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Evidence Guide: CPPSIS6037 - Conduct advanced remote sensing analysis

Student: __________________________________________________

Signature: _________________________________________________

Tips for gathering evidence to demonstrate your skills

The important thing to remember when gathering evidence is that the more evidence the better - that is, the more evidence you gather to demonstrate your skills, the more confident an assessor can be that you have learned the skills not just at one point in time, but are continuing to apply and develop those skills (as opposed to just learning for the test!). Furthermore, one piece of evidence that you collect will not usualy demonstrate all the required criteria for a unit of competency, whereas multiple overlapping pieces of evidence will usually do the trick!

From the Wiki University

 

CPPSIS6037 - Conduct advanced remote sensing analysis

What evidence can you provide to prove your understanding of each of the following citeria?

Determine image processing techniques.

  1. Project specifications are identified and analysed to determine appropriate image, merger and modelling techniques according to organisational requirements.
  2. Appropriate data collection and analysis techniques in remote sensing process are determined according to project specifications.
  3. Suitable digital image processing techniques and digital image data formats are selected according to project specifications.
  4. Additional characteristics of image and metadata are identified according to project specifications.
Project specifications are identified and analysed to determine appropriate image, merger and modelling techniques according to organisational requirements.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Appropriate data collection and analysis techniques in remote sensing process are determined according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Suitable digital image processing techniques and digital image data formats are selected according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Additional characteristics of image and metadata are identified according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Select computing platforms and software systems for image processing.

  1. Appropriate computing platforms and software systems are assessed for suitability according to project specifications.
  2. Availability of suitable data is verified with potential suppliers according to project specifications.
  3. Constraints on use of spatial data are assessed against project specifications and contingencies are planned according to organisational requirements.
  4. Commercially available image processing systems are assessed to determine appropriate components, menu items, characteristics and statistics to meet project specifications.
Appropriate computing platforms and software systems are assessed for suitability according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Availability of suitable data is verified with potential suppliers according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Constraints on use of spatial data are assessed against project specifications and contingencies are planned according to organisational requirements.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Commercially available image processing systems are assessed to determine appropriate components, menu items, characteristics and statistics to meet project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Enhance and manipulate images.

  1. Transformation routines using image calculations are conducted.
  2. Edge enhancements and smoothing filters are applied using convolution matrices.
  3. Image transformation is performed with channels of brightness, greenness and wetness.
  4. Imagery for distribution is determined according to project specifications.
Transformation routines using image calculations are conducted.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Edge enhancements and smoothing filters are applied using convolution matrices.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Image transformation is performed with channels of brightness, greenness and wetness.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Imagery for distribution is determined according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Perform classifications on datasets.

  1. Thematic classifications and relative differentiations between supervised and unsupervised classification algorithms are determined.
  2. Supervised classification algorithms are applied using training samples according to project specifications.
  3. Error analysis is conducted to perform an approximate accuracy assessment of classifications.
  4. Hard copy outputs are produced according to project specifications.
Thematic classifications and relative differentiations between supervised and unsupervised classification algorithms are determined.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Supervised classification algorithms are applied using training samples according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Error analysis is conducted to perform an approximate accuracy assessment of classifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Hard copy outputs are produced according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Conduct data merger and GIS integration.

  1. Integration and merging techniques are identified and documented.
  2. Techniques for integrating GIS data are identified and documented.
  3. Remote sensing data is merged and integrated into GIS according to project specifications.
Integration and merging techniques are identified and documented.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Techniques for integrating GIS data are identified and documented.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Remote sensing data is merged and integrated into GIS according to project specifications.

Completed
Date:

Teacher:
Evidence:

 

 

 

 

 

 

 

Assessed

Teacher: ___________________________________ Date: _________

Signature: ________________________________________________

Comments:

 

 

 

 

 

 

 

 

Instructions to Assessors

Evidence Guide

Elements describe the essential outcomes.

Performance criteria describe the performance needed to demonstrate achievement of the element. Where bold italicised text is used, further information is detailed in the range of conditions.

1.

Determine image processing techniques.

1.1.

Project specifications are identified and analysed to determine appropriate image, merger and modelling techniques according to organisational requirements.

1.2.

Appropriate data collection and analysis techniques in remote sensing process are determined according to project specifications.

1.3.

Suitable digital image processing techniques and digital image data formats are selected according to project specifications.

1.4.

Additional characteristics of image and metadata are identified according to project specifications.

2.

Select computing platforms and software systems for image processing.

2.1.

Appropriate computing platforms and software systems are assessed for suitability according to project specifications.

2.2.

Availability of suitable data is verified with potential suppliers according to project specifications.

2.3.

Constraints on use of spatial data are assessed against project specifications and contingencies are planned according to organisational requirements.

2.4.

Commercially available image processing systems are assessed to determine appropriate components, menu items, characteristics and statistics to meet project specifications.

3.

Enhance and manipulate images.

3.1.

Transformation routines using image calculations are conducted.

3.2.

Edge enhancements and smoothing filters are applied using convolution matrices.

3.3.

Image transformation is performed with channels of brightness, greenness and wetness.

3.4.

Imagery for distribution is determined according to project specifications.

4.

Perform classifications on datasets.

4.1.

Thematic classifications and relative differentiations between supervised and unsupervised classification algorithms are determined.

4.2.

Supervised classification algorithms are applied using training samples according to project specifications.

4.3.

Error analysis is conducted to perform an approximate accuracy assessment of classifications.

4.4.

Hard copy outputs are produced according to project specifications.

5.

Conduct data merger and GIS integration.

5.1.

Integration and merging techniques are identified and documented.

5.2.

Techniques for integrating GIS data are identified and documented.

5.3.

Remote sensing data is merged and integrated into GIS according to project specifications.

Required Skills and Knowledge

Elements describe the essential outcomes.

Performance criteria describe the performance needed to demonstrate achievement of the element. Where bold italicised text is used, further information is detailed in the range of conditions.

1.

Determine image processing techniques.

1.1.

Project specifications are identified and analysed to determine appropriate image, merger and modelling techniques according to organisational requirements.

1.2.

Appropriate data collection and analysis techniques in remote sensing process are determined according to project specifications.

1.3.

Suitable digital image processing techniques and digital image data formats are selected according to project specifications.

1.4.

Additional characteristics of image and metadata are identified according to project specifications.

2.

Select computing platforms and software systems for image processing.

2.1.

Appropriate computing platforms and software systems are assessed for suitability according to project specifications.

2.2.

Availability of suitable data is verified with potential suppliers according to project specifications.

2.3.

Constraints on use of spatial data are assessed against project specifications and contingencies are planned according to organisational requirements.

2.4.

Commercially available image processing systems are assessed to determine appropriate components, menu items, characteristics and statistics to meet project specifications.

3.

Enhance and manipulate images.

3.1.

Transformation routines using image calculations are conducted.

3.2.

Edge enhancements and smoothing filters are applied using convolution matrices.

3.3.

Image transformation is performed with channels of brightness, greenness and wetness.

3.4.

Imagery for distribution is determined according to project specifications.

4.

Perform classifications on datasets.

4.1.

Thematic classifications and relative differentiations between supervised and unsupervised classification algorithms are determined.

4.2.

Supervised classification algorithms are applied using training samples according to project specifications.

4.3.

Error analysis is conducted to perform an approximate accuracy assessment of classifications.

4.4.

Hard copy outputs are produced according to project specifications.

5.

Conduct data merger and GIS integration.

5.1.

Integration and merging techniques are identified and documented.

5.2.

Techniques for integrating GIS data are identified and documented.

5.3.

Remote sensing data is merged and integrated into GIS according to project specifications.

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:

metadata

soil

vegetation bodies

water

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

satellites

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.

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.

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.