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Bruce Wylie

Publications and source records attributed to Bruce Wylie.

16 recordsLinked to original sources
USGS2021

Rapid monitoring of the abundance and spread of exotic annual grasses in the western United States using remote sensing and machine learning

Exotic annual grasses (EAG) are one of the most damaging agents of change in western North America. Despite known socio-environmental effects of EAG there remains a need to enhance monitoring capabilities for better informing conservation and management practices. Here, we integrate field observations, remote sensing and climate data with machine-learning techniques to estimate and assess patterns of historical (1985–2019; R 2 = 0.86 ± 0.05; MAE = 6.7 ± 1.4%), present (2020), and future (2025–2040) EAG abundance (30-m) across much of the western United States. Trend analysis revealed that ∼8% and 1% of the landscape experienced significant rises and declines in historical EAG cover, respectively, with hotspots of invasion generally occurring near roads and along low-to-mid elevation gradients with warmer and drier conditions. Accurate simulations of the response of EAG to changing environmental conditions, disturbances and management treatments indicate that ecosystem resistance to invasion is largely controlled by long-term EAG abundance (surrogate for seed bank), time since and frequency of wildfire, and plant community interactions. Ecological thresholds associated with enhanced probabilities of wildfire occurrence and invasion rates indicate that relatively little (10%) EAG cover is needed to heighten these risks. Climate change is expected to push 8% of the landscape across invasion thresholds by 2040, impacting 6% of existing sage-grouse habitat, and we identify where fuel breaks may be placed to reduce wildfire risks and invasion. Spatially detailed, timely, and accurate depictions of past, present, and future EAG abundance are vital for the protection of life and property and the continued stewardship of sagebrush ecosystems.

California, Colorado, Idaho, Nevada, Oregon, Utah,↗
USGS2021

Tools and technologies for quantifying spread and impacts of invasive species

The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash ( Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass ( Bromus tectorum ) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap. 10 are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap. 10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.

Book chapter↗
USGS2020

Departures of rangeland fractional component cover and land cover from landsat-based ecological potential in Wyoming USA

Monitoring rangelands by identifying the departure of contemporary conditions from long-term ecological potential allows for the disentanglement of natural biophysical gradients driving change from changes associated with land uses and other disturbance types. We developed maps of ecological potential (EP) for shrub, sagebrush ( Artemisia spp.), perennial herbaceous, litter, and bare ground fractional cover in Wyoming, USA. EP maps correspond to the potential natural vegetation cover expected by environmental conditions in the absence of anthropogenic and natural disturbance as represented by the greenest and least disturbed period of the Landsat archive. EP was predicted using regression tree models with inputs of soil maps and spectral data associated with the 75th percentile of the Normalized Difference Vegetation Index in the Landsat archive. We trained our EP models with 2015 component cover maps on ecologically intact sites with relatively lower bare ground than expected. We generated departure of vegetation cover by comparing the EP and 2015 fractional cover. The departures represent land cover change from potential land cover and/or within-state changes in 2015. Next, we converted EP and 2015 fractional cover maps into thematic land cover and evaluated departure to determine if it was great enough to result in land cover change. The 2015 conditions showed reduced shrub, sagebrush, litter, and perennial herbaceous cover and increased bare ground relative to EP. Known disturbances, such as energy development, fires, and vegetation treatments, are clearly visible on the departure maps, but not on EP component maps. The most frequent departure from EP land cover was shrubland conversion to grassland. Land cover departures can be explained only in small part by known disturbance, and instead are ostensibly related to climate and land management practices. These drivers result in land cover departures that broadened the ecotone between shrubland and grassland relative to EP.

Wyoming↗
USGS2019

Monitoring drought impact on annual forage production in semi-arid grasslands: A case study of Nebraska sandhills

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha −1 *yr −1 , was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R 2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses. View Full-Text

Nebraska↗
USGS2018

Multi-scale geophysical mapping of deep permafrost change after disturbance in interior Alaska, USA

Disturbance related to fire or hydrologic processes can cause degradation of deep (greater than 1 m) permafrost. These changes in deep permafrost have the potential to impact landscapes and infrastructure, alter the routing and distribution of surface water or groundwater, and may contribute to the flux of carbon to terrestrial and aquatic ecosystems. However, characterization of deep permafrost over large areas and with high spatial resolution is not possible with traditional remote sensing or surface observations. We make use of multiple ground-based and airborne geophysical methods, as well as numerical simulations, to better understand the distribution of permafrost and how it has changed after disturbance. Together, these geophysical datasets help to fill a critical gap in understanding permafrost landscapes and their response to disturbance.

Alaska↗
USGS2014

Net ecosystem productivity of temperate grasslands in northern China: An upscaling study

Grassland is one of the widespread biome types globally, and plays an important role in the terrestrial carbon cycle. We examined net ecosystem production (NEP) for the temperate grasslands in northern China from 2000 to 2010. We combined flux observations, satellite data, and climate data to develop a piecewise regression model for NEP, and then used the model to map NEP for grasslands in northern China. Over the growing season, the northern China's grassland had a net carbon uptake of 158 ± 25 g C m −2 during 2000–2010 with the mean regional NEP estimate of 126 Tg C. Our results showed generally higher grassland NEP at high latitudes (northeast) than at low latitudes (central and west) because of different grassland types and environmental conditions. In the northeast, which is dominated by meadow steppes, the growing season NEP generally reached 200–300 g C m −2 . In the southwest corner of the region, which is partially occupied by alpine meadow systems, the growing season NEP also reached 200–300 g C m −2 . In the central part, which is dominated by typical steppe systems, the growing season NEP generally varied in the range of 100–200 g C m−2. The NEP of the northern China's grasslands was highly variable through years, ranging from 129 (2001) to 217 g C m −2 growing season −1 (2010). The large interannual variations of NEP could be attributed to the sensitivity of temperate grasslands to climate changes and extreme climatic events. The droughts in 2000, 2001, and 2006 reduced the carbon uptake over the growing season by 11%, 29%, and 16% relative to the long-term (2000–2010) mean. Over the study period (2000–2010), precipitation was significantly correlated with NEP for the growing season (R 2 = 0.35, p-value < 0.1), indicating that water availability is an important stressor for the productivity of the temperate grasslands in semi-arid and arid regions in northern China. We conclude that northern temperate grasslands have the potential to sequester carbon, but the capacity of carbon sequestration depends on grassland types and environmental conditions. Extreme climate events like drought can significantly reduce the net carbon uptake of grasslands.

Inner Mongolia;Gansu;And Ningxia Provinces↗
USGS2013

Monitoring the status of forests and rangelands in the Western United States using ecosystem performance anomalies

The effects of land management and disturbance on ecosystem performance (i.e. biomass production) are often confounded by those of weather and site potential. The current study overcomes this issue by calculating the difference between actual and expected ecosystem performance (EEP) to generate ecosystem performance anomalies (EPA). This study aims to delineate and quantify average EPA from 2000–2009 within the Greater Platte and Upper Colorado River Basins, USA. Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) images averaged over the growing season (GSN) served as a proxy of actual ecosystem performance. Yearly EEP was determined with rule-based piecewise regression tree models of abiotic data (climate, soils, elevation, etc.), independently created for each land cover. EPA were calculated as the residuals of the EEP to GSN relationship, and characterized as normal performing, underperforming, and overperforming at the 90% confidence level. Validation revealed that EPA values were related to biomass production (R 2 = 0.56, P = 0.02) and likely to the proportion of biomass removed by livestock in the Nebraska Sandhills. Overall, 60.6% of the study area was (normal) performing near its EEP, 3.0% was severely underperforming, 5.0% was highly overperforming, and the remainder was slightly underperforming or overperforming. Generally, disturbances such as fires, floods, and insect damage, in addition to high grazing intensity, result in a negative EPA. Conversely, mature stands and appropriate management often result in positive EPA values. This method provides information critical to land managers to evaluate the appropriateness of previous management practices and restoration efforts and quantify disturbance impacts. Results are at a scale sufficient for many of the large management units of the region and for locating areas needing further investigation. Applications of EPA data to monitoring invasive species, grazing impacts, and vulnerability to plant community shifts have been suggested by land management professionals.

Arizona;Colorado;Kansas;Nebraska;New Mexico;South ↗
USGS2013

CO 2 uptake and ecophysiological parameters of the grain crops of midcontinent North America: estimates from flux tower measurements

We analyzed net CO 2 exchange data from 13 flux tower sites with 27 site-years of measurements over maize and wheat fields across midcontinent North America. A numerically robust “light-soil temperature-VPD”-based method was used to partition the data into photosynthetic assimilation and ecosystem respiration components. Year-round ecosystem-scale ecophysiological parameters of apparent quantum yield, photosynthetic capacity, convexity of the light response, respiration rate parameters, ecological light-use efficiency, and the curvature of the VPD-response of photosynthesis for maize and wheat crops were numerically identified and interpolated/extrapolated. This allowed us to gap-fill CO 2 exchange components and calculate annual totals and budgets. VPD-limitation of photosynthesis was systematically observed in grain crops of the region (occurring from 20 to 120 days during the growing season, depending on site and year), determined by the VPD regime and the numerical value of the curvature parameter of the photosynthesis-VPD-response, σ VPD . In 78% of the 27 site-years of observations, annual gross photosynthesis in these crops significantly exceeded ecosystem respiration, resulting in a net ecosystem production of up to 2100 g CO 2 m −2 year −1 . The measurement-based photosynthesis, respiration, and net ecosystem production data, as well as the estimates of the ecophysiological parameters, provide an empirical basis for parameterization and validation of mechanistic models of grain crop production in this economically and ecologically important region of North America.

Agriculture, Ecosystems and Environment↗
USGS2013

Detecting channel riparian vegetation response to best-management-practices implementation in ephemeral streams with the use of spot high-resolution visible imagery

Heavily grazed riparian areas are commonly subject to channel incision, a lower water table, and reduced vegetation, resulting in sediment delivery above normal regimes. Riparian and in-channel vegetation functions as a roughness element and dissipates flow energy, maintaining stable channel geometry. Ash Creek, a tributary of the Bad River in western South Dakota contains a high proportion of incised channels, remnants of historically high grazing pressure. Best management practices (BMP), including off-stream watering sources and cross fencing, were implemented throughout the Bad River watershed during an Environmental Protection Agency (EPA) 319 effort to address high sediment loads. We monitored prairie cordgrass (Spartina pectinata Link) establishment within stream channels for 16 yr following BMP implementation. Photos were used to group stream reaches (n = 103) subjectively into three classes; absent (estimated < 5% cover; n = 64), present (estimated 5–40% cover; n = 23), and dense (estimated > 40% cover; n = 16) based on the relative amount of prairie cordgrass during 2010 assessments of ephemeral channels. Reaches containing drainage areas of 0.54 to 692 ha were delineated with the use of 2010 National Agriculture Imagery Program (NAIP) imagery. Normalized difference vegetation index (NDVI) values were extracted from 5 to 39 sample points proportional to reach length using a series of Satellite Pour l'Observation de la Terre (SPOT) satellite imagery. Normalized NDVI (nNDVI) of 2 152 sample points were determined from pre- and post-BMP images. Mean nNDVI values for each reach ranged from 0.33 to 1.77. ANOVA revealed significant increase in nNDVI in locations classified as present prairie cordgrass cover following BMP implementation. Establishment of prairie cordgrass following BMP implementation was successfully detected remotely. Riparian vegetation such as prairie cordgrass adds channel roughness that reduces the flow energy responsible for channel degradation.

South Dakota↗
USGS2013

Linking phenology and biomass productivity in South Dakota mixed-grass prairie

Assessing the health of rangeland ecosystems based solely on annual biomass production does not fully describe plant community condition; the phenology of production can provide inferences on species composition, successional stage, and grazing impacts. We evaluate the productivity and phenology of western South Dakota mixed-grass prairie using 2000 to 2008 Moderate Resolution Imaging Spectrometer (MODIS) normalized difference vegetation index (NDVI) satellite imagery at 250 m spatial resolution. Growing season NDVI images were integrated weekly to produce time-integrated NDVI (TIN), a proxy of total annual biomass production, and integrated seasonally to represent annual production by cool (C3) and warm (C4) season species. Additionally, a variety of phenological indicators including cool season percentage of TIN were derived from the seasonal profiles of NDVI. Cool season percentage and TIN were combined to generate vegetation classes, which served as proxies of plant community condition. TIN decreased with precipitation from east to west across the study area. Alternatively, cool season percentage increased from east to west, following patterns related to the reliability (interannual coefficient of variation [CV]) and quantity of mid-summer precipitation. Cool season TIN averaged 76.8% of total. Seasonal accumulation of TIN corresponded closely (R2 > 0.90) to that of gross photosynthesis data from a carbon flux tower. Field-collected biomass and community composition data were strongly related to the TIN and cool season percentage products. The patterns of vegetation classes were responsive to topographic, edaphic, and land management influences on plant communities. Accurate maps of biomass production, cool/warm season composition, and vegetation classes can improve the efficiency of land management by adjusting stocking rates and season of use to maximize rangeland productivity and achieve conservation objectives. Further, our results clarify the spatial and temporal dynamics of phenology and TIN in mixed-grass prairie.

South Dakota↗
USGS2013

Optimal placement of off-stream water sources for ephemeral stream recovery

Uneven and/or inefficient livestock distribution is often a product of an inadequate number and distribution of watering points. Placement of off-stream water practices (OSWP) in pastures is a key consideration in rangeland management plans and is critical to achieving riparian recovery by improving grazing evenness, while improving livestock performance. Effective OSWP placement also minimizes the impacts of livestock use radiating from OSWP, known as the “piosphere.” The objective of this study was to provide land managers with recommendations for the optimum placement of OSWP. Specifically, we aimed to provide minimum offset distances of OSWP to streams and assess the effective range of OSWP using Normalized Difference Vegetation Index (NDVI) values, an indicator of live standing crop. NDVI values were determined from a time-series of Satellite Pour l'Observation de la Terre (SPOT) 20-m images of western South Dakota mixed-grass prairie. The NDVI values in ephemeral stream channels (in-channel) and uplands were extracted from pre- and post-OSWP images taken in 1989 and 2010, respectively. NDVI values were normalized to a reference imagine and subsequently by ecological site to produce nNDVI. Our results demonstrate a significant (P < 0.05) increase in the nNDVI values of in-channel vegetation within 1 250 m of OSWP following their implementation. The area of piospheres (n = 9) increased with pasture size (R 2 = 0.49, P = 0.05) and increased with average distance to OSWP in a pasture (R 2 = 0.43, P = 0.07). Piospheric reduction in nNDVI was observed within 200 m of OSWP, occasionally overlapping in-channel areas. The findings of this study suggest placement of OSWP 200 to 1 250 m from streams to achieve optimal results. These results can be used to increase grazing efficiency by effectively placing OSWP and insure that piospheres do not overlap ecologically important in-channel areas.

South Dakota↗
USGS2013

Towards integration of GLAS data into a national fuels mapping program

Comprehensive canopy structure and fuel data are critical for understanding and modeling wildland fire. The LANDFIRE project produces such data nationwide based on a collection of field observations, Landsat imagery, and other geospatial data. Where field data are not available, alternate strategies are being investigated. In this study, vegetation structure data available from GLAS were used to fill this data gap for the Yukon Flats Ecoregion of interior Alaska. The GLAS-derived structure and fuel layers and the original LANDFIRE layers were subsequently used as inputs into a fire behavior model to determine what effect the revised inputs would have on the model outputs. The outputs showed that inclusion of the GLAS data enabled better landscape-level characterization of vegetation structure and therefore enabled a broader wildland fire modeling capability. The results of this work underscore how GLAS data can be incorporated into LANDFIRE canopy structure and fuel mapping.

Alaska↗
USGS2012

A multi-sensor lidar, multi-spectral and multi-angular approach for mapping canopy height in boreal forest regions

Spatially explicit representations of vegetation canopy height over large regions are necessary for a wide variety of inventory, monitoring, and modeling activities. Although airborne lidar data has been successfully used to develop vegetation canopy height maps in many regions, for vast, sparsely populated regions such as the boreal forest biome, airborne lidar is not widely available. An alternative approach to canopy height mapping in areas where airborne lidar data is limited is to use spaceborne lidar measurements in combination with multi-angular and multi-spectral remote sensing data to produce comprehensive canopy height maps for the entire region. This study uses spaceborne lidar data from the Geosciences Laser Altimeter System (GLAS) as training data for regression tree models that incorporate multi-angular and multi-spectral data from the Multi-Angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging SpectroRadiometer (MODIS) to map vegetation canopy height across a 1,300,000 km 2 swath of boreal forest in Interior Alaska. Results are compared to in situ height measurements as well as airborne lidar data. Although many of the GLAS-derived canopy height estimates are inaccurate, applying a series of filters incorporating both data associated with the GLAS shots as well as ancillary data such as land cover can identify the majority of height estimates with significant errors, resulting in a filtered dataset with much higher accuracy. Results from the regression tree models indicate that late winter MISR imagery acquired under snow-covered conditions is effective for mapping canopy heights ranging from 5 to 15 m, which includes the vast majority of forests in the region. It appears that neither MISR nor MODIS imagery acquired during the growing season is effective for canopy height mapping, although including summer multi-spectral MODIS data along with winter MISR imagery does appear to provide a slight increase in the accuracy of resulting height maps. The finding that winter, snow-covered MISR imagery can be used to map canopy height is important because clear sky days are nearly three times as common during the late winter period as during the growing season. The increased odds of acquiring cloud-free imagery during the target acquisition period make regularly updated forest height inventories for Interior Alaska much more feasible. A major advantage of the GLAS–MISR–MODIS canopy height mapping methodology described here is that this approach uses only data that is freely available worldwide, making the approach potentially applicable across the entire circumpolar boreal forest region.

Remote Sensing of Environment↗
USGS2002

Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping

FIA plot data were used to assist in classifying forest land cover from Landsat imagery and relevant ancillary data in two regions of the U.S.: one around the Chesapeake Bay area and the other around Utah. The overall accuracies for the forest/nonforest classification were over 90 percent and about 80 percent, respectively, in the two regions. The accuracies for deciduous/evergreen/mixed and forest type group classifications were around 80 percent and 65 percent, respectively, and were consistent in the two regions. These results suggest that use of FIA plot data together with satellite imagery and relevant ancillary data may substantially improve the efficiency, accuracy, and consistency of large area forest land cover mapping.

Conterminous United States↗
USGS2002

Development of a circa 2000 land cover database for the United States

Multi-Resolution Land Characterization 2000 (MRLC 2000) is a second-generation federal consortium to create an updated pool of nation-wide Landsat 7 imagery, and derive a second-generation National Land Cover Database (NLCD 2000). This multi-layer, multisource database will include a suite of 30-meter resolution data that will serve as standardized ingredients for the production of land cover – both nationally and locally. This database will also provide the framework to allow flexibility in developing and applying suites of independent data layers. These nationally standardized independent data layers or components, will be useful not only within the land-cover classification but as data themes for other applications. This database will consist of the following components: (1) normalized tasseled cap (TC) transformations of Landsat 7 imagery for three time periods per scene (early, peak and late), (2) ancillary data layers, including 30m DEM derivatives of slope, aspect and elevation and three STATSCO soil derivatives, (4) image shape and texture information, (5) image derivatives of percent imperviousness and percent tree canopy per-pixel, (6) classified land-cover data derived from the Tassel Capped imagery, ancillary data and derivatives, (7) classification rules and metadata from the land cover classification, allowing future users the potential to modify rules to derive land cover products tailored to their specific local applications. In a pilot study application of the database concept, two mapping zones (Utah and Virginia) were selected for full generation of the above data components. Three derivative layers including, per-pixel imperviousness, per-pixel canopy and land cover were classified from the database. Cross validation accuracies for land cover ranged from 65-82%, and mean absolute error values of 10-15% were reported for percent tree canopy and imperviousness.

conterminous United States↗