Developing national protocols to map ground cover...

Developing national protocols to map ground cover management practices in cropping and grazing systems

Report on an Australian Collaborative Land Use and Management Program workshop, 12–13 May 2009, Brisbane

Jane B Stewart and Jasmine E Rickards

March 2010

ISBN 978-1-921192-51-7

Stewart, JB and Rickards, JE 2010, Developing national protocols to map ground cover management practices in cropping and grazing systems, Report on an Australian Collaborative Land Use and Management Program workshop, 12–13 May 2009, Brisbane, Bureau of Rural Sciences, Canberra.

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Acknowledgements

The authors would like to thank all participants who presented and/or attended the workshop and contributed to the discussions. A special thank-you goes to Dan Tindall and Skye Byer (from Queensland’s Department of Environment and Resource Management) who organised the presenters and workshop venue and made the workshop run so smoothly.

Figures presented in this report are from the presentations given at the workshop or from the report by Schmidt et al. (2009). The presenters name is acknowledged.

Tables in this report are summaries of information from the workshop presentations or the group discussions.

Developing national protocols to map ground cover management practices 2

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Contents

Executive summary............................................................................................................ 4 Acronyms ............................................................................................................................ 5 Introduction......................................................................................................................... 6

Workshop purpose........................................................................................................... 6

Linkages to the national land product data set................................................................. 7

On-ground impact .......................................................................................................... 10

Monitoring ground cover management practices using remote sensing ................... 12 Lessons from 30 years of remote sensing science........................................................ 12

Fractional cover in context ............................................................................................. 12

Comparing ground cover products................................................................................. 15

Discussion outcomes ..................................................................................................... 15

Reference sites to monitor ground cover and related management practices .......... 19 Roadside surveys .......................................................................................................... 19

Demonstration of SLATS method .................................................................................. 19

Discussion outcomes ..................................................................................................... 20

Workshop recommendations .......................................................................................... 23 Other considerations...................................................................................................... 24

Implementing the recommendations .............................................................................. 26 Appendix 1 – Workshop presentations .......................................................................... 27 Appendix 2 – Workshop participants ............................................................................. 28 Appendix 3 – SLATS generic site data form .................................................................. 29 References ........................................................................................................................ 35 Glossary ............................................................................................................................ 36


Executive summary

Ground cover is plant material (or lack thereof) covering the land surface. It is generally expressed in terms of biomass or proportion of bare ground and measured as the percentage of plant material covering the ground, including crops, stubble, pasture plants and their residues, leaf litter, bark and twigs.

Ground cover provides the protective layer of living and decaying plant material on the soil surface. Management practices that reduce ground cover degrade the soil and water assets that support agricultural production and biodiversity. Programs that achieve adoption of practices that maintain or improve ground cover can be expected to provide a high return on investments.

Remote sensing offers one of a few ways to measure ground cover over large spatial extents. Ground cover management practices with the potential for detection and monitoring using remote sensing include: • timing and length of cultivated fallow • crop residue management (removed or retained) • tillage practices • grazing pressure • perennial pasture content • conversion of continuously low productivity areas to perennial vegetation

The Australian Collaborative Land Use and Management Program (ACLUMP) has been tasked with developing nationally consistent land management practices mapping based on characteristic patterns of ground cover maintenance in agricultural systems. A workshop was held in Brisbane (12–13 May 2009) to recommend feasible and appropriate remote sensing products that would enable ongoing national monitoring and analysis of change in ground cover management under agricultural land uses.

To implement such a national program, the workshop recommended as priorities:

1. on-ground measurements to calibrate and validate remote sensing products 2. measurement of fractional ground cover in the field using the Queensland Statewide Landcover

and Trees Study’s (SLATS) modified discrete point sampling method (with differing approaches for pastoral environments as for intensive agricultural environments)

3. a fractional cover remotely sensed product to detect and monitor land management practices impacting ground cover (existing methods need to be assessed for their accuracy to estimate fractional cover, particularly within cropping areas)

4. implementing the Queensland Department of Environment and Resource Management’s (DERM) Ground Cover Index (GCI) nationally for monitoring ground cover levels.

Success of the proposed national program will depend on:

• understanding the limitation/s of methods/approaches and the imagery source (spatial and temporal resolution) – scientific rigour and consistency

• collaboration and coordination with other initiatives such the Terrestrial Ecosystem Research Network (TERN), the Australian Collaborative Rangeland Information System (ACRIS) and erosion monitoring (roadside surveys) for remote sensing products (i.e. land cover and fractional cover) and to extend the network of reference sites

• engaging with state and territory agencies that collect and maintain land use data and undertake ongoing monitoring programs

• engaging with land managers for data collection on land management practices • access to ancillary data (in particular land cover, land use and climatic data) to put interpretation

of fractional cover in context.

ACLUMP will now seek funding to implement the workshop recommendations.


Acronyms

AATSR Advanced Along Track Scanning Radiometer

ACLUMP Australian Collaborative Land Use and Management Program

ACRIS Australian Collaborative Rangeland Information System

AVHRR Advanced Very High Resolution Radiometer

BGI Bare Ground Index

BRDF Bidirectional Reflectance Distribution Function

BRS Bureau of Rural Sciences (Australian Government)

CAI Cellulose Absorption Index

DCC Department of Climate Change (Australian Government)

DECCW Department of Environment, Climate Change and Water (New South Wales)

DERM Department of Environment and Resource Management (Queensland)

EVI Enhanced Vegetation Index

fPAR Fraction absorbed Photosynthetically Active Radiation

FPC Foliage Projective Cover

GA Geoscience Australia (Australian Government)

GCI Ground Cover Index

GIS Geographical Information System

GPS Global Positioning System

LAI Leaf Area Index

LIDAR LIght Detection And Ranging

MERIS MEdium Resolution Imaging Spectrometer

MODIS MODerate Resolution Imaging Spectroradiometer

NATT Northern Australia Tropical Transect

NBAR Nadir (directly below sensor) BRDF Adjusted Reflectance

NCLUMI National Committee for Land Use and Management Information

NDVI Normalized Difference Vegetation Index

NIR Near Infrared

NRM Natural Resource Management

SLATS Statewide Landcover And Trees Study (Queensland)

SPOT Systeme Pour l'Observation de la Terre

SWIR Short-Wave InfraRed

TERN Terrestrial Ecosystem Research Network


Introduction

Maintenance of ground cover has been identified as a key measure of sustainable farming practices. In order to resolve issues central to the maintenance of ground cover a workshop was held in Brisbane on 12–13 May 2009 to develop national protocols for mapping ground cover management practices. The first day of the workshop dealt with remotely sensed data processing and products relevant to ground cover maintenance and reference site techniques, allowing an opportunity for information sharing. The second day of the workshop involved scoping national protocols and a business plan to implement the protocols in a national program. The workshop was informed by two key reports:

• Improving the capacity to monitor wind and water erosion: a review (Leys et al. 2009), in which ground cover was identified as a key indicator of land management and techniques recommended for estimating ground cover for use in erosion modelling.

• Ground cover monitoring with satellite imagery in agricultural areas and improved pasture (Schmidt et al. 2009), in which the recommended techniques from Leys et al. (2009) were compared for accuracy, functionality and utility in intensive agricultural systems considering the dynamic nature of related land management practices.

The workshop was sponsored by the Australian Collaborative Land Use and Management Program (ACLUMP), a consortium of Australian Government and state and territory government partners, and hosted by the Queensland Department of Environment and Resource Management. Workshop participants (see Appendix 2) provided technical knowledge of the production and use of remotely sensed ground cover products (including vegetation fractions) and on-ground monitoring of ground cover and land management practices. Presentations at the workshop are listed in Appendix 1, with the full presentations available via the ACLUMP GovDex website (contact authors for access). The workshop focused on irrigated and non-irrigated cropping and improved pasture grazing systems, recognising that monitoring of ground cover is well established in the rangelands. The rangeland experience provided an understanding of the techniques that work and which areas require further development.

ACLUMP aims to deliver spatial data sets that inform sustainable agricultural and natural resource management (NRM) decision making. Currently ACLUMP is focussed on developing nationally consistent land management practices mapping on the basis of characteristic patterns of ground cover maintenance during the annual cycle in cropping and grazing systems.

Workshop purpose The workshop was opened by Brian Vandersee, Executive Director, Natural Resource Sciences, Queensland Department of Environment and Resource Management. Mr Vandersee spoke of the importance of measuring ground cover to Queensland policies and environmental planning instruments such as the Delbessie Agreement (for rural leasehold land) and the Reef Water Quality Protection Plan. Queensland has invested in temporal analysis of remote sensing to develop a ground cover index (GCI), which is used as a component in the modelling and monitoring of a range of issues including erosion, land condition, sediments, fire, climate change risk, pasture growth (using AussieGRASS) and biodiversity. Mr Vandersee spoke of the challenge of maintaining the expertise and breadth of experience in remote sensing and the derived products within Queensland. He gave his support for an agreed national standard to underpin the processing and products produced related to ground cover. Mr Vandersee also encouraged partnership and collaboration among workshop participants and linking the outcomes of this workshop with the Terrestrial Ecosystem Research Network (TERN) and Caring for our Country activities.

Dr Michele Barson, Science Advisor to the Australian Government Land and Coast Division, which manages the Caring for Our Country initiative, provided a list of ground cover related practices that the division would like monitored as part of the Caring for our Country ‘Improving Land Management Practices’ target. These practices are:


• tillage passes • crop residue management • stocking rate management to maintain ground cover levels • careful timing or avoidance of long cultivated fallow • increasing perennial content of pastures • increased use of green manure crops • conversion of continuously low productivity areas to perennial vegetation.

Multiple lines of evidence are sought for reporting on adoption of sustainable farming practices under this Caring for our Country target. This evidence will be informed by the Australian Bureau of Statistics’ Agricultural Resource Management Survey, ground cover monitoring (after Leys et al. 2009) and remote sensing of ground cover management practices.

Dr Barson outlined the purpose of the workshop as identifying:

• land management practices with temporal or spatial patterning that will enable remote detection

• remotely sensed products, including the spatial and temporal resolution needed for practice detection

• requirements of field data at reference sites for calibration • ancillary data needed. These points informed discussions on the second day of the workshop.

Linkages to the national land product data set Dr Alex Held, Environmental Remote Sensing Team Leader at the Centre for Australian Weather and Climate Research, provided an overview of the scope and the land product data set (AusCover) that the Terrestrial Ecosystem Research Network (TERN) will be delivering through its consortium of partners. TERN aims to:

• provide a nationally consistent approach to delivery and validation of terrestrial land cover products, for use in ecosystem science and natural resource management

• provide a core set of standardised spatial biophysical data products, including historical time-series, validated for Australian conditions

• operate as a distributed data archive and access capability, with several regional nodes and calibration test sites, interconnected via Australia’s Academic and Research Network (AARNET).

Following from a National Land and Water Resources (NLWRA) land cover workshop in 2007, TERN has developed a list of needs for natural resource management and associate research and development community data (Table 1). In the first year TERN will focus on existing national data sets at low to medium spatial resolution. In the second and third year TERN will look to include data sets at medium to high spatial resolution. Table 2 lists the data sets to be delivered through TERN.


Table 1: Natural resource management and associated research and development community needs for land cover data identified by TERN (source NLWRA 2007) Land cover needs Specifications

Australian Government

Resource condition monitoring Monitoring soil health - soil carbon and acidification Modelling soil erosion by wind and water Monitoring vegetation communities’ integrity Bureau of Meteorology have strong interest in land cover and related spatial data sets for water resource modelling

Annual or monthly updates Automated Resolution 25 to 100 metres Seamless national coverage Broad classification Consistent data across the nation Capacity to design and deliver nested products Prepared to sacrifice accuracy for repeatability

State Monitoring greenhouse emissions Vegetation management planning and compliance Changes in land cover Bare ground as an indicator of land condition Monitoring and evaluation, land condition assessments Extent of native vegetation intact and modified Hot spots of vegetation clearing for possible prosecution Monitoring trend in foliage projective cover NRM land targets 10 and 11

Finer scale than national Diversity in user needs Legislation driven Reflectance products, calibration and validation is important, accuracy of classification Relationship between field data and sensor data Multi-scale

Regional Catchment Management Authorities interested in per cent ground cover ranging from good to bad (ground cover groups are trees and shrubs, chenopod type shrubs, annual grass/forbs, biological soil crusts, stone and rock and fallen timber and dung) Monitoring ground cover is an indicator for soil health, biodiversity and good land management Soil cover is used as a catchment target Catchment scale modelling water quantity and quality

Need ground data at right scales (fine) Point site data (ground data) at local scales Reference sites – temporal consistent data Relationship between field data and sensor data Climate modellers want surface characteristics Multi temporal Multi scale Flexible classification structure Links in chain of data provider/user access Capacity to access biophysical products Reflectance products, calibration and validation is important, accuracy of classification


Table 2: TERN ‘core’ land product data set (Source: A. Held workshop presentation) Year 1 (2010) Years 2–3 (2011–2012)

National scale – low resolution

20+ year NDVI (AVHRR) MODIS land products (BRDF, albedo, LAI, fPAR) for Australia (from year 2000 onwards) MODIS land reflectance (NBAR) composites for Australia (historical data from 2000 onwards) Globcover produced by the European Space Agency MERIS/AATSR – fPAR data

Validated leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (fPAR) products (MODIS, MERIS-AATSR) Vegetation structure product separating woody, herbaceous, grass and bare ground (from MODIS) Bare ground/wood/litter fractions and additional vegetation structure/function metrics, all calibrated against field point/transect-measurement sites across Australia Environmental ‘geofabric’ datasets of value in ecosystem modeling (including spatial datasets on topography, soil chemistry, structure, soil moisture, soil carbon content, mineralogy, climate variables)

National scale – medium resolution

Land-cover types Vegetation-cover trends (from CSIRO ‘VegMachine’) Time-series of forest, non-forest cover National control point chip library Climate data (solar irradiance, optical thickness)

Annual (2) mosaic of Landsat data time-series (nadir-reflectance at bottom of atmosphere directly below the sensor) corrected using standardised and agreed best-practice methods Time-series Landsat nadir-reflectance (for key validation and demonstrator sites)

Local scale – medium plus high resolution

Time-series Landsat reflectance for key validation and demonstration sites Spectral libraries (vegetation, soils) Radiance from vicarious calibration targets Available airborne imagery over key validation and demonstration sites

Time-series Landsat nadir-reflectance for key validation and demonstrator sites Site-based, high resolution (less than 1 metre to 5 metres): Where available, geo-, ortho-rectified and radiometrically corrected optical, radar and Lidar satellite and airborne imagery where available (e.g. Ikonos, Quickbird, Hymap, Carnegie Airborne Observatory (CAO), Casi, Lidar) Repository for targeted remote sensing data (airborne/satellite) collected in support of validation campaigns for TERN, as well as continental and regional TERN Hub data collection activities (campaigns might include Hymap, CAO, soil moisture instruments via 'Linkage Infrastructure, Equipment and Facilities' (LIEF)) building on existing supersites, such as Northern Australia Tropical Transect (NATT), Injune (Queensland) and Tumbarumba (ACT)


On-ground impact Michael Digby, Regional Mapping Services Coordinator for the Northern Gulf Resource Management Group, reminded participants that land holders are users of spatial technology and undertake assessment of land condition. Within the Northern Gulf, graziers are learning GPS and satellite imagery data capture techniques, plus taking charge of data capture and developing property management plans.

Land condition assessment is part of the Delbessie Leasehold Renewal Strategy. This involves assigning land types to a property, based on soil, slope and vegetation (Figure 1). Permanent monitoring sites are GPS located within these land types to assess ground cover, weeds, pasture composition, condition and yield, erosion, tree thickening and basal area. Site monitoring is done post-wet and pre-wet.

SPOT 5 satellite imagery, with a resolution of 10 metres, is used for infrastructure definition and land type mapping for land condition assessments (Figure 1). Lower resolution imagery (MODIS and now Landsat) is used for time-series analysis of ground cover (Figure 2).

Mr Digby concluded that the challenge facing the spatial industry is to foster coordination and collaboration on future technologies that aid land managers in timely decision making. This will avoid duplication of effort. To assist decision making the products must be at a suitable scale.

Figure 1: Land types for a property in the Northern Gulf Region of Queensland derived from SPOT 5 imagery and on-ground sites. Fencing infrastructure is also shown. (Source: M. Digby workshop presentation)


(a) Dry season

(b) Wet season

Figure 2. MODIS 250 metre NDVI images for (a) dry season and (b) wet season used to monitor density and vigour of green vegetation for property in Figure 1. Green shades show healthy vegetation and orange shades show vegetation under stress. Reduced stocking rates and supplementary feeding were employed to manage the impact of prolonged drought on feed availability. Gidyea country has been masked out in black (Source: M. Digby workshop presentation)


Monitoring ground cover management practices using remote sensing

Lessons from 30 years of remote sensing science Dr Tim McVicar, Ecohydrological Time-Series Remote Sensing Team Leader at CSIRO Land and Water, showed some CSIRO examples of differing resolution products relevant to ground cover monitoring. Table 3 summarises these examples and the lessons that have been learned. Some key points were:

• processing decisions need to be carefully considered as results are dependent on these (e.g. a hierarchical classification gave more accurate results than an image stack classification approach)

• one remotely sensed index will not work in all ecosystems or land uses • need to be clear what question/s the remote sensing product is answering • when assessing change in resource condition, climate variability, biophysical functioning and

management all influence the observation variable/s • field data is critical to validate remote sensing.

Fractional cover in context Dr Leo Lymburner, from the Australian Centre for Remote Sensing (ACRES) at Geoscience Australia, demonstrated Geoscience Australia’s approach to producing a land cover map which uses a dynamic mapping system (time-series of imagery) to show current land cover status and changes in a historical context at a national, regional and local scale (Figure 3). Using this approach Geoscience Australia with the Bureau of Rural Sciences have produced a National Dynamic Land Cover Map from the MODIS sensor at a resolution of 250 metres (using data from April 2000 to mid 2008). The land cover map with fractional cover can be used to interpret land management practices (Figure 3 – i.e. ground cover toolkit). Geoscience Australia is currently exploring interpreting and analysing the 500 metre (MODIS) fractional cover product produced by CSIRO Land and Water (Guerschman et al. 2009) with the National Dynamic Land Cover Map.

Figure 4 gives an example of fractional cover for a polygon classified in the land cover map as ‘rainfed cropping’ using Landsat TM imagery. The steps taken to extract a fractional cover time-series were:

• Step 1: Characterise the reflectance characteristics of the image to obtain ‘end members’ (pure reflectance curves).

• Step 2: Use end members to unmix each pixel within an individual Landsat scene (the spectral unmixing gives fractional cover).

• Step 3: Stack unmixed scenes to extract a time-series of each cover fraction.

Some key points were:

• field data should be acquired as close to date of image acquisition • fractional cover needs to be put in context – need to know the land use and land management

practices to interpret the fractional cover for a pixel or object of interest (i.e. polygon/paddock).


Table 3: CSIRO examples and lessons learned from 30 years of remote sensing science (Source: T. McVicar workshop presentation) Extent and purpose Image source

(resolution) Lessons learnt*

Green vegetation*

Australian trends in photosynthetic (green) vegetation – total, recurrent and persistent (Donohue et al. 2009)

AVHRR (1 km)

Trend analysis needs to be interpreted in biophysical frameworks that acknowledge change to drivers of vegetation Total fPAR can be split into persistent and recurrent components to represent vegetation types with different eco-hydrologic functions

Fractional cover*

Mapping of photosynthetic (green) vegetation, non-photosynthetic (yellow) vegetation and bare soil – undertaken for the northern savannas, product available for whole of Australia (Guerschman et al. 2009)

MODIS (500 metres)

Use of SWIR to quantify amount of cellulose in a pixel

Rangelands (extensively managed)*

Rangelands example from the National Carbon Accounting System (NCAS)

Landsat (30 metres)

Cover is best mapped / monitored using red reflectance Trend analysis is needed

Improved pastures (intensively managed)*

Pasture growth rates and biomass (feed on offer) under southern Australian grazing (from Pastures from Space www.pasturesfromspace.csiro.au)

Ikonos (1–4 metres)

Timing of imagery with regards to rotational management and increasing component of senesced grasses through the growing season needs to be accounted for when interpreting imagery

Cropping systems*

Coleambally Irrigation Area – rice, corn, soy and sorghum

Landsat Enhanced Thematic Mapper (ETM) (30 metres)

Need to understand crop type, phenology, management and spectral response LAI best provided by simple ratio (NIR / Red) fPAR / FPC green cover best provided by NDVI [(NIR – Red) / (NIR + Red)] Timing of image acquisition is important (especially if dealing with high resolution data where usually the frequency of observation is less [than with low resolution data])

* Airborne LIDAR provides very valuable structural information (vegetation height, strata, biomass, cover), usually a key for habitat or biodiversity remote sensing.

* Field data, with some assessment of the spatial autocorrelation or spatial variation is critical to validate remote sensing.


http://www.pasturesfromspace.csiro.au/

http://www.pasturesfromspace.csiro.au/

Figure 3: The process undertaken by Geoscience Australia to produce a land cover map which uses the international standard (ISO) for land cover classification and provides fractional cover dynamics for more specific natural resource management and climate change assessments (Source: L. Lymburner workshop presentation)

Figure 4: Fractional cover for a rainfed winter crop in the Gwydir River catchment, northern NSW, showing photosynthetic vegetation (green line), non-photosythetic vegetation (yellow line) and bare soil (brown line) derived from Landsat TM (5 and 7). Fractional cover is extracted by polygon feature from the land cover map according to the International Standards Organisation (ISO) land cover classification (Source: L. Lymburner workshop presentation)


Comparing ground cover products Dr Michael Schmidt from the Queensland Department of Environment and Resource Management (DERM) discussed work undertaken on the assessment and comparison of existing remote sensing derived ground cover products for ACLUMP (Schmidt et al. 2009). Products considered were DERM’s Ground Cover Index (GCI) derived from Landsat (Scarth et al. 2006) and MODIS imagery (Milne et al. 2007) available for Queensland and CSIRO’s fractional cover product from MODIS (Guerschman et al. 2009) available nationally. CSIRO’s fractional cover product was found to underestimate cover conditions (especially non-photosynthetic vegetation) in Queensland. DERM is developing a fractional cover product to estimate photosynthetic vegetation and non-photosynthetic vegetation using Landsat for Queensland. A MODIS NDVI time-series analysis (Pringle et al. 2008) was used for crop identification.

Dr Schmidt highlighted the effect spatial resolution has on the interpretation of a remote sensing product (Figure 5). Key points include:

• climatic data, such as rainfall, aids interpretation of ground cover fraction estimates in time-series.

• time-series of imagery is required throughout the cropping cycle to detect management practices (temporal frequency within a year is important).

• time-series of imagery over a number of years is required to monitor change in management practices.

• sensor resolution limits what we can ‘see’. Spatial resolution of imagery needs to be appropriate for the interpretation of the derived products (the resolution of Landsat is better suited to cropping systems than MODIS).

• any multi-temporal or hyper-temporal analysis of satellite imagery requires scientifically rigorous and consistent, automated, geometric, radiometric and atmospheric corrections to be applied to all imagery.

• extensive field-based calibration sites are required (particularly under different crops) for all image-based ground cover products.

Discussion outcomes In five discussion groups, workshop participants were tasked with assessing whether the ground cover related land management practices to be monitored under Caring for our Country are feasible to observe with remote sensing, and if so, what product/s, sensor resolution and temporal frequency of imagery are required (Table 4). Points raised in discussion included:

• definitions of specific management practices are required • a spatial database of land management practices is needed to inform interpretation of remotely

sensed products. Seek to improve spatial attribution of survey results to paddock level (such as for ABARE farm surveys) with ABS ARM survey

• land use is an underpinning data set • good climate products are important for separating seasonal issues from management (e.g.

ACRIS decile ranking for quality of season) • remote sensing products/indices are available that with a time-series can detect and monitor

some land management practices • exploratory analysis will be required to detect some of the management practices specified by

Caring for our Country using remote sensing • time-series and temporal frequency of remote sensing imagery is important for trend analysis

to detect management practices • remote sensing imagery is required at the scale of paddock to sub-paddock. Paddock size will

determine the spatial resolution required (favouring 25 metre (i.e. Landsat) for cropping belt)


• fractional cover is viewed as important for management practices particularly under cropping (e.g. crop residues)

• a scale-up approach seems appropriate as the questions are at paddock level while deliverables are required nationally. This would involve intensive representative site sampling and engagement with land holders/agricultural consultants to provide details of management practices and targeted acquisition of high resolution imagery.

(a) Landsat (b) MODIS

Figure 5: Results of assessment of foliage projective cover (FPC) and ground cover using (a) Landsat (25 metre) and (b) MODIS (500 metre) for the Burdekin Catchment, Queensland. Ground cover is estimated where FPC is less than 20 per cent (Source: M. Schmidt workshop presentation)


Table 4: Remote sensing products for monitoring ground cover management practices Ground cover related land management practices

Data required Analysis Issues

Tillage passes Number and type of cultivations

Time-series of fractional cover (ideally as many images as possible per year that are cloud free with crop type influencing the minimum number for reasonable estimates) Paddock scale (resolution = ~ Landsat) Database of practices (drawing on existing data e.g. NSW Land Management Database) Extra ground data – i.e. change in stubble orientation, surface roughness under different tillage practices and cropping systems

Relative change in fractions of non-photosynthetic vegetation to bare soil

Changes in stocking rate Spatial distribution of ‘practice database’ data Require agreed terminology and definitions for tillage management practice Exploratory

Crop residue management Remove (burn, bale, mulch, plough) Leave (and spray)

Time-series of fractional cover (ideally as many images as possible per year that are cloud free with crop type influencing the minimum number for reasonable estimates) Sub-paddock scale (resolution = Landsat and finer) Extra ground data

Relative change in fractions of non-photosynthetic vegetation to bare soil

Separating the method of removal is more difficult then whether the stubble is left or removed If measureable, a gradual decay in non-synthetic vegetation would indicate grazing Detecting burning is not easy with remote sensing, if timing is right roadside survey can provide incidence of stubble burning Time lags

Grazing pressure Stocking rate management to maintain ground cover levels

Level fraction of ground cover (green and yellow vegetation) above 50 per cent Cover types Remote sensing cover estimate may include error e.g. +/-20 per cent error needs to be characterised in imagery and propagated through classification

Time-series trend analysis for entire year Sub-paddock to capture variability within paddock Remote sensing should be able to provide land managers with a tool so they can adjust stocking rates.

Knowledge of managed livestock stocking rates (e.g. set stocking rates for SA rangelands) Cattle will leave 10-20 per cent cover, sheep will eat everything Grazing pressure acknowledges effects of all grazing animals (i.e. rabbits, kangaroos) not just managed livestock Provide product to land managers to enable adjustment of stocking rates and fencing of paddocks to land types


Table 4. (continued) Ground cover related land management practices

Data required Analysis Issues

Fallow Careful timing or avoidance of cultivated fallow (Requires a definition of fallow – intercrop period or bare soil, herbicide or cultivation)

Remote sensing data time-series dependant on crop cycle Paddock scale

Days of vulnerability

Access to better climate predictions would enable land managers to time cultivation and planting better to avoid when soils are vulnerable Controlled traffic farming tends to be applied where only cropping not under mixed use

Pasture perenniality Increasing perennial content of pastures

Time-series temporal decomposition of green component into recurrent component Pasture zones from land use map. Property scale MODIS, AVHRR or maybe Landsat Climatic

Greenness index? Climate variability Rule sets would need to deal with soil colour Need to know where perennial pastures are (from land use) Colours of ‘best’ perennial pasture species not necessarily green and will vary between areas Pasture species balance – perennial, palatable, productive

Green manure crops Increased use of green manure crops (often legumes are grown between crops and sprayed rather than harvested to increase nitrogen or carbon and prevent other weed growth)

Remote sensing may be able to detect if green – earth more sudden than a crop which browns off before harvesting Paddock scale

Exploratory analysis informed by land management practices Time-series of high resolution imagery

Need specific land management information

Conversion of areas of continuously low productivity to perennial vegetation

Trend analysis of time-series Scale: Landsat or MODIS Where are the low productivity areas? Repeated low cover Change detection SLATS process

Relative for each area


Reference sites to monitor ground cover and related management practices

Roadside surveys Dr Anna Dutkiewicz and Giles Forward from the Department of Water, Land and Biodiversity Conservation (DWLBC) South Australia, reported on a current joint research project with the University of Adelaide (Dr Ken Clarke and Associate Professor Megan Lewis) that is evaluating the potential of satellite image analysis for monitoring soil erosion hazard in SA’s agricultural zone (wheat-sheep belt with winter dominant rainfall). MODIS derived products are being tested against spatially located ground cover observations collected in DWLBC’s existing roadside ‘windscreen’ survey program, where about 5500 sites are assessed at four critical times per year. The aim is to comprehensively estimate land at risk of erosion based on remote ground cover assessment, both temporally and spatially.

Similar roadside erosion surveys, including in-paddock reference site assessments, are also conducted in parts of NSW, WA and Victoria. Within NSW, the focus has been on dryland agriculture although the rangelands are now monitored. Although exact field methodologies vary between states, ground cover level, erosion severity and land use/management phase are recorded in these programs. A recent project involving representatives from these programs is seeking to increase consistency by proposing standardised national protocols for roadside erosion survey, data management and national database specifications, including a provisional survey manual (Forward 2009).

Dr John Leys from the NSW Department of Environment, Climate Change and Water, presented ‘LandMAPT’ the access database used in NSW as the roadside data entry form (Figure 6). This data entry form also brings up previous site history as a reference at the time of data collection.

Key points on roadside surveys include:

• rapid reporting offering catchment and state-wide multi-variate data • limited spatially and temporally, but with careful design can be representative • geo-referenced site recordings of sequence of land management practices useful for validating

remote sensing fractional cover products • measurement of ground cover, e.g. using the State-wide Land And Trees Survey (SLATS)

methodology, rather than a ranking would increase value of on-ground sites for remote sensing.

Demonstration of SLATS method Workshop participants had the opportunity to apply the SLATS methodology for measuring ground cover (and foliage projective cover). Figure 7 shows the layout of the 100 metre star-shaped transect, the proven method under pastoral environments (most extensively in the rangelands). At one metre intervals along each transect, green leaf, dry leaf, litter, rock, bare ground, cryptogram (biological crust) are measured and recorded on a generic site data form (Appendix 3). Queensland’s DERM has been trialling methods for measuring ground cover under intensive agricultural systems and currently favour a two, 100 metre, 45 degree diagonal across row layout in linearly sown agricultural environments (Figure 8). These methods have been developed for calibrating Landsat imagery.


Figure 6: NSW roadside survey ‘LandMAPT’ data form. Data is recorded as seen from the left and right side of the car with previous recordings also available (Source: J. Leys workshop presentation)

Discussion outcomes Table 5 outlines the requirements of reference sites under intensive and extensive agricultural land uses. Other points of discussion were: • field data, with some assessment of the spatial autocorrelation / spatial variation, is critical to

validate remote sensing • quantitative measurements of cover fractions will be required across a range of soil types

(particularly the colour of A-horizon) • solve end members with high resolution imagery and then on-ground sites with SLATS

measurements • choose homogenous areas first for calibration (to obtain pure end members), then introduce

more difficult (heterogeneous) areas for validation • stratify by land type • a reference site should be representative of the paddock not just a point • scale to MODIS from Landsat • the option to use photographic techniques – though this would need to be calibrated as another

form of remote sensing (standard camera and technique/s).


Figure 7: Applying the SLATS star-shaped transect approach in the field to measure fractional ground cover (under extensive grazing) (Source: M. Schmidt workshop presentation)

(a) pastoral environments (b) intensive agriculture systems

Figure 8. Layout of 100 metre transects for measurement of ground cover at 1 metre intervals under (a) pastoral environments (rangelands and improved pastures) and (b) intensive agricultural systems (cropping) for calibration of Landsat imagery (Source: Schmidt et al. 2009)


Table 5: Requirements of reference sites for monitoring ground cover and related management practices Cropping Extensive Grazing

Indicators to measure

End members Validate Guerschman et al. (2009) data

End members

Method(s) to measure indicator

Ground reference sites for - Landsat image - MODIS pixel Require different methodology to deal with vegetation clumpiness High resolution image at the same time as BRDF measurements + climate station within 1km (super sites)

Same as cropping Vegetation clumpiness is an issue

Other monitoring programs that measure this indicator

SLATS (Queensland and now also NSW)

TERN 1500 -1800 sites TERN super sites (300)

Priority location, land use, land management

Sites not in NT, Queensland – radiometric measurements for some sites Return to the sites multiple times per year Management sequence information – polygon paddock boundaries across a range of soil colours. Number of sites depends on the mean and variance for different soil colours (20-30 sites may be enough)

Different slopes and aspects Sites that are heterogeneous i.e. trees, creeks, hills

Other comments

Overlap with CSIRO soil monitoring sites


Workshop recommendations

To implement a national program that monitors management practices impacting on ground cover levels the workshop participants agreed on four key areas of recommendation:

1. On-ground measurements to calibrate and validate remote sensing products. • Undertake an assessment of paddock sizes to determine where reference sites can be located

and used directly for MODIS imagery (large homogeneous areas) and where paddocks are smaller and Landsat or finer-scale imagery is needed (intensively managed areas).

• Determine sampling strategy for location of reference sites which considers key agricultural land uses and management regimes.

• At an accurately located reference site: − measure fractional cover – photosynthetic (green) vegetation, non-photosynthetic or

senesced (yellow) vegetation and bare soil − measure woody and non-woody vegetation – persistent (non-deciduous perennial

vegetation types) and recurrent (deciduous, annual and ephemeral vegetation types) fractional green vegetation

− measure soil colour (hue, chroma, value for both wet and dry soil) − record calendar of operations for that reference site (paddock).

• Revisit cropping reference sites four to six times per year to measure key management practices that impact on ground cover levels.

2. Field data collected using the Queensland DERM’s SLATS modified discrete point sampling method. This method measures green (separating persistent and recurrent) and yellow vegetation fractions and bare soil.

• Provide a manual on field measurements and training to those undertaking the measurements in each state.

• For pastoral environments, apply the proven star-shaped transect approach of Scarth et al. (2006).

• For intensive agricultural environments with linearly sowed crops, trial the two, 100 metre, 45 degree diagonal across row layout (the cross-transect method of Schmidt et al. (2009).

3. A fractional cover remotely sensed product is required to detect and monitor land management practices impacting ground cover.

• Fractional cover represents the exposed portion of photosynthetic vegetation, non-photosynthetic vegetation and bare soil within each pixel.

• Assess which method provides the most accurate estimate of fractional cover (particularly within cropping areas).

• MODIS (500 metre) product available nationally (Guerschman et al. 2009). • Landsat (30 metre) product currently trialled for limited locations (Queensland DERM,

Geoscience Australia).

4. Queensland DERM’s Ground Cover Index (GCI) would be valuable nationally for monitoring ground cover levels.


Other considerations

References sites • States to provide to TERN, data from existing monitoring sites that is suitable for improvement

(calibration / validation) of MODIS product (following from 2008-09 ACLUMP assessment). • Where practical, co-locate ‘LMP ground cover’ reference sites for remote sensing with existing

state or territory monitoring sites (i.e. soil condition monitoring sites). • Seek to have Queensland DERM’s SLATS modified discrete point sampling method adopted

as a national standard for all ground cover monitoring (i.e. erosion roadside survey reference sites, rangelands reference sites).

• Use regional groups as the contact point with land holders. • Definitions required for land management practices collected (LUMIS handbook). • Standardised data entry into a national database of ground cover measurement cover and

sequence of land management practices (‘calendars of operations’) – suitable for use in the field and linking with GPS and GIS facilities – explore suitability of existing tools.

• Reference site data to be provided to TERN/AusCover activities and then onto international networks such as the CalVal Portal (http://calvalportal.ceos.org).

Ground cover index (GCI) • Queensland DERM produce two Ground Cover Index products:

− Landsat GCI (30 metre): ground cover index calculated from time-series of Landsat imagery for one scene per year corresponding to the end of the local dry season (according to the method of Scarth et al. 2006). Informed by 550 field sites representing the variety of land types in Queensland

− MODIS GCI (500 metre): uses eight day composite images to generate GCI data from early 2000 to present based on a cross calibration with the Landsat GCI data (Milne et al 2007).

NSW DECCW is also developing a GCI based on Queensland’s methodology. • Ideally present a spatial average GCI value for 3x3 pixel location. This is difficult at 500 metre

MODIS resolution in cropping areas but easily identified at the resolution of a 30 metre Landsat pixel (Schmidt et al. 2009).

• There is an option to aggregate the Department of Climate Change’s woody mask for Australia to 500 metres to implement a MODIS GCI nationally.

Fractional cover products • With good field measurements of the green, yellow and bare fractions, the errors of different

methods can be quantified. • Unmixing the signals into the persistent and recurrent components (i.e. using the method of

Donohue et al. 2009) would provide estimates of the green and yellow fractions in the overstorey and understorey, to separate the trees from the grasses.

• Time-series analysis of the recurrent component of the green fraction within cropping land uses could identify a range of crop phenology features including: − number of cropping cycles within a year − number and cumulative time of the fallow periods − timing of crop cycles (whether winter or summer crops) − crop green biomass (estimated by integrating the area under the curve of the crop cycle) − inter-annual changes in crop green biomass.


http://calvalportal.ceos.org/

(Lymburner et al. 2008 has measured crop phenology using an Enhanced Vegetation Index, which considers only greenness not fractional cover.)

• Need to mask for cloud. Queensland’s DERM have developed an automated cloud masking method for their Landsat archive (yet to be applied).

• Difficult to co-locate MODIS data (500 metres) in homogenous cropping areas (Schmidt et al. 2009).

National dynamic land cover map • Uses MODIS Enhanced Vegetation Index (250 metre) data within an object-oriented image

processing environment using a defined set of rules, spatial attributes of objects and ultimately a classification of land cover (Lymburner et al. 2008).

• Fractional cover is not currently available, crop phenology is derived from greenness only (EVI).

• Subject to funding, Guerschman et al. (2009) fractional cover will be added to the dynamic land cover product.


Implementing the recommendations

Drawing on the workshop discussions, participants proposed the following schedule as an approach to deliver a national program to monitor ground cover related practices using remote sensing (Table 6). This schedule is aimed at providing evidence of achievement of Caring for our Country’s ‘Improving Land Management Practices’ target. The available budget would dictate the number of reference sites established, the data collected at these reference sites and the opportunity to compare and analyse the different remote sensing products, such as for fractional cover. ACLUMP will pursue how this proposed national program could be implemented.

Table 6: Key tasks and deliverables for a national program to monitor ground cover related management practices as proposed by workshop participants Key tasks identified at workshop Undertaken

by whom Indicative budget

Time frame

1. Field manual for measuring fractional cover in cropping systems • Methodology to be aligned with current SLATS methodology for

rangelands (trees and pasture) • Consideration of key crop types and management practices (i.e.

row spacing) • (Training staff in all jurisdictions of methods)

Qld DERM $20,000 Dec 2009

2. Calibration sites for fractional cover • Implementing expanded SLATS methodology • Crop sites only in first year (4–6 visits per site, $600/visit/site +

travel time = $4000 per site per year) use ACLUMP calendars of operations to determine number of sites required in a state to capture key land management practices for key broadacre crops • All sites in years 2–3 (adding other crop sites (not necessarily

revisiting previous years sites); rangelands sites generally require only one visit per year) • Opportunities to link with monitoring programs – TERN

(rangelands), soil condition monitoring (broadacre cropping)

All state and territory NCLUMI members

$2 million per year

2010 to 2012

3. Compare MODIS (500 metre) products for Australia of fractional cover (CSIRO) and ground cover index (Qld. DERM) using fractional cover calibration sites • Agree on which product to adopt or use in particular locations for

suitability for estimating within agricultural (cropping) systems (currently lacking calibration sites in cropping)

CSIRO, Qld DERM

$400,000 2011

4. Woody mask (SLATS, NCAS, fPAR persistent) – aggregate to 500 metre product • (NCAS – 25 metre IKONOS, fPAR persistent – 1 kilometre)

Qld DERM / NSW DECCW DCC, BRS

$1,000 2010

5. Landsat scale fractional cover or ground cover index (GCI) • 1 scene per state, all Landsat scenes per year • Scene to coincide with a crop calibration site • Enables scaling up for MODIS product

Qld DERM / GA

$200,000 2010

6. Inter-comparison of Landsat fractional cover Qld DERM / GA

$100,000 2010


Appendix 1 – Workshop presentations

Presentation title Presenter

Caring for our Country – why managing ground cover is important

Dr Michele Barson Science Advisor, Australian Government Land & Coasts Division, Department of Agriculture, Fisheries and Forestry

Terrestrial Ecosystem Research Network – TERN AusCover – Remote Sensing of Terrestrial Vegetation

Dr Alex Held Team Leader, Environmental Remote Sensing, The Centre for Australian Weather and Climate Research, CSIRO Marine & Atmospheric Research

Assessment and comparison of existing remote sensing derived ground cover products for national application

Dr Michael Schmidt Scientist, Remote Sensing Centre, Queensland Department of Environment and Resource Management

Ground cover monitoring - Research and products from CSIRO

Dr Tim McVicar Principal Research Scientist, Ecohydrological Time-Series Remote Sensing Team Leader, CSIRO Land and Water

Mapping photosynthetic vegetation, non photosynthetic vegetation and bare soil using Landsat TM (5 and 7)

Dr Leo Lymburner Remote Sensing Applications Specialist, Remote Sensing Science and Strategy, Australian Centre for Remote Sensing (ACRES), Geoscience Australia

Development of a satellite image-based land condition monitoring system for South Australian agricultural regions

Dr Anna Dutkiewicz1 and Giles Forward2 1Senior Project Officer NRM (Salinity), Department of Water, Land and Biodiversity Conservation, Adoption Manager SA, Future Farm Industries CRC 2Senior Scientific Officer (Land Condition Monitoring), Science, Monitoring and Information Division, SA Department of Water Land and Biodiversity Conservation

Roadside surveys - Visual assessment of ground cover, erosion and land management practices

Dr John Leys Principal Research Scientist, Scientific Services Division, NSW Department of Environment, Climate Change and Water

Mapping of real world remote country by real world people

Michael Digby Regional Mapping Services Coordinator, Northern Gulf Resource Management Group

These presentations are available to workshop participants and interested others via the Australian Collaborative Land Use and Management Program’s GovDex site. Please contact the authors if access to the above presentations is required.


Appendix 2 – Workshop participants

Name State Organisation Michele Barson *# AG Department of Agriculture Fisheries and Forestry Graeme Bell Qld AgForward Roxanne Blackey Qld Murray Darling Committee Lee Blacklock Qld Regional Natural Resource Management Groups Collective Mark Brown Tas. Department of Primary Industries, Parks, Water and Environment John Carter Qld Climate Change Centre of Excellence Rob Clark Vic. Department of Primary Industries Bruce Cowie Qld Fitzroy Basin Association Yang Dang Qld Department of Environment and Resource Management Michael Digby * Qld Northern Gulf Resource Management Group Anna Dutkiewicz * SA Department of Water Land and Biodiversity Conservation Giles Forward * SA Department of Water Land and Biodiversity Conservation Rob Hassett Qld Department of Environment and Resource Management Jeremy Hayden Qld Southern Gulf Catchments Alex Held *# AG Centre for Australian Weather and Climate Research Nik Henry NSW Department of Environment, Climate Change and Water Chris Holloway Qld Department of Primary Industries and Fisheries Bob Karfs Qld Department of Primary Industries and Fisheries Darren Kidd Tas. Department of Primary Industries, Parks, Water and Environment Alex Lau Vic. Department of Sustainability and Environment Paul Lawrence # Qld Department of Environment and Resource Management John Leys * NSW Department of Environment, Climate Change and Water Leo Lymburner * AG Geoscience Australia Tim McVicar * AG CSIRO Land and Water Liz Morse-McNabb VIC Department of Primary Industries Mike Nunweek WA Department of Agriculture and Food Graeme Owen NT Department of Natural Resources, Environment the Arts and Sport Matt Pringle Qld Department of Environment and Resource Management Lucy Randall AG Bureau of Rural Science Jasmine Rickards AG Bureau of Rural Science Peter Scarth Qld Department of Environment and Resource Management Michael Schmidt * Qld Department of Environment and Resource Management Kerry Speller Qld Department of Environment and Resource Management Jane Stewart AG Bureau of Rural Science Phil Tickle AG Geoscience Australia Dan Tindall Qld Department of Environment and Resource Management Peter Wilson AG CSIRO Land and Water Martin Wingett Qld South West Natural Resource Management Christian Witte ^ Qld Department of Environment and Resource Management * Workshop presenter; # Attended Day 1 only; ^ Attended Day 2 only


Appendix 3 – SLATS generic site data form


References

Bureau of Rural Sciences 2006, Guidelines for land use mapping in Australia: principles, procedures and definitions, A technical handbook supporting the Australian Collaborative Land Use Mapping Programme, 3rd edn, Commonwealth of Australia.

Donohue, RJ, McVicar, TR and Roderick, ML 2009, ‘Climate-related changes in Australian vegetation cover as inferred from satellite observations for 1981-2006’, Global Change Biology vol. 15, pp. 1025–1039.

Forward, G 2009, Manual of proposed national minimum standards for roadside erosion survey, DLWBC Report 2009/24, Government of South Australia, through Department of Water, Land and Biodiversity Conservation, Adelaide.

Guerschman, JP, Hill, MJ, Renzullo, L, Barrett, DJ, Marks, AS, & Botha, E 2009, ‘Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors’, Remote Sensing of Environment vol. 113, pp. 928–945.

Leys, J, Smith, J, MacRae, C, Rickards, J, Yang, X, Randall, L, Hairsine, P, Dixon, J & McTainsh, G 2009, Improving the Capacity to Monitor Wind and Water Erosion: A Review, Department of Agriculture, Fisheries and Forestry, Commonwealth of Australia.

Lymburner, L, Li, F, Tan, P, Mueller, N and Reddy, S 2008, ‘Dynamic land cover mapping from space: Baseline information for environmental monitoring’, Ausgeo News issue 92, pp. 6–9.

Milne, J, Danaher, T, Scarth, P, Carter, J, Armston, J, Henry, B, Cronin, N, Hasset, R, Stone, G, Williams, P, Denham, R and Byrne, M 2007, Evaluation of MODIS for groundcover and biomass/feed availability estimates in tropical savannah systems, Meat and Livestock Australia.

National Land and Water Resources Audit 2007, Australian land cover mapping: Proceeding of a workshop to discuss interest in land cover mapping, National Land and Water Resources Audit.

Pringle, M, Denham, R, Witte, C 2008, ‘Time-series analysis of MODIS imagery for the prediction of broadacre crops’, Proceedings of the 14th Australasian Remote Sensing and Photogrammetry Conference, Darwin.

Scarth, P, Byrne, M, Danaher, T, Hasset, R, Carter, J, and Timmers, P 2006, State of the Paddock: Monitoring condition and trend in groundcover across Queensland, Department of Natural Resources and Mines.

Schmidt, M, Tindall, D, Speller, K and Scarth, P (in preparation) 2009, Ground cover monitoring with satellite imagery in agricultural areas and improved pastures, Department of Environment and Resource Management, Queensland.

Author citations are given for the figures and tables included in this report from the presentations given at the workshop. For the author’s presentations see Appendix 1.


Glossary

Landcover The physical surface of the earth, including various combinations of vegetation types, soils, exposed rocks and water bodies as well as anthropogenic elements, such as agriculture and built environments. Land cover classes can be discriminated by characteristic patterns using remote sensing (BRS 2006).

Land use The purpose to which the land cover is committed. Some land uses, such as agriculture, have a characteristic land cover pattern. These usually appear in land cover classifications. Other land uses, such as nature conservation, are not readily discriminated by a characteristic land cover pattern. For example, where land cover is woodland, land use may be timber production or nature conservation (BRS 2006).

Land management practices

The approach taken to achieve a land use outcome (e.g. cultivation practices, such as minimum tillage and direct drilling). Some land management practices, such as stubble disposal patterns and tillage rotation systems, may be discriminated by characteristic land cover patterns and linked to particular issues (BRS 2006).

Ground cover

A sub-component of land cover and can be used to infer land management practices. Ground cover is defined as the material (vegetation, biological crusts and stone) that is in contact with the soil surface. The non-woody ground cover such as crops, grass, forbs and chenopod-type shrubs change monthly rather than annually or decadally, and it is this component that offers the greatest indicator of land management performance (Leys et al. 2009). Related terms (Schmidt et al. 2009):

Ground cover index (GCI) describes the percentage of plant material (dead or alive) that is covering underlying soil or rock material. GCI data are calculated for areas with a low woody vegetation component (less than 15 per cent) as trees interfere with the calculation. GCI estimates ground cover by applying a known statistical relationship between measurements of cover made in the field, and measurements made by satellite of the light reflected from the same field locations, at close to the same time.

Bare ground index (BGI) has a direct relationship to the ground cover index as follows: BGI (%) = 100 – GCI

Fractional cover

The proportion of a given land area covered by either photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) or bare soil (BS). In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. Remote sensing can be used to estimate the fractional cover of vegetation (Guerschman et al. 2009).

Related terms (McVicar pers. comm. 2009): Foliage projective cover (FPC), or foliage cover fraction, is defined as the fractional area (projected vertically) covered by one or more layers of photosynthetic tissue above a given land area.

Vegetation projective cover (VPC), or vegetation cover fraction, is defined as the fractional area (projected vertically) of the vegetation canopy occupying a given land area.

Fraction absorbed PAR (fPAR), is defined as the fraction of incident photosynthetically active radiation (PAR) absorbed by green vegetation.



Reference sites

provide on-site observations and measurements to correlate surface features and localities with their expression in satellite imagery. A reference site for remote sensing can be used for calibration, to determine the accuracy of the image processing and its interpretation into a product, or validation, to confirm or substantiate the product produced.

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