what’s salt got to do with it?artsandsciences.sc.edu/geog/research/cisa/ccrc/...what’s salt got...
TRANSCRIPT
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What’s salt got to do
with it?
Lessons from Current Coastal Agricultural Adaptation in Hyde County, NC
11/14/2018 1
Ellie Davis
University of South Carolina
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Outline
• Background: Agriculture in Hyde County, NC
• Methods
• Results
• Future Work
• Lessons
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Hyde County, NC
3RisingSea.net
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4RisingSea.net
Sea Level Rise & Flooding
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440 out of 613 Square Miles
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Two Identified
Problems
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The extent of
the damage is
unknown.
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Adaptation to
salinization is
undocumented.
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Methods
• Using remote sensing, Model and
Map soil salinity in Hyde County,
NC
• Using interviews, document and
analyze current soil salinization
adaptations and barriers for
farming
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Remote Sensing
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In-situ
Measurements
12
Remote Sensing
Analysis
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13
11 ds/m
5.0 ds/m
2.5 ds/m
Results: In-situ
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Results: Salinity Area Density
Landsat 8 OLI Sentinel-2
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Results: Salinity Area Density
Landsat 8 OLI Sentinel-2
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Results: Soil
Estimate
1.4-2.5%
Bare soil
> 4 dS/m
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Lessons Learned
Remote sensing is
a valuable tool for
tracking soil
salinity
1 2 3
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Any questions so far?
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Identify Adaptation Responses,
Barriers, and Interventions
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Process
Moser and Ekstrom (2010) Framework 20
Understanding
PlanningManaging
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Process
Moser and Ekstrom (2010) Framework 21
Understanding
PlanningManaging
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Process
Moser and Ekstrom (2010) Framework 22
Understanding
PlanningManaging
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Lessons Learned
Remote sensing is
a valuable tool for
tracking soil
salinity
1
Producers are in
each part of the
adaptation cycle
and need support
for each step
2 3
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Barriers
24
Location &
Environment
Coordination &
Communication
Awareness &
Research
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Barriers
25
Location &
Environment
Coordination &
Communication
Awareness &
Research
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Barriers
26
Location &
Environment
Coordination &
Communication
Awareness &
Research
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Lessons Learned
Remote sensing is
a valuable tool for
tracking soil
salinity
1
Producers are in
each part of the
adaptation cycle
and need support
for each step
2
There are multiple
barriers to
adaptation,
external and
internal
3
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Future Work
Expand Interviews to Study
Flooding and Salinity Risk
Perceptions
1
Fly Drone Coverage of
Salinity Change in Test
Plots
2
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Questions
29
Are there any parallels with your work
in adaptation?
What ideas (no matter how crazy) have
you come across that may work to
overcome barriers?
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Acknowledgements
Advisor:
• Dr. Kirstin Dow, University of South Carolina
Committee:
• Dr. Susan Wang, University of South Carolina
• Dr. Gregory Carbone, University of South Carolina
Funding:
• Carolinas Integrated Sciences and Assessments
• SC Sea Grant and SC Space Grant
• Department of Geography, University of South
Carolina
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Works CitedRietbroek, R., Brunnabend, S.-E., Kusche, J., Schröter, J., & Dahle, C. (2016). Revisiting the contemporary sea-level budget on global and regional scales. Proceedings of the National Academy of Sciences of the United States of America, 113(6), 1504–1509. https://doi.org/10.1073/pnas.1519132113
Roberson, R. (2013). North Carolina water management project protecting valuable farmland. Retrieved July 17, 2017, from http://www.southeastfarmpress.com/management/water-management-project-saves-north-carolina-farmland
Sanchez-Arcilla, A., Garcia-Lein, M., Gracia, V., Devoy, R., Stanica, A., & Gault, J. (2016). Managing coastal environments under climate change: Pathways to adaptation. Science of the Total Environment, 572, 1336–1352. https://doi.org/10.1016/j.scitotenv.2016.01.124
Stuart, D., & Schewe, R. L. (2012). Responding to Climate Change : Barriers to Reflexive Modernization in U. S. Agriculture. Organization and Environment, 25(3), 308–327. https://doi.org/10.1177/1086026612456536
Sweet, W. et al. (2017). Global and Regional Sea Level Rise Scenarios for the, (January).
Takahashi, B., Burnham, M., Terracina-Hartman, C., Sopchak, A. R., & Selfa, T. (2016). Climate Change Perceptions of NY State Farmers: The Role of Risk Perceptions and Adaptive Capacity. Environmental Management, 58(6), 946–957. https://doi.org/10.1007/s00267-016-0742-y
Tester, M. (2012). Breeding Technologies to Increase, 818(2010). https://doi.org/10.1126/science.1183700
The Albemarle Peninsula - North Carolina State University. (n.d.). Retrieved April 25, 2017, from http://ncsu-salt.weebly.com/the-albemarle-peninsula.html
USDA NASS. (2012). Hyde County North Carolina. Retrieved from https://www.agcensus.usda.gov/Publications/2012/Online_Resources/County_Profiles/North_Carolina/cp37095.pdf
USDA NRCS. (1998). Soil Quality Resource Concerns: Salinization USDA Natural Resources Conservation Service. Retrieved from https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_053151.pdf
USGS. (2016). Landsat 8 | Landsat Missions. Retrieved March 11, 2017, from https://landsat.usgs.gov/landsat-8
USGS. (2017). USGS EO-1. Retrieved March 11, 2017, from https://eo1.usgs.gov/sensors/hyperion
Whitehead, J. (2017). Personal Communication: Jessica Whitehead.
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Data
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37RisingSea.net
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In-situ
Measurements
• JAZ Spectrometer
191 nm – 889 nm (.37 nm interval)
• TDR 150
• EC (dS/m)
• Soil Moisture (% VWC)
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Multispectral Sensors
Landsat 8 OLI
30 meter resolution
4 bands used
16 day return time
Sentinel-2
10 meter resolution
6 bands used
2-3 day return time
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Pre-processing and collection
40
90 meter transects
Surface spectra
SM and EC at 10
cm
Masks
NDVI – Water & Veg
Clouds
Atmospheric corrections
(ENVI/Sen2Cor)
Radiometric corrections
(ENVI/SNAP)
Field sampling Satellite imagery
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Methods
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1• JAZ data resampled into Landsat OLI and Sentinel-2 bands
2• Wavelength-dependent correlation coefficient calculation between reflectance and
samples
3• OLS regression modeling with satellite bands as independent variables (67/100 samples)
4• Statistical model selection
5• Band math on satellite images
6• Compare Sentinel-2 and Landsat OLI
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FIELD CROPS100% 90% 75% 50%
EC (ds/m) EC (ds/m) EC (ds/m) EC (ds/m)
Cotton 7.7 9.6 13.0 17.0
Sorghum 6.8 7.4 8.4 9.9
Wheat 6.0 7.4 9.5 13.0
Soybean 5.0 5.5 6.3 7.5
Corn 1.7 2.5 3.8 5.9
Maas and Hoffman (1977)
Crop Sensitivity to EC
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Pearson’s r
correlation
with satellite-
like bands &
field EC
Table 1. Pearson 𝑟 correlation coefficients among the OLI-like bands and EC.
Bands in nm b1 b2 b3 b4 EC
b1 (434-451) 1 b2 (452-512) 0.90* 1 b3 (533-590) 0.73* 0.94* 1 b4 (636-673) 0.68* 0.92* 0.98* 1 EC 0.42 0.35* 0.25* 0.21* 1
*Significant at the 0.05 probability level
Table 2. Pearson 𝑟 correlation coefficients for sample spectra averaged into Sentinel-2
bands
Bands in nm b1 b2 b3 b4 b5 b6 EC
b1 (433-453) 1 b2 (457.5-522.5) 0.98* 1 b3 (542.5-577.5) 0.89* 0.95* 1 b4 (650-680) 0.85* 0.93* 0.98* 1 b5 (697.5-712.5) 0.82* 0.90* 0.98* 0.98* 1 b6 (732.5-747.5) 0.78* 0.87* 0.95* 0.96* 0.99* 1 EC 0.39* 0.35* 0.25* 0.21* 0.18* 0.14 1
*Significant at the 0.05 probability level
Results: Correlations
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Day Sensor R2 R2
Adj RMSE VIF AIC
Sep
tem
ber
Landsat OLI 0.54 0.51 0.90 2.17 172.5
Sentinel-2 0.69 0.669 0.73 3.23 165.4
Dec
emb
er Landsat OLI 0.04 -0.02 1.90 1.04 229.6
Sentinel-2 0.04 -0.02 2.83 1.04 228.2
Bo
th D
ay
s Landsat OLI 0.31 0.24 1.15 1.45 333.80
Sentinel-2 0.32 0.27 1.13 1.47 331.90
Results: Model Selection & Evaluation
𝐸𝐶𝑂𝐿𝐼 = 2.0080 + (0.0698 ∗ 𝒃𝟐) − (0.0156 ∗ 𝒃𝟒)
𝐸𝐶𝑆𝑒𝑛𝑡𝑖𝑛𝑒𝑙−2 = 2.1111 + (0.0559 ∗ 𝒃𝟐) − (0.0074 ∗ 𝒃𝟔)
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Results: Mapping of Salinity
Landsat 8 OLI Sentinel-2
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Results: Mapping of Salinity
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a b
d c
Healthy
Soybeans
Mixed
grass &
unhealthy
soybeans
Halophyte
Grass
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48US Census
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Instruments
• JAZ DPU-GPIO (Spectrometer)
https://oceanoptics.com/wp-content/uploads/Jaz-
OEM-Data-Sheet.pdf
• Salinity and Soil Moisture Meter
FieldScout TDR 150
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Equations
50
𝑉𝐼𝐹 =1
1 − 𝑅2
𝐴𝐼𝐶 = 𝑛 ln(𝑅𝑆𝑆
𝑛) + 2𝑘
𝑃𝑒𝑎𝑟𝑠𝑜𝑛 𝑟 = 𝑋𝑌 −
)𝑋)( 𝑌𝑛
𝑋2 − )𝑋 2
𝑛 )( 𝑌2 − )𝑌 2
𝑛
𝑟𝑒𝑓𝑒𝑐𝑡𝑎𝑛𝑐𝑒 =𝑠−𝑑
𝑘−𝑑∗ 100 𝑅2 = 1 −
𝑖=1𝑛 (𝛾 − 𝛾 ,)2
𝑖=1𝑛 (𝛾 , − 𝛾)2
𝑅𝐴𝑑𝑗2 = 1 −
𝑛 − 1
𝑛 − 𝑘 − 11 − 𝑅2
𝑅𝑀𝑆𝐸 = 𝑖=1𝑛 (𝛾−𝛾′)2
𝑛
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Resample
spectrometer
measurements
into satellite-
like bands
Example from Barsi et al. (2011)
Methods: Resampling
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Iterative
Ordinary Least
Squares
Regression
(67% of
samples)
Results: Lab tests
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Results: Hyperspectral Analysis
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Interview Methods
54
NCSU Extension Agents
recommend interviewees
Recruit farmers and build rapport
‣Conduct
interviews with adapted questions from Moser and Ekstrom (2010)
‣Present salinity
maps
Transcribe and code audio recordings
1 2 3 4