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At least 19 recordsLinked to original sources
USGS2003

Comparing potential fuel treatment trade-off models

Understanding the trade-offs between short-term and long-term consequences of fire impacts on ecosystems is needed before a comprehensive fuels management program can be implemented nationally. We are evaluating 3 potential trade-off models at 8 locations in major U.S. fuel types. We present results of the initial testing of the 3 selected models/modelling approaches and a 4 th model on the Bitterroot National Forest (BNF) in western Montana. The selected models/modelling approaches were 1) the Fire Emissions Trade-off Model (FETM), 2) sequential use of the SIMPPLLE and MAGIS models, 3) the Vegetation Dynamics Development Tool (VDDT), and 4) the LANDscape Succession Model (LANDSUM). We simulated 3 fuel treatments over 50 years: 1) no action, 2) prescribed burning in ponderosa pine and Douglas-fir types at 2 different rates, and 3) timber harvesting that returns the stand to a reproduction stage. Simulation results for all models suggested that the acreage of Douglas-fir and ponderosa pine types would not be appreciably altered by the fuel treatments. Most models suggested the harvest treatment would reduce area burned by wildfire and smoke emissions; some models suggested the prescribed fire treatment would reduce wildfire acreage. All models suggested that the harvest treatment would reduce acreage of pole-size trees; some models suggested the fire treatment would increase acreage of sapling-size trees.

Montana↗
USGS1978

Potential applications of satellite imagery in some types of natural resource inventories

Landsat satellite imagery has been routinely available to researchers and resource scientists since 1972. Many studies have demonstrated the application of Landsat imagery for conducting inventories and mapping various natural resources. Examples of applications presented in this paper include: timber volume inventory, range productivity inventory, wildland vegetation mapping, inventory of rangeland conversion and irrigated lands, and mapping strip mine disturbance in forested areas. Costs and accuracies for each of these application areas are presented. Landsat data, used in its proper context with appropriate analysis techniques, supporting data, and sound sampling strategies, can be an effective tool in conducting natural resource inventories. Other Landsat systems, scheduled for launch in 1978 and 1981, will provide continuity in the availability of Landsat data to users. Improvements in the quality and timeliness of data delivered to users, along with improved data handling procedures, analysis techniques, and improved sampling frameworks should increase the use of satellite imagery in natural resource inventories.

Conference Paper↗
USGS2011

Ecoregional-scale monitoring within conservation areas, in a rapidly changing climate

Long-term monitoring of ecological systems can prove invaluable for resource management and conservation. Such monitoring can: (1) detect instances of long-term trend (either improvement or deterioration) in monitored resources, thus providing an early-warning indication of system change to resource managers; (2) inform management decisions and help assess the effects of management actions, as well as anthropogenic and natural disturbances; and (3) provide the grist for supplemental research on mechanisms of system dynamics and cause-effect relationships (Fancy et al., 2009). Such monitoring additionally provides a snapshot of the status of monitored resources during each sampling cycle, and helps assess whether legal standards and regulations are being met. Until the last 1-2 decades, tracking and understanding changes in condition of natural resources across broad spatial extents have been infrequently attempted. Several factors, however, are facilitating the achievement of such broad-scale investigation and monitoring. These include increasing awareness of the importance of landscape context, greater prevalence of regional and global environmental stressors, and the rise of landscape-scale programs designed to manage and monitor biological systems. Such programs include the US Forest Service's Forest Inventory and Analysis (FIA) Program (Moser et al., 2008), Canada's National Forest Inventory, the 3Q Programme for monitoring agricultural landscapes of Norway (Dramstad et al., 2002), and the emerging (US) Landscape Conservation Cooperatives (USDOI Secretarial Order 3289, 2009; Anonymous, 2011). This Special Section explores the underlying design considerations, as well as many pragmatic aspects associated with program implementation and interpretation of results from broad-scale monitoring systems, particularly within the constraints of high-latitude contexts (e.g., low road density, short field season, dramatic fluctuations in temperature). Although Alaska is the focus of most papers in this Special Section, we posit that many of the issues that characterize the remote, relatively undisturbed ecosystems of high northern latitudes are widespread and thus applicable to natural-resource management and conservation across northern portions of the Holarctic ecozone and indeed anywhere broad-scale monitoring is contemplated.

Biological Conservation↗
USGS1979

Satellites monitor Atlanta regional development

Since the adoption of a Regional Development Plan in 1975, the Atlanta Regional Commission has investigated methods for monitoring regional development patterns in a periodic, efficient manner. A promising approach appears to be the use of Landsat satellite data. In cooperation with the Earth Resources Observation Systems (EROS) Data Center, the commission used machine processing of digital temporal overlays of Landsat data collected in 1972, 1974 and 1976 to detect land use and land cover changes in the Atlanta metropolitan area. Results of the analysis revealed the conversion of forested and open space areas to residential, commercial and industrial land use in the urban-rural fringe zone from 1972 to 1974 and from 1974 to 1976. The study indicated that a land use and land cover change-detection program may be used to revise small-area forecasts of land use, population and employment made by planning models.

Atlanta, Georgia↗
USGS2007

Design and implementation of estimation-based monitoring programs for flora and fauna: A case study on the Cherokee National Forest

Science-based monitoring of biological resources is important for a greater understanding of ecological systems and for assessment of the target population using theoretic-based management approaches. When selecting variables to monitor, managers first need to carefully consider their objectives, the geographic and temporal scale at which they will operate, and the effort needed to implement the program. Generally, monitoring can be divided into two categories: index and inferential. Although index monitoring is usually easier to implement, analysis of index data requires strong assumptions about consistency in detection rates over time and space, and parameters are often biasednot accounting for detectability and spatial variation. In most cases, individuals are not always available for detection during sampling periods, and the entire area of interest cannot be sampled. Conversely, inferential monitoring is more rigorous because it is based on nearly unbiased estimators of spatial distribution. Thus, we recommend that detectability and spatial variation be considered for all monitoring programs that intend to make inferences about the target population or the area of interest. Application of these techniques is especially important for the monitoring of Threatened and Endangered (T&E) species because it is critical to determine if population size is increasing or decreasing with some level of certainty. Use of estimation-based methods and probability sampling will reduce many of the biases inherently associated with index data and provide meaningful information with respect to changes that occur in target populations. We incorporated inferential monitoring into protocols for T&E species spanning a wide range of taxa on the Cherokee National Forest in the Southern Appalachian Mountains. We review the various approaches employed for different taxa and discuss design issues, sampling strategies, data analysis, and the details of estimating detectability using site occupancy. These techniques provide a science-based approach for monitoring and can be of value to all resource managers responsible for management of T&E species.

Proceedings of the Annual Conference of the Southe↗
USGS2024

Delineating draft inventory analysis units for National Scenic and Historic Trails inventory, assessment, and monitoring programs

As of 2024, there are 32 National Scenic and Historic Trails (NSHTs) in the system administered by the Bureau of Land Management (BLM), National Park Service, and U.S. Department of Agriculture Forest Service. The BLM administers, manages, and protects 19 of these trails as part of its system of national conservation lands. Various laws, regulations, and policies require that the BLM conduct and maintain an inventory to protect trail-related resources, qualities, values, associated settings, and primary use or uses. There are set procedures for conducting inventory, assessment, and monitoring (IAM) of NSHTs, as outlined in volumes 1 and 2 of BLM Technical Reference 6280-1. One of the first steps in the IAM process is deciding the area along a trail to inventory. However, volumes 1 and 2 of BLM Technical Reference 6280-1 do not specify how the land area to be inventoried should be delineated. The BLM calls these focus areas for IAM efforts “inventory analysis units” (IAUs), which are defined as the geospatial boundary for the location of an inventory along a trail. This report reviews the approach used to delineate the IAUs for an inventory effort and identifies best practices for creating initial IAUs, termed “draft IAUs.” Draft IAUs would provide standardization across multiple management jurisdictions by applying the same parameters for their delineation. These draft IAUs would provide trail managers with an area surrounding NSHTs that would trigger the need for an inventory if a project were proposed within it and are meant to be refined during localized inventory efforts. The best practices herein are for creating draft IAUs using standard parameters for performing a viewshed analysis to identify a proxy of land to include in an initial inventory effort.

New Mexico↗
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↗
USGS2017

Influence of precipitation and crop germination on resource selection by mule deer (Odocoileus hemionus) in southwest Colorado

Mule deer ( Odocoileus hemionus ) populations in the western United States provide many benefits to local economies but can also cause considerable damage to agriculture, particularly damage to lucrative crops. Limited information exists to understand resource selection of mule deer in response to annual variation in crop rotation and climatic conditions. We tested the hypothesis that mule deer select certain crops, and in particular sunflower, based on annual climatic variability. Our objective was to use movements, estimates of home range, and resource selection analysis to identify resources selected by mule deer. We used annually-derived crop-specific datasets along with Global Positioning System collars to monitor 14 mule deer in an agricultural area near public lands in southwestern Colorado, USA. We estimated home ranges for two winter seasons that ranged between 7.68 and 9.88 km 2 , and for two summer seasons that ranged between 5.51 and 6.24 km 2 . Mule deer selected areas closer to forest and alfalfa for most periods during 2012, but selected areas closer to sunflower in a majority of periods during 2013. Considerable annual variation in climate patterns and precipitation levels appeared to influence selection by mule deer because of variability in crop rotation and success of germination of specific crops.

Colorado, Utah↗
USGS2021

Using landscape metrics to characterize towns along an urban-rural gradient

Context Urban-rural gradients are useful tools when examining the influence of human disturbances on ecological, social and coupled systems, yet the most commonly used gradient definitions are based on single broad measures such as housing density or percent forest cover that fail to capture landscape patterns important for conservation. Objectives We present an approach to defining urban–rural gradients that integrates multiple landscape pattern metrics related to ecosystem processes important for natural resources and wildlife sustainability. Methods We develop a set of land cover composition and configuration metrics and then use them as inputs to a cluster analysis process that, in addition to grouping towns with similar attributes, identifies exemplar towns for each group. We compare the outcome of the cluster-based urban-rural gradient typology to outcomes for four commonly-used rule-based typologies and discuss implications for resource management and conservation. Results The resulting cluster-based typology defines five town types (urban, suburban, exurban, rural, and agricultural) and notably identifies a bifurcation along the gradient distinguishing among rural forested and agricultural towns. Landscape patterns (e.g., core and islet forests) influence where individual towns fall along the gradient. Designations of town type differ substantially among the five different typologies, particularly along the middle of the gradient. Conclusions Understanding where a town occurs along the urban-rural gradient could aid local decision-makers in prioritizing and balancing between development and conservation scenarios. Variations in outcomes among the different urban-rural gradient typologies raise concerns that broad-measure classifications do not adequately account for important landscape patterns. We suggest future urban-rural gradient studies utilize more robust classification approaches.

Connecticut. Maine, Massachusetts, New Hampshire, ↗
USGS1993

Fire history of southeastern Glacier National Park: Missouri River Drainage

In 1982, Glacier National Park (GNP) initiated long-term studies to document the fire history of all forested lands in the 410,000 ha. park. To date, studies have been conducted for GNP west of the Continental Divide (Barrett et al. 1991), roughly half of the total park area. These and other fire history studies in the Northern Rockies (Arno 1976, Sneck 1977, Arno 1980, Romme 1982, Romme and Despain 1989, Barrett and Arno 1991, Barrett 1993a, Barrett 1993b) have shown that fire history data can be an integral element of fire management planning, particularly wen natiral fire plans are being developed for parks and wilderness. The value of site specific fire history data is apparent when considering study results for lodgepole pin ( Pinus contorta var. latifolia ) forests. Lodgepole pine is a major subalpine type in the Northern Rockies and such stands experiences a wide range of presettlement fire patterns. On relatively warm-dry sites at lower elevations, such as in GNP's North Fork drainage (Barrett et al. 1991), short to moderately long interval (25-150 yr) fires occurred in a mixed severity pattern ranging from non-lethal underburns to total stand replacement (Arno 1976, Sneck 1977, Barrett and Arno 1991). Markedly different fire history occurred at high elevation lodgepole pine stands on highly unproductive sites, such as on Yellowstone National Park's (YNP) subalpine plateau. Romme (1982) found that, on some sites, stand replacing fires recurred after very long intervals (300-400 yr), and that non-lethal surface fires were rare. For somewhat more productive sites in the Absaroka Mountains in YNP, Barrett (1993a) estimated a 200 year mean replacement interval, in a pattern similar to that found in steep mountain terrain elsewhere, such as in the Middle Fork Flathead River drainage (Barrett et al. 1991, Sneck 1977). Aside from post-1900 written records (ayres 1900; fire atlas data on file, GNP Archives Div. and GNP Resources Mgt. Div.), little fire history information existed for GNP's east-side forests, which are dominated primarily by lodgepole pine. In fall 1992, the park initiated a study to determine the fire history of the Missouri River drainage portion of southeastern GNP. Given the known variation in pre-1900 fire patterns for lodgepole pine, this study was seen as a potentially important contribution to GNP's Fire Management Plan, and to the expanding data base of fire history studies in the region. Resource managers sought this information to assist their development of appropriate fire management strategies for the east-side forests, and the fire history data also would be a useful interactive component of the park's Geographic Information System (GIS). Primary objectives were to: 1) determine pre-1900 fire periodicities, severities, burning patterns, and post-fire succession for major forest types, and 2) document and map the forest age class mosaic, reflecting the history of stand replacing fires at the landscape level of analysis. Secondary objectives were to interpret the possible effects of modern fire suppression on area forests, and to determine fire regime patterns relative to other lodgepole pine ecosystems in the Northern Rockies.

Montana↗
USGS2016

Vulnerability and adaptation to climate-related fire impacts in rural and urban interior Alaska

This paper explores whether fundamental differences exist between urban and rural vulnerability to climate-induced changes in the fire regime of interior Alaska. We further examine how communities and fire managers have responded to these changes and what additional adaptations could be put in place. We engage a variety of social science methods, including demographic analysis, semi-structured interviews, surveys, workshops and observations of public meetings. This work is part of an interdisciplinary study of feedback and interactions between climate, vegetation, fire and human components of the Boreal forest social–ecological system of interior Alaska. We have learned that although urban and rural communities in interior Alaska face similar increased exposure to wildfire as a result of climate change, important differences exist in their sensitivity to these biophysical, climate-induced changes. In particular, reliance on wild foods, delayed suppression response, financial resources and institutional connections vary between urban and rural communities. These differences depend largely on social, economic and institutional factors, and are not necessarily related to biophysical climate impacts per se. Fire management and suppression action motivated by political, economic or other pressures can serve as unintentional or indirect adaptation to climate change. However, this indirect response alone may not sufficiently reduce vulnerability to a changing fire regime. More deliberate and strategic responses may be required, given the magnitude of the expected climate change and the likelihood of an intensification of the fire regime in interior Alaska.

Alaska↗
USGS2011

Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis program, Landsat, and LANDFIRE

The concentrations of CO 2 and other greenhouse gases in the atmosphere have been increasing and greatly affecting global climate and socio-economic systems. Actively growing forests are generally considered to be a major carbon sink, but forest wildfires lead to large releases of biomass carbon into the atmosphere. Aboveground forest biomass carbon (AFBC), an important ecological indicator, and fire-induced carbon emissions at regional scales are highly relevant to forest sustainable management and climate change. It is challenging to accurately estimate the spatial distribution of AFBC across large areas because of the spatial heterogeneity of forest cover types and canopy structure. In this study, Forest Inventory and Analysis (FIA) data, Landsat, and Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) data were integrated in a regression tree model for estimating AFBC at a 30-m resolution in the Utah High Plateaus. AFBC were calculated from 225 FIA field plots and used as the dependent variable in the model. Of these plots, 10% were held out for model evaluation with stratified random sampling, and the other 90% were used as training data to develop the regression tree model. Independent variable layers included Landsat imagery and the derived spectral indicators, digital elevation model (DEM) data and derivatives, biophysical gradient data, existing vegetation cover type and vegetation structure. The cross-validation correlation coefficient ( r value) was 0.81 for the training model. Independent validation using withheld plot data was similar with r value of 0.82. This validated regression tree model was applied to map AFBC in the Utah High Plateaus and then combined with burn severity information to estimate loss of AFBC in the Longston fire of Zion National Park in 2001. The final dataset represented 24 forest cover types for a 4 million ha forested area. We estimated a total of 353 Tg AFBC with an average of 87 MgC/ha in the Utah High Plateaus. We also estimated that 8054 Mg AFBC were released from 2.24 km 2 burned forest area in the Longston fire. These results demonstrate that an AFBC spatial map and estimated biomass carbon consumption can readily be generated using existing database. The methodology provides a consistent, practical, and inexpensive way for estimating AFBC at 30-m resolution over large areas throughout the United States.

Ecological Indicators↗
USGS2004

Assessing biodiversity in Nuevo Leon, Mexico: Are nature reserves the answer?

The Mexican state of Nuevo Leon, located in the northeastern portion of the country, currently has 26 state and three federal nature reserves covering approximately 4.5% of its land area. These reserves were established for a variety of reasons not necessarily related to conservation purposes. In 2000 in response to a growing concern about the lack of organized conservation reserve planning to protect the important biological and physical features of Mexico, the Mexican Commission for Knowledge and Use of Biodiversity proposed 12 new terrestrial reserves for Nuevo Leon. The new reserves, if established, would increase the proportion of protected lands in the state to almost 24% of the state's land area. We compiled a Geographic Information System (GIS) analysis using digital thematic maps of physical and ecological features to examine how well the existing and proposed reserves incorporated the major biological and physical features of the state. The existing reserves are located primarily in regions with elevations > 1,000-1,500 m, on less productive soils, and are dominated by pine and oak forest cover types. As a result, the state's dominant biotic region - low elevation coastal plain with xeric scrub vegetation - is disproportionately under represented in the current reserve system. The new reserves would expand the protection of biophysical resources throughout the state. However, the inclusion of important resources in the low elevation coastal lands would still be limited.

Nuevo Leon↗
USGS2020

Surface elevation change evaluation in mangrove forests using a low‐cost, rapid‐scan terrestrial laser scanner

Mangrove forests have adapted to sea level rise (SLR) increases by maintaining their forest floor elevation via belowground root growth and surface sediment deposits. Researchers use surface elevation tables (SETs) to monitor surface elevation change (SEC) in mangrove forests, after which this information is used to assess SLR resiliency or to dictate active forest management for vulnerable systems. This method requires significant investments in terms of time and human resources and is limited in the number of points it can measure per plot. We use a low‐cost, portable terrestrial laser scanning (TLS) system to assess SEC for three mangrove forests on Pohnpei Island (Federated States of Micronesia). Cloth simulation filtering was used for ground detection, after which results were refined by filtering points using angular orientation. Digital elevation models then were generated via kriging interpolation for data collected in 2017 and 2019, after which the heights of corresponding points were compared across years. Extreme elevation changes, due to disturbances such as footprints or fallen logs, were removed using interquartile range analysis. The TLS‐obtained average SEC ranged between −6.92 and +6.01 mm, which exhibited an average consistency of 72% when compared to simultaneously collected SET data (root mean square error = 1.36 mm). We contend that this approach represents an improvement over the manual method, where very few points typically are used, that is, ≅ 36 points vs. ≅ 30,000 points in the case of TLS, and could contribute to improved monitoring and management of these rapidly changing forest environments.

Limnology and Oceanography Methods↗
USGS2007

A compilation of spatial digital databases for selected U.S. Geological Survey nonfuel mineral resource assessments for parts of Idaho and Montana

This report compiles selected results from 13 U.S. Geological Survey (USGS) mineral resource assessment studies conducted in Idaho and Montana into consistent spatial databases that can be used in a geographic information system. The 183 spatial databases represent areas of mineral potential delineated in these studies and include attributes on mineral deposit type, level of mineral potential, certainty, and a reference. The assessments were conducted for five 1? x 2? quadrangles (Butte, Challis, Choteau, Dillon, and Wallace), several U.S. Forest Service (USFS) National Forests (including Challis, Custer, Gallatin, Helena, and Payette), and one Bureau of Land Management (BLM) Resource Area (Dillon). The data contained in the spatial databases are based on published information: no new interpretations are made. This digital compilation is part of an ongoing effort to provide mineral resource information formatted for use in spatial analysis. In particular, this is one of several reports prepared to address USFS needs for science information as forest management plans are revised in the Northern Rocky Mountains.

Open-File Report↗
USGS2026

High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs

We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi 2 ), of which 13,148,345 ha (52,706 mi 2 ) are located in Mexico and 24,770,640 ha (95,639 mi 2 ) in the United States. We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations. Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections. The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable. The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community. This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.

Arizona, Baja California, California, Nevada, Sina↗
USGS2019

A multi-scale soil moisture monitoring strategy for California: Design and validation

A multi‐scale soil moisture monitoring strategy for California was designed to inform water resource management. The proposed workflow classifies soil moisture response units (SMRUs) using publicly available datasets that represent soil, vegetation, climate, and hydrology variables, which control soil water storage. The SMRUs were classified, using principal component analysis and unsupervised K‐means clustering within a geographic information system, and validated, using summary statistics derived from measured soil moisture time series. Validation stations, located in the Sierra Nevada, include transect of sites that cross the rain‐to‐snow transition and a cluster of sites located at similar elevations in a snow‐dominated watershed. The SMRUs capture unique responses to varying climate conditions characterized by statistical measures of central tendency, dispersion, and extremes. A topographic position index and landform classification is the final step in the workflow to guide the optimal placement of soil moisture sensors at the local‐scale. The proposed workflow is highly flexible and can be implemented over a range of spatial scales and input datasets can be customized. Our approach captures a range of soil moisture responses to climate across California and can be used to design and optimize soil moisture monitoring strategies to support runoff forecasts for water supply management or to assess landscape conditions for forest and rangeland management.

California↗
USGS2003

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).

Wyoming↗