Data Provenance

Our Environment - Statement on Data Accuracy and Fitness for Purpose

Our Environment contains data that Manaaki Whenua has compiled from information derived from a variety of sources which have different data accuracy and hence differing fitness for purpose. These data may not be complete, correct or up to date. Manaaki Whenua advises that users should expect that ALL data contain error and uncertainty. You should therefore familiarise yourself with the information provided below to ensure you understand the limitations of the data presented on this website. Manaaki Whenua cannot be held responsible for inappropriate use of data and does not accept responsibility for any actions taken in reliance on the data or consequences arising from their use.

Quick Links

Use the links below to jump to background information on specific data layers.

 

Land Resource Inventory (LRI)

Land Use Capability (LUC)

Highly Productive Land (HPL)

15m Digital Elevation Model (DEM)

Land Cover Database (LCDB v5.0)

Protected Natural Areas (PNA)

Threatened Indigenous Environments

Potential Natural Vegetation

Basic Ecosystems (2002-2008)

Greenhouse Gas Emissions (2010-2016)

Nitrate Leaching (2015)

Sediment Lost (2012)

Sediment Retained (2012)

Water Yield (2012)

Wetlands

Land Use Opportunities: Whitiwhiti Ora (Crop Suitability)

Nitrogen & Phosphorous loss susceptibility

N₂O emissions / nitrogen loss under dairy farming

Nitrogen leaching loss estimates under crop rotation

Freshwater state - water quality indicators

LINZ Data

 

 

Land Resource Inventory (LRI)

This is a single spatial (polygon) layer with national coverage that contains several physical resource themes: land use capability, lithology, soil, etc. In terms of geographical data accuracy, the polygon boundaries were originally mapped at 1:63,360 scale, except where more recent mapping was carried out at 1:50,000 scale in Northland, Gisborne-East Coast, Wellington and Marlborough regions. This is regional scale mapping according to the Land Use Capability Survey Handbook (Edition 3). The minimum polygon size for the smallest area of interest in the LRI mapping was nominally 10 hectares, although some 2% of polygons fall below this threshold. Average polygon size for 1:63,360 scale mapping is 335 hectares, and for the more recent 1:50,000 scale mapping is 98 hectares, reflecting both increased mapping scale and improved standards of mapping.

The LRI is a regional-scale database and caution should be used when using these data at larger scales.

In addition to the geographical accuracy of the polygon boundaries, each resource theme is subject to uncertainties about the attributes (e.g. soil classification). Users should download and familiarise themselves with the relevant sections of the LRIS Spatial Data Layers Data Dictionary to ensure that the data are fit for their intended analysis. This is available for download from here in the LRIS Portal.

Interpretations in Our Environment based on the LRI and therefore subject to its data accuracy are: Erosion Severity, Surface Geology and all five of the land suitability classifications. 

Land Use Capability (LUC)

The Land Use Capability (LUC) is a classification system that uses the LRI to categorise land into eight classes, according to it's long term capability to sustain one or more productive uses.

In the December 2021 version of Our Environment, revised data were added to the LUC layer. These updated data include a new national classification legend to complement legacy regional LUC classification legends. The revised layer now includes nationally consistent LUC unit descriptions for the entire country. These new codes, along with the legacy regional codes can be found when a point report is generated for the LUC layer. The legacy regional codes are visible in the "Historic regional units" section in the report.

Please note, the December 2021 update was to the mapping concepts (LUC units and descriptions), but not the underlying geometries. 

Highly Productive Land (HPL)

This layer is a visualisation of the baseline extent of Highly Productive Land, represented here as Land Use Capability classes 1, 2 and 3, as mapped in the New Zealand Land Resource Inventory. Note that complex map units containing two LUC units may occur. In this case reports provided will describe the dominant LUC unit only.

The actual boundaries of Highly Productive Land in a particular location will differ to this visualisation, depending on the relevant council rural and urban zoning boundaries as defined in the National Policy Statement for Highly Productive Land (refer to section 3.5). Please refer to your local council for this information.

By late 2025 it is expected that each regional council will have remapped the extent of Highly Productive Land for their region, following the requirements in the National Policy Statement for Highly Productive Land (refer to section 3.4). When this is done that updated regional map will replace the map presented here. Please refer to your local council for this information.

Full information on the National Policy Statement for Highly Productive Land is available here. 

15m Digital Elevation Model (DEM)

Manaaki Whenua produced its national DEM at 15m postings from 1:50,000 scale topographic data, including 20m contours, spot heights, lake shorelines and coastline data available from Land Information New Zealand (LINZ) using the TOPOGRID functions available in ArcGIS (ESRI). This DEM is exactly spatially aligned with the pixels in the pan-sharpened Landsat ETM+ multispectral imagery and has been optimised for slope accuracy to produce accurate standardized reflectance corrections to remove terrain shading effects from EcoSat data products. It is hydrologically consistent with the NIWA digital stream network and is useful for automatically producing catchment boundaries on flat areas.

Aspect and slope surfaces derived from the 15m DEM are currently based on standard ESRI ArcGIS algorithms calculated over a 3x3 cell neighbourhood. Note that the common practice of assigning elevation, slope and aspect values to specified locations by spatial intersection with DEMs or derivative layers should be  treated with great caution. The preferred option should always be to use field measurement of site properties, if at all practicable. While these DEM–based surfaces provide greatly improved definition of surface properties, sensitivity of many environmental systems to topographic variation and the errors inherent in contour-derived DEMs make them better suited to implementing models rather than as a source of specific data values.

Interpretations in Our Environment based on the 15m DEM and therefore subject to its data accuracy are: Direction of Slope (aspect) and Steepness of Slope.

Land Cover Database (LCDB v5.0)

A New Zealand Land Cover Database has been prepared five times from imagery collected in summer 1996/97, summer 2001/02, summer 2008/09, summer 2012/13, and summer 2018/19 as part of New Zealand's response to climate change and the Kyoto Protocol. The data used in Our Environment are taken from LCDB v5.0, published in January 2020, and are made available by Manaaki Whenua under a Creative Commons license. A number of metadata documents are available and those using the LCDB v5.0 should be familiar with this material. These include a User Guide and documents defining Target Classes that can be found on the LRIS Portal.

LCDB v5.0 is a thematic classification of 33 land-cover and land-use classes covering mainland New Zealand, the near shore islands (re-mapping of Chatham Islands was included in version 4.1 - first mapped at version 2, but then not continued through versions 3.0, 3.3, and 4.0 because of resource constraints). LCDB v5.0 was released in January 2020 and used Landsat 7 ETM+ satellite imagery acquired over the summer of 2018/20. Like LCDB1, LCDB2, LCDB v3.3 and LCDB v4.1, LCDB v5.0 adopted a one hectare (ha) minimum mapping unit (MMU) and to maintain compatibility between datasets for change analysis. Users should be aware of the following issues in using geospatial data derived from imagery with a 15m spatial resolution:

• Positional accuracy: In areas with good ground control the Landsat imagery is correct to within 1 pixel. In areas with poor control (typically mountainous terrain) the imagery may have a positional error of 2 or more pixels.
• Linework accuracy: LCDB2 had polygon boundaries created using both automated and manual digitising techniques (at a display screen resolution of 1:15,000). Cartographically this resulted in a mixture of 'rasterised' and smooth polygons. The raster effect becomes noticeable at 1:25000. Given the spatial resolution, ortho-rectification error and scale of manual digitising of LCDB, procedures were introduced to automatically smooth polygon boundaries to create a consistent cartographic standard of representation. Good practice indicates use of LCDB at a scale of 1:25,000 or smaller scales (i.e. 1:50,000).
• Dominant cover rule: Users need to be aware of the dominant cover rule. For example, regenerating Indigenous Forest is classified as Class 69 (Indigenous Forest), providing more than 50% of the patch is characterised by emergent canopy species. A Shrubland polygon with three or more main species (where further subdivision of the patch based on the 1 ha MMU is not possible), is classified according to the dominant species in the matrix.

There was no official accuracy assessment of LCDB v5.0. For LCDB2 a Marlborough pilot study included an accuracy assessment, but this may or may not be applicable to the full dataset. A paper by Brockerhoff et al. (2008) in the New Zealand Journal of Ecology has useful information on class accuracy of LCDB2 (and LCDB1).

The interpretation in Our Environment based on the LCDB v5.0 and therefore subject to its data accuracy is: Vegetation.

Protected Natural Areas (PNA)

This is a parcel-based vector dataset with elements of cadastral land parcels included in the conservation estate, covenants of the Nature Heritage Fund, Nga Whenua Rahui, and QE II National Trust, along with Regional Parks of Auckland, Bay of Plenty, Manawatu, and Wellington. Data accuracy equates closely to standards for property and in many cases will have been subject to land survey with coordinate accuracy well below metre accuracy.

Threatened Indigenous Environments

The Threatened Environment Classification 2012 has been developed by Manaaki Whenua to help identify areas in which much reduced and poorly protected indigenous ecosystems are most likely to occur. It uses LENZ as a framework for representativeness of the abiotic environment. How much of the potential 'full range' remains in indigenous cover and how much is protected are calculated using the LCDB v4.0 and Protected Natural Areas of New Zealand (PNA-NZ) data layers. The Guide for Users of the Threatened Indigenous Environments classification provides the best available indication of methodology and data accuracy.

Potential Natural Vegetation

Potential natural vegetation (PNV) is a vector layer which provides an estimate of what New Zealand’s vegetation cover would have been in the absence of humans.  It has been generated from largely raster data using statistical tools both for the interpolation of point climate data and the analysis of spatial patterns to reconstruct the likely biological character of New Zealand’s pre-human past. More information is available on the LENZ website or in Leathwick, J. R. (2001), 'New Zealand's potential forest pattern as predicted from current species-environment relationships', New Zealand Journal of Botany 39: 447-464. The accuracy of this dataset is difficult to assess since it portrays a concept rather than something that exists in the real-world.  In addition, because it is generated from a number of input layers which include climate layers derived by interpolation, there is significant potential for complex propagation of errors.  Finally, the original raster data layer has also been converted to vectors with some smoothing and simplification of linework.  For this reason the PNV layer cannot be viewed at scales larger than 1:50,000 scale.

Basic Ecosystems (2002-2008)

This layer was derived from three existing data layers: the Land Cover Database 2 (LCDB2) (MfE 2002); the Land Use Map (LUM) from the Land Use Carbon Analysis System (MfE 2008; Dymond et al. 2012); and EcoSat Forests (Shepherd et al. 2002). Indigenous forest classes from EcoSat Forests were combined with classes from LCDB2 to form basic ecosystems classes. Where indigenous forest was mapped by LCDB2, the type of forest was determined from the EcoSat Forests layer. The eight forest types of EcoSat Forests were reduced to three basic types: beech forest; podocarp-broadleaved forest; and mixed beech and podocarp-broadleaved forest. To produce a recent 2008 layer the LUM was used to update indigenous and exotic forest changes since 2002. The mapping was performed using 15 m pixels, which is equivalent to a mapping scale of approximately 1:50 000.

Greenhouse Gas Emissions (2010-2016)

The current New Zealand greenhouse gas inventory derives implied emission factors that vary between animal types (Ministry for the Environment, 2010). The spatial distribution of animal numbers (dairy, sheep, beef, and deer) was modelled using a land-use map derived from AgriBase (AgriQuality New Zealand, July 2015) and the land cover database 2012 (LCDB4.1, Manaaki Whenua, 2015). The number of animals were scaled using statistics of livestock numbers at the regional level (Agricultural Production Survey (APS), Statistics New Zealand, 2016) and spatially distributed the animals using the potential carrying capacity from fundamental soil layers (Landcare Research, 2011a). N.B. Deer numbers were missing from the APS data for 2015-16 for the Taranaki region which results in reduced GHG values for the region. New Zealand-specific emissions factors were then applied using the IPCC (Intergovernmental Panel on Climate Change) methodology for the agriculture sector for methane and nitrous oxide emissions (Ministry for the Environment, 2010) as per method in Ausseil et al (2013).

Nitrate Leaching (2015)

Nitrogen leaching was estimated using OVERSEER farm nutrient budgeting software version 5.4 (Ministry of Agriculture and Forestry et al., 2011) with a modifier to account for OVERSEER version 6. OVERSEER was run for the 100 combinations of soils and climate from level II of LENZ (Leathwick et al., 2003). Stocking rate were set to the carrying capacity of the land according to the New Zealand Land Resource Inventory (Landcare Research, 2011b), and annual leaching rate per stock unit calculated. The nitrogen leaching rates per stock unit were then combined with a map of modelled animal numbers to produce a map of nitrogen leaching for all of New Zealand. The spatial distribution of animal numbers (dairy, sheep, beef, and deer) was modelled using a land-use map derived from AgriBase (AgriQuality New Zealand, July 2015) and the land cover database 2012 (LCDB4.1, Manaaki Whenua, 2015). The number of animals were scaled using statistics of livestock numbers at the regional level (Agricultural Production Survey (APS), Statistics New Zealand, 2016) and spatially distributed the animals using the potential carrying capacity from fundamental soil layers (Landcare Research, 2011a). N.B. Deer numbers were missing from the APS data for 2015-16 for the Taranaki region which results in reduced GHG values for the region.

This layer depicts estimated sediment/soil loss across New Zealand and was created using the NZeem erosion model. Erosion control is defined as the prevention of soil loss by an ecosystem. NZeem has been calibrated from sediment discharges measured in New Zealand rivers (Dymond et al., 2010). This model estimates the long-term mean erosion rate from all sources of erosion, both mass-movement and surficial, and accounts for all sizes of rainfall events. The model was run on the national datasets of rainfall, erosion terrains, and LCDB4.1 (Manaaki Whenua, 2015) to produce a national 1:50,000 scale map of long-term mean erosion rates. Dymond et al. (2010) assessed the accuracy of the model by comparing predictions of specific sediment discharge (assuming sediment delivery ratio of 1 everywhere) with available measurements and obtained a model efficiency of 0.64.

Sediment Retained (2012)

This layer depicts estimated sediment/soil retention across New Zealand and was created using the NZeem erosion model. Sediment retained is defined as the difference between soil loss with and without tree cover (sediment loss avoided). Erosion control is defined as the prevention of soil loss by an ecosystem. NZeem has been calibrated from sediment discharges measured in New Zealand rivers (Dymond et al., 2010). This model estimates the long-term mean erosion rate from all sources of erosion, both mass-movement and surficial, and accounts for all sizes of rainfall events. The model was run on the national datasets of rainfall, erosion terrains, and LCDB4.1 (Manaaki Whenua, 2015) to produce a national 1:50,000 scale map of long-term mean erosion rates. Dymond et al. (2010) assessed the accuracy of the model by comparing predictions of specific sediment discharge (assuming sediment delivery ratio of 1 everywhere) with available measurements and obtained a model efficiency of 0.64.

Water Yield (2012)

This layer represents the net supply of water remaining after evapo-transpiration losses (mm/yr) as an indicator of water-flow regulation. We ran the model WATYIELD (Fahey et al., 2010) for all the different combinations of soil types and climate in New Zealand and stored the results in a lookup table so that a simple computer workflow involving national spatial data layers could be implemented. Water yield (i.e. quickflow and baseflow drainage) (mm) is a result of the mean annual rainfall (mm) times the proportion of rainfall that becomes water yield. This proportion is a function of land cover (forest, scrub, tussock, and pasture), soil type, and climate (Dymond et al, 2012). Input data to this model are the land cover 2012 LCDB4.1 (Manaaki Whenua, 2015), the mean annual rainfall and a lookup table for soil-climate combinations based on level II of LENZ (Leathwick et al., 2003). Although this is a simplified view and does not consider low flows or flood flows, it permits national assessment with the use of a nationally applicable water-balance model.

Wetlands

This wetlands dataset has its origins in the Wetlands of National Importance (WONI) project, which was part of the Sustainable Development Programme of Actions for Freshwaters which had the goal of identifying a list of water bodies that would protect a full range of freshwater biodiversity. The pre-human extent of wetlands was produced using soil information from the New Zealand Land Resource Inventory (NZLRI) and a 15m digital elevation model (DEM) to refine soil boundaries. Current wetlands were defined by combining existing databases including LCDB2 (Land Cover Database version 2), NZMS 260 Topomaps, existing surveys from Regional Councils, Queen Elizabeth II (QEII) covenant wetland polygons, DOC surveys (WERI database), and the 15m DEM, to define a single set of wetland polygons and centre points. All this data was checked against a standardised set of Landsat imagery using the Ecosat technology and where necessary new wetland boundaries delineated. Wetlands were classified into 7 groups at the hydro-class level using fuzzy expert rules.

Land Use Opportunities: Whitiwhiti Ora (Crop Suitability)

The crop suitability layers are visualising how well different locations support the growth and productivity of specific crops, and were created by analyzing various geo-referenced climate, soil, and terrain data. The map layers are either displayed as a suitability score or as suitability classes.

Suitability score is measured from 0 (totally unsuitable) to 1 (perfectly suited) based on various criteria: risk of damage from extreme cold, warmth during the growing season, frost risk, winter chill sufficiency, soil drainage, potential rooting depth, land use capability, and slope. Climate-related criteria scores were calculated annually and averaged over 2006-2016 using weighted geometric means, reflecting their importance. Land-related criteria were also scored and averaged similarly. The final location suitability score combines climate and land scores using a weighted geometric mean.

The method used for the suitability classes layers is adapted from Kidd et al. (2015), and uses GIS rules and simplified parameter ranges to estimate suitability. It translates biophysical attributes into suitability indexes for crops, considering factors like temperature and soil pH. Suitability classes (well-suited, suitable, marginally suited, and unsuitable) are assigned based on these indexes, with the overall suitability determined by the worst limiting factor.

The forestry site suitability layers were defined based on ranges in volume productivity, mean annual temperature, maximum October temperature, October frosts, February water deficits, and wind exposure. This layer was created using Python scripts and ArcGIS python libraries which classified the data into four suitability classes: Well Suited, Suitable, Marginally Suited and Unsuitable.

The soil information used was derived from a combination of S-map and the fundamental soil layers (FSL) from Manaaki Whenua – Landcare Research, resampled on grids of either 1x1km or 500x500m. Climate data is sourced from the 5x5km Virtual Climate Station Network (VCSN) grid from NIWA.

Yield information can be accessed in the GET REPORTS panel by dropping a pin on the map. Yield ranges for each suitability class are estimated by crop experts, with well-suited yields based on maximum observed field yields in New Zealand, suitable yields on national averages, and marginally suited yields varying by environmental conditions. Unsuitable areas predict zero yields or uneconomic harvests.

These datasets were produced as part of the Land Use Opportunities: Whitiwhiti Ora research programme funded by the Our Land and Water National Science Challenge. Further information about these layers can be found at the Whitiwhiti Ora Data Supermarket.

Nitrogen & Phosphorous loss susceptibility

These layers provide a representation of the annual mean susceptibility of nitrogen / phosphorus loss, considering soil and climate factors at each pixel. The data was derived from the Agricultural Production Systems Simulator (APSIM) model, which simulated nitrogen / phosphorus loss from a urine patch in a continuous ryegrass/white clover mixed pasture setup.

The spatial resolution of this dataset is based on a 5km climate grid, using the Fundamental Soil Layers and S-map soil polygons. It is important to note that this analysis does not take into account land use or actual nutrient inputs, focusing solely on inherent soil and climatic conditions. The resulting susceptibility values are scaled between 0 and 1, providing an indication of the relative level of nitrate filtering function at each location.

These datasets were produced as part of the Land Use Opportunities: Whitiwhiti Ora research programme funded by the Our Land and Water National Science Challenge. Further information about these layers can be found at the Whitiwhiti Ora Data Supermarket.

N₂O emissions / nitrogen loss under dairy farming 

These layers represent the estimated maximum potential nitrous oxide (N₂O) emissions / nitrogen leaching losses if the land were under dairy farming. They do not represent the actual land use at each location. The data is derived from a spatial layer that includes various dairy types characterised by specific climate, slope, and soil properties. The Overseer® Nutrient Budgeting software (version 6.5.0) was used which models nitrous oxide emissions and nitrogen run-off.

The data reflects the 2019-2020 production year, based on DairyBase data. Soil data from S-map and FSL, slope data from a 15m DEM, and climate data were used to develop these layers.

These datasets were produced as part of the Land Use Opportunities: Whitiwhiti Ora research programme funded by the Our Land and Water National Science Challenge. Further information about these layers can be found at the Whitiwhiti Ora Data Supermarket.

Nitrogen leaching loss estimates under crop rotation

These layers represent the estimated potential nitrogen leaching losses if the land were under various crop rotations:

• Ryegrass and silage maize
• Broadacre vegetables (squash, onions, beetroot, sweetcorn, beans, and oats)
• Intensive vegetables (lettuce, cabbage, spinach, cauliflower, and oats)

They do not represent the actual land use at each location. The data is derived from Agricultural Production Systems Simulator (APSIM) simulations, which model nitrogen leaching based on soil and climate conditions under different irrigation and fertiliser management scenarios.

The map layers displayed shows nitrogen leaching losses for the 'Irrigation to fully overcome soil water deficit with fixed fertiliser schedule' scenario. Users can access additional scenario data via the GET REPORTS panel by dropping a pin on the map, which will return data for all irrigation and fertilisation scenarios.

The simulations span a 30-year period (1980-2010), using daily weather data from NIWA's Virtual Climate Station Network and soil data from S-map and the Fundamental Soil Layers (FSL). The methodology includes assumptions about idealised management practices and homogeneous application of water and nitrogen, ensuring no restrictions on resource access or management efficiency.

These datasets were produced as part of the Land Use Opportunities: Whitiwhiti Ora research programme funded by the Our Land and Water National Science Challenge. Further information about these layers can be found at the Whitiwhiti Ora Data Supermarket.

Freshwater state - water quality indicators

These layers indicate the necessary load reduction to achieve at least the NOF (National Objectives Framework) C-band for various water quality indicators: E. coli, total nitrogen (TN), total phosphorus (TP) and sediment levels in critical catchments. The NOF C-band, part of New Zealand's freshwater management framework, ensures water quality suitable for secondary contact recreation, such as boating and wading.

The map layers visualise the yield proportion and is used to show the reduction required, expressed as a percentage of the current water quality indicator. For example, if a catchment currently yields a certain amount of E. coli per hectare per year, the E. coli layer shows the proportion of that yield which must be reduced to meet the NOF C-band standard.

The underlying data was derived using spatial statistical models to compare predicted concentrations against criteria defined by the NPSFM (National Policy Statement for Freshwater Management). The analysis spans a 5-year period from 2016 to 2020, providing average annual loads and focused on reducing excess loads to meet the NOF C-band. The data is based on the digital river network used by the River Environment Classification (REC).

These datasets were produced as part of the Land Use Opportunities: Whitiwhiti Ora research programme funded by the Our Land and Water National Science Challenge. Further information about these layers can be found at the Whitiwhiti Ora Data Supermarket.

Last updated: 29 August 2024