Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA

Goetz, Scott J.; Steinberg, Daniel; Dubayah, Ralph; Blair, Bryan

doi:10.1016/j.rse.2006.11.016

Cited by 316 publications

(217 citation statements)

References 43 publications

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“…Efficiency savings have been possible through the recent proliferation of airborne lidar (laser scanning), where it has shown that it is possible to acquire information on three-dimensions of a forested canopy (x,y and z) over much larger areas than possible through ground survey (Maltamo et al, 2005;Goetz et al, 2007). Airborne laser scanning (ALS) is a rapidly growing technology and many technical improvements have featured in recent years.…”

Section: Remote Sensing: Targeted Mappingmentioning

confidence: 99%

“…In either case, the ecologist is provided with information such as forest growth (e.g., Yu et al, 2006); valuable structural information where the vertical distribution of the canopy, for example, can be the most important variable for the accurate prediction of bird species richness (Goetz et al, 2007); habitat assessment variables (Hill et al, 2004;Clawges et al, 2008;Martinuzzi et al, 2009) and biomass assessment for input into initiatives such as the UN-REDD programme (Tollefson, 2009). In addition to work using ALS both spaceborne lidar systems (e.g., ICESat/ GLAS) and terrestrial lidar systems (TLS) have demonstrated potential for provision of ecological information.…”

Section: Remote Sensing: Targeted Mappingmentioning

confidence: 99%

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An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

118

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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Section: Remote Sensing: Targeted Mappingmentioning

confidence: 99%

Section: Remote Sensing: Targeted Mappingmentioning

confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

118

View full text Add to dashboard Cite

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“…Many studies have evaluated the environmental controls of regional patterns of species richness using species field counts (e.g., [37][38][39][40][41][42]), with an increasing number of them taking advantage of remote sensing information (e.g., [33,[43][44][45][46][47][48][49][50]). Globally, these studies found a stronger effect of water than of energy on species richness in the southern hemisphere than in the northern hemisphere [38].…”

Section: Introductionmentioning

confidence: 99%

Environmental and Human Controls of Ecosystem Functional Diversity in Temperate South America

et al. 2013

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Abstract:The regional controls of biodiversity patterns have been traditionally evaluated using structural and compositional components at the species level, but evaluation of the functional component at the ecosystem level is still scarce. During the last decades, the role of ecosystem functioning in management and conservation has increased. Our aim was to use satellite-derived Ecosystem Functional Types (EFTs, patches of the land-surface with similar carbon gain dynamics) to characterize the regional patterns of ecosystem functional diversity and to evaluate the environmental and human controls that determine EFT richness across natural and human-modified systems in temperate South America. The EFT identification was based on three descriptors of carbon gain dynamics derived from seasonal curves of the MODIS Enhanced Vegetation Index (EVI): annual mean (surrogate of primary production), seasonal coefficient of variation (indicator of seasonality) and date of maximum EVI (descriptor of phenology). As observed for species richness in the southern hemisphere, water availability, not energy, emerged as the main climatic driver of 128EFT richness in natural areas of temperate South America. In anthropogenic areas, the role of both water and energy decreased and increasing human intervention increased richness at low levels of human influence, but decreased richness at high levels of human influence.

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“…Biological studies on forest habitat mapping benefit from species specific forest information, since for example, the preferred tree species of some endangered species could be located using remote sensing [4]. Knowledge of tree species is also needed in the forest industry as the species information determines the usability of the wood material.…”

Section: Open Accessmentioning

confidence: 99%

Individual Tree Species Classification by Illuminated—Shaded Area Separation

2009

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Abstract:A new method, called Illumination Dependent Colour Channels (IDCC), is presented to improve individual tree species classification. The method is based on tree crown division into illuminated and shaded parts on a digital aerial image. Colour values of both sides of the tree crown are then used in species classification. Tree crown division is achieved by comparing the projected location of an aerial image pixel with its neighbours on a Canopy Height Model (CHM), which is calculated from a synchronized LIDAR point cloud. The sun position together with the mapping aircraft position are also utilised in illumination status detection. The new method was tested on a dataset of 295 trees and the classification results were compared with ones measured with two other feature extraction methods. The results of the developed method gave a clear improvement in overall tree species classification accuracy.

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Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA

Cited by 316 publications

References 43 publications

An overview of recent remote sensing and GIS based research in ecological informatics

An overview of recent remote sensing and GIS based research in ecological informatics

Environmental and Human Controls of Ecosystem Functional Diversity in Temperate South America

Individual Tree Species Classification by Illuminated—Shaded Area Separation

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