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Elements and Performance Criteria

Range Statement

This section specifies work environments and conditions that may affect performance. Essential operating conditions that may be present (depending on the work situation, needs of the candidate, accessibility of the item, and local industry and regional contexts) are included. Bold italicised wording, if used in the performance criteria, is detailed below.
Metadata must include at least eight of the following:	availability conditions of use coordinate system currency custodian data accuracy data description date of acquisition licence quality source spatial data acquisition methodologies version control.

Characteristics and statistics must include at least two of the following:	band selections hard copy outputs histogram plots look-up tables univariate and multivariate statistics.

Image calculations must include at least one of the following:	greenness ratios greenness ratios plus dark value normalised difference vegetation index (NDVI).

Techniques for integrating GIS data must include at least one of the following:	cartographic modelling environmental modelling land cover classification.

Performance Evidence

A person demonstrating competency in this unit must satisfy the requirements of the elements, performance criteria, foundation skills and range of conditions of this unit. The person must also use a computer and remote sensing software system to conduct advanced remote sensing analysis for two different projects.

While conducting the above advanced remote sensing analysis, the person must:

analyse and define job specifications, constraints and main work activities

analyse remote sensing data to identify and describe its characteristics, including:

vegetation bodies

select and set up appropriate hardware and software systems to meet remote sensing project specifications

assess commercially available image processing systems to ensure their suitability in meeting project specifications

use remote sensing techniques to acquire spatial data from:

airborne platforms

ground observation

comply with organisational and legal requirements for accessing and using spatial data, including copyright, intellectual property and trade practices

comply with organisational requirements and industry-accepted standards relating to:

applying classification algorithms

quality and risk management

working safely when using above equipment

conduct web-based searches to identify available spatial data and verify its suitability to meet project specifications

exercise precision and accuracy when analysing and classifying remote sensing data

identify and assess constraints relating to use of remote sensing data

perform classifications on datasets using supervised and unsupervised classification algorithms and training samples

save digital images in a range of formats, including two of the following:

band interleaved by line (BIL)

band interleaved by pixel (BIP)

band sequential (BSQ)

run length encoding (RLE)

use integration and merging techniques to allow remote sensing data to be integrated into GIS

use digital image processing techniques to enhance and rectify images.

Knowledge Evidence

A person demonstrating competency in this unit must demonstrate knowledge of:

industry-accepted techniques for applying supervised and unsupervised classification algorithms to remote sensing data

computer platforms and software systems for advanced remote sensing analysis and GIS integration

copyright and ownership constraints relating to spatial data

digital image processing techniques

digital image data formats, including BIL, BIP, BSQ and RLE

existing spatial datasets and dataset sources

image calculations required for transformation routines, including:

greenness ratios

greenness ratios plus dark value

normalised difference vegetation index (NDVI)

image enhancement, manipulation and merger techniques

methods for analysing metadata

methods for assessing commercially available image processing systems, including characteristics and statistics

methods for validating spatial data sources and constraints on use

key features of spatial referencing and coordinate systems

techniques for integrating GIS data, including:

cartographic modelling

environmental modelling

land cover classification.