National inventories of down and dead woody material forest carbon stocks in the United States: Challenges and opportunities

Woodall, Christopher W.; Heath, Linda S.; Smith, James E.

doi:10.1016/j.foreco.2008.04.003

Cited by 84 publications

(48 citation statements)

References 32 publications

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“…For signatories to the United Nations Framework Convention on Climate Change, timely assessments of deadwood changes are essential for preparation of the required annual reports on greenhouse gas inventories (Cienciala et al 2008, Woodall et al 2008.…”

Section: Eadwood Is Acknowledged To Be a Criticalmentioning

confidence: 99%

Assessing Deadwood Using Harmonized National Forest Inventory Data

Rondeux¹,

Bertini²,

Bastrup-Birk³

et al. 2012

Forest Science

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Abstract:Deadwood plays an important role in forest ecological processes and is fundamental for the maintenance of biological diversity. Further, it is a forest carbon pool whose assessment must be reported for international agreements dealing with protection and forest management sustainability. Despite wide agreement on deadwood monitoring by national forest inventories (NFIs), much work is still necessary to clarify definitions so that estimates can be directly compared or aggregated for international reporting. There is an urgent need for an international consensus on definitions and agreement on harmonization methods. The study addresses two main objectives: to analyze the feasibility of harmonization procedures for deadwood estimates and to evaluate the impact of the harmonization process based on different definitions on final deadwood estimates. Results are reported for an experimental harmonization test using NFI deadwood data from 9,208 sample plots measured in nine European countries and the United States. Harmonization methods were investigated for volume by spatial position (lying or standing), decay classes, and woody species accompanied by accuracy assessments. Estimates of mean plot volume based on harmonized definitions with minimum length/height of 1 m and minimum diameter thresholds of 10, 12, and 20 cm were on average 3, 8, and 30% smaller, respectively, than estimates based on national definitions. Volume differences were less when estimated for various deadwood categories. An accuracy assessment demonstrated that, on average, the harmonization procedures did not substantially alter deadwood observations (root mean square error 23.17%). FOR. SCI. 58(3):269 -283.

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Section: Eadwood Is Acknowledged To Be a Criticalmentioning

confidence: 99%

Assessing Deadwood Using Harmonized National Forest Inventory Data

Rondeux¹,

Bertini²,

Bastrup-Birk³

et al. 2012

Forest Science

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“…Also, it forms a significant component of forest carbon stocks (Woodall et al, 2008). Furthermore, snags are a source of coarse woody debris which is important in stand succession (Harmon et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

Detection of Single Standing Dead Trees From Aerial Color Infrared Imagery by Segmentation With Shape and Intensity Priors

Polewski

Yao

Heurich

et al. 2015

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

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ABSTRACT:Standing dead trees, known as snags, are an essential factor in maintaining biodiversity in forest ecosystems. Combined with their role as carbon sinks, this makes for a compelling reason to study their spatial distribution. This paper presents an integrated method to detect and delineate individual dead tree crowns from color infrared aerial imagery. Our approach consists of two steps which incorporate statistical information about prior distributions of both the image intensities and the shapes of the target objects. In the first step, we perform a Gaussian Mixture Model clustering in the pixel color space with priors on the cluster means, obtaining up to 3 components corresponding to dead trees, living trees, and shadows. We then refine the dead tree regions using a level set segmentation method enriched with a generative model of the dead trees' shape distribution as well as a discriminative model of their pixel intensity distribution. The iterative application of the statistical shape template yields the set of delineated dead crowns. The prior information enforces the consistency of the template's shape variation with the shape manifold defined by manually labeled training examples, which makes it possible to separate crowns located in close proximity and prevents the formation of large crown clusters. Also, the statistical information built into the segmentation gives rise to an implicit detection scheme, because the shape template evolves towards an empty contour if not enough evidence for the object is present in the image. We test our method on 3 sample plots from the Bavarian Forest National Park with reference data obtained by manually marking individual dead tree polygons in the images. Our results are scenario-dependent and range from a correctness/completeness of 0.71/0.81 up to 0.77/1, with an average center-of-gravity displacement of 3-5 pixels between the detected and reference polygons.

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“…Recent studies show that it is even possible to detect objects located on the ground surface such as coarse woody debris [96,97]. Coarse woody debris is an important indicator of FH, because it provides habitat to a multitude of endangered plant and animal species and plays an important role in the forest carbon cycle [98,99].…”

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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National inventories of down and dead woody material forest carbon stocks in the United States: Challenges and opportunities

Cited by 84 publications

References 32 publications

Assessing Deadwood Using Harmonized National Forest Inventory Data

Assessing Deadwood Using Harmonized National Forest Inventory Data

Detection of Single Standing Dead Trees From Aerial Color Infrared Imagery by Segmentation With Shape and Intensity Priors

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

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