Lidar: shedding new light on habitat characterization and modeling

Vierling, Kerri T.; Vierling, L. A.; Gould, William A.; Martinuzzi, Sebastián; Clawges, Rick M.

doi:10.1890/070001

Cited by 456 publications

(326 citation statements)

References 39 publications

Supporting

Mentioning

319

Contrasting

Unclassified

Order By: Relevance

“…Associated with this is the subsequent potential to make predictions over a wider geographical area for which lidar data are available but species occurrence data are not [11,12]. This makes use of lidar data as an explanatory tool to increase understanding (and quantification) of resource selection by species of known distribution [13]. Such studies have most frequently focussed on bird species [14][15][16][17], but with a growing number examining mammals, such as the bald-faced saki monkey (Pithecia irrorata) [18], Pacific fisher (Martes pennanti) [19], moose (Alces alces) [20], and red squirrel (Sciurus vulgaris) [21].…”

Section: Introductionmentioning

confidence: 99%

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

Hill

Hinsley

2015

Remote Sensing

View full text Add to dashboard Cite

Structure is a fundamental physical element of habitat, particularly in woodlands, and hence there has been considerable recent uptake of airborne lidar data in forest ecology studies. This paper investigates the significance of lidar data characteristics when modelling organism-habitat relationships, taking a single species case study in a mature woodland ecosystem. We re-investigate work on great tit (Parus major) habitat, focussing on bird breeding data from 1997 and 2001 (years with contrasting weather conditions and a demonstrated relationship between breeding success and forest structure). We use a time series of three lidar data acquisitions across a 12-year period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). The lidar data characteristics assessed include time-lag with field data (up to 15 years), spatial sampling density (average post spacing in the range of 1 pulse per 0.14 m 2 -17.77 m 2 ), approach to processing (raster or point cloud), and the complexity of derived structure metrics (with a total of 33 metrics assessed, each generated separately using all returns and only first returns). Ordinary least squares regression analysis was employed to investigate relationships between great tit mean nestling body mass, calculated per brood, and the various canopy structure measures from all lidar datasets. For the 2001 bird breeding data, the relationship between mean nestling body mass and mean canopy height for a sample area around each nest was robust to the extent that it could be detected strongly and with a OPEN ACCESS Remote Sens. 2015, 7 3447 high level of statistical significance, with relatively little impact of lidar data characteristics. In 1997, all relationships between lidar structure metrics and mean nestling body mass were weaker than in 2001 and more sensitive to lidar data characteristics, and in almost all cases they were opposite in trend. However, whilst the optimum habitat structure differed between the two study years, the lidar-derived metrics that best characterised this structure were consistent: canopy height percentiles and mean overstorey canopy height (calculated using all returns or only first returns) and the standard deviation of canopy height (calculated using all returns). Overall, our results suggest that for relatively stable woodland habitats, ecologists should not feel prohibited in using lidar data to explore or monitor organism-habitat relationships because of perceived data quality issues, as long as the questions investigated, the scale of analysis, and the interpretation of findings are appropriate for the data available.

show abstract

Section: Introductionmentioning

confidence: 99%

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

Hill

Hinsley

2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Los datos Lidar han sido aplicados tradicionalmente a los campos de la geomorfología, la silvicultura, y al estudio de los ecosistemas forestales (VIERLING, et al 2008).…”

Section: El Uso Del Lidar En Arqueologíaunclassified

La necrópolis del Monte de Santa Mariña revisitada: aportaciones del LIDAR aéreo para la cartografía megalítica de Galicia

Pazos

Estévez

Fariña

et al. 2015

Gall

View full text Add to dashboard Cite

ResumenEl Lidar aéreo se ha constituido en la última década como una de las herramientas más interesantes para la prospección arqueológica, puesto que permite, entre otras cosas, analizar el terreno con gran detalle obviando la vegetación. Planteamos un ejemplo de las posibilidades que para el Megalitismo la tecnología Lidar puede proporcionar. Para ello, hemos elegido la necrópolis megalítica del Monte de Santa Mariña (provincia de Lugo, Galicia), que cuenta con una treintena de monumentos catalogados. Para el estudio del terreno se ha procedido a diseñar una metodología de prospección arqueológica basada en datos Lidar que, gracias a diferentes análisis visuales propuestos, han permitido situar los monumentos correctamente e incluso encontrar uno nuevo. Palabras claveLidar, Sistemas de Información Geográfica, Megalitismo, Santa Mariña. AbstractOver the last decade, the aerial Lidar has been constituted as one of the most interesting tools for the archaeological survey, because it allows, among other things, to analyze the field in detail, specially obviating the vegetation. Thus, we propose an example of the possibilities that Lidar technology could provide in the case of Megalithic culture. Furthermore, we have chosen the megalithic necropolis of Monte de Santa Mariña (Lugo, Galicia), which had some thirty-four monuments officially cataloged. Consequently, before starting the archaeological survey we have planned a methodology based on Lidar data. In fact, thanks to the study of different types of visual analysis proposed by some authors, we were able to identify correctly the whole of all the monuments and even find a new one.

show abstract

“…Modern forest resource planning, applications such as industrial wood flow optimization, require accurate inventories of forest stands, including information as diameter at breast height (DBH) distribution or tree density [Moberg and Nordmark, 2006] and tree quality parameters. Likewise high resolution 3D-information is needed in many scientific fields and essential for accurate mapping of vegetation biomass [Frolking et al, 2009], habitat quality [Vierling et al, 2008] or carbon storage [Asner, 2009]. To obtain this three-dimensional information Lidar is widely and commercially used in forest inventories [Hyyppä et al, 2008].…”

Section: Dense 3d Reconstruction For Forest Stand Parametersmentioning

confidence: 99%

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Fritz

Kattenborn

Koch

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

138

View full text Add to dashboard Cite

ABSTRACT:In both ecology and forestry, there is a high demand for structural information of forest stands. Forest structures, due to their heterogeneity and density, are often difficult to assess. Hence, a variety of technologies are being applied to account for this "difficult to come by" information. Common techniques are aerial images or ground-and airborne-Lidar. In the present study we evaluate the potential use of unmanned aerial vehicles (UAVs) as a platform for tree stem detection in open stands. A flight campaign over a test site near Freiburg, Germany covering a target area of 120 × 75[m 2 ] was conducted. The dominant tree species of the site is oak (quercus robur) with almost no understory growth. Over 1000 images with a tilt angle of 45°were shot. The flight pattern applied consisted of two antipodal staggered flight routes at a height of 55[m] above the ground. We used a Panasonic G3 consumer camera equipped with a 14 − 42[mm] standard lens and a 16.6 megapixel sensor. The data collection took place in leaf-off state in April 2013. The area was prepared with artificial ground control points for transformation of the structure-from-motion (SFM) point cloud into real world coordinates. After processing, the results were compared with a terrestrial laser scanner (TLS) point cloud of the same area. In the 0.9[ha] test area, 102 individual trees above 7[cm] diameter at breast height were located on in the TLS-cloud. We chose the software CMVS/PMVS-2 since its algorithms are developed with focus on dense reconstruction. The processing chain for the UAV-acquired images consists of six steps: a. cleaning the data: removing of blurry, under-or over exposed and off-site images; b. applying the SIFT operator [Lowe, 2004]; c. image matching; d. bundle adjustment; e. clustering; and f. dense reconstruction. In total, 73 stems were considered as reconstructed and located within one meter of the reference trees. In general stems were far less accurate and complete as in the TLS-point cloud. Only few stems were considered to be fully reconstructed. From the comparison of reconstruction achievement with respect to height above ground, we can state that reconstruction accuracy decreased in the crown layer of the stand. In addition we were cutting 50[cm] slices in z-direction and applied a robust cylinder fit to the stem slices. Radii of the TLS-cloud and the SFM-cloud surprisingly correlated well with a Pearson's correlation coefficient of r = 0.696. This first study showed promising results for UAV-based forest structure modelling. Yet, there is a demand for additional research with regard to vegetation stages, flight pattern, processing setup and the utilisation of spectral information.

show abstract

Lidar: shedding new light on habitat characterization and modeling

Cited by 456 publications

References 39 publications

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

La necrópolis del Monte de Santa Mariña revisitada: aportaciones del LIDAR aéreo para la cartografía megalítica de Galicia

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Contact Info

Product

Resources

About