Assessment of North Coast Floodplain TECs on NSW Crown Forest Estate

Operational map for River-flat Eucalypt Forest:\r\n\r\nThe operational map for River-flat Eucalypt Forest (RFEF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. \r\nIn both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for RFEF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that RFEF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms. \r\nFollowing on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of RFEF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas. \r\nWe used aerial photograph interpretation (API) to assess the floristic and structural attributes of the vegetation cover found on our modelled alluvial environments, and thus delineated polygons likely to contain RFEF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of eucalypt, casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments. \r\nWe then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast the floristic plot data was sourced from both existing flora surveys held in the OEH VIS database and from targeted flora surveys conducted specifically for this project. We compared these plots with those previously assigned to flora communities listed in the determination of RFEF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the RFEF determination. Following this, we developed a predictive statistical model of the probability of occurrence of RFEF using plot data and a selection of environmental and remote-sensing variables. For the north coast, we used a Random Forest model, while for the south coast we used a Boosted Regression Tree model. \r\nTo create the operational map, we assigned every mapped API polygon to RFEF if appropriate based on the plot data, over-storey and understorey attributes, landform features and modelled probabilities underlying each API polygon. \r\nWe mapped 3819 hectares of RFEF on the south coast and 198 hectares of RFEF on the north coast.\r\n\r\nOperational map for Swamp Oak Floodplain Forest:\r\n\r\nThe operational map for Swamp Oak Floodplain Forest (SOFF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast. \r\nIn both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for SOFF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that SOFF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms.\r\nFollowing on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of SOFF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas.\r\nWe used aerial photograph interpretation (API) to assess floristic and structural attributes of the vegetation cover on our modelled alluvial environments, and thus delineated polygons likely to contain SOFF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments.\r\nWe then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast the floristic plot data was sourced from both existing flora surveys held in the OEH VIS database and from targeted flora surveys conducted specifically for this project. We compared these plots with those previously assigned to flora communities listed in the determination of SOFF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the SOFF determination.\r\nTo create the operational map, we assigned every mapped API polygon to SOFF based on the plot data, over-storey and understorey attributes, landform features and model output underlying each API polygon. \r\nIn total, we mapped approximately 272 hectares of SOFF across our full study area.\r\n\r\nOperational map for Swamp Sclerophyll Forest:\r\n\r\nThe operational map for Swamp Sclerophyll Forest (SSF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the coastal Integrated Forestry Operation Agreements. The map was constructed in two parts, with State Forests to the north of Sydney being mapped in a separate process to those to the south of Sydney. We did this to minimise the risk that relationships between regional vegetation communities and the TEC would be confounded or masked by geographical variation or other major ecological gradients, which might otherwise be a significant risk if we had treated the full latitudinal range of the TEC as a single study area. In total, we assessed 1,218,000 hectares of State Forest across coastal NSW. This consisted of 868,000 hectares of State Forest on the north coast and more than 350,000 hectares of State Forest on the south coast.\r\nIn both study areas, the project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) preceded the assessment process by reviewing the determination for SSF and agreeing upon a set of diagnostic parameters for its identification. The Panel found that SSF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms.\r\nFollowing on from these conclusions, we started the mapping process by mapping the distribution of floodplains and alluvial soils and thus identifying possible areas of SSF. For both the north and the south coast we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils within our study areas. \r\nWe used aerial photograph interpretation (API) to assess the floristic and structural attributes of the vegetation cover on our modelled alluvial environments, and thus delineated polygons likely to contain SSF. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of eucalypt, casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments.\r\nWe then compiled floristic plot data for all State Forest areas within our modelled alluvial landforms and API polygons. For both the north and the south coast the floristic plot data was sourced from both existing flora surveys held in the OEH VIS database and from targeted flora surveys conducted specifically for this project. We compared these plots with those previously assigned to flora communities listed in the determination of SSF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that the plots in State forests belonged to one or more of the communities listed in the SSF determination.\r\nFollowing this, we developed a predictive statistical model of the probability of occurrence of SSF using plot data and a selection of environmental and remote-sensing variables. For the north coast, we used a Random Forest model, while for the south coast we used a Boosted Regression Tree model.\r\nTo create the operational map, we assigned every mapped API polygon to SSF if appropriate based on the plot data, over-storey and understorey attributes, landform features and modelled probabilities underlying each API polygon. In total, we mapped approximately 1131 hectares of SSF across out study area.\r\n\r\nOperational map for Subtropical Coastal Floodplain Forest:\r\n\r\nThe operational map for Subtropical Coastal Floodplain Forest (SCFF) was constructed to resolve long-standing issues surrounding its identification, location and extent within the NSW State Forest estate covered by the eastern Regional Forest Agreements. The project’s Threatened Ecological Community (TEC) Reference Panel (the Panel) reviewed the determination for SCFF in conjunction with the determinations of three other TECs associated with coastal floodplain environments. The Panel agreed that SCFF is primarily defined by floristic plot data and that it is mostly located on coastal floodplains and associated alluvial landforms.\r\nThe operational map was constructed in several stages. Firstly, we identified candidate areas for SCFF by mapping the distribution of floodplains and alluvial soils. To do this we used an existing map of coastal landforms and geology in combination with several fine-scale models of alluvial landform features to determine the likely extent of floodplains and alluvial soils in our study area. \r\nSecondly, we compiled floristic plot data for State Forest areas within these alluvial landforms. The floristic plot data was sourced from both existing flora surveys held in the OEH VIS database and from targeted flora surveys conducted specifically for this project. We compared these plots with those assigned to previously defined communities listed in the determinations for SCFF. Both dissimilarity-based methods and multivariate regression methods were used for the comparison. The results of the comparison were then used to assess the likelihood that plots in State forests belonged to one or more of the communities listed in the determination.\r\nThirdly, we used aerial photograph interpretation (API) to assess both floristic and structural attributes found on the modelled alluvial and related environments. We also used API to modify the boundaries of the modelled alluvial areas using a prescribed list of eucalypt, casuarina and melaleuca species in combination with the interpretation of landform elements relevant to alluvial and floodplain environments.\r\nFourthly, we used plot data and a selection of environmental and remote-sensing variables to develop a Random Forest (RF) model of the probability of occurrence of SCFF.\r\nTo create the operational map, we assigned every mapped API polygon to SCFF if appropriate based on the plot data, over-storey and understorey attributes, landform features and modelled probabilities underlying each API polygon. \r\nIn total, we mapped approximately 11,050 hectares of Subtropical Coastal Floodplain Forest. The majority of the mapped SCFF was located between Grafton and Casino.\r\n\r\nOperational TEC Mapping have been derived by API at a viewing scale between 1-4000 using ADS40 50 cm pixel imagery and 1 m derived LIDAR DEM grids for floodplain EECs.

河滨桉树林（River-flat Eucalypt Forest, RFEF）操作地图

本河滨桉树林（RFEF）操作地图的构建，旨在解决新南威尔士州（New South Wales, NSW）沿海综合林业运营协议覆盖范围内的州立森林产业中，长期存在的该群落识别、定位与范围界定难题。本次制图分为两个阶段：悉尼以北的州立森林与悉尼以南的州立森林采用独立流程开展制图。此举旨在规避区域植被群落与受威胁生态群落（Threatened Ecological Community, TEC）之间的关联，因地理变异或其他重大生态梯度而被混淆或掩盖的风险——若将该受威胁生态群落的完整纬度范围作为单一研究区域，此类风险将显著提升。本次研究总计评估了新南威尔士州沿海区域的121.8万公顷州立森林，其中北海岸区域为86.8万公顷，南海岸区域超过35万公顷。

在两个研究区域中，项目组的受威胁生态群落参考专家组（以下简称“专家组”）均在评估流程启动前，完成了RFEF判定文件的审查工作，并商定了一套用于其识别的诊断参数。专家组认定，RFEF主要基于植物区系样地数据进行界定，且其主要分布于沿海洪泛平原及相关冲积地貌中。

基于上述结论，项目组首先开展洪泛平原与冲积土壤的分布制图，以此识别潜在的RFEF分布区域。针对南北海岸区域，项目组均采用现有沿海地貌与地质图件，结合多套冲积地貌特征的精细尺度模型，以确定研究区域内洪泛平原与冲积土壤的潜在范围。

随后，项目组借助航空照片解译（aerial photograph interpretation, API），对建模得到的冲积环境中的植被覆盖的区系与结构特征开展评估，以此圈定可能包含RFEF的多边形区域。同时，项目组还通过API，结合指定的桉属、木麻黄属与白千层属物种列表，以及冲积与洪泛平原相关的地貌要素解译结果，对建模得到的冲积区域的边界进行修正。

接下来，项目组为建模得到的冲积地貌与API多边形覆盖的所有州立森林区域，整理了植物区系样地数据。南北海岸的植物区系样地数据均来源于两个渠道：一是存储于环境与遗产局（Office of Environment and Heritage, OEH）VIS数据库中的现有植被调查数据，二是为本项目专门开展的针对性植被调查。项目组将这些样地数据与此前在RFEF判定文件中列出的植被群落对应的样地进行比对，比对过程同时采用了基于相异性的方法与多元回归方法。比对结果被用于评估州立森林中的样地属于RFEF判定文件所列一个或多个群落的可能性。

在此基础上，项目组利用样地数据及一系列环境与遥感变量，构建了RFEF发生概率的预测统计模型：北海岸区域采用随机森林（Random Forest）模型，南海岸区域则采用提升回归树（Boosted Regression Tree）模型。

为生成最终的操作地图，项目组根据每个API多边形对应的样地数据、乔灌层属性、地貌特征及建模得到的概率，将所有符合条件的API多边形归类为RFEF分布区域。最终，项目组在南海岸区域绘制了3819公顷的RFEF分布范围，北海岸区域则为198公顷。

沼泽橡树洪泛平原林（Swamp Oak Floodplain Forest, SOFF）操作地图

本沼泽橡树洪泛平原林（SOFF）操作地图的构建，旨在解决新南威尔士州沿海综合林业运营协议覆盖范围内的州立森林产业中，长期存在的该群落识别、定位与范围界定难题。本次制图分为两个阶段：悉尼以北的州立森林与悉尼以南的州立森林采用独立流程开展制图。此举旨在规避区域植被群落与受威胁生态群落（TEC）之间的关联，因地理变异或其他重大生态梯度而被混淆或掩盖的风险——若将该受威胁生态群落的完整纬度范围作为单一研究区域，此类风险将显著提升。本次研究总计评估了新南威尔士州沿海区域的121.8万公顷州立森林，其中北海岸区域为86.8万公顷，南海岸区域超过35万公顷。

在两个研究区域中，项目组的受威胁生态群落参考专家组均在评估流程启动前，完成了SOFF判定文件的审查工作，并商定了一套用于其识别的诊断参数。专家组认定，SOFF主要基于植物区系样地数据进行界定，且其主要分布于沿海洪泛平原及相关冲积地貌中。

基于上述结论，项目组首先开展洪泛平原与冲积土壤的分布制图，以此识别潜在的SOFF分布区域。针对南北海岸区域，项目组均采用现有沿海地貌与地质图件，结合多套冲积地貌特征的精细尺度模型，以确定研究区域内洪泛平原与冲积土壤的潜在范围。

随后，项目组借助航空照片解译（API），对建模得到的冲积环境中的植被覆盖的区系与结构特征开展评估，以此圈定可能包含SOFF的多边形区域。同时，项目组还通过API，结合指定的木麻黄属与白千层属物种列表，以及冲积与洪泛平原相关的地貌要素解译结果，对建模得到的冲积区域的边界进行修正。

接下来，项目组为建模得到的冲积地貌与API多边形覆盖的所有州立森林区域，整理了植物区系样地数据。南北海岸的植物区系样地数据均来源于两个渠道：一是存储于OEH VIS数据库中的现有植被调查数据，二是为本项目专门开展的针对性植被调查。项目组将这些样地数据与此前在SOFF判定文件中列出的植被群落对应的样地进行比对，比对过程同时采用了基于相异性的方法与多元回归方法。比对结果被用于评估州立森林中的样地属于SOFF判定文件所列一个或多个群落的可能性。

为生成最终的操作地图，项目组根据每个API多边形对应的样地数据、乔灌层属性、地貌特征及模型输出结果，将所有符合条件的API多边形归类为SOFF分布区域。最终，项目组在全部研究区域内共绘制了约272公顷的SOFF分布范围。

沼泽硬叶林（Swamp Sclerophyll Forest, SSF）操作地图

本沼泽硬叶林（SSF）操作地图的构建，旨在解决新南威尔士州沿海综合林业运营协议覆盖范围内的州立森林产业中，长期存在的该群落识别、定位与范围界定难题。本次制图分为两个阶段：悉尼以北的州立森林与悉尼以南的州立森林采用独立流程开展制图。此举旨在规避区域植被群落与受威胁生态群落（TEC）之间的关联，因地理变异或其他重大生态梯度而被混淆或掩盖的风险——若将该受威胁生态群落的完整纬度范围作为单一研究区域，此类风险将显著提升。本次研究总计评估了新南威尔士州沿海区域的121.8万公顷州立森林，其中北海岸区域为86.8万公顷，南海岸区域超过35万公顷。

在两个研究区域中，项目组的受威胁生态群落参考专家组均在评估流程启动前，完成了SSF判定文件的审查工作，并商定了一套用于其识别的诊断参数。专家组认定，SSF主要基于植物区系样地数据进行界定，且其主要分布于沿海洪泛平原及相关冲积地貌中。

基于上述结论，项目组首先开展洪泛平原与冲积土壤的分布制图，以此识别潜在的SSF分布区域。针对南北海岸区域，项目组均采用现有沿海地貌与地质图件，结合多套冲积地貌特征的精细尺度模型，以确定研究区域内洪泛平原与冲积土壤的潜在范围。

随后，项目组借助航空照片解译（API），对建模得到的冲积环境中的植被覆盖的区系与结构特征开展评估，以此圈定可能包含SSF的多边形区域。同时，项目组还通过API，结合指定的桉属、木麻黄属与白千层属物种列表，以及冲积与洪泛平原相关的地貌要素解译结果，对建模得到的冲积区域的边界进行修正。

接下来，项目组为建模得到的冲积地貌与API多边形覆盖的所有州立森林区域，整理了植物区系样地数据。南北海岸的植物区系样地数据均来源于两个渠道：一是存储于OEH VIS数据库中的现有植被调查数据，二是为本项目专门开展的针对性植被调查。项目组将这些样地数据与此前在SSF判定文件中列出的植被群落对应的样地进行比对，比对过程同时采用了基于相异性的方法与多元回归方法。比对结果被用于评估州立森林中的样地属于SSF判定文件所列一个或多个群落的可能性。

在此基础上，项目组利用样地数据及一系列环境与遥感变量，构建了SSF发生概率的预测统计模型：北海岸区域采用随机森林（Random Forest）模型，南海岸区域则采用提升回归树（Boosted Regression Tree）模型。

为生成最终的操作地图，项目组根据每个API多边形对应的样地数据、乔灌层属性、地貌特征及建模得到的概率，将所有符合条件的API多边形归类为SSF分布区域。最终，项目组在全部研究区域内共绘制了约1131公顷的SSF分布范围。

亚热带沿海洪泛平原林（Subtropical Coastal Floodplain Forest, SCFF）操作地图

本亚热带沿海洪泛平原林（SCFF）操作地图的构建，旨在解决新南威尔士州东部区域林业协议覆盖范围内的州立森林产业中，长期存在的该群落识别、定位与范围界定难题。项目组的受威胁生态群落参考专家组，同步审查了SCFF的判定文件，以及另外3个与沿海洪泛平原环境相关的受威胁生态群落的判定文件。专家组认定，SCFF主要基于植物区系样地数据进行界定，且其主要分布于沿海洪泛平原及相关冲积地貌中。

本次操作地图的构建分为多个阶段：首先，项目组通过绘制洪泛平原与冲积土壤的分布，识别SCFF的候选分布区域。为此，项目组采用现有沿海地貌与地质图件，结合多套冲积地貌特征的精细尺度模型，以确定研究区域内洪泛平原与冲积土壤的潜在范围。

其次，项目组为上述冲积地貌范围内的州立森林区域整理了植物区系样地数据。该数据来源于两个渠道：一是存储于OEH VIS数据库中的现有植被调查数据，二是为本项目专门开展的针对性植被调查。项目组将这些样地数据与此前在SCFF判定文件中列出的植被群落对应的样地进行比对，比对过程同时采用了基于相异性的方法与多元回归方法。比对结果被用于评估州立森林中的样地属于SCFF判定文件所列一个或多个群落的可能性。

第三，项目组借助航空照片解译（API），对建模得到的冲积及相关环境中的植被区系与结构特征开展评估。同时，项目组还通过API，结合指定的桉属、木麻黄属与白千层属物种列表，以及冲积与洪泛平原相关的地貌要素解译结果，对建模得到的冲积区域的边界进行修正。

第四，项目组利用样地数据及一系列环境与遥感变量，构建了SCFF发生概率的随机森林（Random Forest, RF）预测模型。

为生成最终的操作地图，项目组根据每个API多边形对应的样地数据、乔灌层属性、地貌特征及建模得到的概率，将所有符合条件的API多边形归类为SCFF分布区域。最终，项目组在全部研究区域内共绘制了约11050公顷的SCFF分布范围，其中绝大多数分布于格拉夫顿（Grafton）与卡齐诺（Casino）之间。

受威胁生态群落操作制图

本次受威胁生态群落操作制图通过航空照片解译完成，解译观测尺度为1:4000，采用ADS40 50厘米像素分辨率影像，以及用于洪泛平原濒危生态群落的1米分辨率激光雷达DEM格网数据。