Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the Americas
Aims: Natural resource management and biodiversity conservation rely on inventories of vegetation that span multiple management or political jurisdictions. However, while remote sensing data and analytical tools have enabled production of maps at increasing spatial resolution and reliability, there...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Pensoft Publishers
2022-02-01
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| Series: | Vegetation Classification and Survey (VCS) |
| Online Access: | https://vcs.pensoft.net/article/67537/download/pdf/ |
| _version_ | 1818256074108567552 |
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| author | Patrick J. Comer Jon C. Hak Daryn Dockter Jim Smith |
| author_facet | Patrick J. Comer Jon C. Hak Daryn Dockter Jim Smith |
| author_sort | Patrick J. Comer |
| collection | DOAJ |
| description | Aims: Natural resource management and biodiversity conservation rely on inventories of vegetation that span multiple management or political jurisdictions. However, while remote sensing data and analytical tools have enabled production of maps at increasing spatial resolution and reliability, there are limited examples where national or continental-scaled maps are produced to represent vegetation at high thematic detail. We illustrate two examples that have bridged the gap between traditional land cover mapping and modern vegetation classification. Study area: Our two case studies include national (USA) and continental (North and South America) vegetation and land cover mapping. These studies span conditions from subpolar to tropical latitudes of the Americas. Methods: Both case studies used a supervised modeling approach with the International Vegetation Classification (IVC) to produce maps that provide for greater thematic detail. Georeferenced locations for these vegetation types are used by machine learning algorithms to train a predictive model and generate a distribution map. Results: The USA LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) case study illustrates how a history of vegetation-based classification and availability of key inputs can come together to generate standard map products covering more than 9.8 million km2 that are unsurpassed anywhere in the world in terms of spatial and thematic resolution. That being said, it also remains clear that mapping at the thematic resolution of the IVC Group and finer resolution require very large and spatially balanced inputs of georeferenced samples. Even with extensive prior data collection efforts, these remain a key limitation. The NatureServe effort for the Americas - encompassing 22% of the global land surface - demonstrates methods and outputs suitable for worldwide application at continental scales. Conclusions: Continued collection of input data used in the case studies could enable mapping at these spatial and thematic resolutions around the globe. Abbreviations: CART = Classification and Regression Tree; CONUS = Conterminous United States; DSWE = Dynamic Surface Water Extent; EPA = United States Environmental Protection Agency; FGDC = Federal Geographic Data Committee; IVC = International Vegetation Classification; LANDFIRE = Landscape Fire and Resource Management Planning Tools Project; LFRDB = LANDFIRE Reference Database; LiDAR = Light Detection and Ranging; NDVI = Normalized Difference Vegetation Index; NLCD = National Land Cover Database; USNVC = United States National Vegetation Classification; USA = United States of America; WWF = World Wildlife Fund or Worldwide Fund for Nature. |
| first_indexed | 2024-12-12T17:21:58Z |
| format | Article |
| id | doaj.art-f815ecf688a643d986f61e637cfb7f03 |
| institution | Directory Open Access Journal |
| issn | 2683-0671 |
| language | English |
| last_indexed | 2024-12-12T17:21:58Z |
| publishDate | 2022-02-01 |
| publisher | Pensoft Publishers |
| record_format | Article |
| series | Vegetation Classification and Survey (VCS) |
| spelling | doaj.art-f815ecf688a643d986f61e637cfb7f032022-12-22T00:17:38ZengPensoft PublishersVegetation Classification and Survey (VCS)2683-06712022-02-013294310.3897/VCS.6753767537Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the AmericasPatrick J. Comer0Jon C. Hak1Daryn Dockter2Jim Smith3NatureServeUnaffiliatedU.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) CenterThe Nature ConservancyAims: Natural resource management and biodiversity conservation rely on inventories of vegetation that span multiple management or political jurisdictions. However, while remote sensing data and analytical tools have enabled production of maps at increasing spatial resolution and reliability, there are limited examples where national or continental-scaled maps are produced to represent vegetation at high thematic detail. We illustrate two examples that have bridged the gap between traditional land cover mapping and modern vegetation classification. Study area: Our two case studies include national (USA) and continental (North and South America) vegetation and land cover mapping. These studies span conditions from subpolar to tropical latitudes of the Americas. Methods: Both case studies used a supervised modeling approach with the International Vegetation Classification (IVC) to produce maps that provide for greater thematic detail. Georeferenced locations for these vegetation types are used by machine learning algorithms to train a predictive model and generate a distribution map. Results: The USA LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) case study illustrates how a history of vegetation-based classification and availability of key inputs can come together to generate standard map products covering more than 9.8 million km2 that are unsurpassed anywhere in the world in terms of spatial and thematic resolution. That being said, it also remains clear that mapping at the thematic resolution of the IVC Group and finer resolution require very large and spatially balanced inputs of georeferenced samples. Even with extensive prior data collection efforts, these remain a key limitation. The NatureServe effort for the Americas - encompassing 22% of the global land surface - demonstrates methods and outputs suitable for worldwide application at continental scales. Conclusions: Continued collection of input data used in the case studies could enable mapping at these spatial and thematic resolutions around the globe. Abbreviations: CART = Classification and Regression Tree; CONUS = Conterminous United States; DSWE = Dynamic Surface Water Extent; EPA = United States Environmental Protection Agency; FGDC = Federal Geographic Data Committee; IVC = International Vegetation Classification; LANDFIRE = Landscape Fire and Resource Management Planning Tools Project; LFRDB = LANDFIRE Reference Database; LiDAR = Light Detection and Ranging; NDVI = Normalized Difference Vegetation Index; NLCD = National Land Cover Database; USNVC = United States National Vegetation Classification; USA = United States of America; WWF = World Wildlife Fund or Worldwide Fund for Nature.https://vcs.pensoft.net/article/67537/download/pdf/ |
| spellingShingle | Patrick J. Comer Jon C. Hak Daryn Dockter Jim Smith Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the Americas Vegetation Classification and Survey (VCS) |
| title | Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the Americas |
| title_full | Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the Americas |
| title_fullStr | Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the Americas |
| title_full_unstemmed | Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the Americas |
| title_short | Integration of vegetation classification with land cover mapping: lessons from regional mapping efforts in the Americas |
| title_sort | integration of vegetation classification with land cover mapping lessons from regional mapping efforts in the americas |
| url | https://vcs.pensoft.net/article/67537/download/pdf/ |
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