U.S. Geological Survey
Matthew Rigge
Brett Bunde
Kory Postma
Hua Shi
20221130
Rangeland Condition Monitoring Assessment and Projection (RCMAP) Tree Cover Fractional Component Time-Series Across the Western U.S. 1985-2021
Raster Digital Data Set
Earth Resources Observation and Science Center, Sioux Falls, SD
U.S. Geological Survey
Rigge, M., H. Shi, C. Homer, P. Danielson, and B. Granneman. 2019. Long-term trajectories of fractional component change in the Northern Great Basin, USA. Ecosphere: e02762.
Rigge, M., C. Homer, L. Cleeves, D. Meyer, B. Bunde, H. Shi, G. Xian, and M. Bobo. 2020. Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data. Remote Sensing. 12: 412.
Rigge, M., Homer, C., Shi, H., Meyer, D., Bunde, B., Granneman, B., Postma, K., Danielson, P., Case, A., and Xian, G. 2021. Rangeland Fractional Components Across the Western United States from 1985 to 2018. Remote Sensing, 13: 813.
https://www.mrlc.gov/data
https://doi.org/10.5066/P9ODAZHC
Matthew Rigge
Hua Shi
Kory Postma
Brett Bunde
20220805
Trends analysis of rangeland condition monitoring assessment and projection (RCMAP) fractional component time series (1985–2020)
Publication (Journal)
GIScience & Remote Sensing
59:1, 1243-1265
https://doi.org/10.1080/15481603.2022.2104786
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. We have incorporated all available (as of 10/1/22) Bureau of Land Management (BLM), Assessment, Inventory, and Monitoring (AIM), and Landscape Monitoring Framework (LMF) observations. LANDFIRE public reference database training observations spanning 1985-2015 have been added. Neural network models with Keras tuner optimization have replaced Cubist models as our classifier. We have added a tree canopy cover component. Our study area has expanded to include all of California, Oregon, and Washington; in prior generations landscapes to the west of the Cascades were excluded. Additional spectral indices have been added as predictor variables, tasseled cap wetness, brightness, and greenness. Location information (i.e., latitude and longitude/ x and y coordinates) and elevation above sea level have been added as predictor variables. CCDC-Synthetic Landsat images were obtained for 6 monthly periods for each region and were added as predictors. These data augment the phenologic detail of the 2 seasonal Landsat composites.
Post-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Maestas and Campbell 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on (Arkle et al (in press)) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish maximum sage, shrub, and tree cover by time-since fire. Recovery limits in California follow (Keeley and Keeley 1981 and Storey et al. 2016). Second, post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values.
Processing efficiency has been increased using open-source software and USGS High-Performance Computing (HPC) resources. The mapping area included eight regions which were subsequently mosaicked for all nine components. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices.
Component products can be downloaded https://www.mrlc.gov/data.
The goal of this project is to provide a dense Landsat imagery time series of rangeland fractional components across the Western U.S. from 1985 to 2021. These data will provide an inventory of land cover validated products with estimates of precision for the western rangelands. Climate change, shifting fire regimes, and management practices are increasingly impacting the health of the ecosystem. This dataset will fill the need for improved monitoring change within the large rangeland habitat.
Components are defined as:
Bare Ground is a continuous field component including exposed soil, sand and rocks.
Annual Herbaceous is a continuous field component including grasses and forbs whose life history is complete in one growing season. This component is primarily dominated by annual invasive species including Cheatgrass (Bromus tectorum), Medusahead (Taeniatherum caput-medusae), Red Brome (Bromus rebens), or annual mustards such as Tumble Mustard (Sisymbrium altissimum) and Tansy Mustard (Descurainia pinnata), but it may contain substantial native annual herbaceous vegetation at higher elevations and in California. This component is nested within Herbaceous as a secondary component.
Herbaceous is a continuous field component consisting of grasses, forbs and cacti which were photosynthetically active at any point in the year of mapping.
Non-sagebrush shrub is a continuous field component encompassing all shrub species not of the sagebrush (Artemisia spp.) genus. Shrubs, in general, are discriminated by the presence of woody stems and < 6-m in height.
Perennial herbaceous is a continuous field component consisting of grasses, forbs and cacti which were photosynthetically active at any point in the year of mapping and whose lifecycle includes more than one growing season (includes biennials).
Litter is a continuous field component including dead standing woody vegetation, detached plant organic matter and biological soil crusts.
Sagebrush is a continuous field component encompassing almost all species of Sagebrush (Artemisia spp.) including Big Sagebrush (A. tridentata spp.), Low Sagebrush (A. arbuscula), Black Sagebrush (A. nova), Three-tip Sagebrush (A. triparta) and Silver Sagebrush (A. cana). This component is nested within Shrub as a secondary component. Excludes the low stature prairie sage (A. frigida) and white sagebrush (A. ludoviciana).
Shrub is a continuous field component encompassing all shrub species discriminated by the presence of woody stems and < 6-m in height.
Tree cover is defined as vegetation with persistent woody stems > 6m in height. Mature stand of pinyon (Pinus spp. and juniper (Juniperus spp.) are included regardless of height.
Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in Esri format, this metadata file may include some Esri-specific terminology.
19850101
20221231
publication date
As needed
-128.0026
-99.6407
51.5777
26.4827
ISO 19115 Topic Category
biota
environment
geoscientificInformation
imageryBaseMapsEarthCover
USGS Thesaurus
shrubland ecosystems
terrestrial ecosystems
Alexandria Digital Library Feature Type Thesaurus
shrublands
time series
back-in-time
trends
grassland change
shrubland change
vegetation change
climate change
rangeland management
None
shrub
sagebrush
big sagebrush
herbaceous
annual herbaceous
litter
grass
vegetation
bare ground
rangeland
shrubland
Common geographic areas
United States
Washington
Oregon
Montana
North Dakota
South Dakota
Wyoming
Idaho
Nebraska
Nevada
Utah
California
Colorado
Arizona
Texas
New Mexico
Plateau
Desert
Northern Great Salt Lake Desert
Southern Great Salt Lake Desert
North Plains
Plains
The Rockies
None
NM
TX
AZ
CO
CA
UT
NV
NE
ID
WY
SD
ND
MT
OR
WA
Great Basin
Arizona Plateau
Black Hills
Blue Mountains
Chihuahuan Desert
Colorado Plateau
Columbia Plateau
Grand Canyon
Middle Rockies
Rocky Mountains
Gunnison
Sonoran Desert
Southwest Tablelands
Three Forks
Wasatch
Western US
Yellowstone
Northern Mountainous
Mediterranean California
Northern Great Plains
Northern Rocky Mountains
Wyoming Basin
Mojave
Sonoran
Chihuahuan
Southern Rocky Mountains
Sierra Nevada
mts
Any downloading and use of these data signifies a user's agreement to comprehension and compliance of the USGS Standard Disclaimer. Ensure all portions of metadata are read and clearly understood before using these data in order to protect both user and USGS interests.
There is no guarantee of warranty concerning the accuracy of the data. Users should be aware that these data were developed from models which can contain some local error. Users should not use these data for critical applications without a full awareness of their limitations. Acknowledgement of the originating agencies would be appreciated in products derived from these data. Any user who modifies these data is obligated to describe the types of modifications they perform. User specifically agrees not to misrepresent the data, nor to imply that changes made were approved or endorsed by the U.S. Geological Survey. Please refer to https://www.usgs.gov/privacy.html for the USGS disclaimer.
U.S. Geological Survey
Customer Services Representative
mailing
47914 252nd Street
Sioux Falls
SD
57198-0001
U.S.
605-594-6151
605-594-6589
custserv@usgs.gov
U.S. Geological Survey (USGS)
Bureau of Land Management (BLM)
Environment as of Metadata Creation: Microsoft [Unknown] Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.1 (Build 9270) Service Pack N/A (Build N/A)
Jeremy D. Maestas
Steven B. Campbell
Jeanne C. Chambers
Mike Pellant
Richard F. Miller
201606
Maestas and Campbell 2016 - Tapping Soil Survey Information for Rapid Assessment of Sagebrush Ecosystem Resilience and Resistance
publication
https://www.sciencedirect.com/science/article/pii/S0190052816000109?via%3Dihub
https://doi.org/10.1016/j.rala.2016.02.002
Jon E. Keeley
Sterling C. Keeley
19810401
Keeley and Keeley 1981 - POST-FIRE REGENERATION OF SOUTHERN CALIFORNIA CHAPARRAL
publication
https://bsapubs.onlinelibrary.wiley.com/doi/abs/10.1002/j.1537-2197.1981.tb07796.x
https://doi.org/10.1002/j.1537-2197.1981.tb07796.x
Emanuel A. Storey
Douglas A. Stow
John F. O'Leary
20160915
Storey et al. 2016 - Assessing postfire recovery of chamise chaparral using multi-temporal spectral vegetation index trajectories derived from Landsat imagery
publication
https://www.sciencedirect.com/science/article/pii/S0034425716302176
https://doi.org/10.1016/j.rse.2016.05.018
Maps are rigorously validated using field data not included as training (i.e., independent) with data from long-term monitoring sites, and by assessing model fit to training data. Our independent data consists of 1) 1,880 points, each specifically designed to represent a single Landsat pixel, collected from 2013-2020, and 2) long-term monitoring data in southwest Wyoming at 126 plots observed 12 times between 2008 and 2021. The spatial-temporal correlation (n = 1,137) across all 126 plots showed robust correlations for all components, with an R2 of 0.62 for bare ground (RMSE = 11.7%) and 0.47 for shrub cover (RMSE = 8.0%) and average R2 of 0.42 and RMSE of 8.93 across components.
Next, we compared RCMAP data to independent validation sites (n = 1,880) collected from 2013-2020. Correlations between RCMAP and independent validation sites were again robust across all components, with an R2 of 0.75 for bare ground (RMSE = 13.5%) and 0.40 for shrub cover (RMSE = 10.3%) and average R2 of 0.53 and RMSE of 10.4 across components.
Pooling all independent data (long-term monitoring plus independent sites, total n = 3,017): Bare Ground - R2 0.74, RMSE 12.9, Herbaceous - R2 0.66, RMSE 11.5, Litter - R2 0.38, RMSE 8.5, Shrub - R2 0.40, RMSE 9.6, Sagebrush - R2 0.38, RMSE 7.2, Annual Herbaceous - R2 0.56, RMSE 10.1.
We compared RCMAP data to BLM AIM and LMF data (n = 45,132) collected between 2004 and 2021. Correlations between RCMAP and AIM/LMF data were again robust across all components, with an R2 of 0.60 for bare ground, R2 of 0.66 for tree, and 0.35 for shrub cover and average R2 of 0.38 and RMSE of 14.3 across components. Bare Ground - R2 0.60, RMSE 24.4, Herbaceous - R2 0.56, RMSE 21.7, Litter - R2 0.03, RMSE 12.0, Shrub - R2 0.35, RMSE 11.0, Sagebrush - R2 0.42, RMSE 8.5, Annual Herbaceous - R2 0.30, RMSE 15.1, Tree - R2 0.66, RMSE 7.4.
Finally, we conducted a cross-validation of predictions against training data at high-resolution training sites using a random sample of 100,000 points. Cross-validation correlations included an R2 of 0.89 for bare ground (RMSE = 9.7%) and 0.66 for shrub cover (RMSE = 8.3%) and average R2 of 0.76 and RMSE of 7.3 across components. Bare Ground - R2 0.89, RMSE 9.7, Herbaceous - R2 0.82, RMSE 7.9, Litter - R2 0.70, RMSE 5.5, Shrub - R2 0.66, RMSE 8.3, Sagebrush - R2 0.66, RMSE 4.1, Annual Herbaceous - R2 0.75, RMSE 5.4, Tree - R2 0.83, RMSE 9.7.
Changes observed in both the field and RCMAP data were typically gradual, within-state, changes which are most difficult to resolve, which were often successfully captured. It is important to consider that all accuracy assessments described above are designed to evaluate single-pixel level correspondence. Due to fine-scale landscape heterogeneity this is the most rigorous approach, and most applications looking at broader spatial scales would tend to lower error relative to this analysis.
The methods employed to map fractional vegetation components in rangeland ecosystems in the Western U.S. include modelling rangelands as a series of independent continuous field components from 1985 – 2021 and acquiring multiple seasons (leafon and leafoff) for eight regions of 30-meter Landsat imagery for large-area modelling using neural network classifiers.
This fractional estimation of nine rangeland habitat variables ranging from 1985 - 2021 in the Western U.S. is the version dated 20221130. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
A formal accuracy assessment of the horizontal positional information in the data set has not been conducted.
A formal accuracy assessment of the vertical positional information in the data set has either not been conducted or is not applicable.
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, and the temporal trends of each. The five primary components, bare ground, shrub, litter, herbaceous, and tree are designed to sum to 100% in each pixel. The secondary components of annual herbaceous and perennial herbaceous and non-sagebrush shrub and sagebrush are subsets of the primary components herbaceous and shrub, respectively. Processing occurred in seven regions which were subsequently mosaicked across the study area.
The eight regions:
Mediterranean California, Great Basin and Columbia Plateau, Northern Rocky Mountains, Northern Great Plains and Wyoming Basin, Warm Deserts (Mojave, Sonoran, and Chihuahuan), Southern Rocky Mountains and Sierra Nevada, Colorado Plateau and Southwest Tablelands, and Pacific Northwest.
20210601
Image Processing -- Landsat data were ARD pre-processed bundles of Landsat Collection-1 Level-2 archived Landsat data which reduces the time of data processing for time-series analysis. These data were tiled, geo-registered, and atmospherically corrected surface reflectance (SR) values. We selected two different seasons (leaf-on and leaf-off) for each region to enhance change detection and discrimination among components. For each season and year in each region, we used an approach to generate “best pixel” composites. We looked at all Landsat 5 and 8, and 1998-2003 Landsat 7 (pre-slc-off) pixels within the given date range of each year, excluding pixels identified by the QA mask and those where the summation of six evaluated bands (red, green, blue, NIR, SWIR 1 and SWIR 2) had a sum < 1600. Pixels with a low sum in the bands were overwhelmingly shadows missed by the QA mask. The median of the remaining pixels was calculated and the pixel closest to the median value was applied. If any pixel didn’t fit the thresholds for “best pixels” it was added to the fmask (cloud/shadow/water mask). 2012 composites were generated from the 2012 monthly CCDC-synthetic data (described in following paragraph). For the 2016 base year composites we followed the same protocol unless no pixel in the seasonal period passed the thresholds described above (QA mask and band summation). In these cases, we enlarged the date window to a 3-year period (2015- 2017) within the seasonal date windows. In a very few pixels, where no clear images were available with either approach, we further extended the date window to include 2014 and 2018. Regional composites for each seasonal period and year were generated using EROS HPC resources. Landsat 5 and 7 TM utilized Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) Surface Reflectance Code (LaSRC v1.2.0). Landsat 8 OLI/TIRS applied LaSRC v1.2.0. Collection-1 - Earth Resources Observation and Science (EROS) Science Processing Architecture (ESPA) for Level-2 - Quality assessment Tools L2QA_TOOLS v 1.2.0.
Noise in the composites related to clouds/shadows/snow/phenological change was dramatically reduced by applying a secondary strategy. Specifically, for each leaf on and leaf off composite, we calculated the number of observations available to calculate the median observation for each pixel. Pixels with 3 or less observations per pixel available tended to be associated with image artifacts, while those with more than 3 observations were clean. In those pixels with 3 or less observations we considered the corresponding months of CCDC synthetic observations in the calculation of the median value while maintaining the original median value elsewhere. This procedure removed nearly all image artifacts, resulting in a cleaner and more accurate productions process.
Imagery dates for leaf-on and leaf-off composites by region -- Mediterranean California - Leaf-on Dec 01 – May 30. Leaf-off June 01 – Oct 01, Great Basin and Columbia Plateau - Leaf-on March 15 – June 15, Leaf-off July 01 – Nov 01, Northern Rocky Mountains - Leaf-on June 15 – Aug 25, Leaf-off April 15 – May 15, Sept 01 – Oct 15, Northern Great Plains and Wyoming Basin - Leaf-on April 01 – June 30, Leaf-off July 15 – Nov 01, Mojave, Sonoran, and Chihuahuan Deserts - Leaf-on July 20 – Oct 15, Leaf-off March 01 – July 01, Southern Rocky Mountains and Sierra Nevada - Leaf-on June 01 – Aug 15, Leaf-off April 01 – May 01, Sept 01 – Nov 01, Colorado Plateau and Southwest Tablelands - Leaf-on June 01 – Aug 30, Leaf-off Sept 15 – Dec 01, Pacific Northwest – Leaf-on – May 01- July 20 , Leaf-off Sept 15- Dec. 01.
Five to six months of CCDC-synthetic Landsat data were added to augment the phenologic profile of each year, aiding in the discrimination among components. CCDC-synthetic dates by region; Mediterranean California – Jan., Feb., Apr., Jun., Sep., Great Basin and Columbia Plateau – Mar., Apr., May, Jun., Sep., Oct., Northern Rocky Mountains – Jun., Jul., Aug., Sep., Oct., Northern Great Plains and Wyoming Basin – Apr., May., Jun., Jul., Sep., Mojave, Sonoran, and Chihuahuan Deserts – Jan., Mar., Apr. Jun., Aug., Sep., Southern Rocky Mountains and Sierra Nevada – Jun., Jul., Aug., Sep., Oct., Colorado Plateau and Southwest Tablelands – Jun., Jul., Aug., Sep., Nov., Pacific Northwest –Mar, May, Jun., Jul. Oct.
20210130
Ancillary Data -- In addition to Landsat composites and CCDC-synthetic imagery, we included several ancillary data layers as independent variables in the regression modelling of fractional components. 1) Topographic data; slope, aspect, elevation, and position index. In the current generation we used a new version of aspect data, processed into Cartesian (x, y) coordinates. 2) For each Landsat composite, seven spectral indices were calculated, the Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), and tasseled cap wetness, brightness, and greenness. 3) Geographic location information (i.e., latitude and longitude/ x and y coordinates). 4) Water bodies occurring at any point during the time-series were detected using the NDWI. Areas identified as agriculture or water at any point in the time-series were excluded from the training data pool and included in the land cover mask. 5) The residual QA extent in each composite was converted to an fmask equivalent. The leaf-on and leaf-off fmask equivalents were stacked across all target years. 5) Monitoring Trends in Burn Severity (MTBS) and GeoMac burn data were used to produce a suite of burned area products.
20210408
Land Cover Masking -- A land cover mask based on pixels identified as non-rangeland in the circa 2016 base year (Rigge et al. 2020) was applied during the process and to the products. The non-rangeland mask includes urban areas, major roads, snow, and ice identified in NLCD 2019 land cover classes in addition to cultivated crops and water. Cultivated croplands were masked using a combination of the 2013 Cropland Data Layer from the National Agricultural Statistics Service and NLCD 2019 classes. Open water areas were identified using Normalized Difference Water Index (NDWI) thresholds. NLCD hay/pasture and forest areas masked in prior generations of RCMAP data are not masked in the current generation of products.
20210416
Normalization -- An approach was developed to normalize target year composites to the corresponding base year seasonal composite. Linear regression models were fit for the spatial relationship between band x of a given leaf-on/leaf-off target year composite and band x of the leaf-on/leaf-off composite from the base year. Pixels with cloud, shadow, or snow cover and those that were recently burned were removed from the normalization regressions. This procedure was completed for all 6 spectral bands and for both seasons. The regression parameters of slope (m), y-intercept (b), and correlation (R²) were used to adjust the spectral band values of the target year composite, so that spatial mean matched that of the base year. Normalization serves to minimize differences related to phenological variability, sun angle, and atmospheric contamination. Additionally, it helps to stabilize any difference related to Landsat sensor. The normalized composites were only used for identifying change pixels, with non-normalized composites reserved for the component predictions.
20210317
Change detection -- Each normalized target and base composite pair were compared using a Change Vector (CV) approach, finding differences in spectral values by band. Spectral differences were summed across bands to produce a change vector magnitude value for each composite pair. If a pixel had a change vector magnitude beyond a standard deviation threshold in a given target year composite for a given NLCD 2016 land cover class, it was flagged as changed (Rigge et al. 2019). Two out of two season agreement in change was needed for a pixel to be flagged as changed between a target and base year. The requirement for two season agreement was critical to remove cloud, shadow, and haze influence missed by the QA bands. Next, we used an approach which considers the range of CV values through time at each pixel. The CV magnitude range represents the extent of variability through time at each pixel, those pixels with stable conditions have a low value, while those with disturbance/change have a high value. We determine how each yearly CV magnitude compares to the range, allowing us to define the degree of confidence in change. We define this metric as Change Fraction (CF). The CF considers three weighted attributes in change detection. First, the seasonal CV magnitude score for a given composite in relation to the CV magnitude range per pixel. This gives a high value to pixels with a high amount of spectral change relative to the total range of variation in the time-series. Second, the CV magnitude range per pixel is considered, relative to the maximum in each region. This gives a high value to pixels with a high total variation in the time-series relative to those pixels with lower variation through time. Third, the CV magnitude per pixel relative to the maximum CV magnitude in each region, each year. This gives a high value to pixels with a high amount of spectral change relative to the maximum change in that composite. The total score for each season is calculated and the maximum of the two seasons is given as the final CF. Values of CF ranged from 0 (high confidence unchanged) to 100 (high confidence changed). In the current version, we have revised the weighting of the three attributes described above by giving more weight to the first attribute: the CV magnitude score for a given composite in relation to the CV magnitude range per pixel. Pixels with a high amount of difference in CF score between seasons in a year are often indicative of haze/atmospheric contamination (in addition to real change). Specifically, the criteria for change were as follows: CV = 1 and CF >29 or CV = 0 and CF >60, (CV of 1 = change detected and 0 = no change detected). If either scenario was satisfied, then the pixel was flagged as change in that target year. Determination of change in pixels with cloud cover in one season was based on the CV and CF values from the non-cloudy season. This procedure gave a time-series of changed/unchanged areas which allow changed areas to be labeled with the appropriate fractional component cover and unchanged areas to be used to train target year predictions.
20210325
Training -- Within 331 high-resolution training sites, we sampled all available 30-m scale degraded predictions of component cover. To these points, we extracted the dependent and independent variables, in addition to the SAVI temporal mean per pixel. Independent variables were slope, aspect-x, aspect-y, elevation, x/y coordinates, topographic position index, non-normalized Landsat composites, spectral indices (SAVI, NDWI, NDBI, tasseled cap wetness, brightness, and greenness of each Landsat composite, and 5-6 months of CCDC-synthetic imagery. Next, we appended all yearly sampled data files per component into a combined spatio-temporal database. We applied two procedures to the training database to improve dynamic range through space and time. First, to improve the dynamic range through space, we randomly removed half of the observations within 1 standard deviation of the mean for each component (Wylie et al. 2018). Next, to improve temporal dynamic range, we compared the ratio of the yearly leaf-on SAVI conditions to the time-series mean. Wet years had values over 100, and dry years lower than 100. We again removed half of the observations within 1 standard deviation of the mean. The overall objective was to stretch out component histograms through space and time. Using these data, we made preliminary predictions of component cover and conducted a cross-validation of the results relative to training data. We corrected for bias (slope and y-intercept) in the relationships by adjusting training data values. For example, if the cross-validation relationship was: y=original training value × 1/slope – intercept/slope. The objective of this approach was to bias training data values to offset that bias introduced in RT models, with unique adjustment factors by component and region. Cross-validation of predictions made with such adjusted training data, compared to original training data, confirmed that this approach produces prediction with a slope nearer to 1 and y-intercept nearer to 0. Finally, we summed the primary components, and if necessary, we adjusted component cover values so the summation was 100%, but the proportions of each component remained intact. Following these procedures, we placed 30,000 points in recently burned areas to which we assigned a dependent variable value of 0% cover. We then appended those observations to train burned areas in the shrub and sagebrush components. Next, we obtained AIM and LMF plot data (total n = 45,970 from 2011-2021. AIM and LMF plot locations were converted to a 3-by-3 grid of 30-m pixels, to correspond with the area sampled. We extracted the independent variables and spectral values of the Landsat composites from the appropriate year (i.e., 2011 composite values extracted to AIM and LMF plots collected in 2011). Next, we assessed the spectral variability within the 3-by-3 grid of pixels, removing those with spectral conditions significantly different from the overall 9 pixel mean. Following this procedure, a total of 364,910 pixels of AIM and LMF data were added to our training data pool.
20210402
Component Prediction -- Using the spatio-temporal training pool with the adjusted base values we developed a neural network model used to predict all components, using the component values as the dependent variable, and the full set of independent variables. We developed neural network models for the spatio-temporal training dataset but applied the model to data from each target year to produce component cover predictions. A second set of predictions was run for shrub, sagebrush, and tree using training data that included recently burned areas. This burned area prediction for shrub, sagebrush, and tree was inserted in pixels that burned in recently burned areas, while the standard prediction was kept elsewhere.
20210416
Change Labeling -- We used CF data to determine if pixels should be labeled with the target year estimate or if the base value should be maintained. Specifically, the criteria for change were CV = 1 and CF >29 or CV = 0 and CF >60. We limited component cover change in four situations. First, if component change between a given target and base year was greater than defined thresholds described in (Rigge et al. 2019), we lowered it to the limit. Next, we summed the primary fractional components and tree canopy cover, and if necessary, we adjusted component cover values, so the summation was 100% while maintaining the proportions of each component. Third, we identified areas in which most pixels in a 5 by 5 moving window had +/- 1% component cover change between the base and target year. Similarly, we identified areas in which most pixels had no change, and less than 20% had +/- 1% component cover change. Since the change in both above scenarios was likely to be noise, we inserted the base year fractional component cover in these changed pixels, while the target year estimate was maintained in the remaining area. Fourth, we determined the total number of fractional components with change in each target year. If only 1 out of 6 fractional components in a pixel had change, the base year fractional component cover was maintained for all fractional components. The logic was that if one fractional component changes, then other component(s) should change in response. Finally, we ensured that fractional component relationships were intact (shrub > sagebrush, herbaceous > annual herbaceous). If not, we forced the relationships to be intact.
20210417
Post Processing -- Post-processing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience (R and R) classes (Campbell and Maestas 2016) to stratify recovery rates. Ecosystem R and R maps are only available for the sagebrush biome. We intersected classes with 1985-2020 average water year precipitation to identify precipitation thresholds corresponding to R and R classes. Outside of the sagebrush biome, precipitation was used to produce R and R equivalent (low, medium, high). Due to the fast recovery following fire in California chapparal (e.g., Keeley and Keeley 1981, Storey et al. 2016), we used EPA level 3 ecoregions to define a 4th R and R zone. Recovery rates are based on Arkle et al. (in press) who evaluated the recovery of plant functional groups in 1278 post-fire rehab plots by time since disturbance stratified by ecosystem resistance and resilience. We have expanded this analysis by evaluated postfire-recovery in all AIM and LMF data across the West to establish second order polynomial equations defining maximum sage, shrub, and tree cover by time-since fire by R and R class. Recovery limits in California follow Keeley and Keeley 1981 and Storey et al. 2016. Fire correction percent cover limits (y) for sagebrush by each R and R zone were; zone 1) (high R and R) y = 0.0332x2 – 0.1866x + 3.9794, zone 2) (medium R and R) y = 0.0234x2 – 0.0988x + 1.9323, zone 3) (low R and R) y = 0.00074x2 – 0.1535x + 0.676, zone 4) (California chapparal) y = 0.0332x2 – 0.1866x + 3.9794, where x is the number of years since most recent fire. Fire correction percent cover limits (y) for shrub and tree by each R and R zone were; zone 1) (high R and R) y = 0.0019x2 + 1.611x + 3.564, zone 2) (medium R and R) y = 0.0141x2 + 0.5558x + 2.540, zone 3) (low R and R) y = -0.0011x2 + 0.5891x + 1.1267, zone 4) (California chapparal) y = 0.6.7857x2 – 7.6429x + 20.571, where x is the number of years since most recent fire.
Post-processing has been enhanced through a revised noise detection model. For each pixel, we fit a third order polynomial model for each component cover time-series. Observations with a z-score more than 2 standard deviations from the mean are removed, and a new third order polynomial model (i.e., cleaned fit) is fit to observations within this threshold. Finally, looking again at all observations, those observations with a z-score more than 2 standard deviations from the mean of the cleaned fit are replaced with the mean of the prior and subsequent year component cover values. These noise removal procedures were implemented in pixels that did not burn during the time-series, and in pre-burn years in pixels that did burn during the time-series.
20210421
Mosaicking -- The final step was to mosaic data from the eight regions, which we accomplished using ERDAS Imagine software. Regions were designed with a 1.5 km overlap; we used a template to delineate seamlines within this overlap. After this processing we did a visual inspection and addressed any seamlines or data voids among the mapping regions. The secondary components were reconciled to the primary components for sagebrush and shrub, and annual herbaceous to herbaceous. Next, we calculated the perennial herbaceous and non-sagebrush shrub components by subtracting annual herbaceous from herbaceous cover and sagebrush from shrub cover, respectively. Land cover masking and areas outside the mapping region were assigned as a no-data value (i.e., null).
20210504
Trends -- We assess the temporal patterns in each RCMAP component with two approaches, 1) linear trends and 2) a breaks and stable states method with an 8-year temporal moving window based on structural change at the pixel level. Linear trend products include slope and p-value calculated from least squares linear regression. The slope represents the average percent cover change per year over the times-series and the p-value reflects the confidence of change in each pixel. The structural change method partitions the time-series into segments of similar slope values, with statistically significant break-points indicating perturbations to the prior trajectory. The break point trends analysis suite relies on structural break methods, resulting in the identification of the number and timing of breaks in the time-series, and the significance of each segment. We produce the following statistics: 1) for each component, each year, the presence/absence of breaks, 2) the slope, p-value, and standard error of the segment occurring in each year, 3) the overall model R2 (quality of model fit to the temporal profile), and 4) an index, Total Change Intensity. This index reflects the total amount of change occurring across components in that pixel. 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. These data will be part of a separate data release.
20210507
Raster
Grid Cell
83371
66950
1
Albers Conical Equal Area
29.5
45.5
-96.0
23.0
0.0
0.0
row and column
30.0
30.0
meters
WGS_1984
WGS_84
6378137.0
298.257223563
Attribute Table
Table containing attribute information associated with the data set.
Producer defined
Value
Column for value indicating per-pixel percent for bare ground, shrub, herbaceous, litter, sagebrush, annual herbaceous, non-sagebrush shrub and perennial herbaceous components range from 0 to 100, The Error Map contains a column for value indicating per-pixel absolute error in percentage.
Producer defined
0
100
Percent
Count
All raster attribute tables include a column for count describing a nominal integer value that designates the number of pixels that have each value in the file; histogram column in ERDAS Imagine raster attributes table.
Producer defined
0
125551462
Integer
Red
Red color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
Producer defined
0
100
Percent
Green
Green color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
Producer defined
0
100
Percent
Blue
Blue color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
Producer defined
0
100
Percent
Opacity
A measure of how opaque, or solid, a color is displayed in a layer.
Producer defined
0
100
Percent
The entity and attribute information provided here describes the tabular data associated with the data set. Please review the detailed descriptions that are provided (the individual attribute descriptions) for information on the values that appear as fields/table entries of the data set.
The entity and attribute information was generated by the individual and/or agency identified as the originator of the data set. Please review the rest of the metadata record for additional details and information.
U.S. Geological Survey
GS ScienceBase
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Raster Digital Data Set
https://doi.org/10.5066/P9ODAZHC
None
20221202
U.S. Geological Survey
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SD
57198-0001
U.S.
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FGDC-STD-001-1998