Regression Models for Estimating Aboveground Biomass and Stand Volume Using Landsat-Based Indices in Post-Mining Area

Aditya Rizky Priatama, Yudi Setiawan, Irdika Mansur, Muhammad Masyhuri


This paper describes the use of remotely sensed data to measure vegetation variables such as basal area, biomass and stand volume. The objective of this research was developed regression models to estimate basal area (BA), aboveground biomass (AGB), and stand volume (SV) using Landsat-based vegetation indices. The examined vegetation indices were SAVI, MSAVI, EVI, NBR, NBR2 and NDMI.   Regression models were developed based on least-squared method using several forms of equation, i.e., linear, exponential, power, logarithm and polynomial.  Among those models, it was recognized that the best fit of model was obtained from the exponential model, log (y) = ax + b for estimating BA, AGB & SV.  The MSAVI had been identified as the most accurate independent variable to estimates basal area with R² of 0.70 and average verification values of 16.39% (4%-32.66%); while the EVI become the best independent variable for estimating aboveground biomass (AGB) with R2 of 0.72 and average of verification values of 18,10% (9%-28.01%); and the NDMI was recognized to be the best independent variable to estimate stand volume with R2 of 0.69 and average of verification values of 24.37% (-15%-38.11%).


Aguirre-villegas, H. A., & Benson, C. H. (2017). Case history of environmental impacts of an Indonesian coal supply chain. Journal of Cleaner Production, 157, 47–56.

Ahmed, A., Zhang, Yun., & Nichols, S. (2011). Review and evaluation of remote sensing methods for soil-moisture estimation. SPIE Reviews, 2, 028001.

Ainiyah, N., Deliar, A., & Virtriana, R. (2016). The classical assumption test to driving factors of land cover change in the development region of northern part of West Java. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B6, 205–210.

Apriyanto, D. P., Jaya, I. N. S., & Puspaningsih, N. (2019). Examining the object-based and pixel based image analyses for developing stand volume estimator model. Indonesian Journal of Electrical Engineering and Computer Sciences, 15(3), 15861596.

Arshi, A. (2017). Reclamation of coalmine overburden dump through environmental friendly method. Saudi Journal of Biological Sciences, 24(2), 371–378.

Bao, N., Li, W., Gu, X., & Liu, Y. (2019). Biomass estimation for semiarid vegetation and mine rehabilitation using worldview-3 and sentinel-1 SAR imagery. Remote Sensing, 11(23), 2855.

Basuki, T. M., van Laake, P. E., Skidmore, A. K., & Hussin, Y. A., (2009). Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. Forest Ecology and Management, 257(8), 1684–1694.

Cartus, O., Kellndorfer, J., Rombach, M., & Walker, W. (2012). Mapping canopy height and growing stock volume using airborne lidar, alos palsar and Landsat ETM+. Remote Sensing, 4(11), 3320–3345.

Chen, B., Yun, T., Ma, J., Kou, W., Li, H., Yang, C., ..., & Wu, Z., (2020). High-precision stand age data facilitate the estimation of rubber plantation biomass: A case study of Hainan Island, China. Remote Sensing, 12(23), 3853.

Ding, Y., Zhao, K., Zheng, X., & Jiang, T. (2014). Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, 30, 139–145.

Dontala, S. P., Reddy, T. B., & Vadde, R. (2015). Environmental aspects and impacts its mitigation measures of corporate coal mining. Procedia Earth and Planetary Science, 11, 2–7.

Dos Reis, A. A., Franklin, S. E., de Mello, J. M., & Acerbi Junior, F. W. (2019). Volume estimation in a eucalyptus plantation using multi-source remote sensing and digital terrain data: A case study in Minas Gerais State, Brazil. International Journal of Remote Sensing, 40(7), 2683–2702.
Eckert, S. (2012). Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data. Remote Sensing, 4(4), 810–829.

Gallardo-Salazar, J. L., & Pompa-Garcia, M. (2020). Detecting individual tree attributes and multispectral indices using unmanned aerial vehicles: Applications in a pine clonal orchard. Remote Sensing, 12(24), 4144.

Gizachew, B., Solberg, S., Naesset, E., Gobakken, T., Bollandsas, O., M., Breidenbach, J., ..., & Mauya, E., W. (2016). Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data. Carbon Balance and Management, 11, 13(2016).

Hawryło, P., Francini, S., Chirici, G., Giannetti, F., Parkitna, K., Krok, G., ..., & Socha, J. (2020). The use of remotely sensed data and polish NFI plots for prediction of growing stock volume using different predictive methods. Remote Sensing, 12(20), 3331.

Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., & Ferreira. L. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.

Ilyas, S. (2012). Carbon sequestration through reforestation in reclaimed coal mine sites in carbon sequestration through reforestation in reclaimed coal mine sites in East Kalimantan, Indonesia . Journal of Environment and Earth Science, 2(10), 27–35.

Ilyas, S. (2013a). Carbon sequestration and growth of stands of Cassia siamea Lamk. in coal mining reforestation area. Indian Journal of Science and Technology, 6(11), 5405–5410.

Ilyas, S. (2013b). Allometric equation and carbon sequestration of Acacia mangium Willd. in coal mining reclamation areas. Civil and Environmental Research, 3(1), 8–16.

Karan, S. K., Samadder, S. R., & Maiti, S. K. (2016). Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands. Journal of Environmental Management, 182, 272–283.

Kazar, S. A., & Warner, T. (2013). Assessment of carbon storage and biomass on minelands reclaimed to grassland environments using Landsat spectral indices. Journal of Applied Remote Sensing, 7(1), 073583.

Knight, E. J., & Kvaran, G. (2014). Landsat-8 operational land imager design, characterization and performance. Remote Sensing, 6(11), 10286–10305.

Kodir, A., Hartono, D. M., Haeruman, H., & Mansur, I. (2017). Integrated post mining landscape for sustainable land use: A case study in South Sumatera, Indonesia. Sustainable Environment Research, 27(4), 203–213.

Lavista, L., Prasetyo, L. B., & Hermawan, R. (2016). Dynamics change of the above carbon stocks in Bogor Agricultural University, Darmaga campus. Procedia Environmental Sciences, 33, 305–316.

Lestarian, R., & Sutarahardja, S. (2009). Penyusunan tabel volume pohon dalam rangka pelaksanaan IHMB di IUPHHK-HA PT Ratah Timber Kalimantan Timur [Undergraduate Research]. Bogor: IPB University.

Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), 63–105.

Min, K., Turner, B. L., Muller-landau, H. C., Davies, S. J., Larjavaara, M., Faizu, N., & Lum, S. (2013). Forest ecology and management carbon stocks in primary and secondary tropical forests in Singapore. Forest Ecology and Management, 296, 81–89.

Mora, B., Wulder, M. A., White, J. C., & Hobart, G. (2013). Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LIDAR. Remote Sensing, 5, 2308–2326.

Negara, T., Jaya, I., N., S., Kusmana, C., Mansur, I., & Santi, N., A. (2021). Drone image-based parameters for assessing the vegetation condition the reclamation success in post-mining oil exploration. Telkomnika Telecommunication, Computing, Electronics and Control, 19(1), 105114.

Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., ..., & Hayes, D. (2011). A large and persistent carbon sink in the world’s forests. Science, 333, 988–993.

Peng, D., Zhang, H., Liu, L., Huang, W., Huete, A. R., Zhang, X., ..., & Zhang, B. (2019). Estimating the aboveground biomass for planted forests based on stand age and environmental variables. Remote Sensing, 11, 2270.

Qi, J., Chehbouni, A., Huete, A., Kerr, Y., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119–126.

Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia and Analgesia, 126(5), 1763 – 1768.

Šebelíková, L., Řehounková, K., & Prach, K. (2016). Spontaneous revegetation vs. forestry reclamation in post-mining sand pits. Environmental Science and Pollution Research, 23, 13598–13605.

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.

Skaloš, J., Novotný, M., Woitsch, J., Zacharová, J., Berchová, K., Svoboda, M., ..., & Keken, Z. (2015). What are the transitions of woodlands at the landscape level? Change trajectories of forest, non-forest and reclamation woody vegetation elements in a mining landscape in North-western Czech Republic. Applied Geography. 58, 206–216.

Tanaka, S., Takahashi, T., Nishizono, T., Kitahara, F., Saito, H., Iehara, T., ..., & Awaya, Y. (2015). Stand volume estimation using the k-NN technique combined with forest inventory data, satellite image data and additional feature variables. Remote Sensing, 7, 378–394.

Tsai, Y. H., Stow, D., Shi, L., Lewison, R., & An, L. (2016). Quantifying canopy fractional cover and change in Fanjingshan National Nature Reserve, China using multi-temporal Landsat imagery. Remote Sensing Letters, 7, 671–680.

Wahyuni, S., Jaya, I. N. S., & Puspaningsih, N. (2016). Model for estimating above ground biomass of reclamation forest using unmanned aerial vehicles. Indonesian Journal of Electrical Engineering and Computer Science, 4, 586–593.

Yuan, X., Li, L., Tian, X., Luo, G., & Chen, X. (2016). Estimation of above-ground biomass using MODIS satellite imagery of multiple land-cover types in China. Remote Sensing Letters, 7(12), 1141–1149.

Zhu, Y., Feng, Z., Lu, J., & Liu, J. (2020). Estimation of forest biomass in Beijing (China) using multisource remote sensing and forest inventory data. Forests, 11, 163.


Aditya Rizky Priatama (Primary Contact)
Yudi Setiawan
Irdika Mansur
Muhammad Masyhuri
Author Biography

Yudi Setiawan, Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Ring Road Campus IPB Dramaga, Bogor, Indonesia 16680

Recently, I am a lecturer staff at Faculty of Forestry and Environment, IPB University and researcher at Center for Environmental Research, IPB. I have been working as a remote sensing specialist on land change detection at UNDP-REDD Indonesia, and developed a novel method for the change detection that it should be applicable in the development of a near-real time deforestation detection system for Indonesia.

PriatamaA. R., SetiawanY., MansurI., & MasyhuriM. (2022). Regression Models for Estimating Aboveground Biomass and Stand Volume Using Landsat-Based Indices in Post-Mining Area . Jurnal Manajemen Hutan Tropika, 28(1), 1-14.

Article Details

Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

Muhammad Buce Saleh, Rosima Wati Dewi, Lilik Budi Prasetyo, Nitya Ade Santi
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