Land Change Monitoring, Assessment and Projection

Publications

LCMAP Lessons Learned

LCMAP Lessons Learned

Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach.

Lessons Learned

Optimizing LCMAP

Optimizing LCMAP

"Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative"

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Change Detection

Quality Control

Quality Control

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

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Supplemental info
Filter Total Items: 16
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Year Published: 2021

Hotter drought escalates tree cover declines in blue oak woodlands of California

California has, in recent years, become a hotspot of interannual climatic variability, recording devastating climate-related disturbances with severe effects on tree resources. Understanding the patterns of tree cover change associated with these events is vital for developing strategies to sustain critical habitats of endemic and threatened...

Dwomoh, Francis K; Brown, Jesslyn F.; Tollerud, Heather J.; Auch, Roger F.

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Year Published: 2021

The effects of urban land cover dynamics on urban heat Island intensity and temporal trends

Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of...

Xian, George Z.; Shi, Hua; Auch, Roger F.; Gallo, Kevin; Zhou, Qiang; Wu, Zhuoting; Kolian, Michael

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Year Published: 2021

Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability

Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be...

Xing, Dingfan; Stehman, Stephen V; Foody, Giles M; Pengra, Bruce

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Year Published: 2020

Methods for rapid quality assessment for national-scale land surface change monitoring

Providing rapid access to land surface change data and information is a goal of the U.S. Geological Survey. Through the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative, we have initiated a monitoring capability that involves generating a suite of ten annual land cover and land surface change datasets across the United States...

Zhou, Qiang; Barber, Christopher; Xian, George Z.

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Year Published: 2020

Investigating the effects of land use and land cover on the relationship between moisture and reflectance using Landsat Time Series

To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we...

Tollerud, Heather J.; Brown, Jesslyn F.; Loveland, Thomas

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Year Published: 2020

Land change monitoring, assessment, and projection

There is a pressing need to monitor and understand the rapid land change happening around the world. The U.S. Geological Survey is developing a new capability, called Land Change Monitoring, Assessment, and Projection (LCMAP), to innovate the understanding of land change. This capability is the Earth Resources Observation and Science Center's...

Rover, Jennifer; Brown, Jesslyn F.; Auch, Roger F.; Sayler, Kristi L.; Sohl, Terry L.; Tollerud, Heather J.; Xian, George Z.
Rover, J., Brown, J.F., Auch, R.F., Sayler, K.L., Sohl, T.L., Tollerud, H.J., and Xian, G.Z., 2020, Land change monitoring, assessment, and projection: U.S. Geological Survey Fact Sheet 2020–3024, 4 p., https://doi.org/10.3133/fs20203024.

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Year Published: 2020

Gap fill of Land surface temperature and reflectance products in Analysis Ready Data

The recently released Landsat Analysis Ready Data (ARD) over the United States provides the opportunity to investigate landscape dynamics using dense time series observations at 30-m resolution. However, the dataset often contains data gaps (or missing data) because of cloud contamination or data acquisition strategy. We present a new algorithm...

Zhou, Qiang; Xian, George Z.; Shi, Hua

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Year Published: 2020

Training data selection for annual land cover classification for the LCMAP initiative

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative characterizes changes in land cover, use, and condition with the goal of producing land change information that improves understanding of the earth system and provides insight into the impacts of land change on society. For LCMAP, all available high-...

Zhou, Qiang; Tollerud, Heather J.; Barber, Christopher; Smith, Kelcy; Zelenak, Daniel J.

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Year Published: 2019

Lessons learned implementing an operational continuous U.S. national land change monitoring capability: The LCMAP approach

Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more...

Brown, Jesslyn F.; Tollerud, Heather J.; Barber, Christopher; Zhou, Qiang; Dwyer, John L.; Vogelmann, James; Loveland, Thomas; Woodcock, Curtis; Stehman, Stephen V; Zhu, Zhe; Pengra, Bruce; Smith, Kelcy; Horton, Josephine; Xian, George Z.; Auch, Roger F.; Sohl, Terry L.; Sayler, Kristi L.; Gallant, Alisa L.; Zelenak, Daniel; Reker, Ryan R.; Rover, Jennifer R.

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Year Published: 2019

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...

Pengra, Bruce; Stehman, Stephen V; Horton, Josephine; Dockter, Daryn (Contractor); Schroeder, Todd A.; Yang, Zhiqiang; Cohen, Warren B; Healey, Sean P.; Loveland, Thomas

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Year Published: 2018

Analysis ready data: Enabling analysis of the Landsat archive

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor...

Dwyer, John L.; Roy, David P.; Sauer, Brian; Jenkerson, Calli B.; Zhang, Hankui K.; Lymburner, Leo

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Year Published: 2018

U.S. Landsat Analysis Ready Data

U.S. Landsat Analysis Ready Data (ARD) are a revolutionary new U.S. Geological Survey science product that allows the Landsat archive to be more accessible and easier to analyze and reduces the amount of time users spend on data processing for monitoring and assessing landscape change. U.S. Landsat ARD are Level-2 products derived from...

U.S. Geological Survey, 2018, U.S. Landsat Analysis Ready Data: U.S. Geological Survey Fact Sheet 2018–3053, 2 p., https://doi.org/10.3133/fs20183053.