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Name, Brief Description and owner:
Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA
Relevant... more component details: make, model, serial number, firmware version, settings:
Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area.
Cameras used:
2 x Canon HG 25 (forward facing) with the follow settings:
• Focus: Manual (3.0m)
• Rec Program: P)
• Image stabilizer: OFF
• Facial recognition: OFF
• Recording mode: MXP
• Frame rate: PF25
1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds.
Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal.
Contains files:
13.02.*.avi to 23.09.*.aviless
2016 NESP Marine Biodiversity Hub Project D3 - Evaluating and monitoring the status of marine biodiversity assets on the continental shelf MBH - Baited Remote Underwater Video - Published 13 Feb 2021
Name, Brief Description and owner:
Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA
Relevant... more component details: make, model, serial number, firmware version, settings:
Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area.
Cameras used:
2 x Canon HG 25 (forward facing) with the follow settings:
• Focus: Manual (3.0m)
• Rec Program: P)
• Image stabilizer: OFF
• Facial recognition: OFF
• Recording mode: MXP
• Frame rate: PF25
1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds.
Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal.
Contains files:
30.01.*.avi to 31.06.*.aviless
2016 NESP Marine Biodiversity Hub Project D3 - Evaluating and monitoring the status of marine biodiversity assets on the continental shelf MBH - Baited Remote Underwater Video - Published 12 Feb 2021
Name, Brief Description and owner:
Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA
Relevant... more component details: make, model, serial number, firmware version, settings:
Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area.
Cameras used:
2 x Canon HG 25 (forward facing) with the follow settings:
• Focus: Manual (3.0m)
• Rec Program: P)
• Image stabilizer: OFF
• Facial recognition: OFF
• Recording mode: MXP
• Frame rate: PF25
1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds.
Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal.
Contains files:
8.01.*.avi to 12.11.*.aviless
2016 NESP Marine Biodiversity Hub Project D3 - Evaluating and monitoring the status of marine biodiversity assets on the continental shelf MBH - Baited Remote Underwater Video - Published 06 Feb 2021
Name, Brief Description and owner:
Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA
Relevant... more component details: make, model, serial number, firmware version, settings:
Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area.
Cameras used:
2 x Canon HG 25 (forward facing) with the follow settings:
• Focus: Manual (3.0m)
• Rec Program: P)
• Image stabilizer: OFF
• Facial recognition: OFF
• Recording mode: MXP
• Frame rate: PF25
1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds.
Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal.
Contains files:
1.01.*.avi to 7.08.*.aviless
2016 NESP Marine Biodiversity Hub Project D3 - Evaluating and monitoring the status of marine biodiversity assets on the continental shelf MBH - Baited Remote Underwater Video - Published 06 Feb 2021
Name, Brief Description and owner:
Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA
Relevant... more component details: make, model, serial number, firmware version, settings:
Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area.
Cameras used:
2 x Canon HG 25 (forward facing) with the follow settings:
• Focus: Manual (3.0m)
• Rec Program: P)
• Image stabilizer: OFF
• Facial recognition: OFF
• Recording mode: MXP
• Frame rate: PF25
1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds.
Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal.
Contains folders:
Backwards, Forwards, New Convert, Videos Done
less
2016 NESP Marine Biodiversity Hub Project D3 - Evaluating and monitoring the status of marine biodiversity assets on the continental shelf MBH - Baited Remote Underwater Video - Published 04 Feb 2021
The control difficulty index (CDI) has been developed to provide a spatial assessment of the estimated difficulty of enacting an African Swine Fever (ASF) suppression program given an outbreak for any... more region across Australia. This layer is estimated at a spatial grain of 30-arcseconds (approx. 1 km2). The index provides a representation of the estimated difficulty for human enacted control actions and does not include information on the predicted densities of feral pigs or their ecology/behaviours given different habitat types. As such, the index does not integrate difficulties of control that could arise from the ability to find and kill pigs or the efficacy of any particular control strategy (e.g. baiting vs shooting) given differences in terrain.
Using satellite remote sensing data products and distance measures, this index combines several factors that will influence the difficulty of undertaking control across Australia by capturing the difficulty in mobilising resources into a region, the difficulty in undertaking ground control once arrived in the infected zone and the ability to undertake aerial control and/or carcass removal. To achieve this the index integrates measure that include, terrain ruggedness, road and track networks, land use type and canopy cover and remoteness from population centres.
This Index was developed to support national planning for African Swine Fever and was developed rapidly at a coarse resolution. The index can be modified for local conditions and the base code is available on request.
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NESP NAER wetland mgmt - - Published 20 May 2020
This is the data set and code associated with the manuscript:
Pichancourt, J-B., van Klinken, R.D. and Raghu, S. 2019. Understanding the limits to species-wide demographic generalizations: the ecology... more and management of Parkinsonia aculeata. Ecosphere x:xxx-xxx; doi:
The data in the Excel file is the matrix elements corresponding to a 12x12 stage-based population matrix; there are two tabs, once containing the mean value and the other the standard deviation of each matrix element based on a bootstap analysis.
The zip file contains the code used to analyse the data and generate the Figures in the manuscript.less
Parkinsonia biological control RnD4Profit-14-01-040 - - Published 18 Apr 2019
This collection provides additional analyses, figures and tables for an integrated risk assessment of natural, cultural and economic assets in the Kakadu Region of northern Australia, from the combine... more d threats of invasive species (feral animals & aquatic weeds) and climate change induced sea level rise saltwater inundation. It addresses cumulative multiple risks to multiple values over different time frames (Present-day, 2070 & 2100).less
Legacy data - Modelling - Published 25 Sep 2017
0.5m Contours maps as shape files 4903 tiled files (1km x1km) with DBF, PRJ and SHX support files
1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 02 Dec 2014
Model key points are statistically thinned data points that represent the main changes in a sampled surface. The Key Points are classified with code 8 in the LiDAR point classification scheme. Advanta... more ges in their use are significant reductions in data volume and reductions in data noise. There are disadvantages in using this data as has been a loss small features which may be potentially significant for certain applications. eg hydrology less
1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 02 Dec 2014
Intensity Mosaic in ECW format with .ecw.aux.xml, ERS, eww, prj and tab support files
Intensity image as 4943 tiled files (1km x1km) in TIF format with PRJ & TFW support files
1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 02 Dec 2014
Digital Surface Model (DSM) 1metre ESRI Grid Float format as 4942 tiles.
There is no common usage of the terms digital elevation model (DEM), digital terrain model (DTM) and digital surface model (D... more SM) in scientific literature. In most cases the term digital surface model represents the earth's surface and includes all objects on it. In contrast to a DSM, the digital terrain model (DTM) represents the bare ground surface without any objects like plants and buildings. Digital Elevation Model (DEM) and Digital Terrain Model (DTM) appear to be used interchangeably. less
1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 02 Dec 2014
Digital Elevation Model (DEM) 1metre ESRI Grid Float format as 4942 tiles
1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 02 Dec 2014
LiDAR_Point_Clouds, Classified. AHD have been preocessed to conform to the Australian Height Datum and converted from files collected as swaths in to tiles of data. The file formats is LAS.
LAS is ... more an industry format created and maintained by the American Society for Photogrammetry and Remote Sensing (ASPRS). LAS is a published standard file format for the interchange of lidar data. It maintains specific information related to lidar data. It is a way for vendors and clients to interchange data and maintain all information specific to that data.
Each LAS file contains metadata of the lidar survey in a header block followed by individual records for each laser pulse recorded. The header portion of each LAS file holds attribute information on the lidar survey itself: data extents, flight date, flight time, number of point records, number of points by return, any applied data offset, and any applied scale factor. The following lidar point attributes are maintained for each laser pulse of a LAS file: x,y,z location information, GPS time stamp, intensity, return number, number of returns, point classification values, scan angle, additional RGB values, scan direction, edge of flight line, user data, point source ID and waveform information.
Each and every lidar point in a LAS file can have a classification code set for it. Classifying lidar data allows you to organize mass points into specific data classes while still maintaining them as a whole data collection in LAS files. Typically, these classification codes represent the type of object that has reflected the laser pulse. Point classification is usually completed by data vendors using semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. The following table contains the LAS classification codes as defined in the LAS 1.1 standard:
Class code Classification type
0 Never classified
1 Unassigned
2 Ground
3 Low vegetation
4 Medium vegetation
5 High vegetation
6 Building
7 Noise
8 Model key
9 Water
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1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 27 Nov 2014
This is a technical guide for 'AdaptNRM module 2: Invasive plant species and climate change', and a summary table of CLIMEX parameters for invasive plant species. The distribution maps for each of the... more species can be found in other collections in this portal.less
Module 2: Weed projections and invasion - Technical Guide and Parameter Sets for Invasive plant species distribution model using CLIMEX - Published 17 Nov 2014
Canopy Height Model (CHM)
2 metre ESRI Grid Float format as 4944 tiles
1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 17 Sep 2014
Forest Canopy Model (FCM)
10metre ESRI Grid Float format as 4944 tiles
1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 17 Sep 2014
LiDAR_Point_Clouds, Classified. ELL have been preocessed to from swaths in to tiles of data. Points are located by Elevation, Latitude and Longitude. The file formats is LAS.
LAS is an industry form... more at created and maintained by the American Society for Photogrammetry and Remote Sensing (ASPRS). LAS is a published standard file format for the interchange of lidar data. It maintains specific information related to lidar data. It is a way for vendors and clients to interchange data and maintain all information specific to that data.
Each LAS file contains metadata of the lidar survey in a header block followed by individual records for each laser pulse recorded. The header portion of each LAS file holds attribute information on the lidar survey itself: data extents, flight date, flight time, number of point records, number of points by return, any applied data offset, and any applied scale factor. The following lidar point attributes are maintained for each laser pulse of a LAS file: x,y,z location information, GPS time stamp, intensity, return number, number of returns, point classification values, scan angle, additional RGB values, scan direction, edge of flight line, user data, point source ID and waveform information.
Each and every lidar point in a LAS file can have a classification code set for it. Classifying lidar data allows you to organize mass points into specific data classes while still maintaining them as a whole data collection in LAS files. Typically, these classification codes represent the type of object that has reflected the laser pulse. Point classification is usually completed by data vendors using semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. The following table contains the LAS classification codes as defined in the LAS 1.1 standard:
Class code Classification type
0 Never classified
1 Unassigned
2 Ground
3 Low vegetation
4 Medium vegetation
5 High vegetation
6 Building
7 Noise
8 Model key
9 Water
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1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 17 Sep 2014
LiDAR_Point_Clouds, UNClassified. ELL Files swaths of flight collected data. Points are located by Elevation, Latitude and Longitude. The file formats is LAS.
LAS is an industry format created and m... more aintained by the American Society for Photogrammetry and Remote Sensing (ASPRS). LAS is a published standard file format for the interchange of lidar data. It maintains specific information related to lidar data. It is a way for vendors and clients to interchange data and maintain all information specific to that data.
Each LAS file contains metadata of the lidar survey in a header block followed by individual records for each laser pulse recorded. The header portion of each LAS file holds attribute information on the lidar survey itself: data extents, flight date, flight time, number of point records, number of points by return, any applied data offset, and any applied scale factor. The following lidar point attributes are maintained for each laser pulse of a LAS file: x,y,z location information, GPS time stamp, intensity, return number, number of returns, point classification values, scan angle, additional RGB values, scan direction, edge of flight line, user data, point source ID and waveform information.
Each and every lidar point in a LAS file can have a classification code set for it. Classifying lidar data allows you to organize mass points into specific data classes while still maintaining them as a whole data collection in LAS files. Typically, these classification codes represent the type of object that has reflected the laser pulse. Point classification is usually completed by data vendors using semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. The following table contains the LAS classification codes as defined in the LAS 1.1 standard:
Class code Classification type
0 Never classified
1 Unassigned
2 Ground
3 Low vegetation
4 Medium vegetation
5 High vegetation
6 Building
7 Noise
8 Model key
9 Water
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1177.2 Northern NERP - Remote Sensing - ALOS vegetation maps and LIDAR data - Published 17 Sep 2014
Global and Australian maps of Species Distribution Models (SDM) for current and 2070 climates for invasive plant species for which there are published CLIMEX models. The parameters for the model are l... more isted in a cxp file. A GIS suitable file is provided.less
Module 2: Weed projections and invasion - Invasive plant species distribution model using CLIMEX - Published 28 Aug 2014
Global and Australian maps of Species Distribution Models (SDM) for current and 2070 climates for invasive plant species for which there are published CLIMEX models. The parameters for the model are l... more isted in a cxp file. A GIS suitable file is provided.less
Module 2: Weed projections and invasion - Invasive plant species distribution model using CLIMEX - Published 19 Aug 2014
Global and Australian maps of Species Distribution Models (SDM) for current and 2070 climates for invasive plant species for which there are published CLIMEX models. The parameters for the model are l... more isted in a cxp file. A GIS suitable file is provided.less
Module 2: Weed projections and invasion - Invasive plant species distribution model using CLIMEX - Published 19 Aug 2014
Global and Australian maps of Species Distribution Models (SDM) for current and 2070 climates for invasive plant species for which there are published CLIMEX models. The parameters for the model are l... more isted in a cxp file. A GIS suitable file is provided.less
Module 2: Weed projections and invasion - Invasive plant species distribution model using CLIMEX - Published 19 Aug 2014
Global and Australian maps of Species Distribution Models (SDM) for current and 2070 climates for invasive plant species for which there are published CLIMEX models. The parameters for the model are l... more isted in a cxp file. A GIS suitable file is provided.less
Module 2: Weed projections and invasion - Invasive plant species distribution model using CLIMEX - Published 19 Aug 2014
Global and Australian maps of Species Distribution Models (SDM) for current and 2070 climates for invasive plant species for which there are published CLIMEX models. The parameters for the model are l... more isted in a cxp file. A GIS suitable file is provided.less
Module 2: Weed projections and invasion - Invasive plant species distribution model using CLIMEX - Published 14 Aug 2014
