Alluvial and Hillslope Gully Mapping – Digital gully mapping based on lidar data collected 2018-2019 in sections of the Burdekin, Fitzroy, and Normanby catchments. (NESP TWQ 5.10, Griffith University)

This dataset contains maps of alluvial and hillslope gullies across four large blocks of lidar covering portions of the Burdekin, Fitzroy, and Normanby Catchments. The gully polygons were generated using methods developed in the NESP 5.10 project for the extraction of gullies from lidar. Lidar is detailed topographic data collected from aircraft using an airborne laser scanning system.A significant component of the cause of declining water quality and the health of the GBR is increased land based erosion leading to sediment pollution within the rivers draining into the GBR lagoon. Gullies are a significant proportion of the erosional sediment sources within the GBR catchments.Lidar is detailed topographic data collected from aircraft using an airborne laser scanning system. Airborne lidar data and orthophotography were acquired for the three study areas (portions of the Burdekin, Fitzroy, and Normanby Catchments) in 2018 and 2019 as part of a Department of Environment and Energy program to improve investment prioritisation in the management of erosion and fine sediment losses to the reef through the establishment of a new baseline of extent of gully and streambank erosion. CSIRO undertook and oversaw the program, contracting the aerial imaging and mapping company Aerometrex to acquire, process and provide the data. The data provider supplied classified lidar point cloud data, orthophotography imagery, and Digital Elevation Models (DEM), with 0.5 m cell size, derived from point cloud data. To produce DEMs the points within the cloud must be classified into ground and non-ground points. CSIRO performed quality control analyses of the provided point cloud data and the ground/non-ground classification. The lidar acquisition achieved average point densities in the range of 20 to 30 ground points per square metre. The DEMs supplied by the data provider, via CSIRO, were used for this mapping dataset. Griffith University developed methods and processes to map alluvial and hillslope gully polygons, estimates of potential active erosion within gullies, and estimates of total volume of sediment eroded over the lifetime of these gullies. The conceptual model of the gullies that are being mapped, stems from work undertaken through NESP Project 4.9, and prior to that the MTSRF Normanby Sediment Budget Project which followed on from projects undertaken through the TRaCK Program.Methods:The project that produced this dataset was an investigation into developing methods to extract gullies from digital topographic data and is almost entirely a processing method. A full description of the method is available in the NESP 5.10 report.Below is a summary of the method. The data provider (Aerometrex) supplied classified lidar point cloud data, orthophotography imagery, and Digital Elevation Models (DEM), with 0.5 m raster cell size, derived from point cloud data. The DEMs were hydrologically conditioned making the modelled hydrology derived from the DEMs continuous and without disruptions. A mask of channels, roads, and dams was produced and these areas of the DEM removed from the analysis. The landscape setting was analysed and separated into rugged areas and flat to gently undulating areas, that is, hillslope and alluvial landscapes.Landscapes, in certain configuration, exhibit a distinct break in slope that denotes a change of the predominant geomorphological processes acting on the landscape. A commonly observed break in slope in the landscape is the transition from hillslopes to alluvial floodplains. A break in slope can be observed where a mostly stable land surface changes to a predominantly eroding land surface. An example of this change is observed in large floodplains where the near horizontal surface of the floodplain is interrupted by a gully incising into the floodplain material. Another example is on hillslopes where the stable slopes are interrupted by a gully incising into the soil mantle. These breaks in slope (referred to as ‘soft margins’ of erosional landscape features) where mapped. Two different methods were used to map the soft margins in hillslope and alluvial landscapes. The mapping of soft margins within the hillslope landscapes involved a method that performs a statistical smoothing of the elevation data within DEM to produce a smoothed land surface. The elevation of the smoothed land surface is subtracted from the original land surface. Within this subtracted layer the soft margins can be extracted.The mapping of soft margins within the alluvial landscapes involved a combination of landscape concavity and multi-directional hillshade models. The topography stored in the DEM can be used to model the casting of shadows across the landscape when the sun is at a particular bearing and angle to the horizon. In most GIS software a model of the shadow casts by the sun is referred to as a hillshade. A procedure was followed were multiple hillshades were generated with the sun bearing and vertical angle varied so as turn the sun through 360 degrees. From these multiple hillshade layers the parts of the landscape bounded by a break in slope that denote some type of erosional landscape feature were identified. The soft margins contain aggregations of erosional landscape features. The erosional landscape features within the soft margins are disaggregated using a surface hydrology model derive from the DEMs. This disaggregation produces a map of erosional landscape features that are individual hydrologic units.Actively eroding gullies are a type of erosional landscape feature. The gullies are filtered from the erosional landscape features using a combination of; occurrence of bare soil (no vegetation) derive from satellite imagery; a measure of surface roughness derived from the DEM; and a measure of potential active erosion derive from the DEM. The filtering produces polygons identifying actively eroding gullies. Where the boundaries of these gully polygons overlie a distinct scarp edge in the DEM the mapped boundary is referred to as a ‘hard margin’.Estimates of total volume of sediment eroded over the lifetime of these gullies and a range of metrics (include in shapefile attribute table) were calculated for the gullies identified with the application of these mapping procedures.Limitations of the data:Mapping methods, processes, and filtering can generate false negatives and false positives. Within the tens of thousands of polygons there is a small possibility that a polygon is not identifying a gully, but some other closely associated geomorphic erosional landscape feature. If using this dataset to examine and evaluated specific gullies for any sort of assessment, such as, sediment control measures or rehabilitation earth works the polygons require comparison to remotely sense imagery or data and on site ground validation. Covid restrictions during the project limited the amount of ground verification that could be undertaken.Format:This dataset consists of three shapefiles. - BBB_Gullies_withMetrics_for_Upload_v3.shp- Fitzroy_2019_Gullies_withMetrics_for_Upload_v3.shp- Laura_2019_Gullies_withMetrics_for_Upload_v3.shpThese three shapefiles are gully polygons covering a block of the Burdekin, Fitzroy, and Normanby River catchments. The shapefile’s attribute tables contain a range of gully metrics.Data Dictionary:- FID: Feature ID from ArcGIS- Id: ID used by ArcGIS for analysis- ZONAL_ID: Unique value for extracting zonal statistics- Area_m2: Area of the soft margin (m2)- GullyID: Prefix for landscape class plus gully number- GulNum: Unique number for each soft margin- Hard_m2: Area of the hard margin (m2)- Hard_pct: Area of hard margin as % of soft margin- PAE_m2: Area of Potential Active Erosion margin (m2)- PAE_pct: Area of Potential Active Erosion as % of soft margin- VegeData: A note that data on vegetation follows- Ht2m_plus: Area of vegetation >= 2m tall (m2)- pct2mPlus: Area of vegetation >= 2m tall as % of soft margin- Soft_Geom: A note that data on geometry of soft margins follows- SoftLen_m: Length of soft margin as defined by minimum bounding rectangle (m)- SoftWith_m: Width of soft margin as defined by minimum bounding rectangle (m)- SftPerim_m: Perimeter of soft margin (m)- Soft_L_W: Ratio of soft margin Length divided by Width- S_Prm_Area: Ratio of soft margin Perimeter divided by Area- Hard_Geom: A note that data on geometry of hard margins follows- HardLen_m: Length of hard margin as defined by minimum bounding rectangle (m)- HardWith_m: Width of hard margin as defined by minimum bounding rectangle (m)- HrdPerim_m: Perimeter of hard margin (m)- Hard_L_W: Ratio of hard margin Length divided by Width- H_Prm_Area: Ratio of hard margin Perimeter divided by Area- Ht_Range: A note that data on elevation range within soft margins follows- Elev_Range: Maximum elevation minus minimum elevation within soft margin (m)- HtRng_PAE: A note that data on elevation range within potentially active erosion follows- PAE_Range: Maximum elevation minus minimum elevation within potentially active erosion (m)- FlowDist: A note that data on length of maximum flow path within soft margins follows- FlowDist_m: Maximum flow length minus minimum flow length within soft margin, using downstream operator (m)- Slope_FL: A note that data on gully slope derived from flow length follows- Slope_FLen: Gully Slope calc from (Ht Range div by Flow Length) div by Pi times 180- Slope_GL: A note that data on gully slope derived from geometric length follows- Slope_GLen: Slope using gully length from Minimum Bounding Geometry (Ht Range div by Length) div by Pi times 180- Connected: A note that data on gully connection to channel system follows- Conxt_Y_N: Yes or No for connected or disconnected- Disconnect: A note that data on the distance of disconnection follows- DisCnctDis: The distance each individual gully is disconnected from the channel system (m)- ErasedDis: The distance each individual gully is disconnected from the channel system minus the distance the flow path passes through other gullies (m)- Diff: Total disconnected distance minus erased distance. Is the distance of diffuse overland flow (m)- GulVol: A note that data on the excavated volume of material as calculated by the reconstructed lids method follows- GulVol_m3: Volume of eroded material (m3) Derived from Prior Land Surface estimate- AveDepth: Average depth of eroded pixels from Prior Land Surface method- MaxDepth: Max eroded depth of eroded pixels from Prior Land Surface method- UpArea: A note that data on the area of catchment contributing to the gully outlet follows- Catmnt_ha: Area of contributing catchment as defined by the maximum flow accumulation value (m2)- Cat_Ratio: A note that data on the ratio of gully soft margin to contributing catchment follows- CatmtRat: Ratio of gully soft margin divided by contributing catchment area- ContrbArea: A note that data on whether the area of catchment contributing to the gully outlet is completely within the extent of the Lidar follows- WthInLidr1: 1 = contributing area is within Lidar extent. 0 = contributing area runs off Lidar- CircRatio: A note that data on the circularity ratio of gully soft margin follows- CircRato: Circularity Ratio (4*Pi*Gully Area) divided by perimeter squared- SA_Ratio: A note that data on the Berry 2002 method for surface/area ratio of gully soft margin follows.- SAR_SUM: Sum of 3D surface area values- SAR_Norm: Ratio of 3D surface area to 2D surface area- FiltMethd: A note that data on the success or failure to pass filtering criteria for presence of hard margins of PAE within soft margin follows.- HardFilt: 1 = success. 0 = fail- PAE_Filt: 1 = success. 0 = fail- HardCount: A count of the fragments of Hard-edge inside each soft margin- Hard_Count: Count- PAECount: A count of the fragments of PAE inside each soft margin- PAE_Count: Count- Planet3m: A note that data on the area of bare earth identified from Planet Scope 3m imagery within soft margin follows- PS_bare: Area of bare soil from Planet Scope (m2)- PS_pct: Area of Planet Scope 3m bare earth divided by area of soft margin- ecw_Bare: A note that data on the area of bare earth identified orthophoto RGB imagery within soft margin follows- ecwBare_m2: Area of bare soil from ecw imagery (m2)- ecwBarePct: Area of orthophoto bare earth divided by area of soft margin- Fencing: Length of fencing based on minimum bounding perimeter- FenceLen: Length (m)- Clust_10m: A note that data on the clustering of soft margin within 10m of each other follows- Clust10mID: Alphanumeric Identifier of gully clusters within 10m of each other- Clust_50m: A note that data on the clustering of soft margin within 50m of each other follows- Clust50mID: Alphanumeric Identifier of gully clusters within 50m of each other- Roughness: A note that data on the roughness within each soft margin follows- Sum_Rough: Sum of Roughness over any eroding pixels- Sum_RoughA: Sum of roughness normalized to area of soft margin- SoilClass: A note that data on soil type within each soft margin follows- Soil_Desc: A brief description of soil type- Soil_Intgr: Integer classification of soil type- LandClass: A note that data on landscape classification for each soft margin follows- Land_Desc: Landscape class is alluvial, colluvial or rugged- Land_Intgr: Alluvial = 1, colluvial = 2, rugged = 3References:Daley, J., Stout, J.C., Curwen, G., Brooks., A.P., Spencer., J., Pietsch., T. and Thwaites, R.. (2021). Development and application of automated tools for high resolution gully mapping and classification from lidar data. PrESM, Griffith University. pp. 138.Data Location:This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.10_Gully-mapping-classification本数据集包含覆盖伯德金（Burdekin）、菲茨罗伊（Fitzroy）与诺曼比（Normanby）流域部分区域的四幅大型激光雷达（LiDAR）区块内的冲积型与坡地型冲沟分布图。冲沟矢量多边形由国家环境科学计划（National Environmental Science Program, NESP）5.10项目开发的激光雷达冲沟提取方法生成。 激光雷达（LiDAR）是通过机载激光扫描系统从航空器获取的高精度地形数据。大堡礁（Great Barrier Reef, GBR）水质下降与生态健康受损的重要诱因之一，便是陆源侵蚀加剧，导致排入大堡礁潟湖的河流携带泥沙污染物；而冲沟是大堡礁流域内侵蚀性泥沙来源的重要组成部分。 2018至2019年，为通过建立冲沟与河岸侵蚀范围的新基线，优化侵蚀治理与泥沙向大堡礁输运管控的投资优先级，澳大利亚环境与能源部针对三个研究区域（伯德金、菲茨罗伊与诺曼比流域的部分区域）开展了机载激光雷达数据与正射影像的获取工作。联邦科学与工业研究组织（Commonwealth Scientific and Industrial Research Organisation, CSIRO）承接并监管了该项目，委托航空成像与测绘企业Aerometrex负责数据的获取、处理与交付。 数据供应商提供了分类后的激光雷达点云数据、正射影像，以及由点云数据生成的0.5米栅格分辨率数字高程模型（Digital Elevation Model, DEM）。生成DEM时，需将点云中的点分为地面点与非地面点两类；CSIRO对提供的点云数据及地面/非地面点分类结果开展了质量控制分析。本次激光雷达数据采集的平均地面点密度为20~30个/平方米，本数据集的冲沟制图即基于数据供应商通过CSIRO提供的DEM完成。 格里菲斯大学（Griffith University）开发了用于生成冲积型与坡地型冲沟矢量多边形、估算冲沟潜在活跃侵蚀范围，以及冲沟全生命周期内总侵蚀泥沙量的方法与流程。本次制图所采用的冲沟概念模型源自NESP 4.9项目的研究成果，其前期可追溯至基于TRaCK计划项目开展的MTSRF诺曼比流域泥沙预算项目。 方法： 本数据集的研发项目旨在探索从数字地形数据中提取冲沟的方法，核心以数据处理流程为主，完整方法说明可参见NESP 5.10项目报告，下文为方法概要。 数据供应商（Aerometrex）提供了分类后的激光雷达点云数据、正射影像，以及由点云数据生成的0.5米栅格分辨率DEM。首先对DEM进行水文修正，确保由DEM导出的水文模拟连续无间断；随后生成河道、道路与水坝掩膜，并将掩膜覆盖区域从分析中剔除。 对研究区景观进行分类，分为崎岖地形区与平缓/微起伏地形区，即坡地与冲积平原景观。特定景观配置下会出现明显的坡度转折，这代表区域主导地貌过程发生变化——其中最常见的便是坡地向冲积洪泛平原的过渡。坡度转折可通过稳定地表被冲沟切入侵蚀的现象识别，例如大型洪泛平原的近水平地表被冲沟切割，或是稳定坡地上出现冲沟侵蚀。本次制图对这类坡度转折（即侵蚀地貌的“软边界”）进行了提取。 针对坡地与冲积平原景观的软边界，采用两种不同的提取方法： 1. 坡地景观软边界提取：对DEM高程数据进行统计平滑以生成平滑地表，将原始地表高程与平滑地表高程相减，在差值图层中即可提取软边界。 2. 冲积平原景观软边界提取：结合景观凹度与多方向山体阴影模型开展提取。DEM存储的地形数据可用于模拟特定太阳方位角与高度角下的地表阴影投射，在多数地理信息系统（GIS）软件中，太阳阴影模型被称为山体阴影。本次研究生成了多组太阳方位角与高度角变化覆盖360°的山体阴影图层，从中识别出带有坡度转折、代表侵蚀地貌的区域。 软边界区域聚集了多个侵蚀地貌特征，通过由DEM导出的地表水文模型可将这些侵蚀特征解译为独立的水文单元。活跃侵蚀冲沟属于一类侵蚀地貌特征，可通过结合以下三项指标从侵蚀地貌中筛选得到：由卫星影像提取的裸土区域、由DEM导出的地表粗糙度，以及由DEM导出的潜在活跃侵蚀范围。筛选结果生成了标识活跃侵蚀冲沟的矢量多边形，若该多边形边界与DEM中的明显陡崖边缘重合，则将该边界称为“硬边界”。 针对通过上述制图流程识别的冲沟，本数据集计算了其全生命周期总侵蚀泥沙量，以及多项特征指标（存储于Shapefile属性表中）。 数据局限性： 本数据集的制图方法、流程与筛选环节可能产生假阴性与假阳性结果。在数以万计的矢量多边形中，存在极小概率将非冲沟的相关侵蚀地貌特征误识别为冲沟。若使用本数据集开展特定冲沟的相关评估（如泥沙管控措施或修复工程），需将矢量多边形与遥感影像或实地勘测数据进行比对验证。项目实施期间受新冠疫情管控限制，可开展的实地验证工作有限。 数据格式： 本数据集包含3个Shapefile（SHP格式矢量文件）： - BBB_Gullies_withMetrics_for_Upload_v3.shp - Fitzroy_2019_Gullies_withMetrics_for_Upload_v3.shp - Laura_2019_Gullies_withMetrics_for_Upload_v3.shp 三个矢量文件分别覆盖伯德金、菲茨罗伊与诺曼比河流域的冲沟多边形，其属性表包含多项冲沟特征指标。 数据字典： - FID：ArcGIS生成的要素ID - Id：ArcGIS分析所用的ID - ZONAL_ID：用于提取分区统计的唯一值 - Area_m2：软边界面积（单位：平方米） - GullyID：景观类别前缀加冲沟编号 - GulNum：每个软边界的唯一编号 - Hard_m2：硬边界面积（单位：平方米） - Hard_pct：硬边界面积占软边界面积的百分比 - PAE_m2：潜在活跃侵蚀区域面积（单位：平方米） - PAE_pct：潜在活跃侵蚀区域面积占软边界面积的百分比 - VegeData：植被数据说明项 - Ht2m_plus：植被高度≥2米的区域面积（单位：平方米） - pct2mPlus：植被高度≥2米的区域面积占软边界面积的百分比 - Soft_Geom：软边界几何参数说明项 - SoftLen_m：最小外接矩形定义的软边界长度（单位：米） - SoftWith_m：最小外接矩形定义的软边界宽度（单位：米） - SftPerim_m：软边界周长（单位：米） - Soft_L_W：软边界长度与宽度的比值 - S_Prm_Area：软边界周长与面积的比值 - Hard_Geom：硬边界几何参数说明项 - HardLen_m：最小外接矩形定义的硬边界长度（单位：米） - HardWith_m：最小外接矩形定义的硬边界宽度（单位：米） - HrdPerim_m：硬边界周长（单位：米） - Hard_L_W：硬边界长度与宽度的比值 - H_Prm_Area：硬边界周长与面积的比值 - Ht_Range：软边界内高程范围说明项 - Elev_Range：软边界内最大高程与最小高程的差值（单位：米） - HtRng_PAE：潜在活跃侵蚀区域内高程范围说明项 - PAE_Range：潜在活跃侵蚀区域内最大高程与最小高程的差值（单位：米） - FlowDist：软边界内最大汇流路径长度说明项 - FlowDist_m：采用下游算子计算的软边界内最大汇流长度与最小汇流长度的差值（单位：米） - Slope_FL：基于汇流路径长度的冲沟坡度说明项 - Slope_FLen：冲沟坡度计算公式：(高程范围/汇流路径长度)/π×180 - Slope_GL：基于几何长度的冲沟坡度说明项 - Slope_GLen：冲沟坡度计算公式：(高程范围/最小外接矩形长度)/π×180 - Connected：冲沟与河道系统连通性说明项 - Conxt_Y_N：标识冲沟是否与河道系统连通的“是/否”字段 - Disconnect：冲沟与河道系统断开距离说明项 - DisCnctDis：单个冲沟与河道系统的断开距离（单位：米） - ErasedDis：单个冲沟与河道系统的断开距离减去汇流路径穿过其他冲沟的距离（单位：米） - Diff：总断开距离与擦除距离的差值，即分散坡面流的传输距离（单位：米） - GulVol：冲沟开挖体积说明项 - GulVol_m3：由先前地表模型估算的侵蚀泥沙体积（单位：立方米） - AveDepth：由先前地表模型计算的侵蚀像素平均深度 - MaxDepth：由先前地表模型计算的侵蚀像素最大深度 - UpArea：冲沟出口汇流流域面积说明项 - Catmnt_ha：由最大流量累积值定义的汇流流域面积（单位：平方米） - Cat_Ratio：冲沟软边界与汇流流域面积比值说明项 - CatmtRat：冲沟软边界面积与汇流流域面积的比值 - ContrbArea：汇流流域是否完全覆盖于激光雷达数据范围内说明项 - WthInLidr1：1代表汇流区域完全位于激光雷达数据范围内，0代表汇流区域超出激光雷达数据范围 - CircRatio：冲沟软边界圆形度说明项 - CircRato：圆形度计算公式：(4×π×冲沟面积)/周长² - SA_Ratio：Berry 2002提出的冲沟软边界表面积/面积比方法说明项 - SAR_SUM：三维表面积总和 - SAR_Norm：三维表面积与二维表面积的比值 - FiltMethd：软边界内硬边界与潜在活跃侵蚀区域的筛选结果说明项 - HardFilt：1代表通过硬边界筛选，0代表未通过 - PAE_Filt：1代表通过潜在活跃侵蚀区域筛选，0代表未通过 - HardCount：每个软边界内硬边界片段的数量 - Hard_Count：硬边界片段数量 - PAECount：每个软边界内潜在活跃侵蚀区域片段的数量 - PAE_Count：潜在活跃侵蚀区域片段数量 - Planet3m：软边界内由Planet Scope 3米影像识别的裸土面积说明项 - PS_bare：由Planet Scope影像提取的裸土面积（单位：平方米） - PS_pct：Planet Scope 3米影像提取的裸土面积占软边界面积的百分比 - ecw_Bare：软边界内由正射RGB影像识别的裸土面积说明项 - ecwBare_m2：由ecw影像提取的裸土面积（单位：平方米） - ecwBarePct：正射影像提取的裸土面积占软边界面积的百分比 - Fencing：基于最小外接周长计算的围栏长度说明项 - FenceLen：围栏长度（单位：米） - Clust_10m：10米范围内软边界聚类说明项 - Clust10mID：10米范围内冲沟聚类的字母数字标识符 - Clust_50m：50米范围内软边界聚类说明项 - Clust50mID：50米范围内冲沟聚类的字母数字标识符 - Roughness：每个软边界内地表粗糙度说明项 - Sum_Rough：所有侵蚀像素的粗糙度总和 - Sum_RoughA：归一化至软边界面积的粗糙度总和 - SoilClass：每个软边界内土壤类型说明项 - Soil_Desc：土壤类型简要描述 - Soil_Intgr：土壤类型整数分类值 - LandClass：每个软边界的景观分类说明项 - Land_Desc：景观类别，包括冲积型、崩积型或崎岖型 - Land_Intgr：景观类别整数编码：冲积型=1，崩积型=2，崎岖型=3 参考文献： Daley, J., Stout, J.C., Curwen, G., Brooks, A.P., Spencer, J., Pietsch, T. and Thwaites, R. (2021). 基于激光雷达数据的高分辨率冲沟制图与分类自动化工具开发与应用. 格里菲斯大学PrESM项目, 第138页. 数据存储位置： 本数据集存储于eAtlas永久数据仓库中，路径为：datacustodian2019-2022-NESP-TWQ-55.10_Gully-mapping-classification