Geo-Spatial Assessment of the Impact of Land Cover Dynamics and the Distribution of Land Resources...
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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