Abstract
Tropical forests play the main role in the earth’s carbon cycle sources. Nowadays, the study for conservation and management of forest restoration is increasingly needed to preserve the biodiversity of forests and retain the valuable species of tropical forest for the next generation. The accurate tropical tree species recognition is one of the important issues in forest management that have relation to the increasing need to better understand the role of the forest ecosystem. It is essential and valuable information towards an understanding of the ecosystem biodiversity and its function over large spatial scales. Information such as the tree species and location of the trees is crucial for species regeneration and ecological purposes. Currently, machine learning (ML) has been shown a remarkable efficient evolution utilized in artificial intelligence along with the inclination of deep learning (DL) usage in many research, and this includes tropical forest carbon stocks. Therefore, this study aimed to classify the forest aboveground biomass by estimating crown projection area using object-based image analysis (OBIA) and to determine the accuracy assessment for estimating forest aboveground biomass using an artificial neural network (ANN) and random forest (RF). This study involved the use of the object-based technique by fusing SuperView-1 imagery and airborne LiDAR to estimate the aboveground biomass using RF and ANN algorithm. Statistical tools from open-source R will help bridge the gap between analysis and implementation. This study hopes to solve the fundamental issues of forest inventories and carbon stock modeling and will help several organizations for estimating carbon stocks and forest fluxes.
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Acknowledgment
We would like to acknowledge the Forest Research Institute Malaysia (FRIM) for granting access to the study area. We thank the team of research expedition from Universiti Teknologi MARA, Perlis Branch, and Institute for Biodiversity and Sustainable Development (IBSD) for their assistance in the field data collection.
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Mohd Zaki, N.A., Mohd Asri, A., Mohd Zulkiflee, N.I., Abd Latif, Z., Razak, T.R., Suratman, M.N. (2022). Assessment of Forest Aboveground Biomass Estimation from SuperView-1 Satellite Image Using Machine Learning Approaches. In: Suratman, M.N. (eds) Concepts and Applications of Remote Sensing in Forestry . Springer, Singapore. https://doi.org/10.1007/978-981-19-4200-6_6
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