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 SabesanGIS Research Lab

• Located in north-central Florida

• Mixed land use watershed covering 3,585 km2

• 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 water2. Net recharge3. Aquifer media 4. Soil media5. Topography6. Impact of the

vadose zone7. 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 2002.

• 2002, 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

Band Wavelength(µm)

Spectral location

1 0.45-0.52 Blue

2 0.52-0.60 Green

3 0.63-0.69 Red

4 0.76-0.90 Near-infrared

5 1.55-1.75 Mid-infrared

6 10.4-12.5 Thermal infrared

7 2.08-2.35 Mid-infrared

Landsat Satellite Series• NASA and Dept. of Interior• Spatial resolution – 30m

Path 17, Row 39

Landsat TM• August 26th 1990• August 13th 2000Landsat ETM+• February 11th 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 Processing1. 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.

RDOSN = R * (RDO )/ ((Cos (90-θ)*)/180)

To account for atmospheric attenuation factors

Splitting the image into individual bands

Header file

RDOSN = R * (RDO )/ ((Cos (90-θ)*Π)/180)

RDOSM = R * (RDO )/ ((Cos (90-θ)*Π)/180)

Layer stacking the individually calibrated bands

Atmospherically corrected Landsat image.

RDOSN RDOSM

Θ valuesΘ 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 Stage1990 2000

Image Classification: Output Stage2003

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)

0 14,000 28,000 42,000 56,0007,000Meters

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 1Spline with tensionRMSPE: 1.455

Layer 2Spline with tension

RMSPE: 1.369

Layer 3Inverse Distance WeightedRMSPE:1.904

Layer 4Inverse Distance Weighted

RMSPE:1.462

Average profile concentrations

Spline with tensionRMSPE: 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-NO3

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