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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Perfecting a Video Game with Game Metrics
Zhang Yuxing
Academy of Forest Inventory and Planning
Agriculture, Biological Sciences & Forestry Computer Science & IT

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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
Jurnal Manajemen Hutan Tropika Vol. 25 No. 1 (2019)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.635 KB) | DOI: 10.7226/jtfm.25.1.44

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.
Algorithm for detecting deforestation and forest degradation using vegetation indices M. Buce Saleh; I Nengah Surati Jaya; Nitya Ade Santi; Dewayany Sutrisno; Ita Carolita; Zhang Yuxing; Wang Xuenjun; Liu Qian
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.12585

Abstract

In forestry sector, the remote sensing technology hold a key role on forest inventory and monitoring their changes. This paper describes the algorithm for detecting deforestation and forest degradation using high resolution satellite imageries with knowledge-based approach. The main objective of the study is to develop a practical technique for monitoring deforestation and forest degradation occurred within the mangrove and swamp forest ecosystem.  The SPOT 4, 5, and 6 images acquired in 2007, 2012 and 2014 were transformed into three vegetation indices, i.e., Normalized Difference Vegetation Index (NDVI), Green-Normalized Difference Vegetation index (GNDVI) and Normalized Green-Red Vegetation index (NRGI).  The study found that deforestation was well detected and identified using the NDVI and GNDVI, however the forest degradation could be well detected using NRGI, better than NDVI and GNDVI.  The study concludes that the strategy for monitoring deforestation, biomass-based forest degradation as well as forest growth could be done by combining the use of NDVI, GNDVI and NRGI respectively.
Co-Authors Dewayany Sutrisno Dewayany Sutrisno Dwi Noventasari I Nengah Surati Jaya Ita Carolita Liu Qian Liu Qian M. Buce Saleh Muhammad Buce Saleh Nanin Anggraini Nitya Ade Santi Nitya Ade Santi Wang Xuenjun Wang Xuenjun