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

Outline

• Background: Agriculture in Hyde County, NC

• Methods

• Results

• Future Work

• Lessons

2

Hyde County, NC

3RisingSea.net

4RisingSea.net

Sea Level Rise & Flooding

440 out of 613 Square Miles

5

6

Two Identified

Problems

7

The extent of

the damage is

unknown.

8

Adaptation to

salinization is

undocumented.

9

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

10

Remote Sensing

11

In-situ

Measurements

12

Remote Sensing

Analysis

13

11 ds/m

5.0 ds/m

2.5 ds/m

Results: In-situ

14

Results: Salinity Area Density

Landsat 8 OLI Sentinel-2

15

Results: Salinity Area Density

Landsat 8 OLI Sentinel-2

Results: Soil

Estimate

1.4-2.5%

Bare soil

> 4 dS/m

Lessons Learned

Remote sensing is

a valuable tool for

tracking soil

salinity

1 2 3

18

Any questions so far?

18

Identify Adaptation Responses,

Barriers, and Interventions

19

Process

Moser and Ekstrom (2010) Framework 20

Understanding

PlanningManaging

Process

Moser and Ekstrom (2010) Framework 21

Understanding

PlanningManaging

Process

Moser and Ekstrom (2010) Framework 22

Understanding

PlanningManaging

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

18

Barriers

24

Location &

Environment

Coordination &

Communication

Awareness &

Research

Barriers

25

Location &

Environment

Coordination &

Communication

Awareness &

Research

Barriers

26

Location &

Environment

Coordination &

Communication

Awareness &

Research

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

18

Future Work

Expand Interviews to Study

Flooding and Salinity Risk

Perceptions

1

Fly Drone Coverage of

Salinity Change in Test

Plots

2

18

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?

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

30

Thank you!

Ellie Davis

eleanord@email.sc.edu

31

mailto:eleanord@email.sc.edu

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33

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35

Data

36

37RisingSea.net

In-situ

Measurements

• JAZ Spectrometer191 nm – 889 nm (.37 nm interval)

• TDR 150• EC (dS/m)

• Soil Moisture (% VWC)

38

39

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

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

Methods

41

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

42

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

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

44

Day Sensor R2 R2Adj 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)

45

Results: Mapping of Salinity

Landsat 8 OLI Sentinel-2

46

Results: Mapping of Salinity

47

a b

d c

Healthy

Soybeans

Mixed

grass &

unhealthy

soybeans

Halophyte

Grass

48US Census

Instruments

• JAZ DPU-GPIO (Spectrometer)https://oceanoptics.com/wp-content/uploads/Jaz-

OEM-Data-Sheet.pdf

• Salinity and Soil Moisture MeterFieldScout TDR 150

49

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

Resample

spectrometer

measurements

into satellite-

like bands

Example from Barsi et al. (2011)

Methods: Resampling

Iterative

Ordinary Least

Squares

Regression

(67% of

samples)

Results: Lab tests

53

Results: Hyperspectral Analysis

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