PastureYieldNZDefinitions.pdf

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111
6
Updated
24 Nov 2020

This item was last updated on LRIS Portal on 24 Nov 2020

1

New Zealand National Pasture Productivity Map Attribute Definitions

Document ID22890
File namepastureyieldnzdefinitionspdf.pdf
TypePDF
Size56.9 KB

North Island National Pasture Productivity

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719
23
Updated
27 Jul 2021

This dataset was last updated on LRIS Portal on 27 Jul 2021.

The New Zealand National Pasture Productivity map is a multi-temporal approach to estimating pasture (dry matter) yield in New Zealand's grassland area. The approach uses a model generated from collected historical records of pasture yield in 21 locations around New Zealand.It was hypothesised that yield of a paddock planted with pasture species would correlate with some average of normalised difference vegetation index (NDVI) observed at that location. The temporal median of NDVI of vegetated images across New Zealand's grasslands was obtained by calculating the median NDVI of all observations for each pixel where vegetation (NDVI > 0) was observed. Paddock polygons were manually created to surround each of the locations where yields had been obtained, and the spatial means within these polygons of the median NDVIs were plotted against the measured yields. A linear relationship between these quantities was created, which was applied to paddock polygons at a national scale. The uncertainty of the model is +/- 2.2 t/ha/yr for a 70% confidence interval.The data presented in this geodatabase is a segmentation of New Zealand grasslands using an automated multi-temporal approach presented by North, Pairman, and Belliss (2019). The parameters of this segmentation process were selected to achieve minimal missed boundaries between true paddock units. As a side-effect, some boundaries were created between sub-paddock areas with differing spectral response, such as in areas of strip grazing, areas divided by ridges or gullies, and areas with different management patterns. While these artefacts may improve the spatial accuracy of the pasture yield, the result is not intended to provide a one-to-one mapping between true paddock areas and their average pasture yield.The model was applied to each of these polygons. Because the data used to develop the initial model had generally high yields, the model has only been applied to polygons representative of higher-producing grasslands. Where median NDVI for a polygon was outside the domain of the assessed polygons, the model was deemed unfit to estimate pasture yield. Polygons with low median NDVI therefore have an assigned yield of zero.A full list of attribute definitions can be found in the attached PDF.The data used to generate the model has been derived from various published sources, in combination with Sentinel-2 imagery. Complete sets of Sentinel-2 for five passes covering mainland New Zealand were assembled and cloud-cleared in an automated manner using TMASK techinques. The resulting valid data was used to create medians of vegetated images on a per-pixel basis.The segmentation of New Zealand grasslands was derived by initially taking selected Sentinel-2 passes which gave suitable coverage of New Zealand's agricultural land, and applying the automated multi-temporal approach to boundary delineation. Around 200 passes from January - November 2018 were used to achieve the segmentation. These polygons were overlaid with the union of polygons from LCDB v5.0 (lris.scinfo.org.nz/layer/104400-lcdb-v50-land-cove...) which were either High-Producing or Low-Producing Grassland in both 2012 and 2018, and had not changed class between those dates.

Layer ID 105112
Data type Vector multipolygon
Feature count 6893470
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

South Island National Pasture Productivity

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675
22
Updated
26 Jul 2021

This dataset was last updated on LRIS Portal on 26 Jul 2021.

The New Zealand National Pasture Productivity map is a multi-temporal approach to estimating pasture (dry matter) yield in New Zealand's grassland area. The approach uses a model generated from collected historical records of pasture yield in 21 locations around New Zealand.It was hypothesised that yield of a paddock planted with pasture species would correlate with some average of normalised difference vegetation index (NDVI) observed at that location. The temporal median of NDVI of vegetated images across New Zealand's grasslands was obtained by calculating the median NDVI of all observations for each pixel where vegetation (NDVI > 0) was observed. Paddock polygons were manually created to surround each of the locations where yields had been obtained, and the spatial means within these polygons of the median NDVIs were plotted against the measured yields. A linear relationship between these quantities was created, which was applied to paddock polygons at a national scale. The uncertainty of the model is +/- 2.2 t/ha/yr for a 70% confidence interval.The data presented in this geodatabase is a segmentation of New Zealand grasslands using an automated multi-temporal approach presented by North, Pairman, and Belliss (2019). The parameters of this segmentation process were selected to achieve minimal missed boundaries between true paddock units. As a side-effect, some boundaries were created between sub-paddock areas with differing spectral response, such as in areas of strip grazing, areas divided by ridges or gullies, and areas with different management patterns. While these artefacts may improve the spatial accuracy of the pasture yield, the result is not intended to provide a one-to-one mapping between true paddock areas and their average pasture yield.The model was applied to each of these polygons. Because the data used to develop the initial model had generally high yields, the model has only been applied to polygons representative of higher-producing grasslands. Where median NDVI for a polygon was outside the domain of the assessed polygons, the model was deemed unfit to estimate pasture yield. Polygons with low median NDVI therefore have an assigned yield of zero.A full list of attribute definitions can be found in the attached PDF.The data used to generate the model has been derived from various published sources, in combination with Sentinel-2 imagery. Complete sets of Sentinel-2 for five passes covering mainland New Zealand were assembled and cloud-cleared in an automated manner using TMASK techinques. The resulting valid data was used to create medians of vegetated images on a per-pixel basis.The segmentation of New Zealand grasslands was derived by initially taking selected Sentinel-2 passes which gave suitable coverage of New Zealand's agricultural land, and applying the automated multi-temporal approach to boundary delineation. Around 200 passes from January - November 2018 were used to achieve the segmentation. These polygons were overlaid with the union of polygons from LCDB v5.0 (lris.scinfo.org.nz/layer/104400-lcdb-v50-land-cove...) which were either High-Producing or Low-Producing Grassland in both 2012 and 2018, and had not changed class between those dates.

Layer ID 105111
Data type Vector multipolygon
Feature count 4013090
Services Vector Query API, Web Feature Service (WFS), Catalog Service (CS-W), data.govt.nz Atom Feed

Basic ecosystem legend (JPG)

558
88
Added
05 Dec 2018

This item was first added to LRIS Portal on 05 Dec 2018

Jpeg image illustrating values and colours for Basic Ecosystem legend.

Document ID21842
File namebasic-ecosystem-legend-jpg.jpg
TypeJPG
Size69.9 KB

Basic Ecosystems Legend

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684
66
Added
01 May 2018

This item was first added to LRIS Portal on 01 May 2018

ArcGIS layer file for Basic Ecosystems - this may be used most reliably if you load the downloaded data into ArcGIS, right click and select properties, make sure symbology is set to categorical and then click on the folder to import the symbology from this file.

Document ID21590
File namebasic-ecosystems-legend.lyr
TypeLYR
Size9.22 KB

Basic Ecosystems

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4987
380
Added
01 May 2018

This dataset was first added to LRIS Portal on 01 May 2018.

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.

Layer ID 95415
Data type Image/Raster
Resolution 25.000m
Services Catalog Service (CS-W), data.govt.nz Atom Feed

Basic Ecosystems Legend

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461
11
Added
01 May 2018

This item was first added to LRIS Portal on 01 May 2018

Document ID21589
File namebasic-ecosystems-legend.png
TypePNG
Size30.4 KB

Nitrate Leaching

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2575
54
Added
24 Apr 2018

This dataset was first added to LRIS Portal on 24 Apr 2018.

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 the map of animal numbers to produce a map of nitrogen leaching for all of New Zealand.

(Currently unavailable for download - 18/10/2018)

Layer ID 95392
Data type Grid
Resolution 100.000m
Services Raster Query API, Catalog Service (CS-W), data.govt.nz Atom Feed

GHG Fluxes (Greenhouse Gases)

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1818
36
Updated
26 Apr 2018

This dataset was last updated on LRIS Portal on 26 Apr 2018.

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, 2003) and the land cover database (LCDB4.1, Manaaki Whenua, 2015). The number of animals were scaled using statistics of livestock numbers at the regional level (Agricultural Production Census (APS), Statistics New Zealand, 2017) 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).
Units: tonnes of CO2 equivalent/ha/year

(Currently unavailable for download 18/10/2018)

Layer ID 95391
Data type Grid
Resolution 100.000m
Services Raster Query API, Catalog Service (CS-W), data.govt.nz Atom Feed

Sediment Lost

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2103
51
Added
23 Apr 2018

This dataset was first added to LRIS Portal on 23 Apr 2018.

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 land cover 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.

Layer ID 95388
Data type Grid
Resolution 100.000m
Services Raster Query API, Catalog Service (CS-W), data.govt.nz Atom Feed
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