Geo-Spatial Assessment of the Impact of Land Cover Dynamics and the Distribution of Land Resources on Soil and Water Quality in the Santa Fe River Watershed By Aarthy Sabesan GIS Research Lab

Located in north-central Florida Mixed land use watershed covering 3,585 km 2 Encompasses parts of Suwannee, Gilchrist, Columbia, Union, Bradford, Alachua, Baker and Clay Administratively, Suwannee River Water Management District (SRWMD)

1995 Land Use / Land Cover (LULC) classes

Soil Orders

Environmental Geology

1.Depth to water 2.Net recharge 3.Aquifer media 4.Soil media 5.Topography 6.Impact of the vadose zone 7.Hydraulic conductivity DRASTIC Index

Non-point source pollutants are the major source of surface and ground water pollution in U.S today. Increasing concentrations of nitrate-nitrogen are observed in the surface water, ground water and springs in the SRWMD. Contribution of the SFRW has increased by 4% from 2001 to , the Suwannee River Basin: 2,971 tons nitrate-nitrogen. SFRW (5.7% of the Suwannee River Basin): 19.6% of the N loads.

Hypotheses Spatially distributed patterns of land resources and land cover dynamics are useful proxies providing information about nitrogen levels in soils and surface water Land use / land cover (LULC) and soils are the major factors impacting soil and water nitrogen in the SFRW

Characterize the land cover dynamics in the SFRW from 1990 to present Quantify the spatial distribution of soil nitrate-nitrogen across the SFRW Investigate the spatial relationships between watershed characteristics and soil and water quality

Module 1 Land cover dynamics in the SFRW

Objective Identify recent changes within land cover classes Quantify the areal extent of these changes Assess the trend or nature of change within land cover classes

Materials BandWavelength (µm) Spectral location Blue Green Red Near-infrared Mid-infrared Thermal infrared Mid-infrared Landsat Satellite Series NASA and Dept. of Interior Spatial resolution – 30m

Path 17, Row 39 Landsat TM August 26 th 1990 August 13 th 2000 Landsat ETM+ February 11 th 2003

Methods 1.Design of a land cover classification scheme 2.Ground truth data collection 3.Image processing 4.Change trajectory analysis

Design of a Land Cover Classification Scheme Four levels of land use / land cover classification –Aggregation of level 2, 3 and 4 to create level 1 Land cover classes used for the analysis Coniferous pine, Upland forest, Agriculture, Rangeland,Urban,Wetland,Water

Ground Truth Data Collection 487 Ground Control Points (GCP’s) Categorization into training and accuracy assessment sites (60% / 40%)

Image Processing 1.Preprocessing –Geometric correction –Atmospheric correction –Noise removal 2.Pre-classification scene stratification 3.Image classification (Supervised approach) 4.Accuracy assessment

Preprocessing:Geometric Correction 2000 Landsat image imposed over the 2003 image RMS error: 0.5 pixel Correction for distortions in platform attributes

Preprocessing:Atmospheric Correction Dark object subtraction technique Based on the assumption that the reflectance from water bodies is close to zero. R DOSN = R * (R DO )/ ((Cos (90-θ)*  )/180) To account for atmospheric attenuation factors

Splitting the image into individual bands Header file R DOSN = R * (R DO )/ ((Cos (90- θ)*Π)/180) R DOSM = R * (R DO )/ ((Cos (90- θ)*Π)/180) Layer stacking the individually calibrated bands Atmospherically corrected Landsat image. R DOSN R DOSM Θ values Raw Landsat image Pixel value of the dark object in the particular band Identifying a dark object, like a water body Pixel value of the dark object in the particular band

Preprocessing: Noise Removal Masking cloud and cloud shadow Cloud / cloud shadow infested image Cloud / cloud shadow mask Cloud / cloud shadow masked image of SFRW

Pre-Classification Scene Stratification To separate spectrally similar classes of urban, agriculture and rangeland

Image Classification

Image Classification: Training Stage Numerical descriptors of land cover classes Two sets of spectral signatures were developed Summer scene Winter scene

Image Classification: Classification Stage Minimum Distance to Mean Classifier (MDM)

Image Classification: Output Stage

Image Classification: Output Stage 2003 Overall classification accuracy: 82%

Change Trajectory Analysis Three data change image of land cover change classes

Trajectories of Land Cover Change

Conclusions The multi-temporal change detection analysis indicates a increasing trend in agricultural intensification in the watershed Western part: expansion of agriculture on Ultisols and karst topography Eastern part: moderate to weak expansion in agriculture on Spodosols and clayey sand

Module 2 Quantify the spatial distribution of soil nitrate-nitrogen across the SFRW

Tasks Develop a site selection protocol to address the spatial variability of nitrate-nitrogen across the watershed using GIS techniques Soil sampling Laboratory analysis of nitrate-nitrogen Compare different interpolation techniques and identify the method with lowest prediction error Interpret soil nitrate-nitrogen in context of land resources within the SFRW

Land-use and soil combination raster (Illustrated here are the soil orders present under the urban land use class) Stratified Random Sampling Design

101 sites were approved for September 2003 sampling Soil samples were collected at Layer 1 (0 to 30 cm), Layer 2 (30 to 60 cm), Layer 3 (60 to 120 cm) and Layer 4 (120 to 180 cm) Soil nitrate-nitrogen values (  g/g soil)

Layer 1 Spline with tension RMSPE: Layer 2 Spline with tension RMSPE: 1.369

Layer 3 Inverse Distance Weighted RMSPE:1.904 Layer 4 Inverse Distance Weighted RMSPE:1.462

Average profile concentrations Spline with tension RMSPE: 1.306

Pixel Based Prediction of Soil Nitrate-Nitrogen Average nitrate-nitrogen profile values for each LULC-soil combination OPixel soil-N PPixel soil-N Based on LULC-soil combinations

Pixel-Based Prediction of Soil Nitrate- Nitrogen

Conclusion This analysis is the first step in characterizing the spatio-temporal variation of nitrate-nitrogen at a watershed scale The LCLU and the soil data support developing predictive models of soil nitrate-nitrogen in the SFRW

Water Quality Analysis Module 3

Objective Characterize the geographic position and distribution of land resources to understand spatial relationships between watershed characteristics and water quality data Materials Surface water and ground water quality data from SRWMD

Surface Water Quality Observations Time frame of observations: 1989 to 2003

Sub-Basin Attributes Land use / land cover class (2000) Soil order (SSURGO) Geology Mean, maximum and minimum DRASTIC values Mean, maximum and minimum soil organic carbon Mean, maximum and minimum population Mean, maximum and minimum elevation Mean, maximum and minimum slope

Results N-NO 3

Conclusion Results indicate that multiple factors contribute to elevated nitrogen found in soils and water Karst terrain, soil material, and agricultural and urban land uses pose the greatest risk for nitrate leaching In addition the geographic position and spatial distribution of land resource factors and spatial interrelationships between factors influence nitrogen levels observed in soils and surface water Understanding the interrelationships between land cover / land use, soils, geology, topography and other factors in a spatially-explicit context support ongoing efforts to improve the water quality in the SFRW

Acknowledgement My parents Dr. Sabine Grunwald (Chair) Dr. Mark Clark Dr. Michael Binford (Dept. of Geography) Christine Bliss and Isabel Lopez Sanjay Lamsal Kathleen McKee and Rosanna Rivero