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

本数据集包含冲积沟与坡面沟的分布图，覆盖四个大型激光雷达（lidar）数据块，涉及伯德金、菲茨罗伊和诺曼比流域的部分区域。沟谷多边形采用NESP 5.10项目开发的激光雷达沟谷提取方法生成。激光雷达（lidar）是通过机载激光扫描系统从飞行器采集的高精度地形数据。

导致水质下降和大堡礁（Great Barrier Reef，GBR）健康受损的关键因素之一是陆基侵蚀加剧，致使流入大堡礁泻湖的河流中沉积物污染问题突出。沟谷是GBR流域内侵蚀性沉积物来源的重要组成部分。

激光雷达（lidar）是通过机载激光扫描系统从飞行器采集的高精度地形数据。2018-2019年，作为环境与能源部项目的一部分，研究人员针对三个研究区域（伯德金、菲茨罗伊和诺曼比流域的部分区域）采集了机载激光雷达数据与正射影像，旨在通过建立沟谷和河岸侵蚀范围的新基线，优化针对珊瑚礁侵蚀及细泥沙流失管理的投资优先级。澳大利亚联邦科学与工业研究组织（CSIRO）负责并监督该项目，委托航空成像与测绘公司Aerometrex采集、处理并提供数据。数据提供商提供了分类激光雷达点云数据、正射影像以及由点云数据生成的、单元格大小为0.5米的数字高程模型（Digital Elevation Model，DEM）。生成DEM时需将点云中的点分类为地面点与非地面点。CSIRO对所提供的点云数据及地面/非地面分类结果进行了质量控制分析。激光雷达采集的平均地面点密度达到每平方米20-30个点。本制图数据集使用的数据提供商通过CSIRO提供的DEM。

格里菲斯大学开发了绘制冲积沟与坡面沟多边形、估算沟谷内潜在活跃侵蚀区域及沟谷生命周期内总侵蚀沉积物量的方法与流程。所绘制沟谷的概念模型源于NESP 4.9项目的研究成果，更早可追溯至澳大利亚热带河流和河口研究设施（MTSRF）诺曼比泥沙预算项目，该项目是TRaCK项目后续研究的一部分。

方法：生成本数据集的项目旨在开发从数字地形数据中提取沟谷的方法，本质上是一种处理流程。该方法的完整描述见NESP 5.10报告。以下为方法概述：数据提供商（Aerometrex）提供了分类激光雷达点云数据、正射影像及由点云数据生成的、栅格单元大小为0.5米的数字高程模型（DEM）。对DEM进行水文条件处理，确保基于DEM的水文模拟连续无中断。生成河道、道路和堤坝的掩膜，并从分析中排除DEM中的这些区域。分析景观环境，将其划分为崎岖区域与平坦至缓坡区域，即坡面与冲积景观。

在特定构型下，景观会出现明显的坡度突变，表明作用于景观的主导地貌过程发生变化。景观中常见的坡度突变是坡面到冲积洪泛平原的过渡。在相对稳定的地表转变为以侵蚀为主的地表处，可观察到坡度突变。例如，大型洪泛平原中，近乎水平的洪泛平原表面被切入洪泛平原物质的沟谷中断；又如坡面中，稳定的斜坡被切入土壤层的沟谷中断。这些坡度突变（称为侵蚀性景观特征的“软边缘”）被绘制出来。采用两种不同方法绘制坡面与冲积景观中的软边缘。

坡面景观中软边缘的绘制方法包括：对DEM中的高程数据进行统计平滑处理，生成平滑地表；将原始地表减去平滑地表的高程；从差值图层中提取软边缘。

冲积景观中软边缘的绘制结合了景观凹度与多方向山体阴影模型。DEM中存储的地形可用于模拟太阳处于特定方位角和高度角时在景观上投射的阴影。在大多数GIS软件中，太阳投射的阴影模型称为山体阴影。研究中生成了多个山体阴影图层，通过改变太阳方位角和垂直角，模拟太阳360度旋转的效果。从这些多山体阴影图层中，识别出由坡度突变界定的、代表某种侵蚀性景观特征的区域。

软边缘包含侵蚀性景观特征的聚合体。利用基于DEM的地表水文模型，将软边缘内的侵蚀性景观特征分解为独立的水文单元，生成侵蚀性景观特征分布图。

活跃侵蚀沟谷是一种侵蚀性景观特征。通过结合卫星影像提取的裸土（无植被）区域、DEM衍生的地表粗糙度指标及DEM衍生的潜在活跃侵蚀指标，从侵蚀性景观特征中过滤出沟谷。过滤生成识别活跃侵蚀沟谷的多边形。当这些沟谷多边形的边界与DEM中的明显崖壁边缘重叠时，所绘制的边界称为“硬边缘”。

对通过这些制图流程识别出的沟谷，计算其生命周期内总侵蚀沉积物量及一系列指标（包含在shapefile属性表中）。

数据局限性：制图方法、流程及过滤可能产生假阴性与假阳性结果。在数万个多边形中，存在少量多边形未识别沟谷、而是识别了其他密切相关的地貌侵蚀性景观特征的可能性。若使用本数据集对特定沟谷进行评估（如泥沙控制措施或修复工程），需将多边形与遥感影像或数据进行对比，并进行现场验证。项目期间的新冠疫情限制措施减少了可开展的地面验证工作。

格式：本数据集包含三个shapefile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
这三个shapefile文件是覆盖伯德金、菲茨罗伊和诺曼比河流域部分区域的沟谷多边形。shapefile的属性表包含一系列沟谷指标。

数据字典：
- FID：ArcGIS中的要素ID
- Id：ArcGIS分析使用的ID
- ZONAL_ID：用于提取分区统计的唯一值
- Area_m2：软边缘面积（平方米）
- GullyID：景观类别前缀加沟谷编号
- GulNum：每个软边缘的唯一编号
- Hard_m2：硬边缘面积（平方米）
- Hard_pct：硬边缘面积占软边缘面积的百分比
- PAE_m2：潜在活跃侵蚀边缘面积（平方米）
- PAE_pct：潜在活跃侵蚀面积占软边缘面积的百分比
- VegeData：植被数据说明
- Ht2m_plus：高度≥2米的植被面积（平方米）
- pct2mPlus：高度≥2米的植被面积占软边缘面积的百分比
- Soft_Geom：软边缘几何数据说明
- SoftLen_m：软边缘最小外接矩形定义的长度（米）
- SoftWith_m：软边缘最小外接矩形定义的宽度（米）
- SftPerim_m：软边缘周长（米）
- Soft_L_W：软边缘长宽比
- S_Prm_Area：软边缘周长面积比
- Hard_Geom：硬边缘几何数据说明
- HardLen_m：硬边缘最小外接矩形定义的长度（米）
- HardWith_m：硬边缘最小外接矩形定义的宽度（米）
- HrdPerim_m：硬边缘周长（米）
- Hard_L_W：硬边缘长宽比
- H_Prm_Area：硬边缘周长面积比
- Ht_Range：软边缘内高程范围数据说明
- Elev_Range：软边缘内最大高程减最小高程（米）
- HtRng_PAE：潜在活跃侵蚀区域内高程范围数据说明
- PAE_Range：潜在活跃侵蚀区域内最大高程减最小高程（米）
- FlowDist：软边缘内最大流路径长度数据说明
- FlowDist_m：使用下游算子计算的软边缘内最大流长度减最小流长度（米）
- Slope_FL：基于流长度的沟谷坡度数据说明
- Slope_FLen：沟谷坡度（高程范围除以流长度，再除以π乘以180）
- Slope_GL：基于几何长度的沟谷坡度数据说明
- Slope_GLen：沟谷坡度（高程范围除以最小外接几何长度，再除以π乘以180）
- Connected：沟谷与河道系统连接性数据说明
- Conxt_Y_N：连接（Yes）或断开（No）
- Disconnect：断开距离数据说明
- DisCnctDis：每个沟谷与河道系统的断开距离（米）
- ErasedDis：每个沟谷与河道系统的断开距离减去流路径穿过其他沟谷的距离（米）
- Diff：总断开距离减去ErasedDis，即漫流路径距离（米）
- GulVol：基于重建地表方法计算的挖掘体积数据说明
- GulVol_m3：侵蚀物质体积（立方米，基于前期地表估算）
- AveDepth：前期地表方法中侵蚀像素的平均深度
- MaxDepth：前期地表方法中侵蚀像素的最大深度
- UpArea：沟谷出口汇水面积数据说明
- Catmnt_ha：最大流累积值定义的汇水面积（平方米）
- Cat_Ratio：沟谷软边缘与汇水面积比数据说明
- CatmtRat：沟谷软边缘面积除以汇水面积的比值
- ContrbArea：汇水面积是否完全在激光雷达范围内的数据说明
- WthInLidr1：1=汇水面积在激光雷达范围内，0=汇水面积超出激光雷达范围
- CircRatio：沟谷软边缘圆形度比数据说明
- CircRato：圆形度比（4×π×沟谷面积）除以周长平方
- SA_Ratio：基于Berry 2002方法的沟谷软边缘表面积/面积比数据说明
- SAR_SUM：三维表面积值之和
- SAR_Norm：三维表面积与二维表面积的比值
- FiltMethd：软边缘内硬边缘或潜在活跃侵蚀（PAE）过滤标准通过情况数据说明
- HardFilt：1=通过，0=未通过
- PAE_Filt：1=通过，0=未通过
- HardCount：每个软边缘内硬边缘片段的计数
- Hard_Count：计数
- PAECount：每个软边缘内PAE片段的计数
- PAE_Count：计数
- Planet3m：软边缘内从Planet Scope 3米影像识别的裸土面积数据说明
- PS_bare：Planet Scope裸土面积（平方米）
- PS_pct：Planet Scope 3米裸土面积占软边缘面积的百分比
- ecw_Bare：软边缘内从正射影像RGB识别的裸土面积数据说明
- ecwBare_m2：ecw影像裸土面积（平方米）
- ecwBarePct：正射影像裸土面积占软边缘面积的百分比
- Fencing：基于最小外接周长的围栏长度
- FenceLen：长度（米）
- Clust_10m：软边缘10米内聚类数据说明
- Clust10mID：10米内沟谷聚类的字母数字标识符
- Clust_50m：软边缘50米内聚类数据说明
- Clust50mID：50米内沟谷聚类的字母数字标识符
- Roughness：每个软边缘内粗糙度数据说明
- Sum_Rough：所有侵蚀像素的粗糙度之和
- Sum_RoughA：归一化到软边缘面积的粗糙度之和
- SoilClass：每个软边缘内土壤类型数据说明
- Soil_Desc：土壤类型简要描述
- Soil_Intgr：土壤类型整数分类
- LandClass：每个软边缘内景观分类数据说明
- Land_Desc：景观类别（冲积、崩积或崎岖）
- Land_Intgr：冲积=1，崩积=2，崎岖=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沟谷制图与分类自动化工具的开发与应用。PrESM，格里菲斯大学，第138页。

数据位置：本数据集存储于eAtlas长期数据仓库，路径为：datacustodian2019-2022-NESP-TWQ-55.10_Gully-mapping-classification