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USGS2022

Integration of vegetation classification with land cover mapping: Lessons from regional mapping efforts in the Americas

Aims : Natural resource management and biodiversity conservation rely on inventories of vegetation that span multiple management or political jurisdictions. However, while remote sensing data and analytical tools have enabled production of maps at increasing spatial resolution and reliability, there are limited examples where national or continental-scaled maps are produced to represent vegetation at high thematic detail. We illustrate two examples that have bridged the gap between traditional land cover mapping and modern vegetation classification. Study area : Our two case studies include national ( USA ) and continental (North and South America) vegetation and land cover mapping. These studies span conditions from subpolar to tropical latitudes of the Americas. Methods : Both case studies used a supervised modeling approach with the International Vegetation Classification ( IVC ) to produce maps that provide for greater thematic detail. Georeferenced locations for these vegetation types are used by machine learning algorithms to train a predictive model and generate a distribution map. Results : The USA LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) case study illustrates how a history of vegetation-based classification and availability of key inputs can come together to generate standard map products covering more than 9.8 million km 2 that are unsurpassed anywhere in the world in terms of spatial and thematic resolution. That being said, it also remains clear that mapping at the thematic resolution of the IVC Group and finer resolution require very large and spatially balanced inputs of georeferenced samples. Even with extensive prior data collection efforts, these remain a key limitation. The NatureServe effort for the Americas - encompassing 22% of the global land surface - demonstrates methods and outputs suitable for worldwide application at continental scales. Conclusions : Continued collection of input data used in the case studies could enable mapping at these spatial and thematic resolutions around the globe.

Vegetation Classification and Survey↗

USGS2008

A Vegetation Database for the Colorado River Ecosystem from Glen Canyon Dam to the Western Boundary of Grand Canyon National Park, Arizona

A vegetation database of the riparian vegetation located within the Colorado River ecosystem (CRE), a subsection of the Colorado River between Glen Canyon Dam and the western boundary of Grand Canyon National Park, was constructed using four-band image mosaics acquired in May 2002. A digital line scanner was flown over the Colorado River corridor in Arizona by ISTAR Americas, using a Leica ADS-40 digital camera to acquire a digital surface model and four-band image mosaics (blue, green, red, and near-infrared) for vegetation mapping. The primary objective of this mapping project was to develop a digital inventory map of vegetation to enable patch- and landscape-scale change detection, and to establish randomized sampling points for ground surveys of terrestrial fauna (principally, but not exclusively, birds). The vegetation base map was constructed through a combination of ground surveys to identify vegetation classes, image processing, and automated supervised classification procedures. Analysis of the imagery and subsequent supervised classification involved multiple steps to evaluate band quality, band ratios, and vegetation texture and density. Identification of vegetation classes involved collection of cover data throughout the river corridor and subsequent analysis using two-way indicator species analysis (TWINSPAN). Vegetation was classified into six vegetation classes, following the National Vegetation Classification Standard, based on cover dominance. This analysis indicated that total area covered by all vegetation within the CRE was 3,346 ha. Considering the six vegetation classes, the sparse shrub (SS) class accounted for the greatest amount of vegetation (627 ha) followed by Pluchea (PLSE) and Tamarix (TARA) at 494 and 366 ha, respectively. The wetland (WTLD) and Prosopis-Acacia (PRGL) classes both had similar areal cover values (227 and 213 ha, respectively). Baccharis-Salix (BAXX) was the least represented at 94 ha. Accuracy assessment of the supervised classification determined that accuracies varied among vegetation classes from 90% to 49%. Causes for low accuracies were similar spectral signatures among vegetation classes. Fuzzy accuracy assessment improved classification accuracies such that Federal mapping standards of 80% accuracies for all classes were met. The scale used to quantify vegetation adequately meets the needs of the stakeholder group. Increasing the scale to meet the U.S. Geological Survey (USGS)-National Park Service (NPS)National Mapping Program's minimum mapping unit of 0.5 ha is unwarranted because this scale would reduce the resolution of some classes (e.g., seep willow/coyote willow would likely be combined with tamarisk). While this would undoubtedly improve classification accuracies, it would not provide the community-level information about vegetation change that would benefit stakeholders. The identification of vegetation classes should follow NPS mapping approaches to complement the national effort and should incorporate the alternative analysis for community identification that is being incorporated into newer NPS mapping efforts. National Vegetation Classification is followed in this report for association- to formation-level categories. Accuracies could be improved by including more environmental variables such as stage elevation in the classification process and incorporating object-based classification methods. Another approach that may address the heterogeneous species issue and classification is to use spectral mixing analysis to estimate the fractional cover of species within each pixel and better quantify the cover of individual species that compose a cover class. Varying flights to capture vegetation at different times of the year might also help separate some vegetation classes, though the cost may be prohibitive. Lastly, photointerpretation instead of automated mapping could be tried. Photointerpretation would likely not improve accuracies in this case, howev

Open-File Report↗

USGS2020

Associations between riparian plant morphological guilds and fluvial sediment dynamics along the regulated Colorado River in Grand Canyon

Effects of riparian vegetation on fluvial sediment dynamics depend on morphological traits of the constituent species. Determining the effects of different morphological guilds on sedimentation rates, as influenced by multiple aspects of dam operations, can help identify viable strategies for streamflow and vegetation management to achieve riparian resource goals. Plants of increasing size and branching density or complexity have been found to have greater effects on sedimentation in free‐flowing systems; however, this relationship could differ in regulated rivers. We tested the hypothesis that plant guilds of increasing height and branching complexity would be positively associated with sedimentation rates on 23 sandbars deposited in zones of recirculating flow (eddies) along the Colorado River in Grand Canyon. We used an image‐based vegetation classification and digital elevation models from annual topographic surveys to track associations between six plant morphological guilds and topographic change over 5 years. Vegetation had significant associations with deposition after accounting for geomorphic setting, but the ordinal guild scale was not positively correlated with deposition magnitude. Instead, low‐statured rhizomatous and herbaceous guilds were particularly effective at capturing sediment in the separation zone of sandbars, whereas tall herbs and large shrubs were most effective at capturing sediment in reattachment zones. These nuanced interactions between geomorphic position and morphological guild may be a direct consequence of flow regulation through modifications to physical deposition and erosion processes. Flow regulation may also select for a narrow subset of morphological guilds, reducing the diversity of vegetation feedbacks on sedimentation and emphasizing geomorphic drivers.

Arizona↗

USGS2011

Predicting breeding bird occurrence by stand- and microhabitat-scale features in even-aged stands in the Central Appalachians

Spatial scale is an important consideration when managing forest wildlife habitat, and models can be used to improve our understanding of these habitats at relevant scales. Our objectives were to determine whether stand- or microhabitat-scale variables better predicted bird metrics (diversity, species presence, and abundance) and to examine breeding bird response to clearcut size and age in a highly forested landscape. In 2004–2007, vegetation data were collected from 62 even-aged stands that were 3.6–34.6 ha in size and harvested in 1963–1990 on the Monongahela National Forest, WV, USA. In 2005–2007, we also surveyed birds at vegetation plots. We used classification and regression trees to model breeding bird habitat use with a suite of stand and microhabitat variables. Among stand variables, elevation, stand age, and stand size were most commonly retained as important variables in guild and species models. Among microhabitat variables, medium-sized tree density and tree species diversity most commonly predicted bird presence or abundance. Early successional and generalist bird presence, abundance, and diversity were better predicted by microhabitat variables than stand variables. Thus, more intensive field sampling may be required to predict habitat use for these species, and management may be needed at a finer scale. Conversely, stand-level variables had greater utility in predicting late-successional species occurrence and abundance; thus management decisions and modeling at this scale may be suitable in areas with a uniform landscape, such as our study area. Our study suggests that late-successional breeding bird diversity can be maximized long-term by including harvests >10 ha in size into our study area and by increasing tree diversity. Some harvesting will need to be incorporated regularly, because after 15 years, the study stands did not provide habitat for most early successional breeding specialists.

Forest Ecology and Management↗

USGS2017

Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013

The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.

Louisiana↗

USGS2024

2023-2024 Coastal sage scrub and chaparral community monitoring for western San Diego County

Western San Diego County is dominated by shrublands supporting biologically diverse native plant and animal communities. Widespread urbanization has led to regional habitat loss and fragmentation, and many species in these shrubland communities are rare, threatened, or endangered. Large-scale, multiple-species conservation planning has resulted in a regional preserve system that focuses on these shrubland communities. Several large-scale threats are leading to type conversion from shrub-dominated to non-native invasive annual grass-dominated vegetation. To understand the changes that are occurring to native shrublands, we have developed a vegetation monitoring program with several components at multiple spatial scales, focused on quantifying coastal sage scrub (CSS) and chaparral vegetation community characteristics. Several drivers of change associated with type conversion of native shrubland to non-native annual grassland have been identified by previous research including increasing fire frequency, nitrogen deposition from air pollution, and prolonged and intense drought associated with changing climate. Loss of ecological integrity indices have been proposed as useful measures of the threat of degradation and type conversion of shrublands in San Diego County. For this study, ecological integrity is defined as a system’s ability to maintain species’ relationships and functions comparable to natural habitat in the region. Previous studies have identified the percent cover of invasive non-native annual grasses as a proxy for overall ecological degradation (loss of integrity) that is consistent across native taxonomic groups. Increased cover of non-native grass is associated with lower integrity of the shrubland vegetation community as shrub-associated plant and animal species are replaced by species preferring grassy and disturbed habitats. The objectives of this CSS and chaparral vegetation community monitoring plan are to: 1) Determine the distribution, composition, structure, and integrity of CSS and chaparral vegetation communities on conserved lands in western San Diego County, 2) Identify whether these attributes of the vegetation communities are changing over time, and 3) Evaluate relationships of known drivers of change (threats) and environmental factors in association with changes in vegetation community attributes. The CSS and chaparral vegetation community monitoring program is divided into four components: 1) vegetation mapping, 2) GIS/remote sensing office analysis of landscape-scale data, 3) permanent field plots using Unmanned Aerial Systems (UAS) and field data collections, and 4) animal and target species surveys and rapid assessment protocols. The first component, which is not detailed in this vegetation monitoring plan, aims to map vegetation communities every 10-15 years based on a classification developed for western San Diego County. High resolution aerial imagery will be used to update the 2012 vegetation map and expand it across the entire study area. The second component uses remote sensing models to annually track ecological integrity of shrublands across the study area and will include a map of areas of change and areas of stability over several decades. These landscape-scale integrity classifications will be used to analyze ecological processes, threats, and abiotic factors relative to changes to shrubland ecological integrity over time and space. The third component includes field surveys of 100 permanent plots across areas historically mapped as shrublands. Surveys in the 1930s mapped vegetation types using plot data. By using this historical classification map, we included areas that have already type-converted from shrubland to non-native annual grassland. The plots were split between CSS (55 plots) and chaparral (45 plots) and stratified into four geographical eco-subregions to guarantee coverage over small patches of habitat along the heavily developed coast. Surveys will include the collection of UAS imagery at a very high resolution. Species-level identifications will be made from the imagery based on a plant list compiled of all species detected in the plot during a thorough field survey by botanists, combined with geo-referenced samples of plant species locations. In addition, herbaceous cover will be estimated in the field using nine 1-m diameter circles (one per subplot) to obtain ocular estimates of cover for each plant species within the circle frame. Soil samples will be collected and analyzed for important element compositions. These data will be analyzed to evaluate plot-level ecological integrity based on species composition and cover. Repeated monitoring will allow evaluation of changes in vegetation attributes over time with known drivers or threats and other environmental factors. In addition, analyses will focus on indicator species and various measures of biological diversity for the vegetation communities. Finally, animal species and rare plants will be assessed using either taxa-based rapid assessment protocols or specialized species-specific protocols for rarer species. The purpose of these assessments is to document the status, habitat, and threat covariates of specific species and confirm the species composition and diversity of animal taxonomic groups (e.g., pollinators). Diversity and abundance of animal species at vegetation plots will be used to refine measures and thresholds of ecological integrity. Rapid assessment protocols for animal taxonomic groups can include multiple detection methods such as, camera traps, cover boards, and bird point counts. Pollinators will be monitored at plots using a protocol currently being developed in conjunction with but separately from this plan. Target rare plant species will be monitored using the regional Inspect and Manage (IMG) protocol that measures the status of rare plant occurrences and habitat and threat covariates over time. Species-specific animal survey methods will be refined as these species are prioritized for future monitoring. The goal of this monitoring program is to classify CSS and chaparral vegetation community integrity, identify areas of degradation across western San Diego County, and characterize drivers, and environmental factors associated with loss of ecological integrity. A combination of vegetation mapping, landscape-scale remote sensing, and field plots will be used to address all the aspects of our research questions. Data compiled and collected will be available to conservation partners to help inform future management decisions.

California↗

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

Mapping a keystone shrub species, huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains

Black huckleberries ( Vaccinium membranaceum ) provide a critical food resource to many wildlife species, including apex omnivores such as the grizzly bear ( Ursus arctos ), and play an important socioeconomic role for many communities in western North America, especially indigenous peoples. Remote sensing imagery offers the potential for accurate landscape-level mapping of huckleberries because the shrub changes colour seasonally. We developed two methods, for local and regional scales, to map a shrub species using leaf seasonal colour change from remote sensing imagery. We assessed accuracy with ground-based vegetation surveys. The high-resolution supervised random forest classification from one-meter resolution National Agricultural Imagery Program (NAIP) imagery achieved an overall accuracy of 75.31% (kappa = 0.26). The approach using multi-temporal 30-meter Landsat imagery similarly had an overall accuracy of 79.19% (kappa = .31). We found underprediction error was related to higher forest cover and a lack of visible colour change on the ground in some plots. Where forest cover was low, both models performed better. In areas with <10% forest cover, the high-resolution classification achieved an accuracy of 80.73% (kappa = 0.48), while the Landsat model had an accuracy of 82.55% (kappa = 0.47). Based on the fine-scale predictions, we found that 94% of huckleberry shrubs identified in our study area of Glacier National Park, Montana, USA are over 100 meters from human recreation trails. This information could be combined with productivity and phenology information to estimate the timing and availability of food resources for wildlife and to provide managers with a tool to identify and manage huckleberries. The development of the multi-temporal Landsat models sets the stage for assessment of impacts of disturbance at regional scales on this ecologically, culturally, and economically important shrub species. Our approach to map huckleberries is straightforward, efficient and accessible to wildlife and environmental managers and researchers in diverse fields.

Montana↗

USGS2023

Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity

Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, reflectance properties of vegetation. Here, we present a remotely sensed phylogenetic clustering and an evolutionary framework to accommodate spectra, distributions, and traits. Spectral properties evolutionarily conserved in plants provide the opportunity to spatially aggregate species into lineages (interpreted as “lineage functional types” or LFT) with improved classification accuracy. In this study, we use Airborne Visible/Infrared Imaging Spectrometer data from the 2013 Hyperspectral Infrared Imager campaign over the southern Sierra Nevada, California flight box, to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from 1372 surveys and a phylogeny representing 1,572 species. Despite temporal and spatial differences in our training data, we classified plant lineages with moderate reliability (Kappa = 0.76) and overall classification accuracy of 80.9%. We present an assessment of classification error and detail study limitations to facilitate future LFT development. This work demonstrates that lineage-based methods may be a promising way to leverage the new-generation high-resolution and high return-interval hyperspectral data planned for the forthcoming satellite missions with sparsely sampled existing ground-based ecological data.

Proceedings of the Natural Academy of Sciences↗

USGS2015

Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010

Coastal zone managers and researchers often require detailed information regarding emergent marsh vegetation types (that is, fresh, intermediate, brackish, and saline) for modeling habitat capacities and needs of marsh dependent taxa (such as waterfowl and alligator). Detailed information on the extent and distribution of emergent marsh vegetation types throughout the northern Gulf of Mexico coast has been historically unavailable. In response, the U.S. Geological Survey, in collaboration with the Gulf Coast Joint Venture, the University of Louisiana at Lafayette, Ducks Unlimited, Inc., and the Texas A&M University-Kingsville, produced a classification of emergent marsh vegetation types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama. This study incorporates about 9,800 ground reference locations collected via helicopter surveys in coastal wetland areas. Decision-tree analyses were used to classify emergent marsh vegetation types by using ground reference data from helicopter vegetation surveys and independent variables such as multitemporal satellite-based multispectral imagery from 2009 to 2011, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2010 National Agriculture Imagery Program color-infrared aerial photography. The final classification is a 10-meter raster dataset that was produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classification is dated 2010 because the year is both the midpoint of the classified multitemporal satellite-based imagery (2009–11) and the date of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.

Alabama, Louisiana, Mississippi, Texas↗

USGS2010

Conifer health classification for Colorado, 2008

Colorado has undergone substantial changes in forests due to urbanization, wildfires, insect-caused tree mortality, and other human and environmental factors. The U.S. Geological Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotely-sensed imagery for assessing conifer health in Colorado. Two classes were identified for the purposes of this study: healthy and unhealthy (for example, an area the size of a 30- x 30-m pixel with 20 percent or greater visibly dead trees was defined as ?unhealthy?). Medium-resolution Landsat 5 Thematic Mapper imagery were collected. The normalized, reflectance-converted, cloud-filled Landsat scenes were merged to form a statewide image mosaic, and a Normalized Difference Vegetation Index (NDVI) and Renormalized Difference Infrared Index (RDII) were derived. A supervised maximum likelihood classification was done using the Landsat multispectral bands, the NDVI, the RDII, and 30-m U.S. Geological Survey National Elevation Dataset (NED). The classification was constrained to pixels identified in the updated landcover dataset as coniferous or mixed coniferous/deciduous vegetation. The statewide results were merged with a separate health assessment of Grand County, Colo., produced in late 2008. Sampling and validation was done by collecting field data and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and methods used successfully characterized conifer conditions within Colorado. Although forest conditions for Lodgepole Pine (Pinus contorta) are easily characterized, classification uncertainty exists between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), Pi?on (Pinus edulis), and Juniper (Juniperus sp.) vegetation. Some underestimation of conifer mortality in Summit County is likely, where recent (2008) cloud-free imagery was unavailable. These classification uncertainties are primarily due to the spatial and temporal resolution of Landsat, and of the NLCD derived from this sensor. It is believed that high- to moderate-resolution multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009.

Scientific Investigations Map↗

USGS2014

Delineation of marsh types of the Texas coast from Corpus Christi Bay to the Sabine River in 2010

Coastal zone managers and researchers often require detailed information regarding emergent marsh vegetation types for modeling habitat capacities and needs of marsh-reliant wildlife (such as waterfowl and alligator). Detailed information on the extent and distribution of marsh vegetation zones throughout the Texas coast has been historically unavailable. In response, the U.S. Geological Survey, in cooperation and collaboration with the U.S. Fish and Wildlife Service via the Gulf Coast Joint Venture, Texas A&M University-Kingsville, the University of Louisiana-Lafayette, and Ducks Unlimited, Inc., has produced a classification of marsh vegetation types along the middle and upper Texas coast from Corpus Christi Bay to the Sabine River. This study incorporates approximately 1,000 ground reference locations collected via helicopter surveys in coastal marsh areas and about 2,000 supplemental locations from fresh marsh, water, and “other” (that is, nonmarsh) areas. About two-thirds of these data were used for training, and about one-third were used for assessing accuracy. Decision-tree analyses using Rulequest See5 were used to classify emergent marsh vegetation types by using these data, multitemporal satellite-based multispectral imagery from 2009 to 2011, a bare-earth digital elevation model (DEM) based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables believed to be important for delineating the extent and distribution of marsh vegetation communities. Image objects were generated from segmentation of high-resolution airborne imagery acquired in 2010 and were used to refine the classification. The classification is dated 2010 because the year is both the midpoint of the multitemporal satellite-based imagery (2009–11) classified and the date of the high-resolution airborne imagery that was used to develop image objects. Overall accuracy corrected for bias (accuracy estimate incorporates true marginal proportions) was 91 percent (95 percent confidence interval [CI]: 89.2–92.8), with a kappa statistic of 0.79 (95 percent CI: 0.77–0.81). The classification performed best for saline marsh (user’s accuracy 81.5 percent; producer’s accuracy corrected for bias 62.9 percent) but showed a lesser ability to discriminate intermediate marsh (user’s accuracy 47.7 percent; producer’s accuracy corrected for bias 49.5 percent). Because of confusion in intermediate and brackish marsh classes, an alternative classification containing only three marsh types was created in which intermediate and brackish marshes were combined into a single class. Image objects were reattributed by using this alternative three-marsh-type classification. Overall accuracy, corrected for bias, of this more general classification was 92.4 percent (95 percent CI: 90.7–94.2), and the kappa statistic was 0.83 (95 percent CI: 0.81–0.85). Mean user’s accuracy for marshes within the four-marsh-type and three-marsh-type classifications was 65.4 percent and 75.6 percent, respectively, whereas mean producer’s accuracy was 56.7 percent and 65.1 percent, respectively. This study provides a more objective and repeatable method for classifying marsh types of the middle and upper Texas coast at an extent and greater level of detail than previously available for the study area. The seamless classification produced through this work is now available to help State agencies (such as the Texas Parks and Wildlife Department) and landscape-scale conservation partnerships (such as the Gulf Coast Prairie Landscape Conservation Cooperative and the Gulf Coast Joint Venture) to develop and (or) refine conservation plans targeting priority natural resources. Moreover, these data may improve projections of landscape change and serve as a baseline for monitoring future changes resulting from chronic and episodic stressors.

Texas↗

USGS2023

Trends and population estimate of the threatened Buff-breasted Sandpiper Calidris subruficollis wintering in coastal grasslands of southern Brazil

Information about population sizes, trends, and habitat use is key for species conservation and management. The Buff-breasted Sandpiper Calidris subruficollis (BBSA) is a long-distance migratory shorebird that breeds in the Arctic and migrates to south-eastern South America, wintering in the grasslands of southern Brazil, Uruguay, and Argentina. Most studies of Nearctic migratory species occur in the Northern Hemisphere, but monitoring these species at non-breeding areas is crucial for conservation during this phase of the annual cycle. Our first objective was to estimate trends of BBSA at four key areas in southern Brazil during the non-breeding season. We surveyed for BBSA and measured vegetation height in most years from 2008/09 to 2019/20. We used hierarchical distance sampling models in which BBSA abundance and density were modelled as a function of vegetation height and corrected for detectability. Next, we used on-the-ground surveys combined with satellite imagery and habitat classification models to estimate BBSA population size in 2019/20 at two major non-breeding areas. We found that abundance and density were negatively affected by increasing vegetation height. Abundance fluctuated five- to eight-fold over the study period, with peaks in the middle of the study (2014/15). We estimated the BBSA wintering population size as 1,201 (95% credible interval [CI]: 637–1,946) birds in Torotama Island and 2,232 (95% CI: 1,199–3,584) in Lagoa do Peixe National Park during the 2019/20 austral summer. Although no pronounced trend was detected, BBSA abundance fluctuated greatly from year to year. Our results demonstrate that only two of the four key areas hold high densities of BBSA and highlight the positive effect of short grass on BBSA numbers. Short-grass coastal habitats used by BBSA are strongly influenced by livestock grazing and climate, and are expected to shrink in size with future development and climatic changes.

Bird Conservation International↗

USGS2015

General classification handbook for floodplain vegetation in large river systems

This handbook describes the General Wetland Vegetation Classification System developed as part of the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration (UMRR) Program, Long Term Resource Monitoring (LTRM) element. The UMRR is a cooperative effort between the U.S. Army Corps of Engineers, U.S. Geological Survey, U.S. Fish and Wildlife Service, and the states of Illinois, Iowa, Minnesota, Missouri, and Wisconsin. The classification system consists of 31 general map classes and has been used to create systemic vegetation data layers throughout the diverse Upper Mississippi River System (UMRS), which includes the commercially navigable reaches of the Mississippi River from Minneapolis, Minnesota, in the north to Cairo, Illinois, in the south, the Illinois River, and navigable portions of the Kaskaskia, Black, St. Croix, and Minnesota Rivers. In addition, this handbook describes the evolution of the General Wetland Vegetation Classification System, discusses the process of creating a vegetation data layer, and describes each of the 31 map classes in detail. The handbook also acts as a pictorial guide to each of the map classes as they may appear in the field, as well as on color-infrared imagery. This version is an update to the original handbook published in 2004.

Techniques and Methods↗

USGS2017

National Park Service Vegetation Mapping Inventory Program: Appalachian National Scenic Trail vegetation mapping project

The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program classifies, describes, and maps existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VMI Program is managed by the NPS I&M Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, NPS Northeast Temperate Network, and NPS Appalachian National Scenic Trail (APPA) have completed vegetation classification and mapping of APPA for the NPS VMI Program. Mappers, ecologists, and botanists collaborated to affirm vegetation types within the U.S. National Vegetation Classification (USNVC) of APPA and to determine how best to map the vegetation types by using aerial imagery. Analyses of data from 1,618 vegetation plots were used to describe USNVC associations of APPA. Data from 289 verification sites were collected to test the field key to vegetation associations and the application of vegetation associations to a sample set of map polygons. Data from 269 validation sites were collected to assess vegetation mapping prior to submitting the vegetation map for accuracy assessment (AA). Data from 3,265 AA sites were collected, of which 3,204 were used to test accuracy of the vegetation map layer. The collective of these datasets affirmed 280 USNVC associations for the APPA vegetation mapping project. To map the vegetation and land cover of APPA, 169 map classes were developed. The 169 map classes consist of 150 that represent natural (including ruderal) vegetation types in the USNVC, 11 that represent cultural (agricultural and developed) vegetation types in the USNVC, 5 that represent natural landscapes with catastrophic disturbance or some other modification to natural vegetation preventing accurate classification in the USNVC, and 3 that represent nonvegetated water (non-USNVC). Features were interpreted from viewing 4-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems (GIS). (Digital aerial imagery was collected each fall during 2009–11 to capture leaf-phenology change of hardwood trees across the latitudinal range of APPA.) The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in GIS. Polygon units were mapped to either a 0.5-hectare (ha) or 0.25-ha minimum mapping unit, depending on vegetation type or scenario; however, polygon units were mapped to 0.1 ha for alpine vegetation. A geodatabase containing various feature-class layers and tables provide locations and support data to USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, validation sites, AA sites, project boundary extent and zones, and aerial image centers and flight lines. The feature-class layer and related tables of the vegetation map layer provide 30,395 polygons of detailed attribute data covering 110,919.7 ha, with an average polygon size of 3.6 ha; the vegetation map coincides closely with the administrative boundary for APPA. Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 28,242 polygons (92.9% of polygons) and cover 106,413.0 ha (95.9%) of the map extent for APPA. The map layer indicates APPA to be 92.4% forest and woodland (102,480.8 ha), 1.7% shrubland (1866.3 ha), and 1.8% herbaceous cover (2,065.9 ha). Map classes representing park-special vegetation (undefined in the USNVC) apply to 58 polygons (0.2% of polygons) and cover 404.3 ha (0.4%) of the map extent. Map classes representing USNVC cultural types apply to 1,777 polygons (5.8% of polygons) and cover 2,516.3 ha (2.3%) of the map extent. Map classes representing nonvegetated water (non-USNVC) apply to 332 polygons (1.1% of polygons) and cover 1,586.2 ha (1.4%) of the map extent.

Appalachian National Scenic Trail↗

USGS2004

Two-dimensional hydrodynamic simulation of surface-water flow and transport to Florida Bay through the Southern Inland and Coastal Systems (SICS)

Successful restoration of the southern Florida ecosystem requires extensive knowledge of the physical characteristics and hydrologic processes controlling water flow and transport of constituents through extremely low-gradient freshwater marshes, shallow mangrove-fringed coastal creeks and tidal embayments, and near-shore marine waters. A sound, physically based numerical model can provide simulations of the differing hydrologic conditions that might result from various ecosystem restoration scenarios. Because hydrology and ecology are closely linked in southern Florida, hydrologic model results also can be used by ecologists to evaluate the degree of ecosystem restoration that could be achieved for various hydrologic conditions. A robust proven model, SWIFT2D, (Surface-Water Integrated Flow and Transport in Two Dimensions), was modified to simulate Southern Inland and Coastal Systems (SICS) hydrodynamics and transport conditions. Modifications include improvements to evapotranspiration and rainfall calculation and to the algorithms that describe flow through coastal creeks. Techniques used in this model should be applicable to other similar low-gradient marsh settings in southern Florida and elsewhere. Numerous investigations were conducted within the SICS area of southeastern Everglades National Park and northeastern Florida Bay to provide data and parameter values for model development and testing. The U.S. Geological Survey and the National Park Service supported investigations for quantification of evapotranspiration, vegetative resistance to flow, wind-induced flow, land elevations, vegetation classifications, salinity conditions, exchange of ground and surface waters, and flow and transport in coastal creeks and embayments. The good agreement that was achieved between measured and simulated water levels, flows, and salinities through minimal adjustment of empirical coefficients indicates that hydrologic processes within the SICS area are represented properly in the SWIFT2D model, and that the spatial and temporal resolution of these processes in the model is adequate. Sensitivity analyses were conducted to determine the effect of changes in boundary conditions and parameter values on simulation results, which aided in identifying areas of greatest uncertainty in the model. The parameter having the most uncertainty (most in need of further field study) was the flow coefficient for coastal creeks. Smaller uncertainties existed for wetlands frictional resistance and wind. Evapotranspiration and boundary inflows indicated the least uncertainty as determined by varying parameters used in their formulation and definition. Model results indicated that wind was important in reversing coastal creek flows. At Trout Creek (the major tributary connecting Taylor Slough wetlands with Florida Bay), flow in the landward direction was not simulated properly unless wind forcing was included in the simulation. Simulations also provided insight into the major influence that wind has on salinity mixing along the coast, the varying distribution of wetland flows at differing water levels, and the importance of topography in controlling flows to the coast. Slight topographic variations were shown to highly influence the routing of water. A multiple regression analysis was performed to relate inflows at the northern boundary of Taylor Slough bridge to a major pump station (S-332) north of the SICS model area. This analysis allows Taylor Slough bridge boundary conditions to be defined for the model from operating scenarios at S-332, which should facilitate use of the SICS model as an operational tool.

Florida↗

USGS2024

Using surficial geologic maps, vegetation, and monitoring to address erosion impacts from grazing in Channel Islands National Park, California

Employing a map-unit classification scheme based on geomorphic process and age, the U.S. Geological Survey (USGS) mapped Quaternary surficial deposits of the five islands comprising Channel Islands National Park (CHIS), California, as no such maps previously existed. Mapping was motivated through an agreement with the National Park Service (NPS) to aid natural resource assessments, including post-grazing disturbance recovery and identification of mass wasting and tectonic hazards. The resulting detailed (1:12,000 scale) maps portray areas of upland erosional transport processes and alluvial, fluvial, eolian, beach, marine terrace, mass wasting, and mixed depositional processes at a scale commensurate with the process signal on the landscape. Detailed, GPS-located observations of sedimentology, pedogenic development, landscape position, and geomorphology constrain map unit identification. In addition, map-unit boundary delineation was determined in a GIS using a variety of high-resolution (sub-meter) aerial imagery and lidar-based DEMs complemented by derivative raster products. The GIS geodatabase schema used is GeMS compliant. Resolvable map-unit widths are as small as 10-15 meter. Locational accuracy of unit boundaries is estimated to be as low as 10 m or better. Detailed mapping is at a scale allowing determination of geomorphic transport processes operating on the landscape and conveys geologic information to land managers at a resolution commensurate with park resources. Map unit ages are constrained by relative soil chronostratigraphy, vertebrate and invertebrate fossils, and materials collected for dating control using radiogenic, luminescence, and cosmogenic radionuclide techniques. Associated topical studies examined connections between geology, surface water, and vegetation distribution and restoration.

California↗

USGS1979

Integrated terrain mapping with digital Landsat images in Queensland, Australia

Mapping with Landsat images usually is done by selecting single types of features, such as soils, vegetation, or rocks, and creating visually interpreted or digitally classified maps of each feature. Individual maps can then be overlaid on or combined with other maps to characterize the terrain. Integrated terrain mapping combines several terrain features into each map unit which, in many cases, is more directly related to uses of the land and to methods of land management than the single features alone. Terrain brightness, as measured by the multispectral scanners in Landsat 1 and 2, represents an integration of reflectance from the terrain features within the scanner's instantaneous field of view and is therefore more correlatable with integrated terrain units than with differentiated ones, such as rocks, soils, and vegetation. A test of the feasibilty of the technique of mapping integrated terrain units was conducted in a part of southwestern Queensland, Australia, in cooperation with scientists of the Queensland Department of Primary Industries. The primary purpose was to test the use of digital classification techniques to create a 'land systems map' usable for grazing land management. A recently published map of 'land systems' in the area (made by aerial photograph interpretation and ground surveys), which are integrated terrain units composed of vegetation, soil, topography, and geomorphic features, was used as a basis for comparison with digitally classified Landsat multispectral images. The land systems, in turn, each have a specific grazing capacity for cattle (expressed in beasts per km 2 ) which is estimated following analysis of both research results and property carrying capacities. Landsat images, in computer-compatible tape form, were first contrast-stretched to increase their visual interpretability, and digitally classified by the parallelepiped method into distinct spectral classes to determine their correspondence to the land systems classes and to areally smaller, but readily recognizable, 'land units.' Many land systems appeared as distinct spectral classes or as acceptably homogeneous combinations of several spectral classes. The digitally classified map corresponded to the general geographic patterns of many of the land systems. Statistical correlation of the digitally classified map and the published map was not possible because the published map showed only land systems whereas the digitally classified map showed some land units as well as systems. The general correspondence of spectral classes to the integrated terrain units means that the digital mapping of the units may precede fieldwork and act as a guide to field sampling and detailed terrain unit description as well as measuring of the location, area, and extent of each unit. Extension of the Landsat mapping and classification technique to other arid and semi-arid regions of the world may be feasible.

Professional Paper↗