Skip to main content
U.S. flag

An official website of the United States government

Publications

Find publications related to USGS Land Change Monitoring, Assessment, and Projection (LCMAP) here.

Filter Total Items: 25

Development of the LCMAP annual land cover product across Hawai'i

Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the

Tree regrowth duration map from LCMAP collection 1.0 land cover products in the conterminous United States, 1985–2017

Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities, has increased the demand for information on forest regrowth and recovery fol

Analyzing the effects of land cover change on the water balance for case study watersheds in different forested ecosystems in the USA

We analyzed impacts of interannual disturbance on the water balance of watersheds in different forested ecosystem case studies across the United States from 1985 to 2016 using a remotely sensed long-term land cover monitoring record (U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science products), gridded precipitation and evaporation data, and st

Conterminous United States land-cover change (1985-2016): New insights from annual time series

Sample-based estimates augmented by complete coverage land-cover maps were used to estimate area and describe patterns of annual land-cover change across the conterminous United States (CONUS) between 1985 and 2016. Most of the CONUS land cover remained stable in terms of net class change over this time, but a substantial gross change dynamic was captured by the annual and cumulative time interval

A novel regression method for harmonic analysis of time series

Harmonic analysis of time series is an important technique in remote sensing to reveal seasonal land surface dynamics. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite da

Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product

The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land

Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling

Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This interpreter variability is typically not accounted for in variance estimators applied to area estimates of land cover. A simple meas

A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for c

Validation of the U.S. Geological Survey’s Land Change Monitoring, Assessment and Projection (LCMAP) collection 1.0 annual land cover products 1985–2017

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) has released a suite of annual land cover and land cover change products for the conterminous United States (CONUS). The accuracy of these products was assessed using an independently collected land cover reference sample dataset produced by analysts interpreting Landsat data, high-resolution aerial photograp

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 vegetation communities. We assessed patterns of tree cov

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 Landsat land surface temperature products and annual l

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 assigned. Two approaches for accommodating interpr