Review of renewable energy-based hydrogen production processes for sustainable energy innovation

In this review,we primarily analyze the hydrogen production technologies based on water and biomass,including the economic,technological,and environmental impacts of different types of hydrogen production technologies based on these materials,and comprehensively compare them.Our analyses indicate that all renewable energy-based approaches for hydrogen production are more environmentally friendly than fossil-based hydrogen generation approaches.However,the technical ease and economic efficiency of hydrogen production from renewable sources of energy needs to be further improved in order to be applied on a large scale.Compared with other renewable energy-based methods,hydrogen production via biomass electrolysis has several advantages,including the ease of directly using raw biomass.Furthermore,its environmental impact is smaller than other approaches.Moreover,using a noble metal,catalyst-free anode for this approach can ensure a considerably low power consumption,which makes it a promising candidate for clean and efficient hydrogen production in the future.

Generation of ready to use (RTU) products over China based on Landsat series data

Earth observation community has entered into the era of big data.Family of Landsat sensors have collected massive medium resolution satellite images,which are valuable for long-term land surface monitoring.In order to significantly reduce the magnitude of data processing for remote sensing data users,Landsat-based Ready to Use(RTU)products have been produced.Main RTU products,including orthorectified products,land surface reflectance,land surface temperature,large-area mosaic image,and standard image map products,are described.The resulting Landsat RTU products are hosted on the RSGS earth observation data sharing web site for free download(http://ids.ceode.ac.cn/rtu/).These new products will provide consistent,standardized,multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences.In the coming years,CASEarth DataBank system will be constructed,which is an intelligent data service platform for providing not only the RTU products from multi-source satellite data,but also big earth data analysis methods.

A Systematic Overview of Blockchain Research

Blockchain has been receiving growing attention from both academia and practices.This paper aims to investigate the research status of blockchain-related studies and to analyze the development and evolution of this latest hot area via bibliometric analysis.We selected and explored 2451 papers published between 2013 and 2019 from the Web of Science Core Collection database.The analysis considers different dimensions,including annual publications and citation trends,author distribution,popular research themes,collaboration of countries(regions)and institutions,top papers,major publication journals(conferences),supportive funding agencies,and emerging research trends.The results show that the number of blockchain literature is still increasing,and the research priorities in blockchain-related research shift during the observation period from bitcoin,cryptocurrency,blockchain,smart contract,internet of thing,to the distributed ledger,and challenge and the inefficiency of blockchain.The findings of this research deliver a holistic picture of blockchain research,which illuminates the future direction of research,and provides implications for both academic research and enterprise practice.

Monitoring vegetation dynamics in East Rennell Island World Heritage Site using multi-sensor and multi-temporal remote sensing data

East Rennell of Solomon Island is the first natural site under customary law to be inscribed on UNESCO’s World Heritage List.Potential threats due to logging,mining and agriculture led to the site being declared a World Heritage in Danger in 2013.For East Rennell World Heritage Site(ERWHS)to‘shed’its‘Danger’status the management must monitor forest cover both within and outside of ERWHS.We used satellite data from multiple sources to track forest cover changes for the entire East Rennell island since 1998.95%of the island is still covered by undisturbed forests;annual average normalized difference vegetation index(NDVI)for the whole island was above 0.91 in 2015.However,vegetation cover in the island has been slowly decreasing,at a rate of–0.0011 NDVI per year between 2000 and 2015.This decrease less pronounced inside ERWHS compared to areas outside.While potential threats due to forest clearing outside ERWHS remain the forest cover change from 2000 to 2015 has been below 15%.We suggest ways in which the Government of Solomon Islands could use our data as well as unmanned air vehicles and field surveys to monitor forest cover change and ensure the future conservation of ERWHS.

Eighteen years(2001–2018)of forest habitat loss across the Asian elephant’s range and its drivers

The Asian elephant(Elephas maximus)-the largest living land animal in Asia-is categorized as an endangered species according to the International Union for Conservation of Nature(IUCN)Red List and is listed in the Convention on International Trade in Endangered Species of Wild Fauna and Flora(CITES)Appendix I[1].Over the past 100 years,the range of the Asian elephant has declined by as much as 90%[2].At present,the Asian elephant inhabits forest-dominated habitat in only 13 countries across South and Southeast Asia(Fig.S1 online),and the IUCN Species Survival Commission(SSC)Asian Elephant Specialist Group(AsESG)estimated the global wild population size of Asian elephants to number 45,671-49,028 individuals[3].Forest habitat loss,along with human-elephant conflict,poaching and the illegal trade in ivory and elephant body parts,is weakening global conservation efforts towards the recovery of wild Asian elephant populations at local,national and range-wide scales[3,4].

Forest status assessment in China with SDG indicators based on high-resolution satellite data

To assess the status and change trend of forest in China,an indicator framework was developed using SDG sub-indicators.In this paper,we propose an improved methodology and a set of workflows for calculating SDG indicators.The main modification include the use of moderate and high spatial resolution satellite data,as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass(AGB).This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover,while time series Landsat data is used to estimate forest AGB across the whole territory of China.The study calculate two SDG sub-indicators:SDG_(15.1.1) for forest area and SDG_(15.2.1) for sustainable forest management.The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021,accounting for about 23.51%of the land area.The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha,and the overall trend of forest AGB change in China was positive,albeit with some spatial differences.