On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping: A focus on soil properties
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F23%3A103312" target="_blank" >RIV/60460709:41210/23:103312 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s11119-022-09955-7" target="_blank" >https://doi.org/10.1007/s11119-022-09955-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11119-022-09955-7" target="_blank" >10.1007/s11119-022-09955-7</a>
Alternative languages
Result language
angličtina
Original language name
On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping: A focus on soil properties
Original language description
The benefits of digital soil maps cannot be overemphasised. For many years, researchers have mapped different soil classes, properties and processes while identifying the spatial associations between soil properties using side-by-side visualization maps. Although this is acceptable, it may be difficult to identify complex spatial associations between the mapped soil properties. For some, the task may be challenging owing to multiple times of side-by-side placing of the maps and the possible application of none user-friendly colour palettes and or schemes. Innovative tools are proposed for visualizing and identifying spatial associations between digital soil maps (raster layers) using bivariate and trivariate maps. These tools are applied in a case study to identify the spatial interactions between pH and selected macro-nutrients [nitrogen (N) and potassium (K)] of similar locality (Czech Republic), resolution and scale. This study further gives a brief overview of the applicability of bivariate and trivariate maps following the digital soil mapping process. Results show that bivariate and trivariate maps are effective for visualizing complex associations between pH and macro-nutrients. However, precautionary measures should be taken while applying bivariate and trivariate maps to ensure they are self-explanatory and that the legend colour schemes applied are user-friendly. Also, the variables mapped should be related. In this case, pH is a key soil quality indicator that affects macro-nutrient availability in soils.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
40104 - Soil science
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Precision Agriculture
ISSN
1385-2256
e-ISSN
1573-1618
Volume of the periodical
24
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
21
Pages from-to
511-532
UT code for WoS article
000847055900001
EID of the result in the Scopus database
2-s2.0-85137740229