SPOT Site Classification System Updates
Dr. Andrew Trlica | 24 January 2025
Introduction
The FPC SPOT (Site Productivity Optimization for Trees) system captures potential resource availability and limitations for intensive pine plantation management. The system classifies NRCS soils, geology, and physiographic province into one geospatial layer to inform silvicultural decisions. This summary covers changes in 2023–2024.
The new updated SPOT v3.1 is now available for download. The new manual is available here. The SPOT database is available for the whole Southeastern US and on a state-by-state basis in both geodatabase form (.gdb) for ArcMap and geoopackage form (.gpkg) for other GIS programs.
In the SPOT database, each map unit from NRCS SSURGO soils data is combined with additional geologic data from the US Geological Survey State Geologic Map Compilation, LiDAR-based coastal plain terraces, and physiographic province from the NRCS Major Land Resource Area. Each SPOT code provides relevant forest management recommendations. The FPC SPOT System consists of the following components:
Major soil group (primarily texture)
Depth to increase in clay (depth class)
Drainage code
Nature of surface (modifier 1)
Nature of subsoil (modifier 2)
Additional resources or limitation (modifier 3)
Geological/coastal plain code (geocode)
Physiographic province
As an example, SPOT code “B3WekoGgPD” can be quickly translated as major soil group B (‘fine loamy’), depth code 3 (‘10-20 in [25-51 cm] to clay subsoil’), drainage code W (‘well drained’), surface modifier e (‘eroded’), subsoil modifier k (‘kaolinitic’), resource/limitation code o (‘other/no specific information’), geocode Gg (‘Granite/gneiss group’), in physiographic province PD (‘Piedmont’).
Information added in v3.1
In addition to the SPOT code information already provided, the v3.1 update also includes information on:
Slope %
Soil landscape position (e.g., “river terraces”)
Topographic setting (e.g., “toe slopes”)
Soil pH (top 50 cm)
Site index predictions (base age 25 yr) based on 1) long-term monitoring of the FPC’s Regionwide Trials (Cook et al., 2024) and 2) Forest Inventory and Analysis data (Ribas-Costa et al., 2024)
Furthermore, we added the state of Tennessee and now exclude the far western parts of Oklahoma and Texas where pine plantations are not viable.
Figure 2. Physiographic province groups used in SPOT v3.1, derived from MLRA.
Code Review & Systematization
SPOT v3.1 converted all manual geoprocessing steps (many in Arc) in the previous version to systematized and thoroughly documented R and Python scripts (backed up in Github). This code handles the entire data process from the raw data inputs to the final export as user-ready geospatial files. An updated user manual as well as extensive README, code-commentary, and formal code-running documentation have also been completed. This documentation and code systematization effort will help ensure continuity of maintenance of the data set and more easily allow future FPC researchers to update the map as new versions of input data become available or as new needs are identified.
During code documentation, the classification of geological data and physiographic province data was extensively reviewed. Two new geocodes were added (Tuscaloosa ‘Ts’, and Midway ‘Md’), as well as the classification of regions in Tennessee. In addition, the boundary definition of the Atlantic Coastal Plain (ACP) terraces was modified to include only the eastern margins of the states of FL, GA, SC, NC, and VA, generally east of the Fall Line (Figure 1, top). The underlying data for creating the ACP map was also updated to use higher-quality 10 m elevation data exported from Google Earth Engine.
Figure 3. Range of southern pine species Pinus clausa (sand), P. elliottii (slash), P. palustris (longleaf), and P. taea (loblolly).
Modification to Management Recommendations
Management recommendations were systematized from Cook (2024) into a processing script, with a handful of minor corrections. Species are now recommended only in their native historic range (Figure 3). Fertility ratings for each soil polygon were updated to better reflect the underlying Lithology of the geological members of each region (Figure 4).
Figure 4. Fertility rating based on major lithologies of underlying geological formations.
Site Index Estimates
Cook et al. (2024) modeled loblolly pine site index values from the FPC Regionwide trials (Figure 5), now available in SPOT v3.1 for similar soils. Ribas-Costa et al., 2024 developed two additional site index models using USGS LiDAR on intensively managed FPC member land and USFS Forest Inventory and Analysis (FIA) from thousands of plots. The FIA-based site index estimates are available in SPOT v3.1 (Figure 6). The site index estimates from the industry-based model are provided to the participating FPC members as a separate dataset.
Figure 5. Range of site index values observed for different soil SPOT codes, based on Regionwide height data (ordered by magnitude). This empirical data was used to predict site index across a wide range of soil polygons in the SPOT map across the Southeastern US.
Figure 6. Predictions of site index (assuming plantation establishment year of 2000) based on the FIA random forest model.
Reference:
Cook, R.L., T.R. Fox, H.L. Allen, C.W. Cohrs, V. Ribas-Costa, A. Trlica, M. Ricker, D.R. Carter, R.A. Rubilar, O.C. Campoe, T.J. Albaugh, P. Kleto, E. O'Brien, K. McEachern. Forest soil classification for intensive pine plantation management: “Site Productivity Optimization for Trees” system. Forest Ecology and Management (2024) 556: 121732. DOI: https://doi.org/10.1016/j.foreco.2024.121732
Ribas–Costa, V.A., A. Gaston, S.A. Bloszies, J.D. Henderson, A.Trlica, D.R. Carter, R. Rubilar, T.J. Albaugh, R.L. Cook. 2024. Nature vs. nurture: Drivers of site productivity in loblolly pine (Pinus taeda L.) forests in the southeastern US. Forest Ecology and Management 572: 122334. DOI: https://doi.org/10.1016/j.foreco.2024.122334
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