High spatial resolution remote sensing of forest trees

McGraw, James B.; Warner, Timothy A.; Key, Thomas; Lamar, W. Robert

doi:10.1016/s0169-5347(98)01414-1

Cited by 16 publications

(7 citation statements)

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“…A new generation of high spatial resolution (< 4 m), multispectral sensors now exists for use by tropical resource managers and scientists that can resolve individual tree crowns (ITCs) as groups of image pixels McGraw et al, 1998). ITCs can be identified to species and regrouped into communities, stands or patches Leckie et al, 2003).…”

Section: Contribution Of Remote Sensing To Tropical Rain Forest Assesmentioning

confidence: 99%

“…New digital forms of high spatial resolution imagery from cameras, videography, and multispectral airborne and spaceborne sensors (e.g., IKONOS, Quickbird) have stimulated the development of automated techniques for ITC detection, crown delineation and subsequent measurement of crown architecture and floristic information (reviewed in McGraw et al, 1998 andNagendra, 2001). Automated ITC delineation and classification algorithms have been optimized for distinguishing trees in northern-latitude forests dominated by conifers and deciduous hardwoods (Gougeon, 1995;Key et al, 2001;Leckie & Gougeon, 1999;Meyer et al, 1996), and it is unclear how these algorithms will perform in TRF with high species diversity and complex canopies.…”

Section: Contribution Of Remote Sensing To Tropical Rain Forest Assesmentioning

confidence: 99%

“…Operational applications involving individual tree inventory over large spatial extents will require automated methods of isolating crowns in the imagery. Automated ITC delineation is a subdiscipline of image segmentation and is an area of intense research (reviewed extensively in McGraw et al, 1998 andKey et al, 2001). Most segmentation methods have been developed for forestry applications in conifer-dominated stands and there are still many challenges to crown delineation in complex, old-growth hardwood forests, where trees and lianas may intertwine and overlap, inter-tree shadows are narrow, and canopy-gap shadows are prevalent, causing automated segmentation schemes to identify clusters of trees rather than individual crowns (McGraw et al, 1998).…”

Section: Potential For Tropical Rain Forest Natural Resource Managementmentioning

confidence: 99%

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Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales

Clark

Roberts

Clark

2005

Remote Sensing of Environment

648

503

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Section: Contribution Of Remote Sensing To Tropical Rain Forest Assesmentioning

confidence: 99%

Section: Contribution Of Remote Sensing To Tropical Rain Forest Assesmentioning

confidence: 99%

Section: Potential For Tropical Rain Forest Natural Resource Managementmentioning

confidence: 99%

See 1 more Smart Citation

Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales

Clark

Roberts

Clark

2005

Remote Sensing of Environment

648

503

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“…Repeated censuses can be strengthened when coupled with spatial information about the population (Pavlik, 1994). High spatial resolution remote sensing is becoming more common (Coultier et al, 2000;Peters et al, 2000), and offers largely untapped potential for censusing, monitoring, and managing rare plant populations (McGraw et al, 1998). First, in environments where access and terrain limit the intensity and extent of field-based census methods, remote sensing may be particularly valuable.…”

Section: Introductionmentioning

confidence: 99%

Potential of Digital Color Imagery for Censusing Haleakala Silverswords in Hawaii

Landenberger¹,

McGraw²,

Warner³

et al. 2003

photogramm eng remote sensing

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Spatially explicit, high spatial resolution remotely sensed imagery offers a largely untapped potential for censusing and monitoring rare plant populations that exist in remote, exposed environments. Using digital color imagery acquired over the Haleakala Crater on Maui, Hawai'i, we evaluated the accuracy of photointerpretation and automated censuses by imaging nine silversword census plots characterized by individuals of known size, life cycle status, and location. Due to spatial resolution limitations, both methods tended to omit small individuals, but omissions varied by size class and type of omission. Omission rates were low for demographically important medium and large plants; however, the automated method often failed to segment and census tightly clustered plants. The photointerpreter commission error rate was lower than that of the automated method, and both methods tended to overestimate mean silversword size. These data outline the issues and challenges that will likely emerge as spatially explicit, high spatial resolution aerial censuses become more common.

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“…Quantifying forage availability across large landscapes in places such as Alaska at a resolution and accuracy useful to land managers requires new methodologies that meet the following requirements: 1) they are relatively inexpensive; 2) they can be scaled up to landscape levels; and 3) they can be implemented efficiently in terms of ground‐truthing requirements. High‐resolution aerial photography or satellite imagery has been used to map vegetation to the species level with high accuracy (Harvey and Hill , Everitt et al , Wang and Sousa ), but these applications have been limited to the mapping of tree or overstory canopy species (McGraw et al ), or were spatially limited (Petersen et al ). Thus, previous applications of these techniques have been of little value for large herbivore forage inventories.…”

mentioning

confidence: 99%

High spatial resolution vegetation mapping for assessment of wildlife habitat

et al. 2013

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Management of large herbivore populations requires assessment of both the quality and quantity of habitats available to the animals. Classifying and quantifying their habitats, particularly the availability of forage, is often challenging because of the time and expense associated with collection of the appropriate data over large landscapes. Land-cover maps generated from remote-sensing imagery generally provide only qualitative information regarding species composition of the vegetation, and do not adequately quantify forage species availability. Our objective was to determine the feasibility of mapping important forage species for moose (Alces americanus) in South-central Alaska, USA, using fine-resolution aerial photography. We used infrared and color digital photography acquired at an altitude of 150 m across 2 study areas with distinctly different vegetation to provide spectral data for supervised classifications of plant species using the maximum likelihood algorithm. We analyzed spring, autumn, and composite sets of the imagery to determine which imagery provides the most accurate classifications. Willow (Salix spp.) was separable from non-forage species in autumn images with an average overall accuracy of 76% in the Nelchina study area and 78% in Placer Valley. The classifications of composite images separated important moose forages, but with lower average overall accuracies (68% for Nelchina study area and 75% for Placer Valley) compared with the autumn classifications. Spring classifications were least accurate (57% for Nelchina study area and 63% for Placer Valley), likely because the green-up of plants in the spring occurred rapidly for all species in both study sites, and this resulted in relatively small differences in spectral response patterns between species.

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High spatial resolution remote sensing of forest trees

Cited by 16 publications

References 0 publications

Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales

Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales

Potential of Digital Color Imagery for Censusing Haleakala Silverswords in Hawaii

High spatial resolution vegetation mapping for assessment of wildlife habitat

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