Geology topics

Kory Postma

Publications and source records attributed to Kory Postma.

7 recordsLinked to original sources

USGS2025

An automated compositing method for producing annual clear images from Landsat Collection 2 for annual NLCD production

Quality image input is fundamental to the quality of derived land cover products. Substantial time and effort are usually required to prepare images. Here, we present a novel and streamlined compositing algorithm that ingests Landsat Collection 2 Analysis Ready Data (ARD) and outputs cloud-free and gap-free composite imagery, which can be directly used for classification. This method leverages and improves the previous National Land Cover Database (NLCD) Virtual Median Value Point (VMVP) compositing method, the first part of the image preparation for NLCD 2019 operational production. The NLCD 2019 image preparation approach includes a second part, a residual cloud and cloud shadow detection and gap-filling method, to produce final cloud-free and gap-free composite imagery. The second part requires one clear reference image for each target year. Additional reference images are needed for producing reasonable observations for perennial ice/snow areas because Pixel QA (Quality Assessment) from ARD has difficulties differentiating ice/snow areas from clouds. Unlike the NLCD 2019 image preparation approach, our new compositing method, which is referred to as Automated VMVP (AVMVP), uses Landsat ARD as the only input and does not require reference images and extra steps. In this method, we developed new spectral filter criteria coupled with counts of clear observations using Pixel QA to identify potential cloud and cloud shadow observations on initially selected observations from the NLCD VMVP compositing algorithm. We also automate “gap-filling” using clear observations retrieved from a maximum of ±2 years around the target year when needed. Finally, a percentile-filtered compositing method was developed for the perennial ice/snow areas. All these steps are streamlined, pixel-based, and directly run on Landsat Collection 2 ARD. We have run successful tests on the conterminous United States (CONUS). Composite images derived from our innovative method were used to produce the CONUS Annual NLCD Collection 1 product suite that covers the period from 1985 to 2023.

conterminous 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

Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085

Climate change over the past century has altered vegetation community composition and species distributions across rangelands in the western United States. The scale and magnitude of climatic influences are unknown. While many studies have projected the effects of climate change using several modeling approaches, none has evaluated the impacts to fractional component cover at a 30-m resolution across the full sagebrush ( Artemisia spp.) biome. We used fractional component cover data for rangeland functional groups and weather data from the 1985 to 2018 reference period in conjunction with soils and topography data to develop empirical models describing the spatiotemporal variation in component cover. To investigate the ramifications of future change across the western United States, we extended models based on historical relationships over the reference period to model landscape effects based on future weather conditions from two emission scenarios and three time periods (2020s, 2050s, and 2080s). We tested both generalized additive models (GAMs) and regression tree models, finding that the former led to superior spatial and statistical results. Our results indicate more xeric vegetation across most of the study area, with an increasing dominance of non-sagebrush shrubs, annual herbaceous cover, and bare ground over herbaceous and sagebrush cover in both the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. In general, both scenarios yielded similar results, but RCP 8.5 tended to be more extreme, with greater change relative to the reference period. Results demonstrate that in cool sites some degree of warming to growing season maximum temperature or nongrowing season minimum temperature could be beneficial to sagebrush and shrub growth. However, warming nongrowing season maximum temperature was beneficial to shrub, but not to sagebrush growth. Our results inform rangeland managers of potential future vegetation composition, cover, and species distributions, which could improve prioritization of conservation and restoration efforts.

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

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↗

USGS2020

Analyzing vegetation change in a sagebrush ecosystem using long-term field observations and Landsat imagery in Wyoming

The importance of monitoring shrublands to detect and understand changes through time is increasingly recognized as critical to management. This research focuses on ecological change observed over 10 yr of field observation at 126 plots and over 35 yr of the Landsat archive in a shrubland ecosystem. Field data consisting of the fractional cover of shrubs, sagebrush, herbs, litter, and bare ground components were collected to be directly comparable to Landsat time-series predictions at an ecoregion level. We used these data to test three hypotheses. First, that precipitation and temperature govern changes in the proportions of shrubland components on an interannual time scale. Second, that longer-term component change is related to climate change. Finally, that change intensity varies by shrubland communities clustered by biophysical conditions. We found that the field observations and Landsat times-series predictions generally responded similarly to interannual variation in weather, chiefly driven by precipitation. Landsat times-series data provided a reasonable means of scaling up the findings of the field observations to a larger temporal and spatial window. The results of the analysis indicate that shrubland component change intensity significantly varies by biophysical clusters, and indicate a significant increase in the cover of shrubs and sagebrush in long-term monitoring plots between 2008 and 2017 and in the Landsat time-series data across the Wyoming Basin study area from 1985 to 2017 and from 2008 to 2017. Our results indicate that the Landsat time series can be used to answer critical questions regarding the influence of climate change and the suitability of management practices in shrubland ecosystems.

Wyoming↗