Geology topics

Catherine Costello

Publications and source records attributed to Catherine Costello.

4 recordsLinked to original sources

USGS2023

National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product

The National Land Cover Database (NLCD) 2016 products show that, between 2001 and 2016, nearly half of the land cover change in the conterminous United States (CONUS) involved forested areas. To ensure the quality of NLCD land cover and land cover change products, it is important to accurately detect the location and time of forest disturbance. We designed a comprehensive strategy to integrate a continuous time series forest change detection method and a discrete 2-date forest change detection method to produce the NLCD 1986–2019 forest disturbance product, which shows the most recent forest disturbance date between the years 1986 and 2019 for every 2- to 3-year interval. This method, the Time-Series method Using Normalized Spectral Distance (NSD) index (TSUN), uses NSD to detect multi-date forest land cover changes and was shown to be easily extended to a new date even when new images were processed in a different way than previous date images. The discrete 2-date method uses the Multi-Index Integrated Change Analysis (MIICA) method to detect changes between 2-date images. A method based on confidence and object grouping was designed to combine the multiple MIICA outputs to improve change detection accuracy. Finally, an aggregation scheme was implemented to combine the TSUN output, the integrated MIICA results, and ancillary data to produce the NLCD 2019 forest disturbance 1986–2019 product. The initial accuracy assessments from 1,600 samples over 4 Landsat path/rows show that the producer’s and user’s accuracies of the 2001–2019 forest disturbance map are 76% and 74%, respectively. The final CONUS-wide forest disturbance product is provided at https://www.mrlc.gov/nlcd-2019-science-research-products .

conterminous United States↗

USGS2023

National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images

National Land Cover Database (NLCD) 2019 is a new epoch of national land cover products for the conterminous United States. Image quality is fundamental to the quality of any land cover product. Image preprocessing has often taken a considerable proportion of overall time and effort for this kind of national project. An approach to prepare image inputs for NLCD 2019 production was developed to ensure efficiency and quality of operational production. Here, we introduce a new and comprehensive strategy to produce clear Landsat composite images for NLCD 2019 production. First, we developed a new median-value compositing method. Second, we designed parameter settings for selecting images and pixels to generate 4 composite images (leaf-on, leaf-off, primary reference, and complementary reference) for a target year based on the US Landsat Analysis Ready Data surface reflectance dataset. Third, we developed a method, referred to as Detection and Filling with Simulated Image, to detect and replace clouds and cloud shadow pixels to produce the final clean leaf-on and leaf-off image composites. This image compositing and processing strategy was implemented for the entire conterminous United States to produce images for NLCD 2019. Our image results and NLCD 2019 change detection and land cover products, which were released in July 2021, showed this new strategy to be effective and efficient.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2020

Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database

The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001-2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001-2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this time. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4,778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001-2006 at twice the rate of 2011-2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2018

A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping.

Conterminous United States↗