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At least 1,027 records · Page 57Linked to original sources

USGS2011

Northern goshawk monitoring in the western Great Lakes bioregion

Uncertainties about factors affecting Northern Goshawk ( Accipiter gentilis ) ecology and the status of populations have added to the challenge of managing this species. To address data needs for determining the status of goshawk populations, Hargis and Woodbridge (2006) developed a bioregional monitoring protocol based on estimating occupancy. The goal of our study was to implement this protocol and collect data to determine goshawk population status in the western Great Lakes (WGL) bioregion, which encompasses portions of Minnesota, Wisconsin, and Michigan, and is a mixture of private and public property. We used 366 goshawk nest locations obtained between 1979 and 2006 throughout the WGL bioregion to develop a model of landscape use consisting of forest canopy cover and land-cover covariates. We then used the model to develop a stratified sampling design for selecting 600-ha Primary Sampling Units (PSUs) to survey for goshawks. Project collaborators surveyed 86 PSUs for goshawk presence using broadcasted calls twice between mid-May and mid-August 2008, and recorded 30 goshawk detections in 21 different PSUs. Seventy-four percent of detections occurred at call stations with canopy closure >75%. Goshawk detection probabilities were 0.549 ± 0.118 (standard error) for the first visit to PSUs and 0.750 ± 0.126 for the second visit. We estimated the proportion of PSUs occupied by goshawks as 0.266 ± 0.047, which corresponded to 5184 ± 914 PSUs occupied by goshawks in our study area and suggested that goshawks are widely, but sparsely, distributed throughout the WGL bioregion.

Michigan, Minnesota, Wisconsin↗

USGS2024

Fine-resolution land cover mapping over large and mountainous areas for Lāna‘i, Hawaii using posterior probabilities, and expert knowledge

The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna‘i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna‘i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna‘i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.

Hawaii↗

USGS2013

Clarity versus complexity: land-use modeling as a practical tool for decision-makers

The last decade has seen a remarkable increase in the number of modeling tools available to examine future land-use and land-cover (LULC) change. Integrated modeling frameworks, agent-based models, cellular automata approaches, and other modeling techniques have substantially improved the representation of complex LULC systems, with each method using a different strategy to address complexity. However, despite the development of new and better modeling tools, the use of these tools is limited for actual planning, decision-making, or policy-making purposes. LULC modelers have become very adept at creating tools for modeling LULC change, but complicated models and lack of transparency limit their utility for decision-makers. The complicated nature of many LULC models also makes it impractical or even impossible to perform a rigorous analysis of modeling uncertainty. This paper provides a review of land-cover modeling approaches and the issues causes by the complicated nature of models, and provides suggestions to facilitate the increased use of LULC models by decision-makers and other stakeholders. The utility of LULC models themselves can be improved by 1) providing model code and documentation, 2) through the use of scenario frameworks to frame overall uncertainties, 3) improving methods for generalizing key LULC processes most important to stakeholders, and 4) adopting more rigorous standards for validating models and quantifying uncertainty. Communication with decision-makers and other stakeholders can be improved by increasing stakeholder participation in all stages of the modeling process, increasing the transparency of model structure and uncertainties, and developing user-friendly decision-support systems to bridge the link between LULC science and policy. By considering these options, LULC science will be better positioned to support decision-makers and increase real-world application of LULC modeling results.

Journal of Environmental Management↗

USGS2004

Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series

Time series of rainfall data and normalized difference vegetation index (NDVI) were used to evaluate land cover performance in Senegal, Africa, for the period 1982–1997, including analysis of woodland/forest, agriculture, savanna, and steppe land cover types. A strong relationship exists between annual rainfall and season-integrated NDVI for all of Senegal ( r =0.74 to 0.90). For agriculture, savanna, and steppe areas, high positive correlations portray ‘normal’ land cover performance in relation to the rainfall/NDVI association. Regions of low correlation might indicate areas impacted by human influence. However, in the woodland/forest area, a negative or low correlation (with high NDVI) may reflect ‘normal’ land cover performance, due in part to the saturation effect of the rainfall/NDVI association. The analysis identified three areas of poor performance, where degradation has occurred over many years. Use of the ‘Standard Error of the Estimate’ provided essential information for detecting spatial anomalies associated with land degradation.

Journal of Arid Environments↗

USGS2006

An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data

Remote sensing data from both Landsat 5 and Landsat 7 systems were utilized to assess urban area thermal characteristics in Tampa Bay watershed of west-central Florida, and the Las Vegas valley of southern Nevada. To quantitatively determine urban land use extents and development densities, sub-pixel impervious surface areas were mapped for both areas. The urban–rural boundaries and urban development densities were defined by selecting certain imperviousness threshold values and Landsat thermal bands were used to investigate urban surface thermal patterns. Analysis results suggest that urban surface thermal characteristics and patterns can be identified through qualitatively based urban land use and development density data. Results show the urban area of the Tampa Bay watershed has a daytime heating effect (heat-source), whereas the urban surface in Las Vegas has a daytime cooling effect (heat-sink). These thermal effects strongly correlated with urban development densities where higher percent imperviousness is usually associated with higher surface temperature. Using vegetation canopy coverage information, the spatial and temporal distributions of urban impervious surface and associated thermal characteristics are demonstrated to be very useful sources in quantifying urban land use, development intensity, and urban thermal patterns.

Remote Sensing of Environment↗

USGS2014

Change in agricultural land use constrains adaptation of national wildlife refuges to climate change

Land-use change around protected areas limits their ability to conserve biodiversity by altering ecological processes such as natural hydrologic and disturbance regimes, facilitating species invasions, and interfering with dispersal of organisms. This paper informs USA National Wildlife Refuge System conservation planning by predicting future land-use change on lands within 25 km distance of 461 refuges in the USA using an econometric model. The model contained two differing policy scenarios, namely a ‘business-as-usual’ scenario and a ‘pro-agriculture’ scenario. Regardless of scenario, by 2051, forest cover and urban land use were predicted to increase around refuges, while the extent of range and pasture was predicted to decrease; cropland use decreased under the business-as-usual scenario, but increased under the pro-agriculture scenario. Increasing agricultural land value under the pro-agriculture scenario slowed an expected increase in forest around refuges, and doubled the rate of range and pasture loss. Intensity of land-use change on lands surrounding refuges differed by regions. Regional differences among scenarios revealed that an understanding of regional and local land-use dynamics and management options was an essential requirement to effectively manage these conserved lands. Such knowledge is particularly important given the predicted need to adapt to a changing global climate.

Environmental Conservation↗

USGS2018

Analysis of different sensor performances in impervious surface mapping

The U.S. Geological Survey (USGS) has developed the National Land Cover Database (NLCD) to provide consistent land cover and land cover change products for the nation since 2001. As one of products in the NLCD, the percent impervious surface area (ISA), which was estimated with Landsat imagery, represents the fraction of human-made impervious area in a 30-m grid and has been used to quantify urban land cover types and extents for the United States. However, it is still a challenge to clearly determine urban land cover intensity and extents using remote sensing data with spatial and spectral resolutions similar to Landsat in part because of highly heterogeneous features of urban land cover. Most urban areas, especially in low intensity development areas, exhibit sub-pixel characteristics that mix impervious surface with other land covers (e.g., grass and trees) in the 30-m resolution satellite imagery. Furthermore, the influence of highly heterogeneous features in many urban areas and how they alter the spectral signature of urban landscapes has not yet been fully studied. Recent advances in remote sensing technology have provided multiple spectral and spatial resolution data from several satellites including WorldView (WV), Sentinel-2, and the Landsat Operational Land Imager (OLI). Remote sensing images having different spectral bands and high spatial resolution provide the potential to derive detailed information on the nature and properties of different surface materials on the urban ground. This study focuses on performance of mapping impervious surface using data collected from WorldView-3, Sentinel-2, and Landsat OLI. We compared ISA results estimated from these sensors and evaluated benefits and limitations of radiometric and spatial resolutions for mapping impervious surface in a study area on the Eastern corridor between Washington, D.C., and Baltimore, where developed impervious surface containing both residential housings, office buildings, and roads, in the United States. The impact of different band combinations in Sentinel-2 imagery on mapping urban impervious surface and urban land cover was also evaluated.

Conference Paper↗

USGS2022

Nebraska and Landsat

The rolling plains of Nebraska occupy a storied place in the American psyche. For those living outside the Midwest, the Cornhusker State may be seen as a symbol of the Nation’s heartland, cropped border to border, with country churches and barely standing barns to be found around every turn of its gravel roads. Although the pioneer history and agricultural heritage of the 37th State lend credence to this idyllic view, Nebraska’s varied landscapes and modern economy make the reality of life in the State more complex than its rural image would suggest. Agriculture remains Nebraska’s top industry, but manufacturing now represents 12 percent of the State’s gross domestic product. The financial services and insurance industries account for 8 percent of the gross domestic product in Nebraska, which is the headquarters of Berkshire Hathaway and Mutual of Omaha, the latter of which is named after Nebraska’s largest city, which grew nearly 12 percent between 2010 and 2020. Cropland is indeed a prominent feature in Nebraska, but the State also is home to 8 State parks, 5 national parks, 2 national forests, and 3 national grasslands. The grasses and dunes of the Nebraska Sand Hills that stretch across the north-central quarter of the State are a National Natural Landmark. Data from the Landsat satellite program contribute to the study and management of Nebraska’s land in myriad ways, from monitoring crop productivity and aiding in rangeland management to tracking damage from floods, droughts, or hurricanes. Land cover maps produced using data pulled from the 50-year Landsat archive can offer important insights into urban growth, land use trends, and land change patterns. Here are a few examples of how Landsat has been used in Nebraska.

Nebraska↗

USGS2014

Land cover change on the Seward Peninsula: The use of remote sensing to evaluate the potential influences of climate warming on historical vegetation dynamics

Vegetation on the Seward Peninsula, Alaska, which is characterized by transitions from tundra to boreal forest, may be sensitive to the influences of climate change on disturbance and species composition. To determine the ability to detect decadal-scale structural changes in vegetation. Change Vector Analysis (CVA) techniques were evaluated for Landsat Thematic Mapper (TM) imagery of the Seward Peninsula from 1986 to 1999. Scenes were geographically corrected to sub-pixel accuracy and then radiometrically rectified. Between the 1986 and 1992 satellite scenes, the CVA detected changes in direction and magnitude of the two indices (TM Band 4/TM Band 3. TM Band 5). For Row 14, change was detected for 135,518 ha and for Row 15, change was detected for 111,831 ha. Between the 1992 and 1999 scenes, change was detected by CVA for 93,278 ha. CVA results and photo interpretation together show that shrub advance is approximately 100 metres in valleys north of the Bendeleben Mountains and that shrubs have increased along riverbed bottoms. Across Path 78 Row 14 and 15, the unsupervised classification detected that 55% of the pixels changed between 1986 and 1992. Overall, approximately 759,610 ha changed to a class with a more developed canopy and only 268,132 ha changed to a class with a less developed canopy. Thus, the change detection analysis based on the unsupervised classification indicates that land-cover change on the Seward Peninsula was predominantly in the direction of increased shrubbiness. Taken together, our comparison of CVA results, unsupervised classification results, and visual interpretation of aerial photographs suggests that shrub cover may be increasing on the Seward Peninsula, which is consistent with results from experimental warming in tundra. The use of both CVA and unsupervised classification together provided a more powerful interpretation of change than either method alone in transitional regions between tundra and boreal forest.

Canadian Journal of Remote Sensing↗

USGS2016

Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014)

Remote sensing has proven a useful way of evaluating long-term trends in vegetation “greenness” through the use of vegetation indices like Normalized Differences Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In particular, analyses of greenness trends have been performed for large areas (continents, for example) in an attempt to understand vegetation response to climate. These studies have been most often used coarse resolution sensors like Moderate Resolution Image Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, trends in greenness are also important at more local scales, particularly in and around cities as vegetation offers a variety of valuable ecosystem services ranging from minimizing air pollution to mitigating urban heat island effects. To explore the ability to monitor greenness trends in and around cities, this paper presents a new way for analyzing greenness trends based on all available Landsat 5, 7, and 8 images and applies it to Guangzhou, China. This method is capable of including the effects of land cover change in the evaluation of greenness trends by separating the effects of abrupt and gradual changes, and providing information on the timing of greenness trends. An assessment of the consistency of surface reflectance from Landsat 8 with past Landsat sensors indicates biases in the visible bands of Landsat 8, especially the blue band. Landsat 8 NDVI values were found to have a larger bias than the EVI values; therefore, EVI was used in the analysis of greenness trends for Guangzhou. In spite of massive amounts of development in Guangzhou from 2000 to 2014, greenness was found to increase, mostly as a result of gradual change. Comparison of the greening magnitudes estimated from the approach presented here and a Simple Linear Trend (SLT) method indicated large differences for certain time intervals as the SLT method does not include consideration for abrupt land cover changes. Overall, this analysis demonstrates the importance of considering land cover change when analyzing trends in greenness from satellite time series in areas where land cover change is common.

Guangzhou↗

USGS2004

Has the conversion of natural wetlands to agricultural land increased the incidence and severity of damaging freezes in south Florida?

On several occasions, winter freezes have wrought severe destruction on Florida agriculture. A series of devastating freezes around the turn of the twentieth century, and again during the 1980s, were related to anomalies in the large-scale flow of the ocean–atmosphere system. During the twentieth century, substantial areas of wetlands in south Florida were drained and converted to agricultural land for winter fresh vegetable and sugarcane production. During this time, much of the citrus industry also was relocated to those areas to escape the risk of freeze farther to the north. The purpose of this paper is to present a modeling study designed to investigate whether the conversion of the wetlands to agriculture itself could have resulted in or exacerbated the severity of recent freezes in those agricultural areas of south Florida. For three recent freeze events, a pair of simulations was undertaken with the Regional Atmospheric Modeling System. One member of each pair employed land surface properties that represent pre-1900s (near natural) land cover, whereas the other member of each pair employed data that represent near-current land-use patterns as derived from analysis of Landsat data valid for 1992/93. These two different land cover datasets capture well the conversion of wetlands to agriculture in south Florida during the twentieth century. Use of current land surface properties resulted in colder simulated minimum temperatures and temperatures that remained below freezing for a longer period at locations of key agricultural production centers in south Florida that were once natural wetlands. Examination of time series of the surface energy budget from one of the cases reveals that when natural land cover is used, a persistent moisture flux from the underlying wetlands during the nighttime hours served to prevent the development of below-freezing temperatures at those same locations. When the model results were subjected to an important sensitivity factor, the depth of standing water in the wetlands, the outcome remained consistent. These results provide another example of the potential for humans to perturb the climate system in ways that can have severe socioeconomic consequences by altering the land surface alone.

Monthly Weather Review↗

USGS2010

AVHRR composite period selection for land cover classification

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, near-infrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite data-set as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa) of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).

International Journal of Remote Sensing↗

USGS2016

Estimating black bear density in New Mexico using noninvasive genetic sampling coupled with spatially explicit capture-recapture methods

During the 2004–2005 to 2015–2016 hunting seasons, the New Mexico Department of Game and Fish (NMDGF) estimated black bear abundance (Ursus americanus) across the state by coupling density estimates with the distribution of primary habitat generated by Costello et al. (2001). These estimates have been used to set harvest limits. For example, a density of 17 bears/100 km2 for the Sangre de Cristo and Sacramento Mountains and 13.2 bears/100 km2 for the Sandia Mountains were used to set harvest levels. The advancement and widespread acceptance of non-invasive sampling and mark-recapture methods, prompted the NMDGF to collaborate with the New Mexico Cooperative Fish and Wildlife Research Unit and New Mexico State University to update their density estimates for black bear populations in select mountain ranges across the state. We established 5 study areas in 3 mountain ranges: the northern (NSC; sampled in 2012) and southern Sangre de Cristo Mountains (SSC; sampled in 2013), the Sandia Mountains (Sandias; sampled in 2014), and the northern (NSacs) and southern Sacramento Mountains (SSacs; both sampled in 2014). We collected hair samples from black bears using two concurrent non-invasive sampling methods, hair traps and bear rubs. We used a gender marker and a suite of microsatellite loci to determine the individual identification of hair samples that were suitable for genetic analysis. We used these data to generate mark-recapture encounter histories for each bear and estimated density in a spatially explicit capture-recapture framework (SECR). We constructed a suite of SECR candidate models using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We used Akaike’s Information Criterion corrected for small sample size (AICc) to rank and select the most supported model from which we estimated density. We set 554 hair traps, 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 M, 358 F) individuals; the sex ratio for each study area was approximately equal. Our density estimates varied within and among mountain ranges with an estimated density of 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) for the NSC, 19.74 bears/100 km2 (95% CI: 13.77 – 28.30) in the SSC, 25.75 bears/100 km2 (95% CI: 13.22 – 50.14) in the Sandias, 21.86 bears/100 km2 (95% CI: 17.83 – 26.80) in the NSacs, and 16.55 bears/100 km2 (95% CI: 11.64 – 23.53) in the SSacs. Overall detection probability for hair traps and bear rubs, combined, was low across all study areas and ranged from 0.00001 to 0.02. We speculate that detection probabilities were affected by failure of some hair samples to produce a complete genotype due to UV degradation of DNA, and our inability to set and check some sampling devices due to wildfires in the SSC. Ultraviolet radiation levels are particularly high in New Mexico compared to other states where NGS methods have been used because New Mexico receives substantial amounts of sunshine, is relatively high in elevation (1,200 m – 4,000 m), and is at a lower latitude. Despite these sampling difficulties, we were able to produce density estimates for New Mexico black bear populations with levels of precision comparable to estimated black bear densities made elsewhere in the U.S. Our ability to generate reliable black bear density estimates for 3 New Mexico mountain ranges is attributable to our use of a statistically robust study design and analytical method. There are multiple factors that need to be considered when developing future SECR-based density estimation projects. First, the spatial extent of the population of interest and the smallest average home range size must be determined; these will dictate size of the trapping array and spacing necessary between hair traps. The number of technicians needed and access to the study areas will also influence configuration of the trapping array. We believe shorter sampling occasions could be implemented to reduce degradation of DNA due to UV radiation; this might help increase amplification rates and thereby increase both the number of unique individuals identified and the number of recaptures, improving the precision of the density estimates. A pilot study may be useful to determine the length of time hair samples can remain in the field prior to collection. In addition, researchers may consider setting hair traps and bear rubs in more shaded areas (e.g., north facing slopes) to help reduce exposure to UV radiation. To reduce the sampling interval it will be necessary to either hire more field personnel or decrease the number of hair traps per sampling session. Both of these will enhance detection of long-range movement events by individual bears, increase initial capture and recapture rates, and improve precision of the parameter estimates. We recognize that all studies are constrained by limited resources, however, increasing field personnel would also allow a larger study area to be sampled or enable higher trap density. In conclusion, we estimated the density of black bears in 5 study areas within 3 mountains ranges of New Mexico. Our estimates will aid the NMDGF in setting sustainable harvest limits. Along with estimates of density, information on additional demographic rates (e.g., survival rates and reproduction) and the potential effects that climate change and future land use may have on the demography of black bears may also help inform management of black bears in New Mexico, and may be considered as future areas for research.

New Mexico↗

USGS2023

Application of the Precipitation-Runoff Modeling System (PRMS) to simulate the streamflows and water balance of the Red River Basin, 1980–2016

The Precipitation-Runoff Modeling System (PRMS) was used to develop and calibrate a streamflow and water balance model for the Red River Basin as part of the U.S. Geological Survey National Water Census, a research effort focused on developing innovative water accounting tools and conducting assessments of water use and availability at regional and national spatial scales. The PRMS is a deterministic model that simulates the effects of climate, land cover, and water use on watershed hydrology on the basis of physical processes and spatial attributes of the watershed. The model was used to estimate streamflow at daily and monthly temporal scales for the 1980–2016 period and to evaluate the impacts of natural and anthropogenic influences on streamflow and water budget components. Sixty-three percent of streamgages were calibrated successfully for the monthly time step and 43 percent of streamgages were successfully calibrated for the daily time step. Some of the challenges of calibrating streamgages included estimating low amounts of streamflow in dry areas of the basin and accurately representing watershed characteristics related to evapotranspiration in the basin, among other factors. The model estimated streamflow with some accuracy for 42 percent and 29 percent of the 73 streamgages used to evaluate the model at monthly and daily time steps, respectively. Relative to no-water-use conditions, water use increased streamflow volumes (that is, return flow from reservoir releases) the most on the main stem of the Red River, the North Fork of the Red River, and the Ouachita River. Water withdrawal decreased streamflow volumes most in the Red River near the outlet of the basin and in Caney Creek. Streamflow volumes on the North Fork of the Red River changed most as a result of water use. The Red River Basin PRMS model provided estimates of streamflow that were limited in their accuracy by (1) the availability of accurate water-use data; (2) the coarse resolution of spatial parameters (such as those for impervious area or plant canopy), which leads to the homogenization of physical features in small watersheds in the model domain; and (3) the accuracy of spatial patterns of precipitation distribution across the model domain. Improvements in the quality and quantity of available water-use data and finer resolution spatial parameter and climate data could lead to the development of better-informed models in the future that are capable of making more accurate estimates of streamflow, because they are more representative of physical and hydrologic conditions in the Red River Basin.

Arkansas, Louisiana, Texas, Oklahoma↗

USGS2025

Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration

Often found along the estuarine-marine interface, barrier islands and mainland coastal zones are shaped by tides, currents, extreme storms, and relative sea-level rise. These systems provide ecosystem services such as storm surge and wave attenuation, erosion protection to inland areas, habitat for fish and wildlife, recreation, and tourism. Given the importance of these ecosystems coupled with their dynamic nature, information on how these coastal systems are changing can help to inform natural resource management. Remote sensing advancements have led to an abundance of data for monitoring change in coastal settings. This study developed a multiscale framework that can provide trajectory information from screening-level analyses by using existing or custom moderate spatial resolution land cover maps. Using the north-central Gulf Coast as a case study, the trajectory of land cover area for barrier islands and mainland coastal zones was assessed using several geospatial data sets, including: (1) long-term moderate-resolution remote sensing products with an annual (or more frequent) temporal frequency; (2) a restoration database ( e.g. , beach/dune restoration, sediment placement, and dune enhancement); and (3) a tropical storm database. Due to the coarser spatial resolution of data sets used for screening-level analyses, detailed or application-specific analyses are often needed to reduce uncertainty in smaller changes that may not be captured. These may include land cover change analyses ( i.e. this study), periodic land cover maps with higher spatial resolution and more detailed land cover classes, or elevation-related analyses ( e.g. , dune change or inundation change). Using this framework, abrupt changes in land cover on Dauphin Island, Alabama, resulting from extreme storms were detected using moderate spatial resolution screening-level data, while restoration impact analyses may require higher resolution data. Further, land cover change analyses that incorporate change allocation provide robust information for understanding land cover change in dynamic coastal settings.

Alabama, Florida↗

USGS2015

The Southern Piedmont’s continued land-use evolution, 1973–2011

The southern Piedmont in the U.S. was an important farming region during the 19 th century, but by the end of the 20th century, agricultural land use had decreased substantially with forest becoming the majority land cover by the 1970s. Geographical literature has documented this change but has not concentrated on the region’s contemporary land uses. The Piedmont currently has three main types of land use and land cover changes: cyclic forestry, changes between forest and agriculture, and urbanization. The first and second groupings are reversible and land uses and land covers can change among them, but urbanization is normally a permanent change that increases in area through time. U.S. Geological Survey findings indicate that cyclic forestry of cutting (clearing) and regrowth dominated recent land change in the Piedmont. This paper explores the Piedmont’s current land uses and some of their driving forces.

Alabama, Georgia, North Carolina, South Carolina, ↗

USGS2019

Landsat: The cornerstone of global land imaging

Since 1972, the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites have provided an uninterrupted space-based data record of the Earth’s land surface to help advance scientific research towards the understanding of our planet and the environmental impact of its inhabitants. Early Landsat satellites offered a wealth of new data that improved mapping of remote areas and geologic features along with digital analysis of vegetation. The utility of Landsat’s spatial and spectral resolution has advanced its use for applications that benefit society such as global crop forecasting, forest monitoring, water use, carbon assessments, and the base for Google Maps. Landsat’s long-term data record provides an unrivaled resource for observing land cover and land-use change over a timescale of decades. The free and open Landsat data policy in 2008 was a paradigm shift for the world. Today, due to improved analytical and computing capabilities, the Landsat archive is poised to shift into a more real-time monitoring and understanding of the Earth.

GIM International↗

USGS2018

Density of American black bears in New Mexico

Considering advances in noninvasive genetic sampling and spatially explicit capture–recapture (SECR) models, the New Mexico Department of Game and Fish sought to update their density estimates for American black bear ( Ursus americanus ) populations in New Mexico, USA, to aide in setting sustainable harvest limits. We estimated black bear density in the Sangre de Cristo, Sandia, and Sacramento Mountains, New Mexico, 2012–2014. We collected hair samples from black bears using hair traps and bear rubs and used a sex marker and a suite of microsatellite loci to individually genotype hair samples. We then estimated density in a SECR framework using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We sampled the populations using 554 hair traps and 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 male, 358 female) individuals. Our density estimates varied from 16.5 bears/100 km 2 (95% CI = 11.6–23.5) in the southern Sacramento Mountains to 25.7 bears/100 km 2 (95% CI = 13.2–50.1) in the Sandia Mountains. Overall, detection probability at the activity center (g0) was low across all study areas and ranged from 0.00001 to 0.02. The low values of g0 were primarily a result of half of all hair samples for which genotypes were attempted failing to produce a complete genotype. We speculate that the low success we had genotyping hair samples was due to exceedingly high levels of ultraviolet (UV) radiation that degraded the DNA in the hair. Despite sampling difficulties, we were able to produce density estimates with levels of precision comparable to those estimated for black bears elsewhere in the United States.

New Mexico↗