Improving forest sampling strategies for assessment of fuel reduction burning

Gharun, Mana; Possell, Malcolm; Jenkins, Meaghan E.; Poon, Lai Fan; Bell, Tina L.; Adams, Mark A.

doi:10.1016/j.foreco.2017.03.001

Forest Ecology and Management

2017

DOI: 10.1016/j.foreco.2017.03.001

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Improving forest sampling strategies for assessment of fuel reduction burning

Mana Gharun

Malcolm Possell

Meaghan E. Jenkins

et al.

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Cited by 4 publications

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“…Many studies have focused on gaining information on green biomass volume and vegetation productivity (Hame et al, 1997;Hall et al, 2006;Ji et.al., 2012;Zhang et al, 2014). Use of optical remote sensing data for the quantification of forest fuel stocks (Arroyo et al, 2008;Gharun et al, 2017) or the volume of stem wood for logging purposes (Fazakas et al, 1999;Zheng et al 2014) is of greater interest. Such use of this type of data is, however, much less common.…”

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Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug)

Sochilova¹,

Surkov²,

Ershov³

et al. 2019

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The paper describes assessment of spatial biomass of top wood layer based on combination of high-resolution Landsat-8 satellite images and selected ground forest inventory data measurements. Test area is one of forestry of Khanty-Mansiysk region. Segmentation of satellite images for spectral homogeneous land sites (segments) mapping is applied. Land category, dominated specie, age and wood stock volume for these sites are defined. Ground forest inventory data and segments used for selection of segments for dominated specie classification and validation of obtained map. The first, nine types of land cover are classified, four of them belong to forest cover with dominating of pine, spruce, cider and birch. The reference sample is updated by segments of such non-forest classes as fires, cuts and other non-forested lands, swamps, water internal bodies. Twelve spectral metrics are used for classification: reflectance in blue, green, red and near-infrared bands of Landsat-8. There are following vegetation seasons: and of winter, beginning of spring and middle of summer. The most significant informative metrics are the reflectance in the NIR band of the spring image, also green and red bands of the summer image. Random Forest algorithm is applied for training classification. The total accuracy of land categories and dominated species classification is 86,3%. Cross-validation of the classification based on the control sample was 0.712. In the second stage, we used regression models to relate the reflectance in the red band of the winter image with the taxation characteristics of the wood stock and age of the forest species in the selected reference segments. The level of relationship between the reflectance and wood stock values were equal to 0.80 for pine, 0.56 for dark coniferous species and 0.73 for birch. Between the reflectance and the specie height is following 0.75 for pine, 0.61 for birch and 0.64 for dark coniferous species. A check with control data showed that the error in estimating the wood stock above 250 m 3 / ha for birch is 15.4%, for pine-19.0% and for dark coniferous species-5.5%. We used regional growth tables and the mean heights reconstructed from the regression equations for calculation mean specie ages. Then the age groups (according regional felling age) for each species are determined and the wood stocks are converted into wood biomass by conversion coefficients. As a result, maps of mean ages, heights, wood stock in m 3 /ha and biomass in t/ha were created. Based on these maps quarter assessments of the areas and stocks of the main dominated forest species of our test area, including felling age forest stands, were carried out.

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confidence: 99%

Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug)

Sochilova¹,

Surkov²,

Ershov³

et al. 2019

FSI

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Uncertainties in above ground tree biomass estimation

et al. 2020

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Spatial variability-based sample size allocation for stratified sampling

Shao

Zhang

Fan

et al. 2021

CATENA

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Improving forest sampling strategies for assessment of fuel reduction burning

Cited by 4 publications

References 63 publications

Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug)

Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug)

Uncertainties in above ground tree biomass estimation

Spatial variability-based sample size allocation for stratified sampling

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