Promoting forest landscape dynamic prediction with an online collaborative strategy.

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.

Carbon mitigation potential afforded by rooftop photovoltaic in China.

Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km2 rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios.

Sustainable development goals (SDGs) in the United Nations 2030 Agenda call for action by all nations to promote economic prosperity while protecting the planet. Projection of future land-use change under SDG scenarios is a new attempt to scientifically achieve the SDGs. Herein, we proposed four scenario assumptions based on the SDGs, including the sustainable economy (ECO), sustainable grain (GRA), sustainable environment (ENV), and reference (REF) scenarios. We forecasted land-use change along the Silk Road (resolution: 300 m) and compared the impacts of urban expansion and forest conversion on terrestrial carbon pools. There were significant differences in future land use change and carbon stocks, under the four SDG scenarios, by 2030. In the ENV scenario, the trend of decreasing forest land was mitigated, and forest carbon stocks in China increased by approximately 0.60% compared to 2020. In the GRA scenario, the decreasing rate of cultivated land area has slowed down. Cultivated land area in South and Southeast Asia only shows an increasing trend in the GRA scenario, while it shows a decreasing trend in other SDG scenarios. The ECO scenario showed highest carbon losses associated with increased urban expansion. The study enhances our understanding of how SDGs can contribute to mitigate future environmental degradation via accurate simulations that can be applied on a global scale.

Mitigation of China's carbon neutrality to global warming.

Projecting mitigations of carbon neutrality from individual countries in relation to future global warming is of great importance for depicting national climate responsibility but is poorly quantified. Here, we show that China's carbon neutrality (CNCN) can individually mitigate global warming by 0.48 °C and 0.40 °C, which account for 14% and 9% of the global warming over the long term under the shared socioeconomic pathway (SSP) 3-7.0 and 5-8.5 scenarios, respectively. Further incorporating changes in CH4 and N2O emissions in association with CNCN together will alleviate global warming by 0.21 °C and 0.32 °C for SSP1-2.6 and SSP2-4.5 over the long term, and even by 0.18 °C for SSP2-4.5 over the mid-term, but no significant impacts are shown for all SSPs in the near term. Divergent responses in alleviated warming are seen at regional scales. The results provide a useful reference for the global stocktake, which assesses the collective progress towards the climate goals of the Paris Agreement.

Prediction for Origin-Destination Distribution of Dockless Shared Bicycles: A Case Study in Nanjing City.

Shared bicycles are currently widely welcomed by the public due to their flexibility and convenience; they also help reduce chemical emissions and improve public health by encouraging people to engage in physical activities. However, during their development process, the imbalance between the supply and demand of shared bicycles has restricted the public's willingness to use them. Thus, it is necessary to forecast the demand for shared bicycles in different urban regions. This article presents a prediction model called QPSO-LSTM for the origin and destination (OD) distribution of shared bicycles by combining long short-term memory (LSTM) and quantum particle swarm optimization (QPSO). LSTM is a special type of recurrent neural network (RNN) that solves the long-term dependence problem existing in the general RNN, and is suitable for processing and predicting important events with very long intervals and delays in time series. QPSO is an important swarm intelligence algorithm that solves the optimization problem by simulating the process of birds searching for food. In the QPSO-LSTM model, LSTM is applied to predict the OD numbers. QPSO is used to optimize the LSTM for a problem involving a large number of hyperparameters, and the optimal combination of hyperparameters is quickly determined. Taking Nanjing as an example, the prediction model is applied to two typical areas, and the number of bicycles needed per hour in a future day is predicted. QPSO-LSTM can effectively learn the cycle regularity of the change in bicycle OD quantity. Finally, the QPSO-LSTM model is compared with the autoregressive integrated moving average model (ARIMA), back propagation (BP), and recurrent neural networks (RNNs). This shows that the QPSO-LSTM prediction result is more accurate.

Vectorized rooftop area data for 90 cities in China.

Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km2 across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.

An online participatory system for SWMM-based flood modeling and simulation.

In the context of the continuous development of urbanization and global climate change, urban flooding risk has become a well-publicized research issue. The Storm Water Management Model (SWMM) performs very well in urban rain-runoff simulations and is widely used to build flood models in specific areas. Because of the complicated and tedious processing work for urban flood modeling and simulation, multifield participants' cooperation is becoming a trend. To promote the research and application of flood modeling and simulation, some resource sharing-oriented systems and platforms have been proposed with the advantages of network technology. However, they still require a participatory environment that can help modeling participants overcome the difficulties of distributed cooperation in the process of SWMM-based flood modeling and simulation. Therefore, we designed and implemented an online participatory system to coordinate the effective collaboration of modeling participants in this process. By referring to the scenarios and specific participatory demands in the modeling process, the system provides a guiding framework that consists of multiple participatory activities and prepares a series of online auxiliary tools designed for these activities. Using the main urban area of Lishui City as the study area, it was confirmed that the process of SWMM-based flood modeling and simulation can be demonstrated collaboratively on the online participatory system developed in this study.

Service-oriented interface design for open distributed environmental simulations.

Modeling and simulations are important methods in environmental research. Currently, massive simulation resources from different domains have been developed to simulate various dynamic phenomena and processes to address different environmental problems. These heterogeneous simulation resources (e.g., models, data, and servers) can be wasted if they are not shared and reused effectively. Recently, experts may exchange resources and conduct simulations in the open web environment via these shared and distributed services. However, some challenges remain, such as the heterogeneity and reusability of simulation resources. The goal of this study was to analyze typical scenarios involved in simulation tasks and design a set of service-oriented interfaces for different simulation resources. These interfaces, including the model description interface, model encapsulation interface, server management interface and sim-task operation interface, can be used to describe, encapsulate, manage and invoke environmental simulation resources, which can further contribute to resource assembly for environmental simulation tasks. This study evaluated the case of PM2.5 concentration distribution simulation by meteorological data, land cover data and a random forest model in 2014. Using the designed interface, this study conducted the simulation and explored the influence of different interpolation methods (inverse distance weighting (IDW) and kriging) for meteorological data in the random forest-based PM2.5 concentration simulation. For this case, the results show that kriging is a more suitable interpolation method than IDW for meteorological data in the simulation, and this interface design can organize simulation resources, configure tasks, and balance task loads in different servers on the open web.