Q10 – S. Furze

A Digital Soil Mapping Framework for Assessing Plantation Productivity Summary

This research focused on developing a geocentric approach to assessing plantation productivity with which measured forest attributes are compared to underlying variations in topography and soil properties. The limitation with geocentric approaches is the lack of consistent and accurate information pertaining to soil variability. Traditionally, soils are delineated as broad tessellated polygons depicting soil associations, groupings of soil types via similarities in regolith and morphology, with each soil type differentiated by drainage class. With this, intra-variability of soil properties is omitted while inter-variability is too broad for stand- and site- level assessments. Additionally, all pertinent information regarding soils is found separately in the form of soil surveys. Thus, there is no spatial representation of changing soil properties with changes in topography, hydrology, and landforms. Soils must be delineated at a continuous extent for application in geocentric productivity assessments. This is possible via digital soil mapping (DSM).
Variations in soil properties at any location are a result of the influence of soil forming factors over an extended period of time. With this, Jenny (1941) introduced CLORPT where soil properties are a function of climate (CL), organisms (O), relief (topography, R), parent material (P), and time (T). With enhancements in GIS and geo-spatial statistical analyses, McBratney et al. (2003) introduced DSM as a continuation of Jenny’s formulation:

Soil = SCORPAN

Where Soil is a soil property of interest, S represents other soil properties, C is climate, O for organisms, R for relief, P for parent material, A for age, and N for surrounding neighborhood. With this, any soil property at any location is a function of these factors and surrounding environment. DSM is a method to mapping soil property variation as it is influenced by soil forming factors. This approach is based on three fundamental principles:

  1. soil formation and soil forming factors (Jenny, 1941; Birkeland, 1999; Wu et al., 2008; Adhikari et al., 2012),
  2. pedometrics, mathematical and statistical approaches to modeling soil properties and relationships, including predotransfer functions (PTFs) (Florinsky, 2012), and
  3. soil-landscape relationships (Gerrard, 1981; Birkeland, 1999; McBratney et al., 2000; MacMillan et al., 2005; Barka et al., 2011).

This research followed the framework of DSM as such:

1. Soil Formation and Soil Forming Factors:

Topography – a new DEM with full provincial coverage was developed at 10m resolution by way of DEM fusion on open-sourced DEMs with comparison to LiDAR-derived DEMs. With this, topo-hydrologic derivatives were produced and assessed for application in DSM in terms of representing hillslope influences on soil properties.
Climate – climate normal from past 29 years were assigned to climate stations for N.B. and Ontario (n=150). This information was compared to underlying changes in elevation, latitude and longitude, resulting in predictions of annual average temperature, annual average rainfall, and daily maximum high temperature at 10m resolution.
Geology – surficial geology delineations were updated by way of a similarity model on 5 vector data sets resulting in improved delineations of mode of deposition, lithology, mineral hardness, dominant grain size, and dominant rock type.

2. Pedometrics:

An aspatial database was developed from the amalgamation and harmonization of ancillary soil surveys for New Brunswick. With this, PTFs (regression equations) were created to predict soil properties from other soil properties. These were created for predicting texture (sand, silt, and clay), coarse fragment content, organic matter content, bulk density, cation exchange capacity, water retention at field capacity and permanent wilting point, and base saturation.
A spatial database was developed from the amalgamation of soil profiles collected from numerous sources (n=12,058).

3. Soil-landscape Relationships:

Updated delineations of topo-hydrologic, geological, and climatic data dets were compared to measured soil properties within the spatial database resulting in predictions of soil drainage, A horizon depth, B horizon depth, solum depth, and sand, silt, clay, organic matter, coarse fragment content, and bulk density at 10m and at depth intervals of 0-15cm, 15-30cm, 30-60cm, and 60-90cm for all New Brunswick. With these, PTFs from (2) were applied spatially to show cation exchange capacity and water retention at both field capacity and permanent wilting point as these vary across the province.


The final stage of this research (still ongoing) is comparing the LiDAR-derived plantation metrics for black spruce, white spruce, and Norway spruce to updated soil delineations and topo-hydrologic data to develop a measure of productivity for Black Brook management zone.

 

Literature Cited

Adhikari, K., Kheir, R.B., Greve, M.B., Bøcher, P.K., Malone, B.P., Minasny, B., McBratney, A.B., Greve, M.H. 2012. High-Resolution 3-D Mapping of Soil Texture in Denmark. Soil Sci. Soc. Am. J. 77: 1–17.

Barka, I., Vladovic, J., Máli, Š. 2011. Landform Classification and Its Application in Predictive Mapping of Soil and Forest Units. GIS Ostrava. 11.

Birkeland, P. 1999. Soils and Geomorphology, 3rd ed. Oxford University Press, Oxford, England, UK.

Florinsky, I.. 2012. Digital Terrain Analysis in Soil Science and Geology, 1st ed. Elsevier. Elsevier, The Boulevard, Langford Lane, Kidlington, Oxford, United Kingdom.

Gerrard, A.J. 1981. Soils and Landforms. An Integration of Geomorphology and Pedology. George Allen & Unwin Ltd., London, United Kingdom.

Jenny, H. 1941. Factors of Soil Formation: a System of Quantitative Pedology. McGraw-Hill, New York, New York, USA.

MacMillan, R. a., Pettapiece, W.W., Brierley, J. a. 2005. An expert system for allocating soils to landforms through the application of soil survey tacit knowledge. Can. J. Soil Sci. 85: 103–112.

McBratney, A.., Mendonça Santos, M.., Minasny, B. 2003. On digital soil mapping. Geoderma. 117: 3–52.

McBratney, A.B., Odeh, I.O.A., Bishop, T.F.A., Dunbar, M.S., Shatar, T.M. 2000. An overview of pedometric techniques for use in soil survey. Geoderma. 97: 293–327.

Wu, W., Fan, Y., Wang, Z., Liu, H. 2008. Assessing effects of digital elevation model resolutions on soil–landscape correlations in a hilly area. Agric. Ecosyst. Environ. 126: 209–216.

 

Publications

Furze, S., Castonguay M., Ogilvie J., Nasr, M., Cormier, P., Gagnon, R., Adams, G., Arp, P.A. 2017. Assessing Soil-Related Black Spruce and White Spruce Plantation Productivity. Open Journal of Forestry, 7, 209-227.

Furze, S., Ogilvie, J. and Arp, P.A. 2017. Fusing Digital Elevation Models to Improve Hydrological Interpretations. Journal of Geographic Information System, 9, 558-575.

Furze, S., Arp, P.A. 2018. Amalgamation and harmonization of soil survey reports into a multi-purpose database: an example. Submitted to the Open Journal of Soil Science

Furze, S., Arp, P.A. 2018. From ancillary soil surveys to pedotransfer function development and perfomance assessment. Submitted to the Open Journal of Soil Science

 

Conference Presentations

Improving Available DEMs for Enhanced Applications (Poster). Canadian Symposium on Remote Sensing, St. Johns, Newfoundland, June, 2015

Enhancing Digital Elevation Models for Improved Soils Mapping. Canadian Symposium on Soil Science, Montreal, Quebec, July, 2015.

Enhancing Digital Elevation Models (DEMs) for Improved Soils Mapping. ESRI Atlantic User Conference, Halifax, NS, October, 2015.

Enhancing Digital Elevation Models (DEMs) for Improved Soils Mapping (Poster). ESRI International User Conference, San Diego, California, July, 2016.

 

Other Presentations

Furze, S. High-resolution Soil Property Mapping for Black Brook. Irving Update. Fredericton, February, 2015.

Furze, S. Digital Elevation Model Fusion. Ontario Ministry of Natural Resources and Forestry. Fredericton, April, 2015.

Furze, S. A Digital Soil Mapping Framework for New Brunswick, Canada. New Brunswick EFI Update. Fredericton, March, 2016.

Furze, S. High-resolution Soil Property Mapping for Black Brook. Irving Update. Fredericton, June, 2016.

Furze, S. High-resolution Soil Property Mapping for Black Brook. Irving Update. Fredericton, August, 2016.

Furze, S. High-resolution Soil Property Mapping for Black Brook. Irving Update. Fredericton, October, 2016.

Furze, S. A Digital Soil Mapping Framework for New Brunswick, Canada. Canada-Wide Digital Soil Mapping Group. Quebec City, February, 2017.

Furze, S. High-resolution Soil Property Mapping for Black Brook. Irving and N.B. Energy and Resource Development Update. Fredericton, March, 2017.

Furze, S. A Digital Soil Mapping Framework for New Brunswick, Canada. N.B. Department of Energy and Resource Development and Agriculture Canada Update. Fredericton, June, 2017.

Furze, S. A Digital Soil Mapping Framework for New Brunswick, Canada. N.B. Department of Energy and Resource Development and Agriculture Canada Update. Fredericton, June, 2017.

Furze, S. Fusing Digital Elevation Models to Improve Hydrological Interpretations. Service New Brunswick Update. Fredericton, July, 2017.

Furze, S. A Digital Soil Mapping Framework for New Brunswick, Canada. New Brunswick EFI Update. Fredericton, March, 2018.