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

  • I Nengah Surati Jaya Department of Forest Management, Faculty of Forestry, IPB University, Dramaga Campus, Bogor, Indonesia 16680
  • Muhammad Buce Saleh Department of Forest Management, Faculty of Forestry, IPB University, Dramaga Campus, Bogor, Indonesia 16680
  • Dwi Noventasari Department of Forest Management, Faculty of Forestry, IPB University, Dramaga Campus, Bogor, Indonesia 16680
  • Nitya Ade Santi Department of Forest Management, Faculty of Forestry, IPB University, Dramaga Campus, Bogor, Indonesia 16680
  • Nanin Anggraini Indonesian National Institute of Aeronautics and Space (LAPAN), Jl. Pemuda Persil No.1 Jakarta, Indonesia 13220
  • Dewayany Sutrisno Geospatial Information Agency, Jl. Raya Jakarta-Bogor KM. 46 Cibinong, Indonesia 16911
  • Zhang Yuxing Academy of Forest Inventory and Planning
  • Wang Xuenjun Academy of Forest Inventory and Planning, SFA, P.R., 18 Hepingli East Street, Dongcheng District, Beijing, China 100010
  • Liu Qian Academy of Forest Inventory and Planning, SFA, P.R., 18 Hepingli East Street, Dongcheng District, Beijing, China 100010
Keywords: Gompertz model, growth curve model, Richards model, standard classical model, Weibull model

Abstract

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.

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

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Published
2019-05-10
How to Cite
Jaya, I. N. S., Saleh, M. B., Noventasari, D., Santi, N. A., Anggraini, N., Sutrisno, D., Yuxing, Z., Xuenjun, W., & Qian, L. (2019). The Examination of The Satellite Image-Based Growth Curve Model Within Mangrove Forest. Jurnal Manajemen Hutan Tropika, 25(1), 44. Retrieved from https://jagb.journal.ipb.ac.id/index.php/jmht/article/view/25414
Section
Articles