Mapping spatial variation in acorn production from airborne hyperspectral imagery

Masting is a well-marked variation in yields of oak forests. In Japan, this phenomenon is also related to wildlife management and oak regeneration practices. This study demonstrates the capability of integrating remote sensing techniques into map- ping spatial variation of acorn production. The hyperspectral images in 72 wavelengths （407-898 nm） were acquired over the study area ten times over a period of three years （2003-2005） during the early growing season of Quercus serrata using the Airborne Im- aging Spectrometer Application （AISA） Eagle System. With the canopy spectral reflectance values of 22 sample trees extracted from the images, yield estimation models were developed via multiple linear regression （MLR） analyses. Using the object-oriented classi- fication approach in eCognition, canopies representative of individual oak trees （Q. serrata） were identified from the corresponding hyperspectral imagery and combined with the fitted estimation models developed, acorn yield over the entire forest were estimated and visualized into maps. Three estimation models, obtained for June 27 in 2003, July 13 in 2004 and June 21 in 2005, showed good performance in acorn yield estimation both for the training and validation datasets, all with R2 〉 0.4, p 〈 0.05 and RRMSE 〈 1 （the relative root mean square of error）. The present study shows the potential of airborne hyperspectral imagery not only in estimating acorn yields during early growing seasons, but also in identifying Q. serrata from other image objects, based on which of the spatial distribution patterns of acorn production over large areas could be mapped. The yield map can provide within-stand abundance and valuable information for the size and spatial synchrony of acorn production.

Homotopy classification of maps between r-1 connected 2r dimensional manifolds

In this paper,we study the homotopy classification of continuous maps between two r-1 connected 2r dimensional topological manifolds M,N.If we assume some knowledge on the homotopy groups of spheres,then the complete classification can be obtained from the homotopy invariants of M,N.We design an algorithm and compose a program to give explicit computations.

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.

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

For evaluating the progresses towards achieving the Sustainable Development Goals(SDGs),a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators.In this paper,we propose an improved methodology and a set of workflows for calculating SDGs indicators.The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity.Within the European Network for Observing our Changing Planet(ERA-PLANET),three SDGs indicators are calculated.In this research,harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment,as well as Landsat 8,Sentinel-2 and Sentinel-1 time series are utilized for crop mapping.We calculate for the whole territory of Ukraine SDG indicators:15.1.1–‘Forest area as proportion of total land area’;15.3.1–‘Proportion of land that is degraded over total land area’;and 2.4.1–‘Proportion of agricultural area under productive and sustainable agriculture’.Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform.We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.

Understanding satellite images:a data mining module for Sentinel images

The increased number of free and open Sentinel satellite images has led to new applications of these data.Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images,in particular,the identification and quantification of their temporal changes.In this paper,we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes,and presenting these as classification maps and/or statistical analytics.This represents a new systematic validation approach for semantic image content verification.We will focus on a number of different scenarios proposed by the user community using Sentinel data.From a large number of potential use cases,we selected three main cases,namely forest monitoring,flood monitoring,and macro-economics/urban monitoring.