201239 walden, dave monitoring wetland weeds using remotely sensed data in kakadu national park,...

8/2/2019 201239 Walden, Dave Monitoring Wetland Weeds Using Remotely Sensed Data in Kakadu National Park, Northern Territory, Australia

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Supervising Scientist - working to protect the environment from the impacts of uranium mining

www.environment.gov.au/ssd

Monitoring the wetland weed paragrass (Urochloa mutica) using

remotely sensed data in KakaduNational Park, Northern Territory

Dave Walden, James Boyden & Renee Bartolo

Environmental Research Institute of the Supervising Scientist


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2Supervising Scientist Division

Project Overview

July 2004 a multispectral QuickBird satellite captureof 64 km2 of the central region (highest density ofpara grass) of the Magela floodplain in KNP

Airboat and helicopter field validation surveys inMarch 2003 and June 2004

Able to estimate the cover of para grass within the

image boundary and the rate of spread usinghistorical data

Data also incorporated into cost-of-control and

Bayesian habitat suitability models


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Para grass as a weed

A trailing perennial grass with long, robust creeping culms(stolons or stems) that sprout new roots at the nodes whereverthey contact the ground

Also colonises floating vegetation mats that can break apart

and float downstream, thus increasing spread

Fast growing with no pests/diseases and a very broadenvironmental niche tolerant of inundation at a wide range ofwater depth from moist ground to 200 cm

Widespread distribution as it is valued by pastoralists as idealbuffalo/cattle fodder and actively planted in the region as earlyas 1922 and right up to the KNP declaration in 1979


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Key impacts of para grass

Can modify landscapes by forming monocultures overlarge areas thus reducing biodiversity

Para grass readily outcompetes (amongst other flora)

wild rice (Oryza spp.) and sedges (Eleocharis spp.).Species that are essential food and nesting resources formagpie geese and some other waterbirds

The denser structure and higher biomass reduces waterflows thus increasing sediment deposition. Greater fuelloads increase intensity and extent of floodplain fireswhich can kill fringing paperbarks and other species


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Kakadu NationalPark, the Magelafloodplain & theextent of theQuickBird image


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Raw 2004

QuickBirdimage

Woodland

Wetland


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Image analyses vegetation map

Supervised classification using six training classes para

grass (2 forms), dominant native vegetation types, and openwater

Training areas for classification were selected using the

spatially referenced ground and low-level helicopter surveydata. Wherever possible, training sites were selected fromwithin discreet homogeneous patches of a particular class

Map class Producer accuracy User accuracy

Para grass (high greenness) 90 % 96 %

Para grass (low greenness) 96 % 92 %

Overall (all classes) 86 %



Vegetation

class map fromsupervisedclassification



Image analyses para grass cover

The vegetation class map was resampled to 5 mpixels. From this map a raster layer was producedfor para grass only (other map classes removed)

Using this data (and a 250 m zone-grid overlay),the percentage cover of para grass within each250 m grid cell was estimated i.e. dividing thetotal number of 5 m para grass classed pixels

falling within each 250 m grid cell, by the totalarea of each grid cell



Para grass cover

map derived fromthe vegetation map



Early para grass spread on the Magela

Small patches observed amongst the nativeHymenachne acutigluma on the Western Plains of theMagela floodplain KNP in 1982-83

An eriss project (Knerr 1998) based on aerialphotography and ground mapping of the same regionshowed that para grass cover in 1991 was 132 ha andby 1996 had spread to 422 ha (more than tripled in 5-6years)

This initial spread increased rapidly as the grazing andtrampling impacts of buffalo were reduced during the1980s early 1990s



More recent para grass spread

In 2004 the total area of para grass cover in this central

floodplain region as derived from the cover map was 1250ha

Para grass was distributed over 35% of the floodplain with

10% displacement of the native vegetation being largelywild rice mixed with sedges. Many satellite infestationsparticularly to the north (ie downstream)

Average spread rate calculated at 14% p.a. or a doubling in

extent every 5 years

Comprehensive helicopter surveys (Parks, NT WeedsBranch and CDU) in 2009 calculated 3513 ha of para grassover the central region



Para grass spread (not including 2009 data)

Linear regression between Loge extent (km2) of para grass and time (yrs)

(R2=69%, n=5, P



Future work

The Spatial Sciences and Data Integration group at eriss now

acquires an annual (VHSR) WorldView-2 image capture of theMagela floodplain and Ranger minesite

Using the GEOBIA approach it should be possible to continuemonitoring and detect more subtle changes (including any control

efforts) in para grass cover compared to previous mapping efforts

In addition, SSDI will soon acquire a LiDAR 30cm DEM of theMagela floodplain. This will yield valuable hydrological/inundationdata, factors which are critical in determining the distribution of

floodplain flora

Such information should further our knowledge of spread rates andhabitat suitability, information that can be used to develop andimprove management strategies



WV-2 image May 2010

Para grass



WV-2 image May 2010QuickBird July 2004



Bayesian HabitatSuitability Model byKeith Ferdinands



WV-2 image May 2010

Gratuitous crocodile photo

Thank you

201239 walden, dave monitoring wetland weeds using remotely sensed data in kakadu national park,...

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