Land status data · 2015. 4. 24. · Land Status Data Mapping for the Mount Lofty Ranges Watershed For further information please contact: Information Officer Environment Protection

Land status datamapping

for the

Mount Lofty RangesWatershed

Jane Bradley and Karla Billington

Watershed Protection Office Environment Protection Agency

Department for Environment and Heritage

February 2002

Land Status Data Mapping for the Mount Lofty Ranges Watershed

For further information please contact: Information Officer Environment Protection Agency Department for Environment and Heritage GPO Box 2607 Adelaide SA 5001

Telephone: (08) 8204 2004

Facsimile: (08) 8204 9393

Free call (country): 1800 623 445

ISBN 1 876562 31 5

FEBRUARY 2002

¢ Environment Protection Authority

This document may be reproduced in whole or part for the purpose of study or training, subject to the inclusion of an acknowledgment of the source and to its not being used for commercial purposes or sale. Reproduction for purposes other than those given above requires the prior written permission of the Environment Protection Authority.

Printed on chlorine-free and partly recycled paper

Land status data mapping for the Mount Lofty Ranges Watershed

ACKNOWLEDGEMENTS

The land status data mapping project was a large, collaborative project involving a range of stakeholders. The Watershed Protection Office thanks those who attended the early workshops when the mapping trial was being developed and acknowledges the following people who gave their time and effort throughout the project.

Department for Environment and Heritage Environmental & Geographic Information Division--Spatial Projects Unit Lou Panico, Supervisor Graham Baggs, Co-ordinator Gary Maguire, Senior GIS Analyst Katrina Oppermann Linley Golat Gareth Walters Joe Caristo Shaun Dwyer

Industry representatives Jackie Crampton, South Para Biodiversity Group Ray James, Adelaide Hills Council John Munday, Horticultural Executive Committee, South Australian Farmers

Federation Michael Bowe and Alan Dean, Adelaide Hills Wine Region Richard Jong, Floriculture South Australia Peter Hackworth, Phylloxera and Grape Industry Board of South Australia Kym Green, Cherry Growers Association Don Parker, Berry Fruits Association David Kerber and David Kretchmer, dairy industry Ernie Sullivan, Australian Wine and Brandy Corporation Trevor Ranford and the members of the Apple and Pear Growers Association Sally Robbins, chestnut grower Keith Jones, SA Wine and Brandy Industry Association Colin Fox, Dairy Farmers Graham Webster, District Council of Victor Harbor Roger Brockhoff, horticulturist

Government representatives Judith Kirk, Planning SA Russell Flavel, Primary Industries and Resources South Australia Brian Hughes, PIRSA Rural Solutions

Watershed Protection OfficeíFebruary 2002 iii


TABLE OF CONTENTS

Acknowledgements................................................................................................ iii

Executive summary................................................................................................ vii

1. Introduction.................................................................................................1

1.1 Purpose ............................................................................................................... 1

1.2 Background ........................................................................................................ 1

1.3 Project objectives ............................................................................................... 2

1.4 Land status data mapping trial ....................................................................... 2

1.5 Land status data mapping of the watershed ................................................. 2

2. Methodology ................................................................................................3

2.1 Development of methodology......................................................................... 3

2.2 Mapping principles........................................................................................... 3

2.2.1 Definitions .................................................................................................. 3

2.2.2 Mapping small land titles........................................................................... 3

2.2.3 Roadside reserves and easements ................................................................ 3

2.3 Land status data classification......................................................................... 4

2.4 Aerial photograph interpretation ................................................................... 4

2.4.1 Land cover mapping at 1:4000 ................................................................... 6

2.4.2 Land use mapping at 1:4000....................................................................... 8

2.5 Validation of data.............................................................................................. 8

2.5.1 Internal validation ...................................................................................... 8

2.5.2 External validation ..................................................................................... 8

2.6 Outcomes from the project............................................................................... 9

2.6.1 Maps ........................................................................................................... 9

2.6.2 Digital data ................................................................................................. 9

2.6.3 Web data ..................................................................................................... 9

2.6.4 CD............................................................................................................... 9

3. Discussion.................................................................................................. 11

3.1 Accuracy of aerial photograph interpretation ............................................ 11

3.2 Factors influencing interpretation ................................................................ 11

3.2.1 Timing of obtaining the aerial photographs.............................................. 11

3.2.2 Timing of mapping and fieldwork............................................................. 11

3.2.3 Time lapse ................................................................................................. 11

4. Metadata/appropriate use of data ................................................................ 13

4.1 Requirements for metadata............................................................................ 13

4.2 Limitations to use ............................................................................................ 13

5. Recommendations and future directions....................................................... 15

5.1 Changes to methodology/classification systems ....................................... 15

5.1.1 Methodology ............................................................................................. 15

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5.1.2 Classification system................................................................................. 15

5.2 Updates ............................................................................................................. 16

5.3 Increase in spatial extent ................................................................................ 17

6. References .................................................................................................19

List of appendices

Appendix 1: Definitions....................................................................................................... 21

Appendix 2: Land cover classification system ................................................................. 23

Appendix 3: Land use classification system ..................................................................... 31

Appendix 4: Land cover classification accuracies............................................................ 39

Appendix 5: Land use classification accuracies ............................................................... 41

Appendix 6: MetadataíPage 0 elements ......................................................................... 43

Appendix 7: MetadataíPage 1 elements ......................................................................... 47

List of figures

Figure 1: Land status data model ......................................................................................... 5

Figure 2: Heads-up digitising of land cover at a scale of 1:4,000 ..................................... 6

Figure 3: Classification of digitised land cover .................................................................. 7

List of tables

Table 1: Classification of emerging industries for land cover........................................ 16

Table 2: Classification of emerging industries for land use ........................................... 16

Table 3: Additional layers ................................................................................................... 17

Table 4: Update features...................................................................................................... 17

Watershed Protection Office ë February 2002vi


Executive summary

Land status data (land cover, land use and land management) is essential for undertaking a variety of natural resource assessments. The validity and success of these assessments is often constrained by the availability of suitable data.

A number of methods have previously been used to obtain land status data, including spatial and non-spatial data, at varying scales. This has resulted in complexities in determining the most suitable data for localised assessments in the Mount Lofty Ranges. The principal requirement for land status data in the Mount Lofty Ranges is to ensure that it is suitable for assessment at a local level, while having the potential for use in regional planning. A secondary objective is to meet national and State classification standards.

After assessing the available land status data, it was determined that the business needs of the Watershed Protection Office (WPO) of the Environment Protection Agency (EPA) and other stakeholders were not being fully met. As a result, the WPO decided to undertake a land status data mapping trial.

The trial was carried out to evaluate the suitability, accuracy and economics of several different methods of mapping land status data in the Mount Lofty Ranges. The trial focused on a 50-km2 area around Lobethal, in the upper Onkaparinga catchment, where there is a wide range of land covers and land uses. Two methods of capturing land status data were consideredíaerial photograph interpretation and classification of hyper-spectral imagery. Interpretation of 1:20,000 aerial photographs at a scale of 1:4000 proved to be the most suitable, accurate and cost-effective method.

The overall objective of the land status data mapping project was to develop a cost-effective, multi-layered classification system to map land cover and land use at a resolution suitable for sub-catchment analysis in the Mount Lofty Ranges. The data model developed during the trial was expanded to include a link to the Australian New Zealand Draft Land Use Codes (ANZLUC), as well as elements that indicated land cover and land use change.

From the results of the trial, the methodology and classification system was refined to enable mapping of land status data for the Mount Lofty Ranges Watershed. Mapping occurred during 2001 and early 2002 as a collaborative project between the EPA and the Environmental & Geographic Information (EGI) division of the Department for Environment and Heritage (DEH).

Land cover and land use attributes were mapped via heads-up digitising, while viewing 1:20,000 ortho-rectified aerial photographs at a scale of 1:4000. A series of menus developed by EGI assisted the process. Line work accuracy was checked as part of the overall assessment regime followed throughout the project.

Classification occurred through another series of menus developed by EGI and followed the multi-level classification system developed by the WPO. Classifications were determined firstly by interpreting the aerial photographs and then compared with other catchment reference layers. Classification accuracy was then checked both on-screen and in the field.

As data acquisition and classification accuracy were of paramount importance, the assessment regime instigated at the beginning of the project ensured that accuracy checks were conducted after each stage of mapping. This ensured that mapping

Watershed Protection Office ë February 2002 vii


principles were adhered to and that there was consistency throughout the dataset. The spatial accuracy of the final dataset is ±5 m, while attribute accuracy is between 80% and 95%.

An important part of the land status data mapping project was to consult with relevant industry groups to ensure that they had an input to the validation of the data, and confidence in the accuracy of the final dataset. This extremely valuable consultation was completed during January 2002. The dataset was finalised early in February 2002.

The outcomes from the project encompass a range of products including two sets of 19 maps for land cover and land use within the watershed, two spatial datasets available in a variety of formats, metadata and the final report detailing the various project principles, methods and standards. The report will be placed on the EPA website and be linked to the EGI website where the spatial information will reside.

This land status data will provide valuable information on the distribution of land uses in catchments and how the uses relate to water quality. The data will also enable extension and industry groups to target their education campaigns to specific regions where relevant crop types occur, to calculate areas of crop for specific regions and to calculate water use.

Watershed Protection Office ë February 2002viii


1. Introduction

1.1 Purpose Land status data has been generated for the Mount Lofty Ranges Watershed. This report describes the process of acquiring the data, outlines the level of accuracy achieved, provides metadata on the dataset, identifies improvements which can be made in the mapping and classification methodology and makes recommendations on mapping updates.

1.2 Background Land status data (see Appendix 1) is required for natural resource management in the Mount Lofty Ranges. This information is essential when considering the sustainability of our land and water resources and the risks associated with resource use. Land status information is useful for relatively simple tasks such as determining crop areas through to more complex applications in water catchments such as modelling, estimating irrigation water requirements, or as an aid in tracing pollutants. The success of these assessments is often constrained by the lack of suitable data.

While land status information has been available for some time (land use valuation data, Australian Bureau of Statistics records on agricultural land cover and various mapping projects), the advent of computer mapping and storage of land attributes on a geographic information system (GIS) has enabled the collection and wider use of the data. In particular, the data can be integrated with other relevant datasets. In the past, data products have focused on differing aspects of the informationí enterprise productivity, spatial extents, seasonal changes and land management have influenced the interpretation of the land (classification) for particular projects. In addition, mapping and data collection scales and accuracies have considerably influenced the final information.

As a result, it has been difficult to obtain appropriate, consistent and correlatable land status information for resource assessment. The difficulties are associated with the mapping or classification that has been undertaken, or mis-interpretations because of a lack of knowledge regarding the purpose of the information and its use limitations.

The current business needs of the Watershed Protection Office (WPO) of the Environment Protection Agency (EPA) and other stakeholders were not being fully met by the data available. A broad suggestion was to refine the methods for collecting land status information in order to:

• distinguish between land cover, land use and land management data

• map land cover and land use within separate GIS layers

• incorporate higher resolution imagery and improved mapping accuracy to support sub-catchment data acquisition.

The project objectives included other specific requirements.

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1.3 Project objectives The overall objective of the land status data mapping project was to develop a cost-effective, multi-layered classification system to map land cover and land use at a resolution suitable for sub-catchment analysis in the Mount Lofty Ranges.

Secondary objectives of the project included:

• establishing a classification system with a hierarchy of levels, suitable for regional and localised resource assessments

• setting discrete classification fields that minimise ambiguity and are consistent with the draft Australian and New Zealand Land Use Codes without compromising the requirements at the local level

• local validating by industry and catchment stakeholders

• providing suitable metadata and information to assist in interpreting the data

• creating a mapping system that would enable the ready capture of additional layers of data or the updating of fields.

1.4 Land status data mapping trial In order to meet the WPOçs requirements for suitable land status data, a land status data mapping trial was undertaken to evaluate the different mapping mechanisms available, and to develop a classification system for the Mount Lofty Ranges.

The trial was conducted between December 2000 and May 2001. An evaluation of the trial recommended the most suitable, accurate and economic method of capturing the data at a sub-catchment scale, and a classification system. The review discussed the potential for extending the preferred method to include land status data mapping for the watershed.

1.5 Land status data mapping of the watershed The recommendations of the land status data mapping trial (Bradley & Billington 2001) led to the decision to map the land status of the watershed using the methodology developed from mapping the 1:20,000 aerial photographs. The Environmental Geographic & Information (EGI) division of the Department for Environment and Heritage (DEH) was contracted to carry out the mapping and classification work. The same team member from the EPA who undertook the trial provided training in aerial photograph interpretation and undertook accuracy assessments throughout the project. Mapping of the watershed was conducted between June and December 2001.

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2. Methodology

2.1 Development of methodology Prior to mapping the Mount Lofty Ranges Watershed for land status data (land cover and land use), a trial was carried out in a 50-km2 area around Lobethal to determine the preferred method of mapping land status data at the sub-catchment scale. Bradley & Billington (2001) documented the results of the trial and recommended 1:20,000 aerial photographs as the most suitable, accurate and cost-effective method.

The land status data mapping project for the watershed utilised the method and the data model developed during the trial (see section 2.3). The important aspects of the method defined through the trial included mapping principles, the classification system and the data model; these are discussed below.

2.2 Mapping principles

2.2.1 Definitions

To aid data capture and classification of features, definitions (see Appendix 1) were developed to describe the primary function of the land (land cover) and the principal management objective (land use). They also assisted in determining which features should be distinguished as mapped units. For example, if the primary function of the land was pasture then scattered trees in a pasture would not be mapped. However, a significant patch of trees would be more appropriately defined as a vegetation feature within land cover. The land use for this feature could be mapped as animal husbandry or vegetation, depending on the presence of understorey vegetation, fencing and stock tracking. Similarly, watercourses and headlands within a vineyard were not mapped separately as the primary management objective and function was still viticulture.

2.2.2 Mapping small land titles

Mapping small land titles (titles that are less than 1 ha) is problematic because the land cover and land use vary greatly. Considerable effort is required to record this data but the information may not have a substantial impact on the overall accuracy of the dataset. Notwithstanding this, the cumulative effect of these small titles on natural resource management may be significant in relation to certain assessments and so should not be disregarded.

As such, mapping principles for small land titles were defined as follows to ensure a consistent approach to recording this data:

• a contiguous series of small titles (three or more) was mapped as the one feature and coded for the dominant land cover and land use within the titles

• an isolated small title, if considered not to impact on the primary function of the land or the principal management objective, was not mapped: e.g. a single residence within a grazing area.

2.2.3 Roadside reserves and easements

Mapping principles for roadside vegetation were defined as follows to ensure a consistent approach in recording this data:

• roads defined as the road itself and any normal cleared road reserve area did not require any additional mapping



• surveyed roads which did not have a defined track were mapped according to the land cover and land use visible on the aerial photograph and the land title boundaries were removed.

• easements were considered in a similar manner to surveyed roads

• any area of vegetation within the road polygon that was wider than 5 m and longer than 20 m was mapped as a separate unit and classified accordingly.

2.3 Land status data classification A classification system was devised during the mapping trial (Fig. 1). The system was designed to provide multiple levels of information regarding land cover and land use (see Appendices 2 and 3). This hierarchy of levels provided broad åcategoriesç and more detailed åclassesç. A ådescriptionç field was also provided in the classification system to document valid features within each class, but this was not coded during the classification process.

Provision was also made in the classification system to indicate when more than one crop is grown at a particular site. The årotationç field is used in this situation to indicate that the land cover or land use is not a perennial feature.

During the early stages of capturing the data in the watershed, it was noted that a considerable amount of development had occurred since the aerial photographs had been flown. It was felt that new development needed to be included in the data, so an additional field was incorporated in the dataset to cater for land cover and land use changes.

While the collection of land management data was not within the scope of the project, concurrent work by the WPO is assessing management practices for a range of land uses. This data will have the potential to be linked to the land cover and land use spatial data at a later date.

Where possible, the classification codes have been linked to the draft Australian and New Zealand Land Use Codes at the land cover and land use level.

2.4 Aerial photograph interpretation EGI supplied ortho-rectified aerial photographs of the watershed in .bil, .ers and .hdr file formats. The aerial photographs, flown during September 1999, were at a scale of 1:20,000 and scanned in at a resolution of 900 dpi.

The digital cadastre database (DCDB) coverage for the watershed was supplied by EGI and used as a map base. Other reference layers used were:

• valuation land use data (Department for Environment and Heritage [DEH])

• regional land use planing data, 1999 (Primary Industries and Resources South Australia [PIRSA])

• forestry data (Forestry SA)

• heritage agreement data, 2001 (Planning SA [PLNSA])

• land holdings data, 1999 (SA Water Corporation)

• park and reserve boundaries, 2000 (National Parks and Wildlife SA [NPWS])

• dairy farm pick-up points (SA Milk).



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Figure 1: Land status data model



Data capture was carried out using the ArcEdit function within ESRIçs ArcInfo 8.1. EGI developed a series of menus, which considerably aided the digitising process.

Given the size of the watershed (~1600 km2), it was necessary to break the area up into discrete sections to keep the dataset to manageable sizes. This was achieved by creating åsuper blocksç based on the location of roads on the DCDB and the number of aerial photographs within each area. The super blocks enabled several areas to be worked on at the same time by different team members.

2.4.1 Land cover mapping at 1:4000 Initially, the land cover was mapped from the aerial photograph. Discrete areas of land cover were digitised on-screen while viewing the 1:20,000 aerial photograph at a scale of 1:4000 (Fig. 2).

Figure 2: Heads-up digitising of land cover at a scale of 1:4000

The DCDB was used as the base layer, and land cover features were mapped within a title boundary and common boundaries between titles dissolved. This occurred for an entire super block.

Using the DCDB as the mapping base supported the project objective of allowing the development of additional GIS layers and feature updates, while maintaining a consistent and relatable map base.

The DCDB was assumed to be accurate to at least 1:4000 but, at the same time, it was recognised that there were inconsistencies in the geometric accuracy within this GIS

Watershed Protection Officeí February 20026


layer. As a result, it is appreciated that the addition of data for the broad-scale mapping will have to overcome possible issues with small geometric shifts.

After digitising each super block, the EPA project officer assessed the accuracy of the line work. Digitising accuracy was checked on-screen against the aerial photographs to ensure that no feature had been missed and mapping had been in accordance with the mapping principles defined in the project plan.

During the initial stages of the project each member of the EGI team undertook classification of a small portion of a super block. This included a field trip so the team member could become familiar with different cover types. The fieldwork was particularly important when differentiating between and within orchards and vineyards.

The classification process used the series of menus developed by EGI (Fig. 3). This aided the data coding process as drop-down menus were utilised to assign codes. Classifications were determined firstly by interpreting the aerial photographs and then checked against the reference layers listed above; these layers were used as a guide only and amendments were made as necessary.

Figure 3: Classification of digitised land cover

Maps were printed after the classification process and another on-screen accuracy assessment against the aerial photographs was undertaken by the EPA project officer to check the coverage for unclassified and misclassified areas. Corrections were made where necessary.



Fieldwork was undertaken to validate the classifications for each super block, to determine a classification for an area where it was not known and also to note different land uses (as distinguished from land cover), particularly with regard to pasture areas. Land cover and land use changes identified through the fieldwork were noted on the field maps. Subsequent digitising included these areas in the dataset, and they classified at the åclassç level.

The final maps used solid colours to display the land cover and land use digitised from the aerial photographs. The land cover and land use changes were displayed using a semi-transparent layer so that the original land cover and land use can be observed.

It needs to be recognised that, whilst the land cover and land use change information has been included in the dataset, it is not spatially accurate and so should only be used as a preliminary guide to land cover and land use changes. Updates of the dataset will identify these areas of change and ensure that they are spatially accurate.

2.4.2 Land use mapping at 1:4000

The land cover dataset prepared for each super block was copied and renamed for the land use dataset, as many of the land covers were the same as the land use (see Appendices 2 and 3). As with the land cover dataset, the aerial photographs were viewed at 1:4000 and discrete areas of land use were digitised on-screen, checked by the EPA project officer and amended as necessary. The classifications were made from interpretations of the aerial photographs, field notes, and with reference to the layers listed above. The information was included in the spatial dataset by using the menus created in ArcEdit and in line with the classification system. The EPA project officer again checked the accuracy of the classifications both on-screen and in the field.

2.5 Validation of data

2.5.1 Internal validation

As the accuracy of the data was of paramount importance to the WPO, an internal checking regime that included an assessment of the digitising and classification accuracy was followed throughout the mapping process (see sections 2.4.1 and 2.4.2). This checking regime ensured that the accuracy targets set for each class within the classification system were met (see Appendices 4 and 5).

2.5.2 External validation

Following the mapping of the watershed, consultation with various industry and government groups was conducted during the validation process. Sixteen representatives were involved in validating the data:

• South Para Biodiversity Group

• Adelaide Hills Council

• Horticultural Executive Committee, South Australian Farmers Federation

• Adelaide Hills Wine Region

• PIRSA Rural Solutions

• Floriculture South Australia

• Phylloxera and Grape Industry Board of South Australia



• Cherry Growers Association

• Berry Fruits Association

• dairy industry members

• Australian Wine and Brandy Corporation

• Apple and Pear Growers Association

• chestnut growers

• SA Wine and Brandy Industry Association

• Dairy Farmers

• District Council of Victor Harbor

Each representative validated the accuracy of their specific interest. In some cases, local knowledge was also applied to ensure the accuracy of other areas of classification. After the consultation process, final adjustments were made to the datasets and final maps were prepared.

2.6 Outcomes from the project To make the information created through the project accessible to a wide range of stakeholders, who may not necessarily have access to a GIS, a number of products were created in the final stages of the project in addition to this report.

2.6.1 Maps

Two series of 19 maps for land cover and land use were produced in a standard format and will be available through EGI.

2.6.2 Digital data

The dataset is available in several formats and projections suitable for use in a range of GIS systems.

2.6.3 Web data

This report will be available in pdf format on the EPA website, with a link to the EGI website where the dataset resides.

2.6.4 CD

A CD containing this report, the dataset and the metadata for the dataset is also available.



3. Discussion

3.1 Accuracy of aerial photograph interpretation As previously discussed, a checking regime was instigated at the beginning of the project to ensure that an assessment of the accuracy was carried out after each stage of mapping. This ensured that the mapping principles were adhered to and that there was consistency throughout the dataset.

When preparing the project plan, a series of accuracy targets was set for each class within the classification system (see Appendices 4 and 5). The rigorous assessment and validation processes throughout the project, and the industry validation, have enabled these targets to be met; however, no formal external validation process was implemented.

Rapid land use change has occurred, and is still occurring, throughout the Mount Lofty Ranges. Therefore, the dataset, including the land cover and land use change elements, may not be as accurate as when it was mapped.

3.2 Factors influencing interpretation Factors involved in mapping and classification accuracy were the timing of aerial photograph collection, the timing of the mapping and the fieldwork and the time frame between obtaining the aerial photographs and interpreting them.

3.2.1 Timing of obtaining the aerial photographs

The 1:20,000 aerial photographs were flown in spring 1999. In regards to farm dams, this was an ideal time for flying because the dams were relatively full. However, this was not the best season for land cover and land use mapping purposes, particularly when interpreting irrigated pasture. The spring flush meant that all pastures, whether irrigated or not, were quite green and it was difficult to interpret whether irrigation was occurring. The ability to distinguish between irrigated and un-irrigated pasture would have assisted in mapping the differences between broadscale animal husbandry (which is based on pasture grazing) and intensive animal husbandry (which often uses strip grazing of improved/irrigated pastures).

3.2.2 Timing of mapping and fieldwork

Mapping and fieldwork occurred throughout the winter and spring of 2001. This made the identification of different orchards very difficult because they are planted with deciduous species. Consultation with industry representatives helped to remedy errors in this respect. Ideally, data capture and fieldwork should occur during summer; it is easier to identify different types of orchards then as there are leaves and/or fruit present.

3.2.3 Time lapse

Although the aerial photographs were flown in September 1999, it is worth noting that the ortho-rectification of the photographs for the watershed was not completed until mid 2001. In the two-year period between flying the aerial photographs, ortho-rectification and image interpretation, classification and validation, considerable development had occurred. Thus, even if land status data mapping is carried out rapidly, the resultant data is still dated. Indeed, within a month of having interpreted several areas, in three sites there had been changes to the land coverífrom nut trees

Watershed Protection OfficeíFebruary 2002 11


to vines at one, from pasture to strawberries at a second and at a third site the apple trees had been bulldozed and left as pasture for future development as vines.

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4. Metadata/appropriate use of data

4.1 Requirements for metadata A mandatory function of the EPA is to collect the core metadata elements as required by the South Australian Spatial Information Directory. This information was collected as part of the project and now resides with the DEH co-ordinator. The metadata provides information on the data, spatial location, levels of accuracy, attributes and contact details. In addition, more detailed metadata has been developed to support the mapped layers and adheres to the DEH metadata standards for page 0 and page 1 data elements. Metadata at this level has been developed to support information requirements when features are updated within the data layers (see Appendices 6 and 7).

4.2 Limitations to use The land status dataset is not intended to be used for land use change comparisons with previously collected GIS land use data. Land cover and land use data has not previously been collected at this resolution and to compare data at this scale with much broader scale data could provide misleading results. Future updates using this method, however, will ensure that meaningful land use change comparisons are possible.



5. Recommendations and future directions

5.1 Changes to methodology/classification systems

5.1.1 Methodology The mapping methodology was developed during the land status data trial. However, the trial encompassed only 50 km2 in the Lobethal area and did not cover the full range of land cover and land uses within the watershed. The mapping of the watershed was an iterative process; changes occurred as different land cover and land uses were identified. While the methodology caters for all land cover and land use within the Mount Lofty Ranges Watershed, it is also applicable to other areas of the Mount Lofty Ranges and South Australia generally.

5.1.2 Classification system

The proposed classification system was amended after the land status data mapping trial to incorporate a link to the Australian and New Zealand Land Use Codes classification system.

Where possible the classification system adopted similar features to the Planning SA land cover system, which is used in irrigation areas along the River Murray and, potentially, within the River Murray Catchment Water Management Boardçs area.

The methodology captured land status information at the localised level. This information can be simplified where regional assessments are appropriate. This will involve the consideration of appropriate coding detail and resolution of the mapped features.

The mapping process for the watershed revealed several emerging and existing industries that were not classified separately under the classification system. As these industries have the potential to impact on the watershed (e.g. on its water quality), it is recommended that they be classified separately in the next round of land status data mapping.

Those industries requiring separate classification include pine and olive plantations, farm forestry and home orchards. Several small, mixed orchards were identified close to houses; they were for household use. The dominant tree type (e.g. pomefruit) was used for classification purposes during the mapping process. Tables 1 and 2 show the suggested system of classifying these industries for land cover and land use in the next round of mapping. It should be noted that the description field is for the readerçs information only, and is not included in the dataset.

If the recommended changes are adopted prior to the next round of mapping, the new classification system will need to be correlated with the existing system to enable the changes in land use to be calculated.



Table 1: Classification of emerging industries for land cover

Land cover Category Class Description

Horticultureëtrees Orchards Olives Olives

Mixed/home Mixed orchard types (e.g. orchards stone/pome/citrus/nut)

Forestry/protected area

Native vegetation Native farm forestry Blue gum Spotted gum Blackwood Sugar gum Red gum Saltbush etc.

Native vegetation Remnant vegetation Revegetation

Forestry Exotic vegetation Exotic farm forestry Paulownia Cricket-bat willow etc.

Exotic vegetation Pines Willows Ash etc.

Table 2: Classification of emerging industries for land use

Land use Category Class Description

Horticultureëtrees Orchards Olives Olives

Mixed/home orchards Mixed orchard types (e.g. stone/pome/citrus/nut)

Forestry/protected area

Native forestry Native farm forestry Blue gum Spotted gum Blackwood Sugar gum Red gum etc.

Managed native Managed native Remnant vegetation vegetation vegetation Revegetation

Forestry Exotic forestry Exotic plantation Pines forestry Christmas trees

Exotic farm forestry Paulownia Cricket-bat willow etc.

Managed exotic Managed exotic Pines (windbreaks) vegetation vegetation Ash

Willow etc.

5.2 Updates One objective of the project was to support the acquisition of additional layers of data and to provide for the updating of features. Thus the data model and the methodology were designed to cater for this. Stakeholders with particular requirements can assist in collecting this information by using the methodology developed for this project. The specific studies can collect imagery to maximise the



detection and resolution of features of interest. Tables 3 and 4 provide examples of additional layers and features in which some stakeholders are interested.

Table 3: Additional layers

Additional layer Collection criteria

Irrigated pasture Late summer-flown aerial photographs

Rural living Any property within a certain size range

Table 4: Update features

Update feature Collection criteria

Dairies Every 2 years

Dams Every 2 yearsëpreferably late spring for full capacity

Vegetables Every 2 yearsësummer/winter alternately

Vineyards Every 2 years

By following this process, ongoing work in the watershed will build on the land status information developed through this project and will ensure a consistent and correlatable dataset.

5.3 Increase in spatial extent The WPO and EGI have now moved on to map land status data in the Northern Adelaide & Barossa Catchment Water Management Board area. It is hoped that other bodies will also adopt the methodology developed by the WPO, and continue to collect land status data for use by industry associations and community groups.



6. References Bradley, J. & Billington, K. (2001). Land status data mapping trial. Unpublished report, Watershed Protection Office, Department for Environment and Heritage, Adelaide.



Appendix 1: Definitions

land coveríthe permanent or semi-permanent cover of the land surface. It is the combination of vegetation, water and man-made structures that make up the earthçs landscape.

land cover changeíany change that has occurred to the permanent or semi-permanent cover of the land surface since September 1999. This information is not spatially correct, as these changes were not visible on the aerial photographs.

land status dataíinformation on land cover, land use and land management which enables analysis of the sustainability of land and water resources and the risks associated with resource use. The land status data displayed in the maps produced by the project has been captured from 1:20,000 ortho-rectified aerial photographs flown during September 1999.

land useíthe way in which the land is used for principal management objectives (e.g. economic production activities, conservation).

land use changeíany change that has occurred to the principal management objectives for the land since September 1999. This information is not spatially correct, as these changes were not visible on the aerial photographs.



Appendix 2: Land cover classification system1

Code Land cover Category Class Description Rotation

01 01 01 Horticultureëtrees Orchards Citrus Grapefruit n

Lemons n

Limes n

Mandarins n

Oranges n

Tangelos n

01 01 02 Horticultureëtrees Orchards Stonefruit Apricots n

Cherries n

Nectarines n

Peaches n

Plums n

Quandong n

01 01 03 Horticultureëtrees Orchards Pomefruit Apples n

Feijoas n

Loquat n

Nashi n

Pears n

Pomegranate n

Quinces n

01 01 04 Horticultureëtrees Orchards Nuts Almonds n

Cashews n

Chestnuts n

Hazelnuts n

Macadamias n

Pecans n

Pistachios n

Walnuts n

01 01 05 Horticultureëtrees Orchards Tropical fruit Avocados n

Bananas n

Coconuts n

Custard apples n

Dates n

1 An additional field, land cover change, in the land cover dataset indicates developments that have occurred since September 1999.




Lychees n

Mango n

Tamarillo n

Paw paw n

Pineapple n

Rambutan n

01 01 06 Horticultureëtrees Orchards Orchards/ miscellaneous

Olives n

Figs n

Persimmon n

Tea n

Coffee n

Tobacco n

01 01 07 Horticultureëtrees Orchards Orchards/berries Blueberry n

Elderberry n

Gooseberry n

Mulberry n

02 01 01 Horticultureërow crops

Vines Vines Grapes n

Hop n

Kiwifruit n

Passionfruit n


Rowëberries Berries Blackcurrant n

Blackberry n

Boysenberry n

Loganberry n

Raspberry n

Redcurrant n

Strawberry n

Tayberry n

Youngberry n


Vegetables Root vegetables Beet y

Carrot y

Parsnip y

Radish y

Rutabagas y




Swede y

Turnip y

02 03 02 Horticulture ê row crops

Vegetables Legumes Bean y

Chickpeas y

Lentils y

Peas y


Vegetables Brassicas Broccoli y

Brussels sprouts y

Cabbage y

Cauliflower y

Chinese cabbage y

Kale/collards y

Kohlrabi y


Vegetables Leafy greens Celery y

Chinese greens y

Endive y

Lettuce y

Silverbeet y

Spinach y


Vegetables Cucurbits Cucumber y

Squash y

Melons y

Watermelon y

Pumpkin y

Zucchini y


Vegetables Aliums Garlic y

Leeks y

Onion y

Shallots y


Vegetables Solanums Capsicum y

Eggplant y

Potatoes y

Tomatoes y





Vegetables Perennials Artichoke y

Asparagus y

Horseradish y

Rhubarb y


Vegetables Vegetables/ miscellaneous

Okra y

Sweet corn y

Sweet potatoes y

Mushrooms y


Floriculture Native flowers Kangaroo paw n

Geraldton wax n

Boronia n

Ixodia n

Flannel flower n

Thryptomene n

Banksia n

Tea tree n

NSW Christmas bush n

Waratah n

Dryandra n

Eucalyptus n kruseana


Floriculture Exotic flowers Proteas n

Leucodendron n

Carnations n

Irises n

Daffodils n

Tulips n

Lilies n

Gerberas n

Lisianthus n


Floriculture Herbs Basil n

Oregano n

Parsley n

Chives n




Echinacea n

Jojoba n

Ginseng n

Lavender n

Coriander n

Tarragon n

Rosemary n

03 01 01 Field crops Cropping Legumes Lucerne n

Lupins n

Field peas n

Fava beans n

03 01 02 Field crops Cropping Cereals Barley n

Oats n

Wheat n

Rye n

03 01 03 Field crops Cropping Oil seeds Canola n

Indian mustard n

03 01 04 Field crops Cropping Cropping/ miscellaneous

Turf farming n

03 02 01 Field crops Pasture Pasture Native pasture n

Unimproved pasture n

Improved pasture n

04 01 01 Forestry Exotic vegetation

Exotic vegetation Pines n

Paulownia n

Willows n

Ash n

05 01 01 Forestry/protected Native Native Remnant n areas vegetation vegetation vegetation

Revegetation n

Forestry n

06 01 01 Manufacturing/ Manufacturing/ Industry/ Manufacturing n commerce urban commercial

Winery n

Abattoir/stockyard n

Landfill n

Recycling yards n

Composting depots n




Tourism n development

Retail n

Plant nursery n

07 01 01 Accommodation Accommodation/ urban

Residential Suburban n

Residential Caravan park n


Vacant allotment Vacant allotment n

08 01 01 Services Services/urban Cultural Historical building/precinct

n

Cemetery/ n crematorium

Community n building/facility

Education n

Religion n

09 01 01 Transport/storage/ Transport/urban Utilities/other Airport n utilities and

communication

Military facility n

Wastewater n treatment plant

Service n easement/utility corridor

Power station/sub- n station

Road n

10 01 01 Mining and Mining/ Mining/ Open cut n

extractive extraction extraction

industries

Soil dump n

Gravel extraction n

Shafts/pit heads n

Alluvial mining n

Restored lands n

Sand mining n

11 01 01 Water bodies Water bodies Dam Dam n

11 01 02 Water bodies Water bodies Reservoir Reservoir n

11 01 03 Water bodies Water bodies Sewage pond Sewage pond n




11 01 04 Water bodies Water bodies Wetland Wetland n

11 01 05 Water bodies Water bodies Lake Lake n



Appendix 3: Land use classification system2

Code Land use Category Class Description Rotation

01 01 01 Horticultureëtrees Orchards Citrus Grapefruit n

Lemons n

Limes n

Mandarins n

Oranges n

Tangelos n

01 01 02 Horticultureëtrees Orchards Stonefruit Apricots n

Cherries n

Nectarines n

Peaches n

Plums n

Quandongs n

01 01 03 Horticultureëtrees Orchards Pomefruit Apples n

Feijoas n

Loquats n

Nashi n

Pears n

Pomegranates n

Quinces n

01 01 04 Horticultureëtrees Orchards Nuts Almonds n

Cashews n

Chestnuts n

Hazelnuts n

Macadamias n

Pecans n

Pistachios n

Walnuts n

01 01 05 Horticultureëtrees Orchards Tropical fruit Avocados n

Bananas n

Coconuts n

Custard apples n

Dates n

2 An additional field, land use change, in the land use dataset indicates developments that have occurred since September 1999.




Lychees n

Mangos n

Paw paw n

Pineapple n

Rambutan n

Tamarillo n

01 01 06 Horticultureëtrees Orchards Orchards/ miscellaneous

Olives n

Figs n

Persimmon n

Tea n

Coffee n

Tobacco n

01 01 07 Horticultureëtrees Orchards Orchards/berries Blueberry n

Elderberry n

Gooseberry n

Mulberry n


Vines Vines Grapes n

Hop n

Kiwifruit n

Passionfruit n


Rowëberries Berries Blackcurrant n

Blackberry n

Boysenberry n

Loganberry n

Raspberry n

Redcurrant n

Strawberry n

Tayberry n

Youngberry n


Vegetables Root vegetables Beet y

Carrot y

Parsnip y

Radish y

Rutabagas y




Swede y

Turnip y


Vegetables Legumes Bean y

Chickpeas y

Lentils y

Peas y


Vegetables Brassicas Broccoli y

Brussels sprouts y

Cabbage y

Cauliflower y

Chinese cabbage y

Kale/collards y

Kohlrabi y


Vegetables Leafy greens Celery y

Chinese greens y

Endive y

Lettuce y

Silverbeet y

Spinach y


Vegetables Cucurbits Cucumber y

Squash y

Melons y

Watermelon y

Pumpkin y

Zucchini y


Vegetables Aliums Garlic y

Leeks y

Onion y

Shallots y


Vegetables Solanums Capsicum y

Eggplant y

Potatoes y

Tomatoes y





Vegetables Perennials Artichoke y

Asparagus y

Horseradish y

Rhubarb y


Vegetables Vegetables/ miscellaneous

Okra y

Sweet corn y

Sweet potatoes y

Mushrooms y


Floriculture Native flowers Kangaroo paw n

Geraldton wax n

Boronia n

Ixodia n

Flannel flower n

Thryptomene n

Banksia n

Tea tree n

NSW Christmas bush n

Waratah n

Dryandra n

Eucalyptus n kruseana


Floriculture Exotic flowers Proteas n

Leucodendron n

Carnations n

Irises n

Daffodils n

Tulips n

Lilies n

Gerberas n

Lisianthus n


Floriculture Herbs Basil n

Oregano n

Parsley n

Chives n




Echinacea n

Jojoba n

Ginseng n

Lavender n

Coriander n

Tarragon n

Rosemary n

03 01 01 Field crops Cropping Legumes Lucerne n

Lupins n

Field peas n

Fava beans n

03 01 03 Field crops Cropping Cereals Barley n

Oats n

Wheat n

Rye n

03 01 02 Field crops Cropping Oil seeds Canola n

Indian mustard n

03 01 04 Field crops Cropping Cropping/ miscellaneous

Turf farming n

04 01 01 Forestry Exotic vegetation

Exotic vegetation Pines n

Paulownia n

Willows n

Ash n

05 01 01 Forestry/protected areas

Native vegetation

Native vegetation Revegetation n

Remnant n vegetation

Forestry n

06 01 01 Manufacturing/ Manufacturing/ Industry/ Manufacturing n commerce urban commercial

Winery n

Abattoir/stockyard n

Landfill n

Recycling yards n

Composting depots n

Tourism n development

Retail n


1

2

3

4

5



Plant nursery n


Residential Suburban n

Caravan park n


Vacant allotment Unknown n

Vacant allotment n

08 01 01 Services Services/urban Cultural Historical building/precinct

n

Cemetery/ n crematorium

Community n building/facility

Education n

Religion n

09 01 01 Transport/storage/ Transport/ urban Utilities/other Airport n utilities and communication

Military facility n

Wastewater n treatment plant

Service easement/ n utility corridor

Power station/sub- n station

10 01 01 Mining and Mining/ Mining/ Open cut n

extractive extraction extraction

industries

Soil dump n

Gravel extraction n

Shafts/pit heads n

Alluvial mining n

Restored lands n

Sand mining n

11 01 0 Water bodies Water bodies Dam Dam n

11 01 0 Water bodies Water bodies Reservoir Reservoir n

11 01 0 Water bodies Water bodies Sewage pond Sewage pond n

11 01 0 Water bodies Water bodies Wetland Wetland n

11 01 0 Water bodies Water bodies Lake Lake n

12 01 01 Livestock Animal husbandry

Broadscale grazing

Sheep n




Horse n

Beef n

Goats n


Intensive grazing Horse n

Dairy n

Deer n

Alpacas n

Free-range hens n

Ostriches n

Emus n


Housed/confined Poultry farm n

Piggeries n

Cattle feedlots n

Aquaculture n

13 01 01 Cultural and recreation services

Recreation Recreation Golf course n

Football/soccer/ n

cricket oval

14 01 01 Protected Recreation/ Recreation/ Conservation parks n area/recreation protected area protected area area

Reserves n

National parks n

Wetlands n

Road/water n reserves

Parklands/open n spaces



Appendix 4: Land cover classification accuracies

Land cover Category Class Accuracy

Horticultureëtrees Orchards Citrus 95%

Stonefruit 95%

Pomefruit 95%

Nuts 90%

Tropical fruit 90%

Orchards/miscellaneous 90%

Orchards/berries 90%

Horticultureërow crops Vines Vines 95%

Horticultureërow crops Rowëberries Berries 90%

Horticultureërow crops Vegetables Root vegetables 80%

Vegetables/legumes 80%

Brassicas 80%

Leafy greens 80%

Cucurbits 80%

Aliums 80%

Solanums 80%

Perennials 80%

Vegetables/miscellaneous 80%

Horticultureërow crops Floriculture Native flowers 80%

Exotic flowers 80%

Herbs 80%

Field crops Cropping Cropping/legumes 80%

Cereals 80%

Oil seeds 80%

Cropping/miscellaneous 80%

Field crops Pasture Pasture 90%

Forestry Exotic vegetation Exotic vegetation 90%

Forestry/protected areas Native vegetation Native vegetation 90%

Manufacturing/ commerce Manufacturing/ urban Industry/commercial 95%

Accommodation Accommodation/urban Residential 95%

Services Services/urban Cultural 95%

Transport/storage/ utilities and communication

Transport/urban Utilities/other 95%

Mining and extractive

industries

Mining/extraction Mining/extraction 95%




Water bodies Water bodies Dams 95%

Reservoirs 95%

Sewage ponds 95%

Wetlands 95%

Lake 95%



Appendix 5: Land use classification accuracies

Land use Category Class Accuracy


Stonefruit 95%

Pomefruit 95%

Nuts 90%

Tropical fruit 90%







Brassicas 80%

Leafy greens 80%

Cucurbits 80%

Aliums 80%

Solanums 80%

Perennials 80%



Exotic flowers 80%

Herbs 80%

Field crops Cropping Legumes 80%

Oil seeds 80%

Cereals 80%




Manufacturing/commerce Manufacturing/urban Industry/commercial 95%



Transport/storage/utilities and communication


Mining and extractive industries



Reservoirs 95%



Land use Category Class Accuracy

Sewage ponds 95%

Wetlands 95%

Lake 95%

Livestock Animal husbandry Broadscale grazing 90%

Intensive 90%

Housed/confined 90%

Cultural and recreational services

Recreation Golf course 90%

Football/soccer/cricket oval 90%

Protected area/recreation area

Recreation/protected area Recreation/protected area 95%



Appendix 6: MetadataëPage 0 elements

Dataset information Title: Land status dataset for the Mt Lofty Ranges Watershed (land cover)

Custodian: Environmental and Geographic Information (EGI)

Owner: Environment Protection Agency (EPA)

Jurisdiction: South Australia

Description Abstract: The EPAçs Mount Lofty Ranges Watershed Protection Office (WPO) has developed a method to map land status data that is suitable for natural resource assessments at a sub-catchment scale. The land status dataset consists of two spatial layers: land cover and land use. Several data acquisition methods were evaluated and it was determined that interpretation of 1:20,000 aerial photos at a scale of 1:4000 (using the Digital Cadastral Data Base (DCDB) as a base layer) was the most suitable, accurate and cost effective method.

The data model designed during the trial provides multiple levels of information regarding land cover and land use. The hierarchy of levels provides broad åcategoriesç and more detailed åclassesç. The data model includes links to the Australian New Zealand Draft Land Use Codes (ANZLUC), plus elements that indicate land cover and land use change.

To ensure consistency and accuracy the method included the application of mapping principles, comprehensive line work and classification validation procedures, and industry validation.

Search words:

Land status

Land cover

Land management

Water

Water management

Mount Lofty Ranges

Watershed

Defined region North: -34.638640

South: -35.477230

East: 139.084991

West: 138.408108



Data currency Beginning date: 1 June 2001 (2001-06-01)

End date: 1 March 2002 (2002-03-01)

Data status Progress: Complete.

Update: As required

During early 2002, the spatial extent of the dataset is being extended to include the North Para River catchment of the Northern Adelaide & Barossa Catchment Water Management Board area. It is anticipated that further areas will be added at later dates.

Access Stored data format

Non digital:

Digital: ARC/INFO 8.1 coverage

Available data format

Non digital: Plotted maps.

Digital: ARC/INFO coverages and export files; ArcView shapefiles; DXF; MapInfo; Microstation design files.

Data quality Lineage

Source data history: The line work has been captured using the most up-to-date cadastral digital dataset available as a base layer. Land cover attributes were captured via åheads upç digitising using September 1999 1:20,000 ortho-rectified aerial photography. Line work was checked prior to classification to ensure adherence to the mapping principles and to keep the dataset consistent.

Classifications were determined firstly by interpreting the aerial photo images, then checked against other catchment reference layers. The EPA officer then checked classification accuracy both on-screen and in the field.

An important part of the land status mapping project was consultation with industry to ensure the relevant industry groups had input into the validation of the data and confidence in the accuracy of the final datasets. This consultation was carried out in January 2002 and proved extremely valuable.

Attribute accuracy: Accuracy targets were set at the beginning of the project, and through a series of accuracy assessments, including industry validation, it is felt that these targets have been met.





Stonefruit 95%

Pomefruit 95%

Nuts 90%

Tropical fruit 90%







Brassicas 80%

Leafy greens 80%

Cucurbits 80%

Aliums 80%

Solanums 80%

Perennials 80%



Exotic flowers 80%

Herbs 80%

Field crops Cropping Cropping/legumes 80%

Cereals 80%

Oil seeds 80%


Field crops Pasture Pasture 90%



Manufacturing/commerce Manufacturing/ urban Industry/commercial 95%



Transport/storage/ utilities and communication


Mining and extractive industries






Reservoirs 95%

Sewage ponds 95%

Wetlands 95%

Lakes 95%

Logical consistency: The data is topologically correct.

Completeness: The dataset is currently complete. It is suggested that future updates should occur as shown below.

Update feature Collection criteria

Dams Every 2 yearsëpreferably late spring for full capacity

Vineyards Every 2 years

Vegetables Every 2 yearsësummer/winter alternately

Dairies Every 2 years

Entire dataset Every 4 years

Contact information Organisation: EGI Position: Co-ordinator, Spatial Projects Unit Address: PO Box 550

NETLEY SA 5033 Phone: 8226 4907 Fax: 8226 4898 Email: [email protected]

Metadata date: 15 February 2002

Supplementary information: Page 1 metadata

Bradley, J & Billington, K. 2001. Land status data mapping trial, unpublished report, Watershed Protection Office, Department for Environment and Heritage, Adelaide.

Bradley, J & Billington, K. 2002. Land status data mapping for the Mount Lofty Ranges Watershed, Watershed Protection Office, Department for Environment and Heritage, Adelaide.




Appendix 7: MetadataëPage 1 elements

Responsible partyThe Watershed Protection Office developed the method for capturing land statusdata and is the owner of the data for the Mount Lofty Ranges Watershed.

DescriptionDataset classification: Principal version

Spatial representation type: Vector

Feature type: Polygon

Dimension: x, y

Sample graphic:

UsagePurpose: The dataset is the master digital copy of land cover for the Mount LoftyRanges Watershed.

Use: Land cover data for the Mount Lofty Ranges Watershed was collected as abaseline dataset for the catchment risk assessment being undertaken by the EPAçsWPO. The land cover data was captured at a resolution suitable for use at a sub-catchment scale.

The land status dataset will also enable extension and industry groups to target theireducation campaigns to specific regions, identify where relevant crop types occur,calculate areas of crop for specific regions, and calculate water use.

Use limitation: The land cover data is not intended to be used for land cover changecomparisons with previously collected GIS land use data. Land cover and land usedata has not previously been collected at this resolution and to compare data at thisscale with much broader scale data could provide misleading results. Future updates


using this method, however, will ensure that meaningful land use change comparisons are possible.

Dataset associations Dependent dataset title: Digital Cadastral Data Base (DCDB)

Attribute information The classification system was designed to provide multiple levels of information regarding land cover and land use. This hierarchy of levels provided broad åcategoriesç and more detailed åclassesç. A description field was also provided in the classification system to document valid features within each class, but was not coded within the actual classification processes. The data model created for the project is shown on the following page.

Attribute table type: Vectorípolygon

Attribute name Type Description Completeness Reliability

Land cover C Broad description linked to ANZLUC

100% Between 80-95%*

Category C Land cover typeëe.g. orchards 100% Between 80-95%*

Class C Land cover type according to similar cropsëe.g. pomefruit (apple & pear)

100% Between 80-95%*

Rotation C Indicates whether or not crop rotation occurs

100% Between 80-95%*

Land cover change C Indicates areas of development that had occurred since aerial photographs were flown in September 1999

100% for areas

identified

100%

*Attribute accuracy requirements were set at the beginning of the project for each land cover class.

Origin Dataset size:

Projection:

Datum:

31.7 MB

Geographicals

GDA94

Metadata management Date modified:

Modification officer:

Date authorised:

Authorisation officer:

15 February 2002

Oppermann, Katrina (EGI)

15 February 2002

Bradley, Jane (EPA)



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eg i il livi

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eg Field crops Hort cu ture - trees


Land cover nked to ANZLUC,

where appropr ate

eg Pasture

Category Class

eg Pasture

Class

Inc native pasture Un mproved pasture

mproved pasture

Descr pt on st o d features

with n c ass

Rotat on Y/N

Orchards aneous

Land cover change

Land cover

eg L vestock Hort cu ture - trees


Land use nked to ANZLUC,

where app cab

eg Animal husbandry

Category

eg Broadsca e grazing

Class

Sheep

Horse

Descr pt on st o d features

with n c ass

Rotat on Y/N

Vines

Land use change

Land use

Spat and management data Su tab e use of project methodology

rr gated pasture rura ng

Non-spat and management data ternat ve method requ red

eg pest de management so management

Land management

Land

Sta

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Info

rmat

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Land status data · 2015. 4. 24. · Land Status Data Mapping for the Mount Lofty Ranges Watershed For further information please contact: Information Officer Environment Protection

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