Abstract
Advances in remote sensing combined with the emergence of sophisticated methods for large-scale data analytics from the field of data science provide new methods to model complex interactions in biological systems. Using a data-driven philosophy, insights from experts are used to corroborate the results generated through analytical models instead of leading the model design. Following such an approach, this study outlines the development and implementation of a whole-of-forest phenotyping system that incorporates spatial estimates of productivity across a large plantation forest. In large-scale plantation forestry, improving the productivity and consistency of future forests is an important but challenging goal due to the multiple interactions between biotic and abiotic factors, the long breeding cycle, and the high variability of growing conditions. Forest phenotypic expression is highly affected by the interaction of environmental conditions and forest management but the understanding of this complex dynamics is incomplete. In this study, we collected an extensive set of 2.7 million observations composed of 62 variables describing climate, forest management, tree genetics, and fine-scale terrain information extracted from environmental surfaces, management records, and remotely sensed data. Using three machine learning methods, we compared models of forest productivity and evaluate the gain and Shapley values for interpreting the influence of categorical variables on the power of these methods to predict forest productivity at a landscape level. The most accurate model identified that the most important drivers of productivity were, in order of importance, genetics, environmental conditions, leaf area index, topology, and soil properties, thus describing the complex interactions of the forest. This approach demonstrates that new methods in remote sensing and data science enable powerful, landscape-level understanding of forest productivity. The phenotyping method developed here can be used to identify superior and inferior genotypes and estimate a productivity index for individual site. This approach can improve tree breeding and deployment of the right genetics to the right site in order to increase the overall productivity across planted forests.
Highlights
Plantation forestry research seeks to optimise the productivity, profitability, health, and sustainability of commercial forests
All three models included a hyperparameter for the number of estimators and we tried to keep them in the same range, the values verifying Eq 2 varied between models with 6,877 for XGBoost, 10,369 for CatBoost, and 6,571 for LightGBM (Table 1)
We have developed and optimised a processing pipeline for a data-driven forest phenotyping platform using a state-of-the-art machine learning approach
Summary
Plantation forestry research seeks to optimise the productivity, profitability, health, and sustainability of commercial forests. This vital fibre supply system provides many ecosystem services and is critical in meeting sustainable development goals to support the global population's increasing wood and fibre demands. Managed plantation forests must assume an increasingly prominent role in providing for the future demand in wood and fibre products. Increasing forest productivity whilst safeguarding forest health and sustainability will be critical to ensuring that this can be achieved (Sedjo and Botkin, 1997; Powers, 1999; Dash et al, 2019). This research has helped to deliver improved productivity, profitability and helped to ensure wood fibre security
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
