Local Maximum Filtering for the Extraction of Tree Locations and Basal Area from High Spatial Resolution Imagery

By M. Wulder, K. Niemann, D. Goodenough. 2000

422 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/108d7f83d13a259509e55c088e8dee20cb5c0aeb

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In Ecology

In Environmental Engineering

In Nature and Landscape Conservation

Citations in the corpus

A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing (2011, methodology): “Local maximum filtering (LMF) was developed based on the assumption, described above, of the mountainous spatial structure that is typical of forested images.”
Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data (2016, background): “Using most traditional approaches, tree detection is generally based on finding local maxima within the image, and crown delineation requires outlining minimum valleys regardless of whether the input is a passive image or a CHM/DSM/CMM raster [40,62,63,66].”
Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery (2006, methodology): “…been estimated using semi-variogram parameters (Levesque & King, 1999; Muinonen et al., 2001; StOnge & Cavayas, 1995, 1997; Wulder et al., 1998), statistical texture indices (Franklin &Mcdermid, 1993) and also through individual tree crown delineation (Gougeon & Leckie, 1999; Wulder et al., 2000).”
Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013, methodology): “A best-of-both-worlds approach might be achieved by combining our approach with filtering techniques such as local maximum filtering (Wulder et al., 2000), or with pattern-recognition techniques using the object-oriented classification software eCognition (Definiens, 2009).”
Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest (2017, methodology): “As the tree detection is based on FWS, having a small FWS results in selection of non-existent trees or multiple trees for an individual tree crown [65].”
Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration (2002, methodology): “Comparisons with field data (Brandtberg & Walter, 1998; Wulder et al., 2000) are less common due to the difficulty and associated costs of identifying tree locations in the field.”
Individual tree detection based on variable and fixed window size local maxima filtering applied to IKONOS imagery for even-aged Eucalyptus plantation forests (2011, methodology): “Finally, local maxima filtering (Wulder et al. 2000, Pouliot et al. 2002, Wang et al. 2004, Wulder and White 2004, Korpela 75 et al. 2006), where the maximum pixel brightness value is assumed to represent the tree peak, applies a moving window of a specified window size.”
Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery (2008, methodology): “With regards to automatic analysis of such data, actual cases of practical application are still hardly established (Leckie and Gougeon 1999, Wulder et al. 2000, Culvenor 2002, Ehlers et al. 2003, Yu et al. 2006).”