An End to End Process Development for UAV-SfM Based Forest Monitoring: Individual Tree Detection, Species Classification and Carbon Dynamics Simulation
| Autor: | Kiichiro Hayashi, Satoru Sugita, Chihiro Haga, Takanori Matsui, Takashi Machimura, Hiroaki Takagi, Ayana Fujimoto |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Forest dynamics business.industry crown segmentation Environmental resource management Forest management 0211 other engineering and technologies Forestry Bio-energy with carbon capture and storage 02 engineering and technology lcsh:QK900-989 01 natural sciences Ecosystem services carbon simulation Greenhouse gas Forest ecology Sustainability Reducing emissions from deforestation and forest degradation lcsh:Plant ecology Environmental science species classification tree top detection business 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | Forests, Vol 10, Iss 8, p 680 (2019) Forests Volume 10 Issue 8 |
| ISSN: | 1999-4907 |
| Popis: | To promote Bio-Energy with Carbon dioxide Capture and Storage (BECCS), which aims to replace fossil fuels with bio energy and store carbon underground, and Reducing Emissions from Deforestation and forest Degradation (REDD+), which aims to reduce the carbon emissions produced by forest degradation, it is important to build forest management plans based on the scientific prediction of forest dynamics. For Measurement, Reporting and Verification (MRV) at an individual tree level, it is expected that techniques will be developed to support forest management via the effective monitoring of changes to individual trees. In this study, an end-to-end process was developed: (1) detecting individual trees from Unmanned Aerial Vehicle (UAV) derived digital images (2) estimating the stand structure from crown images (3) visualizing future carbon dynamics using a forest ecosystem process model. This process could detect 93.4% of individual trees, successfully classified two species using Convolutional Neural Network (CNN) with 83.6% accuracy and evaluated future ecosystem carbon dynamics and the source-sink balance using individual based model FORMIND. Further ideas for improving the sub-process of the end to end process were discussed. This process is expected to contribute to activities concerned with carbon management such as designing smart utilization for biomass resources and projecting scenarios for the sustainable use of ecosystem services. |
| Databáze: | OpenAIRE |
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