REMOTE SENSING APPLICATIONS FOR MONITORING ......The Buntine-Marchagee Natural Diversity Recovery...

REMOTE SENSING APPLICATIONS FOR

MONITORING PERENNIAL VEGETATION IN THE

BUNTINE-MARCHAGEE NATURAL DIVERSITY

RECOVERY CATCHMENT

Katherine Zdunic and Graeme Behn

Department of Environment and Conservation

September 2010

DOCUMENT REVISION HISTORY

Revision Description Originator Reviewed Date

A Review all content Katherine

Zdunic

Gavan Mullan, David Pongracz,

Melissa Cundy and Lindsay Bourke

June 2010

B Edit with reviewers

comments

Katherine

Zdunic

September

2010

Remote sensing applications for monitoring perennial vegetation in the Buntine-

Marchagee Natural Diversity Recovery Catchment

Katherine Zdunic and Graeme Behn


Buntine-Marchagee Natural Diversity Recovery Catchment Department of Environment and Conservation

Geraldton Regional

1st Floor, The Foreshore Centre

201 Foreshore Drive

Geraldton Western Australia 6531

Telephone +61 899215955

Facsimile +61 8 99215713

www.dec.wa.gov.au

© Government of Western Australia 2009

September 2010

This work is copyright. You may download, display, print and reproduce this material in unaltered form only

(retaining this notice) for your personal, non-commercial use or use within your organisation. Apart from any

use as permitted under the Copyright Act 1968, all other rights are reserved. Requests and enquiries

concerning reproduction and rights should be addressed to the Department of Environment and Conservation.

This document has been commissioned/produced as part of the Buntine-Marchagee Natural Diversity

Recovery Catchment Recovery Plan 2007-2027.

Acknowledgements

Gavan Mullan and David Pongracz for supporting this project, report feedback and field work in mid

summer. Contributions of time, advice and software from the Mapping and Monitoring team at CSIRO

Mathematical and Information Science at the Leeuwin Centre Floreat. Report feedback from Lindsay

Bourke and Melissa Cundy. Other members of the Remote Sensing Unit Ricky van Dongen and Kathy

Murray for project and field work.

Government of Western Australia


http://www.dec.wa.gov.au/

The Buntine-Marchagee Natural Diversity Recovery Catchment

Remote Sensing Applications in the BMNDRC i

Contents

Introduction 7

Background 7

Study Area and Datasets 9

Satellite Imagery 10

Aerial Photography 11

Ground Data 11

Methodology 12

Vegetation Index Derivation 13

Field Data Acquisition 13

Projective Foliage Cover (PFC) and Imagery 14

Time series and Trend Images 15

Delivery in VegMachine 15

Results and Discussion 15

Vegetation Index 15

Field Data 16

Projective Foliage Cover (PFC) and Landsat TM Imagery 17

Time series and Trend Images 18

Validation 20

Limitations 21

Continue monitoring and refine expression 23

Application in VegMachine 24

Extended Analysis utilising GIS 28

Workshops and training 31

Conclusion 31

References 32

Appendix A 35

Appendix B 37


Remote Sensing Applications in the BMNDRC ii

Figures

Figure 1: The Buntine-Marchagee Natural Diversity Recovery Catchment boundary (blue

line), 2008 (orange) and 2009 (green) field sites shown with Landsat TM 2009 imagery

with spectral bands 3, 2, 1 in red, green, blue respectively. ................................................ 10

Figure 2: Conceptual plan of remote sensing analysis in the BMNDRC. .......................... 12

Figure 3: Aerial photo density scale template and application to a homogenous site

identified in the aerial photography. .................................................................................... 13

Figure 4: Testing linear relationship between vegetation indices and canopy density

determined from aerial photograph; 2005 Landsat TM band 3 and canopy cover density

from aerial photograph. ....................................................................................................... 16

Figure 5: 2009 Landsat TM Band 3 homogeneous site average pixel values versus field

PFC values computed using canopy closure observed with templates and aerial photo

canopy density. .................................................................................................................... 18

Figure 6: Trend image using all PFC time series image dates 1988 to 2009. Left to right:

1988 Landsat 5 TM image in with spectral bands 5,4,2 in red, green, blue respectively;

trend image using all dates between 1988 and 2009 red represents loss in cover, blue gain

in cover and green fluctuations in cover over the time period; 2009 Landsat 5 TM image in

with spectral bands 5,4,2 in red, green, blue respectively. .................................................. 19

Figure 7: Effect of different time periods on trend image display. Right – plot of PFC

values of area delineated with red line in left. Left clockwise: trend image 1988 to 2009;

trend image 2004 to 2009, red represents loss in cover, blue gain in cover and green

fluctuations in cover over the time period; 2006 aerial photograph; 2009 Landsat 5 TM

image in with spectral bands 5,4,2 in red, green, blue respectively. ................................... 20

Figure 8: 2008 field derived PFC values versus the image values of the developed 2009

PFC expression as applied to the 2008 Landsat TM image. ............................................... 21

Figure 9: Soil colour of field sites shown on 2009 Landsat TM Band 3 homogeneous site

average pixel values versus field PFC values computed using canopy closure observed

with templates and aerial photo canopy density. ................................................................. 22

Figure 10: Vegetation cover changes at corridor revegetation site. Top left: plot of time

series cover values at revegetation site, top right: 2009 Landsat 5 TM image in with

spectral bands 5,4,2 in red, green, blue respectively with green dot indicating location of

pixel used to produce the plot, bottom left: site photo of revegetation site captured 29

January 2009, bottom right: site photo of revegetation site captured 26 February 2010. ... 25

Figure 11: Tagasaste planting site vegetation cover history. Left to right: plot of PFC

vegetation cover values over time of pixels in area delineated in purple on satellite images;

1988 Landsat 5 TM image in with spectral bands 5,4,2 in red, green, blue respectively with

purple rectangle indicating location of pixels used to produce the plot; 2009 Landsat 5 TM

image in with spectral bands 5,4,2 in red, green, blue respectively with purple rectangle

indicating location of pixels used to produce the plot. ........................................................ 26

Figure 12: Remnant degradation. Top row LR: 1998 Aerial Photo with red rectangle

indicating location of pixels used to produce the plot; 2006 Aerial Photo; trend image 1988

to 2009; trend image 2004 to 2009, red represents loss in cover, blue gain in cover and

green fluctuations in cover over the time period. Bottom row L R: 1988 Landsat 5 TM

image in with spectral bands 5,4,2 in red, green, blue respectively with red rectangle


Remote Sensing Applications in the BMNDRC iii

indicating location of pixels used to produce the plot; 2009 Landsat 5 TM image in with

spectral bands 5,4,2 in red, green, blue respectively used to produce the plot; plot of PFC

vegetation cover values over time of pixels in area delineated by red rectangle on images.

............................................................................................................................................. 27

Figure 13: GIS analysis using vegetation density and trends near Jocks Well. Top left:

CSIRO remnant vegetation patch mapping (Huggett et al. 2004) on 2006 aerial

photograph; top right: 2009 PFC image classified into six classes; bottom left: 1988 to

2009 vegetation cover linear trend classified into five classes; bottom right: regions of

declining vegetation cover trends larger than 1000m2 annotated with vegetation type and

density on 2006 aerial photograph....................................................................................... 29


Remote Sensing Applications in the BMNDRC iv

Tables

Table 1: Dates of Satellite imagery used. Major scene date refers to the Landsat scene that

covers the majority of the BMNDRC area. ......................................................................... 11

Table 2: 2009 Canopy observations and derived PFC values. Revegetation sites (annotated

rev in site name) estimate of canopy density is observed in the field due to planting and

growth post the 2005 aerial photo image date. .................................................................... 17

Table 3: Datasets used in GIS analysis to examine vegetation cover trends in differing

vegetation types and vegetation cover densities. ................................................................. 28

Table 4: Identification of declining vegetation cover in CSIRO remnant vegetation patch

mapping (Huggett et al. 2004) near Jocks Well, red text indicates percentage of total

remnant declining is greater than 25. .................................................................................. 30


Remote Sensing Applications in the BMNDRC v

Glossary

Calibration A process of making image data values (pixel values)

comparable through time.

ETM+ Enhanced Thematic Mapper Plus, a sensor in the Landsat 7

satellite.

Geolink A link of two or more image windows in geographic

coordinate space.

GIS A system of hardware and software used for storage,

retrieval, mapping, and analysis of geographic data.

Homogeneous Site An area that has the same cover type and consistent spatial

arrangement.

Landsat Various satellites operated by U.S. government

organisations, used to gather data for images of the earth's

land surface and coastal regions. These satellites are

equipped with sensors that respond to earth-reflected

sunlight and infrared radiation.

Monitoring The process of repeatedly observing and measuring using a

consistent method at regular intervals.

NDVI Normalised Difference Vegetation Index, a mathematical

combination of spectral bands in imagery based on

normalised ratios, used to measure the amount of green

vegetation cover over soil.

Pixel The smallest single component of a digital image. Indicator

of spatial resolution eg/ 25m pixel.

Orthogonal rectification Also known as ortho-rectification, a process of making

corrections within a photograph so that the scale is uniform

throughout the resulting image.

Orthophoto mosaic A mosaic of aerial photographs that has been rectified such

that it is equivalent to a map of the same scale.


Remote Sensing Applications in the BMNDRC vi

Reflectance Value A measure of the light reflectance characteristics of a

surface.

Remote Sensing The science and art of obtaining information about an object,

area, or phenomenon through the analysis of data acquired

by a device that is not in contact with the object, area, or

phenomenon under investigation.

Spatial Resolution An indicator of how well a sensor can record spatial detail,

often referred to as pixel size.

Spectral Band An interval in the electromagnetic spectrum defined by two

wavelengths, frequencies, or wave numbers, for example the

Visible Spectrum has a range of wavelengths between 0.4µm

to 0.7µm.

Temporal Resolution How often a satellite records imagery of a particular area.

Time series A sequence of data gathered at spaced intervals of time.

Trend A generalisation of the direction of variation in a quantity

over time or space.

TM Themathic Mapper, a sensor in Landsat 4 & Landsat 5

Satellites.

Vegetation Index A mathematical combination of spectral bands in satellite

imagery, which is sensitive indicators of the presence and

condition of living vegetation.

VegMachine A software package and an extension program which enable

land managers to interactively view and interrogate many

dates (time series) of imagery, informing on management

actions and assisting reporting.

http://www.timbercon.com/electromagnetic-spectrum.shtml

http://www.timbercon.com/wave.shtml


Remote Sensing Applications in the BMNDRC 7

Remote sensing applications for monitoring perennial vegetation in

the Buntine-Marchagee Natural Diversity Recovery Catchment

Introduction

Remote sensing technology is a proven vegetation monitoring tool (Caccetta et al. 2000, Pelkey et al.

2000). The ability to repeatedly capture an area using the same instrument at an affordable cost targets

the use of imagery for monitoring. Combined with the application of consistent processing methods

makes imagery data suitable for examination over long time periods. Imagery data from the Landsat

satellites has been extensively used to observe vegetation changes at the landscape level (Kuhnell et al.

1998, Pickup et al. 1998, Hostert et al. 2003, Wallace et al. 2006).

In the Buntine-Marchagee Natural Diversity Recovery Catchment (BMNDRC) remote sensing

applications for vegetation monitoring have been implemented. Processed Landsat imagery and field

data are delivered via the software program VegMachineTM

(Karfs et al. 2004). VegMachine is the

name of both a software package and an extension program which has been successfully implemented

in rangeland areas of the Northern Territory and Queensland to deliver remote sensing technology. The

software enables land managers to interactively view and interrogate many dates (time series) of

imagery, informing on management actions and assisting reporting. Continued field observations and

feedback from users will assist the development and improve the delivery of vegetation monitoring

satellite data into the future.

Background

Satellite imagery data digitally records response (reflectance) values from the earth in sections of the

light spectrum referred to as bands. Variations in how the same object will reflect in different parts of

the light spectrum can be used to examine the qualities of the object. For example most vegetation in

the visible part of the spectrum has the strongest reflectance in the green section; hence we view

vegetation as green. However, in the near infrared part of the spectrum vegetation has a much larger

reflectance and this response is often used to examine the vigour of vegetation. The ability to discern

objects on the ground using satellite remotely sensed data depends on the spatial resolution (pixel size)



of the data. Landsat TM data has a spatial resolution of 25m, and thus objects much smaller than 25m

by 25m will be difficult to distinguish from the background.

Monitoring systems require repeatable quantitative measures through time (Wallace et al. 2004).

Archives of satellite imagery can provide a repeated series of data across the landscape, which provides

the temporal aspect required for monitoring, but consistent processing is required to make the different

dates of imagery data comparable. This processing consists of two tasks; rectification and calibration.

Rectification is the method by which satellite imagery is located to known ground positions. For

monitoring purposes image pixels from different dates of the same location need to overlap. This is

often achieved by using one particular image date which is rectified using ground data as the „base‟ and

rectifying all other image dates to this image. Calibration involves making the image data values (pixel

values) comparable through time. For example dry bright white sand that has not changed, should have

the same pixel values, but variations in sun angle and illumination among other factors may contribute

to different values being recorded. Once the imagery is calibrated the pixel values should be very

similar. This processing, if consistently carried out, enables measurement of real changes on the

ground.

Consistently rectified and calibrated Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced

Thematic Mapper Plus (ETM+) satellite imagery is available over the south west agricultural region of

Western Australia through the Land Monitor Project (Caccetta et al. 2000;

http://www.landmonitor.wa.gov.au). Image dates available range from 1988 to 2009 (Table 1). These

data have a pixel size of 25m and are provided with six spectral bands including; the visible bands blue,

green and red (bands 1, 2 and 3), a near infrared band (band 4) and two short wave infrared bands

(bands 5 and 7). Each band contains values of reflectance between 0 and 255.

In order to examine vegetation response many imagery band combinations, referred to as indices, have

been developed. A common index to examine vegetation vigor is the normalised difference vegetation

index (NDVI; Lillesand and Kiefer 1994). The vegetation index developed during the Land Monitor

Project adds together band 3 and band 5. This index can be used to examine vegetation cover

dynamics (Furby et al. 2009). In the BMNDRC a vegetation index has been determined to examine

vegetation cover changes in this particular environment. This developed vegetation index should

linearly represent differences in canopy density.

http://www.landmonitor.wa.gov.au/



Projective foliage cover (PFC) in McDonald et al (1990) is defined as „…percentage of the sample site

occupied by the vertical projection of foliage only.‟ Vegetation cover captured by Landsat TM data is

similarly portrayed by PFC, thus this measure of classifying cover can be applied to the imagery (Behn

et al. 2001). Field derived values of PFC can be calculated by combining ground observations of

canopy openness with canopy density measures derived from aerial photographs. Images portraying

PFC may be generated by developing a correlation between the satellite data and field derived PFC

values (Behn et al. 2001). When a sequence of image dates has been converted to PFC they may be

combined to create a time series.

Viewing changes in vegetation cover from 15 dates of imagery (Table 1) is difficult to assimilate.

Index trend images enable the geographic viewing of changes in vegetation cover by summarizing the

15 dates using linear and quadratic regression (Furby et al. 2009). Each pixel in the image area has 15

vegetation cover values, one for each available date. By determining the slope of a line of linear

regression through these values a general indication of whether the vegetation cover has increased,

decreased or remained stable is possible. A red, green and blue image display can be created where red

shows linear loss of cover, green displays fluctuating cover and blue shows linear gain in cover. The

resultant image explicitly identifies the location, extent and magnitude of changes in vegetation cover

(Figure 6; Figure 7).

Study Area and Datasets

The Buntine-Marchagee Natural Diversity Recovery Catchment (BMNDRC) is located in the north

eastern part of the agricultural district, 280km north east of Perth and contains 181,000 hectares (Figure

1; DEC 2007). It is located across two biogeographic regions, the Geraldton sandplain and the Avon-

wheatbelt. The BMNDRC is a sub catchment of the Moore River and the land use is mostly grain

crops and sheep with small patches of remnant vegetation containing 11% of pre-european vegetation.

It is the only Natural Diversity Recovery Catchment with a primary saline braided wetland channel

system. The climate is warm temperate to semi arid with winter dominated rainfall. The catchment

contains 27% of the aquatic invertebrates found in the wheatbelt and includes many threatened plant

taxa and priority fauna (DEC 2007).



Figure 1: The Buntine-Marchagee Natural Diversity Recovery Catchment boundary (blue line), 2008 (orange)

and 2009 (green) field sites shown with Landsat TM 2009 imagery with spectral bands 3, 2, 1 in red, green, blue

respectively.

Satellite Imagery

Rectified and calibrated Landsat 5 TM and Landsat 7 ETM+ data available from the Land Monitor

Project have been utilised (Caccetta et al. 2000; http://www.landmonitor.wa.gov.au). Images are

obtained in summer and dates range from 1988 to 2009 (Table 1), annual updates will be provided.

http://www.landmonitor.wa.gov.au/



Table 1: Dates of Satellite imagery used. Major scene date refers to the Landsat scene that covers the majority

of the BMNDRC area. Epoch Scene Date Sensor

Major Minor

1988 03/01/1988 11/02/1988 TM

1990 25/02/1990 14/12/1989 TM

1992 13/12/1991 22/02/1992 TM

1994 19/01/1994 26/01/1994 TM

1996 25/01/1996 01/02/1996 TM

1998 13/12/1997 26/03/1998 TM

2000 19/01/2000 13/02/2000 ETM+

2002 06/03/2002 09/02/2002 ETM+

2003 17/01/2003 05/02/2003 TM

2004 23/02/2004 19/03/2004 TM

2005 02/02/2005 NA TM

2006 10/04/2006 27/01/2006 TM

2007 07/01/2007 NA TM

2008 02/02/2008 26/01/2008 TM

2009 27/12/2008 NA TM

Aerial Photography

Aerial orthophoto mosaics of the BMNDRC area were captured in the December 2004 to April 2005

time period; and in January 2006. The orthophoto mosaics have been provided and orthogonally

rectified by Landgate (Western Australian Land Information Authority).

Ground Data

General location descriptions are recorded across each one hectare site including main vegetation

species and type, landscape type, vegetation height, slope and aspect, soil colour and shadow,

Appendix A contains full descriptions of these. In addition across site estimates of canopy, mid storey,

ground cover, litter and exposed soils are recorded adding up to 100 percent. In each one hectare site

three 5m quadrants are established. Within each quadrant the vegetation height is estimated, canopy

openness determined from templates and vertically photographed, GPS location, across quadrant

vegetation cover estimated and site photograph with direction recorded (Appendix A). On the 27th

and

28th

March 2008 field data was collected from 12 one hectare sites, with canopy openness observed at

eight sites (Figure 1). On the 28th

and 29th

January 2009 field data was collected from 15 one hectare

sites and on 28th

and 29th

July field data was collected from four one hectare sites, with canopy

openness observed at all 19 sites (Figure 1).



Methodology

Conceptual Plan

The conceptual plan displayed in Figure 2 shows how the remote sensing analysis contributes to

answering the key monitoring questions. The remote sensing methodology is detailed in the following

sections.

Figure 2: Conceptual plan of remote sensing analysis in the BMNDRC.

Domain What quantity do we need to monitor At what scales – spatial and temporal

What data do we have available

Perennial Native

Vegetation Cover

Sub Remnant

Annual

Landsat TM 25m

annual summer

satellite data

Remote

Sensing

Analysis

Vegetation Index

Derivation

Field Data Acquisition

Projective Foliage Cover and Imagery

Time series and Trend

Images

Delivery in VegMachine

Feedback to RS Unit

Refi

nin

g A

naly

sis

An

nu

al

Mo

nit

ori

ng

Extended

Analysis in GIS



Vegetation Index Derivation

Estimates of canopy cover from aerial photographs can aid in determining a vegetation index that

describes differences in vegetation cover. One hectare sites where the spatial distribution of vegetation

is uniform can be identified from aerial photographs and canopy cover values assigned using templates

(Figure 3). The measure assumes the canopy is opaque. This produces a set of locations with canopy

density values which may be compared with Landsat TM pixel values of a similar date. Exploration of

the linear relationship between various combinations of bands or indices with the assigned canopy

density will establish the most appropriate index for use in the BMNDRC. Fifty five one hectare

homogenous sites were selected across a range of vegetation types and densities. Each of these sites

was assigned a canopy density value and the pixel values for each Landsat TM band were extracted.

Figure 3: Aerial photo density scale template and application to a homogenous site identified in the aerial

photography.

Field Data Acquisition

The canopy density assigned to the homogenous sites using an aerial photograph do not take into

account the gaps or openness of the canopy. Satellite imagery captures the combined response of

canopy, undergrowth, bare ground and gaps in the one pixel, therefore to relate the imagery to canopy

cover the openness of the canopy must be taken into account. Canopy openness may be observed in

the field using a few methods. We have utilised two methods, templates from the Australian Soil and

Land Survey Field Handbook (McDonald et al. 1990) and vertical digital photographs of the canopy

using a standard digital camera and tripod. These methods produce a measure of canopy closure which

may be converted to canopy openness by subtracting from 100.



Vertical digital photographs have been used in recent times to derive canopy measures (Nobis and

Hunziker 2005, Crane and Shearer 2007, Macfarlane et al. 2007). To determine the canopy closure

from the vertical digital photographs the images were analysed using ER Mapper 7.1. In this program

image values defining the dark areas of leaves and twigs can be identified from the lighter sky of the

image, this technique is referred to as thresholding. The area of dark values can then be compared to

the total area of the image to produce a canopy closure measure. Where required the canopy

photographs have been cropped to remove non representative parts of the canopy foliage such as the

trunk.

Within a previously identified one hectare homogeneous site a general site description is recorded

including main vegetation species and type, landscape type, vegetation height, slope and aspect, soil

colour and shadow. At the three 5m quadrants within the site; canopy openness among other location

and vegetation characteristics are recorded (Appendix A).

Projective Foliage Cover (PFC) and Imagery

PFC is a measure of foliage cover that combines canopy density and openness to approximate the

vertical projection of foliage. Multiplying the canopy density percentage derived from the aerial

photographs and the field observed canopy openness percentage, as shown by equation (1) below,

results in a field measure of PFC.

(Canopy Cover Density % x Canopy Openness %) / 100 = PFCField (1)

Applying this process to the field data produces three field estimates of PFC for each one hectare site.

In order to compare these values with the Landsat image data the three PFC values are averaged for

each one hectare site.

To convert the Landsat image pixel values to a PFC value a relationship between the PFC determined

from field information and the Landsat data needs to be established. If a mathematical relationship can

be resolved between the Landsat vegetation index and field PFC this can be applied to the image pixels

to produce a PFC image. As the imagery is calibrated this relationship should be consistent throughout

the image sequence and may be applied to the other dates of imagery.



Time series and Trend Images

Interrogation and examination of the developed PFC images is enabled through creation of time series

and trend images. A time series image contains each date of imagery converted to PFC values and can

be used to extract the PFC values at specific locations for all image dates. Trend images summarize

several PFC image dates by calculating the linear and quadratic trends of each individual pixel over

time. Varying sets of dates can be used to examine short and long term effects. Trend images were

created using CSIRO Mathematical and Information Sciences software which utilises orthogonal

polynomials (Draper & Smith, 1981) to independently estimate the linear and quadratic elements.

Displaying the trends in a red, green and blue image using the following schema allows geographic

identification of areas of change and an indication of magnitude;

Red negative linear trend (slope), indicates loss of vegetation cover,

Green positive quadratic trend (curvature), indicates loss and recovery of vegetation cover,

Blue positive linear trend (slope), indicates gain in vegetation cover.

Delivery in VegMachine

VegMachineTM

software (Karfs et al. 2004) facilitates everyday use of the PFC time series and trend

image displays. The program uses a split screen displaying two geolinked images which are employed

to determine areas of interest and query the time series PFC values in a location. This query is

displayed as a plot of imagery date versus average PFC value. This allows the examination of the

history of vegetation cover over a site and can be used to assess the effect of impacts. With everyday

use by land managers any issues with particular vegetation types or imagery dates can be

communicated to the Remote Sensing Unit to further refine and develop the remote sensing analysis.

Results and Discussion

Vegetation Index

The vegetation index with the strongest linear relationship with canopy cover consists of Landsat TM

band 3 with a Pearson product moment correlation coefficient of 0.8508 (Figure 4). The spread of

density values observed is sufficient, but would be improved with observations from more sites that



have very dense and very sparse cover densities. These are difficult to obtain as very dense vegetation

is not typical within the catchment and very sparse cover levels rarely occur in homogenous patches

large enough to compare with the imagery. The spread of values around the line of best fit indicate that

assessment of vegetation cover from the aerial photograph does not entirely correlate with the satellite

imagery. Observation of the gaps in the canopy should improve the relationship.

y = -1.395x + 132.66

R2 = 0.7238

0

10

20

30

40

50

60

70

80

90

100

30 40 50 60 70 80 90 100

Landsat TM Band 3

Ae

ria

l P

ho

to D

en

sit

y

Band3

Linear (Band3)

Figure 4: Testing linear relationship between vegetation indices and canopy density determined from aerial

photograph; 2005 Landsat TM band 3 and canopy cover density from aerial photograph.

Field Data

Field data used to derive the PFC imagery relationship was captured in 2009 (Appendix B). Table 2

displays the observed canopy closure using two techniques and the computed field PFC values. The

variation in the observed canopy closure techniques may be due to several factors including photo

positions not being representative of the canopy; both techniques include some woody material in the

assessment but the amounts may vary; differences in vegetation height affects the photographs focal

length and the area of canopy captured. The techniques in photographing the canopy are in

development and may not be as consistent as an experienced interpreter using templates. These issues

have also been experienced in the mangrove environment (Human et al. 2009).



Table 2: 2009 Canopy observations and derived PFC values. Revegetation sites (annotated rev in site name)

estimate of canopy density is observed in the field due to planting and growth post the 2005 aerial photo image

date.

Identified from Aerial Photo Date

Captured

Aerial Photo % Estimate of Canopy

Density (2005 image date)

Observed at Three Points per Homogeneous Site and Averaged (%)

PFCField using Aerial Photo Estimate of Canopy Cover Density

Homogeneous Site

Canopy Closure from Template

Canopy Closure from Photo

PFCField (canopy from template)

PFCField (canopy from photo)

rem_019_40 29/01/2009 40 65.0 45.9 14.0 21.7

rem_033_85 28/01/2009 85 38.3 49.1 52.4 43.3

rem_035_45 28/01/2009 45 56.7 46.3 19.5 24.2

rem_043_70 29/01/2009 70 51.7 41.5 33.8 41.0

rem_045_70 29/01/2009 70 51.7 36.2 33.8 44.7

rem_101_70 28/07/2009 70 41.7 44.1 40.8 39.2

rem_102_35 28/07/2009 35 36.7 49.8 22.2 17.6

rem_106_40 29/07/2009 40 33.3 43.8 26.7 22.5

rem_108_50 29/07/2009 50 50.0 53.2 25.0 23.4

res_052_70 28/01/2009 70 45.0 36.9 38.5 44.2

res_054_75 28/01/2009 75 46.3 39.9 40.3 45.1

rev_060_30 29/01/2009 30 58.3 53.7 12.5 13.9

rev_061_27 29/01/2009 27 75.0 55.8 6.8 11.9

rev_062_36 28/01/2009 36 53.3 63.7 16.8 13.1

wet_007_30 28/01/2009 30 40.0 52.7 18.0 14.2

wet_024_45 29/01/2009 45 58.3 54.7 18.8 20.4

wet_026_40 29/01/2009 40 88.3 85.6 4.7 5.8

wet_040_35 29/01/2009 35 86.7 62.5 4.7 13.1

The initial field work completed in 2008 has been used for validation, see the following validation

section. Field data has also been collected in February 2010, this data will be used to further refine and

develop the image analysis once processed imagery becomes available.

Projective Foliage Cover (PFC) and Landsat TM Imagery

In developing the relationship between field observed PFC and the Landsat TM imagery many

variations of field derived PFC (Appendix B) were tested. The strongest linear relationship was

achieved between Landsat TM Vegetation Index Band 3 and field PFC computed using canopy closure

observed with templates and aerial photo canopy density (Figure 5), with a Pearson‟s product moment

correlation coefficient of 0.8805. The linear equation is then applied to the sequence of calibrated

imagery to produce a time series of PFC imagery. This expression could be improved by more

sampling at the dense and sparse ends of the vegetation cover spectrum and ensuring the most common

vegetation communities are represented adequately.



PFC Average Canopy Cover vs Band 3

y = -0.7585x + 73.448

R2 = 0.7752

0

10

20

30

40

50

60

30 40 50 60 70 80 90 100

Landsat TM Band 3

PF

C A

vera

ge C

an

op

y C

over

Figure 5: 2009 Landsat TM Band 3 homogeneous site average pixel values versus field PFC values computed

using canopy closure observed with templates and aerial photo canopy density.

Time series and Trend Images

A set of time series images representing derived PFC was created by applying the developed linear

regression equation (Figure 5) to Landsat TM band 3 of each available year of imagery. As the

imagery is calibrated to a common base year the pixel values are comparable through time, and thus the

same regression equation developed for one year can confidently be applied to other years in the

sequence. By creating a time series this one dataset can be used to investigate changes in vegetation

cover employing a variety of methods such as trend analysis and plotting.

Interactive plotting for users has been enabled in the VegMachine program (Figure 7). A single pixel

location or a group of pixels may be selected and a plot displaying the average PFC value of each

image date generated. Plotting, along with examination of the individual imagery dates, enables

identification of dates of impacts and periods of increases or decreases of vegetation cover.

Trend images that summarise changes of a selection of years in the time series using linear and

quadratic regression are created to geographically examine changes in cover over time. A red, green

and blue image display can be used to display the calculated trends by displaying values of linear loss



in cover in red, linear gain in blue and the positive quadratic displaying fluctuations in cover in green

(Figure 6). This method of summarising changes enables identification of areas of change and an

indication of magnitude, as brighter colours signify a more dramatic change in cover values. The trend

image displayed in Figure 6 shows a small narrow fire impact starting in the middle of the remnant and

moving to the north west corner. Without the use of trend imagery to encapsulate the changes in

vegetation cover each image date would need to be examined in turn before the impact would be

discovered.

Figure 6: Trend image using all PFC time series image dates 1988 to 2009. Left to right: 1988 Landsat 5 TM

image in with spectral bands 5,4,2 in red, green, blue respectively; trend image using all dates between 1988 and

2009 red represents loss in cover, blue gain in cover and green fluctuations in cover over the time period; 2009

Landsat 5 TM image in with spectral bands 5,4,2 in red, green, blue respectively.

Trend image displays were also created for time periods 1988 to 2000, 2000 to 2009 and 2004 to 2009.

The first and last years in the image sequences can dramatically change the observed trend, therefore

setting time periods to coincide with events or management actions can enhance understanding of the

changes observed. Figure 7 displays this effect with the recent trend image display of dates 2004 to

2009 showing more stable cover and less variation than the 1988 to 2009 trend image. The plot (Figure

7) illustrates the PFC values for each image date on which the stable values of the last five years can be

compared with the variations across the whole time period.

1988 1988-2009 Trend 2009



Figure 7: Effect of different time periods on trend image display. Right – plot of PFC values of area delineated

with red line in left. Left clockwise: trend image 1988 to 2009; trend image 2004 to 2009, red represents loss in

cover, blue gain in cover and green fluctuations in cover over the time period; 2006 aerial photograph; 2009

Landsat 5 TM image in with spectral bands 5,4,2 in red, green, blue respectively.

Validation

Field data on canopy closure was observed at eight one hectare sites on the 27th

and 28th

March 2008.

These field measures can be combined with 2005 aerial photo estimations of canopy cover to produce a

set field derived PFC measurements. The linear relationship between the 2008 field derived PFC

values and the 2008 PFC image pixel values (Figure 8) can be examined to determine the robustness of

the expression derived from the 2009 field and image data. The linear relationship between the 2008

PFC image and 2008 field data achieves a Pearson‟s product moment correlation coefficient of 0.8759.

Although the eight validation sites cover a range of PFC values, more sites especially at the denser end

would better examine the relationship. The clustering of values around PFC image value 15 indicates

1988-2009 Trend 2004-2009 Trend

2009 Landsat 5 TM 2006 Aerial Photo



investigation is required into the range of PFC values observed at this value. The 2010 field data will

enable further validation to be carried out.

MJ_FPC vs 2008 FPC09 Image

y = 1.1387x + 9.1514

R2 = 0.7672

0

10

20

30

40

50

60

0 10 20 30 40 50

PFC 2009 Expression Applied to 2008 Landsat TM Image

20

08

Fie

ld D

eri

ve

d P

FC

Figure 8: 2008 field derived PFC values versus the image values of the developed 2009 PFC expression as

applied to the 2008 Landsat TM image.

Limitations

Limitations in field data capture, satellite imagery specifications and image analysis influence the

results. Time series PFC dataset and trend display images provide the ability to geographically identify

where a change in vegetation has occurred, the time period the change occurs in and the magnitude of

the change but not the cause of the change. Land managers need to apply their knowledge of processes

in the catchment and field visits to determine the cause of changes.

The pixel size of 25m2 limits the ability to monitor small areal changes in vegetation cover. For

example rehabilitation plantings may not be visible on Landsat TM imagery for up to four years as the

vegetation cover over a 25m2 area may take this long to become significant. Also rehabilitation

plantings less than 50m wide are difficult to monitor as pixels are likely to be mixed with adjacent

paddock areas and hence not provide a clear observation of the rehabilitation planting growth. Higher

spatial resolution imagery can improve the scale changes are observed but unfortunately do not yet



have the historical archive for analysis. The development of an archive is unlikely to occur unless there

is an effort towards annual planned capture which would involve a considerable cost.

Monitoring very sparse vegetation can be difficult as the majority of the pixel value is made up of the

background soil and litter. Changes in the background such as soil moisture may be shown as false

change in vegetation cover if not taken into account. Soil background colour can also affect the

estimation of vegetation cover (Pickup et al. 1993, Peter et al. 2003). There are many different soil

colours across the BMNDRC (Figure 9), and there appears to be some relationship between colour and

density of vegetation cover, with whiter soils with less cover moving to red, orange and brown soils

with greater cover. More sites sampled for soil properties are required to determine whether this is due

to difficult to farm sites on darker gravelly soils being left as remnants or if vegetation cover and types

do vary with soil colour and properties. The imagery may be stratified prior to analysis by soil

colour/properties if it impacts the results, however this does require more field data.

PFC Average Canopy Cover vs Band 3 with Soil Colour

Light orange/red

Light brown

Light brown

Light orange-brown

Light orange-brown

Dark brown-red

Red-orangeOrange

Brown with yellow

Light white-yellow

Brown red-orangeYellow, brown/orange

YellowOrange-brown

Orange-red

Orange-red

YellowWhite-grey w brown

0

10

20

30

40

50

60

30 40 50 60 70 80 90 100

Landsat TM Band 3

PF

C A

ve

rag

e C

an

op

y C

ov

er

Light orange/red

Light brown

Orange-brown

Orange-red

Light orange-brown

Dark brown-red

Red-orange

Orange

Brown with yellow

Light white-yellow

Brown red-orange

Yellow, brown/orange

Yellow

White-grey w brown

Figure 9: Soil colour of field sites shown on 2009 Landsat TM Band 3 homogeneous site average pixel values

versus field PFC values computed using canopy closure observed with templates and aerial photo canopy

density.

During the analysis the estimates across the homogeneous site of canopy cover used to produce the

field projective foliage cover (PFC) measure was observed from aerial photographs captured in the



period December 2004 to April 2005. This is at least three years different from the first date of field

capture and can not be used for field sites located in rehabilitation plantings (field site estimates are

used instead). Ideally the field observations and the aerial photograph estimates are captured at

approximately the same time. This is often an issue when applying this method. Depending on the

application sometimes it is only possible to develop the relationship between the aerial photo observed

canopy and the satellite image captured close to the same date. The catchment was aerially captured by

Landgate, at the beginning of 2010, however provision of processed orthophoto mosaics will probably

not be until mid 2011. Once the 2010 aerial photos are available these should be used with 2010 field

data to refine the PFC expression.

The use of Landsat TM band 3 as the spectral index is based on variation in vegetation cover regardless

of vegetation type or community. The changes in vegetation density of some vegetation types may be

better represented by a different spectral index. Use of the developed index by land managers may

expose some vegetation types where observed changes in vegetation cover may be adequately

represented by the developed PFC expression. Once these vegetation types are identified further

analysis can lead to a different spectral index being developed. For example on the lake floor of Lake

Toolibin, near Narrogin in the wheatbelt, the variation in vegetation cover of the Casuarina obesa and

Melaleuca strobophylla vegetation types present are better represented by the spectral index Landsat

TM Band 5 (Zdunic, 2010).

Continue monitoring and refine expression

Continuous monitoring requires the provision of summer (dry season) satellite data and field data on an

annual basis. Currently the Landsat satellite imagery is provided through the Land Monitor program of

which DEC is a member. Should Landsat 5 TM imagery not be available due to satellite failure

contingency plans using Landsat 7 ETM+ and other satellites have been investigated (Furby and Wu

2006). Consistent processing through the Land Monitor program should smooth any transitional

changes to a different satellite. However, annual capture of field information will greatly aid in adding

the new imagery data into the historical archive.

Annual capture of field data will enable further development of the relationship between satellite data

and vegetation cover and allow field data capture methods to be expanded and improved. 2010 field

data was captured in February and can be analysed with the 2010 Landsat data when it is provided in



September 2010. Additionally the eventual provision of 2010 aerial photos by Landgate will present

the opportunity to create a PFC expression using field and image data of completely consistent dates.

Feedback from VegMachine users is essential in continuing to improve the imagery products provided

and extend the applications of the analysis. Responses of how the PFC expression performs across the

catchment, will lead to further refinement of the imagery analysis. This could include stratifying the

landscape by soils or vegetation type. Knowledge of management requirements can lead to the

development of GIS analyses using other variables such as climate.

Application in VegMachine

Image enhancements of every date of satellite imagery, the derived time series and trend display

images are enabled for operation by end users in the VegMachine program. This program allows

different dates of imagery and trend displays to be viewed in two geolinked windows, and the creation

of plots of values from the time series by selecting areas using vectors. In this way changes in

vegetation cover can be investigated by land managers in a quick, easy to use package.

An application of the time series plotting function in VegMachine is to examine the performance of

revegetation sites. Figure 10 displays the time series of a corridor revegetation site planted in July

2004. As the satellite imagery is captured at the beginning of each year, in 2005 no change in

vegetation cover is registered, however in 2006 there is a large increase in cover which is maintained in

2007. This is consistent with significant growth in rehabilitation sites often observed around 18 months

or the second summer after planting (D. Pongracz pers. comms.). The presence of second year weeds

could have also influenced the increased vegetation cover observed in the 2006 imagery. In 2008 there

is a loss of cover, followed by a smaller loss of cover in 2009. There are a few possible explanations

for this loss in cover including; lack of rainfall, death of some of the plantings, weaker seedlings being

outcompeted by stronger seedlings, loss of short lived native „increaser‟ species like Ptilotus spp.

(mulla mulla), subtle variations in soil type and changes in tree shape, leaf shape and vigour of the

young plantings as they mature. Of these explanations the loss observed in 2009 is unlikely to be due

to rainfall as there was significant winter rainfall in 2008. As the tree plantings start to mature they

achieve a more upright, less bushy form and this change in vegetation structure could affect the cover

observed by the satellite (Figure 10).



Figure 10: Vegetation cover changes at corridor revegetation site. Top left: plot of time series cover values at

revegetation site, top right: 2009 Landsat 5 TM image in with spectral bands 5,4,2 in red, green, blue

respectively with green dot indicating location of pixel used to produce the plot, bottom left: site photo of

revegetation site captured 29 January 2009, bottom right: site photo of revegetation site captured 26 February

2010.

The planting by farmers of tagasaste (Chamaecytisus proliferus) as a perennial fodder shrub can affect

water table levels in the catchment (Seymour 2001). Knowledge of the planting dates and the length of

the initial rapid growth period of tagasaste is valuable information for water management. The time

series plot in Figure 11 illustrates the probable planting of an area of tagasaste between 1988 and 1990,

the rapid growth period between 1990 and 2000 and the stabilisation in vegetation cover from 2002

onwards. Consultation with the previous landholder indicates the planting of the tagasaste was in 1988

(M. Cundy pers. comms). The higher PFC values in the tagasaste plantation (Figure 11) compared

with the rehabilitation planting (Figure 10) illustrate a difference in density of planting and species.

January 2009 February 2010

0

10

20

30

40

50

60

70

80

90

100

1988 1990 1992 1994 1996 1998 2000 2002 2003 2004 2005 2006 2007 2008 2009

Year

PF

C

Revegetation Site July 2004

Landsat TM 2009



There are no records maintained on this type of perennial shrub establishment, therefore the data

delivered via VegMachine fills a knowledge gap.

Figure 11: Tagasaste planting site vegetation cover history. Left to right: plot of PFC vegetation cover values

over time of pixels in area delineated in purple on satellite images; 1988 Landsat 5 TM image in with spectral

bands 5,4,2 in red, green, blue respectively with purple rectangle indicating location of pixels used to produce

the plot; 2009 Landsat 5 TM image in with spectral bands 5,4,2 in red, green, blue respectively with purple

rectangle indicating location of pixels used to produce the plot.

Other changes in vegetation cover may be more subtle and occur over a period of years. Remnant

degradation by sheep grazing can cause vegetation cover changes of varying amounts. The losses of

vegetation cover at different periods from 2002 onwards displayed in Figure 12 may be due to sheep

grazing (G. Mullan pers. comms.). The 1998 aerial photo illustrates the greater cover in the northern

part of the remnant as compared to the 2006 aerial photo. The bright red colour trend image display

using all dates 1988 to 2009 shows there has been a dramatic decrease in vegetation cover. The more

recent 2004 to 2009 trend display shows continuing decline however the initial cover levels in this time

period are much lower and so the red colours are not as bright (Figure 12). Comparison of satellite

image dates at the beginning and end of the sequence confirm the changes in vegetation cover.

0

10

20

30

40

50

60

70

80

90

100

1988 1990 1992 1994 1996 1998 2000 2002 2003 2004 2005 2006 2007 2008 2009

Year

PF

C

417735.97E 6683577.5N

1988 Landsat TM 2009 Landsat TM



Figure 12: Remnant degradation. Top row LR: 1998 Aerial Photo with red rectangle indicating location of

pixels used to produce the plot; 2006 Aerial Photo; trend image 1988 to 2009; trend image 2004 to 2009, red

represents loss in cover, blue gain in cover and green fluctuations in cover over the time period. Bottom row

L R: 1988 Landsat 5 TM image in with spectral bands 5,4,2 in red, green, blue respectively with red rectangle

indicating location of pixels used to produce the plot; 2009 Landsat 5 TM image in with spectral bands 5,4,2 in

red, green, blue respectively used to produce the plot; plot of PFC vegetation cover values over time of pixels in

area delineated by red rectangle on images.

Other applications the VegMachine program could be used for in the BMNDRC are to monitor

rehabilitation plantings and adjacent existing remnants, the effects of drainage works such as contour

banks and culverts on perennial vegetation and the monitoring of fenced versus unfenced grazed

remnants. VegMachine provides the ability to examine vegetation cover responses to extreme events

such as fire and flooding. Examination of the vegetation cover prior to an event provides knowledge of

the previous state and investigating the vegetation cover response post an event provides information

on recovery periods. In the case of a flood event comparison of recovery periods in different locations,

soil or vegetation types could provide insights into where areas are under stress due to elevated and

hyper saline water tables or are waterlogged and now have high surface salt levels.

0

5

10

15

20

25

30

35

40

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Date

PF

C

415098.59E 6671807N



Extended Analysis utilising GIS

The remotely sensed data products derived in this analysis can be integrated into GIS applications to

conduct integrated investigations. For example layers of information containing rehabilitation planting

areas can be intersected with the trend information to provide a synoptic view of increases in vegetation

cover since planting. The vegetation cover density images for each year could be used to divide the

landscape into categories of vegetation cover from sparse to dense and these categories could be used

to examine relationships with elevation, changes in cover, rainfall, fencing, density of tracks or a

myriad of other datasets. Proximity analysis could examine whether there is a relationship with

changes in vegetation cover and distance from wetlands or channels. Another use of the vegetation

cover density images is as input into sampling strategies to ensure the most appropriate vegetation

densities per vegetation type are sampled.

An analysis to investigate vegetation cover changes in different densities of vegetation cover per

vegetation type was conducted with ArcGISTM

9.2 ArcMap product. The datasets utilised are 2009

PFC vegetation cover image classified into six classes, CSIRO remnant vegetation patch mapping

(Huggett et al. 2004) and the 1988 to 2009 vegetation cover linear trend classified into five classes

(Furby et al. 2009) (Table 3). Subsets of these datasets near Jocks Well are displayed in Figure 13.

Table 3: Datasets used in GIS analysis to examine vegetation cover trends in differing vegetation types and

vegetation cover densities.

Dataset Data Type Classification

2009 Projected Foliage Cover (PFC) image

Raster

0 < PFC < 10 – Sparse vegetation cover 10 ≤ PFC < 20 – Medium/sparse vegetation cover 20 ≤ PFC < 30 – Medium vegetation cover 30 ≤ PFC < 40 – Medium/dense vegetation cover 40 ≤ PFC < 50 – Dense vegetation cover 50 ≤ PFC < 60 – Very dense vegetation cover

1988 to 2009 linear trend (scaled 0-255)

Raster

0 – Not processed, or masked as never perennial vegetation 0 ≤ Linear trend < 90 – Large decrease in vegetation density 90 ≤ Linear trend < 110 – Decrease in vegetation density 110 ≤ Linear trend < 145 – Stable vegetation density 145 ≤ Linear trend < 190 – Increase in vegetation density 190 ≤ Linear trend < 256 – Large increase in vegetation density

CSIRO vegetation patch mapping

Vector Twenty four terrestrial native vegetation associations and their relationships were identified by Huggett et al. 2004

Classifying the images enables use of intersections, hence when all three datasets are intersected the

resultant dataset contains regions attributed with vegetation type, vegetation density and linear trend.



Examination of this dataset can provide information on trends in vegetation cover per vegetation type

and the densities most affected by losses or gains in cover. By selecting regions that have a declining

trend in vegetation cover and an area greater than 1000m2, the most affected vegetation types and

densities can be identified. Figure 13 displays the result of this selection, at a remnant near Jocks Well.

In this remnant almost all of the vegetation types have experienced some decline in vegetation cover

over the last twenty years, but the declines are predominantly in the more sparse vegetation densities.

Figure 13: GIS analysis using vegetation density and trends near Jocks Well. Top left: CSIRO remnant

vegetation patch mapping (Huggett et al. 2004) on 2006 aerial photograph; top right: 2009 PFC image classified

into six classes; bottom left: 1988 to 2009 vegetation cover linear trend classified into five classes; bottom right:

regions of declining vegetation cover trends larger than 1000m2 annotated with vegetation type and density on

2006 aerial photograph.



The analysis presented in Figure 13 has been produced for the whole the BMNDRC where the CSIRO

remnant vegetation patch mapping (Huggett et al. 2004) exists. Some vegetated areas such as samphire

present in the channels are not included in this mapping. Another way of presenting the GIS analysis is

in tabular form (Table 4). By calculating the area of each region the percentage change of vegetation

cover per vegetation patch can be collated and converted to a percentage. Identification of areas

greater than 25 percent of the total patch area show the medium/sparse category of the 2009 PFC image

is the most affected by vegetation cover decline and the vegetation associations most affected are

sedgeland and mixed shrublands.

Table 4: Identification of declining vegetation cover in CSIRO remnant vegetation patch mapping (Huggett et al.

2004) near Jocks Well, red text indicates percentage of total remnant declining is greater than 25.

% Declining Vegetation Cover of Total

Vegetation within Patch

Patch Number

Vegetation Association Sparse Medium/ Sparse

Medium Medium/ Dense

Dense

BM478/1 Mixed shrublands (sandplain) 4.89 26.36 6.34 0.34 0.00

BM478/10 River Red Gum woodland 0.00 26.41 3.84 0.00 0.00

BM478/11 Sandplain Cypress shrublands 3.98 15.28 12.37 0.95 0.00

BM478/12 Tamma/Wodjil/Melaleuca shrublands 1.13 0.77 0.00 0.00 0.00


BM478/14 Banksia/Woody Pear shrublands 0.00 2.26 6.43 2.29 0.00


BM478/2 Sedgeland 0.00 1.03 0.00 0.00 0.00

BM478/20 Sedgeland 18.46 54.57 7.86 0.85 0.00

BM478/26 Sedgeland 0.61 5.09 0.04 0.00 0.00

BM478/27 Sedgeland 32.77 25.68 7.13 0.36 0.00



















BM478/9 Grevillea/Jam/Dodonaea/Eremophila shrub 5.26 6.96 6.53 0.00 0.00



Workshops and training

To facilitate integration of the satellite imagery analysis via VegMachine two workshops have been

held at the Geraldton office. The first was conducted on the 31st October 2008 and concentrated on the

use of satellite imagery and ground data to produce a Projective Foliage Cover time series in the

BMNDRC. Examples were presented of how similar datasets are used in DEC and other organisations;

and the use of the data products in VegMachine, ArcGIS and reports was explained. A second

workshop carried out on 28th

April 2010 focused on the use of time series satellite data in the Midwest

region at a strategic level with examples in Shark Bay and the BMNDRC. With identification of

specific projects and project officers‟ future one on one training will aid in integrating VegMachine as

a regularly used tool for examining vegetation history and monitoring changes. Continued

communication between the users and the Remote Sensing Unit will enable further refinement of

requirements, applications, analyses and data products.

Conclusion

The application of consistently provided satellite imagery, annual field data capture and reliable

processing has filled a need to monitor, and examine the history of all the vegetation remnants in the

BMNDRC. Continuous improvement to the analysis, tools and products provided is achieved by

feedback from the land managers and sustained research by the Remote Sensing Unit. Ongoing field

work is vital to maintain the monitoring program, as well as support for the use of satellite imagery in

this domain. Future developments in software, techniques and available imagery need to be identified

early and assessed for appropriateness for inclusion in the analysis. For example higher spatial

resolution digital imagery is available, but would need to be pre-ordered each year to ensure capture.

This involves a considerably higher cost for purchase and processing than the current image supply and

any benefits would need to balance this cost.



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Appendix A

Vegetation Canopy Cover Estimation Methods for filling out the Field Observation form Aim: To calibrate the imagery with ground measurements Three point sites will be captured within the pre-selected homogenous sites. Homogenous sites are delineated by a polygon boundary with location extents. Record keeping: Site Number Date ________ Recorded by___________ General Site Description Use the GPS waypoint to guide you into the centre of the homogeneous site, observe the landscape as you walk through the area, and record this in the General site Description and Across site estimates. (If there is more than one GPS way point go to all and use these points as your plot sites). Site Location Description: Across site estimates: All fields should add to 100% Canopy cover x % Mid storey cover x % Ground cover x % Litter x % Exposed Soil x % Total 100% Soil colour: in absence of Mansell colour chart, do your best as describing the colour. Shadow (% on ground): this may be difficult if it is early morning of midday, so try to imagine the shadow cast at 10.30am. Site Measurements Within each homogenous area, three representative 5 by 5m plots will be selected randomly within the mangrove. In the absence of 5m poles cross the 4m poles in the centre and imagine adding ½ a meter on the end of the poles to make a 5 by 5m plot area. Each plot (5m by 5m area) is used to estimate canopy cover, site vegetation cover, vegetation height and take photos of canopy and site.

A general site description of each homogenous site should be recorded (location eg: slope (%), aspect, soil type, roads, distance from track, nearby major features eg/wetland, main vegetation type, condition of vegetation or age of vegetation, landscape type, vegetation height). General tree height – use the poles marked every meter to estimate a range (to the nearest half metre).



Site 1 - Waypoint ID Record from GPS

Take GPS Waypoint from the centre of the plot (cross of poles).See figure 1.

Latitude/Northing

Of Waypoint

Record from GPS GPS Quality - PDOP At waypoint

Record from GPS

Longitude/Easting Of Waypoint

Record from GPS Vegetation Height

Use the poles to estimate

Canopy Cover % (Just live canopy cover)

Look directly up through the crown and use Crown Types Keys 1 or 2

Canopy Photo ID Turn camera skyward so that it is as level (use a tripod with level if can) and place under canopy. Use the timer to take photo. Try to take a photo that is representative of the canopy, See Figure 2.

Record from camera filename

Site Vegetation Cover % (all live vegetation cover including understorey and ground cover)

Use Crown Types Keys 1 or 2 and imagine a birds eye view of the site

Photo ID & Direction Take a few steps back and take a photo of the whole plot if possible. Use a compass for direction (not GPS)

Record from camera filename and compass direction

If there is the opportunity: Random Validation: Select areas outside the pre-selected homogenous sites. At each plot the site description (species composition), GPS location, shadow percentage (%), soil colour, canopy cover (%) and photos will be recorded, for use as validation for post processing and future mangroves monitoring work. Preferably these sites should be located in homogeneous areas. Figure 1: Example of plot photo and GPS waypoint position. Figure 2: Example of a canopy photo.



Appendix B

2009 Field Data and derived PFC values

Identified from Aerial Photo

Observed at Three Points per Homogeneous Site and Averaged

(%)

Estimated % across each Homogeneous Site (Total 100% excluding shadow)

Location

(GDA94 MGA50) PFC using Aerial Photo Estimate

PFC using Site Vegetation Estimate

PFC using Site Canopy Estimate

Homogeneous Site

Canopy Closure

from Template

Canopy Closure

from Photo

Site Vegetation

Cover

Canopy Cover

Estimate

Mid Storey

Estimate

Ground Cover

Estimate

Litter Estimate

Exposed Soil

Estimate

Shadow Estimate

Soil Colour Easting Northing

PFC (canopy

from template)

PFC (canopy

from photo)

PFC (canopy

from template)

PFC (canopy

from photo)

PFC (canopy

from template)

PFC (canopy

from photo)

rem_019_40 65.0 45.9 41.7 10 40 10 10 30 5 Light orange/red 426280 6662562 14.0 21.7 14.6 22.6 17.5 27.1

rem_033_85 38.3 49.1 80.0 85 0 0 10 5 25 Light brown 437835 6684371 52.4 43.3 49.3 40.7 52.4 43.3

rem_035_45 56.7 46.3 60.0 20 35 5 15 25 15 Orange-brown 457382 6683011 19.5 24.2 26.0 32.2 23.8 29.5

rem_043_70 51.7 41.5 50.0 50 10 0 15 25 10 Orange-red 447395 6657407 33.8 41.0 24.2 29.3 29.0 35.1

rem_045_70 51.7 36.2 70.0 15 45 0 5 35 10 Light orange-brown 466377 6658744 33.8 44.7 33.8 44.7 29.0 38.3

rem_101_70 41.7 44.1 53.3 50 0 0 10 40 40 Orange-red 425568 6670773 40.8 39.2 31.1 29.8 29.2 28.0

rem_102_35 36.7 49.8 45.0 50 25 0 5 20 30 Dark brown-red 425672 6670037 22.2 17.6 28.5 22.6 47.5 37.6

rem_106_40 33.3 43.8 35.0 35 0 0 5 60 15 Red-orange 425144 6674857 26.7 22.5 23.3 19.7 23.3 19.7

rem_108_50 50.0 53.2 45.0 40 10 0 5 45 5 Orange 425837 6674679 25.0 23.4 22.5 21.1 25.0 23.4

res_052_70 45.0 36.9 78.3 80 0 0 20 0 30 Brown with yellow 466133 6681121 38.5 44.2 43.1 49.4 44.0 50.5

res_054_75 46.3 39.9 68.8 20 40 0 10 30 10 Light orange-brown 466020 6678736 40.3 45.1 37.0 41.4 32.3 36.1

rev_060_30 58.3 53.7 33.3 10 30 0 10 50 5 Light white-yellow 430084 6655338 12.5 13.9 13.9 15.4 16.7 18.5

rev_061_27 75.0 55.8 25.0 0 25 10 35 30 5 Light brown 462167 6671268 6.8 11.9 6.3 11.0 6.3 11.0

rev_062_36 53.3 63.7 16.7 0 0 20 40 40 0 Brown red-orange 425788 6679147 16.8 13.1 7.8 6.0 9.3 7.3

wet_007_30 40.0 52.7 43.3 15 15 20 15 35 5 Yellow, brown/orange 419811 6681205 18.0 14.2 26.0 20.5 18.0 14.2

wet_024_45 58.3 54.7 50.0 50 10 5 5 30 10 Yellow 434461 6658398 18.8 20.4 20.8 22.6 25.0 27.2

wet_026_40 88.3 85.6 48.3 10 0 40 10 40 5 Yellow 437485 6659259 4.7 5.8 5.6 7.0 1.2 1.4

wet_040_35 86.7 62.5 43.3 0 0 40 20 40 0 White-grey with brown 455256 6662984 4.7 13.1 5.8 16.3 5.3 15.0

REMOTE SENSING APPLICATIONS FOR MONITORING ......The Buntine-Marchagee Natural Diversity Recovery...

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