Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug)
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 homogen...
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Russian Academy of Sciences, Center for Forest Ecology and Productivity
2019
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ftdoajarticles:oai:doaj.org/article:f42f46ff04e54d71a35935897294c62f 2023-05-15T17:02:49+02:00 Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug) E.N. Sochilova N.V. Surkov D.V. Ershov V.A. Khamedov 2019-06-01T00:00:00Z https://doi.org/10.31509/2658-607x-2019-2-2-1-20 https://doaj.org/article/f42f46ff04e54d71a35935897294c62f EN RU eng rus Russian Academy of Sciences, Center for Forest Ecology and Productivity http://jfsi.ru/wp-content/uploads/2019/06/2-2-2019-Sochoilova_et_all.pdf https://doaj.org/toc/2658-607X doi:10.31509/2658-607x-2019-2-2-1-20 2658-607X https://doaj.org/article/f42f46ff04e54d71a35935897294c62f Вопросы лесной науки, Vol 2, Iss 2, Pp 1-19 (2019) stand biomass wood stock volume remote sensing data landsat-8 forest classification random forest forestry SD1-669.5 article 2019 ftdoajarticles https://doi.org/10.31509/2658-607x-2019-2-2-1-20 2022-12-31T05:49:10Z 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 ... Article in Journal/Newspaper khanty khanty-mansi Mansi Directory of Open Access Journals: DOAJ Articles Forest science issues 2 2 1 19 |
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stand biomass wood stock volume remote sensing data landsat-8 forest classification random forest forestry SD1-669.5 |
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stand biomass wood stock volume remote sensing data landsat-8 forest classification random forest forestry SD1-669.5 E.N. Sochilova N.V. Surkov D.V. Ershov V.A. Khamedov Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug) |
topic_facet |
stand biomass wood stock volume remote sensing data landsat-8 forest classification random forest forestry SD1-669.5 |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
E.N. Sochilova N.V. Surkov D.V. Ershov V.A. Khamedov |
author_facet |
E.N. Sochilova N.V. Surkov D.V. Ershov V.A. Khamedov |
author_sort |
E.N. Sochilova |
title |
Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug) |
title_short |
Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug) |
title_full |
Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug) |
title_fullStr |
Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug) |
title_full_unstemmed |
Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug) |
title_sort |
assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of khanty-mansi autonomous okrug) |
publisher |
Russian Academy of Sciences, Center for Forest Ecology and Productivity |
publishDate |
2019 |
url |
https://doi.org/10.31509/2658-607x-2019-2-2-1-20 https://doaj.org/article/f42f46ff04e54d71a35935897294c62f |
genre |
khanty khanty-mansi Mansi |
genre_facet |
khanty khanty-mansi Mansi |
op_source |
Вопросы лесной науки, Vol 2, Iss 2, Pp 1-19 (2019) |
op_relation |
http://jfsi.ru/wp-content/uploads/2019/06/2-2-2019-Sochoilova_et_all.pdf https://doaj.org/toc/2658-607X doi:10.31509/2658-607x-2019-2-2-1-20 2658-607X https://doaj.org/article/f42f46ff04e54d71a35935897294c62f |
op_doi |
https://doi.org/10.31509/2658-607x-2019-2-2-1-20 |
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Forest science issues |
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