Estimation of Grassland Biomass in Eastern Mongolia RS-Based Vegetation Indices

Amarsaikhan Damdinsuren; Byambadolgor Batdorj; Enkhjargal Damdinsuren; Nyamjargal Erdenebaatar; Tsogzol Gurjav

doi:10.2991/978-94-6463-278-1_20

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Estimation of Grassland Biomass in Eastern Mongolia RS-Based Vegetation Indices

Authors

Amarsaikhan Damdinsuren¹^{, *}, Byambadolgor Batdorj¹, Enkhjargal Damdinsuren¹, Nyamjargal Erdenebaatar¹, Tsogzol Gurjav¹

¹Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia

^*Corresponding author. Email: amarsaikhan@mas.ac.mn

Corresponding Author

Amarsaikhan Damdinsuren

Available Online 29 October 2023.

DOI: 10.2991/978-94-6463-278-1_20 How to use a DOI?
Keywords: Eastern grassland area; biomass; MODIS data; vegetation indices
Abstract: The aim of this study is to estimate above-ground biomass (AGB) in the eastern grassland area of Mongolia applying two machine learning methods, such as the support vector machine (SVM) and random forest (RF), and determine the appropriate method by comparing the results. For this purpose, 21 vegetation indices derived from MODIS data of August 2016 are used. As ground-truth information, reference biomass samples are available from 38 sites of a field survey. To select the appropriate prediction variables, the correlations between the measured biomass in the field and the defined indices are calculated. For further analysis, eight indices with correlation coefficients (r)>0.6 are selected. When the classification results are compared, the RF method demonstrates higher accuracy. Therefore, we can conclude that it can be used efficiently for the estimation of AGB in the selected grassland area of Mongolia.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Fourth International Conference on Environmental Science and Technology (EST 2023)
Series: Advances in Engineering Research
Publication Date: 29 October 2023
ISBN: 10.2991/978-94-6463-278-1_20
ISSN: 2352-5401
DOI: 10.2991/978-94-6463-278-1_20 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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ris enw bib

TY  - CONF
AU  - Amarsaikhan Damdinsuren
AU  - Byambadolgor Batdorj
AU  - Enkhjargal Damdinsuren
AU  - Nyamjargal Erdenebaatar
AU  - Tsogzol Gurjav
PY  - 2023
DA  - 2023/10/29
TI  - Estimation of Grassland Biomass in Eastern Mongolia RS-Based Vegetation Indices
BT  - Proceedings of the Fourth International Conference on Environmental Science and Technology (EST 2023)
PB  - Atlantis Press
SP  - 205
EP  - 215
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-278-1_20
DO  - 10.2991/978-94-6463-278-1_20
ID  - Damdinsuren2023
ER  -

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