ISO Land Cover for Agricultural Regions of Canada, Circa 2000 Data Product Specification. Revision: A

Transcription

1 ISO Land Cover for Agricultural Regions of Canada, Circa 2000 Data Product Specification Revision: A

2 Data specification: Land Cover for Agricultural Regions, circa 2000 Table of Contents 1. OVERVIEW... 4 a. Informal description... 4 b. Data product specification metadata... 4 c. Terms and definitions... 4 d. Abbreviations SPECIFICATION SCOPE DATA PRODUCT IDENTIFICATION Data Series Identification Data product identification Land Cover for Agricultural Regions of Canada by UTM Zone, circa Land Cover for Agricultural Regions of Canada by UTM Zone, circa 2000, Date Index DATA CONTENT AND STRUCTURE Feature-based application schema Feature catalogue Land Cover (Circa 2000), Feature Catalog Feature attribute... 8 Page 2 of Landsat path and row numbers Landsat path number Landsat row number Early season sensor code Mid-season sensor code Late season sensor code Early season acquisition date Mid-season acquisition date Late season acquisition date Project name REFERENCE SYSTEMS DATA QUALITY Completeness Completeness omission Logical consistency... 10

3 Conceptual consistency Positional accuracy Temporal accuracy Thematic accuracy Quantitative attribute accuracy Lineage statement DATA CAPTURE Data inputs: Image Pre-Processing: Classification: Product Integration: Filtering and post editing: Mosaic: Quality assurance/controls: Accuracy components: DATA MAINTENANCE PORTRAYAL DATA PRODUCT DELIVERY METADATA Page 3 of 13

4 Data specification: Land Cover for Agricultural Regions, circa OVERVIEW a. Informal description A thematic land cover classification representative of circa 2000 conditions for agricultural regions of Canada. Land cover is derived from Landsat 5 TM and/or 7 ETM+ multi-spectral imagery by inputting imagery and ground reference training data into a Decision-Tree or Supervised image classification process. Object segmentation, pixel filtering, and/or post editing is applied as part of the image classification. Mapping is corrected to the GeoBase Data Alignment Layer. National Road Network (1:50,000) features and other select existing land cover products are integrated into the product. UTM Zone mosaics are generated from individual 30 meter resolution classified scenes. A table and related spatial scene index are available indicating the Landsat imagery dates input in the classification. b. Data product specification metadata This section provides metadata about the creation of this data product specification. Dataset title: Land Cover for Agricultural Regions of Canada, circa 2000 Dataset reference date: Dataset responsible party: Dataset language: Dataset topic category: Agri-Geomatics English, French Imagery & Base Maps c. Terms and definitions Attribute Characteristic of a feature. (For example the feature validity date) circa 2000 circa (sometimes italicized to show it is Latin) means "in approximately", generally referring to a year when the dates of events are approximately known. Circa 2000 refer to the year 2000 more or less a few years. Class Description of a group of objects sharing the same attributes, operations, methods, relations and semantic. A Class d. Abbreviations AAFC PFRA Agriculture and Agri-Food Canada Prairie Farm Rehabilitation Administration 2. SPECIFICATION SCOPE This data specification has only one scope, the general scope. NOTE: The term specification scope originates from the International Standard ISO Specification scope does not express the purpose for the creation of a data specification or the potential use of data, but identifies partitions of the data specification where specific requirements apply. Page 4 of 13

5 3. DATA PRODUCT IDENTIFICATION 3.1. Data Series Identification Title: Page 5 of 13 Land Cover for Agricultural Regions of Canada, circa 2000 Alternate title: Abstract: Purpose: Circa 2000 Landcover The Land Cover for Agricultural Regions of Canada, circa 2000 is a thematic land cover classification representative of Circa 2000 conditions for agricultural regions of Canada. Land cover is derived from Landsat5-TM and/or 7-ETM+ multi-spectral imagery by inputting imagery and ground reference training data into a Decision-Tree or Supervised image classification process. Object segmentation, pixel filtering, and/or post editing is applied as part of the image classification. Mapping is corrected to the GeoBase Data Alignment Layer. National Road Network (1:50,000) features and other select existing land cover products are integrated into the product. UTM Zone mosaics are generated from individual 30 meter resolution classified scenes. A spatial index is available indicating the Landsat imagery scenes and dates input in the classification. This product is published and compiled by Agriculture and Agri-Food Canada (AAFC), but also integrates products mapped by other provincial and federal agencies; with appropriate legend adaptations. This release includes UTM Zones 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, and 22 for corresponding agricultural regions in Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick, Québec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia covering approximately 370,000,000 hectares of mapped area. Mapped classes include: Water, Exposed, Built-up, Shrubland, Wetland, Grassland, Annual Crops, Perennial Crops and Pasture, Coniferous, Deciduous and Mixed forests. However, emphasis is placed on accurately delineating agricultural classes, including: annual crops (cropland and specialty crops like vineyards and orchards), perennial crops (including pastures and forages), and grasslands. This product aims to offer a Canadian integrated Land Cover base produced from various available classified satellite data. The Land Cover base dating extended from 1996 to 2005 nevertheless 80% of the Land Cover base come from 1999 to 2001 defined by circa Because it was produced from classified imagery, LCC2000 data product is linked to a given thematic data accuracy related to classified images process. This means it is possible that entities may not represent the right classes, for instance, a wetland polygon may be overlapping an agricultural area, or a forest may be in reality a plain. These omission or commissions may either be temporal variations and/or classification errors. Topic category: Imagery & BaseMaps Spatial representation type: Vector, Raster Spatial resolution: 30 meter resolution raster format. Geographic description: This specification is applicable to the extent of Canada. Constraints:

6 Keywords: Scope: Data is subject to the Government of Canada Open Data Licence : Earth sciences, Land use, Remote sensing (Government of Canada Core Subject Thesaurus, ). series 3.2. Data product identification Land Cover for Agricultural Regions of Canada by UTM Zone, circa 2000 Title: Land Cover for Agricultural Regions of Canada by UTM Zone, circa (lcv_utm9_aafc_30m_2000_v12, lcv_utm10_aafc_30m_2000_v12, lcv_utm11_aafc_30m_2000_v12, lcv_utm12_aafc_30m_2000_v11, lcv_utm13_aafc_30m_2000_v11, lcv_utm14_aafc_30m_2000_v11, lcv_utm15_aafc_30m_2000_v11, lcv_utm16_aafc_30m_2000_v11, lcv_utm17_aafc_30m_2000_v11, lcv_utm18_aafc_30m_2000_v11, lcv_utm19_aafc_30m_2000_v11, lcv_utm20_aafc_30m_2000_v11, lcv_utm21_aafc_30m_2000_v11, lcv_utm22_aafc_30m_2000_v11,) Abstract: A 30 meter resolution raster format land cover classification produced from Landsat satellite imagery. Mapping is complete for agricultural regions of Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia. The Product is available based on corresponding UTM Zones. Purpose: Agriculture and Agri-Food Canada (AAFC) has compiled land cover mapping for agricultural regions across Canada, with the goal of accurately classifying lands at a moderate scale that are annually cultivated (cropland), permanently cultivated (pasture/forages), and grasslands; as well as mapping other contextual classes (such as built-up or forested lands). The purpose is to provide information that can be used to support land use decision making and a range of other agri-environmental; federal, provincial, municipal and private projects, applications, programs and activities. Some examples of potential applications are: land use decision making and monitoring, impact assessment on ecosystem services, biodiversity conservation, water quality assessment, evaluation of temporal trends in surface properties, estimates of rates of change, and causes and consequences of change. Spatial representation type: Raster Constraints: Data is subject to the Government of Canada Open Data Licence : Keywords: Earth sciences, Land use, Remote sensing (Government of Canada Core Subject Thesaurus, ). Scope: dataset Feature Attribute Names: Page 6 of Land Cover for Agricultural Regions of Canada by UTM Zone, circa 2000, Date Index Title: Land Cover for Agricultural Regions of Canada, circa 2000, Date Index.

7 (LCV_CA_AAFC_DATE_INDEX_2000) Abstract: The Land Cover for Agricultural Regions of Canada (circa 2000), Date Index dataset is a geospatial data layer containing polygon features representing the Landsat scene number, associated dates and other products that were incorporated into the thematic land cover classification which is contained within the AAFC Landcover (circa 2000) product. Purpose: The purpose of this dataset is to provide auxiliary / reference information related to the AAFC Landcover (circa 2000) product. Spatial representation type: Vector Constraints: Data is subject to the Government of Canada Open Data Licence : Keywords: Earth sciences, Land use, Remote sensing (Government of Canada Core Subject Thesaurus, ). Scope: dataset Feature Attribute Names: Landsat path and rows numbers, Landsat path number, Landsat row number, Early season sensor code, Mid-season sensor code, Late season sensor code, Early season acquisition date, Mid-season acquisition date, Late season acquisition date, Project Name 4. DATA CONTENT AND STRUCTURE The data is structured by features. An application schema expressed in UML details the content and an associated feature catalogue provides the semantics of the model elements Feature-based application schema Page 7 of 13

8 4.2. Feature catalogue Land Cover (Circa 2000), Feature Catalog Title: Land Cover (Circa 2000), Feature Catalogue Scope: Imagery & BaseMaps Version Number: 1.0 Version Date: Producer: Agri-Geomatics Feature attribute Landsat path and row numbers Name: Landsat path and row numbers (LANDSAT_WRSPR_ID) Definition: Landsat path and row numbers separated by a zero. Value Data Type: Integer Value Domain Type: 0 (not enumerated) Landsat path number Name: Landsat path number (LANDSAT_TRACK_NUM) Definition: Landsat path number. Value Data Type: Integer Value Domain Type: 0 (not enumerated) Landsat row number Name: Landsat row number (LANDSAT_FRAME_NUM) Definition: Landsat row number. Value Data Type: Integer Value Domain Type: 0 (not enumerated) Early season sensor code Name: Early season sensor code (SENSOR_1_CODE) Definition: Landsat sensor (TM or ETM+) used as early season image. Value Data Type: Text Value Domain Type: 1 (enumerated) Value Domain Type: Feature Attribute Value: Label: ETM+ TM Definition Enhanced thematic mapper. Thematic mapper Mid-season sensor code Name: Mid-season sensor code (SENSOR_2_CODE) Definition: Landsat sensor (TM or ETM+) used as mid-season image. Value Data Type: Text Value Domain Type: 1 (enumerated) Value Domain Type: Feature Attribute Value: Label: ETM+ TM Definition Enhanced thematic mapper. Thematic mapper. Page 8 of 13

9 Late season sensor code Name: Late season sensor code (SENSOR_3_CODE) Definition: Landsat sensor (TM or ETM+) used as late season image. Value Data Type: Text Value Domain Type: 1 (enumerated) Value Domain Type: Feature Attribute Value: Label: ETM+ TM Definition Enhanced thematic mapper. Thematic mapper Early season acquisition date Name: Early season acquisition date (CAPTURE_1_DATE) Definition: Acquisition date of the early season image. Value Data Type: Date Mid-season acquisition date Name: Mid-season acquisition date (CAPTURE_2_DATE) Definition: Acquisition date of the midseason image. Value Data Type: Date Late season acquisition date Name: Late season acquisition date (CAPTURE_3_DATE) Definition: Acquisition date of the late season image. Value Data Type: Date Project name Name: Project Name (PROJECT_NAME) Definition: Producer of the original landcover data. Value Data Type: Text 5. REFERENCE SYSTEMS Spatial reference system: Horizontal coordinate reference system: WGS 84 Map projection: UTM zone 10N; EPSG : 32611, UTM zone 12N; EPSG : 32612, UTM zone 13N; EPSG : 32613, UTM zone 14N; EPSG : 32614, UTM zone 15N; EPSG : 32615, UTM zone 16N; EPSG : 32616, UTM zone 17N; EPSG : 32617, UTM zone 18N; EPSG : 32618, UTM zone 19N; EPSG : 32619, UTM zone 20N; EPSG : Temporal reference system: Gregorian calendar Page 9 of 13

10 6. DATA QUALITY 6.4. Completeness Completeness omission MEASURE DESCRIPTION: UTM Zones include 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, and 22 for corresponding agricultural regions in Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia. Mapped areas are based on Landsat scene coverage of corresponding agricultural regions in Canada Logical consistency Conceptual consistency MEASURE DESCRIPTION: Please note the following: 1) It is not feasible to obtain cloud free imagery within a single growing season over a large area. As such, the classification product is based on a set of images that range within the time period. This may produce some temporal inconsistencies across the product. The presence of cloud in Landsat images may limit the completeness of classes within specific regions of the product. 2) Comparison with National Topographic Database 1:50,000 features indicate some miss-representation of wetland and water features. This may be due to the use of early spring imagery (high water level) in the classification or error introduced by the classification process. Confusion between or poor separation of forested and wetland and/or shrubland covers was observed in the classification results in landscapes where such covers typically intermix. 3) Small (less than 0.4 ha) features may be omitted from the classification, due to segmentation and filtering. 4) Some confusion between Cropland and Grassland features has been observed (specifically, along rivers and along roadways). 5) The classification process is designed to classify barren fields as annual cropland when tilled soils in the spring image become green with vegetation in the summer image. Tilled fields are occasionally miss-classified as exposed. 6) When possible, areas under cloud, cloud shadows and mountain shadows were interpreted manually. 7) Agricultural classes were often confused under cloud and cloud shadow cover Positional accuracy Measure not defined at this time Temporal accuracy Measure not defined at this time Thematic accuracy Quantitative attribute accuracy MEASURE DESCRIPTION: The classification is not without error. There are many aspects of satellite image classification that introduce uncertainty in the final product, for example spatial resolution and spectral mixing. The following indicators of product quality and thematic accuracy are calculated by Agriculture and Agri-Food Canada. Please also refer to the Logical Consistency Report section for additional comments on product quality. 1) A cross-validation measure based on training data input into each scene by scene classification was performed. As a result, based on 97 scenes evaluated, the overall accuracies estimates recorded for each scene are summarized as follows: Minimum: 72.8%, Maximum: 96.3%, Mean 86.8%. 2) A thematic (pixel) consistency measurement of overlapping areas between classified scenes was performed. As a result, based on 151 overlapping cases, the percentage consistency measured between scenes is summarized as follows: Minimum: 72.9%, Maximum: 98.9%, Mean 92.1%. 3) An overall accuracy measure using independent ground reference information collected based on a stratified systematic sampling grid across Canada was performed. A total of 3164 sample points were collected and evaluated against approximately 370,000,000 hectares of mapped area. As a result, an overall thematic class accuracy of approximately 82% was calculated. Considering the proportion of class areas represented and the density of sampling, this is estimated to be within +/- 1.5% at the 95% confidence level. 4) Areas reported by the 2001 Census of Agriculture (Statistics Canada, 2002) are compared with common Annual Cropland, grassland and Perennial Crops and Pasture. Approximately 239,434,510 hectares across Canada are analyzed. Results for four relative comparisons between the mapped land cover Page 10 of 13

11 Page 11 of 13 areas versus the area reported by the Census (mapped/census*100) are as follows: a) Annual Cropland = 103.0%; b) Perennial Crops and Improved Pasture = 139.6%; c) Unimproved Pasture (Grassland) = 76.8% (Note: unimproved pasture is not always associated with grassland, and grasslands may not be used as unimproved pasture); d) Perennial Crops, Improved Pasture and Unimproved Pasture (Grassland) combined = 105.0% Lineage statement Compilation of Date Index feature class based on information relating to the original data Providers. This dataset is an index file intended to be used as an associated dataset with the AAFC Landcover (circa 2000) product. As such, it has a coverage area over the agricultural regions of Canada. Land cover is derived from Landsat5-TM and/or 7-ETM+ multi-spectral imagery by inputting imagery and ground reference training data into a Decision-Tree or Supervised image classification process. Object segmentation, pixel filtering, and/or post editing is applied as part of the image classification. Mapping is corrected to the GeoBase Data Alignment Layer. National Road Network (1:50,000) features and other select existing land cover products are integrated into the product. 7. DATA CAPTURE This product is compiled and published by Agriculture and Agri-Food Canada (AAFC). The product integrates select products mapped by other provincial and federal agencies; with appropriate legend adaptations. The following summaries the general classification processes and aspects of the map compilation; including: Data inputs Image pre-processing Classification Product integration Post-processing Mosaic Quality assurance measures Accuracy components. 7.1 Data inputs: Landsat-5 TM or Landsat-7 ETM+ satellite imagery. One to three image dates within the growing season were used including imagery ranging from Priority was given to using imagery closest to year Landsat images from different years may be used within a scene. More than one image is preferred in order to accurately separate annual crops from perennial crops and pasture, as the classes have varying spectral characteristics over the growing season. Reference data used to train the classifier was collected or interpreted with reference to a variety of sources such as crop insurance databases, air photos, airphoto or satellite imagery (SPOT, IKONOS, Quickbird, visual interpretation of Landsat images, other topographic map references, or through field collection where possible. Ancillary data such as DEM, DEM-derived products (slope, aspect) and soils information may be used as appropriate. Data was sourced from many agencies and departments: Canada Centre for Remote Sensing; GeoBase; Northern Forestry Centre, Canadian Forest Service, Natural resources Canada; Ontario Ministry of Natural Resources; Environment Canada; Ducks Unlimited Canada; Faune Québec; Ministère des Ressources naturelle; Ministère de l'agriculture, des Pêcheries et de l'alimentation du Québec. 7.2 Image Pre-Processing: Ortho-rectification of images using a standard 3-D multi-sensor physical model that positionally corrected images to the GeoBase Data Alignment Layer ( Atmospheric correction using the ATCOR algorithm; however, in most cases, Landsat images were not atmospherically corrected except where obvious heterogeneous conditions occur. A Tasseled Cap (TC) transformation (for scenes classified using a decision tree classifier) that converts bands 1 to 5 and 7 of image pairs into three channels (brightness, greenness, wetness).

12 Creation of masks to identify areas contaminated with cloud and shadow, or to mask other land cover types are created where required. Computation of an intra-pixel texture band based on the local variance in an adaptively placed 3 by 3 window of the green band on the mid-season image. 7.3 Classification: A Decision Tree (DT) classifier was principally implemented by AAFC using software that generates a set of decision rules defined by combinations of features and a set of linear discriminate functions that are applied to each training node. The training sites, tassel cap and texture channels and other ancillary data for each Landsat scene were input into See5 software to produce 12 classes. A two-level hierarchical classification scheme is implemented in the DT process that allows the landscape to be divided into general classes within which more detailed classes can be discriminated. The two-level classification assists the delineation of sub-classes within agricultural and forest areas. AAFC developed custom processes, scripts and software to perform the classification. 7.4 Product Integration: Select existing products were integrated by adapting legends using defined cross-walk tables and using defined mosaic seam lines or integration procedures. This included the following three products: The Earth Observation for Sustainable Development of Forests (EOSD) Land Cover, circa 2000 published by Natural Resources Canada, Canadian Forest Service was produced using a supervised classification approach. The Manitoba Remote Sensing Centre (MRSC) used image processing software to map 17 classes for agricultural regions of Manitoba. Classification was based on landsat imagery using a supervised classification approach. The original 17 classes are converted by AAFC to 10 comparable classes. The Saint-Lawrence in Quebec valley was classified using Landsat ETM and TM imagery and a supervised classification methodology. The original 25 classes were converted by AAFC to 10 comparable classes. 7.5 Filtering and post editing: In order to remove mis-classified or noisy pixels from the classification two approaches were used: An object-oriented filter approach that best accommodates landscape physical properties. For this, segment objects used for filtering are generated using multitemporal Tasseled Cap bands using Definiens software A majority filter is applied to the DT generated per-pixel classification using the resulting segmented objects. This technique was primarily used in prairie regions of Canada were it fit the more homogeneous landscape In other more heterogeneous landscapes, a SIEVE filter was applied to the classification. Finally, manual editing of pixel values was applied as needed to correct obvious identified classification errors. 7.6 Mosaic: Two approaches were used to mosaic individual classified scenes together. An approach based on the object-based filtered segments are input to an ArcGIS model that prioritises scenes based on consistency results and case files. Selected segments are then converted to raster format and a mosaic is generated. This process optimizes final product accuracy and minimizes visual inconsistencies by generating cut features along segmented object edges. Cut lines are manually created through areas with high levels of consistency between scenes. This minimizes visual inconsistencies in the mosaic. In all regions, raster sub-set of National Road Network (1:50,000) features were burned or integrated into the classification to preserve features lost through segmentation or filtering. 7.7 Quality assurance/controls: An initial cross-validation accuracy calculation of scenes classified by AAFC based on the centroids of the training site polygons. The goal of this step is not to evaluate absolute accuracy, but to ensure for each scene that sufficient training data were collected, as well as, ensure reasonable class representation and consistency of interpretation of classes. Page 12 of 13

13 Qualitative visual evaluation of the thematic classification by an independent interpreter for each completed scene. Although this can be subjective, reference to ancillary data and source imagery are used. If significant errors are found, images are reclassified or post editing is performed. A consistency analysis of overlapping classified scenes to identify thematic differences between scenes was performed. This helped determine which scene should be given priority in the mosaic process, and potentially identify if re-classification or post-editing is required. High consistency between scenes provides a level of confidence in the classification. Areas reported by the 2001 Census of Agriculture (Statistics Canada, 2002) are compared with common classified agricultural land areas as validation that resulting classification areas are reasonable. 7.8 Accuracy components: Collection of independent reference data base on a stratified systematic sampling grid. The density of points is defined by determining the precision desired and by considering spatial variability of land cover classes. A confidence level of 95% or greater is targeted in the selection of the sampling density. High resolution imagery are primarily used by interpreters to populate the ground reference databases. Error matrix analysis is used to calculate overall accuracy statistics. 8. DATA MAINTENANCE There are no planned updates to this product. 9. PORTRAYAL Not applicable. 10. DATA PRODUCT DELIVERY Delivery medium information: units of delivery: package medium name: online via HTTP, online via direct access Delivery format information: File Geodatabase format name: Esri Geodatabase (File-based) format version: 10.0 specification: A collection of various types of GIS datasets held in a file system folder. ( atabases/003n /) languages: character set: eng utf8 11. METADATA GeoTIFF format name: GeoTIFF (Tagged Image File Format), format version: 1.0 Specification: GeoTIFF is format extension for storing georeference and geocoding information in a TIFF 6.0 compliant raster file by tying a raster image to a known model space or map projection. The metadata requirements are following the Treasury Board Standard on Geospatial Data (ISO 19115). Page 13 of 13