張 北辰

Abstract Understanding the land cover is crucial to comprehending the functioning of the Earth’s system. The land cover of Siberia is characterized by uncertainty because it is wide-ranging and comprises various classification types. However, comparisons among land cover products reveal substantial discrepancies and uncertainties. Therefore, a reliable land cover product for Siberia is necessary. In this study, we generated new land cover data for Siberia using random forest (RF) classifiers with global land cover datasets. To assess their accuracy and characteristics, we individually validated global land cover products in Siberia using multi-source sample datasets. We trained the RF classifiers with multiple land cover products to produce a more precise land cover product for Siberia. The validations showed that: (a) the generated new land cover data achieved the highest overall accuracy (85.04%) and kappa coefficient (82.62%); (b) the classifications of mixed forest (user accuracy: 97.85%) and grasses (user accuracy: 94.85%) demonstrated improvements, showing higher performance compared to most other types; and (c) by comparing the distribution of land cover across climate zones, we discovered that temperature is a critical factor throughout Siberia. However, in warm summer climates, precipitation plays a critical role in vegetation distribution. The more accurate and detailed land cover created in this study enhances the reliability of analyses in Siberia and fosters a deeper understanding of the impact of the carbon cycle.

Abstract To facilitate forest research, simulations of the whole forest growth process can be employed to analyze forest dynamics and predict forest yields. Different forest growth models can be integrated for comprehensive process simulation and thus can assist forest growth research. With the development of network technologies, a web environment can provide cross‐platform capability and wide availability for distributed researchers. In order to serve the simulation of complex forest growth processes and help online forest growth research, this article proposes a web‐based integrated modeling and simulation method for forest growth research. The proposed method includes three steps, namely, model preparation, model integration, and forest growth simulation. The corresponding implementation strategies are designed to prepare forest growth models, integrate different models, preprocess model data, and implement forest growth simulations for integrated modeling and simulations via the web. Two applications in the comprehensive prediction of forest growth and comparison of different forest management decisions are introduced to verify the feasibility and capability of the proposed method. The results show that the proposed web‐based integrated modeling and simulation method can be used conveniently for comprehensive simulations of forest growth research.