This dataset is the inventory level output from the MPI SLMACC Northland project. It is derived from raster data through a series of modelling steps based on field data collected at over 400 observation points. Raster maps are then subjected to segmentation processing to create polygons that are assigned inventory attribute values based on zonal statistics from the original raster datasets. The majority of mapping is carried out by automated processing, the exceptions currently being erosion mapping and parent material (because of scale of available source information - Qmap).
Data is derived from LiDAR and field soil sampling data. LiDAR point cloud has been processed into a digital elevation model from which a number of terrain attribute layers (covariate layers) have been derived. All raster modelling has been carried out at 5 m resolution. Digital soil mapping has been used to model soil distribution using a Random Forest model that relates soil mapping units to covariate layers. Segmentation processing of soil and slope raster data was used to convert raster to polygon data and create a set of land resource inventory polygons that were also assigned additional soil properties (depth, texture, drainage and stoniness) using zonal statistics to record dominant soil properties within segmented polygons.
This data is currently in draft form as is being made available via the LRIS Portal as "view only" until LUC coding is finalised.