et al. (2006) used image texture features from IKONOS-2 satellite images with 1m × 1m ground resolution and linear modelling to explain forest stand age and other structure-related variables.”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “Use of textural images or object-based methods has the potential to solve these problems (Kayitakire et al. 2006).”
Pine plantation structure mapping using WorldView-2 multispectral image (2013, methodology): “…for remote sensing applications, according to the literature (Baraldi and Parmiggiani 1995; Solberg 1999; Lu 2005; Tuominen and Pekkarinen 2005; Kayitakire, Hamel, and Defourny 2006), namely mean (ME), variance (VAR), standard deviation (ST), contrast (CON), angular second moment (ASM),…”
Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses (2008, methodology): “Kayitakire et al. (2006), deduced forest-structure variables (age, top height, circumference and basal area) from image-texture analysis and IKONOS-2 imagery.”
Modelling tree size diversity from airborne laser scanning using canopy height models with image texture measures (2013, methodology): “The selected texture measures, Contrast, Entropy, and Correlation, have been widely used in previous works (Kayitakire et al., 2006; Ota et al., 2011; Chang et al., 2011; Gu et al., 2012).”
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture (2012, methodology): “The GLCM method is widely accepted and several studies have used it for forest structure modelling (Castillo-Santiago et al., 2010; Kayitakire et al., 2006; Ozdemir et al., 2008; Pasher and King, 2010; Wunderle et al., 2007), habitat modelling (Estes et al.”