Land Use and Land Cover Semantics - Principles, Best Practices and Prospects. Ola Ahlqvist

Transcription

1 Acknowledgements: Dalia Varanka, USGS; Steffen Fritz, IIASA, Austria; Helle Skånes, Stockholm University-NORDSCAPE; Krzysztof Janowicz, UCSB Land Use and Land Cover Semantics - Principles, Best Practices and Prospects ahlqvist.1@osu.edu

2 Land Use and Land Cover Semantics Land use and land cover data Importance climate modeling, urban planning, landscape change assessment, hydrological models, and unknown future issues Demand that data can be re-purposed for a variety of end uses Often produced and disseminated as categorical data variation in nomenclature and class definitions pose significant hurdles to effective and synergistic use of Land Use & Land Cover information resources Standards Initiatives National Vegetation Classification Standard (Vegetation Subcommittee, 1997), the Nordic Landscape Monitoring Project (Groom, 2005), the CORINE Land Cover (CEC, 1995 and 1999; Bossard et al., 2000), the standard classification for land cover of South Africa (Thompson, 1996), GLC2000 (Bartholomé and Belward, 2005), UNEP/FAO Land Cover Classification System (LCCS) (Di Gregorio and Jansen, 2000)

3 Despite standards, information semantics is a growing problem Cognitive science suggest that humans need categories in order to process experiences, form memories, for analysis, or to summarize and communicate knowledge (Lakoff, 1987; Rosch, 1978) Mixed regeneration forest Coniferous mature forest Mixed regeneration forest Developed industrial The use of categorical data in computer based land analysis poses a significant problem because it usually leads to a binary treatment of the information in subsequent analysis, harmonization.

4 Land Use and Land Cover Semantics A traditional land cover taxonomy Land Cover Chester County, PA 1992 Typically follows divisions into classsubclass relations But no agreement on a unified taxonomy CORINE GlobCover MODIS/IGBP Even the same agency (ex. USGS) system change from one time to the other National Land Cover Data (NLCD) used slightly different classes in 1992 and 2001 t Kilometers 11 Open Water 21 Low Intensity Residential 22 High Intensity Residential 23 Commercial/Industrial/Transport. 32 Quarries/Strip Mines/Gravel Pits 33 Transitional 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 81 Pasture/Hay 82 Row Crops 85 Urban/Recreational Grasses 91 Woody Wetlands 92 Emergent Herbaceous Wetlands

5 Land Use and Land Cover Semantics National Land Cover Data (NLCD) change example Land Cover Chester County, PA 1992 Land Cover Chester County, PA 2001 t t Kilometers Kilometers Different classification systems create problems!? 11 Open Water 21 Low Intensity Residential 22 High Intensity Residential 23 Commercial/Industrial/Transport. 32 Quarries/Strip Mines/Gravel Pits 33 Transitional 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 81 Pasture/Hay 82 Row Crops 85 Urban/Recreational Grasses 91 Woody Wetlands 92 Emergent Herbaceous Wetlands? 11 Open Water 21 Developed, Open Space 22 Developed, Low Intensity 23 Developed, Medium Intensity 24 Developed, High Intensity 31 Barren Land (Rock/Sand/Clay) 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 81 Pasture/Hay 82 Cultivated Crops 90 Woody Wetlands 95 Emergent Herbaceous Wetlands

6 A fundamental issue...the gradation of properties in the world means that our smallish number of categories will never map perfectly onto all objects: The distinction between member and nonmembers will always be difficult to draw or will even be arbitrary in some cases [ ] if the world consists of shadings and gradations and a rich mixture of different kinds of properties, then a limited number of concepts would almost have to be fuzzy. (Murphy, 2004).

7 Tree height (m) Land Use and Land Cover Semantics Same issues around global definitions of forest Zimbabwe Turkey Sudan Tanzania 6 4 United States China UNESCO Jamaica 2 0 Estonia Canopy cover (%) after Lund (2006) and Comber et al. (2006)

8 Current situation Increased recognition that variation in nomenclature and class definitions pose significant hurdles to effective and synergistic use of Land Use & Land Cover information resources Example initiatives: FAO LCCS EU EBONE USGS Ontology for the National Map INTEROP

9 Land Use and Land Cover Semantics FAO/LCCS - Land Cover Meta Language (LCML) Domain Scale Range watercov.owl Ratio [ ] waterphase.owl Nominal {Ice, Water} imperviouspct.owl Ratio [ ] vegetationcov.owl Ratio [ ] development.owl surfacetype.owl Nominal {Residental, Commercial, Mining} Nominal {Earthen material, Constructed} treecov.owl Ratio [ ] treeheight.owl Ratio [ ] deciduouspct.owl Ratio [ ] evergreenpct.owl Ratio [ ] shrubcoverpct.owl Ratio [ ] woodytenure.owl grassherbtenure.owl Nominal {(Semi)Natural, Cultivated/Planted} Nominal {(Semi)Natural, Cultivated/Planted} grassherbcoverpct.owl Ratio [ ] crop.owl waterpersistence.owl U.S. NLCD 1992 Low Intensity Residential Nominal {RowCrops, SmallGrains, Fallow, Hay, Grass} Nominal {Permanent, Periodically, Waterlogged} Ahlqvist, O., 2004, A parameterized representation of uncertain conceptual spaces, Transactions in GIS, 8(4),

10 Land Use and Land Cover Semantics Attributes values allow for evaluation of category semantics In Ahlqvist (2004) I suggested two metrics of semantic relations Distance Overlap Distance Woodland (Hyytiäinen, 1995) Woodland (USDA F.S., 1997) Overlap Canopy cover (%) U.S. NLCD 1992 Low Intensity Residential Domain Scale Range watercov.owl Ratio [ ] waterphase.owl Nominal {Ice, Water} imperviouspct.owl Ratio [ ] vegetationcov.owl Ratio [ ] development.owl Nominal {Residental, Commercial, Mining} surfacetype.owl Nominal {Earthen material, Constructed} treecov.owl Ratio [ ] treeheight.owl Ratio [ ] deciduouspct.owl Ratio [ ] evergreenpct.owl Ratio [ ] U.S. NLCD 2001 Developed, Low Intensity Domain Scale Range watercov.owl Ratio [ ] waterphase.owl Nominal {Ice, Water} imperviouspct.owl Ratio [ ] vegetationcov.owl Ratio [ ] development.owl Nominal {Residental, Commercial, Mining} surfacetype.owl Nominal {Earthen material, Constructed} treecov.owl Ratio [ ] treeheight.owl Ratio [ ] deciduouspct.owl Ratio [ ] evergreenpct.owl Ratio [ ]

11 Land Use and Land Cover Semantics Attributes values allow for evaluation of category semantics Two metrics of semantic relations Distance Overlap Distance Woodland (Hyytiäinen, 1995) Woodland (USDA F.S., 1997) Overlap Similar but Disjoint classes Very similar classes Overlap -1 Distance Very different classes Class/ subclass relationship Canopy cover (%) Bivariate color scheme Show different combinations of these two metrics Ahlqvist, O., 2008, Extending post classification change detection using semantic similarity metrics to overcome class heterogeneity: a study of 1992 and 2001 National land Cover Database changes, Remote Sensing of Environment, 112(3):

12 Land Use and Land Cover Semantics Michigan Massachusetts NLCD Level Indicator variogram Semantic variogram Mass Mich Mich Mass m Mich Mass Mich Mass Distance (m) Contagion (%) Total Edge Contrast (%) Ahlqvist, O., & Shortridge, A. (2010). Spatial and semantic dimensions of landscape heterogeneity. Landscape Ecology, 25(4),

13 Land Use and Land Cover Semantics Michigan Massachusetts NLCD Level m Indicator variogram Semantic variogram Mich Mass Mich Mich Mass 0.2 Mass Mich Mass Distance (m) Contagion (%) Total Edge Contrast (%) Ahlqvist, O., & Shortridge, A. (2010). Spatial and semantic dimensions of landscape heterogeneity. Landscape Ecology, 25(4),

14 As I see it, these are some needs: Identifying a coherent set of tools, guidelines or standards to help researchers, data producers and practitioners. Review foundational aspects of semantics in data modeling to be aware of in order to providing clear and consistent semantic metadata. Provide a resource on current best practices for handling semantics in work on Land Use and Land Cover. Present a forward-looking collection of ongoing research and development across the entire spectrum of LULC semantics.

15 Calling for contributions Springer / CRC Press Book Land Use and Land Cover Semantics: Principles, Best Practices and Prospects I. Principles and foundations II. Use cases and best practice III. Ongoing development and future prospects

16 What should be in this book? -TOC Principles and foundations LULC as information on human-environment systems Data sources Categories and information Formal modeling of semantics Semantics as metadata Semantic support for Spatial Data Infrastructures What would make you read/contribute to the book? Use cases and best practice INTEROP/SOCoP GeoVoCamps Ontology for the National Map EAGLE EU monitoring UN/FAO LCCS Ongoing development and future prospects Earthcube iplant derivatives

17 Next steps Tentative timeline/due dates Extended abstracts March 1, 2014 Full chapter manuscripts June 1, 2014 Sessions at meetings Association of Am. Geographers, April 2014 AGILE, June 2014 Other ideas?