Geology topics

Daryn Dockter

Publications and source records attributed to Daryn Dockter.

4 recordsLinked to original sources

USGS2022

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 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 km 2 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.

Vegetation Classification and Survey↗

USGS2021

Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups

Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1 & 2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have >17% of their extent in Status 1 & 2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.

Forests↗

USGS2020

Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as "duplicate interpretations"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.

Remote Sensing of Environment↗

USGS2019

LANDFIRE remap prototype mapping effort: Developing a new framework for mapping vegetation classification, change, and structure

LANDFIRE (LF) National (2001) was the original product suite of the LANDFIRE program, which included Existing Vegetation Cover (EVC), Height (EVH), and Type (EVT). Subsequent refinements after feedback from data users resulted in updated products, referred to as LF 2001, that now served as LANDFIRE’s baseline datasets and are the basis for all subsequent LANDFIRE updates. These updates account for disturbances and vegetation transitions changes that may not represent current vegetation conditions. Therefore, in 2016 LANDFIRE initiated the Remap prototype to determine how to undertake a national-scale remap of the LANDFIRE primary vegetation datasets. EVC, EVH, and EVT were produced (circa 2015) via modeling for ecologically variable prototyping areas in the Pacific Northwest (NW) and Grand Canyon (GC). An error analysis within the GC suggested an overall accuracy of 52% (N = 800) for EVT, and a goodness of fit of 51% (N = 38) for percent cover (continuous EVC) and 53% (N = 38) for height (continuous EVH). The prototyping effort included a new 81-class map using the National Vegetation Classification (NVC) within the NW. This paper presents a narrative of the innovative methodologies in image processing and mapping used to create the new LANDFIRE vegetation products.

Fire↗