Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.

Mapping the potential distribution suitability of 16 tree species under climate change in northeastern China using Maxent modelling

Knowledge on the potential suitability of tree species to the site is very important for forest management planning.Natural forest distribution provides a good reference for afforestation and forest restoration.In this study,we developed species distribution model(SDM)for 16 major tree species with 2,825 permanent sample plots with natural origin from Chinese National Forest Inventory data collected in Jilin Province using the Maxent model.Three types of environmental factors including bioclimate,soil and topography with a total of 33 variables were tested as the input.The values of area under the curve(AUC,one of the receiver operating characteristics of the Maxent model)in the training and test datasets were between 0.784 and 0.968,indicating that the prediction results were quite reliable.The environmental factors affecting the distribution of species were ranked in terms of their importance to the species distribution.Generally,the climatic factors had the greatest contribution,which included mean diurnal range,annual mean temperature,temperature annual range,and iosthermality.But the main environmental factors varied with tree species.Distribution suitability maps under current(1950-2000)and future climate scenarios(CCSM4-RCP 2.6 and RCP 6.0 during 2050)were produced for 16 major tree species in Jilin Province using the model developed.The predicted current and future ranges of habitat suitability of the 16 tree species are likely to be positively and negatively affected by future climate.Seven tree species were found to benefit from future climate including B etula costata,Fraxinus mandshurica,Juglans mandshurica,Phellodendron amurense,Populus ussuriensis,Quercus mongolica and Ulmus pumila;five tree species will experience decline in their suitable habitat including B.platyphylla,Tilia mongolica,Picea asperata,Pinus sylvestris,Pinus koraiensis;and four(Salix koreensis,Abies fabri,Pinus densiflora and Larix olgensis)showed the inconsistency under RCP 2.6 and RCP 6.0 scenarios.The maps of the habitat suitability can be used as a basis for afforestation and forest restoration in northeastern China.The SDMs could be a potential tool for forest management planning.

Commercial Credit and Corporate Productivity

As an important form of informal finance,commercial credit is widely used among enterprises.Does commercial credit promote the total factor productivity of enterprises?According to the theoretical literature and the reality,using the large sample data of Chinese industrial enterprises,the paper empirically tests the impact of commercial credit on the productivity of enterprises from three aspects:the provision and acquisition of commercial credit and the net commercial credit.The study finds that the provision of commercial credit reduces the productivity level of enterprises;the acquisition of commercial credit fails to promote productivity;while the net commercial credit as a short-term financial buffer for enterprises can alleviate the financing constraints,faced by enterprises,especially private enterprises,which help to increase their productivity levels.In addition,the study found that the higher the marketization process in the region,the more favorable the commercial credit is to the improvement of the production efficiency of private enterprises.

DAPR-tree: a distributed spatial data indexing scheme with data access patterns to support Digital Earth initiatives

This paper proposes a novel data indexing scheme,the distributed access pattern R-tree(DAPR-tree),for spatial data retrieval in a distributed computing environment.As compared to traditional distributed indexing schemes,the DAPR-tree introduces the data access patterns during the indexing utilization stage so that a more balanced indexing structure can be provided for spatial applications(e.g.Digital Earth data warehouse).In this new indexing scheme,(a)an indexing penalty matrix is proposed by considering the balance of data number,topology and access load between different indexing nodes;(b)an‘access possibility’element is integrated to a classic‘Master-Client’structure for a distributed indexing environment;and(c)indexing algorithm for the DAPR-tree is provided for index implementations.By using a duplication of official GEOSS Clearinghouse system as a case study,the DAPR-tree was evaluated in a number of scenarios.The results show that our indexing schemes generally outperform(around 9%)traditional distributed indices with the utilization of data access patterns.Finally,we discuss the applicability of the DARP-tree and document DARP-tree shortcomings to encourage researchers pursuing related topics in Big Data indexing for Digital Earth and other geospatial initiatives.

Building geospatial infrastructure

Many visions for geospatial technology have been advanced over the past half century.Initially researchers saw the handling of geospatial data as the major problem to be overcome.The vision of geographic information systems arose as an early international consensus.Later visions included spatial data infrastructure,Digital Earth,and a nervous system for the planet.With accelerating advances in information technology,a new vision is needed that reflects today’s focus on open and multimodal access,sharing,engagement,the Web,Big Data,artificial intelligence,and data science.We elaborate on the concept of geospatial infrastructure,and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.

Sustainable development and geospatial information:a strategic framework for integrating a global policy agenda into national geospatial capabilities

The 2030 Agenda for Sustainable Development provides a new global policy to guide the way countries collectively manage and transform the social,economic,and environmental dimensions of people and the planet over the next 15 years.Achieving sustainable development presents all countries and the global policy community with a set of significant development challenges that are almost entirely geographic in nature.Many of the issues impacting sustainable development can be analyzed,modeled,and mapped within a geographic context,which in turn can provide the integrative framework necessary for global collaboration,consensus and evidence-based decision-making.However,and despite significant advances in geospatial information technologies,there is a lack of awareness,understanding and uptake,particular at the policy and decision-making level,of the vital and integrative role of geospatial information and related enabling architectures such as National Spatial Data Infrastructures.This paper reasons that the role of geospatial information in contributing to sustainable development has not adequately been described by either the sustainable development policy practice or by the geospatial professional community.This lack of policy and guidance,with commensurate critical gaps and connection points with national geospatial frameworks,is a visible impediment to developing countries and those most affected by the challenges and need to achieve sustainable development.The global geospatial community now has a unique opportunity to integrate and connect geospatial information into the global development agenda in a more holistic and sustainable manner,specifically in contributing their data resources toward measuring and monitoring the 17 Sustainable Development Goals,and their 169 associated targets,through the global indicator framework that anchors the 2030 Agenda for Sustainable Development.This paper introduces and discusses a new strategic framework for linking a global policy to national geospatial capabilities.