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- W4313482564 abstract "Tropical forests play the main role in the earth’s carbon cycle sources. Nowadays, the study for conservation and management of forest restorationForest restoration is increasingly needed to preserve the biodiversityBiodiversity 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 managementForest management that have relation to the increasing need to better understand the role of the forest ecosystemForest ecosystems. It is essential and valuable information towards an understanding of the ecosystem biodiversityBiodiversity 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 learningMachine learning (ML) (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 stocksCarbon stocks. Therefore, this study aimed to classify the forest aboveground biomassBiomass by estimating crown projection areaCrown projection area (CPA) using object-based image analysis (OBIAObject based image analysis (OBIA)) and to determine the accuracy assessmentAccuracy assessments for estimating forest aboveground biomassBiomass using an artificial neural network (ANN)Artificial neural network (ANN) and random forest (RF). This study involved the use of the object-based technique by fusing SuperView-1 imagery and airborneAirborne LiDAR to estimate the aboveground biomassBiomass using Random forestRF and ANNArtificial neural network (ANN) algorithmAlgorithms. 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 inventoriesForest inventories and carbon stockCarbon stocks modeling and will help several organizations for estimating carbon stocksCarbon stocks and forest fluxes." @default.
- W4313482564 created "2023-01-06" @default.
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- W4313482564 date "2022-01-01" @default.
- W4313482564 modified "2023-10-18" @default.
- W4313482564 title "Assessment of Forest Aboveground Biomass Estimation from SuperView-1 Satellite Image Using Machine Learning Approaches" @default.
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- W4313482564 doi "https://doi.org/10.1007/978-981-19-4200-6_6" @default.
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