Changes
On March 13, 2024 at 8:52:03 PM +1100, National Native Title Tribunal:
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Added resource Operational and Indicative Maps for the Assessment of Threatened Ecological Communities on NSW Crown Forest Estate to Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Added resource Operational Map for River-Flat Eucalypt Forest Threatened Ecological Community on NSW Crown Forest Estate to Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Added resource Operational Map for Swamp Oak Floodplain Forest Threatened Ecological Community on NSW Crown Forest Estate to Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Added resource Operational Map for Subtropical Coastal Floodplain Forest Threatened Ecological Community on NSW Crown Forest Estate to Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Added resource Native Forestry Map Viewer to Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Added resource Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate to Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Added resource Data Quality Statement to Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Added resource Operational Map for Swamp Sclerophyll Forest Threatened Ecological Community on NSW Crown Forest Estate to Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Deleted resource Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate from Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Deleted resource Operational Map for Subtropical Coastal Floodplain Forest Threatened Ecological Community on NSW Crown Forest Estate from Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Deleted resource Operational and Indicative Maps for the Assessment of Threatened Ecological Communities on NSW Crown Forest Estate from Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Deleted resource Data Quality Statement from Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Deleted resource Native Forestry Map Viewer from Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Deleted resource Operational Map for Swamp Oak Floodplain Forest Threatened Ecological Community on NSW Crown Forest Estate from Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Deleted resource Operational Map for Swamp Sclerophyll Forest Threatened Ecological Community on NSW Crown Forest Estate from Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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Deleted resource Operational Map for River-Flat Eucalypt Forest Threatened Ecological Community on NSW Crown Forest Estate from Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate
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n | 25 | "metadata_modified": "2024-02-08T01:28:35.793030", | n | 25 | "metadata_modified": "2024-03-13T09:52:02.958051", |
26 | "name": "assessment-of-floodplain-tecs-of-the-north-coast", | 26 | "name": "assessment-of-floodplain-tecs-of-the-north-coast", | ||
27 | "notes": "Operational map for River-flat Eucalypt Forest:\r\n\r\nThe | 27 | "notes": "Operational map for River-flat Eucalypt Forest:\r\n\r\nThe | ||
28 | operational map for River-flat Eucalypt Forest (RFEF) was constructed | 28 | operational map for River-flat Eucalypt Forest (RFEF) was constructed | ||
29 | to resolve long-standing issues surrounding its identification, | 29 | to resolve long-standing issues surrounding its identification, | ||
30 | location and extent within the NSW State Forest estate covered by the | 30 | location and extent within the NSW State Forest estate covered by the | ||
31 | coastal Integrated Forestry Operation Agreements. The map was | 31 | coastal Integrated Forestry Operation Agreements. The map was | ||
32 | constructed in two parts, with State Forests to the north of Sydney | 32 | constructed in two parts, with State Forests to the north of Sydney | ||
33 | being mapped in a separate process to those to the south of Sydney. We | 33 | being mapped in a separate process to those to the south of Sydney. We | ||
34 | did this to minimise the risk that relationships between regional | 34 | did this to minimise the risk that relationships between regional | ||
35 | vegetation communities and the TEC would be confounded or masked by | 35 | vegetation communities and the TEC would be confounded or masked by | ||
36 | geographical variation or other major ecological gradients, which | 36 | geographical variation or other major ecological gradients, which | ||
37 | might otherwise be a significant risk if we had treated the full | 37 | might otherwise be a significant risk if we had treated the full | ||
38 | latitudinal range of the TEC as a single study area. In total, we | 38 | latitudinal range of the TEC as a single study area. In total, we | ||
39 | assessed 1,218,000 hectares of State Forest across coastal NSW. This | 39 | assessed 1,218,000 hectares of State Forest across coastal NSW. This | ||
40 | consisted of 868,000 hectares of State Forest on the north coast and | 40 | consisted of 868,000 hectares of State Forest on the north coast and | ||
41 | more than 350,000 hectares of State Forest on the south coast. \r\nIn | 41 | more than 350,000 hectares of State Forest on the south coast. \r\nIn | ||
42 | both study areas, the project\u2019s Threatened Ecological Community | 42 | both study areas, the project\u2019s Threatened Ecological Community | ||
43 | (TEC) Reference Panel (the Panel) preceded the assessment process by | 43 | (TEC) Reference Panel (the Panel) preceded the assessment process by | ||
44 | reviewing the determination for RFEF and agreeing upon a set of | 44 | reviewing the determination for RFEF and agreeing upon a set of | ||
45 | diagnostic parameters for its identification. The Panel found that | 45 | diagnostic parameters for its identification. The Panel found that | ||
46 | RFEF is primarily defined by floristic plot data and that it is mostly | 46 | RFEF is primarily defined by floristic plot data and that it is mostly | ||
47 | located on coastal floodplains and associated alluvial landforms. | 47 | located on coastal floodplains and associated alluvial landforms. | ||
48 | \r\nFollowing on from these conclusions, we started the mapping | 48 | \r\nFollowing on from these conclusions, we started the mapping | ||
49 | process by mapping the distribution of floodplains and alluvial soils | 49 | process by mapping the distribution of floodplains and alluvial soils | ||
50 | and thus identifying possible areas of RFEF. For both the north and | 50 | and thus identifying possible areas of RFEF. For both the north and | ||
51 | the south coast we used an existing map of coastal landforms and | 51 | the south coast we used an existing map of coastal landforms and | ||
52 | geology in combination with several fine-scale models of alluvial | 52 | geology in combination with several fine-scale models of alluvial | ||
53 | landform features to determine the likely extent of floodplains and | 53 | landform features to determine the likely extent of floodplains and | ||
54 | alluvial soils within our study areas. \r\nWe used aerial photograph | 54 | alluvial soils within our study areas. \r\nWe used aerial photograph | ||
55 | interpretation (API) to assess the floristic and structural attributes | 55 | interpretation (API) to assess the floristic and structural attributes | ||
56 | of the vegetation cover found on our modelled alluvial environments, | 56 | of the vegetation cover found on our modelled alluvial environments, | ||
57 | and thus delineated polygons likely to contain RFEF. We also used API | 57 | and thus delineated polygons likely to contain RFEF. We also used API | ||
58 | to modify the boundaries of the modelled alluvial areas using a | 58 | to modify the boundaries of the modelled alluvial areas using a | ||
59 | prescribed list of eucalypt, casuarina and melaleuca species in | 59 | prescribed list of eucalypt, casuarina and melaleuca species in | ||
60 | combination with the interpretation of landform elements relevant to | 60 | combination with the interpretation of landform elements relevant to | ||
61 | alluvial and floodplain environments. \r\nWe then compiled floristic | 61 | alluvial and floodplain environments. \r\nWe then compiled floristic | ||
62 | plot data for all State Forest areas within our modelled alluvial | 62 | plot data for all State Forest areas within our modelled alluvial | ||
63 | landforms and API polygons. For both the north and the south coast the | 63 | landforms and API polygons. For both the north and the south coast the | ||
64 | floristic plot data was sourced from both existing flora surveys held | 64 | floristic plot data was sourced from both existing flora surveys held | ||
65 | in the OEH VIS database and from targeted flora surveys conducted | 65 | in the OEH VIS database and from targeted flora surveys conducted | ||
66 | specifically for this project. We compared these plots with those | 66 | specifically for this project. We compared these plots with those | ||
67 | previously assigned to flora communities listed in the determination | 67 | previously assigned to flora communities listed in the determination | ||
68 | of RFEF. Both dissimilarity-based methods and multivariate regression | 68 | of RFEF. Both dissimilarity-based methods and multivariate regression | ||
69 | methods were used for the comparison. The results of the comparison | 69 | methods were used for the comparison. The results of the comparison | ||
70 | were then used to assess the likelihood that the plots in State | 70 | were then used to assess the likelihood that the plots in State | ||
71 | forests belonged to one or more of the communities listed in the RFEF | 71 | forests belonged to one or more of the communities listed in the RFEF | ||
72 | determination. Following this, we developed a predictive statistical | 72 | determination. Following this, we developed a predictive statistical | ||
73 | model of the probability of occurrence of RFEF using plot data and a | 73 | model of the probability of occurrence of RFEF using plot data and a | ||
74 | selection of environmental and remote-sensing variables. For the north | 74 | selection of environmental and remote-sensing variables. For the north | ||
75 | coast, we used a Random Forest model, while for the south coast we | 75 | coast, we used a Random Forest model, while for the south coast we | ||
76 | used a Boosted Regression Tree model. \r\nTo create the operational | 76 | used a Boosted Regression Tree model. \r\nTo create the operational | ||
77 | map, we assigned every mapped API polygon to RFEF if appropriate based | 77 | map, we assigned every mapped API polygon to RFEF if appropriate based | ||
78 | on the plot data, over-storey and understorey attributes, landform | 78 | on the plot data, over-storey and understorey attributes, landform | ||
79 | features and modelled probabilities underlying each API polygon. | 79 | features and modelled probabilities underlying each API polygon. | ||
80 | \r\nWe mapped 3819 hectares of RFEF on the south coast and 198 | 80 | \r\nWe mapped 3819 hectares of RFEF on the south coast and 198 | ||
81 | hectares of RFEF on the north coast.\r\n\r\nOperational map for Swamp | 81 | hectares of RFEF on the north coast.\r\n\r\nOperational map for Swamp | ||
82 | Oak Floodplain Forest:\r\n\r\nThe operational map for Swamp Oak | 82 | Oak Floodplain Forest:\r\n\r\nThe operational map for Swamp Oak | ||
83 | Floodplain Forest (SOFF) was constructed to resolve long-standing | 83 | Floodplain Forest (SOFF) was constructed to resolve long-standing | ||
84 | issues surrounding its identification, location and extent within the | 84 | issues surrounding its identification, location and extent within the | ||
85 | NSW State Forest estate covered by the coastal Integrated Forestry | 85 | NSW State Forest estate covered by the coastal Integrated Forestry | ||
86 | Operation Agreements. The map was constructed in two parts, with State | 86 | Operation Agreements. The map was constructed in two parts, with State | ||
87 | Forests to the north of Sydney being mapped in a separate process to | 87 | Forests to the north of Sydney being mapped in a separate process to | ||
88 | those to the south of Sydney. We did this to minimise the risk that | 88 | those to the south of Sydney. We did this to minimise the risk that | ||
89 | relationships between regional vegetation communities and the TEC | 89 | relationships between regional vegetation communities and the TEC | ||
90 | would be confounded or masked by geographical variation or other major | 90 | would be confounded or masked by geographical variation or other major | ||
91 | ecological gradients, which might otherwise be a significant risk if | 91 | ecological gradients, which might otherwise be a significant risk if | ||
92 | we had treated the full latitudinal range of the TEC as a single study | 92 | we had treated the full latitudinal range of the TEC as a single study | ||
93 | area. In total, we assessed 1,218,000 hectares of State Forest across | 93 | area. In total, we assessed 1,218,000 hectares of State Forest across | ||
94 | coastal NSW. This consisted of 868,000 hectares of State Forest on the | 94 | coastal NSW. This consisted of 868,000 hectares of State Forest on the | ||
95 | north coast and more than 350,000 hectares of State Forest on the | 95 | north coast and more than 350,000 hectares of State Forest on the | ||
96 | south coast. \r\nIn both study areas, the project\u2019s Threatened | 96 | south coast. \r\nIn both study areas, the project\u2019s Threatened | ||
97 | Ecological Community (TEC) Reference Panel (the Panel) preceded the | 97 | Ecological Community (TEC) Reference Panel (the Panel) preceded the | ||
98 | assessment process by reviewing the determination for SOFF and | 98 | assessment process by reviewing the determination for SOFF and | ||
99 | agreeing upon a set of diagnostic parameters for its identification. | 99 | agreeing upon a set of diagnostic parameters for its identification. | ||
100 | The Panel found that SOFF is primarily defined by floristic plot data | 100 | The Panel found that SOFF is primarily defined by floristic plot data | ||
101 | and that it is mostly located on coastal floodplains and associated | 101 | and that it is mostly located on coastal floodplains and associated | ||
102 | alluvial landforms.\r\nFollowing on from these conclusions, we started | 102 | alluvial landforms.\r\nFollowing on from these conclusions, we started | ||
103 | the mapping process by mapping the distribution of floodplains and | 103 | the mapping process by mapping the distribution of floodplains and | ||
104 | alluvial soils and thus identifying possible areas of SOFF. For both | 104 | alluvial soils and thus identifying possible areas of SOFF. For both | ||
105 | the north and the south coast we used an existing map of coastal | 105 | the north and the south coast we used an existing map of coastal | ||
106 | landforms and geology in combination with several fine-scale models of | 106 | landforms and geology in combination with several fine-scale models of | ||
107 | alluvial landform features to determine the likely extent of | 107 | alluvial landform features to determine the likely extent of | ||
108 | floodplains and alluvial soils within our study areas.\r\nWe used | 108 | floodplains and alluvial soils within our study areas.\r\nWe used | ||
109 | aerial photograph interpretation (API) to assess floristic and | 109 | aerial photograph interpretation (API) to assess floristic and | ||
110 | structural attributes of the vegetation cover on our modelled alluvial | 110 | structural attributes of the vegetation cover on our modelled alluvial | ||
111 | environments, and thus delineated polygons likely to contain SOFF. We | 111 | environments, and thus delineated polygons likely to contain SOFF. We | ||
112 | also used API to modify the boundaries of the modelled alluvial areas | 112 | also used API to modify the boundaries of the modelled alluvial areas | ||
113 | using a prescribed list of casuarina and melaleuca species in | 113 | using a prescribed list of casuarina and melaleuca species in | ||
114 | combination with the interpretation of landform elements relevant to | 114 | combination with the interpretation of landform elements relevant to | ||
115 | alluvial and floodplain environments.\r\nWe then compiled floristic | 115 | alluvial and floodplain environments.\r\nWe then compiled floristic | ||
116 | plot data for all State Forest areas within our modelled alluvial | 116 | plot data for all State Forest areas within our modelled alluvial | ||
117 | landforms and API polygons. For both the north and the south coast the | 117 | landforms and API polygons. For both the north and the south coast the | ||
118 | floristic plot data was sourced from both existing flora surveys held | 118 | floristic plot data was sourced from both existing flora surveys held | ||
119 | in the OEH VIS database and from targeted flora surveys conducted | 119 | in the OEH VIS database and from targeted flora surveys conducted | ||
120 | specifically for this project. We compared these plots with those | 120 | specifically for this project. We compared these plots with those | ||
121 | previously assigned to flora communities listed in the determination | 121 | previously assigned to flora communities listed in the determination | ||
122 | of SOFF. Both dissimilarity-based methods and multivariate regression | 122 | of SOFF. Both dissimilarity-based methods and multivariate regression | ||
123 | methods were used for the comparison. The results of the comparison | 123 | methods were used for the comparison. The results of the comparison | ||
124 | were then used to assess the likelihood that the plots in State | 124 | were then used to assess the likelihood that the plots in State | ||
125 | forests belonged to one or more of the communities listed in the SOFF | 125 | forests belonged to one or more of the communities listed in the SOFF | ||
126 | determination.\r\nTo create the operational map, we assigned every | 126 | determination.\r\nTo create the operational map, we assigned every | ||
127 | mapped API polygon to SOFF based on the plot data, over-storey and | 127 | mapped API polygon to SOFF based on the plot data, over-storey and | ||
128 | understorey attributes, landform features and model output underlying | 128 | understorey attributes, landform features and model output underlying | ||
129 | each API polygon. \r\nIn total, we mapped approximately 272 hectares | 129 | each API polygon. \r\nIn total, we mapped approximately 272 hectares | ||
130 | of SOFF across our full study area.\r\n\r\nOperational map for Swamp | 130 | of SOFF across our full study area.\r\n\r\nOperational map for Swamp | ||
131 | Sclerophyll Forest:\r\n\r\nThe operational map for Swamp Sclerophyll | 131 | Sclerophyll Forest:\r\n\r\nThe operational map for Swamp Sclerophyll | ||
132 | Forest (SSF) was constructed to resolve long-standing issues | 132 | Forest (SSF) was constructed to resolve long-standing issues | ||
133 | surrounding its identification, location and extent within the NSW | 133 | surrounding its identification, location and extent within the NSW | ||
134 | State Forest estate covered by the coastal Integrated Forestry | 134 | State Forest estate covered by the coastal Integrated Forestry | ||
135 | Operation Agreements. The map was constructed in two parts, with State | 135 | Operation Agreements. The map was constructed in two parts, with State | ||
136 | Forests to the north of Sydney being mapped in a separate process to | 136 | Forests to the north of Sydney being mapped in a separate process to | ||
137 | those to the south of Sydney. We did this to minimise the risk that | 137 | those to the south of Sydney. We did this to minimise the risk that | ||
138 | relationships between regional vegetation communities and the TEC | 138 | relationships between regional vegetation communities and the TEC | ||
139 | would be confounded or masked by geographical variation or other major | 139 | would be confounded or masked by geographical variation or other major | ||
140 | ecological gradients, which might otherwise be a significant risk if | 140 | ecological gradients, which might otherwise be a significant risk if | ||
141 | we had treated the full latitudinal range of the TEC as a single study | 141 | we had treated the full latitudinal range of the TEC as a single study | ||
142 | area. In total, we assessed 1,218,000 hectares of State Forest across | 142 | area. In total, we assessed 1,218,000 hectares of State Forest across | ||
143 | coastal NSW. This consisted of 868,000 hectares of State Forest on the | 143 | coastal NSW. This consisted of 868,000 hectares of State Forest on the | ||
144 | north coast and more than 350,000 hectares of State Forest on the | 144 | north coast and more than 350,000 hectares of State Forest on the | ||
145 | south coast.\r\nIn both study areas, the project\u2019s Threatened | 145 | south coast.\r\nIn both study areas, the project\u2019s Threatened | ||
146 | Ecological Community (TEC) Reference Panel (the Panel) preceded the | 146 | Ecological Community (TEC) Reference Panel (the Panel) preceded the | ||
147 | assessment process by reviewing the determination for SSF and agreeing | 147 | assessment process by reviewing the determination for SSF and agreeing | ||
148 | upon a set of diagnostic parameters for its identification. The Panel | 148 | upon a set of diagnostic parameters for its identification. The Panel | ||
149 | found that SSF is primarily defined by floristic plot data and that it | 149 | found that SSF is primarily defined by floristic plot data and that it | ||
150 | is mostly located on coastal floodplains and associated alluvial | 150 | is mostly located on coastal floodplains and associated alluvial | ||
151 | landforms.\r\nFollowing on from these conclusions, we started the | 151 | landforms.\r\nFollowing on from these conclusions, we started the | ||
152 | mapping process by mapping the distribution of floodplains and | 152 | mapping process by mapping the distribution of floodplains and | ||
153 | alluvial soils and thus identifying possible areas of SSF. For both | 153 | alluvial soils and thus identifying possible areas of SSF. For both | ||
154 | the north and the south coast we used an existing map of coastal | 154 | the north and the south coast we used an existing map of coastal | ||
155 | landforms and geology in combination with several fine-scale models of | 155 | landforms and geology in combination with several fine-scale models of | ||
156 | alluvial landform features to determine the likely extent of | 156 | alluvial landform features to determine the likely extent of | ||
157 | floodplains and alluvial soils within our study areas. \r\nWe used | 157 | floodplains and alluvial soils within our study areas. \r\nWe used | ||
158 | aerial photograph interpretation (API) to assess the floristic and | 158 | aerial photograph interpretation (API) to assess the floristic and | ||
159 | structural attributes of the vegetation cover on our modelled alluvial | 159 | structural attributes of the vegetation cover on our modelled alluvial | ||
160 | environments, and thus delineated polygons likely to contain SSF. We | 160 | environments, and thus delineated polygons likely to contain SSF. We | ||
161 | also used API to modify the boundaries of the modelled alluvial areas | 161 | also used API to modify the boundaries of the modelled alluvial areas | ||
162 | using a prescribed list of eucalypt, casuarina and melaleuca species | 162 | using a prescribed list of eucalypt, casuarina and melaleuca species | ||
163 | in combination with the interpretation of landform elements relevant | 163 | in combination with the interpretation of landform elements relevant | ||
164 | to alluvial and floodplain environments.\r\nWe then compiled floristic | 164 | to alluvial and floodplain environments.\r\nWe then compiled floristic | ||
165 | plot data for all State Forest areas within our modelled alluvial | 165 | plot data for all State Forest areas within our modelled alluvial | ||
166 | landforms and API polygons. For both the north and the south coast the | 166 | landforms and API polygons. For both the north and the south coast the | ||
167 | floristic plot data was sourced from both existing flora surveys held | 167 | floristic plot data was sourced from both existing flora surveys held | ||
168 | in the OEH VIS database and from targeted flora surveys conducted | 168 | in the OEH VIS database and from targeted flora surveys conducted | ||
169 | specifically for this project. We compared these plots with those | 169 | specifically for this project. We compared these plots with those | ||
170 | previously assigned to flora communities listed in the determination | 170 | previously assigned to flora communities listed in the determination | ||
171 | of SSF. Both dissimilarity-based methods and multivariate regression | 171 | of SSF. Both dissimilarity-based methods and multivariate regression | ||
172 | methods were used for the comparison. The results of the comparison | 172 | methods were used for the comparison. The results of the comparison | ||
173 | were then used to assess the likelihood that the plots in State | 173 | were then used to assess the likelihood that the plots in State | ||
174 | forests belonged to one or more of the communities listed in the SSF | 174 | forests belonged to one or more of the communities listed in the SSF | ||
175 | determination.\r\nFollowing this, we developed a predictive | 175 | determination.\r\nFollowing this, we developed a predictive | ||
176 | statistical model of the probability of occurrence of SSF using plot | 176 | statistical model of the probability of occurrence of SSF using plot | ||
177 | data and a selection of environmental and remote-sensing variables. | 177 | data and a selection of environmental and remote-sensing variables. | ||
178 | For the north coast, we used a Random Forest model, while for the | 178 | For the north coast, we used a Random Forest model, while for the | ||
179 | south coast we used a Boosted Regression Tree model.\r\nTo create the | 179 | south coast we used a Boosted Regression Tree model.\r\nTo create the | ||
180 | operational map, we assigned every mapped API polygon to SSF if | 180 | operational map, we assigned every mapped API polygon to SSF if | ||
181 | appropriate based on the plot data, over-storey and understorey | 181 | appropriate based on the plot data, over-storey and understorey | ||
182 | attributes, landform features and modelled probabilities underlying | 182 | attributes, landform features and modelled probabilities underlying | ||
183 | each API polygon. In total, we mapped approximately 1131 hectares of | 183 | each API polygon. In total, we mapped approximately 1131 hectares of | ||
184 | SSF across out study area.\r\n\r\nOperational map for Subtropical | 184 | SSF across out study area.\r\n\r\nOperational map for Subtropical | ||
185 | Coastal Floodplain Forest:\r\n\r\nThe operational map for Subtropical | 185 | Coastal Floodplain Forest:\r\n\r\nThe operational map for Subtropical | ||
186 | Coastal Floodplain Forest (SCFF) was constructed to resolve | 186 | Coastal Floodplain Forest (SCFF) was constructed to resolve | ||
187 | long-standing issues surrounding its identification, location and | 187 | long-standing issues surrounding its identification, location and | ||
188 | extent within the NSW State Forest estate covered by the eastern | 188 | extent within the NSW State Forest estate covered by the eastern | ||
189 | Regional Forest Agreements. The project\u2019s Threatened Ecological | 189 | Regional Forest Agreements. The project\u2019s Threatened Ecological | ||
190 | Community (TEC) Reference Panel (the Panel) reviewed the determination | 190 | Community (TEC) Reference Panel (the Panel) reviewed the determination | ||
191 | for SCFF in conjunction with the determinations of three other TECs | 191 | for SCFF in conjunction with the determinations of three other TECs | ||
192 | associated with coastal floodplain environments. The Panel agreed that | 192 | associated with coastal floodplain environments. The Panel agreed that | ||
193 | SCFF is primarily defined by floristic plot data and that it is mostly | 193 | SCFF is primarily defined by floristic plot data and that it is mostly | ||
194 | located on coastal floodplains and associated alluvial | 194 | located on coastal floodplains and associated alluvial | ||
195 | landforms.\r\nThe operational map was constructed in several stages. | 195 | landforms.\r\nThe operational map was constructed in several stages. | ||
196 | Firstly, we identified candidate areas for SCFF by mapping the | 196 | Firstly, we identified candidate areas for SCFF by mapping the | ||
197 | distribution of floodplains and alluvial soils. To do this we used an | 197 | distribution of floodplains and alluvial soils. To do this we used an | ||
198 | existing map of coastal landforms and geology in combination with | 198 | existing map of coastal landforms and geology in combination with | ||
199 | several fine-scale models of alluvial landform features to determine | 199 | several fine-scale models of alluvial landform features to determine | ||
200 | the likely extent of floodplains and alluvial soils in our study area. | 200 | the likely extent of floodplains and alluvial soils in our study area. | ||
201 | \r\nSecondly, we compiled floristic plot data for State Forest areas | 201 | \r\nSecondly, we compiled floristic plot data for State Forest areas | ||
202 | within these alluvial landforms. The floristic plot data was sourced | 202 | within these alluvial landforms. The floristic plot data was sourced | ||
203 | from both existing flora surveys held in the OEH VIS database and from | 203 | from both existing flora surveys held in the OEH VIS database and from | ||
204 | targeted flora surveys conducted specifically for this project. We | 204 | targeted flora surveys conducted specifically for this project. We | ||
205 | compared these plots with those assigned to previously defined | 205 | compared these plots with those assigned to previously defined | ||
206 | communities listed in the determinations for SCFF. Both | 206 | communities listed in the determinations for SCFF. Both | ||
207 | dissimilarity-based methods and multivariate regression methods were | 207 | dissimilarity-based methods and multivariate regression methods were | ||
208 | used for the comparison. The results of the comparison were then used | 208 | used for the comparison. The results of the comparison were then used | ||
209 | to assess the likelihood that plots in State forests belonged to one | 209 | to assess the likelihood that plots in State forests belonged to one | ||
210 | or more of the communities listed in the determination.\r\nThirdly, we | 210 | or more of the communities listed in the determination.\r\nThirdly, we | ||
211 | used aerial photograph interpretation (API) to assess both floristic | 211 | used aerial photograph interpretation (API) to assess both floristic | ||
212 | and structural attributes found on the modelled alluvial and related | 212 | and structural attributes found on the modelled alluvial and related | ||
213 | environments. We also used API to modify the boundaries of the | 213 | environments. We also used API to modify the boundaries of the | ||
214 | modelled alluvial areas using a prescribed list of eucalypt, casuarina | 214 | modelled alluvial areas using a prescribed list of eucalypt, casuarina | ||
215 | and melaleuca species in combination with the interpretation of | 215 | and melaleuca species in combination with the interpretation of | ||
216 | landform elements relevant to alluvial and floodplain | 216 | landform elements relevant to alluvial and floodplain | ||
217 | environments.\r\nFourthly, we used plot data and a selection of | 217 | environments.\r\nFourthly, we used plot data and a selection of | ||
218 | environmental and remote-sensing variables to develop a Random Forest | 218 | environmental and remote-sensing variables to develop a Random Forest | ||
219 | (RF) model of the probability of occurrence of SCFF.\r\nTo create the | 219 | (RF) model of the probability of occurrence of SCFF.\r\nTo create the | ||
220 | operational map, we assigned every mapped API polygon to SCFF if | 220 | operational map, we assigned every mapped API polygon to SCFF if | ||
221 | appropriate based on the plot data, over-storey and understorey | 221 | appropriate based on the plot data, over-storey and understorey | ||
222 | attributes, landform features and modelled probabilities underlying | 222 | attributes, landform features and modelled probabilities underlying | ||
223 | each API polygon. \r\nIn total, we mapped approximately 11,050 | 223 | each API polygon. \r\nIn total, we mapped approximately 11,050 | ||
224 | hectares of Subtropical Coastal Floodplain Forest. The majority of the | 224 | hectares of Subtropical Coastal Floodplain Forest. The majority of the | ||
225 | mapped SCFF was located between Grafton and Casino.\r\n\r\nOperational | 225 | mapped SCFF was located between Grafton and Casino.\r\n\r\nOperational | ||
226 | TEC Mapping have been derived by API at a viewing scale between 1-4000 | 226 | TEC Mapping have been derived by API at a viewing scale between 1-4000 | ||
227 | using ADS40 50 cm pixel imagery and 1 m derived LIDAR DEM grids for | 227 | using ADS40 50 cm pixel imagery and 1 m derived LIDAR DEM grids for | ||
228 | floodplain EECs.", | 228 | floodplain EECs.", | ||
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458 | "title": "Assessment of North Coast Floodplain TECs on NSW Crown | 458 | "title": "Assessment of North Coast Floodplain TECs on NSW Crown | ||
459 | Forest Estate", | 459 | Forest Estate", | ||
460 | "type": "dataset", | 460 | "type": "dataset", | ||
461 | "unpublished": "false", | 461 | "unpublished": "false", | ||
462 | "update_freq": "irregular", | 462 | "update_freq": "irregular", | ||
463 | "url": null, | 463 | "url": null, | ||
464 | "version": null | 464 | "version": null | ||
465 | } | 465 | } |