文献类型：期刊文献

英文题名：Promoting forest landscape dynamic prediction with an online collaborative strategy

作者：Ma, Zaiyang[1,2,3] Wu, Chunyan[4] Chen, Min[1,2,3] Li, Hengyue[1,2,3] Lin, Jian[5] Zheng, Zhong[1,2,3] Yue, Songshan[1,2,3] Wen, Yongning[1,2,3] Lue, Guonian[1,2,3]

第一作者：Ma, Zaiyang

通信作者：Chen, M[1]

机构：[1]Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PR China, Nanjing, Jiangsu, Peoples R China;[2]Jiangsu Ctr Collaborat Innovat, Nanjing, Peoples R China;[3]Nanjing Normal Univ, State Key Lab Cultivat Base Geog Environm Evolut J, Nanjing, Jiangsu, Peoples R China;[4]Chinese Acad Forestry, Res Inst Forestry, Beijing, Peoples R China;[5]Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong, Peoples R China

年份：2024

卷号：352

外文期刊名：JOURNAL OF ENVIRONMENTAL MANAGEMENT

收录：;EI(收录号：20240415425177);Scopus(收录号：2-s2.0-85182743818);WOS:【SCI-EXPANDED(收录号:WOS:001169377800001)】；

基金：We appreciate the detailed suggestions and comments from the ed- itor and anonymous reviewers. We express heartfelt thanks to the other members of the OpenGMS team. This work was supported by the Na- tional Key R & D Program of China [grant numbers 2022YFF0711605] , the National Natural Science Foundation (NSF) of China [grant numbers 42071363, 42171406, and 41930648] , and the Project funded by China Postdoctoral Science Foundation [grant numbers 2022M721660] . The case study can be accessed from: https://geomodeling.njnu.edu.cn/PExploration/projectInfo/a18cd719-e97e-4618-8dcd-dd5e701cc358? content = info & token = fdf97606-1c3b-4c96-9788-f535c10941e0. The used OpenGMS model and data service container can be accessed from https://gitee.com /opengms/NGIS_Solution, and the implementation of the online collaborative framework can be accessed at: https://github.com /TsaiYoung/GeoProblemSolving3.0.

语种：英文

外文关键词：Collaborative framework; Web -based prediction; Forest landscape modeling; OpenGMS; LANDIS-II

摘要：Modeling and predicting forest landscape dynamics are crucial for forest management and policy making, especially under the context of climate change and increased severities of disturbances. As forest landscapes change rapidly due to a variety of anthropogenic and natural factors, accurately and efficiently predicting forest dynamics requires the collaboration and synthesis of domain knowledge and experience from geographically dispersed experts. Owing to advanced web techniques, such collaboration can now be achieved to a certain extent, for example, discussion about modeling methods, consultation for model use, and surveying for stakeholders' feedback can be conducted on the web. However, a research gap remains in terms of how to facilitate online joint actions in the core task of forest landscape modeling by overcoming the challenges from decentralized and heterogeneous data, offline model computation modes, complex simulation scenarios, and exploratory modeling processes. Therefore, we propose an online collaborative strategy to enable collaborative forest landscape dynamic prediction with four core modules, namely data preparation, forest landscape model (FLM) computation, simulation scenario configuration, and process organization. These four modules are designed to support: (1) voluntary data collection and online processing, (2) online synchronous use of FLMs, (3) collaborative simulation scenario design, altering, and execution, and (4) participatory modeling process customization and coordination. We used the LANDIS-II model as a representative FLM to demonstrate the online collaborative strategy for predicting the dynamics of forest aboveground biomass. The results showed that the online collaboration strategy effectively promoted forest landscape dynamic prediction in data preparation, scenario configuration, and task arrangement, thus supporting forest-related decision making.