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On January 17, 2019 at 11:23:20 AM +1100, National Native Title Tribunal:
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f | 1 | { | f | 1 | { |
2 | "author": null, | 2 | "author": null, | ||
3 | "author_email": null, | 3 | "author_email": null, | ||
4 | "creator_user_id": "8d2ac61a-d63a-4456-a00d-b35fee4c4025", | 4 | "creator_user_id": "8d2ac61a-d63a-4456-a00d-b35fee4c4025", | ||
5 | "id": "bac38cc3-9280-4b46-b7c1-da0d3462f764", | 5 | "id": "bac38cc3-9280-4b46-b7c1-da0d3462f764", | ||
6 | "license_id": "cc-by", | 6 | "license_id": "cc-by", | ||
7 | "maintainer": null, | 7 | "maintainer": null, | ||
8 | "maintainer_email": null, | 8 | "maintainer_email": null, | ||
9 | "metadata_created": "2018-06-15T08:52:52.979894", | 9 | "metadata_created": "2018-06-15T08:52:52.979894", | ||
t | 10 | "metadata_modified": "2019-01-16T04:13:08.389626", | t | 10 | "metadata_modified": "2019-01-17T00:23:20.007215", |
11 | "name": "assessment-of-swamp-sclerophyll-forest-tec-south", | 11 | "name": "assessment-of-swamp-sclerophyll-forest-tec-south", | ||
12 | "notes": "The operational map for Swamp Sclerophyll Forest (SSF) was | 12 | "notes": "The operational map for Swamp Sclerophyll Forest (SSF) was | ||
13 | constructed to resolve long-standing issues surrounding its | 13 | constructed to resolve long-standing issues surrounding its | ||
14 | identification, location and extent within the NSW State Forest estate | 14 | identification, location and extent within the NSW State Forest estate | ||
15 | covered by the coastal Integrated Forestry Operation Agreements. The | 15 | covered by the coastal Integrated Forestry Operation Agreements. The | ||
16 | map was constructed in two parts, with State Forests to the north of | 16 | map was constructed in two parts, with State Forests to the north of | ||
17 | Sydney being mapped in a separate process to those to the south of | 17 | Sydney being mapped in a separate process to those to the south of | ||
18 | Sydney. We did this to minimise the risk that relationships between | 18 | Sydney. We did this to minimise the risk that relationships between | ||
19 | regional vegetation communities and the TEC would be confounded or | 19 | regional vegetation communities and the TEC would be confounded or | ||
20 | masked by geographical variation or other major ecological gradients, | 20 | masked by geographical variation or other major ecological gradients, | ||
21 | which might otherwise be a significant risk if we had treated the full | 21 | which might otherwise be a significant risk if we had treated the full | ||
22 | latitudinal range of the TEC as a single study area. In total, we | 22 | latitudinal range of the TEC as a single study area. In total, we | ||
23 | assessed 1,218,000 hectares of State Forest across coastal NSW. This | 23 | assessed 1,218,000 hectares of State Forest across coastal NSW. This | ||
24 | consisted of 868,000 hectares of State Forest on the north coast and | 24 | consisted of 868,000 hectares of State Forest on the north coast and | ||
25 | more than 350,000 hectares of State Forest on the south coast.\r\nIn | 25 | more than 350,000 hectares of State Forest on the south coast.\r\nIn | ||
26 | both study areas, the project\u2019s Threatened Ecological Community | 26 | both study areas, the project\u2019s Threatened Ecological Community | ||
27 | (TEC) Reference Panel (the Panel) preceded the assessment process by | 27 | (TEC) Reference Panel (the Panel) preceded the assessment process by | ||
28 | reviewing the determination for SSF and agreeing upon a set of | 28 | reviewing the determination for SSF and agreeing upon a set of | ||
29 | diagnostic parameters for its identification. The Panel found that | 29 | diagnostic parameters for its identification. The Panel found that | ||
30 | SSF is primarily defined by floristic plot data and that it is mostly | 30 | SSF is primarily defined by floristic plot data and that it is mostly | ||
31 | located on coastal floodplains and associated alluvial | 31 | located on coastal floodplains and associated alluvial | ||
32 | landforms.\r\nFollowing on from these conclusions, we started the | 32 | landforms.\r\nFollowing on from these conclusions, we started the | ||
33 | mapping process by mapping the distribution of floodplains and | 33 | mapping process by mapping the distribution of floodplains and | ||
34 | alluvial soils and thus identifying possible areas of SSF. For both | 34 | alluvial soils and thus identifying possible areas of SSF. For both | ||
35 | the north and the south coast we used an existing map of coastal | 35 | the north and the south coast we used an existing map of coastal | ||
36 | landforms and geology in combination with several fine-scale models of | 36 | landforms and geology in combination with several fine-scale models of | ||
37 | alluvial landform features to determine the likely extent of | 37 | alluvial landform features to determine the likely extent of | ||
38 | floodplains and alluvial soils within our study areas. \r\nWe used | 38 | floodplains and alluvial soils within our study areas. \r\nWe used | ||
39 | aerial photograph interpretation (API) to assess the floristic and | 39 | aerial photograph interpretation (API) to assess the floristic and | ||
40 | structural attributes of the vegetation cover on our modelled alluvial | 40 | structural attributes of the vegetation cover on our modelled alluvial | ||
41 | environments, and thus delineated polygons likely to contain SSF. We | 41 | environments, and thus delineated polygons likely to contain SSF. We | ||
42 | also used API to modify the boundaries of the modelled alluvial areas | 42 | also used API to modify the boundaries of the modelled alluvial areas | ||
43 | using a prescribed list of eucalypt, casuarina and melaleuca species | 43 | using a prescribed list of eucalypt, casuarina and melaleuca species | ||
44 | in combination with the interpretation of landform elements relevant | 44 | in combination with the interpretation of landform elements relevant | ||
45 | to alluvial and floodplain environments.\r\nWe then compiled floristic | 45 | to alluvial and floodplain environments.\r\nWe then compiled floristic | ||
46 | plot data for all State Forest areas within our modelled alluvial | 46 | plot data for all State Forest areas within our modelled alluvial | ||
47 | landforms and API polygons. For both the north and the south coast the | 47 | landforms and API polygons. For both the north and the south coast the | ||
48 | floristic plot data was sourced from both existing flora surveys held | 48 | floristic plot data was sourced from both existing flora surveys held | ||
49 | in the OEH VIS database and from targeted flora surveys conducted | 49 | in the OEH VIS database and from targeted flora surveys conducted | ||
50 | specifically for this project. We compared these plots with those | 50 | specifically for this project. We compared these plots with those | ||
51 | previously assigned to flora communities listed in the determination | 51 | previously assigned to flora communities listed in the determination | ||
52 | of SSF. Both dissimilarity-based methods and multivariate regression | 52 | of SSF. Both dissimilarity-based methods and multivariate regression | ||
53 | methods were used for the comparison. The results of the comparison | 53 | methods were used for the comparison. The results of the comparison | ||
54 | were then used to assess the likelihood that the plots in State | 54 | were then used to assess the likelihood that the plots in State | ||
55 | forests belonged to one or more of the communities listed in the SSF | 55 | forests belonged to one or more of the communities listed in the SSF | ||
56 | determination.\r\nFollowing this, we developed a predictive | 56 | determination.\r\nFollowing this, we developed a predictive | ||
57 | statistical model of the probability of occurrence of SSF using plot | 57 | statistical model of the probability of occurrence of SSF using plot | ||
58 | data and a selection of environmental and remote-sensing variables. | 58 | data and a selection of environmental and remote-sensing variables. | ||
59 | For the north coast, we used a Random Forest model, while for the | 59 | For the north coast, we used a Random Forest model, while for the | ||
60 | south coast we used a Boosted Regression Tree model.\r\nTo create the | 60 | south coast we used a Boosted Regression Tree model.\r\nTo create the | ||
61 | operational map, we assigned every mapped API polygon to SSF if | 61 | operational map, we assigned every mapped API polygon to SSF if | ||
62 | appropriate based on the plot data, over-storey and understorey | 62 | appropriate based on the plot data, over-storey and understorey | ||
63 | attributes, landform features and modelled probabilities underlying | 63 | attributes, landform features and modelled probabilities underlying | ||
64 | each API polygon. In total, we mapped approximately 1131 hectares of | 64 | each API polygon. In total, we mapped approximately 1131 hectares of | ||
65 | SSF across out study area.\r\n\r\nOperational TEC Mapping have been | 65 | SSF across out study area.\r\n\r\nOperational TEC Mapping have been | ||
66 | derived by API at a viewing scale between 1-4000 using ADS40 50 cm | 66 | derived by API at a viewing scale between 1-4000 using ADS40 50 cm | ||
67 | pixel imagery and 1 m derived LIDAR DEM grids for floodplain EECs.", | 67 | pixel imagery and 1 m derived LIDAR DEM grids for floodplain EECs.", | ||
68 | "owner_org": "83a21590-19cd-49af-a188-f06f5d4fe231", | 68 | "owner_org": "83a21590-19cd-49af-a188-f06f5d4fe231", | ||
69 | "private": false, | 69 | "private": false, | ||
70 | "revision_id": "f04fe3a6-c90e-4a7d-a862-cec12e79fc09", | 70 | "revision_id": "f04fe3a6-c90e-4a7d-a862-cec12e79fc09", | ||
71 | "state": "active", | 71 | "state": "active", | ||
72 | "title": "Assessment of Swamp Sclerophyll Forest on Coastal | 72 | "title": "Assessment of Swamp Sclerophyll Forest on Coastal | ||
73 | Floodplains TEC on NSW Crown Forest Estate (South Coast Region)", | 73 | Floodplains TEC on NSW Crown Forest Estate (South Coast Region)", | ||
74 | "type": "dataset", | 74 | "type": "dataset", | ||
75 | "url": "", | 75 | "url": "", | ||
76 | "version": null | 76 | "version": null | ||
77 | } | 77 | } |