Mapping from space: Remote Sensing for migratory shorebird … · 2020-07-14 · Bahía Lomas...

Mapping from space: Remote Sensing for migratory shorebird conservation

at Bahia Lomas Ramsar Site

Dr. Paula Lightfoot

Dr. Fabio A. Labra

Webinar structure

• Introduction

• Using Sentinel-2 to Map Intertidal Extent at Bahía

Lomas Ramsar Site

• Using Remote Sensing Imagery to Model Migrant Bird

Habitat at Bahía Lomas Ramsar Site

• Questions and answers

Background

• This webinar presents the results of the pilot project

• "USING EARTH OBSERVATION TO DETERMINE DISTRIBUTION OF SUITABLE

HABITAT FOR MIGRATORY SHOREBIRDS: CASE STUDY OF BAHÍA LOMAS

RAMSAR SITE“

• This project was funded by the Department for the Environment, Food & Rural

Affairs of the United Kingdom government

• This is the first project managed and executed under the Southern Observation

Alliance of the Earth (Austral Earth Observation Alliance, AEOA).

Austral Earth Observation Alliance

• The Austral Earth Observation Alliance is a virtual centre of excellence to bring

together providers and users of EO enabling them to work together on common

problems to exploit EO in the South Atlantic and South American region

• Joint Nature Conservation Committee

• South Atlantic Environmental Research Institute

• Universidad de Magallanes

• Universidad Santo Tomás

• Universidad de Chile

• University of Dundee

Using Sentinel-2 to Map Intertidal Extent at Bahía Lomas Ramsar Site

Dr Paula Lightfoot, Earth Observation Specialist

Dr Gwawr Jones, Senior Earth Observation Specialist

July 15th, 2020

Introduction

Bahía Lomas Ramsar site is a 150km2 tidal flat in Tierra del Fuego, southern Chile.

It is a key wintering site for many wading birds, including Red Knot, Calidris canutus.

Red Knot have suffered a severe global population decline, possibly due to poor food

supply at their wintering sites.

Information on the distribution of key

habitats and prey species is essential

to inform conservation management.

Red Knots in flight over Bahía Lomas © Antonio LarreaRed Knot © G. Breese

Intertidal Extent Mapping

A map of the extent of the intertidal area at Bahía Lomas was needed to:

• help predict the distribution of shorebird habitat and prey species

• monitor change e.g. due to seismic movement or sea level rise

• validate the shoreward boundary of the Ramsar site.

This project investigated using Sentinel-2 imagery to map intertidal extent at Bahía Lomas.

Sentinel-2Sentinel-2 is a mission from Copernicus,

the Earth Observation programme run by

the European Commission and European

Space Agency.

Twin satellites, Sentinel-2A and Sentinel-2B

collect multispectral imagery, acquiring new data

every few days since 2015.

JNCC process Sentinel-2 imagery to create ‘analysis-

ready data’, a surface reflectance raster containing 10

bands at 10 metres resolution.

Sentinel-2 analysis-ready data has many applications,

including habitat mapping, condition monitoring and

change detection.

Sentinel-2

• Sentinel-2 imagery is supplied as a grid of tiles with unique alphanumeric codes.

Each tile measures 100km x 100km.

• Bahía Lomas Ramsar site is covered by tiles 19FDB and 19FEB and relative

orbits 67 and 110.

• Image acquisition time at Bahía Lomas is around 14:00 UCT (11:00 local time).

Coast X-Ray

Coast X-Ray is an approach for mapping intertidal extent in near real-time by

measuring water occurrence frequency using Sentinel-2.

It was developed by Scottish Government’s Dynamic Coast project to meet national

requirements for monitoring and coastal management.

The Dynamic Coast team kindly supported JNCC by trialling the Coast X-Ray

approach with Sentinel-2 data for Bahía Lomas.

http://www.dynamiccoast.com/

http://www.dynamiccoast.com/

Coast X-Ray Method• Create a grid of 90 km2 hexagons covering the

coastal area.

• In each grid cell, create a ‘stack’ of cloud-free

Sentinel-2 images for a date range (September

2015 – January 2020).

• Generate Normalised Difference Water Index

(NDWI) each Sentinel-2 image in the stack.

• Classify pixels with NDWI > 0.2 as ‘water’.

• For each pixel in the image stack, calculate the % of

total images in which it is classified as ‘water’.

• The final output is a raster file in which each pixel

has a water occurrence value of 0 – 100.

𝐍𝐃𝐖𝐈 =𝐆𝐫𝐞𝐞𝐧 − 𝐍𝐞𝐚𝐫 𝐈𝐧𝐟𝐫𝐚𝐫𝐞𝐝

𝐆𝐫𝐞𝐞𝐧 + 𝐍𝐞𝐚𝐫 𝐈𝐧𝐟𝐫𝐚𝐫𝐞𝐝

Developed in Google Earth Engine

Coast X-Ray Water Occurrence RasterUsers can view the results and the input data on an interactive map here:

https://jamesmfitton.users.earthengine.app/view/coastxray

https://jamesmfitton.users.earthengine.app/view/coastxray

Coast X-Ray Water Occurrence Raster

The water occurrence

raster produced by the

Coast X-Ray method has

been provided to the

Universidad Santo Tomás

as an output of this

project.

• A map of the intertidal

extent can be created by

reclassifying the water

occurrence raster values.

• In theory, water occurrence

values should be

reclassified as:

0 = land

1-99 = intertidal zone

100 = sea

• However, this misclassifies

large areas of both sea and

land as intertidal.

Intertidal Extent Map

• A water occurrence

value of 5 appears best

for defining the high tide

limit.

• A value of 97.5 appears

best for defining the low

tide limit.

• Both values cause

some misclassification

which requires manual

editing.


Two intertidal extent vector files derived from the Coast X-Ray water occurrence raster

have been provided to Universidad Santo Tomás as outputs of this project.


Map 1. Clipped to a 12 km buffer around the Bahía

Lomas Ramsar site boundary, but not edited.

Map 2: Clipped to a 12 km buffer around the Bahía

Lomas Ramsar site boundary and manually edited

to remove misclassifications.

NDWI generated from Sentinel-2 imagery acquired on

a low spring tide on 12th May 2019

Normalised Difference Water Index

• In this example, there is good separation of NDWI value ranges for ‘sea’ and ‘intertidal’.

• ‘Land’ samples have lower NDWI values than ‘intertidal’, but there is overlap between the value ranges.

• The range of ‘intertidal’ NDWI values exceeds 0.2 (the threshold for water in the Coast X-Ray method)

• Turbid areas of sea have low NDWI values, which can lead to misclassification of water as land.

• Topographic shadows have high NDWI values, which can lead to misclassification of land as water.

NDWI values of randomly generated sample points

(n = 500 per class)

• As expected, ‘intertidal’ and ‘sea’ have similar NDWI value ranges when the intertidal zone is immersed.

• Low outlier values for ‘sea’ are the result of unmasked cloud.

• High outlier values for ‘land’ are the result of topographic shadow misclassified as water. The effect is

greater in this imagery from July than in the May imagery on the previous slide.

• Despite overlapping NDWI value ranges for ‘land’ and ‘intertidal’, there is a clear high water boundary.

Normalised Difference Water IndexNDWI generated from Sentinel-2 imagery acquired on

a high spring tide on 14th July 2018

NDWI values of randomly generated sample points

(n = 500 per class)

Future Research Opportunities• Explore the use of other indices in combination with NDWI to classify ‘water’ and ‘land’.

• Normalised Difference Vegetation Index (NDVI) could help to delineate the shoreward boundary and reduce

misclassification due to topographic shadow.

• Modified Normalised Difference Water Index (mNDWI) could reduce misclassification caused by turbidity.

• JNCC have produced analysis-ready Sentinel-2 data for 33 dates from 2016 to 2020, providing cloud-free

imagery of high and low spring tides. This will aid future research.

Future Research Opportunities

• The stack of Sentinel-2 images used to generate the Coast X-Ray water occurrence map may

not represent the full tidal range.

• Obtain locally referenced tidal data for Punta Arenas to cross-reference with the dates of the

imagery used.

• Ideally the image stack should include examples of the highest and lowest tides, but this may

not be the case due to cloud cover and satellite acquisition time.

http://www.ioc-sealevelmonitoring.org/station.php?code=ptar2


• Investigate potential of using Sentinel-1 radar data for mapping water occurrence

• This could improve the likelihood of obtaining usable imagery for the full tidal range

➢ radar data is not affected by cloud.

➢ Sentinel-1 acquisition times are around 09:00 and 23:45 UCT (06:00 and 20:45 local time)

• Research is needed into the consistency of radar backscatter values for land and water using

imagery from different dates.

• JNCC have produced analysis-ready Sentinel-1 data for 27 dates from March to November

2019, providing imagery of high and low spring tides.

Acknowledgements

• The Dynamic Coast Team, particularly

James Fitton and Alistair Rennie, for the

Coast X-Ray outputs.

• UK Government Department for

Environment, Food & Rural Affairs for

funding this collaborative project.

• Universidad Santo Tomás and Joint

Nature Conservation Committee as the

main partners in this project.

• Austral Earth Observation Alliance for

enabling this project by fostering

collaboration opportunities.

Mapping from space: Remote Sensing for migratory shorebird conservation

at Bahia Lomas Ramsar Site

Dr Fabio A. Labra

15th July 2020

Introduction

• Conservation and management efforts often require spatially explicit description of

habitats and species distribution as an input for research or decision process

• In addition to Calidris canutus, Bahia Lomas harbours other important

conservation targets

© Antonio Larrea

Introduction

• Conservation and management efforts often require spatially explicit description of

habitats and species distribution as an input for research or decision process

• In addition to Calidris canutus, Bahia Lomas harbours other important

conservation targets

• Accurate habitat maps are needed to inform conservation management

© Antonio Larrea

Earth Observation can assist conservation

• Remote sensing data such as satellite imagery can help us to obtain accurate distribution maps, by using statistical models to infer the distribution of species from those images together with geo-located points where species have been observed

Darina solenoidesDarina solenoides

Workflow

SDMMaxEnt

Satellite image

Intertidal habitat extent

Species presence

Clip, RTOA

Gathering spatial information

Processing Modelling Mapping

Workflow: Darina solenoides

SDMMaxEnt

Clip, RTOA

Gathering spatial information

Processing Modelling Mapping

Satellite Images

• Landsat 8

• Sentinel 2

• WorldView-2

BandsRed, Green, Blue, Near Infrared



Landsat 8 (30m)

February 6, 2020 August 25, 2019

Sentinel 2 (10m)

February 8, 2020 August 25, 2019

WorldView-2 (0.5 m)2013-2016

Model fit:• We observed very good model

fit to observed presence data sets across all three EO imagery scenes

• In both Polychaete worms and Sarcocornia magellanica, a scale effect is apparent

• This may reflect the imbalance in the presence sample size with the resolution and number of image pixels

a)

b)

c)

Darina solenoides

Polychaete worms

Sarcocornia magellanica

L8, 6 febrero 2020 L8, 25 agosto 2019

S2, 8 febrero 2020 S2, 25 agosto 2019

Darina solenoides Polychaetes

Sarcocornia magellanica True color image

Distribution area:

• We estimated the covered pixel area for each of these conservation targets, and examined the possible effects of Sensor image scale of resolution

• Intertidal invertebrates exhibit a strong effect of scale, while there´s no significant effect for Sarcocornia magellanica

Results

• It is possible to characterize the distribution of habitats for migratory birds by using earth observation images

• Darina solenoides shows a wide distribution in the Bahía Lomas Nature Sanctuary, covering between 35% and 40% of the surface area of the Nature Sanctuary.

• In the case of polychaete worms, they cover between 43% and 48% of the Sanctuary surface, while Sarcocornia magellanicahas a more restricted distribution, which covers between 12% and 13% of the Sanctuary surface.


• Investigate the distribution pattern at a species level for the most

abundant polychaete taxa

• Estimate Darina solenoids and polychaete biomass or standing stock by

building onto the distribution modelling work. This may be achieved by

further field sampling, including the empirical estimation of biomass.

• Further development of sampling and modelling to assess temporal and

spatial variability in Sarcocornia magellanica distribution and phenology

across the Nature Sanctuary.

• Incorporate other modelling approaches, including machine learning

algorithms such as Boosted Regression Trees, Random Forest or

Support Vector Machines, and contrasting their performance with other

methods such as GLM, GAM. Lm GLM GAM RF


• Exploring the opportunities to apply these methods in other migratory bird habitats, by adding

sampling and image analysis to the tools available for conservation effors focused on migrant

shorebird species

Darina solenoides Sarcocornia magellanicaPolychaetes

Acknowledgements

• The Centro Bahia Lomas Team, particularly

Carmen Espoz and Gabriela Garrido.

• UK Government Department for

Environment, Food & Rural Affairs for

funding this collaborative project.

• Field sampling has been funded over the

years by several institutions including MMA,

ENAP, UST and Manomet.

• Sampling was made possible by the

collaboration and help in the fieldwork and lab

by Ricardo Matus, Alejandra Ponce, Olivia

Blank, Gabriela González, Antonio Larrea,

Melissa Villagrán, among many others

Mapping from space: Remote Sensing for migratory shorebird … · 2020-07-14 · Bahía Lomas...

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