Abstract
The NPP was calculated for 2-m resolution image of Ulu Muda Forest Reserve (UMFR), in Sik, North of Peninsular Malaysia. The GeoEye-1 image was first preprocessed, and land use was classified based on object-based image analysis (OBIA) based on PCI Geomatics Catalyst Professional image processing software. Attributes were segmented by employing three segmentation methods, namely, finer, 50 scale; moderate, 200; and coarse, 350. Based on accuracy assessment, a moderate scale of 200 showed the best kappa with 0.67, whereas for finer and coarse were 0.44 and 0.27, respectively. The moderate segmentation method showed a moderate number of attributes that sufficiently assist in collecting accuracy sampling that resulted in a higher kappa coefficient in the study. Biophysical indices, such as Absorbed Photosynthetically Active Radiation (APAR), Normalized Difference Vegetation Index (NDVI) and the fraction of Photosynthetically Active Radiation (fAPAR), were calculated for the study based on the satellite images. The study showed that the coarse method of NDVI had the highest mean value of 0.709, followed by 0.698 for moderate and 0.966 for finer method. A high NDVI value indicated that the area in UMFR is covered by high-density vegetation dominated by lowland forest. Meanwhile, the recorded NPP ranged between 6.7 g C m−2 month−1 and 300.04 g C m−2 month−1, with a mean value of 231.85 g C m−2 month−1 for the study area. Satellite remote sensing allows for the estimation of NPP while also generating land use/land cover and forest density estimates for lowland forests in the peninsular. The findings indicate the presence of intricacies between NDVI, forest density and land cover in explaining NPP variations within this type of forest. The Ulu Muda FR community assessed by the study extracted no major NPP. This type of research can be used in forest management planning in the forestry department to improve forest extraction policy.
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Acknowledgements
The authors would like to thank WWF-Malaysia for funding this project, even though this paper reports only a part of the project implementation. Gratitude is also extended to the Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia, for funding for software and data acquisition for this study.
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Razali, S.M., Jamil, N.R., Sulaiman, M.S., Radzi, M.A. (2023). Characterizing and Assessing Forest Density and Productivity of Ulu Muda Forest Reserve Based on Satellite Imageries. In: Samdin, Z., Kamaruddin, N., Razali, S.M. (eds) Tropical Forest Ecosystem Services in Improving Livelihoods For Local Communities. Springer, Singapore. https://doi.org/10.1007/978-981-19-3342-4_4
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