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Promoting forest landscape dynamic prediction with an online collaborative strategy.

Zaiyang Ma1, Chunyan Wu2, Min Chen1

  • 1Key Laboratory of Virtual Geographic Environment (Ministry of Education of PR China), Nanjing Normal University, Nanjing, Jiangsu, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, Jiangsu, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, Jiangsu, China.

Journal of Environmental Management
|January 18, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an online strategy for collaborative forest landscape modeling. It enhances data preparation, scenario configuration, and task arrangement for better forest dynamics prediction and decision-making.

Keywords:
Collaborative frameworkForest landscape modelingLANDIS-IIOpenGMSWeb-based prediction

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Area of Science:

  • Forestry and Environmental Science
  • Computational Ecology
  • Geospatial Analysis

Background:

  • Forest landscape dynamics are critical for management and policy, especially with climate change and disturbances.
  • Predicting these dynamics requires expert collaboration, but challenges exist with decentralized data and offline computations.
  • Existing web tools facilitate some collaboration, but a gap remains in core modeling tasks.

Purpose of the Study:

  • To propose and demonstrate an online collaborative strategy for forest landscape dynamic prediction.
  • To overcome challenges of decentralized data, offline computations, and complex scenarios in collaborative modeling.
  • To support participatory modeling processes and decision-making in forest management.

Main Methods:

  • Developed a four-module online collaborative strategy: data preparation, model computation, scenario configuration, and process organization.
  • Enabled voluntary data collection, online processing, and synchronous use of Forest Landscape Models (FLMs).
  • Facilitated collaborative design, alteration, and execution of simulation scenarios using the LANDIS-II model.

Main Results:

  • The online strategy effectively improved forest landscape dynamic prediction.
  • Significant advancements were observed in data preparation, scenario configuration, and task arrangement.
  • The approach successfully supported the prediction of forest aboveground biomass dynamics.

Conclusions:

  • The proposed online collaborative strategy enhances forest landscape dynamic prediction capabilities.
  • It addresses key challenges in collaborative modeling, promoting efficient and participatory processes.
  • This approach provides valuable support for forest-related decision-making and policy development.