The Koala Likelihood Map (KLM) predicts the likelihood of finding a koala relative to other arboreal mammals across a 10-km grid covering NSW. It is built using existing arboreal mammal records from the past 20 years (currently 1999 to 2019) and represents the likelihood of koalas as the proportion of all records within a grid cell that are koalas. The records of other arboreal mammals provide a measure of survey effort independent of koalas and allow identification of areas where other arboreal mammals have been recorded, but not koalas. The map also includes a measure of the confidence in the koala likelihood estimate. This enables deficiencies in the data to be highlighted, and recommendations to be made for areas requiring further survey. The KLM is a useful tool that can be used to inform a range of koala conservation and management issues, however it is not static and should be updated regularly as new data become available. The KLM was first developed in 2014 for use in private native forestry regulation, on behalf of the NSW Environment Protection Authority. An updated and refined version of the map (NSW Koala Baseline Likelihood Map 2016) was produced in 2016 and has been used to inform provisions for koala protection under the Coastal Integrated Forestry Operations Approvals and is planned to inform the future review of the Private Native Forestry Code of Practice. This latest version of the KLM (v2.0 August 2019) includes new data from BioNet and Spot Assessment Technique (SAT) survey databases, as well as SAT data from a targeted state-wide field survey program. The KLM v2.0 (August 2019) is delivered under the NSW Koala Strategy's Koala Habitat Information Base. This comprises several layers of spatial information, including: Koala Habitat Suitability Model (KHSM) – the probability of finding koala habitat at any location; Koala Tree Suitability Index (KTSI) – the probability of finding a tree species that koalas are known to use for food or shelter; Koala Likelihood Map (KLM) including a confidence layer – predicts the likelihood of finding a koala at a location; Areas of Regional Koala Significance (ARKS) – identifies key koala populations and management areas with potential for long-term viability as well as priority threats to key koala populations; Native vegetation of NSW – this is a high-resolution map of native tree cover and water bodies; and all koala sightings recorded in NSW Bionet. All Koala Habitat Information Base (KHIB) datasets are available for download below under 'Dataset Relationship'.

This layer reflects the probability of finding a tree species that a koala is known to use for food or shelter. The statewide Koala Habitat Information Base (KHIB) has been developed as part of the NSW Koala Strategy. It delivers the best available state-wide spatial data on koala habitat, likelihood, koala preferred trees and koala sightings for NSW. It will be an important resource to assist government agencies, local councils and private land holders with koala conservation decisions. The Koala Tree Index v1.1 is one dataset under the KHIB. Tree indices were developed for each of the nine Koala Modelling Regions and mosaiced together into a statewide raster. Boosted Regression Tree (BRT) individual tree species distribution models were developed for important food and shelter species, with each KMR possessing a characteristic set of species whose combined distributions added to create index. The choice which species to include in each region was determined by a process of expert elicitation. The statewide product is available for download below as a zipped 5m tif image readable in any spatial software package. An ArcGIS mxd is also supplied for suggested symbology. All Koala Habitat Information Base datasets are available for download at the links below under 'Dataset relationship'. For further information on the data layers and their development, please see the Koala Habitat Information Base Technical Guide.

The PNF Koala Prescription Map identifies areas that are mapped as high koala habitat suitability for the purpose of implementing the koala protections under the Private Native Forestry Codes of Practice. The dataset represents a predictive habitat model with inputs from a range of scales and accuracies. While the map has been produced at a 5x5m scale, on-ground verification at the site scale is provided for under the PNF Codes of Practice. For more information, including details of map lineage (production method) and regulatory prescriptions, please see: https://www.lls.nsw.gov.au/help-and-advice/private-native-forestry/private-native-forestry-code-of-practice

The raster image represents a continuous surface of estimated land cover type probabilities for the western Kenai Peninsula circa 1973, circa 2002, and circa 2017. The estimated land cover types (Needleleaf Forest, Mixed Forest, Broadleaf Forest, Herbaceous, Wetland, Alpine, Barren, Shrub, Water) were derived from a random forest classifier executed in R (version 3.5.0). Predictor variables from training data included known landcover types deduced from high resolution aerial imagery, summer and winter spectral indices obtained from historical Landsat scenes, and topographic parameters derived from a digital elevation model. For each era (c. 1973, c. 2002, and c. 2017) 3,600 training points (400 points for each land cover type) were randomly distributed within training areas and training areas were opportunistically distributed to capture the regional and geomorphic extent of each land cover type to the extent possible given availability of aerial imagery. Each training point was assigned feature list values from the Landsat mosaics and a digital elevation model while land cover was manually interpreted using high-resolution areal imagery. Model output included predicted landcover type and a corresponding probability score and were rasterized for each era with the raster image featuring land cover type probability. For the 1973 era, these raster images are at a 60 meter resolution. For the 2002 and 2017 era, raster image resolution is 30 meters.

Yukon Flats National Wildlife Refuge (YKF NWR) and Koyukuk NWR (KUK NWR), U.S. Fish and Wildlife Service (USFWS), initiated a project with the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center to acquire map products needed for moose habitat assessment. The objective of this work was to create a suite of products which included: Estimated Vegetation Heights, Willow-Not Willow Estimates, and Vegetation Type Maps. These products are based on spectral characteristics found in bands 2 through 7 of Landsat 8 OLI scenes processed to surface reflectances, acquired in summer of 2013, and late winter of 2014. Training data was collected by fixed wing aircraft and helicopter by USFWS refuge staff, and extrapolated by the methods described. This project, “ Yukon Flats NWR willow mapping” (PI: Delia Vargas Kretsinger) was funded through the USFWS Inventory and Monitoring program via an Interagency Agreement between the USGS EROS and the USFWS – Alaska Regional Office. The data products are provisional in nature and are intended to support USFWS land management decisions. These data have not been validated with independent test data but received favorable qualitative assessment by local field experts.

The spatial data maps koala populations and habitat for three study areas in the Midcoast LGA of New South Wales. The three study areas, Kundle, Khappinghat and Tea Gardens are geographically separate, and each are approximately 16, 000 hectares and square or rectangular in shape. The data maps both ‘Likely koala habitat’ and Occupied koala habitat’ on all lands except NPWS Estate and Forestry Corporation of NSW estate. ‘Likely koala habitat’ occurs where there is greater than 15% dominance of preferred koala food trees. ‘Occupied koala habitat’ is a spatial subset of ‘Likely koala habitat’ where koala populations are currently viable based upon known presence and their generational persistence over time. The maps were produced under the NSW Government's Koala Strategy in collaboration with Midcoast Council to inform Council’s 2024 MidCoast Koala Conservation Strategy.

URL: https://geoscience.data.qld.gov.au/dataset/mr001992 The Koolatah series map was compiled in 2018 at 1:100 000 as part of the Geological 1:100 000 Compilation series to provide an interpretation of known surface geology information. The map product is available to all government agencies, industry and the public for reference and is located within the Koolatah (7366) 1:100 000 map area.

This dataset contains map data relevant to the Site Investigation Area for Koala Plans of Management Map, as referenced in Chapter 4 of State Environmental Planning Policy (Biodiversity and Conservation) 2021 (Biodiversity and Conservation SEPP). The map is referenced in Chapter 4 of the Biodiversity and Conservation SEPP as the ‘State Environmental Planning Policy (Koala Habitat Protection) 2021—Site Investigation Area for Koala Plans of Management Map’. However, the 2021 SEPP has been repealed and replaced by Chapter 4 of the Biodiversity and Conservation SEPP. The description and method for this layer is detailed below. Site Investigation Area for Koala Plans of Management Map This map identifies areas that are likely to have koala use trees, as well as environmental features such as soil type, topography and climate suitable for sustaining koalas. This map was developed from the Koala Habitat Information Base. The map does not show core koala habitat, and is only relevant as an investigation area, when councils resolve to prepare a Koala Plan of Management. Outside of this process, the map is not relevant and is not used. The map only captures land in the LGAs listed in schedule 21 of the SEPP. The map also excludes the lands that the SEPP does not apply to, such as national parks and state forests. The Site Investigation Area Map is not publicly available. To access this map, please contact your local council or the Department of Planning, Housing and Infrastructure. Contact data.broker@environment.nsw.gov.au for more information.

A predictive model has been established and tested to account for variations in the landscape to reflect changes in agricultural land capability class (on a progressive rating of 1: good - 7: poor). This dataset (and map) provides a prediction of the most likely land capability class to be expected in a particular location based on several layers of readily available information. These layers included geology, rainfall, slope, elevation, forest cover and surface drainage status. These data layers were input into a Geographic Information System modelling framework. Using previous experience and limited visits in the field, the output has been produced as a digital dataset and 1: 100,000 map. It was found to provide a relatively good impression of the landscapes potential for agricultural persuits (ie cropping and grazing). It was found to represent changes in capability class very well where geology, climate or slope control capability. In those areas where subsurface drainage controlled land capability it was found to be less reliable. Overall however as these areas of the State were previously devoid of any broadscale land resource information for this purpose - this map provides a valuable fist step in discerning land capability.

A predictive model has been established and tested to account for variations in the landscape to reflect changes in agricultural land capability class (on a progressive rating of 1: good - 7: poor). This dataset (and map) provides a prediction of the most likely land capability class to be expected in a particular location based on several layers of readily available information. These layers included geology, rainfall, slope, elevation, forest cover and surface drainage status. These data layers were input into a Geographic Information System modelling framework. Using previous experience and limited visits in the field, the output has been produced as a digital dataset and 1: 100,000 map. It was found to provide a relatively good impression of the landscapes potential for agricultural persuits (ie cropping and grazing). It was found to represent changes in capability class very well where geology, climate or slope control capability. In those areas where subsurface drainage controlled land capability it was found to be less reliable. Overall however as these areas of the State were previously devoid of any broadscale land resource information for this purpose - this map provides a valuable fist step in discerning land capability.