Logging history overlay of most recent harvesting activities
dataset:
LASTLOG25
This layer has been derived from LOG_SEASON and represents the spatial extent of the most recent timber harvesting activity
recorded for any given area in State forest. Where harvest events overlap previous harvesting events the most recent harvesting
event is shown.
The layer stores details of the last time an area was known to be harvested, the species harvested and the harvesting method
used.
The dataset has been updated with the 2022-23 information. Complete to 30 June 2023
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Citation proposal Citation proposal
(2023) Logging history overlay of most recent harvesting activities Department of Energy, Environment and Climate Action https://metashare.maps.vic.gov.au/geonetwork/srv/eng/catalog.search#/metadata/3a367d37-6dfe-5d66-ba7a-3d8f864cb417 |
- Description
- Temporal
- Spatial
- Maintenance
- Format
- Contacts
- Keywords
- Resource Constraints
- Lineage
- Metadata Constraints Metadata Constraints
- Quality
- Acquisition Info
- Raster Data Details
- Raster Type Details
- Point Cloud Data Details
- Contour Data Details
- Survey Details
Description
- Title
- Logging history overlay of most recent harvesting activities
- Alternate title
- LASTLOG25
- Purpose
- This data is derived from the LOG_SEASON dataset. The layer stores details of the last time an area of State forest was known to be harvested.
- Supplemental Information
- History: The current version incorporates data from the 2007/2008 season. As this dataset is wholly derived from LOG_SEASON, attributes are the same as for the LOG_SEASON dataset with the exception of the addition of a decade item derived from the season field. Relationship to other Datasets: This dataset is wholly derived from LOG_SEASON. LOG_SEASON is updated annually and this layer will need to be re-derived to maintain its currency. Current Design Issues: Because this dataset is automatically derived from LOG_SEASON it has the same design issues as follows: The current layer design has been kept as simple as possible as this layer is used at the forest district level and therefore must be commensurate with currently available resources for update, maintenance and accessibility. The design also has to accommodate the needs of the Statewide Forest Resource Inventory (SFRI) in quantifying the start and end dates of logging events. Logging history data is an important input to periodic updates in the SFRI, where logging event data is correlated with aerial photography. The unique polygon identifier used in this layer is LOGHISTID. This field is a composite string that comprises Forest Management Area number, forest block number, forest compartment number, coupe number, logging season identifier and coupe section number separated by slashes to make interpretation easier. The items which make up the LOGHISTID identifier are also held separately as string type items to facilitate attribute editing via ArcView. The logging event data must be able to be linked to a variety of textual databases and spreadsheets. Some examples being LOGSALES and locally maintained spreadsheets for coupe management. The link to this external information is the field COUPEADD. This is used as the concept of a "coupe" incorporates both single and multiple polygons. Related Documents: None
- Status
- Completed
Spatial
- Code
- 4283
Maintenance
- Maintenance and update frequency
- Annually
Contacts
Point of contact
Department of Energy, Environment and Climate Action
-
Boyd Eggleston (left)
(Policy Analyst)
1 Spring Street
Melbourne
Vic
3000
Australia
Cited responsible party
No information provided.
Cited responsible party
No information provided.
Cited responsible party
No information provided.
Keywords
- Topic category
-
- Biota
- Farming
Resource Constraints
- Use limitation
- General Access Subject to Custodial Approval Access Subject to Custodial Approval
- Classification
- Unclassified
- Creative Commons Attribution 4.0 (CC-BY)
Lineage
- Statement
- Dataset Source: Derived from LOG_SEASON, which was constructed from regional logging history records. (Refer to LOG_SEASON for further information) Dataset Originality: Derived
- Description
- Collection Method: Consecutive overlay of layers, from the oldest to the most recent.
- Description
- Dataset standardisation has occurred to bring all regional logging history datasets into line with a common structure. Polygons for each season are extracted from LOG_SEASON and updated with the season before, allowing for the most recent information to be the last information overlayed. Polygons less than 100m2 are deleted. Areas less than 0.5 ha and slivers are then eliminated into neighbouring polygons. Therefore, coupe areas will differ between LASTLOG25 and LOG_SEASON.
Metadata Constraints Metadata Constraints
- Classification
- Unclassified
Quality
Attribute Quality
- Comments
- Required to be 100% for reporting purposes. Datasets will be subjected to standard CGDL audit processes.
Positional Accuracy
- Comments
- As per LOG_SEASON namely: Precision: 10m to 100m depending on survey method used for each coupe as documented in dataset. Determination: Comparisons to existing 1:25,000 topographic mapping and internal database consistencies. GPS sourced data may be inconsistent with existing 1:25000 map base roads and hydrology.
Conceptual Consistency
- Comments
- As per LOG_SEASON namely: 1.LOGHISTID must be consistent with FMA, BLOCK, COMPARTMENT COUPE SEASON and SECTION. 2.COUPEADD must be consistent with FMA, BLOCK, COMPARTMENT COUPE. 3. ENDDATE cannot be earlier than STARTDATE 4. STARTDATE and ENDDATE must be consistent with logging season identifier. 5. SILVSYS cannot be inconsistent with FORESTYPE 6. FMA, BLOCK and COMPARTMENT must be consistent with data stored in the FORMB100 and FORMB25 layers. 7. FMA, BLOCK, COMPARTMENT must be consistent with data stored in FORMB100 layer.
Missing Data
- Comments
- (Refer to Fire100_XY for further information) The extent of annual records varies from one FMA to another. All FMA's should have complete records from season 199798 onwards.
Excess Data
Overviews
Graphic Overview of Data Footprint
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