Latest Contributions by larryzhang

Traditionally, geospatial and G&G techniques belong to different discipline. For last decade, however, geospatial community has been facing new challenges, because more and more custmers and applications demand surface-subsurface data integration (saying,  creating drill hole traces in 3D plan/ profile/ section) and geological analysis 'within' geospatial solutions, which are commonly used in geological studies, subsurface engineering (utility, civil engineering), hydrology, river and flood rish analysis, earthquake study, mining, energy, ... Even though huge challenges ahead, it also offers big opportunities ... How closely to combine both disciplines to reach those diverse goals, in particular, via comining both spatial data models and G&G data models 'within' geospatial database/ servers, with rich client applications? We see some good moves in this direction. For example, Aquaveo at Subsurface Analyst | Aquaveo.com and C Tech at EnterVol Product Suite | C Tech Development Corporation , partially or fully combine some of G&G data models within ArcGIS FGDB. Worth to mention, major commercial G&G data models are aleady well-designed to combine both geospatial and G&G data models 'inside' G&G domain, meanwhile also allowing integrated or seamlessly interoperated with major geospatial database (like a WMS Server or ESRI ArcGIS Server) and other G&G project database (OpenWorks, GeoFrame). For example,  Paradigm SKUA-GOCAD (Epos), which is  'proprietary' G&G data models with geospatial data models widely available for petroleum and mining industries, offers this type of 'integrated' features (complete geospatial and G&G data models in one domain) to give users much more efficiency and production for geological exploration and resource appraisals in operation... Technically, SKUA-GOCAD is a complete 3D GIS - G&G modelling system, which covers both surface 3D and subsurface 3D. With SKUA-GOCAD, seamless interaction between 3D model visualization, multiple data space views, and a powerful query environment for selecting subsets of both geospatial and G&G data through one central software application. Schlumberger Seabed E&P data model (Petrel) and GEOVIA Surpac™ (Gems) offer similar capabilities and functionalities... So far, some offer good starter with some real geological analysis capabilities in geospatial workflows as an extension inside ArcGIS to connect their own G&G database, even not completely in geospatial database yet. RockWare GIS Link 16 - cross-sections, fence diagrams in ArcView (sold by RockWare) Geological Software | Geology Software | Geosoft Solutions GSI3D -Free Near-surface 3D modeling package GSI3D - Wikipedia, the free encyclopedia Certainly, some open data models are also trying to do so, including BGS OpenGeoscience, USGS and PPDM ... However, those data models still are facing development challenges in geospatial platform like ArcGIS... ++++++++++++ OneGeology OneGeology - To be the provider of geoscience data globally Open Geoscience data models | British Geological Survey (BGS) Borehole data model from BGS: Borehole index & interpretations | British Geological Survey (BGS) Geochemistry data model: Geochemistry data model | Geochemistry | British Geological Survey (BGS) Lithology data model: Lexicon data model | British Geological Survey (BGS) NADM - The North American Geologic Map Data Model FGDC Proposal for Geologic Data Model Standard National Geologic Map Database -- Standards Development ... View more

From variant index images (like NDVI) derived from time-serial remote sensing images, both the ‘spatial variation’ and ‘temporal variation’ for multiple disciplines could be defined, which are widely available, in particular, in the literatures. However, most of those research results show high uncertainty and low reliability. Examples of spatial and temporal variations of some fine-grained suspended particle characteristics in two Danish coastal water bodies (by Ole Aarup Mikkelsen 2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 (by Liming Zhou el Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 - Resear…) Annual integral changes of time serial NDVI in mining subsidence area (Ma Chao el 2011) In practice, in order to *accurately* define spatial variations or detect spatial changes directly from time-serial images or index images, many technical challenges are required to be solved. For example, in GIS and land management, how *effectively* to detect spatial changes of landcover over time (i.e., landuses, building lots, fences, tree crowns, etc.)? Obviously, algorithms for change detection /defining spatial variation are mostly different from feature extraction, including traditional change detection algorithms and object-oriented algorithms. For last few years, many researches and practitioners have been discussing object-oriented Change Detection with eCognition and ERDAS Objective. With eCognition, it uses the multivariate alteration detection (MAD) transformation (by Allan et al, 1998; Nielsen & Conradsen, 1997), which is based on the established canonical correlations analysis. http://www.isprs.org/proceedings/xxxviii/4-c1/sessions/Session8/6674_Johansen_Proc.pdf‌ http://www.ecognition.com/sites/default/files/357_esarda03_nieca.pdf, http://clas-pages.uncc.edu/gang-chen/files/2012/12/Chen_2012_TRES_332.pdf, http://www.ecognition.com/document/object-oriented-analysis-change-detection However, it looks that MAD might be challenging, as a ‘real' object-oriented solution for Change Detection in accurate way. Inversely, ERDAS Objective uses Discriminant Function Change algorithm to help extract change features, which demonstrates direct and efficient way to map ‘spatial variation’ and perform change detection... ... View more