Abstract
Purpose
Forest as a vital natural resource in China plays an irreplaceable important role in safeguarding ecological security and human survival and development. Due to the vast territory, huge population and widespread forest landscape of China, forest management is a complex system involving massive data and various management activities. To effectively implement sustainable forest management, the big data technology has been utilized to analyze China’s forestry resources. Thus, the purpose of this paper is to clarify the role of big data technology in China’s forest management.
Design/methodology/approach
In this paper, the authors revisited the roles of big data in forest ecosystem monitoring, forestry management system development, and forest policy implementation.
Findings
It demonstrates that big data technology has a great potential in forest ecosystem protection and management, as well as the government’s determination for forest ecosystem protection. However, to deepen the application of big data in forest management, several challenges still need to be tackled.
Originality/value
Thus, enhancing modern science and technology to improve big data, cloud computing, and information technologies and their combinations will contribute to tackle the challenges and achieve wisdom of forest management.
Keywords
Citation
Hasan, S.S., Zhang, Y., Chu, X. and Teng, Y. (2019), "The role of big data in China’s sustainable forest management", Forestry Economics Review, Vol. 1 No. 1, pp. 96-105. https://doi.org/10.1108/FER-04-2019-0013
Publisher
:Emerald Publishing Limited
Copyright © 2019, Shaikh Shamim Hasan, Yue Zhang, Xi Chu and Yanmin Teng
License
Published in Forestry Economics Review. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Forest is one of the key components of ecosystem, which provide various benefits to humans, including timber products, climate mitigation, biodiversity preservation, etc. (Mori et al., 2017). Although forests are under great pressure due to human activities, especially in tropical forests (Sloan and Sayer, 2015), facing climate change issues and acknowledging the serious problem of forest loss, different countries have made great efforts in forest conservation and afforestation. China initiated a series of reforestation and afforestation programs to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change since the early 1980s (Liu et al., 2008; Zhang et al., 2000). With a dozen years of efforts, China has contributed greatly to the global net gain of tree cover, which alone accounts for 25 percent of the global net increase in leaf area with only 6.6 percent of global vegetated area since 2000 (Chen et al., 2019). It should still be noted that tree cover is not necessarily the forest cover; however, the net gain in the forest cover is the result of a net loss of high-quality forests being outweighed by a net gain in the afforestation in bare lands (Song et al., 2018). Therefore, it is still essential to monitor forest resources for forestry policy making and management.
Forest management is a complex system (ecological, economic and social) that involves massive data and various management activities, including those focused on the ecology of the ecosystem or on the economics of forest businesses (Bettinger et al., 2016). For example, global climate mitigation and adaptation requires effective systems for monitoring forest cover to calculate carbon stock (Bustamante et al., 2016); forestry ecological engineer constructions require evaluation of forest ecosystem services to properly decide payment for environmental services from forests (Sheng et al., 2017); and timber or revenue production in a sustainable manner requires more efficient production processing cycle monitoring (Zelinka et al., 2018). In these cases, better information technologies and more timely inventories on forest resources will result in more reasonable and efficient decision making, such as providing more options for conserving biodiversity, planning and implementing sustainable productive use of resources, including determination of allowable cut and plantation and natural forest management, etc. Here, big data technology is playing a very important role in global forest management. Moreover, emerging big data technology is playing a significant role in forest management at multiple spatiotemporal level. Big data generally refers to massive volumes of data not readily handled by the usual data tools and practices and presents unprecedented opportunities for advancing science and informing resource management through data-intensive approaches, and big data technology is making possible a new kind of environmental activism in the process (Strickland, 2014). When a tree falls in the forest, these days, it does not just make a sound, it causes a computer program to generate an alert that is sent out to activists, researchers, and environmental policymakers around the planet. Forest cover change trends since the year of 2000 and forest loss at a resolution of 30 meters can be examined with a global map. Big data techniques are frequently enough to track deforestation hot spots in places such as Indonesia and Brazil. Forest managers can easily find signs of illegal logging or slash-and-burn agriculture in the tropics. Collectively, researchers are encouraged to join the larger scientific community in global initiatives to address major scientific and societal problems such as forest protection by harnessing big data power (Hampton et al., 2013).
China’s forestry resources development has been challenging due to serious problems such as shortage, inferiority and uneven distribution of forestry resources. The vast territory, huge population and widespread forest landscape of China have led to the numerous indexes and complex data (Li, Hao and Chi, 2017; Li, Cui and Liu, 2017); therefore, it is important to improve efficiency and accuracy of forestry resources investigation and forest cover monitoring to ensure the sustainable development of the forestry resource. In this case, the theory and technology that are developed on the basis of big data can be widely applied to analyze China’s forestry resources (Li, Hao and Chi, 2017; Li, Cui and Liu, 2017). Thus, the main purpose of this paper is to clarify the role of big data in China’s forest management. The second part of this paper discusses the applications of big data to research works on forest ecosystem. The third part explains how big data promotes forest businesses, whereas the fourth part reviews the policies and measures on forest management issued by governments driven by big data technology. The paper ends with conclusions and discussion about the prospects of big data in forest management.
2. Forest ecosystem services analysis and forest fire predication using big data
2.1 Forest ecosystem services analysis
Forest ecosystem is the main body of the terrestrial ecosystem; understanding the spatial and temporal distribution of forest ecosystem services is as vital as their actual value for forest management. The existing problems in China’s effective organization and management of forest ecological environment mainly stem from the lack of spatial and temporal data sets and insufficient study of the dynamic changes of forest ecosystem services. Accurate and detailed measures of changing pattern in forestry area have been difficult to obtain in China. In the past, most studies have relied on forest inventory data, which are reported by the State Forestry Administration in every five to six years. Although some land use data are available through the annual statistical year books, the quality of such data has long been questioned. Whereas forest ecosystem service assessment, at present, has entered the period of big data after the period of small data and surface big data (Gijzen, 2013; Hampton et al., 2013), many studies have applied big data, especially remote sensing (RS) data, to measure forest inventory as well as to monitor and manage forests ecosystem at regional levels. With high-resolution RS data, a 1-km area percentage data model (1-km APDM) with RS data are developed and used to monitor forestry area, which examines the conversions of forests, and to identify the spatiotemporal characteristics of land conversions between forests and other land uses/covers and internal conversions between forest cover types (Deng et al., 2010). The 1-km APDM is a feasible and important tool to trace the spatiotemporal patterns of the forest conversions at a finer resolution. Based on the 1-km area percentage data, Deng, Huang, Huang, Rozelle and Gibson (2011), Deng, Huang, Uchida, Rozelle and Gibson (2011) tested whether the existence and the size of roads (ranging from expressways to tertiary roads) affected the level of forest and grasslands cover within 1 km2 (pixel) units. Furthermore, Deng, Yin, Uchida, and Rozelle (2012) made a great progress in measuring forestry area by providing the first detailed statistics for changes in forestry area for the entire China, based on RS data. Importantly, the RS-based forestry area data are complementary with the official government statistics for future monitoring of forest cover in China.
Apart from forest monitoring based on RS data, forest ecosystem service assessment based on long-term monitoring data from forest ecological stations can obtain more detailed information from the big data, and thus multi-scale evaluation can be carried out. At the same time, the random error brought by selection of small data sample can be avoided to make the evaluation results more reliable. Such long-term monitoring data from forest ecological stations provide support for forest resources protection and sustainable development (Song et al., 2015; Tien, 2013). With the development of big data, it has become feasible to set up a database to monitor the changes of forest ecosystem services and achieve a more accurate visualization in the future (Li, Hao and Chi, 2017; Li, Cui and Liu, 2017). Additionally, the development of field observation technology and the application of advanced observation equipment have led to a rapid increase in the amount of ecosystem observation data, providing a good data foundation for the evaluation of ecosystem services based on big data. Research works have shown that ecosystem services can only be fully expressed by humans on a larger scale such as landscapes and regions (Prager et al., 2012; Tscharntke et al., 2012). In recent years, Chinese forest ecosystem research network has developed rapidly and continuously improved. The long-term monitoring data of forest ecological stations provide data support for the evaluation of forest ecosystem service functions (Niu et al., 2013). Due to the particularity of big data, traditional data processing methods are no longer suitable. Niu et al. (2012) carried out a distributed measurement method for forest ecosystem service functions, which decomposes the complex evaluation process into several measurement units, and then the final evaluation result is accumulated step by step. This allows for the integration of big data from forest ecological stations in each assessment unit, avoiding the cumbersome steps of processing big data on a larger scale. Management of ecological performance and forest protection could be improved by utilizing big data to acquire more intelligence or latent knowledge (Xie et al., 2017).
Forest ecosystem service assessment based on long-term observations of forest ecological stations can reveal deeper phenomena, such as analyzing different spatial and temporal patterns of forest types, and leading functions of forest ecosystems in a certain region, and carrying out driving force analysis (Song et al., 2015; Niu and Wang, 2012). These studies can deepen people’s environmental awareness, strengthen the dominant position of forestry construction in the national economy, improve the level of forest management, and accelerate the integration of the environment into the national economic accounting system and correctly handle the relationship between social economic development and ecological environmental protection.
2.2 Forest fire prediction
Forest fires are one of the threats to the forest ecosystem and environment; thus, its predication or early detection based on big data will contribute greatly to sustainable forest management. Generally, the forest area or wildland has the high possibility to have forest fires and to have a significant damage to the environment (Ramachandran et al., 2008). According to the statistics of China, from 1952 to 2012, there were 798,500 forest fires and 38,060,000 hectares of forest was destroyed, which caused about $22m loss a year (Niu and Zhai, 2012). There exist complex factors that cause forest fires, among which local weather and human behaviors are the factors that mostly relate to forest fires. Additionally, high incidence and destructiveness of forest fire determine the importance of forest fire prediction or early detection. With the development of big data technology, such as rechargeable wireless sensor networks (RWSNs), forest fire prediction can be implemented and forest fire risk can be calculated. Humans have the ability to obtain the information of forest environment using RWSNs and analyze the complex relationship between the forest fire and the information. The RWSNs collect continuous 24-hour weather information, which can reflect the high accurate status of forest environment. Based on big data integration in study, Dutta et al. (2016) found that Australian bush-fire frequency was increasing significantly. The high possibility of potential forest fires risk can be measured by the big data analysis algorithm (Lin et al., 2018); therefore, more attention needs to be paid to prevent forest fire condition.
Forest fire warning is one of the key fields of fire security (Rossi et al., 2011). The overall goal of a big data-based forest fire management system is to improve the visibility of the fire protection system and to narrow the gap between strategy and execution (Bellatreche et al., 2015). As the forest fire has different time and space attributes, it is necessary to consider the combined effects of various factors on forest fire prevention at different time points and different regions (Wang et al., 2017). Video fire alert system based on big data is a fast and effective way for such application, which is also active for the study of early warning (Mahdipour and Dadkhah, 2014). Big data model has the characteristics of security and stability, which can be suitable for more complex application scenarios and can guarantee the stability of the system for a long time (Zhu et al., 2015).
Management of ecological performance and forest protection could be improved by utilizing big data to acquire more intelligence or latent knowledge. On the one hand, forest ecosystem service assessment based on big data can obtain more detailed information and be carried out on a multi-scale. At the same time, it can avoid the random errors caused by the selection of small data samples, making the evaluation results more reliable. On the other hand, forest fire predication or early detection based on big data will contribute greatly to sustainable forest management. The forest fire early warning system based on big data shows its feasibility and effectiveness in practical applications.
3. Forestry resources management system based on big data
Recent advancements in information and big data technologies have led to an increasing development of forest resources management systems, which include forestry production management system, forest information platform and comprehensive forest resources management systems, etc. Forestry industry has the characteristics of long production cycle; once a decision-making error occurs, it is hard to trace back and recover. In this case, a comprehensive production management system supported by information and big data technology, which could accelerate information feedback and help adjust strategies, is needed in realizing forestry protection and sustainable production. In this case, the development of “precision forestry” system has become an innovative solution to increase yield and efficiency of forestry industries while simultaneously reducing the environmental impact, which uses information technologies and analytic tools to guide forestry management strategies (Fardusi et al., 2017). In the system, big data technologies connect data from satellite, airborne, unmanned aerial vehicles, global positioning systems, and many sensors, devices and other geospatial tools, which enables highly repeatable measurements, actions and processes to manage and harvest forest stands, simultaneously allowing information linkages between production and wood supply chain, involving resource managers and environmental community; all these factors are contributing to the wider goal of sustainable forest management (Corona et al., 2017).
China continues to be the world’s largest producer, consumer and trader in forest products. It is necessary to build a forestry informatization platform for forestry integrated development. In this case, big data could integrate forestry resources, forestry industry, spatial geography, natural society and other information, promote large-scale production and intensive management of forestry, and vigorously promote the development of modern forestry. In 2016, China Forestry Big Data Center and China Forestry Right Trading (Storage) Center (Abbreviated as “Dual Center”) was founded in Yunnan province. The forestry informatization platform depends on rich forestry resources in Yunnan province and aims at constructing a network of forestry information in China, an intelligent and multidimensional visual picture of forestry for ecological construction and industrial development, as well as performing as a dual center. Analyzed results at the platform based on big data technology could be used by forestry administrator, farmers and companies; forestry experts could solve problems for farmers online. In particular, users could complete forestry production, forestry right and other trades online. In general, the informatization platform not only makes forestry trades convenient, but also guides forestry industrial to develop scientifically. Based on informatization platform, forestry resources and industries, natural and social data will be combined deeply for realizing forestry large-scale production and intensive management in the future. Forest management is a complex process that integrates environmental management and also economic development, thus making comprehensive forest resources management a topic of great current interest. The National Forestry and Grassland Administration of China (NFGA) launched a program named Forest Land “One Map,” which is a coordinated and integrated mapping program for the whole country involving different ministries and agencies, covering basic geographical factors, land conditions, forest cover factors, planning and management factors, forest economics, and many other topics (Xu et al., 2015). In addition to national-level forest resources management, many provinces and cities have conducted pilot constructions of forest management systems. For instance, Qinghai province promoted the pilot construction of Intelligent Management and Control System in 2013, in which a real-time dynamic monitoring system on forestry was also developed with video surveillance, computer networking and other high-tech means in order to warn forest fires, insect pest and theft early. Similarly, in Henan province, Shanxi province, Chuzhou city and many other cities and provinces have employed forestry resources management system, which could collect, manage, analyze, applicate and update forestry-related data. By constructing a basic geographic information environment in two and three dimensions (3D), massive dynamic 3D data could be seamlessly displayed in exhibition subsystem; many kinds of thematic maps and shared information could be built, as well as forestry resources statistics and tables could be searched. Except for constructions of forest management system to monitoring their current situations, to assisting comprehensive forest management decision making, to analyze scenario prediction with empirical statistical methods is good for making plans of regional forest development. Meanwhile, Deng et al. (2008), Deng, Yin, Lin, Jin and Qu (2012) developed the computable general equilibrium models of land use change (CGELUC) and dynamics of land system (DLS) for policy simulation and land spatial modeling, which are useful tools for sustainable land management. The CGELUC is a mechanistic model based on microeconomic theory, and it not only can effectively reflect mechanisms in structural changes in regional land use, but also provide support for forestry resources management strategies. The DLS is a collection of programs that simulates pattern changes in land uses under given land use scenarios by integrating several driving factors of spatial pattern changes, land use conversion rules and dynamic spatiotemporal processes of land use changes. The combination of CGELUC and DLS models can be applied to simulate and predict regional land use change, including forest land under different scenarios (Jin et al., 2017), whereas Luo et al. (2014) applied the CGELUC to predict changes of forest resources in the Yunnan province during the period of 2008–2020 under the baseline scenario, a low total factor productivity (TFP) scenario, and a high TFP scenario. Overall, forestry management strategies will depend on the dynamic changes of forest resources, which can be presented clearly through big data technologies and scenario-based modeling.
Generally speaking, forestry resources management system is a useful tool for ensuring quantity, quality, construction and distribution of forest resources. With the help of both information and big data technologies, forest resources monitoring is more scientific and accurate. It is more reasonable for managers to adjust forestry management and production plans and forest cutting quotas.
4. Implementations of forest management policies driven by big data
Facing severe degradation of forest and rapid development of big data, the NFGA has already launched forestry information research and “intelligent forestry” research. Since 2013, the NFGA issued policy-related documents such as “China wisdom forestry development guidance” and “Guidelines on promoting the development of China forestry Internet of things.” Especially, the NFGA officially released “Three-year work plan for implementation ‘The outline of action program to promote the development of big data’” and “The guidance on accelerating the development of China forestry big data” in 2016. All of these indicate that promoting the application of big data in forest ecosystem protection and forest resources development has become an important content of national forestry management policy.
According to the work plan and guidance on development of China forestry big data, the main task is to build four systems, namely forestry big data acquisition system, application system, open sharing system and technical system. In the meantime, five demonstration projects, including the ecological big data sharing service system, the Beijing‒Tianjin‒Hebei region forestry data and resources sharing platform, the Belt and Road forestry data and resource collaborative sharing platform, the Yangtze river economic belt forestry data and resource collaborative sharing platform and ecosystem services big data intelligent decision-making platform, will be constructed. In addition, based on construction of the forestry big data, national ecological big data center, national ecological big data research institute and national ecological big data application engineering laboratory will be established. Currently, relying on the forestry data sub-center of national natural resources and geospatial basic information database, the basic environment database management system, 91 forestry databases, relevant standards and management methods have been established. A national satellite RS data application platform for forestry has been established, providing a large number of RS data products for forest resource monitoring, annual update of woodland and wetland monitoring.
Along with the implementation of the plan and guidance, forestry big data has been gradually constructed and applied. For instance, an ecological big data public service platform of “where to go tomorrow” and “fruit is ripe” has been built up, which is providing big data support for the development of forest tourism, fruit industry and other forestry industries. Furthermore, the construction of China forestry network intelligent decision-making system applied big data technology to conduct a comprehensive collection and analysis of users’ access data of China forestry site, accurately tracking user needs and hot spot trends. Special reports on ecological big data, such as “analysis report on the Internet repercussions of the reform of state-owned forest farms and state-owned forest regions,” “spatial-temporal characteristics of China-ASEAN regional ecological environment” and “distribution and change characteristics of global large international important wetlands” were developed to provide scientific basis for major decision making.
The formulation and implementation of forestry management policies driven by big data, which include accelerating the establishment of forestry big data system, forestry big data demonstration area and forestry big data research institutions as well as promoting big data technology research and application in forestry, demonstrated the great role and potential of big data technology in forest ecosystem protection and management, as well as the government’s determination for forest ecosystem protection.
5. Conclusions and discussions
Forest as a vital natural resource in China plays an irreplaceable important role in safeguarding ecological security, human survival and development. In this sense, China has emphasized the importance of sustainable forest management to tackle climate change, improve forest ecosystem services, increase forestry resources management efficiency, etc. At the same time, big data as a new idea and technology has been widely applied in China’s forest management, such as forest ecosystem monitoring, forestry management system developing, and forest policy implementing, etc.
By utilizing big data, more intelligent knowledge and information can be integrated to accurately assess the ecological performance of forest and effectively predict forest fire on a multi-scale. Multi-category monitoring and management systems for the forestry industry have been developed on the basis of big data technology and widely piloted at the city, provincial and even national level, which can effectively improve the efficiency of forestry production and management. Additionally, China’s recent major forestry planning and management policies, which are driven by big data technology, promote the application of forestry big data in the protection of forest ecosystems and development of forestry resources. The implementations of the polices with the assistance of big data technologies have achieved remarkable results, which include accelerating the construction of forestry big data system, establishing forestry big data demonstration area and forestry big data research institutions as well as promoting forestry big data technology research and application. These results demonstrate the great role and potential of big data technology in forest ecosystem protection and management, as well as the government’s determination for forest ecosystem protection.
Big data technology has been recognized as an innovative tool for sustainable forest management; however, there still exist many challenges. China’s forestry big data processing is very complex, which needs to establish a complete database to integrate various types of information, such as natural resources and geographical data, forestry policies and regulations, forestry statistical data, and many other thematic data sets. It is urgent to eliminate data islands and to expand and improve data quantity. Besides, big data analytics requires forestry workers to accurately find the available data and extract laws and information from a large amount of data. However, in the process of extraction, dealing with inaccurate data and analyzing data reliability are frequently troubling the forestry workers. Additionally, improving real-time forestry data analysis efficiency is another challenge in big data development. The real-time analysis of forestry data will help the administrator to provide feedback quickly. Only by finding the problems immediately, they can be solved more effectively. With rapid development of modern technologies, combining big data, cloud computing, and information technology can ensure wisdom of forest management, which will be effective to tackle the challenges and to form a modern forest management mode with international leading information base, high quality of ecological management, and optimized forestry industrial structure and innovation ability development.
References
Bellatreche, L., Cuzzocrea, A. and Song, I.-Y. (2015), “Advances in data warehousing and OLAP in the big Data Era”, Information Systems, Vol. 53, pp. 39-40.
Bettinger, P., Boston, K., Siry, J.P. and Grebner, D.L. (2016), Forest Management and Planning, Academic Press.
Bustamante, M.M., Roitman, I., Aide, T.M., Alencar, A., Anderson, L.O., Aragão, L., Asner, G.P., Barlow, J., Berenguer, E. and Chambers, J. (2016), “Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity”, Global Change Biology, Vol. 22 No. 1, pp. 92-109.
Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturvedi, R.K., Fuchs, R., Brovkin, V., Ciais, P. and Fensholt, R. (2019), “China and India lead in greening of the world through land-use management”, Nature Sustainability, Vol. 2 No. 2, p. 122.
Corona, P., Chianucci, F., Quatrini, V., Civitarese, V., Clementel, F., Costa, C., Floris, A., Menesatti, P., Puletti, N. and Sperandio, G. (2017), “Precision forestry: riferimenti concettuali, strumenti e prospettive di diffusione in Italia”, Forest@-Journal of Silviculture and Forest Ecology, Vol. 14 No. 1, p. 1.
Deng, X., Su, H. and Zhan, J. (2008), “Integration of multiple data sources to simulate the Dynamics of Land Systems”, Sensors, Vol. 8 No. 2, pp. 620-634.
Deng, X., Jiang, Q., Su, H. and Wu, F. (2010), “Trace forest conversions in Northeast China with a 1-km area percentage data model”, Journal of Applied Remote Sensing, Vol. 4.
Deng, X., Yin, F., Uchida, E. and Rozelle, S. (2012), “A complementary measurement of changes in China’s forestry area using remote sensing data”, Journal of Food, Agriculture & Environment, Vol. 10, pp. 1355-1358.
Deng, X., Huang, J., Huang, Q., Rozelle, S. and Gibson, J. (2011), “Do roads lead to grassland degradation or restoration? A case study in Inner Mongolia”, China.Environment Development Economics, Vol. 16, pp. 751-773.
Deng, X., Huang, J., Uchida, E., Rozelle, S. and Gibson, J. (2011), “Pressure cookers or pressure valves: do roads lead to deforestation in China?”, Journal of Environmental Economics Management, Vol. 61, pp. 79-94.
Deng, X., Yin, F., Lin, Y., Jin, Q. and Qu, R. (2012), “Equilibrium analyses on structural changes of land uses in Jiangxi province”, Journal of Food, Agriculture & Environment, Vol. 10 No. 1, (Part 2), pp. 846-852.
Dutta, R., Das, A. and Aryal, J. (2016), “Big data integration shows Australian bush-fire frequency is increasing significantly”, Royal Society Open Science, Vol. 3 No. 2.
Fardusi, M.J., Chianucci, F. and Barbati, A. (2017), “Concept to practice of geospatial-information tools to assist forest management and planning under precision forestry framework: a review”, Annals of Silvicultural Research, Vol. 41 No. 1, pp. 3-14.
Gijzen, H. (2013), “Big data for a sustainable future”, Nature, Vol. 502 No. 7469, pp. 38-38.
Hampton, S.E., Strasser, C.A., Tewksbury, J.J., Gram, W.K., Budden, A.E., Batcheller, A.L., Duke, C.S. and Porter, J.H. (2013), “Big data and the future of ecology”, Frontiers in Ecology and the Environment, Vol. 11 No. 3, pp. 156-162.
Jin, G., Deng, X., Chu, X., Li, Z. and Wang, Y. (2017), “Optimization of land-use management for ecosystem service improvement: a review”, Physics and Chemistry of the Earth, Vol. 101, pp. 70-77.
Li, L., Hao, T. and Chi, T. (2017), “Evaluation on China’s forestry resources efficiency based on big data”, Journal of Cleaner Production, Vol. 142, pp. 513-523.
Li, T., Cui, Y. and Liu, A. (2017), “Spatiotemporal dynamic analysis of forest ecosystem services using ‘big data’: a case study of Anhui province, central-eastern China”, Journal of Cleaner Production, Vol. 142, pp. 589-599.
Lin, H., Liu, X., Wang, X. and Liu, Y. (2018), “A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks”, Sustainable Computing: Informatics and Systems, Vol. 18, pp. 101-111.
Liu, J., Li, S., Ouyang, Z., Tam, C. and Chen, X. (2008), “Ecological and socioeconomic effects of China’s policies for ecosystem services”, Proceedings of the National academy of Sciences, Vol. 105 No. 28, pp. 9477-9482.
Luo, J., Zhan, J., Lin, Y. and Zhao, C. (2014), “An equilibrium analysis of the land use structure in the Yunnan Province, China”, Frontiers of Earth Science, Vol. 8, pp. 393-404.
Mahdipour, E. and Dadkhah, C. (2014), “Automatic fire detection based on soft computing techniques: review from 2000 to 2010”, Artificial Intelligence Review, Vol. 42 No. 4, pp. 895-934.
Mori, A.S., Lertzman, K.P. and Gustafsson, L. (2017), “Biodiversity and ecosystem services in forest ecosystems: a research agenda for applied forest ecology”, Journal of Applied Ecology, Vol. 54 No. 1, pp. 12-27.
Niu, R. and Zhai, P. (2012), “Study on forest fire danger over Northern China during the recent 50 years”, Climatic Change, Vol. 111 Nos 3-4, pp. 723-736.
Niu, X. and Wang, B. (2012), “Evaluation on forest ecosystem services in Fujian Province based on distributions measurement methodology”, Science of Soil & Water Conservation, Vol. 10 No. 2, pp. 36-43, (in Chinese).
Niu, X., Wang, B. and Wei, W.J. (2013), “Chinese forest ecosystem research network: a platform for observing and studying sustainable forestry”, Journal of Food, Agriculture & Environment, Vol. 11 No. 2, pp. 1008-1016.
Niu, X., Wang, B., Liu, S., Liu, C., Wei, W. and Kauppi, P.E. (2012), “Economical assessment of forest ecosystem services in China: characteristics and implications”, Ecological Complexity, Vol. 11, pp. 1-11.
Prager, K., Reed, M. and Scott, A. (2012), “Encouraging collaboration for the provision of ecosystem services at a landscape scale – rethinking agri-environmental payments”, Land Use Policy, Vol. 29 No. 1, pp. 244-249.
Ramachandran, C., Misra, S. and Obaidat, M.S. (2008), “A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks”, International Journal of Communication Systems, Vol. 21 No. 10, pp. 1047-1073.
Rossi, L., Akhloufi, M. and Tison, Y. (2011), “On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics”, Fire Safety Journal, Vol. 46 Nos 1-2, pp. 9-20.
Sheng, W., Zhen, L., Xie, G. and Xiao, Y. (2017), “Determining eco-compensation standards based on the ecosystem services value of the mountain ecological forests in Beijing, China”, Ecosystem Services, Vol. 26, pp. 422-430.
Sloan, S. and Sayer, J.A. (2015), “Forest resources assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries”, Forest Ecology and Management, Vol. 352, pp. 134-145.
Song, Q., Niu, X. and Wang, B. (2015), “Review on forest ecosystem services assessment based on big data”, Chinese Journal of Ecology, Vol. 34 No. 10, pp. 2914-2921.
Song, X.P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F. and Townshend, J.R. (2018), “Global land change from 1982 to 2016”, Nature, Vol. 560 No. 7720, p. 639.
Strickland, E. (2014), “Big data comes to the forest”, IEEE Spectrum, Vol. 51 No. 6, pp. 11-12.
Tien, J.M. (2013), “Big data: unleashing information”, Journal of Systems Science and Systems Engineering, Vol. 22 No. 2, pp. 127-151.
Tscharntke, T., Tylianakis, J.M., Rand, T.A., Didham, R.K., Fahrig, L., Batary, P., Bengtsson, J., Clough, Y., Crist, T.O. and Dormann, C.F. (2012), “Landscape moderation of biodiversity patterns and processes-eight hypotheses”, Biological Reviews, Vol. 87 No. 3, pp. 661-685.
Wang, D., Zhou, A., Cong, J. and Zhao, M.S. (2017), “Forest-fire prevention management system design based on big data processing”, Journal of Central South University of Forestry & Technology, Vol. 37 No. 11, pp. 30-37, (in Chinese).
Xie, H., He, Y. and Xie, X. (2017), “Exploring the factors influencing ecological land change for China’s Beijing–Tianjin–Hebei Region using big data”, Journal of Cleaner Production, Vol. 142, pp. 677-687.
Xu, D., Li, H., Pang, L., Zhang, Y., Huang, G. and Han, A. (2015), “Research of key technology for national forest-land “One Map” database”, Forest Resources Management, Vol. 5, pp. 36-43, (in Chinese).
Zelinka, S., Kordziel, S., Pei, S., Glass, S. and Tabares-Velasco, P. (2018), “Moisture monitoring throughout the construction and occupancy of mass timber buildings”, 1st International Conference on New Horizons in Green Civil Engineering, pp. 32-35.
Zhang, P., Shao, G., Zhao, G., Le Master, D.C., Parker, G.R., Dunning, J.B. and Li, Q. (2000), “China’s forest policy for the 21st century”, Science, Vol. 288 No. 5474, pp. 2135-2136.
Zhu, S., Zhang, J., Zhou, X. and Yang, J. (2015), “A simulation model of big data analysis for fire alarm”, International Conference on Advances in Energy, Environment and Chemical Engineering, Atlantis Press.
Acknowledgements
The authors declare no conflict of interests. This research was supported by the National Natural Science Funds of China for Distinguished Young Scholars (Grant No. 71225005), Chang Jiang Scholar Program, and Young and Middle-aged Leading Scientists, Engineers and Innovators Program.