1. Reader Beware! Land Use Classification Nomenclature Issues
Presented at
Sustainability & Land Use Change Workshop
27 September 2018
Sharon Bard
Centrec Consulting Group

2. What prompted this exploration?
ILUC project
Which classification nomenclature to use?
How can our work help advance knowledge in this area?
As we dug into classification nomenclature and suggestions made by the Advisory Team, issues surfaced:
Inconsistent land cover classification
Inconsistent land cover categorization
Census of Ag: cropland pasture versus permanent pasture
Implications of these issues on research output / conclusions

3. Dimensions of Classification of Land Cover and Land Use
PRIMARY
Various US:
USDA NASS C of Ag
USDA CDL
GCL2000
CORINE
GTAP
GLOBIOM
CATEGORIZED
FAO
Data Collection Methods
Land Cover &/or Land Use Data Sets
Classification Nomenclature
Modeling
Conclusions
Output
Analysis
IGBP
UN LCSS
Intergovernmental Panel on Climate Change (IPCC)
Remote sensing
Surveys
On-ground

4. Sources of Error in LC/LU Classification
Inappropriate dataset
Incorrect assumptions
Inconsistent categorizations
Missing data points
Classification error
Data Collection Methods
Land Cover &/or Land Use Data Sets
Classification Nomenclature
Modeling
Conclusions
Output
Analysis
Surveys – respondent error; collection method: measurement / reporting
Classification nomenclature – confusion; inconsistent categories between data sets but often merged (or is this modeling?)
Modeling – incorrect use of data sets; incorrect assumptions
Inconsistent terminology
Vague definitions
Complicated or incomplete nomenclature
Spatial & spectral resolution of satellite imagery
Temporal
Respondent / survey error

5. Classification Nomenclature Key Issues
Missing data points
Accuracy of data
Proper accounting for double cropping / intensification
Complexity of delineating land cover vs land use vs land management
For example, CRP land
Aggregation of land cover types
Land use change methodology
Explicit categories omitted
Defining marginal land

6. Classification Nomenclature Key Issues: Examples
Grassland/ pasture
Permanent pasture
Cropland pasture
Idle/fallow cropland
Cropland
Grassland
Hay ground
Cultivated
Alfalfa
Other
Non cultivated (grass)
Pasture and grass-related land cover categories
Idle vs fallow vs failed vs ??? – idle cropland is difficult to categorize

7. Focus: Respondent Misclassification
PRIMARY
Various US:
USDA NASS C of Ag
USDA CDL
GCL2000
CORINE
GTAP
GLOBIOM
CATEGORIZED
FAO
Data Collection Methods
Land Cover &/or Land Use Data Sets
Classification Nomenclature
Modeling
Conclusions
Output
Analysis
Spatial & spectral resolution of satellite imagery
Temporal
Respondent / survey error

8. Bridging Data: Source to Use
USDA Census of Ag
FAO
GTAP
Cropland harvested
Cropland used for crops
Total cropland
Crop failure
Arable land & permanent crops
Cropland
Cultivated summer fallow
Total Ag Area
Idle cropland
Cropland pasture
Pasture excl. CLP & woodland pasture
Grass- land
pasture & range
Permanent
pasture & meadows
Pasture land
Pasture (rented by AUM or not in farms)

9. Focus: Cropland Pasture vs Permanent Pasture
From footnote in ERS Table:
“Between 2002 and 2007, total cropland decreased by 34 million acres to its lowest level since this series began in 1945, even though harvested cropland (which accounts for most land planted to crops) increased 5 million acres due to a recovery of failed cropland from severe droughts in A 26-million acre decline in cropland pasture contributed to this trend, partly due to methodological changes in the 2007 Census of Agriculture that reclassified some cropland pasture to permanent grassland pasture and range.”

10. Focus: Cropland Pasture vs Permanent Pasture
Cropland Pasture – generally considered to be in a long-term rotation; includes crop acres that have been hogged or grazed but not harvested; some land used for pasture that could have been cropped without additional improvement
Slide 10
Permanent Pasture and Rangeland – all open land used primarily for pasture and grazing, including shrub and brushland types of pasture, grazing land with sage brush…..

11. Focus: Cropland Pasture vs Permanent Pasture
Potential causes of the change in acres between categories
Non-measured errors or reporting artifacts (USDA reporting error)
Respondent and enumerator error
Processing error
Item nonresponse
Record matching error
Model uncertainty error
Respondent misclassification of land cover in either Census year
Missing information – non-response or under-coverage
Actual land cover change

12. Change between 2002 and 2007 in: Cropland Pasture: Decrease Permanent Pasture: Increase
Courtesy: Ploughman-Analytics

13. Change between 2002 and 2007 in: Cropland Pasture: Increase Permanent Pasture: Increase
Courtesy: Ploughman-Analytics

14. Focus: Cropland Pasture vs Permanent Pasture Potential Implications
Problematic “fuzzy” data
Changes in the western states => large number of acres
Would use aggregate numbers of cropland pasture and permanent pasture and trends with caution
“Reclassification” impacts available cropland acres
What should be done about historical classification?
May impact modeling efforts that account for intensification
Intensification – increasing production on existing area through yield improvement per harvested area, greater use of double cropping and/or cultivating the existing unused marginal land

15. Focus: Inconsistent Nomenclature
PRIMARY
Various US:
USDA NASS C of Ag
USDA CDL
GCL2000
CORINE
Data Collection Methods
Land Cover &/or Land Use Data Sets
Classification Nomenclature
Modeling
Conclusions
Output
Analysis
IGBP
UN LCSS
Intergovernmental Panel on Climate Change (IPCC)
Remote sensing
Surveys
On-ground

16. Focus: Inconsistent Nomenclature

17. Focus: Inconsistent Categorization
PRIMARY
Various US:
USDA NASS C of Ag
USDA CDL
GCL2000
CORINE
Data Collection Methods
Land Cover &/or Land Use Data Sets
Classification Nomenclature
Modeling
Conclusions
Output
Analysis
Researcher categorization of land cover data
IGBP
UN LCSS
Intergovernmental Panel on Climate Change (IPCC)
Remote sensing
Surveys
On-ground

18. Focus: Categorization of CDL Data

19. How can the errors be minimized?
Increase the accuracy of land cover classification
Increase the application of standardized classification nomenclature for land cover across data sets
Increase the use of standardized methodology for land use change
Be aware of the issues / limitations of the data / methods and account for them in the research
Slide 16

20. Questions / Suggestions for further exploration? Thank you!