Connection to the Larger Landscape?Pasture assessment " Pastures from Space/ Pasture Watch " light use efficiency model " integrates MODIS satellite imagery, 250 m, 10 day composites

Remote Sensing Tools in Grasslands: Connection to the Larger Landscape?

Anne Smith Agriculture and Agri-Food Canada

Lethbridge Research and Development Centre Transboundary Grasslands Partnership Workshop

December 4th, 2018 Lethbridge, Alberta

Landsat 5 TM True Colour Composite July 6, 1991

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Landsat 5 TM True Colour Composite July 6, 1991

Year Cost Processing time

1991 $4500 1 week++ 2018 $0 1 hour

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LANDSAT 8

4

Remote Sensing MODIS

PLANET LABS FLOCKS/DOVES

RADARSAT CONSETLLATION MISSION

RAPIDEYE

SENTINEL QUICKBIRD

SPOT

Update ~2000 EO ~684

2014

Remote Sensing !  the acquisition of data about a physical object without touching it

Passive Active

e.g. MODIS , Landsat Sentinel-2, SPOT, RapidEye, Planet Labs Doves….. airborne and drone cameras

e.g. Radarsat-2, TerraSAR, Sentinel-1

airborne and drone sensors.

**RADARSAT Constellation

Mission February 2019

http://www.seos-project.eu leadertechnic.com 5

Remote Sensing Tools for Grassland Monitoring

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Three broad categories of remote sensing research in grasslands: !  Mapping grassland

spatial extent and changes over time,

!  Estimating productivity

!  Mapping invasive plant species

MAPPING SPATIAL EXTENT

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!  Alberta Native Prairie Baseline (~1993) !  Alberta Grassland Vegetation Inventory (2006-2014)

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!  Agriculture Financial Services Corporation grassland mask

Mapping Grassland Spatial Extent

Image courtesy of Barry Adams, Alberta Environment and Sustainable Resource Development

"  Colour infrared digital airborne imagery 0.5 m "  Polygon-based, 5.0 ha for upland site types

and 1.0 ha for wetland site types. "  335,366 polygons in 1204 townships (110

townships remaining) "  Accuracy estimated at >90% "  > 8 years to compile (2006-2014)

"  Comprehensive biophysical, anthropogenic and land use inventory


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NEWELL TEST SITE RADARSAT-2 PAULI-RGB

July 21, 2009

PAKOWKI TEST SITE RADARSAT-2 PAULI-RGB

July 03, 2009


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Ground data (collated from a variety of databases)

Remote sensing image (optical or radar)

Signatures

Classification

Land cover map

Training samples Validation samples

“Windshield surveys” Crop insurance Irrigation district Native Prairie Baseline

AGRASID Hydrology Township fabric


!  FQ4 fine quad-pol images (HH, VH, HV and VH)

!  Multiple dates !  Yamaguchi decomposition !  July/August provide best

results (overall classification 81-82%; GVI polygons correctly identified 81-84% )

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Landsat TM image acquired in July 2011, R:band 4, G:band 3, B: band 2

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To investigate the potential to use satellite remote sensing products to update the grassland vegetation inventory: –  Wholesale change (native

grassland to cropping) –  Partial change (oil, gas,

transportation infrastructure)

Provide a tool for directing ground-based surveys


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5185 Upland-Grassland polygons covering 14 site types Method development database:

74 native grassland polygons 152 cropped polygons

Method validation data: 34 native grassland polygons in both 2006 and 2011 26 native grassland polygons converted to cropping systems between 2006 and 2011


!  Landsat imagery (6 bands BGR, NIR, 2MIR)

–  functional vegetation indices (Greenness/photosynthesis; vegetation/landscape water content; senescent vegetation)

!  Hybrid change detection –  Update changed areas only –  2006, 2011, 2013

GVI polygons overlaid on July 6, 2006 Landsat TM5 False Colour Composite Image

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Native grasslands converted to croplands Native grasslands

v

Edit polygon

Field validate

July August

Results- July and August change detection map


July 2006-2013

19 Results-Change detection 2006-2011 and 2006-2013

July 2006-2011 Native grasslands converted to croplands Native grasslands


Updated GVI

2007 SPOT Image 2006 GVI

Database Record Add/edit/delete

Slides courtesy of Barry Adams AESRD 20


ESTIMATING ANNUAL PRODUCTIVITY OF GRASSLANDS

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!  Point 1 !  Point 2

Introduction

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Estimating Annual Productivity of Grasslands !  Canada initial testing 1980s !  African, North American and Australian savannahs

"  Rangeview "  MODIS – 250 m, daily coverage "  Difference from average

State of Arizona, 2001 versus 2003, February 17 and May 8 23

!  Point 1 !  Point 2

Introduction

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Estimating Annual Productivity of Grasslands

Pasture assessment

"  Pastures from Space/ Pasture Watch "  light use efficiency model "  integrates MODIS satellite imagery, 250 m, 10 day composites and climate data

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!  Empirical relationships –  Landsat (multiple scenes) –  16 day repeat cycle –  30 m spatial resolution

!  Simple modelling –  Moderate Resolution Imaging Spectrometer –  Daily coverage –  250 m spatial resolution

!  Validation data?

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!  Rangeland Reference Area Monitoring Program

–  Alberta Environment and Sustainable Resource Development

–  5-20 years of clipping data –  Spatially discrete


Caveat: •  Measure potential

production •  Scale

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Y= 4522.23X - 460.13, R² = 0.68

0

1000

2000

3000

4000

5000

0.0 0.2 0.4 0.6 0.8 1.0

Gree

n bi

omas

s (k

g ha

-1)

NDVI

Y = 7106.76x + 276.19, R² = 0.78

0

1000

2000

3000

4000

5000

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Gree

n bi

omas

s (k

g ha

-1)

MTVI2

Community Types R2 (2008) R2 (2009) R2 (2008+2009)

NDVI MTVI1 MTVI2 NDVI MTVI1 MTVI2 NDVI MTVI1 MTVI2 Foothills rough fescue (FRF) 0.54 0.68 0.65 0.86 0.95 0.95 0.67 0.76 0.73 Needle-and –thread (NT) 0.65 0.67 0.69 0.70 0.63 0.64 0.66 0.66 0.67 Northern wheatgrass (NW) 0.54 0.46 0.50 0.91 0.92 0.90 0.58 0.53 0.55

Cages


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LANDSAT DATA LANDSAT DATA

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30 30

fAPAR=(1.21*NDVI)-0.04 PAR=(0.5*average daily solar

radiation (MJ m-2 day-1))

APAR (MJ m-2 day-1) =fAPAR*PAR

APARu (MJ m-2 day-1) = APAR*MI*TI

GT (kg ha-1 day-1) = APARu*LUE*10 where GT is total (above ground and below ground) grass growth

10 is a factor to convert NPPT to kg ha-1.

MODIS IMAGERY TI=temperature stress index calculated from weather data and optimal values derived from literature (0-1) MI = moisture stress index derived from AGRASID soil information and Nix (1981) (0-1)

AGA (kg ha-1 day-1) = GT * root-shoot ratio where AGA is above ground grass growth

AAG(kg/ha annum) = sumjan-dec(AGA*10) where AAG is annual above ground grass growth


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APAR


-30

-20

-10

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0221

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0821

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Mea

n Ai

r Tem

epra

ture

(oC)

Tem

pera

ture

Inde

x

Aden (NT) Opt T=15oC Opt T=20oC Opt T=20oC Mean T

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Temp. Index

Moist. Index

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Considerations !  Multiples spatial resolutions !  Size of sampling units (MODIS pixel

versus clipping) !  Timing of field clipping !  Model integrated over time versus clipping

is from a single time period !  Carryover from different years (influences

NDVI itself, masks green vegetation)

MAPPING NOXIOUS WEEDS

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Mapping Noxious Weeds in Grasslands

# Noxious weeds # Reduce productivity and biodiversity # Compete for water resources # Toxic to livestock # Economic impacts (loss of production and cost of control)

# Quantitative surveys of invasive species occurrence on the Prairie grasslands are rare due to the cost and accessibility

# Remote sensing shows promise

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!  Extent of coverage •  5 million acres in 15 states and 6 Canadian

provinces •  Manitoba alone 340,000 acres •  river banks, pastures, and native grasslands

!  Economic Impacts •  $130 million annually in North Dakota, South

Dakota, Montana, and Wyoming •  $19 million annually in Manitoba •  control, loss of forage, loss of livestock

!  Control •  biological (insects and sheep) •  herbicides

!  Mapping !  by people spraying or releasing insects for

biocontrol !  remote sensing

Control by Aphthona sp.

Mapping Leafy Spurge in Grasslands

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!  Remote sensing –  Coverage of large and inaccessible

areas –  Lower cost –  Distinct yellow bracts –  Unique spectral signature –  Success in the USA

!  No wide adopted

–  Considerations !  Density, Patch size

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Mixed-Tuned Matched Filter !  Only requires end-member for target of choice !  Provides presence or absence

AISA imagery


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Site 1c Site 1c Site 1c

AISA Simulated WorldView 2 Simulated Quickbird

!  >30% ground cover of flowering leafy spurge can be detected

!  <30% detection depends on proximity to higher density patches

!  potential to use high spatial resolution satellites

0

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<1 to

10%

<10

to 2

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<20

to 3

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to 3

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to 4

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>40%

AISA Quickbird Worldview-2

Sam

pes

poin

ts d

etec

ted

(%)

Ground cover of yellow flowers (%)Yes No

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Fixed-wing, larger areas, 30 mins. Multi-rotor, up and down, 12-15 mins.

Draganfly Commander

DJI Inspire 1 Precision Hawk

eBee


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!  9 sampling areas !  Transects (3) !  Quadrats (19)

!  Photograph (% total & flowering leafy spurge

!  Visual density (L, M, H) !  Stem count


Site Collection date

Flight height (m)

Scenes Spatial resolution (cm)

Covering area(m2)

Site1 9 am, July 30, 2016

15 1 0.65 507 30 1 1.26 1905 45 1 1.88 4242 60 1 2.54 7742 75 1 3.15 11907 90 1 3.77 17055 105 1 4.41 23338 120 1 5.07 30846


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Mapped leafy spurge cover versus ground measured leafy

spurge cover

Mapped leafy spurge cover versus ground measured

flowering leafy spurge cover

Mapped leafy spurge cover versus ground measured

flowering leafy spurge density


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# UAV Images !  Image acquired on June 28, 2015 !  Flight height 100-110 m !  Spatial resolution: 3.2 cm


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Leafy spurge mapped using Mixed Tune Matched Filter Classification

Leafy spurge


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Leafy spurge

Original classification map Classification map with tree and shrub mask applied


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Ground samples

1. Transect: 11 2. Quadrats: 110 (High, Medium, Low density leafy spurge and no leafy spurge) 3. Stems of leafy spurge and flowering leafy spurge were recorded with each quadrat


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Leafy spurge

Overall classification 70% Producer Accuracy 67% User Accuracy 88%

# Reconcile spatial resolution of image <4 cm) with global position system accuracy (>1 m)


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Remote Sensing in Grasslands

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! Potential applications of remote sensing –  Mapping grassland spatial extent and changes over

time –  Estimating productivity –  Mapping invasive plant species –  Others????

Remote sensing offers the potential to

map broader landscapes

Documents

Connection to the Larger Landscape?Pasture assessment " Pastures from Space/ Pasture Watch " light use efficiency model " integrates MODIS satellite imagery, 250 m, 10 day composites