Estimation of forest above ground biomass using Sentinel-1 data

Authors

  • Tsolmon Altanchimeg Division of GIS and Remote Sensing,  Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia https://orcid.org/0000-0002-0116-4862
  • Amarsaikhan Damdinsuren Division of GIS and Remote Sensing,  Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia

DOI:

https://doi.org/10.5564/mjgg.v60i44.2935

Keywords:

Above Ground Biomass (AGB), Random Forest (RF), rincipal Component Analysis (PCA), Vegetation Index (VI)

Abstract

Estimation of aboveground biomass is important for sustainable forest management and climate change mitigation. Traditional methods for estimating aboveground biomass rely on data collected from field measurements, which is spatially limited and also very expensive. Over the last years, remotely sensed (RS) datasets have been widely used for forest biomass assessment. So, the main aim of this research is to estimate and then map the above ground biomass (AGB) of forested site using modern radar satellite data. The study has the following objectives: I. Estimate the biomass of forest land, II. Estimating AGB using vegetation indices and Sentinel-1 satellite data recorded in the C-band with 5.55 cm wavelength. III. Conduct a comparative study of principal component analysis and random forest methods. As a test site, the area around Khangal sum of Bulgan Province was selected. In the study, the random forest method showed good results, and for Level 1 GRD data R2=0.823, RMSE=0.116 t/ha, while for Level 1 SLC data R2=0.815, RMSE=0.105 t/ha. Overall, sthe reseach indicated that it is possible to determine the AGB of forests in the temperate zone of Mongolia using radar satellite data.

Sentinel-1 дагуулын мэдээ ашиглан ойн газрын дээрх биомассыг тооцоолох нь

ХУРААНГУЙ: Газрын дээрх биомассыг тооцоолох нь ойн тогтвортой менежмент болон уур амьсгалын өөрчлөлтийг бууруулахад чухал үүрэгтэй. Газрын дээрх биомассыг тооцох уламжлалт аргууд нь хээрийн хэмжилтээр цуглуулсан өгөгдлийг ашиглан үнэлгээ хийх зарчимд тулгуурлах бөгөөд энэ нь орон зайн хувьд хязгаарлагдмал, өртөг өндөртэй юм. Орчин үед зайнаас тандсан мэдээг боловсруулан ойн биомассын үнэлгээнд ихээхэн ашиглаж байна. Энэхүү судалгааны ажил нь ойн газрын дээрх биомассыг сүүлийн үеийн радарын хиймэл дагуулын мэдээ ашиглан тооцоолж, улмаар зураглах үндсэн зорилготой. Тус зорилгын хүрээнд i) ойн газрын дээрх биомассыг тооцох, ii) Sentinel-1 дагуулын 5.55 см урттай радарын С-сувгийн мужид бүртгэгдсэн мэдээ болон ургамлын индексүүдийг ашиглан газрын дээрх биомассыг тооцоолох, iii) гол компонентын шинжилгээ болон санамсаргүй форестын аргыг харьцуулан судлах гэсэн зорилтуудыг дэвшүүлсэн. Судалгааны талбайгаар Булган аймгийн Хангал сум орчмын талбайг сонгон авсан. Судалгааны үр дүнгээс харахад санамсаргүй форестын арга сайн үр дүнг үзүүлж байсан бөгөөд Level 1 Ground Range Detected (GRD) мэдээний хувьд детерминацийн коэффициент (R2)=0.823, дундаж квадрат алдаа (RMSE)=0.116 тн га-1 байсан бол Level 1 Single Look Complex (SLC) мэдээний хувьд R2=0.815, RMSE=0.105 тн га-1 байлаа. Энэхүү судалгаа нь Монгол орны сэрүүн бүсийн ойн газрын дээрх биомассыг радарын мэдээ ашиглан тодорхойлох боломжтой гэдгийг баталж байна.

Түлхүүр үгс: Газрын дээрх биомасс (ABG), Санамсаргүй форестын арга (RF), Гол компонентын шинжилгээ (PCA), Ургамлын индекс (VI)

Abstract
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References

. D. Ellison, C. E. Morris, B. Locatelli, D. Sheil, J. Cohen, D. Murdiyarso, V. Gutierrez, M. v. Noordwijk, I. F. Creed, J. Pokorny, D. Gaveau, D. V. Spracklen, A. B. T. Tobella, U. Ilstedt, A. J. Teuling, S. G. Gebrehiwot, D. C. Sands, B. Muys, B. Verbist, E. Springgay, Y. Sugandi and C. A. Sullivan, "Trees, forests and water: Cool insights for a hot world". Global Environmental Change, vol. 9. no. 43, pp. 51-61, 2017. https://doi.org/10.1016/j.gloenvcha.2017.01.002

. T. Altanchimeg, A. Damdinsuren, B. Tseveen and B. Batdorj, "Analysis of Relations Between Aboveground Biomass and Vegetation Indices Derived from Sentinel-2 Satellite Data". Journal of Institute of Mathematics and Digital Technology, vol 4, pp. 94-100, 2022. https://doi.org/10.5564/jimdt.v4i1.2666

. M. O. N. H. Ravindranath, "Carbon Inventory Methods: Handbook for Greenhouse Gas Inventory, Carbon Mitigation and Roundwood Production Projects". Heidelberg, Berlin: Springer, 2008. https://doi.org/10.1007/978-1-4020-6547-7

. L. Chen, C. Ren, B. Zhang, Z. Wang and Y. Xi, "Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery". Forests, vol. 9, pp. 1-20, 2018. https://doi.org/10.3390/f9100582

. N. Georgopoulos, C. Sotiropoulos, A. Stefanidou and I. Z. Gitas, "Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain". Forests, vol. 13, pp. 1-18, 2022. https://doi.org/10.3390/f13122157

. Ч. Болдбаатар, Ой, уур амьсгалын өөрчлөлт, Улаанбаатар: БОАЖЯ, ШУТИС, Ой модны сургалт судалгааны хүрээлэн, 2018.

. N. Georgopoulos, I. Z. Gitas, A. Stefanidou, L. Korhonen and D. Stavrakoudis, "Estimation of Individual Tree Stem Biomass in an Uneven-Aged Structured Coniferous Forest Using Multispectral LiDAR Data". vol. 13, p. 4827, 2021. https://doi.org/10.3390/rs13234827

. T. Renchin, R. Tateishi and J. T. S. Sumantyo, "A method to estimate forest biomass and its application to monitor Mongolian Taiga using JERS-1 SAR". International Journal of Remote Sensing, vol. 23, pp. 4971-4978, 2002. https://doi.org/10.1080/01431160210133554

. N. Zagdaa, T. Renchin and N. Davaa, "Monitoring of forest biomass in Selenge province". Proceedings of 27th Asian Conference on Remote Sensing, Ulaanbaatar, 2006.

. B. Norovsuren, B. Tseveen, V. Batomunkuev and T. Renchin, "Estimation for forest biomass and coverage using Satellite data in small scale area, Mongolia". IOP Conference Series: Earth and Environmental Science, p. 320, 2019. https://doi.org/10.1088/1755-1315/320/1/012019

. T. Altanchimeg, T. Renchin, B. Darkhijav, P. D. Maeyer, B. Tseveen and B. Norovsuren, "Biomass estimation methodology for forest in Bulgan province, Mongolia". Proceedings of 41st Asian conference on Remote sensing, Deqing, 2020.

. D. Chimednyam, D. Chultem, T. Gonchig, T. Jargalsaikhan and K. Battuvshin, "Mongolia's forestry -rehabilitation of plants". Forest Day in Mongolia, Ulaanbaatar, 2021.

. Л. Цэрэндэжид "Сибирь шинэс (larix sibirica ledeb)-ний бичил ургамлыг гаргах, бойжуулахад өсөлт in vitro орчинд идэвхжүүлэгчийн нөлөө". ХАА-н шинжлэх ухаан сэтгүүл, 26, хх. 108-116, 2019.

. A. Damdinsuren, N. Erdenebaatar, J. Enkhtuya, M.-E. Altangerel, R. Tovuudorj ба T. Gurjav, "Interpretation of Scattering Characteristics of Radar L and C-Channel Data". Journal of Institute of Mathematics and Digital Technology, Vol. 4, pp. 85-93, 2023. https://doi.org/10.5564/jimdt.v4i1.2665

. ESA, "Sentinel-1 SAR User Guide". ESA, 2016.

. T. Altanchimeg, T. Renchin, P. D. Maeyer, E. Natsagdorj, B. Tseveen and N. Bayanmunkh, "Estimation methodology for forest biomass in Mongolia using remote sensing". The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 7-12, 2019. https://doi.org/10.5194/isprs-archives-XLII-5-W3-7-2019

. Д. Амарсайхан, Радарын тандан судлал, радарын мэдээнд дүн шинжилгээ хийх зарчмууд, Улаанбаатар: Эрдэм хэвлэл, 2013.

. P. Wallisch, . M. Lusignan, M. Benayoun, T. I. Baker, A. S. Dickey and N. G. Hatsopoulos, "Principal Components Analysis". Matlab for Neuroscientists, pp. 183-192, 2009. https://doi.org/10.1016/B978-0-12-374551-4.00014-2

. L. Breiman, "Random forest". Machine Learning, б. 45, p. 5-32, 2001. https://doi.org/10.1023/A:1010933404324

. S. Chakraborty, Bayesian Additive Regression Tree for Seemingly Unrelated Regression with Automatic Tree Selection, Handbook of Statistics, pp. 229-251, 2016. https://doi.org/10.1016/bs.host.2016.07.007

. Y. Kim and J. J. van Zyl, "A Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data". IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 2519 - 2527, 2009. https://doi.org/10.1109/TGRS.2009.2014944

. D. Mandal, V. Kumar, D. Ratha, S. Dey, A. Bhattacharya, J. M. Lopez-Sanchez, H. McNairn and Y. S. Rao, "Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data". Remote Sensing of Environment, vol. 247, pp. 1954, 2020. https://doi.org/10.1016/j.rse.2020.111954

. J. J. Faraway, Linear Models with R, Taylor & Francis, 2014.

. X.P. Song, W. Huang, M. C. Hansen and P. Potapov, "An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping". Science of Remote Sensing, vol. 3, pp. 10018, 2021. https://doi.org/10.1016/j.srs.2021.100018

. T. K. Ho, "The random subspace method for constructing decision forests". IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 832-844, 1998. https://doi.org/10.1109/34.709601

. Y. Amit and D. Geman, "Shape Quantization and Recognition with Randomized Trees". Neural Computation, vol. 9, pp. 1545-1588, 1997. https://doi.org/10.1162/neco.1997.9.7.1545

. A. Tariqa, M. R. Javed, M. I. Majeeda, H. Nawaza, N. Rashidc, R. M. Pallaresd, A. Ijazb, N. Hudaa, H. Tahseen, A. Namana, S. Aziza, R. Tariqa and R. M. Pallaresd, "Characterization of Aspergillus niger DNA by Surface-Enhanced Raman Spectroscopy (SERS) with Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) with Application for the Production of Cellulase". Analytical Letters , pp. 1-14, 2023. https://doi.org/10.1080/00032719.2023.2241938

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Published

2023-12-28

How to Cite

Altanchimeg, T., & Damdinsuren, A. (2023). Estimation of forest above ground biomass using Sentinel-1 data. Mongolian Journal of Geography and Geoecology, 60(44), 116–124. https://doi.org/10.5564/mjgg.v60i44.2935

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