TY - JOUR
T1 - On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping
T2 - A focus on soil properties
AU - Kebonye, Ndiye M.
AU - Agyeman, Prince C.
AU - Seletlo, Zibanani
AU - Eze, Peter N.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022
Y1 - 2022
N2 - 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. Graphical abstract: [Figure not available: see fulltext.].
AB - 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. Graphical abstract: [Figure not available: see fulltext.].
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U2 - 10.1007/s11119-022-09955-7
DO - 10.1007/s11119-022-09955-7
M3 - Article
AN - SCOPUS:85137740229
SN - 1385-2256
VL - 24
SP - 511
EP - 532
JO - Precision Agriculture
JF - Precision Agriculture
IS - 2
ER -