Fences and hydropower:Important but overlooked Human Footprint

Human activities have impacted 77%of the terrestrial ecosystems(excluding Antarctica),and the remaining areas are becoming increasingly endangered.Mapping spatiotemporal dynamics of Human Footprint has been used to evaluate the cumulative interference on terrestrial environments globally.However,fences and hydropower,two widespread and rapidly expanding infrastructures,have not been considered regarding Human Footprint,despite their complicated and extensive effects on ecosystem functioning and species survival.Previous work has proved that fences increase habitat fragmentation,disrupt migratory routes,inadvertently trap and kill wildlife,and hinder genetic exchange.Hydropower construction also caused habitat loss,fragmentation,and degradation.These impacts have received global concern,but fences around the world are difficult to be detected due to the limitations of current cartographic technologies.Furthermore,the effect of hydropower on the terrestrial environment has been underestimated,making the research on this topic at a global scale still in its infancy.Therefore,building an observation network of global fences and hydropower is a necessary step to move forward in the assessment of the impact of human activities on our planet,but also to better provide scientific support for policy-making regarding global biodiversity conservation,the identification of protected areas,and the prioritization of ecological restoration areas.

Russian section of GGOS： Network status and progress

Establishment of the Russian section in the framework of Global Geodetic Observing System （GGOS） is under progress. New components of ＂Quasar＂ network observatories, which are included into GGOS global network as core stations, are presented. Recent developments include： two new generation radio telescopes with 13 m antennas at Badary and Zelenchukskaya observatories, water vapor radiometers installed at all observatories and software correlator at the Institute of Applied Astronomy. New and potential developments within other networks belonging to different agencies are also considered in the context of widening of Russian section activity in GGOS project, The paper gives a short overview of status, new components and plans, concerning 5 sub-networks of Federal Agency of Scientific Organi- zations, Roskosmos, Rosstandard, and Rosreestr. Short overview of the plans on creating Data and Analysis Distributed Center is also ~iven.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsuffcient precondition of the downstream in a new notion of Space Economy 4.0-Part 1:Problem background in Artificial General Intelligence(AGI)

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsufficient precondition of the downstream in a new notion of Space Economy 4.0-Part 2:Software developments

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.