Geology ReportsSearch

USGS · 70232210

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

Abstract

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 following natural and anthropogenic disturbances (e.g., fire, harvest, or thinning). However, a wall-to-wall mapping of the CONUS tree regrowth duration at an annual time interval and 30-m resolution is still challenging. In this study, we utilized the annual land cover products to develop a dataset to quantify forest regrowth duration for CONUS over 1985–2017. The land cover data used to derive the tree regrowth duration map is from the primary land cover product in the U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) collection. The LCMAP product used all available Landsat images to detect disturbances over forest and classify Grass/Shrub to Tree Cover transitions on an annual basis. The average regrowth duration was then calculated for each pixel. The regrowth duration map was validated using human interpreted annual reference data that were collected independently. The validation results show one-year of underestimation and 6-year standard deviation of error between the reference data and regrowth duration map. In southeastern CONUS, where major tree regrowth activities have been observed, our map showed higher accuracy with less than one-year bias and 3.6 years standard deviation of error. Forest in the southeast took around 5 years to recover, which was faster than other regions of CONUS. Many pixels had multiple disturbances during the 33-year study period in the region. The spatial pattern of the tree regrowth indicated intense harvesting activities in this region. The Pacific Northwest coast region was the second main area of tree regrowth, but this region often took multiple decades to recover. Given increasing interest in forests as nature-based climate solutions, the tree regrowth duration map can be used to assess reforestation activities as well as forest recovery following natural disturbance and harvesting.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 25.08° to 49.38905° latitude; -124.68721° to -66.96466° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Qiang Zhou, George Z. Xian, Josephine Horton, Danika F. Wellington, Grant Domke, Roger F. Auch, Congcong Li, Zhe Zhu. 2022-06-13. Tree regrowth duration map from LCMAP collection 1.0 land cover products in the conterminous United States, 1985–2017. https://doi.org/10.1080/15481603.2022.2083790

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

KEEP EXPLORING

Related USGS reports

Thematic accuracy assessment of the National Land Cover Database (NLCD2021) for the conterminous United States

The MultiResolution Land Characteristics (MRLC) consortium’s National Land Cover Database (NLCD) was established as an operational, stakeholder-oriented land cover monitoring program with the release of the NLCD2001 database. Both land cover and land cover change data became available with the release of NLCD2006. Here, we report the land cover and land cover change accuracies for NLCD2021, documenting Level II and I accuracies for the 2016, 2019, and 2021 land cover datasets and 2016–2021 land cover change. An additional objective, not addressed in previous NLCD assessments, is the estimation of the total variance of area estimates, where total variance includes sampling variance and variability in reference label assignment. We estimate the total variance of the area estimates for the stable and change classes targeted by the strata used in the sampling design. For the 16 Level II classes, the overall accuracy (OA) was 73% ± 1% when agreement was defined using only the primary reference class label and 89% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label (± standard error, SE). For the eight Level I classes, the corresponding OA estimates were 81% ± 0.9% and 89% ± 0.7%. Level II and Level I OA tended to be higher for 2016 land cover and lower and more similar for 2019 and 2021 land cover. Land cover change user and producer accuracies (UA and PA) tended to be <50%. When the alternate reference label was included in the definition of agreement, exceptions of high class-specific accuracies (≥75%) were more prevalent for PA than UA. The consistency of reference label interpretations was greater for no-change classes than for change classes. The estimates of the total variance incorporating interpreter variability were often smaller than the standard variance estimates, indicating the possibility that the total variance estimator is highly unstable. Further study is needed to improve the utility of this total variance estimator for practical applications.

conterminous United States

Thematic accuracy assessment of the NLCD 2019 land cover for the conterminous United States

The National Land Cover Database (NLCD), a product suite produced through the MultiResolution Land Characteristics (MRLC) consortium, is an operational land cover monitoring program. Starting from a base year of 2001, NLCD releases a land cover database every 2–3-years. The recent release of NLCD2019 extends the database to 18 years. We implemented a stratified random sample to collect land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only, and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label. At Level I of the classification hierarchy, land cover OA was 83.1% ± 0.9% for both 2016 and 2019 when agreement was defined as a match between the map label and primary reference label only, and increased to 90.3% ± 0.7% when agreement also included the alternate reference label. The Level II and Level I OA for the 2016 land cover in the NLCD2019 database were 5% higher compared to the 2016 land cover component of the NLCD2016 database when agreement was defined as a match between the map label and primary reference label only. No improvement was realized by the NLCD2019 database when agreement also included the alternate reference label. User’s accuracies (UA) for forest loss and grass gain were>70% when agreement included either the primary or alternate label, and UA was generally<50% for all other change themes. Producer’s accuracies (PA) were>70% for grass loss and gain and water gain and generally<50% for the other change themes. We conducted a post-analysis review for map-reference agreement to identify patterns of disagreement, and these findings are discussed in the context of potential adjustments to mapping and reference data collection procedures that may lead to improved map accuracy going forward.

conterminous United States

Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020)

Rangelands have a dynamic response to climate change, fire, and other anthropogenic disturbances. The Rangeland Condition, Monitoring, Assessment, and Projection (RCMAP) product aims to capture this response by quantifying the percent cover of eight rangeland components, associated error, and trends across the western United States using Landsat from 1985 to 2020. The current generation of RCMAP has been improved with more training data, regional-scale Landsat composites, and more robust change detection. We assess the temporal patterns in each component with a linear model and a structural change method that determines break points using an 8-year temporal moving window. The linear and structural change methods generally agreed on patterns of change, but the latter found breaks more often, with at least one break point in most pixels. The structural change model provides more robust statistics on the significant minority of pixels with non-monotonic trends, while detrending some interannual signal potentially superfluous from a long-term perspective. Although break point density within one year of fire and vegetation treatments was ~10× and ~4× that of unburned areas, respectively, break point detection in the correct year of fire was only moderately accurate. Climate responses in break points proved more robust, with strong spatiotemporal relation in break point density with both aridity index values and aridity index change. Break point density strongly responds to both increased and decreased aridity and is reflective of ecosystem resilience. Data provide spatiotemporal information on the occurrence of breaks, but even more importantly, attribute those change events to specific component(s).

Arizona, California, Colorado, Idaho, Kansas, Mont