Digitization and mapping of national legacy soil data of Montenegro

https://doi.org/10.17221/81/2017-SWRCitation:Salković E., Djurović I., Knežević M., Popović-Bugarin V., Topalović A. (2018): Digitization and mapping of national legacy soil data of Montenegro. Soil & Water Res., 13: 83-89.
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This paper describes the process of digitizing Montenegro’s legacy soil data, and an initial attempt to use it for digital soil mapping (DSM) purposes. The handwritten legacy numerical records of physical and chemical properties for more than 10 000 soil profiles and semi-profiles covering whole Montenegro have been digitized, and, out of those, more than 3000 have been georeferenced. Problems and challenges of digitization addressed in the paper are: processing of non-uniform handwritten numerical records, parsing a complex textual representation of those records, georeferencing the records using digitized (scanned) legacy soil maps, creating a single computer database containing all digitized records, transforming, cleaning and validating the data. For an initial assessment of the suitability of these data for mapping purposes, inverse distance weighting (IDW), ordinary kriging (OK), multiple linear regression (LR), and regression-kriging (RK) interpolation models were applied to create thematic maps of soil phosphorus. The area chosen for mapping is a 400 km2 area near the city of Cetinje, containing 125 data points. LR and RK models were developed using publicly available digital elevation model (DEM) data and satellite global land survey (GLS) data as predictor variables. The digitized phosphorus quantities were normalized and scaled. The predictor variables were scaled, and principal component analysis was performed. For the best performing RK model an R2 value of 0.23 was obtained.

References:
Arrouays Dominique, Leenaars Johan G.B., Richer-de-Forges Anne C., Adhikari Kabindra, Ballabio Cristiano, Greve Mogens, Grundy Mike, Guerrero Eliseo, Hempel Jon, Hengl Tomislav, Heuvelink Gerard, Batjes Niels, Carvalho Eloi, Hartemink Alfred, Hewitt Alan, Hong Suk-Young, Krasilnikov Pavel, Lagacherie Philippe, Lelyk Glen, Libohova Zamir, Lilly Allan, McBratney Alex, McKenzie Neil, Vasquez Gustavo M., Mulder Vera Leatitia, Minasny Budiman, Montanarella Luca, Odeh Inakwu, Padarian Jose, Poggio Laura, Roudier Pierre, Saby Nicolas, Savin Igor, Searle Ross, Solbovoy Vladimir, Thompson James, Smith Scott, Sulaeman Yiyi, Vintila Ruxandra, Rossel Raphael Viscarra, Wilson Peter, Zhang Gan-Lin, Swerts Martine, Oorts Katrien, Karklins Aldis, Feng Liu, Ibelles Navarro Alexandro R., Levin Arkadiy, Laktionova Tetiana, Dell'Acqua Martin, Suvannang Nopmanee, Ruam Waew, Prasad Jagdish, Patil Nitin, Husnjak Stjepan, Pásztor László, Okx Joop, Hallett Stephen, Keay Caroline, Farewell Timothy, Lilja Harri, Juilleret Jérôme, Marx Simone, Takata Yusuke, Kazuyuki Yagi, Mansuy Nicolas, Panagos Panos, Van Liedekerke Mark, Skalsky Rastislav, Sobocka Jaroslava, Kobza Josef, Eftekhari Kamran, Alavipanah Seyed Kacem, Moussadek Rachid, Badraoui Mohamed, Da Silva Mayesse, Paterson Garry, Gonçalves Maria da Conceição, Theocharopoulos Sid, Yemefack Martin, Tedou Silatsa, Vrscaj Borut, Grob Urs, Kozák Josef, Boruvka Lubos, Dobos Endre, Taboada Miguel, Moretti Lucas, Rodriguez Dario (2017): Soil legacy data rescue via GlobalSoilMap and other international and national initiatives. GeoResJ, 14, 1-19  https://doi.org/10.1016/j.grj.2017.06.001
 
Conrad O., Bechtel B., Bock M., Dietrich H., Fischer E., Gerlitz L., Wehberg J., Wichmann V., Böhner J. (2015): System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development Discussions, 8, 2271-2312  https://doi.org/10.5194/gmdd-8-2271-2015
 
Cressie N. (2015): Statistics for Spatial Data. New York, John Wiley & Sons.
 
Egnér H., Riehm H., Domingo W.R. (1960): Untersuchungen über die chemische Bodenanalyse als Grundlage für die Beurteilung des Nährstoffzustandes der Böden. II. Chemische Extraktionsmethoden zur Phosphor-und Kaliumbestimmung. Kungliga Lantbrukshögskolans Annaler, 26: 204–209.
 
Ferranti J. de (2014): Worldwide 3” DEM. Available at http://www.viewfinderpanoramas.org/dem3.html (accessed May 2015)
 
Fuštić B., Đuretić G. (2000): The Soils of Montenegro. Podgorica, University of Montenegro. (in Montenegrin)
 
Hengl T. (2009): A Practical Guide to Geostatistical Mapping. Amsterdam, University of Amsterdam.
 
Hengl Tomislav, Husnjak Stjepan (2006): Evaluating Adequacy and Usability of Soil Maps in Croatia. Soil Science Society of America Journal, 70, 920-  https://doi.org/10.2136/sssaj2004.0141
 
Hengl T., Rossiter D. G., Husnjak S. (2002): Mapping soil properties from an existing national soil data set using freely available ancillary data. In: Proc. 17th World Congress of Soil Science. Bangkok, IUSS: 1140-1–1140-10.
 
Hengl Tomislav, de Jesus Jorge Mendes, MacMillan Robert A., Batjes Niels H., Heuvelink Gerard B. M., Ribeiro Eloi, Samuel-Rosa Alessandro, Kempen Bas, Leenaars Johan G. B., Walsh Markus G., Gonzalez Maria Ruiperez, Bond-Lamberty Ben (2014): SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE, 9, e105992-  https://doi.org/10.1371/journal.pone.0105992
 
Hiemstra P.H., Pebesma E.J., Twenhöfel C.J.W., Heuvelink G.B.M. (2008): Real-time automatic interpolation of ambient gamma dose rates from the Dutch Radioactivity Monitoring Network. Computers & Geosciences, 35: 1711–1721.
 
Keshavarzi Ali, Sarmadian Fereydoon, Omran El-Sayed Ewis, Iqbal Munawar (2015): A neural network model for estimating soil phosphorus using terrain analysis. The Egyptian Journal of Remote Sensing and Space Science, 18, 127-135  https://doi.org/10.1016/j.ejrs.2015.06.004
 
King Jacquelynne R., Jackson Donald A. (1999): Variable selection in large environmental data sets using principal components analysis. Environmetrics, 10, 67-77  https://doi.org/10.1002/(SICI)1099-095X(199901/02)10:1<67::AID-ENV336>3.0.CO;2-0
 
Liu Z.-P., Shao M.-A., Wang Y.-Q. (2013): Spatial patterns of soil total nitrogen and soil total phosphorus across the entire Loess Plateau region of China. Geoderma, 197: 67–78.
 
McBratney A.B, Mendonça Santos M.L, Minasny B (2003): On digital soil mapping. Geoderma, 117, 3-52  https://doi.org/10.1016/S0016-7061(03)00223-4
 
R Core Team (2015): R: A Language and Environment for Statistical Computing. Vienna, R Foundation for Statistical Computing.
 
Roger Aurélien, Libohova Zamir, Rossier Nicolas, Joost Stéphane, Maltas Alexandra, Frossard Emmanuel, Sinaj Sokrat (2014): Spatial variability of soil phosphorus in the Fribourg canton, Switzerland. Geoderma, 217-218, 26-36  https://doi.org/10.1016/j.geoderma.2013.11.001
 
Rubæk Gitte H., Kristensen Kristian, Olesen Svend E., Østergaard Hans S., Heckrath Goswin (2013): Phosphorus accumulation and spatial distribution in agricultural soils in Denmark. Geoderma, 209-210, 241-250  https://doi.org/10.1016/j.geoderma.2013.06.022
 
Sarmadian F., Keshavarzi A., Rooien A., Iqbal M., Zahedi G., Javadikia H. (2014): Digital mapping of soil phosphorus using multivariate geostatistics and topographic information. Australian Journal of Crop Science, 8: 1216–1223.
 
Schachtman Daniel P., Reid Robert J., Ayling S.M. (1998): Phosphorus Uptake by Plants: From Soil to Cell. Plant Physiology, 116, 447-453  https://doi.org/10.1104/pp.116.2.447
 
Shepard D. (1968): A two-dimensional interpolation function for irregularly-spaced data. In: Proc. 23rd ACM National Conf. New York, ACM: 517–524.
 
Topalovic Ana, Pfendt Lidija, Perovic Natalija, Djordjevic Dragana, Trifunovic Snezana, Pfendt Petar (2006): The chemical characteristics of soil which determine phosphorus partitioning in highly calcareous soils. Journal of the Serbian Chemical Society, 71, 1219-1236  https://doi.org/10.2298/JSC0611219T
 
USGS (2008a): Collection Name: Global Land Survey, Epoch: 1975, Sensor name: Landsat MSS, Image Name: 60 meter scene p201r030_3dm19780706. Sioux Falls, United States Geological Survey.
 
USGS (2008b): Collection Name: Global Land Survey, Epoch: 1990, Sensor name: Landsat TM, Image Name: 60 meter scene p187r031_5dt19870724. Sioux Falls, United States Geological Survey.
 
Vrščaj B., Prus T., Lobnik F. (2005): Soil Information and soil data use in Slovenia. In: Jones R. J., Houšková B., Bullock P., Montanarella L. (eds): Soil Resources of Europe. 2nd Ed. Luxembourg, Office for Official Publications of the European Communities: 331–344.
 
Wang Yunqiang, Zhang Xingchang, Huang Chuanqin (2009): Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China. Geoderma, 150, 141-149  https://doi.org/10.1016/j.geoderma.2009.01.021
 
Xiao Rong, Bai Junhong, Gao Haifeng, Huang Laibin, Deng Wei (2012): Spatial distribution of phosphorus in marsh soils of a typical land/inland water ecotone along a hydrological gradient. CATENA, 98, 96-103  https://doi.org/10.1016/j.catena.2012.06.008
 
Yang X., Post W. M., Thornton P. E., Jain A. (2013): The distribution of soil phosphorus for global biogeochemical modeling. Biogeosciences, 10, 2525-2537  https://doi.org/10.5194/bg-10-2525-2013
 
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