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

Works CitedAdam, E., Mutanga, O., & Rugege, D. (2009). Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol Management. Retrieved from https://www.researchgate.net/profile/Onisimo_Mutanga/publication/225456909_Multispectral_and_hyperspectral_remote_sensing_for_identification_and_mapping_of_wetland_vegetation_A_review/links/09e4150e6b7617a58a000000/Multispectral-and-hyperspectral-remote-s

Arbuckle, J. G., Morton, L. W., & Hobbs, J. (2013). Understanding farmer perspectives on climate change adaptation and mitigation: the roles of trust in sources of climate information, climate change beliefs, and perceived risk. Environment and Behavior, 1–30. https://doi.org/10.1177/0013916513503832

Azhoni, A., Holman, I., & Jude, S. (2016). Contextual and interdependent causes of climate change adaptation barriers: Insights from water management institutions in Himachal Pradesh, India. Science of the Total Environment, 576, 817–828. https://doi.org/10.1016/j.scitotenv.2016.10.151

Bai, L., Wang, C., Zang, S., Zhang, Y., Hao, Q., & Wu, Y. (2016). Remote sensing of soil alkalinity and salinity in the Wuyu’er-Shuangyang river basin, Northeast China. Remote Sensing, 8(2). https://doi.org/10.3390/rs8020163

Beck, R. (Ed.). (2003). EO-1 User Guide (2.3). University of Cincinnati. Retrieved from https://eo1.usgs.gov/documents/EO1userguidev2pt320030715UC.pdf

Biesbroek, R., Klostermann, J., Termeer, C., & Kabat, P. (2011). Barriers to climate change adaptation in the Netherlands. Climate Law, 2(2), 181–199. https://doi.org/10.3233/CL-2011-033

Brady, N. C., & Weil, R. R. (2010). Elements of the Nature and Properites of Soils (Third Edit). Upper Saddle River: Prentice Hall.

California Institute of Technology. (n.d.). Welcome to HyspIRI Mission Study Website — Hyperspectral Infrared Imager. Retrieved January 22, 2017, from https://hyspiri.jpl.nasa.gov/

Climate Central. (2016). Sea level rise and coastal flood risk: Summary for Hyde County, NC Sea level rise and flood forecast. Retrieved from http://ssrf.climatecentral.org.s3-website-us-east-1.amazonaws.com/Buffer2/states/NC/downloads/pdf_reports/County/NC_Hyde_County-report.pdf

Drouin, R. (2016). Ghost Forests: How Rising Seas Are Killing Southern U.S. Woodlands — Earth Law Center. Retrieved January 22, 2017, from http://www.earthlawcenter.org/newsfeed/2016/11/ghost-forests-how-rising-seas-are-killing-southern-us-woodlands

Eakin, H., York, A., Aggarwal, R., Waters, S., Welch, J., Rubiños, C., … Anderies, J. M. (2016). Cognitive and institutional influences on farmers’ adaptive capacity: insights into barriers and opportunities for transformative change in central Arizona. Regional Environmental Change, 16(3), 801–814. https://doi.org/10.1007/s10113-015-0789-y

Eisenack, K., Moser, S. C., Hoffmann, E., Klein, R. J. T., Oberlack, C., Pechan, A., … Termeer, C. J. A. M. (2014). Explaining and overcoming barriers to climate change adaptation. Nature Climate Change, 4(October), 867–872. https://doi.org/10.1038/NCLIMATE2350

Ghosh, G., Kumar, S., & Saha, S. K. (2012). Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis. Journal of the Indian Society of Remote Sensing, 40(1), 129–136. https://doi.org/10.1007/s12524-011-0143-x

Haden, V. R., Niles, M. T., Lubell, M., Perlman, J., & Jackson, L. E. (2012). Global and Local Concerns: What Attitudes and Beliefs Motivate Farmers to Mitigate and Adapt to Climate Change? PLoS ONE, 7(12). https://doi.org/10.1371/journal.pone.0052882

32

Works CitedHarris Geospatial Solutions. (n.d.-a). Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) Users Manual. Retrieved March 12, 2017, from http://www.harrisgeospatial.com/portals/0/pdfs/envi/flaash_module.pdf

Harris Geospatial Solutions. (n.d.-b). FLAASH Background (Using ENVI) [Harris Geospatial Docs Center]. Retrieved March 12, 2017, from https://www.harrisgeospatial.com/docs/backgroundflaash.html

Hodgson, M. E., & Kar, B. (2008). Modeling the Potential Swath Coverage of Nadir and Off-Nadir Pointable Remote Sensing Satellite-Sensor Systems. Cartography and Geographic Information Science, 35(3), 147–156. Retrieved from http://people.cas.sc.edu/hodgsonm/Published_Articles_PDF/CAGIS_Hodgson_Kar_Modeling Satellite Opportunities_CAGIS_2008.pdf

Hossain, M. A. (2010). Global Warming induced Sea Level Rise on Soil, Land and Crop Production Loss in Bangladesh. Soil Resource Development Institute. Retrieved from http://iuss.org/19th WCSS/Symposium/pdf/0419.pdf

Kruse, F., Boardman, J. W., & Huntington, J. F. (2003). Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Transactions on Geoscience and Remote Sensing, 41(6 PART I), 1388–1400. https://doi.org/10.1109/TGRS.2003.812908

Li, C., Roberts, H., Stone, G., Weeks, E., & Luo, Y. (2011). Wind surge and saltwater intrusion in Atchafalaya bay during onshore winds prior to cold front passage. Hydrobiologia, (658), 27–39.

Li, J., Pu, L., Zhu, M., Dai, X., Xu, Y., Chen, X., … Zhang, R. (2015). Monitoring soil salt content using HJ-1A hyperspectral data: A case study of coastal areas in Rudong County, Eastern China. Chinese Geographical Science, 25(2), 213–223. https://doi.org/10.1007/s11769-014-0693-2

Manda, A. K., Giuliano, A. S., & Allen, T. R. (2014). Influence of artificial channels on the source and extent of saline water intrusion in the wind tide dominated wetlands of the southern Albemarle estuarine system (USA). Environmental Earth Sciences, 71(10), 4409–4419. https://doi.org/10.1007/s12665-013-2834-9

Marshall, N. (2010). Understanding social resilience to climate variability in primary enterprise industries. Global Environmental Change, 20, 36–43. Retrieved from https://www.researchgate.net/profile/Nadine_Marshall/publication/230757177_Understanding_social_resilience_to_climate_variability_in_primary_enterprises_and_industries/links/53daf5500cf2631430cb1696.pdf

Marshall, N., Dowd, A.-M., Fleming, A., Gambley, C., Howden, M., Jakku, E., … Park, S. (2014). Transformational capacity in Australian peanut farmers for better climate adaptation Transformational capacity in Australian peanut farmers for better climate adaptation. Agron- omy for Sustainable Development. EDP Sciences/INRA, 34(3), 583–591. Retrieved from https://hal.archives-ouvertes.fr/hal-01234807

Mccarl, B. A., Musumba, M., Smith, J. B., Kirshen, P., Jones, R., & El-ganzori, A. (2015). Climate change vulnerability and adaptation strategies in Egypt ’ s agricultural sector, 1097–1109. https://doi.org/10.1007/s11027-013-9520-9

McMullan Jr., P. S., Rich Jr., C., Landino, J., & Barnes, S. (2016). North Carolina’s Blacklands Treasure. (P. W. Carroll, Ed.). Nags Head, NC: Pamlico & Albemarle Publishing.

McNulty, S., Wiener, S., Moore-Myers, J., Farahani, H., Fouladbash, L., Marshall, D., & Steele, R. F. (2015). Southeast Regional Climate Hub Assessment of Climate Change Vulnerability and Adaptation and Mitigation Strategies. (T. Anderson, Ed.). United States Department of Agriculture.

33

Works CitedMerenlender, A. M., Huntsinger, L., Guthey, G., & Fairfax, S. K. (2004). Land Trusts and Conservation Easements : Who Is Conserving What for Whom ?, 18(1), 65–75.

Metternicht, G., & Zinck, J. (Eds.). (2009). Remote sensing of soil salinization: Impact on Land Management . Boca Ration, FL: CRC Press.

Metternicht, G. ., & Zinck, J. . (2003). Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85(1), 1–20. https://doi.org/10.1016/S0034-4257(02)00188-8

Moorhead, K., & Brinson, M. (1995). Response of Wetlands to Rising Sea Level in the Lower Coastal Plain of North Carolina. Ecological Society of America, 5(1), 261–271. Retrieved from http://www.jstor.org.pallas2.tcl.sc.edu/stable/pdf/1942068.pdf

Moser, S. C., & Ekstrom, J. A. (2010). A framework to diagnose barriers to climate change adaptation. Proceedings of the National Academy of Sciences, 107(51), 22026–22031. https://doi.org/10.1073/pnas.1007887107

Munns, R., Gilliham, M., Munns, R., & Gilliham, M. (2015). Tansley insight Salinity tolerance of crops – what is the cost ?, (C).

Niles, M. T., Lubell, M., & Brown, M. (2015). How limiting factors drive agricultural adaptation to climate change. Agriculture, Ecosystems and Environment, 200, 178–185. https://doi.org/10.1016/j.agee.2014.11.010

Niles, M. T., & Mueller, N. D. (2016). Farmer perceptions of climate change: Associations with observed temperature and precipitation trends, irrigation, and climate beliefs. Global Environmental Change, 39, 133–142. https://doi.org/10.1016/j.gloenvcha.2016.05.002

Nyman, J., La Peyre, M., Caldwell, A., Piazza, S., Thom, C., & Winslow, C. (2009). Defining restoration targets for water depth and salinity in wind-dominated Spartina patens. J Hydrology, (376), 327–336.

Oberlack, C. (2017). Diagnosing institutional barriers and opportunities for adaptation to climate change. Mitigation Adaptation Strategy Global Change, 22, 805–838. https://doi.org/10.1007/s11027-015-9699-z

Pereira, C. S., Lopes, I., Sousa, J. P., & Chelinho, S. (2015). Effects of NaCl and seawater induced salinity on survival and reproduction of three soil invertebrate species. Chemosphere, 135, 116–122. https://doi.org/10.1016/j.chemosphere.2015.03094

Pons, X., Pesquer, L., Cristóbal, J., & González-Guerrero, O. (2014). Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images. International Journal of Applied Earth Observation and Geoinformation, 33, 243–254. https://doi.org/10.1016/j.jag.2014.06.002

Poulter, B., Feldman, R. L., Brinson, M. M., Horton, B. P., Orbach, M. K., Pearsall, S. H., … Whitehead, J. C. (2009). Sea-level rise research and dialogue in North Carolina: Creating windows for policy change. Ocean and Coastal Management, 52(3–4), 147–153. https://doi.org/10.1016/j.ocecoaman.2008.09.010

Rashid, S., Shamsi, F., Zare, S., & Abtahi, S. A. (2013). Soil salinity characteristics using moderate resolution imaging spectroradiometer ( MODIS ) images and statistical analysis. Archives of Agronomy and Soil Science, 59(4), 471–489. https://doi.org/10.1080/03650340.2011.646996

34

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.

35

Data

36

37RisingSea.net

In-situ

Measurements

• JAZ Spectrometer

191 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 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)

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 Meter

FieldScout 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