Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT

Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.

KEYWORDS:

• Landsenses ecology
• meliorization model evaluation framework
• various ecological factors
• model behavior uncertainty
• mix-marching data
• ecological forecasting
• process-based ecological models

Acknowledgments

This work was supported by the National Key Research Program of China (2016YFC0502704); National Science Foundation of China (31670645, 31470578 and 31200363), National Forestry Public Welfare Foundation of China (201304205), Fujian Provincial Department of S&T Project (2013YZ0001-1, 2016Y0083, 2014J05044 and 2015Y0083), Xiamen Municipal Department of Science and Technology (3502Z20130037 and 3502Z20142016), and Youth Innovation Promotion Association CAS (2014267). We are grateful to Professor Li Hu for his helpful suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

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Additional information

Funding

This work was supported by the National Key Research Program of China (Grant Number 2016YFC0502704); the National Science Foundation of China : [Grant Numbers 31670645; 31470578; 31200363]; National Forestry Public Welfare Foundation of China: [Grant Number 201304205]; Fujian Provincial Department of Science and Technology Project: [Grant Numbers 2013YZ0001-1; 2016Y0083; 2014J05044; 2015Y0083]; Xiamen Municipal Department of Science and Technology: [Grant Numbers 3502Z20130037; 3502Z20142016]; Youth Innovation Promotion Association CAS: [Grant Number 2014267].