This is the poster I presented at the 2015 Georgia Water Resources Conference. It focuses on my M.S. thesis research that seeks to answer this fundamental question: "why do sinkholes form where they do?". This question was answered using an improved remote sensing sinkhole mapping procedure, integration of many datasets (i.e., hydrologic, anthropogenic, geologic, geomorphologic, and hydrogeologic), and spatial statistics (i.e., ordinary least squares and geographically weighted regression). This poster / my presentation was voted as one of the top 3 posters at the conference.

1. An Integrated Approach for Subsidence Monitoring and Sinkhole
Formation in the Karst Terrain of Dougherty County, Georgia
Matthew Cahalan1 and Adam Milewski1
1 Department of Geology, University of Georgia, Athens, GA, 30602, USA
Abstract:
The Upper Floridan Aquifer (UFA) is an important water source for agricultural and municipal
purposes in Dougherty County. However, prolonged water demand, dissolution of the carbonate
aquifer (Ocala Limestone), and short and long-term fluctuations in precipitation, surface water
discharge, and groundwater levels have placed many areas at risk for sinkhole formation and
broader scale land deformation (i.e., subsidence) in the covered karst terrain. Evaluating the
causes of sinkhole formation and subsidence is essential for reducing risk within karst settings.
In this study, we evaluate the controlling factors on subsidence feature formation within a 71 mi2
area of southern Dougherty County, Georgia, by applying a sinkhole delineation and regression
analysis procedure. ArcGIS 10.1 software was used to process digital elevation models (DEMs)
from 1999 (10m) and 2011 (1m and resampled 10m) to locate subsidence features that formed
between 1999 and 2011. Different types of subsidence features were delineated based on area
values from the high spatial resolution LiDAR DEM (1m). Results show that small-scale
subsidence features (i.e., cover collapse sinkholes less than 100 m2) form close to streams in
areas with relatively thin overburden thickness. This is most likely due to flooding and
subsequent liquefaction processes transporting overburden material into subsurface voids.
Our conceptual model of subsidence feature controlling factors, or independent variables,
included geologic, topographic, hydrologic, anthropogenic, and hydrogeologic variables.
Regression analysis allows you to examine the influences of various factors driving observed
spatial patterns. Ordinary least squares (OLS) and geographically weighted regression (GWR)
statistical models were applied to the recently formed subsidence features between 1999 and
2011 to determine the factors that are most influential in subsidence feature development.
The OLS global regression tool was applied to examine if we had a properly specified model
with limited biases. OLS results showed that distance to lineaments, elevation, aspect, distance
to roads, and overburden thickness were statistically significant (p-value < 0.05), or the most
influential controlling factors in subsidence feature density. The 11 included independent
variables did not show any redundancy (Variance Inflation Factors < 7.5). Overall, the OLS
model explained 42% of the spatial variation of subsidence feature density (R2 = 0.42).
GWR models, a local form of regression that illustrates how relationships vary across space by
calculating a regression equation for each feature in the study area, were applied. GWR analysis
resulted in a R2 value of 0.70 when 9 of the independent variables were included in the model
(slope, aspect, land use, overburden thickness, distance to lineaments, distance to roads, distance
to surface water features, distance to rivers, and distance to wetlands).
.
Subsidence Feature Delineation and Regression
Procedure
OLS Regression Results
Area of InvestigationObjectives:
1. Apply statistical regression models
(OLS and GWR) to evaluate the
influences of topographic,
hydrologic, geologic,
hydrogeologic, and anthropogenic
variables on subsidence feature
formation.
2. Locate recently formed (1999 –
2011) subsidence features using a
semi-automated mapping procedure.
Acknowledgements
• Randy Weathersby – Albany GIS
• UGA Provost Fund for financial support of the project
Subsidence Feature Density Maps
Fig. 1 – Outlined study area location in
southern Dougherty County, GA (71 mi2).
Fig. 4 – 1999 (10m). Fig. 5 – 2011 (10m).
Fig. 6 – Subsidence features that formed between 1999 and 2011.
Subsidence Feature Types
Fig. 3 - Processing procedure for the 1999 and 2011 digital elevation models (DEMs),
which were used to locate subsidence features and analyze the factors controlling their
development in the covered karst setting of southern Dougherty County, Georgia.
Fig. 9 – Lidar-derived map of subsidence features within study area. Cover subsidence
features are > 100 m2, and cover collapse features are < 100 m2.
Conclusions
The proposed processing procedure was successful at delineating sinkholes temporally and
attributing their formation to various factors spatially. Regression results showed that the
increase in subsidence features between 1999 and 2011 was primarily influenced by geologic
(i.e., distance to lineaments and overburden thickness), topographic (i.e., elevation and
aspect), and anthropogenic (i.e., distance to roads) factors. The cover-collapse features’
distributions along streams is evidence of liquefaction processes occurring in areas with
shallow overburden thickness being a primary control on their formation, while cover
subsidence features form in areas with greater overburden thickness at higher elevations.
Data acquisition
Digital elevation
model
Fill in sinks to spill
level
Digital elevation models,
geology, land use/land cover,
soils, hydrography, fractures,
overburden thickness
Climatic, stream
discharge, well
levels, roads, urban
structures, aerial
imagery
Generate a “fill-
difference” raster
Validate with aerial imagery
and previous subsidence
feature maps
Attribute all variables to subsidence
feature points
Perform ordinary least squares and
geographically weighted regression
analysis
Begin regression procedure on
mapped subsidence features
Create layers for each independent
variable
1. Land use/land cover
2. Overburden thickness
3. Upper Floridan Aquifer
fluctuations
4. Distance to fractures
5. Distance to streams
6. Distance to roads
7. Distance to lakes and
ponds
8. Distance to wetlands
9. Elevation
10. Slope
11. Aspect
Apply threshold filters based
on depth, geometry, area,
and intersection with human
structures
Convert to polygon layer
Calculate subsidence feature point
density (dependent variable)
Subsidence Feature Development
Fig. 2 – Subsidence features can be triggered by flooding events (i.e., liquefaction) and
drought or groundwater withdrawal (i.e., loss of hydrostatic support).
Dobecki and Upchurch, 2006
Category Description Source Method OLS p-value
Elevation NED 10m DEM Raster to polygon 0.016007*
Topography Aspect NED 10m DEM ArcMap Aspect tool 0.049568*
Slope NED 10m DEM ArcMap Slope tool 0.287726
Distance to ponds Georgia GIS Clearinghouse
ArcMap Generate Near Table
tool
0.560277
Hydrology Distance to streams
National Hydrography
Dataset
ArcMap Generate Near Table
tool
0.153236
Distance to wetlands National Wetland Inventory
ArcMap Generate Near Table
tool
0.235599
Distance to lineaments Brook and Allison (1986)
ArcMap Generate Near Table
tool
0.000403*
Overburden thickness
Borehole data and USGS
cross-sections (48 wells)
Empirical Bayesian Kriging
interpolation (RMS = 2.42)
0.000000*
Hydrogeology
Upper Floridan Aquifer
fluctuations
USGS well data (12 wells)
Kriging interpolation (RMS =
3.22)
0.211642
Land use National Land Cover Dataset Raster to polygon 0.339056
Distance to roads Georgia GIS Clearinghouse
ArcMap Generate Near Table
tool
0.001548*
Fig. 8 – Independent variable descriptions. Statistically significant variables (p-values < 0.05)
are in bold.
Independent Variables
Fig. 7 – Maps of independent variables used in the ordinary least squares and
geographically weighted regression models.
Geology
Anthropogenic
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Density (features/km2)