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Implementation methodology for spatial data collation and analysis


1. Introduction

The following describes process that will be used for developing the GIS system, transferring skills and providing information to inform management planing.

The development of the spatial data system involves the following six tasks.

  • Data Collation.

The first task is to access pre-existing spatial data for inclusion in an ARC-View based GIS.

·         Satellite Image analysis.

Image processing for land cover classification  and fire mapping will provide the primary derived data for analysis.

  • Field collection and ground truthing

GPS based surveys will provide additional spatial data and accuracy assessment for the derived satellite data.

·         GIS analysis

Spatial analysis will be conducted examining the relationship of derived fire histories to various biophysical and cultural landscape layers.

·         Derived map products
Maps will be produced as a primary means of communication and field verification.

·         Training

On-going training in Satellite image processing, GIS development, analysis and field survey technique


2. Methodology


2.1 Data Collation.

The following data sets are being collated for this project.

Topographic map sheets (1:50000) have been acquired as both hard copy and digital coverages. The hard copy map sheets have been scanned and will be registered to the rectified digital coverage. These mapped data are the most accurate available and so will form the base layer for co-registration of  all other spatial data layers. These map data where developed from recent aerial photography (~1995) and so provide reliable infrastructure and land-cover mapping.

Ariel Photography will be sourced from the appropriate Indonesian government archive. Photography used for the topographic mapping over the study sites will be used to add to and verify the topographic mapping. Historic aerial photography will be obtained, where available, for the study sites to be used in land cover change analysis. All photography will be scanned and rectified to the topographic map base.

Satellite Imagery:

Landsat-ETM imagery has been obtained as ninth scenes (60x60km). Two of these sub-scenes are required to cover the study sites of Dorameli and Dhersa in Flores and Kiritana and Lukuwingir in Sumba respectively. Two images will be purchased for each year to cover early dry season (May to early August) and the  late dry season (August to November). This imagery will be used for fire mapping, landcover mapping and landcover change detection.

Early Landsat-MSS imagery will be used for landcover change analysis. We currently have  a full MSS scene from 1973 for the Sumba study area and a 1973 scene has been ordered for the Flores study area.


MODIS and NOAA satellites imagery will be used for hot-spot mapping. Current mapping being undertaken by the CSIRO, using MODIS imagery, covers NTT. It is proposed that this mapping be combined with NOAA derived hot-spot data by the Western Australian Dept of Land Administration (DOLA) to produce comprehensive mapping that would be posted daily through DOLA’s established web based hot-spot distribution system.

2.2 Satellite Image analysis

Fire Scar Mapping

Fire scar mapping will be conducted on the ninth scene ETM images. Using ER-Mapper the two yearly images will be combined to produce one multi-date image. From this a difference image will be produced for each date by subtracting one image date from following date to highlight change between dates and thus highlight fire-scars. A Principle Components Analysis image will then be produced from the difference image to further highlight this change. Training sites will be selected from areas of significant change and a supervised classification run. The resulting imagery will be filtered to reduce speckle misclassification. The classification will then exported to ARC-View as a vector file for manual cleaning. The PCA-difference and raw imagery will be used as an underlay to guide this process.

MODIS data will be examined for is ability to augment the fire scar mapping being undertaken with Landsat.

Vegetation-Land cover mapping

A broad scale land cove map will be produced from the ETM imagery for the study areas on Sumba and Flores. Using field surveys and Ariel photography, classification training sites will be selected and a supervised classification conducted. Further ground truthing will be used to refine the map product. This vegetation map will be used as a base layer for analysis of the fire derived fire histories.

Land Cover Change

It is of interest to this project to develop an understanding of recent and historic landscape change. By comparing Landsat-MSS imagery going back to 1972 to current imagery we will quantify land cover change for the overlapping scene. Landsat MSS imagery has a lower spectral and spatial resolution (80x80m than current ETM imagery. This will limit the accuracy of cover change mapping.

Once co-registered a difference image will be created from the MSS and ETM imagery highlighting changes in spectral response in  the overlapping scene area. This difference image will then be classified into positive and negative change, spectral response. The classified \image will then be compared to the two image dates for manually interpretation of the change response. Classification will be altered or annotated according pattern response and local knowledge. Ground truthing of change areas will be conducted through site visits and discussion with locals.


Hot Spot Detection

Using thermal sensors on the MODIS and NOAA satellites hot spots are detected and mapped on a daily basis through out Australia using an automated detection algorithm.

Due to the coarse resolution of NOAA imagery the location accuracy of the hotspot data is, at best, ±1–2 km. Typical daily fluctuations of fire intensity and the timing of the satellite data collection effect hotspot detection. The inclusion of the higher resolution MODIS data (±250m) increases both the spatial and temporal resolution of the mapping.

Training will be provided in the interpretation of hot-spot data. A ground truthing program will be developed to ascertain the accuracy and effectiveness  of hot-spot data for management. 

2.3 Field collection and ground truthing

Field GPS surveys will map the following:


 -Traditional and government tenure boundaries will be mapped for relation to current burning regimes and management options.


Significant sites

-Important cultural sites need to be mapped as integral features in developing fire management plans. This is also true for…
- Areas of particular ecological significance.

Vegetation Boundaries

-Through ground based mapping of land cover features such as forest edges and cultivated land a base layer for developing a strategic burning approach. It will be base data for accessing land cover change through comparison to historic data and ongoing monitoring.




Satellite mapping

Ground truthing of satellite imagery and derived data is important for classification attribution and accuracy assessment.


This is particularly important for the determining the accuracy of hot-spot and fire scar mapping.  It is assumed that image processing techniques developed in Australia will readily transfer to the rugged savanna landscapes of eastern Indonesia. This assumption needs to be tested.


2.4 GIS processing and analysis

Digital elevation models will be created from the 1:50000 topographic map contours. These will be used for landscape 3D visualisation, Land-cover mapping and derived management maps ie. erosion potential.

Fire scar mapping will be will be  aggregated into two seasonal coverages:  fire maps for May to early August will be combined to produce an Early Dry Season (EDS) coverage, and fire maps for late August to November will be combined to produce a Late Dry Season (LDS) coverage.  Over time these data will be combined to produce fire history parameters:  total fire frequency – the number of times burnt over the study period;  frequency of EDS fires; frequency of LDS fires. These fire history parameters will be intersected with landcover, tenure, management strategy and land cover change data and relationships quantified.


 2.5 Derived map products

It will be an important and on-going part of the project to provide hard copy and digital map products to land managers, local and provincial government administrators for verification, analysis and discussion.


Satellite image maps of the study areas, with and without infrastructure vector over lays, will form a base layer for discussion and explanation. Image maps will be provided at the start of the project to each project site village head-men (Kepala-Desa).

Land cover maps will be produced for the study areas and provided to villages and field officers to assist in developing and implementing management options.

Hot spot maps will be provided in near real time over the internet.

Fire history maps will be produced as the imagery is processed. They will form a basis for ground truthing and field discussion.

Management maps, such as erosion potential or burning sensitivity, derived from GIS analysis of combined spatial layers could be produced to aid land management decisions.

2.6 Training

Training will be provided in combination with development of the GIS and subsequent analysis primarily to the two GIS project officers. Software training will specifically focus on the use of ER-Mapper for satellite image analysis and ARC-View for GIS analysis. It is envisaged that simple hand books will be created in English and Indonesian describing technical methodology and processing pathways developed for this project. Field training will focus on the use of GPS for mapping and accuracy assessment.

Training in the interpretation of satellite imagery and the derived products, will be provided to all team members and relevant government administrators., This will create an understanding of management opportunities and  limitations of these tools. This process will be particularly important for assessing the utility of hot spot mapping

CIFOR will provide training in the integration of social cultural data into a spatial context for analysi





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