Dr. rer. nat. Michael Denk

Start – Staff

Research focus

Multi- and hyperspectral remote sensing
Field and laboratory spectrometry
Exploration of anthropogenic deposits
Remote sensing in the mine life cycle

Scientific career

since 08/2021
Scientist in the Department of Geoecology, Institut of Geosciences and Geography, Martin Luther University Halle-Wittenberg
07/2018
PhD (Dr. rer. nat.) on „Qualitative and quantitative spectral characterisation of iron- and steelworks by-products: combining information from the visible light to the longwave infrared”
08/2015 – 07/2021
Research assistent in the Department of Geoscientific Remote Sensing, Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg
08/2012 – 07/2015
Research assistant in the BMBF joint project “REStrateGIS”, Institute of Geosciences and Geography, Martin Luther University Halle-Wittenberg
08/2011 – 07/2012
Scholarship holder supported by the Graduate Funding of the State of Saxony-Anhalt
12/2010
Diploma Thesis „Reflexionsspektrometrische Inventarisierung mineralischer Oberflächen und sekundärer Mineralneubildungen in der Bergbaufolgelandschaft bei Teutschenthal“
10/2003 – 05/2011
Study of geography (major), geology and soil science (minors) at the Martin Luther University Halle-Wittenberg

Current projects

Publications

Peer-Reviewed

2355391 MDLREVTX PeerReview Denk, Michael 1 american-journal-of-plant-sciences 50 date desc 1 Denk, M. 5372 https://geooeko.geo.uni-halle.de/wp-content/plugins/zotpress/

%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A150%2C%22request_next%22%3A50%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22LSTBNYZU%22%2C%22library%22%3A%7B%22id%22%3A2355391%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Mammadov%20et%20al.%22%2C%22parsedDate%22%3A%222024-01-29%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%20style%3D%26quot%3Bclear%3A%20left%3B%20%26quot%3B%26gt%3B%5Cn%20%20%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-left-margin%26quot%3B%20style%3D%26quot%3Bfloat%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%26quot%3B%26gt%3B1%20%26lt%3B%5C%2Fdiv%26gt%3B%26lt%3Bdiv%20class%3D%26quot%3Bcsl-right-inline%26quot%3B%20style%3D%26quot%3Bmargin%3A%200%20.4em%200%201.5em%3B%26quot%3B%26gt%3BMammadov%2C%20E.%2C%20%26lt%3Bstrong%26gt%3BDenk%2C%20M.%26lt%3B%5C%2Fstrong%26gt%3B%2C%20Mamedov%2C%20A.I.%20and%20Glaesser%2C%20C.%20%282024%29%20Predicting%20Soil%20Properties%20for%20Agricultural%20Land%20in%20the%20Caucasus%20Mountains%20Using%20Mid-Infrared%20Spectroscopy.%20%26lt%3Bi%26gt%3BLand%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bb%26gt%3B13%26lt%3B%5C%2Fb%26gt%3B%2C%20154.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20target%3D%26%23039%3B_blank%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.3390%5C%2Fland13020154%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.3390%5C%2Fland13020154%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%20%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Predicting%20Soil%20Properties%20for%20Agricultural%20Land%20in%20the%20Caucasus%20Mountains%20Using%20Mid-Infrared%20Spectroscopy%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Elton%22%2C%22lastName%22%3A%22Mammadov%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Denk%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Amrakh%20I.%22%2C%22lastName%22%3A%22Mamedov%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cornelia%22%2C%22lastName%22%3A%22Glaesser%22%7D%5D%2C%22abstractNote%22%3A%22Visible-near%20infrared%20%28Vis-NIR%29%20and%20mid-infrared%20%28MIR%29%20spectroscopy%20are%20increasingly%20being%20used%20for%20the%20fast%20determination%20of%20soil%20properties.%20The%20aim%20of%20this%20study%20was%20%28i%29%20to%20test%20the%20use%20of%20MIR%20spectra%20%28Agilent%204300%20FTIR%20Handheld%20spectrometer%29%20for%20the%20prediction%20of%20soil%20properties%20and%20%28ii%29%20to%20compare%20the%20prediction%20performances%20of%20MIR%20spectra%20and%20Vis-NIR%20%28ASD%20FieldSpecPro%29%20spectra%3B%20the%20Vis-NIR%20data%20were%20adopted%20from%20a%20previous%20study.%20Both%20the%20MIR%20and%20Vis-NIR%20spectra%20were%20coupled%20with%20partial%20least%20squares%20regression%2C%20different%20pre-processing%20techniques%2C%20and%20the%20same%20114%20soil%20samples%2C%20collected%20from%20the%20agricultural%20land%20located%20between%20boreal%20forests%20and%20semi-arid%20steppe%20belts%20%28Kastanozems%29.%20The%20prediction%20accuracy%20%28R2%20%3D%200.70%5Cu20130.99%29%20of%20both%20techniques%20was%20similar%20for%20most%20of%20the%20soil%20properties%20assessed.%20However%2C%20%28i%29%20the%20MIR%20spectra%20were%20superior%20for%20estimating%20CaCO3%2C%20pH%2C%20SOC%2C%20sand%2C%20Ca%2C%20Mg%2C%20Cd%2C%20Fe%2C%20Mn%2C%20and%20Pb.%20%28ii%29%20The%20Vis-NIR%20spectra%20provided%20better%20results%20for%20silt%2C%20clay%2C%20and%20K%2C%20and%20%28iii%29%20the%20hygroscopic%20water%20content%2C%20Cu%2C%20P%2C%20and%20Zn%20were%20poorly%20predicted%20by%20both%20methods.%20The%20importance%20of%20the%20applied%20pre-processing%20techniques%20was%20evident%2C%20and%20among%20others%2C%20the%20first%20derivative%20spectra%20produced%20more%20reliable%20predictions%20for%2011%20of%20the%2017%20soil%20properties%20analyzed.%20The%20spectrally%20active%20CaCO3%20had%20a%20dominant%20contribution%20in%20the%20MIR%20predictions%20of%20spectrally%20inactive%20soil%20properties%2C%20followed%20by%20SOC%20and%20Fe%2C%20whereas%20particle%20sizes%20and%20hygroscopic%20water%20content%20appeared%20as%20confounding%20factors.%20The%20estimation%20of%20spectrally%20inactive%20soil%20properties%20was%20carried%20out%20by%20considering%20their%20secondary%20correlation%20with%20carbonates%2C%20clay%20minerals%2C%20and%20organic%20matter.%20The%20soil%20information%20covered%20by%20the%20MIR%20spectra%20was%20more%20meaningful%20than%20that%20covered%20by%20the%20Vis-NIR%20spectra%2C%20while%20both%20displayed%20similar%20capturing%20mechanisms.%20Both%20the%20MIR%20and%20Vis-NIR%20spectra%20seized%20the%20same%20soil%20information%2C%20which%20may%20appear%20as%20a%20limiting%20factor%20for%20combining%20both%20spectral%20ranges.%20The%20interpretation%20of%20MIR%20spectra%20allowed%20us%20to%20differentiate%20non-carbonated%20and%20carbonated%20samples%20corresponding%20to%20carbonate%20leaching%20and%20accumulation%20zones%20associated%20with%20topography%20and%20land%20use.%20The%20prediction%20capability%20of%20the%20MIR%20spectra%20and%20the%20content%20of%20nutrient%20elements%20was%20highly%20related%20to%20soil-forming%20factors%20in%20the%20study%20area%2C%20which%20highlights%20the%20importance%20of%20local%20%28site-specific%29%20prediction%20models.%22%2C%22date%22%3A%222024-01-29%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.3390%5C%2Fland13020154%22%2C%22ISSN%22%3A%222073-445X%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.mdpi.com%5C%2F2073-445X%5C%2F13%5C%2F2%5C%2F154%22%2C%22collections%22%3A%5B%22MDLREVTX%22%5D%2C%22dateModified%22%3A%222024-01-31T15%3A04%3A46Z%22%7D%7D%2C%7B%22key%22%3A%22DU892HRC%22%2C%22library%22%3A%7B%22id%22%3A2355391%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Tepanosyan%20et%20al.%22%2C%22parsedDate%22%3A%222023-04-15%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%20style%3D%26quot%3Bclear%3A%20left%3B%20%26quot%3B%26gt%3B%5Cn%20%20%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-left-margin%26quot%3B%20style%3D%26quot%3Bfloat%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%26quot%3B%26gt%3B1%20%26lt%3B%5C%2Fdiv%26gt%3B%26lt%3Bdiv%20class%3D%26quot%3Bcsl-right-inline%26quot%3B%20style%3D%26quot%3Bmargin%3A%200%20.4em%200%201.5em%3B%26quot%3B%26gt%3BTepanosyan%2C%20G.%2C%20Muradyan%2C%20V.%2C%20Tepanosyan%2C%20G.%2C%20Avetisyan%2C%20R.%2C%20Asmaryan%2C%20S.%2C%20Sahakyan%2C%20L.%2C%20%26lt%3Bstrong%26gt%3BDenk%2C%20M.%26lt%3B%5C%2Fstrong%26gt%3B%20and%20Gl%26%23xE4%3B%26%23xDF%3Ber%2C%20C.%20%282023%29%20Exploring%20Relationship%20of%20Soil%20PTE%20Geochemical%20and%20%26%23x201C%3BVIS-NIR%20Spectroscopy%26%23x201D%3B%20Patterns%20near%20Cu%26%23×2013%3BMo%20Mine%20%28Armenia%29.%20%26lt%3Bi%26gt%3BEnvironmental%20Pollution%26lt%3B%5C%2Fi%26gt%3B.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20target%3D%26%23039%3B_blank%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2Fhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1016%5C%2Fj.envpol.2023.121180%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2Fhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1016%5C%2Fj.envpol.2023.121180%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%20%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Exploring%20relationship%20of%20soil%20PTE%20geochemical%20and%20%5Cu201cVIS-NIR%20spectroscopy%5Cu201d%20patterns%20near%20Cu%5Cu2013Mo%20mine%20%28Armenia%29%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Garegin%22%2C%22lastName%22%3A%22Tepanosyan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Vahagn%22%2C%22lastName%22%3A%22Muradyan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Gevorg%22%2C%22lastName%22%3A%22Tepanosyan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rima%22%2C%22lastName%22%3A%22Avetisyan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Shushanik%22%2C%22lastName%22%3A%22Asmaryan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Lilit%22%2C%22lastName%22%3A%22Sahakyan%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Denk%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cornelia%22%2C%22lastName%22%3A%22Gl%5Cu00e4%5Cu00dfer%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%222023-04-15%22%2C%22language%22%3A%22%22%2C%22DOI%22%3A%22https%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1016%5C%2Fj.envpol.2023.121180%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22%22%2C%22collections%22%3A%5B%22MDLREVTX%22%5D%2C%22dateModified%22%3A%222023-08-25T09%3A58%3A44Z%22%7D%7D%2C%7B%22key%22%3A%22QL4ZRMVX%22%2C%22library%22%3A%7B%22id%22%3A2355391%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ogen%20et%20al.%22%2C%22parsedDate%22%3A%222022-12-31%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%20style%3D%26quot%3Bclear%3A%20left%3B%20%26quot%3B%26gt%3B%5Cn%20%20%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-left-margin%26quot%3B%20style%3D%26quot%3Bfloat%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%26quot%3B%26gt%3B1%20%26lt%3B%5C%2Fdiv%26gt%3B%26lt%3Bdiv%20class%3D%26quot%3Bcsl-right-inline%26quot%3B%20style%3D%26quot%3Bmargin%3A%200%20.4em%200%201.5em%3B%26quot%3B%26gt%3BOgen%2C%20Y.%2C%20%26lt%3Bstrong%26gt%3BDenk%2C%20M.%26lt%3B%5C%2Fstrong%26gt%3B%2C%20Glaesser%2C%20C.%20and%20Eichstaedt%2C%20H.%20%282022%29%20A%20Novel%20Method%20for%20Predicting%20the%20Geochemical%20Composition%20of%20Tailings%20with%20Laboratory%20Field%20and%20Hyperspectral%20Airborne%20Data%20Using%20a%20Regression%20and%20Classification-Based%20Approach.%20%26lt%3Bi%26gt%3BEuropean%20Journal%20of%20Remote%20Sensing%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bb%26gt%3B55%26lt%3B%5C%2Fb%26gt%3B%2C%20453%26%23×2013%3B470.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20target%3D%26%23039%3B_blank%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1080%5C%2F22797254.2022.2104173%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1080%5C%2F22797254.2022.2104173%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%20%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22A%20novel%20method%20for%20predicting%20the%20geochemical%20composition%20of%20tailings%20with%20laboratory%20field%20and%20hyperspectral%20airborne%20data%20using%20a%20regression%20and%20classification-based%20approach%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yaron%22%2C%22lastName%22%3A%22Ogen%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Denk%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cornelia%22%2C%22lastName%22%3A%22Glaesser%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Holger%22%2C%22lastName%22%3A%22Eichstaedt%22%7D%5D%2C%22abstractNote%22%3A%22%22%2C%22date%22%3A%222022-12-31%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1080%5C%2F22797254.2022.2104173%22%2C%22ISSN%22%3A%222279-7254%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.tandfonline.com%5C%2Fdoi%5C%2Ffull%5C%2F10.1080%5C%2F22797254.2022.2104173%22%2C%22collections%22%3A%5B%22MDLREVTX%22%5D%2C%22dateModified%22%3A%222023-01-20T08%3A52%3A53Z%22%7D%7D%2C%7B%22key%22%3A%22HEB3WZV9%22%2C%22library%22%3A%7B%22id%22%3A2355391%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Mammadov%20et%20al.%22%2C%22parsedDate%22%3A%222022-03-02%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%20style%3D%26quot%3Bclear%3A%20left%3B%20%26quot%3B%26gt%3B%5Cn%20%20%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-left-margin%26quot%3B%20style%3D%26quot%3Bfloat%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%26quot%3B%26gt%3B1%20%26lt%3B%5C%2Fdiv%26gt%3B%26lt%3Bdiv%20class%3D%26quot%3Bcsl-right-inline%26quot%3B%20style%3D%26quot%3Bmargin%3A%200%20.4em%200%201.5em%3B%26quot%3B%26gt%3BMammadov%2C%20E.%2C%20%26lt%3Bstrong%26gt%3BDenk%2C%20M.%26lt%3B%5C%2Fstrong%26gt%3B%2C%20Riedel%2C%20F.%2C%20Ka%26%23x17A%3Bmierowski%2C%20C.%2C%20Lewinska%2C%20K.%2C%20%26%23×141%3Bukowiak%2C%20R.%2C%20Grzebisz%2C%20W.%2C%20Mamedov%2C%20A.I.%20and%20Glaesser%2C%20C.%20%282022%29%20Determination%20of%20Mehlich%203%20Extractable%20Elements%20with%20Visible%20and%20Near%20Infrared%20Spectroscopy%20in%20a%20Mountainous%20Agricultural%20Land%2C%20the%20Caucasus%20Mountains.%20%26lt%3Bi%26gt%3BLand%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bb%26gt%3B11%26lt%3B%5C%2Fb%26gt%3B%2C%20363.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20target%3D%26%23039%3B_blank%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.3390%5C%2Fland11030363%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.3390%5C%2Fland11030363%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%20%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Determination%20of%20Mehlich%203%20Extractable%20Elements%20with%20Visible%20and%20Near%20Infrared%20Spectroscopy%20in%20a%20Mountainous%20Agricultural%20Land%2C%20the%20Caucasus%20Mountains%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Elton%22%2C%22lastName%22%3A%22Mammadov%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Denk%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Frank%22%2C%22lastName%22%3A%22Riedel%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cezary%22%2C%22lastName%22%3A%22Ka%5Cu017amierowski%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Karolina%22%2C%22lastName%22%3A%22Lewinska%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Remigiusz%22%2C%22lastName%22%3A%22%5Cu0141ukowiak%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Witold%22%2C%22lastName%22%3A%22Grzebisz%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Amrakh%20I.%22%2C%22lastName%22%3A%22Mamedov%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cornelia%22%2C%22lastName%22%3A%22Glaesser%22%7D%5D%2C%22abstractNote%22%3A%22Soil%20spectroscopy%20is%20a%20promising%20alternative%20to%20evaluate%20and%20monitor%20soil%20and%20water%20quality%2C%20particularly%20in%20mountainous%20agricultural%20lands%20characterized%20by%20intense%20degradation%20and%20limited%20soil%20tests%20reports%3B%20a%20few%20studies%20have%20evaluated%20the%20feasibility%20of%20VIS-NIR%20spectroscopy%20to%20predict%20Mehlich%203%20%28M3%29%20extractable%20nutrients.%20This%20study%20aimed%20to%20%28i%29%20examine%20the%20potential%20of%20VIS-NIR%20spectroscopy%20in%20combination%20with%20partial%20least%20squares%20regression%20to%20predict%20M3-extractable%20elements%20%28Ca%2C%20K%2C%20Mg%2C%20P%2C%20Fe%2C%20Cd%2C%20Cu%2C%20Mn%2C%20Pb%2C%20and%20Zn%29%20and%20basic%20soil%20properties%20%28clay%2C%20silt%2C%20sand%2C%20CaCO3%2C%20pH%2C%20and%20soil%20organic%20carbon-SOC%29%2C%20%28ii%29%20find%20optimal%20pre-processing%20techniques%2C%20and%20%28iii%29%20determine%20primary%20prediction%20mechanisms%20for%20spectrally%20featureless%20soil%20properties.%20Topsoil%20samples%20were%20collected%20from%20a%20representative%20area%20%28114%20samples%20from%20525%20ha%29%20located%20in%20the%20mountainous%20region%20of%20NW%20Azerbaijan.%20A%20series%20of%20pre-processing%20steps%20and%20transformations%20were%20applied%20to%20the%20spectral%20data%2C%20and%20the%20models%20were%20calibrated%20and%20evaluated%20based%20on%20the%20coefficient%20of%20determination%20%28R2%29%2C%20root%20mean%20square%20error%20%28RMSE%29%2C%20and%20the%20residual%20prediction%20deviation%20%28RPD%29.%20The%20leave-one-out%20cross-validated%20predictions%20showed%20that%20the%20first%20derivative%20spectra%20produce%20higher%20prediction%20accuracies%20%28R2%20%3D%200.51%5Cu20130.91%3B%20RPD%20%3D%201.20%5Cu20132.29%29%20for%20most%20soil%20properties.%20The%20evaluation%20of%20the%20model%20performance%20with%20optimal%20pre-processing%20techniques%20revealed%20that%20both%20calibration%20and%20validation%20models%20produce%20considerable%20differences%20in%20RPD%20values%20associated%20with%20sample%20size%20and%20the%20random%20partition%20of%20the%20calibration%20or%20validation%20subsets.%20The%20prediction%20models%20were%20excellent%20or%20very%20good%20%28RPD%20%26gt%3B%202.0%29%20for%20CaCO3%2C%20SOC%2C%20sand%2C%20silt%2C%20Ca%2C%20and%20Pb%2C%20good%20or%20fair%20%281.4%20%26lt%3B%20RPD%20%26lt%3B%202.0%29%20for%20clay%2C%20K%2C%20Cd%2C%20pH%2C%20Fe%2C%20Mn%2C%20and%20Cu%2C%20and%20poor%20%281.0%20%26lt%3B%20RPD%20%26lt%3B%201.4%29%20for%20Mg%2C%20P%2C%20and%20Zn.%20Principal%20component%20and%20correlation%2C%20stepwise%20regression%20analysis%2C%20and%20variable%20importance%20in%20projection%20procedures%20allowed%20to%20elucidate%20the%20underlying%20prediction%20mechanisms.%20Unlike%20the%20previous%20studies%2C%20the%20spectral%20estimations%20of%20pH%2C%20Ca%2C%20Mg%2C%20P%2C%20Fe%2C%20Pb%2C%20and%20Cd%20concentrations%20were%20linked%20to%20their%20correlation%20with%20CaCO3%20rather%20than%20soil%20organic%20matter%2C%20whereas%20Mg%20and%20P%20concentrations%20were%20also%20connected%20to%20Fe-oxides.%20Soil%20particle%20sizes%20contributed%20to%20predicting%20K%20concentration%20but%20confounded%20the%20prediction%20of%20P%20and%20Zn%20concentration.%20The%20weaker%20correlations%20of%20Mn%2C%20Cu%20or%20Zn%20with%20CaCO3%2C%20particle%20sizes%2C%20SOC%2C%20Fe%2C%20and%20spectral%20data%20yielded%20to%20their%20lower%20prediction%20accuracy.%20The%20major%20prediction%20mechanisms%20for%20M3-extractable%20elements%20relied%20on%20their%20relations%20with%20CaCO3%2C%20pH%2C%20clay%20content%20and%20mineralogy%2C%20and%20exchangeable%20cations%20in%20the%20context%20of%20their%20association%20with%20land%20use.%20The%20results%20can%20be%20used%20in%20mountain%20lands%20to%20evaluate%20and%20control%20the%20effect%20of%20management%20on%20soil%20quality%20indices%20and%20land%20degradation%20neutrality.%20Further%20studies%20are%20needed%20to%20develop%20most%20advantageous%20sampling%20schemes%20and%20modeling.%22%2C%22date%22%3A%222022-03-02%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.3390%5C%2Fland11030363%22%2C%22ISSN%22%3A%222073-445X%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.mdpi.com%5C%2F2073-445X%5C%2F11%5C%2F3%5C%2F363%22%2C%22collections%22%3A%5B%22MDLREVTX%22%5D%2C%22dateModified%22%3A%222022-03-14T08%3A41%3A37Z%22%7D%7D%2C%7B%22key%22%3A%22JWES5CSE%22%2C%22library%22%3A%7B%22id%22%3A2355391%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ogen%20et%20al.%22%2C%22parsedDate%22%3A%222021-12-06%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%20style%3D%26quot%3Bclear%3A%20left%3B%20%26quot%3B%26gt%3B%5Cn%20%20%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-left-margin%26quot%3B%20style%3D%26quot%3Bfloat%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%26quot%3B%26gt%3B1%20%26lt%3B%5C%2Fdiv%26gt%3B%26lt%3Bdiv%20class%3D%26quot%3Bcsl-right-inline%26quot%3B%20style%3D%26quot%3Bmargin%3A%200%20.4em%200%201.5em%3B%26quot%3B%26gt%3BOgen%2C%20Y.%2C%20%26lt%3Bstrong%26gt%3BDenk%2C%20M.%26lt%3B%5C%2Fstrong%26gt%3B%2C%20Glaesser%2C%20C.%2C%20Eichstaedt%2C%20H.%2C%20Kahnt%2C%20R.%2C%20Loeser%2C%20R.%2C%20Suppes%2C%20R.%2C%20Chimeddorj%2C%20M.%2C%20Tsedenbaljir%2C%20T.%2C%20Alyeksandr%2C%20U.%20and%20Oyunbuyan%2C%20T.%20%282021%29%20Quantification%20of%20the%20Spectral%20Variability%20of%20Ore-Bearing%20Granodiorite%20under%20Supervised%20and%20Semisupervised%20Conditions%3A%20An%20Upscaling%20Approach.%20%26lt%3Bi%26gt%3BJournal%20of%20Spectroscopy%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bb%26gt%3B2021%26lt%3B%5C%2Fb%26gt%3B%2C%20e2580827.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20target%3D%26%23039%3B_blank%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1155%5C%2F2021%5C%2F2580827%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1155%5C%2F2021%5C%2F2580827%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%20%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Quantification%20of%20the%20Spectral%20Variability%20of%20Ore-Bearing%20Granodiorite%20under%20Supervised%20and%20Semisupervised%20Conditions%3A%20An%20Upscaling%20Approach%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yaron%22%2C%22lastName%22%3A%22Ogen%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Michael%22%2C%22lastName%22%3A%22Denk%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Cornelia%22%2C%22lastName%22%3A%22Glaesser%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Holger%22%2C%22lastName%22%3A%22Eichstaedt%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rene%22%2C%22lastName%22%3A%22Kahnt%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Ralf%22%2C%22lastName%22%3A%22Loeser%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Rudolf%22%2C%22lastName%22%3A%22Suppes%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Munkhjargal%22%2C%22lastName%22%3A%22Chimeddorj%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Tugsbuyan%22%2C%22lastName%22%3A%22Tsedenbaljir%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Undrakhtamir%22%2C%22lastName%22%3A%22Alyeksandr%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Tsedendamba%22%2C%22lastName%22%3A%22Oyunbuyan%22%7D%5D%2C%22abstractNote%22%3A%22Reflectance%20spectroscopy%20is%20a%20nondestructive%2C%20rapid%2C%20and%20easy-to-use%20technique%20which%20can%20be%20used%20to%20assess%20the%20composition%20of%20rocks%20qualitatively%20or%20quantitatively.%20Although%20it%20is%20a%20powerful%20tool%2C%20it%20has%20its%20limitations%20especially%20when%20it%20comes%20to%20measurements%20of%20rocks%20with%20a%20phaneritic%20texture.%20The%20external%20variability%20is%20reflected%20only%20in%20spectroscopy%20and%20not%20in%20the%20chemical-mineralogical%20measurements%20that%20are%20performed%20on%20crushed%20rock%20in%20certified%20laboratories.%20Hence%2C%20the%20spectral%20variability%20of%20the%20surface%20of%20an%20uncrushed%20rock%20will%2C%20in%20most%20cases%2C%20be%20higher%20than%20the%20internal%20chemical-mineralogical%20variability%2C%20which%20may%20impair%20statistical%20models%20built%20on%20field%20measurements.%20For%20this%20reason%2C%20studying%20ore-bearing%20rocks%20and%20evaluating%20their%20spectral%20variability%20in%20different%20scales%20is%20an%20important%20procedure%20to%20better%20understand%20the%20factors%20that%20may%20influence%20the%20qualitative%20and%20quantitative%20analysis%20of%20the%20rocks.%20The%20objectives%20are%20to%20quantify%20the%20spectral%20variability%20of%20three%20types%20of%20altered%20granodiorite%20using%20well-established%20statistical%20methods%20with%20an%20upscaling%20approach.%20With%20this%20approach%2C%20the%20samples%20were%20measured%20in%20the%20laboratory%20under%20supervised%20ambient%20conditions%20and%20in%20the%20field%20under%20semisupervised%20conditions.%20This%20study%20further%20aims%20to%20conclude%20which%20statistical%20method%20provides%20the%20best%20practical%20and%20accurate%20classification%20for%20use%20in%20future%20studies.%20Our%20results%20showed%20that%20all%20statistical%20methods%20enable%20the%20separation%20of%20the%20rock%20types%2C%20although%20two%20types%20of%20rocks%20have%20exhibited%20almost%20identical%20spectra.%20Furthermore%2C%20the%20statistical%20methods%20that%20supplied%20the%20most%20significant%20results%20for%20classification%20purposes%20were%20principal%20component%20analysis%20combined%20with%20k-nearest%20neighbor%20with%20a%20classification%20accuracy%20for%20laboratory%20and%20field%20measurements%20of%2068.1%25%20and%20100%25%2C%20respectively.%22%2C%22date%22%3A%222021%5C%2F12%5C%2F06%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1155%5C%2F2021%5C%2F2580827%22%2C%22ISSN%22%3A%222314-4920%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fwww.hindawi.com%5C%2Fjournals%5C%2Fjspec%5C%2F2021%5C%2F2580827%5C%2F%22%2C%22collections%22%3A%5B%22MDLREVTX%22%5D%2C%22dateModified%22%3A%222021-12-07T10%3A08%3A57Z%22%7D%7D%5D%7D

Mammadov, E., Denk, M., Mamedov, A.I. and Glaesser, C. (2024) Predicting Soil Properties for Agricultural Land in the Caucasus Mountains Using Mid-Infrared Spectroscopy. Land, 13, 154. https://doi.org/10.3390/land13020154.

Tepanosyan, G., Muradyan, V., Tepanosyan, G., Avetisyan, R., Asmaryan, S., Sahakyan, L., Denk, M. and Gläßer, C. (2023) Exploring Relationship of Soil PTE Geochemical and “VIS-NIR Spectroscopy” Patterns near Cu–Mo Mine (Armenia). Environmental Pollution. https://doi.org/https://doi.org/10.1016/j.envpol.2023.121180.

Ogen, Y., Denk, M., Glaesser, C. and Eichstaedt, H. (2022) A Novel Method for Predicting the Geochemical Composition of Tailings with Laboratory Field and Hyperspectral Airborne Data Using a Regression and Classification-Based Approach. European Journal of Remote Sensing, 55, 453–470. https://doi.org/10.1080/22797254.2022.2104173.

Mammadov, E., Denk, M., Riedel, F., Kaźmierowski, C., Lewinska, K., Łukowiak, R., Grzebisz, W., Mamedov, A.I. and Glaesser, C. (2022) Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains. Land, 11, 363. https://doi.org/10.3390/land11030363.

Ogen, Y., Denk, M., Glaesser, C., Eichstaedt, H., Kahnt, R., Loeser, R., Suppes, R., Chimeddorj, M., Tsedenbaljir, T., Alyeksandr, U. and Oyunbuyan, T. (2021) Quantification of the Spectral Variability of Ore-Bearing Granodiorite under Supervised and Semisupervised Conditions: An Upscaling Approach. Journal of Spectroscopy, 2021, e2580827. https://doi.org/10.1155/2021/2580827.

Book contributions

2355391 MDLREVTX BookContributions Denk, Michael 1 american-journal-of-plant-sciences 50 date desc 1 Denk, M. 5372 https://geooeko.geo.uni-halle.de/wp-content/plugins/zotpress/

Proceedings

2355391 MDLREVTX ConferencePaper Denk, Michael 1 american-journal-of-plant-sciences 50 date desc 1 Denk, M. 5372 https://geooeko.geo.uni-halle.de/wp-content/plugins/zotpress/

Presentations/Posters

2355391 Presentation Denk, Michael 1 american-journal-of-plant-sciences 50 date desc 1 Denk, M. 5372 https://geooeko.geo.uni-halle.de/wp-content/plugins/zotpress/

Non-Peer-Reviewed

2355391 MDLREVTX Others Denk, Michael 1 american-journal-of-plant-sciences 50 date desc 1 Denk, M. 5372 https://geooeko.geo.uni-halle.de/wp-content/plugins/zotpress/

Supervised theses

PhD theses

2355391 2XDMJIQC dissertation Denk, Michael 1 annals-of-hematology 50 date desc 5372 https://geooeko.geo.uni-halle.de/wp-content/plugins/zotpress/

Master

2355391 2XDMJIQC master Denk, Michael 1 annals-of-hematology 50 date desc 5372 https://geooeko.geo.uni-halle.de/wp-content/plugins/zotpress/

Bachelor

2355391 2XDMJIQC bachelor Denk, Michael 1 annals-of-hematology 50 date desc 5372 https://geooeko.geo.uni-halle.de/wp-content/plugins/zotpress/

Dr. rer. nat. Michael Denk