The Examination of The Satellite Image-Based Growth Curve Model Within Mangrove Forest

I Nengah Surati Jaya, Muhammad Buce Saleh, Dwi Noventasari, Nitya Ade Santi, Nanin Anggraini, Dewayany Sutrisno, Zhang Yuxing, Wang Xuenjun, Liu Qian


Developing growth curve for forest and environmental management is a crucial activity in forestry planning. This paper describes a proposed technique for developing a growth curve based on the SPOT 6 satellite imageries. The most critical step in developing a model is on pre-processing the images, particularly during performing the radiometric correction such as reducing the thin cloud. The pre-processing includes geometric correction, radiometric correction with image regression, and index calculation, while the processing technique include training area selection, growth curve development, and selection. The study found that the image regression offered good correction to the haze-distorted digital number.  The corrected digital number was successfully implemented to evaluate the most accurate growth-curve for predicting mangrove.  Of the four growth curve models, i.e., Standard classical, Richards, Gompertz, and Weibull models, it was found that the Richards is the most accurate model in predicting the mean annual increment and current annual increment.  The study concluded that the growth curve model developed using high-resolution satellite image provides comparable accuracy compared to the terrestrial method.  The model derived using remote sensing has about 9.16% standard of error, better than those from terrestrial data with 15.45% standard of error.


Ali, S. A., Abbood, C. E. A, Abdul Kadhm, C. S. (2016). Salt and pepper noise removal using resizable window and Gaussian estimation function. International Journal of Electrical and Computer Engineering (IJECE), 6(5), 2219–2224.

Birch, P. D. (1999). A new generalized logistic sigmoid growth equation compared with the Richards growth equation. Annals of Botany, 83, 713–723.

Cita, F. A. (2014). Estimation of increment and biomass of mangrove in Kandelia Alam and Bina Ovivipari Semesta Co. Ltd concession areas, West Kalimantan. [bachelor thesis] Bogor: IPB University.

Damgaard, C., Weiner, J., & Nagashima, H. (2002). Modelling individual growth and competition in plant populations: Growth curves of Chenopodium album at two densities. Journal of Ecology, 90, 666–671.

Devaranavadgi, S. B., Bassappa, S., Jolli, R. B., Wali, S. Y., & Bagali, A. N. (2013). Height-age growth curve modelling for different tree species in drylands of North Karnataka. Global Journal of Science Frontier Research Agriculture and Veterinary Sciences, 13(1), 11–21.

Dimyati, R. D., Danoedoro, P., Hartono, & Kustiyo. (2018). A minimum cloud cover mosaic image model of the operational land imager Landsat-8 multitemporal data using tile based. International Journal of Electrical and Computer Engineering (IJECE), 8(1), 360–371.

Gunawansyah, H. (2006). The analysis of the increment of the gmelina (Gmelina arborea Linn) plantation for evaluating the cutting cycle in the timber estate of the Aya Yayang Indonesia Co. Ltd South Kalimantan. Journal of Borneo Tropical Forest, 18, 1–14.

Hardjana, A. K. (2010). The biomass and carbon potential on the plantation forest of Acacia mangium within the Surya Hutani Jaya Co. Ltd concession area, East Kalimantan. Journal of Social and Forestry Economic, 7(4), 237–249.

Istomo, Wibowo, C., & Hidayati, N. (1999). The evaluation of the growth of meranti (Shorea spp.) in Haurbentes Sub-Management (BKPH) of Jasinga, Management Unit of Bogor, Perum Perhutani Unit III, West Java. Jurnal Manajemen Hutan Tropika, 5(2), 15–22.

Karmali, I. (2015). The increment of the mangrove forest in Bina Silva Nusa Group concession areas, West Kalimantan [bachelor thesis]. Bogor: IPB University.

Prodan, M. (1968). Forest biometrics. Oxford: Pergamon Press.
Sánchez-González, M., Tomé, M., & Montero, G. (2005). Modelling height and diameter growth of dominant cork oak trees in Spain. Annals of Forest Science, 62(7), 633–643.

Santoso, H. (2008). The estimation model of standing stock of dry land forest using SPOT 5 Supermode dan Quickbird [bachelor thesis]. Bogor: IPB University.

Sedmák, R, & Scheer, L. (2012). Modelling of tree diameter growth using growth functions parameterized by least squares and Bayesian methods. Journal of Forest Science, 58(6), 245–252.

Vanclay, J. K. (1994). Modelling forest growth and yield: Applications to mixed tropical forests. Wallingford: CAB International.

Yin, X., Goudriaan, J., Lantinga, E. A., Vos, J., & Spiertz, H. J. (2003). A flexible sigmoid function of determinate growth. Annals of Botany, 91(3), 361–371.

Yusandi. S. (2015). Mangrove biomass estimation model using medium resolution satellite imageries, BSN Group concession area, West Kalimantan. [bachelor thesis] Bogor: IPB University.


I Nengah Surati Jaya (Primary Contact)
Muhammad Buce Saleh
Dwi Noventasari
Nitya Ade Santi
Nanin Anggraini
Dewayany Sutrisno
Zhang Yuxing
Wang Xuenjun
Liu Qian
Author Biography

Zhang Yuxing, Academy of Forest Inventory and Planning

Academy of Forest Inventory and Planning, SFA, P.R., 18 Hepingli East Street, Dongcheng District, Beijing, China 100010
JayaI. N. S., SalehM. B., NoventasariD., SantiN. A., AnggrainiN., SutrisnoD., YuxingZ., XuenjunW., & QianL. (2019). The Examination of The Satellite Image-Based Growth Curve Model Within Mangrove Forest. Jurnal Manajemen Hutan Tropika, 25(1), 44.

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