Airborne laser scanning-based prediction of coarse woody debris volumes in a conservation area

Pesonen, Annukka; Maltamo, Matti; Eerikäinen, Kalle; Packalén, Petteri

doi:10.1016/j.foreco.2008.02.017

Cited by 116 publications

(99 citation statements)

References 41 publications

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“…The European CORINE Land Cover (CLC2000) database in a 25 m grid can help in coarse assessments of some biophysical characteristics of the environment (Vihervaara et al 2010), but it cannot provide accurate information on local species assemblages and biotope types that form the basis of ecosystem service supply. More detailed tools, such as airborne laser scanning (ALS), are needed (Hill and Thomson 2005;Pesonen et al 2008;Vehmas et al 2009;Asner et al 2012;Vihervaara et al 2012;Pippuri et al 2012Pippuri et al , 2013. ALS has revolutionized the ways to measure forest structure in 3D.…”

Section: Introductionmentioning

confidence: 99%

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

et al. 2014

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Context Biodiversity and ecosystem functioning underpins the delivery of all ecosystem services and should be accounted for in all decision-making related to the use of natural resources and areas. However, biodiversity and ecosystem services are often inadequately accounted for in land use management decisions. Objective We studied a boreal forest ecosystem by linking citizen-science bird data with detailed information on forest characteristics from airborne laser scanning (ALS). In this paper, we describe this method, and evaluate how similar kinds of biological data sets combined with remote sensing can be used for ecosystem assessments at landscape scale. Methods We analysed data for 41 boreal forest bird species and for 14 structural ALS-based forest parameters.Results The results support the use of the selected method as a basis for quantifying spatially-explicit biodiversity indicators for ecosystem assessments, while suggestions for improvements are also reported. Finally, we evaluate the capacity of those indicators to describe biodiversity-ecosystem service relationships, for example with carbon trade-offs. The results showed clear distinctions between the different species as measured, for example, by above-ground forest biomass at the observation sites. We also assess how Electronic supplementary material The online version of this article

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Section: Introductionmentioning

confidence: 99%

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

et al. 2014

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“…ALS data have been related to animal habitat data, this far mostly for birds (e.g., Hill et al, 2004;Hinsley et al, 2008;Lesak et al, 2011), but also for forest-dwelling beetles (Müller and Brandl, 2009). The amount of lying dead wood (Pesonen et al, 2008) and standing dead wood (Bater et al, 2009) in forests has been estimated from ALS data with area-based approaches (i.e., based on the correlation between the total amount of dead wood and measures derived from the ALS data in 900 to 1600 m 2 raster cells). The analyses above have not utilized the detailed 3D structure of ALS data, which may be done with, for example, object based image analysis (Blanchard et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Detection of lying tree stems from airborne laser scanning data using a line template matching algorithm

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Mücke

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Dead wood is an important habitat characteristic in forests. However, dead wood lying on the ground below a canopy is difficult to detect from remotely sensed data. Data from airborne laser scanning include measurement of surfaces below the canopy, thus offering the potential to model objects on the ground. This paper describes a new line template matching algorithm for detecting lines along the ground. The line template matching is done directly to the laser point cloud and results in a raster showing the support of the line in each raster cell. Line elements are vectorized based on the raster to represent lying tree stems. The results have been validated versus field-measured lying tree stems. The number of detected lines was 845, of which 268 could be automatically linked to the 651 field-measured stems. The line template matching produced a raster which visually showed linear elements in areas where lying tree stems where present, but the result is difficult to compare with the field measurements due to positioning errors. The study area contained big piles of storm-felled trees in some places, which made it an unusually complex test site. Longer line structures such as ditches and roads also resulted in detected lines and further analysis is needed to avoid this, for example by specifically detecting longer lines and removing them.

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“…While the differentiation between deciduous and coniferous trees can be performed with a high accuracy (>80%, up to~97%) the differentiation within these classes is more difficult and leads to a higher classification error [111]. Moreover, it is possible to distinguish between living trees, standing dead trees and snags [112][113][114] or to map dead trees on the plot or stand level [115,116]. However, 3D LiDAR has its limitations for differentiating between trees species and dead trees unless it is not combined with multi-and hyperspectral optical data.…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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Airborne laser scanning-based prediction of coarse woody debris volumes in a conservation area

Cited by 116 publications

References 41 publications

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

Detection of lying tree stems from airborne laser scanning data using a line template matching algorithm

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

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