Geology topics

Brett Bunde

Publications and source records attributed to Brett Bunde.

10 recordsLinked to original sources

USGS2025

Spatial scale dependence of error in fractional component cover maps

Geospatial products such as fractional vegetation cover maps often report overall, pixel-wise accuracy, but decision-making with these products often occurs at coarser scales. As such, data users often desire guidance on the appropriate spatial scale to apply these data. We worked toward establishing this guidance by assessing RCMAP (Rangeland Condition Monitoring Assessment and Projection) accuracy relative to a series of high-resolution predictions of component cover. We scale the 2-m and RCMAP predictions to various focal window sizes scales ranging from 30 to 1 500 m using focal averaging. We also evaluated variation in scaling effects on error at ecoregion and pasture (mean area of 1 050 ha) scales. Our results demonstrate increased accuracy at broader windows, across all components, and most increases in accuracy level off at ∼200–600 m scales. At the scale with highest accuracy, cross-component average correlation ( r ) increased by 6.5%, and root mean square error (RMSE) was reduced 46.4% relative to 30-m scale data. Scaling-related improvements to accuracy were greatest in components such as shrub and tree with more spatially heterogeneous cover and in ecoregions with more spatially heterogenous cover. When components were aggregated at the pasture scale, r increased 10% and RMSE decreased 34.3% on average relative to the 30-m scale. Our results provide empirical data on the scale dependence of error, which fractional cover data users may consider alongside their needs when using these data. Although the general principle remains that remotely sensed products are intended to address landscape-scale questions, our analysis indicates that applying data at finer than landscape spatial scales and grouping even a handful of pixels resulted in lowered error compared to pixel-level comparisons. Our results quantify the trade-offs between data granularity and error related to scale for fractional vegetation cover.

western United States↗

USGS2025

Correspondence between satellite-derived and long-term field observations of vegetation cover at Great Basin experimental treatments.

Vegetation treatments are frequently utilized in Western US rangelands to reduce woody plant cover in sagebrush stands threatened by increased wildfire risk and in pinyon-juniper woodlands expanding into formerly high-value sagebrush habitats. Despite widespread use of these treatments, monitoring data to evaluate long-term vegetation responses are often insufficient or absent. Long-term field experiments and remote-sensing based vegetation data may be complementary for assessing treatment effectiveness across temporal and spatial scales. The SageSTEP project experimentally implemented treatments at numerous sites across the Intermountain West and monitored the subsequent response of vegetation cover components with 15+ yr of field observations. However, while pretreatment data were collected in the year of implementation, long-term observations of pretreatment vegetation conditions are lacking in the SageSTEP database. Remote-sensing based time-series maps (1985–2023) of vegetation cover from Rangeland Condition Monitoring Assessment and Projection (RCMAP) could fill temporal gaps in monitoring data and scale findings across broader extents. We evaluate the relationship between pretreatment vegetation cover in the RCMAP data and the post-treatment response in both the RCMAP and field observations. Additionally, we explore the correspondence between SageSTEP field observations and RCMAP at various scales, and examine key factors related to the strength of relationships. Overall, SageSTEP and RCMAP data show a similar direction of treatment effect for each component, and to a lesser extent the magnitude of effect. SageSTEP and RCMAP data tended to agree most strongly where treatment effects were strong; when averaged across broader spatial scales; and for components such as tree and bare ground that are more easily distinguished spectrally. Remote sensing tools such as RCMAP, in combination with field-based climate and vegetation observations, can help assess postdisturbance recovery trajectories and facilitate regional decision-making around treatment alternatives, fire risk reduction, and protection of critical habitats.

Great Basin↗

USGS2024

Application of normalized radar backscatter and hyperspectral data to augment rangeland vegetation fractional classification

Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project provides fractional vegetation cover maps across western North America using Landsat imagery and artificial intelligence from 1985 to 2023 at yearly time-steps. The objectives of this case study are to apply hyperspectral data from several new data streams, including Sentinel Synthetic Aperture Radar (SAR) and Earth Surface Mineral Dust Source Investigation (EMIT), to the RCMAP model . We run a series of five tests (Landsat-base model, base + SAR, base + EMIT, base + SAR + EMIT, and base + Landsat NEXT [LNEXT] synthesized from EMIT) over a difficult-to-classify region centered in southwest Montana, USA. Our testing results indicate a clear accuracy benefit of adding SAR and EMIT data to the RCMAP model, with a 7.5% and 29% relative increase in independent accuracy ( R 2 ), respectively. The ability of SAR data to observe vegetation height allows for more accurate classification of vegetation types, whereas EMIT’s continuous characterization of the spectral response boosts discriminatory power relative to multispectral data. Our spectral profile analysis reveals the enhanced classification power with EMIT is related to both the improved spectral resolution and representation of the entire domain as compared to legacy Landsat. One key finding is that legacy Landsat bands largely miss portions of the electromagnetic spectrum where separation among important rangeland targets exists, namely in the 900–1250 nm and 1500–1780 nm range. Synthesized LNEXT data include these gaps, but the reduced spectral resolution compared to EMIT results in an intermediate 18% increase in accuracy relative to the base run. Here, we show the promise of enhanced classification accuracy using EMIT data, and to a smaller extent, SAR.

Idaho, Montana, Wyoming↗

USGS2022

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↗

USGS2021

Rangeland fractional components across the western United States from 1985 to 2018

Monitoring temporal dynamics of rangelands to detect and understand change in vegetation cover and composition provides a wealth of information to improve management and sustainability. Remote sensing allows the evaluation of both abrupt and gradual rangeland change at unprecedented spatial and temporal extents. Here, we describe the production of the National Land Cover Database (NLCD) Back in Time (BIT) dataset which quantified the percent cover of rangeland components (bare ground, herbaceous, annual herbaceous, litter, shrub, and sagebrush ( Artemisia spp. Nutt.) across the western United States using Landsat imagery from 1985 to 2018. We evaluate the relationships of component trends with climate drivers at an ecoregion scale, describe the nature of landscape change, and demonstrate several case studies related to changes in grazing management, prescribed burns, and vegetation treatments. Our results showed the net cover of shrub, sagebrush, and litter significantly ( p < 0.01) decreased, bare ground and herbaceous cover had no significant change, and annual herbaceous cover significantly ( p < 0.05) increased. Change was ubiquitous, with a mean of 92% of pixels with some change and 38% of pixels with significant change ( p < 0.10). However, most change was gradual, well over half of pixels have a range of less than 10%, and most change occurred outside of known disturbances. The BIT data facilitate a comprehensive assessment of rangeland condition, evaluation of past management actions, understanding of system variability, and opportunities for future planning.

Arizona, California, Colorado, Idaho, Kansas, Mont↗

USGS2021

Ecological potential fractional component cover based on Long-Term satellite observations across the western United States

Rangelands have immense inherent spatial and temporal variability, yet land condition and trends are often assessed at a limited number of spatially “representative” points. Spatially comprehensive, and quantitative, Ecological Potential (EP) data provide a baseline for comparison to current rangeland vegetation conditions and trends. Here, we define EP as potential fractional cover (bare ground, herbaceous, litter, shrub, and sagebrush) represented in the least disturbed areas and most productive years of the Landsat satellite archive (1985-present) for each 30-m pixel. We produce EP maps across rangelands in the western United States by training regression tree models using Rangeland Condition Monitoring Assessment and Projection (RCMAP) time-series fractional cover maps in ecologically intact sites (with limited annual herbaceous cover, no recent disturbance or vegetation treatment, and less bare ground cover than expected). As independent predictor variables in these models, we use digital soils and topography data and six bimonthly composites of the 90th percentile of Normalized Difference Vegetation Index (NDVI) and associated spectral bands from the 1985–2020 Landsat archive. EP predictions were successful in capturing biophysical gradients present in the independent variables and depicting potential cover in the absence of disturbance; we found no influence of fires or land treatments in the data. Next, we compared EP to contemporary (2018) cover, to create departure maps that can be used as a screening tool indicating degradation and providing an early warning of vegetation state change. Finally, we used a dichotomous key to convert the 1985 and 2018 RCMAP cover and EP cover into vegetation states important to land management decisions (invaded sagebrush steppe , annual grasslands, etc.). We found that in 1985, 21.2% of the study area had a different vegetation state than EP, and this percentage increased to 24.2% by 2018. More than 50% of the EP native sagebrush steppe was converted to an annual grassland, perennial grassland, or non-sagebrush shrub by 2018, and an additional 7% was classified as invaded sagebrush steppe, at risk of transition to another state. EP products provide a spatio-temporal reference of vegetation conditions from the last three decades across rangelands in the western United States. Use of the EP reference can improve adaptive management practice by providing monitoring and control data, which are often lacking, and assist in differentiating treatment effect from confounding factors.

western United States↗

USGS2020

Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data

Quantifying western U.S. rangelands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. Nine rangeland ecosystem components, including percent shrub, sagebrush ( Artemisia ), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30 m resolution. Extensive ground measurements, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree models were used to create component predictions. In the mapped area (2,993,655 km²), bare ground averaged 45.5%, shrub 15.2%, sagebrush 4.3%, big sagebrush 2.9%, herbaceous 23.0%, annual herbaceous 4.2%, and litter 15.8%. Component accuracies using independent validation across all components averaged R 2 values of 0.46 and an root mean squared error (RMSE) of 10.37, and cross-validation averaged R 2 values of 0.72 and an RMSE of 5.09. Component composition strongly varies by Environmental Protection Agency (EPA) level III ecoregions ( n = 32): 17 are bare ground dominant, 11 herbaceous dominant, and four shrub dominant. Sagebrush physically covers 90,950 km², or 4.3%, of our study area, but is present in 883,449 km², or 41.5%, of the mapped portion of our study area.

Arizona, California, Colorado, Idaho, Montana, Nev↗

USGS2019

Using remote sensing to quantify ecosystem site potential community structure and deviation in the Great Basin, United States

The semi-arid Great Basin region in the Northwest U.S. is impacted by a suite of change agents including fire, grazing, and climate variability to which native vegetation can have low resilience and resistance. Assessing ecosystem condition in relation to these change agents is difficult due to a lack of a consistent and objective Site Potential (SP) information of the conditions biophysically possible at each site. Our objectives were to assess and quantify patterns in ecosystem condition, based on actual fractional component cover and a SP map and to evaluate drivers of change. We used long-term 90th percentile Landsat NDVI(Normalized Difference Vegetation Index) and biophysical variables to produce a map of SP. Ecosystem condition was assessed using two methods, first we integrated fractional components into an index which was regressed against SP. Regression confidence intervals were used to segment the study area into normal, over-, and under-performing relative to SP. Next, the relationships between SP and fractional component cover produced SP expected component cover, from which we mapped the actual cover deviation. Much of the study area is within the range of conditions expected by the SP model, but degraded conditions are more common than those above SP expectations. We found that shrub cover deviation is more positive at higher elevation, while herbaceous cover deviation has the opposite pattern, supporting the hypothesis that more resistant and resilient sites are less likely to change from the shrub dominated legacy. Another key finding was that regions with significant annual herbaceous invasions tend to have lower than expected bare ground and shrub cover.

Great Basin↗

USGS2017

Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management

Understanding the causes and consequences of component change in sagebrush steppe is crucial for evaluating ecosystem sustainability. The sagebrush ( Artemisia spp.) steppe ecosystem of the northwest USA has been impacted by the invasion of exotic grasses, increasing fire return intervals, changing land management practices, and fragmentation, often lowering the overall resilience to change. We utilized contemporary and historical Landsat imagery, field data, and regression tree models to produce fractional cover maps of rangeland components (shrub, sagebrush, herbaceous, bare ground, and litter) through the last 30 years. Our main goals were to (1) investigate rangeland component trends over 30 years, (2) evaluate the magnitude and direction of trends in components and climate drivers and their relationship, and (3) assess component trends influenced by climate. Results indicated that over the study period, shrub, sage, herbaceous, and litter cover decreased, while bare ground cover increased. Measured rates of change ranged from − 0.14% decade −1 for shrub cover to 0.05% decade −1 for bare ground, whereas herbaceous and litter cover trends were negligible. Net landscape cover changes were consistent with expectations of climate change and disturbance producing a loss of biotic cover, and converting a portion of shrub and sagebrush to herbaceous cover. Overall, fire and related successional recovery was the greatest change agent for all components in terms of area and cover change, while increasing minimum temperature, at a rate of 0.66°C decade −1 , was found to be the most significant climate driver.

California, Nevada, Oregon↗

USGS2012

Quantifying urban land cover change between 2001 and 2006 in the Gulf of Mexico region

We estimated urbanization rates (2001–2006) in the Gulf of Mexico region using the National Land Cover Database (NLCD) 2001 and 2006 impervious surface products. An improved method was used to update the NLCD impervious surface product in 2006 and associated land cover transition between 2001 and 2006. Our estimation reveals that impervious surface increased 416 km 2 with a growth rate of 5.8% between 2001 and 2006. Approximately 1110.1 km 2 of non-urban lands were converted into urban land, resulting in a 3.2% increase in the region. Hay/pasture, woody wetland, and evergreen forest represented the three most common land cover classes that transitioned to urban. Among these land cover transitions, more than 50% of the urbanization occurred within 50 km of the coast. Our analysis shows that the close-to-coast land cover transition trend, especially within 10 km off the coast, potentially imposes substantial long-term impacts on regional landscape and ecological conditions.

Gulf of Mexico↗