Estimating vertical vegetation density through a SPOT5 imagery at multiple radiometric correction levels

There is growing concern about remote sensing of vertical vegetation density in rapidly expanding peri-urban interfaces. A widely used parameter for such density, i.e., leaf area index （LAI）, was measured in situ in Nanjing, China and then correlated with two vegetation indices （VI） derived from multiple radiometric correction levels of a SPOT5 imagery. The VIs were a normal- ized difference vegetation index （NDVI） and a ratio vegetation index （RVI）, while the four radiometric correction levels were i） post atmospheric correction reflectance （PAC）, ii） top of atmosphere reflectance （TOA）, iii） satellite radiance （SR） and iv） digital number （DN）. A total of 157 LAI-VI relationship models were established. The results showed that LA! is positively correlated with VI （r varies from 0.303 to 0.927, p 〈 0.001）. The R： values of＂pure＂ vegetation were generally higher than those of mixed vegetation. The average R2 values of about 40 models based on DN data （0.688） were higher than that of the routinely used PAC （0.648）. Independent variables of the optimal models for different vegetation quadrats included two vegetation indices at three radiometric correction lev- els, indicating the potential of vegetation indices at multiple radiometric correction levels in LAI inversion. The study demonstrates that taking heterogeneities of vegetation structures and uncertainties of radiometric corrections into account may help full mining of valuable information from remote sensing images, thus improving accuracies of LAI estimation.

Radiometric normalization of overlapping LiDAR intensity data for reduction of striping noise

Airborne LiDAR data are usually collected with partially overlapping strips in order to serve a seamless and fine resolution mapping purpose.One of the factors limiting the use of intensity data is the presence of striping noise found in the overlapping region.Though recent researches have proposed physical and empirical approaches for intensity data correction,the effect of striping noise has not yet been resolved.This paper presents a radiometric normalization technique to normalize the intensity data from one data strip to another one with partial overlap.The normalization technique is built based on a second-order polynomial function fitted on the joint histogram plot,which is generated with a set of pairwise closest data points identified within the overlapping region.The proposed method was tested with two individual LiDAR datasets collected by Teledyne Optech’s Gemini(1064 nm)and Orion(1550 nm)sensors.The experimental results showed that radiometric correction and normalization can significantly reduce the striping noise found in the overlapping LiDAR intensity data and improve its capability in land cover classification.The coefficient of variation of five selected land cover features was reduced by 19–65%,where a 9–18%accuracy improvement was achieved in different classification scenarios.With the proven capability of the proposed method,both radiometric correction and normalization should be applied as a pre-processing step before performing any surface classification and object recognition.

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.