Search USGS⌕ Search
Home/Archive/USGS

USGS · 70188068

Status and trends of land change in selected U.S. ecoregions - 2000 to 2011

Kristi L. Sayler·William Acevedo·Janis Taylor
View original source ↗Export metadata ↓

Abstract

U.S. Geological Survey scientists developed a dataset of 2006 and 2011 land-use and land-cover ( LULC ) information for selected 100-km 2 sample blocks within 29 U.S. Environmental Protection Agency ( EPA ) Level III ecoregions across the conterminous United States. The data can be used with the previously published Land Cover Trends Dataset: 1973 to 2000 to assess landuse/land-cover change across a 37-year study period. Results from analysis of these data include ecoregion-based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km 2 in the Northern Basin and Range Ecoregion to a high of 78,782 km 2 in the Southeastern Plains Ecoregion. The Southeastern Plains continues to encompass one of the most intense forest harvesting and regrowth regions in the country, with 16.6 percent of the ecoregion changing between 2001 and 2011. These LULC change statistics provide a new, valuable resource that complements other reference data and field-verified LULC data. Researchers can use this resource to independently validate other land change products or to conduct regional land change assessments.

Explore related subjects

Photogrammetric Engineering and Remote SensingArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kristi L. Sayler, William Acevedo, Janis Taylor. 2016. Status and trends of land change in selected U.S. ecoregions - 2000 to 2011. https://doi.org/10.14358/pers.82.9.687

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2025

Evaluating Three-Dimensional Elevation Program lidar consistency and accuracy at scale using cloud-native, open-source methods

The U.S. Geological Survey three-dimensional elevation program (3DEP) has significantly expanded national lidar coverage, necessitating scalable, reproducible methods for assessing data quality across diverse terrains and acquisition conditions. This study introduces a cloud-native, open-source workflow designed to evaluate the geometric accuracy and consistency of 3DEP lidar data sets at a national scale. Leveraging tools such as the Point Data Abstraction Library, Open3D, and Amazon Web Services infrastructure, the workflow integrates global navigation satellite system‐surveyed ground control points and terrestrial laser scanning data to validate airborne lidar collections. Two case studies demonstrate the application of this process. In Puerto Rico, the process identified vertical biases and inconsistencies in vegetated areas, while in Iowa and Arizona, the process confirmed high vertical accuracy with minimal bias. The results underscore the effectiveness of combining cloud computing with open-source tools to perform large-scale lidar data quality assessments. This process offers a reproducible, efficient solution for nationwide validation of 3DEP data sets, supporting enhanced decision-making in geospatial applications.

Photogrammetric Engineering and Remote Sensing↗
USGS2025

Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data

Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.

California↗