In 2017, **** percent of students from the lowest three decile groups leaving secondary school in New Zealand did so without attaining NCEA Level *. In comparison, only *** percent of school-leavers from the top three socio-economic groups did not have an NCEA qualification. In New Zealand, students must attend school until they are 16 years old. The National Certificate of Educational Achievement (NCEA) is the official secondary school qualification in New Zealand. Students can gain NCEA Level *, *, or * if they meet the requirements set out by the standard.

Link to the NCEA Ministry of Education webpage that explains the Geography 1.2 Standard.

This table includes information on the number of enterprises and institutions broken down by the National Classification of Economic Activity 2008 (NCEA 2008, based on NACE Rev 2.0). Besides the breakdown by NCEA, the number of enterprises is broken down by size class or legal form. The number of enterprises is rounded by a multiple of 5. The data refer to the situation on 1 January of the year concerned.

The number of agricultural enterprises in the Economic Demographic Statistics (EDS) is based on the Farm structure survey. This survey classifies main economic activities of agricultural enterprises in accordance with the NEG system of farm size units. However, Statistics Netherlands uses the National Classification of Economic Activity (NCEA) to determine the branch of industry in which an enterprise is active. A conversion table has been drawn up to go from NEG to NCEA in the EDS. According to this table, the complete number of enterprises in one NEG branch are included in one NCEA branch. However, some NEG branches also comprise enterprises with activities in other NCEA branches. The number of enterprises in those NCEA branches is then unknown.

Data available from: 2006

Status of the figures: All data recorded in this publication are definite.

Last changes: Data broken down by NCEA 1993 have been converted to data broken down by NCEA 2008.

When will new figures be published? Changes as of 20 April 2012: This table has been stopped. Two points in the processing of the data have changed: - the statistical unit has been changed. - the check on economic activity of an enterprise has been broadened. As a consequence of this, more entrepeneurs have entered the population of Dutch enterprises. Due to these changes, the figures are no longer comparable to those of the previous years. Therefore a new table has been started from 2007 onwards.

National Centre for Engineering in Agriculture Archive for Concluded Research Projects.

This table includes information on the number of local units of enterprises and institutions (establishments) broken down by the National Classification of Economic Activity 2008 (NCEA 2008, based on NACE Rev 2.0). Besides the breakdown by NCEA, the number of local units is broken down by municipality. The regional characteristics are derived from the municipality in which the local unit is located. The regional totals shown concern cumulated municipal data. Where changes of municipal boundaries transect regional boundaries, the municipal classifications concerns the most recent situation. The municipality of Woerden, for example, was annexed by the province of Utrecht on 1 January 1989, and is classified under the province of Utrecht in the Table. The number of local units is rounded by a multiple of 5. The data refer to the situation on 1 January of the year of observation.

Data available from: 2006

Status of the figures: All data recorded in this publication are final data.

Last changes: Data broken down by NCEA 1993 are transformed into data broken down by NCEA 2008.

When will new figures be published? Changes as of 20 April 2012: This table has been stopped. Two points in the processing of the data have changed: - the statistical unit has been changed. - the main economic activity (NCEA 2008) of the local unit has been implemented. Due to these changes, the figures are no longer comparable to those of the previous years. Therefore a new table has been started from 2007 onwards.

This table includes information on the number of local units of enterprises and institutions (establishments) broken down by the National Classification of Economic Activity 2008 (NCEA 2008, based on NACE Rev 2.0). Besides the breakdown by NCEA, the number of local units is broken down by municipality. The regional characteristics are derived from the municipality in which the local unit is located. The regional totals shown concern cumulated municipal data. Where changes of municipal boundaries transect regional boundaries, the municipal classifications concerns the most recent situation. The municipality of Woerden, for example, was annexed by the province of Utrecht on 1 January 1989, and is classified under the province of Utrecht in the Table. The number of local units is rounded by a multiple of 5. The data refer to the situation on 1 January of the year of observation. Data available from: 2006 Status of the figures: All data recorded in this publication are final data. Last changes: Data broken down by NCEA 1993 are transformed into data broken down by NCEA 2008. When will new figures be published? Changes as of 20 April 2012: This table has been stopped. Two points in the processing of the data have changed: - the statistical unit has been changed. - the main economic activity (NCEA 2008) of the local unit has been implemented. Due to these changes, the figures are no longer comparable to those of the previous years. Therefore a new table has been started from 2007 onwards.

The Natural Capital and Ecosystem Assessment (NCEA) Product Brochure provides an overview of some of the key product collections that the NCEA programme is delivering with examples of how they are currently being used. A product collection will often include a series of underpinning datasets, map layers, protocols, standards, summary reports or other publications. We intend to release all relevant components of a product collection under Open Government Licence wherever possible.

The NCEA is a Defra-led programme working in partnership with its arms-length bodies: the Environment Agency (EA), Forest Research (FR), Joint Nature Conservation Committee (JNCC), Natural England (NE), and the Royal Botanic Gardens, Kew (RBG Kew).

Statistics on Products in the Services Sector: Legal services (7411 NCEA 93) Results by breakdown of turnover by activity sector of the client and employed personnel. National.

This table includes information on the number of local units of enterprises and institutions (establishments) broken down by the National Classification of Economic Activity 2008 (NCEA 2008, based on NACE Rev 2.0). Besides the breakdown by NCEA, the number of local units is broken down by regional characteristics. All characteristics of a region are derived from the municipality in which the local unit is located. The regional totals shown concern cumulated municipal data. Where changes of municipal boundaries transect regional boundaries, the municipal classifications concerns the most recent situation. The municipality of Woerden, for example, was annexed by the province of Utrecht on 1 January 1989, and is classified under the province of Utrecht in the Table. The number of local units is rounded by a multiple of 5. The data refer to the situation on 1 January of the year of observation.

Data available from: 2006

Status of the figures: All data recorded in this publication are final data.

Last changes: Data broken down by NCEA 1993 are transformed into data broken down by NCEA 2008.

When will new figures be published? Changes as of 20 April 2012: This table has been stopped. Two points in the processing of the data have changed: - the statistical unit has been changed. - the main economic activity (NCEA 2008) of the local unit has been implemented. Due to these changes, the figures are no longer comparable to those of the previous years. Therefore a new table has been started from 2007 onwards.

The National Variety Trials (NVT) involve a yearly coordination of 630 grain trials conducted across 250 locations in Australia. At different stages of the crop season Trial Service Providers visually assess the attributes of the grain plants in each trial-plot to evaluate the growth and development of the different grain varieties. This involves manual measurements related to: (i) plant dimensions (height, canopy size); (ii) different stages of growth (seedling, tillering, jointing, boot and flowering); and (iii) germination rate. However, the availability of personnel to perform this monitoring is likely to be constrained to larger research stations. These plant attributes can be visually monitored and automatically detected using remote camera-based machine vision technologies to improve the timeliness and consistency of assessment of the grain varieties. In addition to streaming visual data of the crop, there is potential for machine vision technology to automatically analyse the images to determine a range of plant attributes and performance indicators from video-frame samples collected daily; such as flowering behaviour (50% of the plot to anthesis) and crop height. The data captured, processes and stored will be used to determine variation between varieties of grains across Australia.

Statistics on R&D Activities in the Business Sector: Company Sector. Resources allocated to R+D by research and development companies (NCEA-93 division 73) by branch of activity served, internal expenditure/personnel and type of indicator. National.

Annual Labour Cost Survey: Labour cost by NCEA-93 economic activity and cost components. National.

NDWI is used to identify water bodies and detect changes in their extent. It is calculated as the ratio of the green and near-infrared (NIR) bands. Positive index values generally indicate the presence of water, with higher values corresponding to water bodies.

NDWI = (GREEN – NIR) / (GREEN + NIR)

Sentinel-2 NDWI (Defra/JNCC ARD bands) = (B02 – B07) / (B02 + B07)

Equivalent ESA Sentinel-2 bands: B03, B08

Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.

These data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.

The dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices derived from Defra and JNCC Sentinel-2 ARD. Index files have been generated for Sentinel-2 granules covering England and Scotland for the period from 2015 to 2025. Note that new unmasked index files (v2) have superseded the previous masked index files (v1). Masked files will no longer be produced. Users can mask the new index files if required using the cloud and topographic shadow masks provided with the ARD, or masks of their choice.

Contains modified Copernicus Sentinel data 2015-2025

NBR enables identification of burned areas and can also provide an indication of burn severity. It is calculated as the ratio between the near-infrared (NIR) and short-wave infrared (SWIR) bands, with lower values indicating burned areas. Burn severity can be assessed by subtracting post-fire NBR from pre-fire NBR.

NBR = (NIR – SWIR) / (NIR + SWIR)

Sentinel-2 NBR (Defra/JNCC ARD bands) = (B07 - B10) / (B07 + B10)

Equivalent Sentinel-2 bands: B08, B12

Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.

These data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.

The dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices derived from Defra and JNCC Sentinel-2 ARD. Index files have been generated for Sentinel-2 granules covering England and Scotland for the period from 2015 to 2025. Note that new unmasked index files (v2) have superseded the previous masked index files (v1). Masked files will no longer be produced. Users can mask the new index files if required using the cloud and topographic shadow masks provided with the ARD, or masks of their choice.

Contains modified Copernicus Sentinel data 2015-2025

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of LOW marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). Low marsh (LM) was defined as regularly flooded marsh [SLAMM category 8]. LM is normally inundated by tidal water at least once per day. Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories. 1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years. 2) In Raster Calculator, set the SLAMM codeequal to8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year. 3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh). Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities. Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

Environmental Accounts: Other Environmental Accounts: Origin of wood and some products derived by economic activities NCEA-93, year and products NCPA-96 . National.

NDVI is a widely used index that quantifies vegetation greenness and can provide an indication of vegetation health and vegetation density. It is calculated as the ratio between the near-infrared (NIR) and red bands. Positive values generally correspond to vegetation, with higher values reflecting healthy, dense vegetation.

NDVI = (NIR – RED) / (NIR + RED)

Sentinel-2 NDVI (Defra/JNCC ARD bands) = (B07 – B03) / (B07 + B03)

Equivalent ESA Sentinel-2 bands: B08, B04

Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.

These data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.

The dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices derived from Defra and JNCC Sentinel-2 ARD. Index files have been generated for Sentinel-2 granules covering England and Scotland for the period from 2015 to 2025. Note that new unmasked index files (v2) have superseded the previous masked index files (v1). Masked files will no longer be produced. Users can mask the new index files if required using the cloud and topographic shadow masks provided with the ARD, or masks of their choice.

Contains modified Copernicus Sentinel data 2015-2025

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of HIGH marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). High marsh (HM) was defined as the aggregation of irregularly-flooded marsh [SLAMM category 7] and transitional salt marsh [SLAMM category 20]. HM is covered by water only sporadically (once per day or less). Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories. 1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years. 2) In Raster Calculator, set the SLAMM code equal to 8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year. 3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh). Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities. Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

The England species map was funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme. The map was created using satellite remote sensing data (Sentinel-2) and machine learning to classify common tree species in England. The model was trained to distinguish 35 common tree species, with minority species grouped into “Other broadleaf” or “Other conifer” classes for better classification performance. The final product comprises a species classification and confidence raster output.

The species map represents a predicted distribution of common tree species in England, produced using a time series of multispectral satellite remote sensing data (Sentinel-2) and machine learning. A classifier based on the XGBoost algorithm was trained to distinguish tree species, utilising a time-series of surface reflectance data and labelled training samples from the sub-compartment database (SCDB). To enhance classification performance, minority species with fewer than 1,000 training samples were grouped into broader categories, resulting in a total of 35 classes. Given the significant class imbalances, a sample weighting strategy was employed to guard against significant underfitting of the minority classes. Model evaluation demonstrated strong classification performance, with an overall accuracy of 89% and balanced class accuracy of 90%. Predictions were made at the pixel level and used to generate a species classification and confidence raster output. Field validation for Norway spruce within the Ips typographus demarcated area, confirmed a precision of 69%, aligning with test data results for this class. Additional validation using National Forest Inventory (NFI) data further reinforced model reliability, though accuracy was observed to be worse for underrepresented species.

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of total marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). Total marsh (TM) was defined as the sum of low marsh and high marsh [SLAMM category 8 + SLAMM category 7 + SLAMM category 20]. Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories. 1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years. 2) In Raster Calculator, set the SLAMM codeequal to8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year. 3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh). Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities. Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).