A review of forest biomass estimation and modeling methods by remote sensing

Journal of Nature and Spatial Sciences (JONASS)

دوره 3، شماره 1 - شماره پیاپی 5، خرداد 2023، صفحه 79-84   

نوع مقاله: Review Article

شناسه دیجیتال (DOI): 10.30495/jonass.2023.1985526.1070

نویسندگان

Razieh Hadavand^{* 1}؛ Sadegh Mokhtarisabet¹؛ Reza Abedzadegan Abdi²

¹Department of Remote Sensing and GIS, Yazd Branch, Islamic Azad University, Yazd, Iran

²Faculty of Agriculture and Natural Resources, Chalous Branch, Islamic Azad University, Chalous, Iran

چکیده

Background and objective: Detailed evaluation of biomass using Remote Sensing and Geographic Information systems is very important to manage the forest and its role as a carbon source and climate change. Ground sensing data have made a big change in compiling and exploiting information about forest biomass, But non-local equations and the use of different radar and optical images, and also huge expenses have caused ambiguities in the accurate estimation of biomass. This study aims to investigate the capabilities of different remote sensing data for modeling and estimating forest biomass.
Materials and methods:  Today, by using the conducted research and also by examining the conducted methods, it is possible to have an accurate assessment of biomass estimation, which brings the lowest cost and the highest efficiency. In this study, the challenges of the forest biome were investigated by reading numerous domestic and international articles and also with the opinion of natural resources experts in Iran.
Results and conclusion: After reviewing the opinions of experts, all the solutions and challenges of the existing methods for estimating and modeling the forest biomass, it was concluded that to increase the accuracy and reduce the costs, the use of remote sensing capabilities can be useful in the assessment of the forest biomass. Decision makers and managers, especially in the natural resources area, can use remote sensing capabilities to prevent crises and monitor forests.Materials and methods: Today, by using the conducted research and also by examining the conducted methods, it is possible to have an accurate assessment of biomass estimation, which brings the lowest cost and the highest efficiency. In this study, the challenges of the forest biome were investigated by reading numerous domestic and international articles and also with the opinion of natural resources experts in Iran.
Results and conclusion: After reviewing the opinions of experts, all the solutions and challenges of the existing methods for estimating and modeling the forest biomass, it was concluded that to increase the accuracy and reduce the costs, the use of remote sensing capabilities can be useful in the assessment of the forest biomass. Decision makers and managers, especially in the natural resources area, can use remote sensing capabilities to prevent crises and monitor forests.

کلیدواژه‌ها

Biomass؛ Remote sensing؛ Radar؛ Optics؛ GIS

مراجع

Amini.J, Sadeghi.Y, (2013). Optical and radar satellite imagery modeling forest biomass in northern Iran”, Iranian Remote Sensing &GIS, 4,4,69-82 .

Amini, J., & Sumantyo, J. T. S. (2009). Employing a method on SAR and optical images for forest biomass estimation. IEEE Transactions on Geoscience and Remote Sensing, 47(12), 4020-4026. https://doi.org/10.1109/TGRS.2009.2034464 

Aslan, A., Rahman, A. F., Warren, M. W., & Robeson, S. M. (2016). Mapping spatial distribution and biomass of coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data. Remote sensing of environment, 183, 65-81. https://doi.org/10.1016/j.rse.2016.04.026 

Baharluie.M & Azizi.Z, (2016), Geoeye Application in Forest Studies ,Fifth Islamic Conference of Iran Progress, Basic Model of Progress.

Cleve, C., Kelly, M., Kearns, F. R., & Moritz, M. (2008). Classification of the wildland–urban interface: A comparison of pixel-and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems, 32(4), 317-326. https://doi.org/10.1016/j.compenvurbsys.2007.10.001 

Dube, T., & Mutanga, O. (2015). Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 36-46. https://doi.org/10.1016/j.isprsjprs.2014.11.001 

FAO, (2016). Global forest resources assessment. State of the World’s Forests, Paper 16. Forests and agriculture: land-use challenges and opportunities. Rome, PP20.

Galidaki, G., Zianis, D., Gitas, I., Radoglou, K., Karathanassi, V., Tsakiri–Strati, M., ... & Mallinis, G. (2017). Vegetation biomass estimation with remote sensing: focus on forest and other wooded land over the Mediterranean ecosystem. International journal of remote sensing, 38(7), 1940-1966. https://doi.org/10.1080/01431161.2016.1266113 

Ghasemi, N., Sahebi, M. R., & Mohammadzadeh, A. (2012). Biomass estimation of a temperate deciduous forest using wavelet analysis. IEEE Transactions on Geoscience and Remote Sensing, 51(2), 765-776. https://doi.org/10.1109/TGRS.2012.2205260 

Karl, J. W., & Maurer, B. A. (2010). Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentation. Landscape Ecology, 25, 591-605. https://doi.org/10.1007/s10980-009-9439-4 

Lu, D., Chen, Q., Wang, G., Moran, E., Batistella, M., Zhang, M., ... & Saah, D. (2012). Aboveground forest biomass estimation with data and uncertainty analysis of the estimates. International Journal of Forestry Research, 2012. https://doi.org/10.1155/2012/436537 

Lu, D., Mausel, P., Brondı́zio, E., & Moran, E. (2004). Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest ecology and management, 198(1-3), 149-167. https://doi.org/10.1016/j.foreco.2004.03.048 

Mokhtari, M. H., Busu, I., & Parvizi, S. (2021). METRIC based evapotranspiration mapping of pistachio orchard in the semi-arid region. Journal of Nature and Spatial Sciences (JONASS), 1(2), 1-15.

Mahdavi Saeidi, A., Babaie Kafaky, S., & Mataji, A. (2020). Detecting the development stages of natural forests in northern Iran with different algorithms and high-resolution data from GeoEye-1. Environmental Monitoring and Assessment, 192, 1-15. https://doi.org/10.1007/s10661-020-08612-8

Nelson, R., Margolis, H., Montesano, P., Sun, G., Cook, B., Corp, L., ... & Prisley, S. (2017). Lidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observations. Remote Sensing of Environment, 188, 127-140. https://doi.org/10.1016/j.rse.2016.10.038 

Nichol, J. E., & Sarker, M. L. R. (2010). Improved biomass estimation using the texture parameters of two high-resolution optical sensors. IEEE Transactions on Geoscience and Remote Sensing, 49(3), 930-948. https://doi.org/10.1109/TGRS.2010.2068574 

Ramezani, M. R., & Sahebi, M. R. (2015). Forest biomass estimation using SAR and optical images. Engineering Journal of Geospatial Information Technology, 3(1), 15-26. https://doi.org/10.29252/jgit.3.1.15 

Sadeghi.Y, (2010)."Estimation of forest biomass using light and sunny images and SAR", MASTER, Thesis,P 149.

Sinha, S., Jeganathan, C., Sharma, L. K., & Nathawat, M. S. (2015). A review of radar remote sensing for biomass estimation. International Journal of Environmental Science and Technology, 12, 1779-1792. https://doi.org/10.1007/s13762-015-0750-0 

Smithson, P. A. (2002). IPCC, 2001: climate change 2001: the scientific basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by JT Houghton, Y. Ding, DJ Griggs, M. Noguer, PJ van der Linden, X. Dai, K. Maskell and CA Johnson (eds). Cambridge University Press, Cambridge, UK, and New York, USA, 2001. No. of pages: 881. Price£ 34.95, US 49.95,ISBN0‐521‐01495‐6(paperback).£90.00,US 130.00, ISBN 0‐521‐80767‐0 (hardback).

Zarei, M., Tazeh, M., Moosavi, V., & Kalantari, S. (2021). Evaluating the changes in Gavkhuni Wetland using MODIS satellite images in 2000-2016. Journal of Nature and Spatial Sciences (JONASS), 1(1), 27-41.