National Land Cover Dataset Research Articles

Cooling benefits from urban planting depend on local background canopy cover: A continental cross-city comparison

Numerous studies have shown that the cooling efficiency (CE) of urban trees varies by cities with different climate backgrounds, and recent research further indicated that there may be large within-city variations in CE. However, how such within-city variations differ across cities, and their relations to the local percent of urban tree canopy (Ptree) remain poorly understood. This study aims to fill this gap based on a comparison study across 118 cities with different biomes and climates in the continental USA. We used the tree canopy layer of the National Land Cover Dataset (NLCD) 2011 to measure urban tree canopy (UTC), and calculated land surface temperature (LST) based on Landsat thermal bands. We found: 1) CE had larger within-city and cross-city spatial variations in cities located in arid and semi-arid biomes. 2) CE was related to Ptree in nonlinear ways in >90 % of the study cities. In most cities (approximately 70 %), CE had an L-shaped relationship with Ptree, showing that CE first declined quickly with the increase of Ptree, but then gradually dropped in a slower way or became relatively stable after Ptree reached a certain threshold. 3) While there was no significant difference in the types of CE-Ptree relationship among biomes and climates, the threshold of Ptree in CE-Ptree nonlinear ways was smaller in arid cities. The results of this threshold linking cooling benefits and current UTC can serve as a useful tool to prioritize locations for urban planting to maximize cooling benefits.

Assessing and predicting land cover dynamics for environmental sustainability in Jordan's arid ecosystems using CA-Markov model

Jordan's arid ecosystems face a threat of environmental degradation due to increasing human activities driven by a growing population and economy. This study assesses Land Use and Land Cover (LULC) changes in regional and ecoregional landscapes, particularly in a region with an east-west environmental gradient, spanning 41 years (1990–2030). The study utilizes the Jordan National Land Cover Dataset (JNLCD) for historical data and employs the CA-Markov model to generate a future land cover map. The results revealsignificant changes, particularly the tripling in size of urban areas at the expense of primary croplands. The Steppe ecoregion, known for its fragility, experienced shifts into irrigated agriculture practices and loss of natural rangelands. The period between 2000 and 2015 marked as the most dynamic period as the region landscape underwent significant change due to population growth and influx of refugees. During this period, 123 km2 of rainfed agricultural areas were consumed by urbanization while rainfed agriculture activities expanded into sparsely-vegetated/barren areas (55 km2). Additionally, 33 km2 of sparsely-vegetated/barren areas were transformed into irrigated agriculture. This study offers a comprehensive and innovative analysis of LULC changes in Jordan's arid ecosystems. While it highlights the ecological importance of the region and its potential to enhance environmental resilience, it also underscores the negative impact of human interventions, which have disrupted spatial arrangement, connectivity, stability, and structure. By bridging historical data with future projections, employing detailed landscape metrics, and addressing socio-economic drivers, this work provides valuable insights for sustainable land use planning and environmental conservation in Jordan.

Landscape changes and watershed erosion in Prince George's County, Maryland

AbstractThis study evaluated erosion rates and sediment production in streams, and factors potentially influencing them throughout the Anacostia, Patuxent, and Potomac (non‐Anacostia) River watersheds within Prince George's County, Maryland, US. As part of the County's watershed‐scale biological monitoring program, from approx. 1999 to 2008, permanent monuments were established to allow measurement of stream channel cross‐sectional (XS) area. The intent of this study was to characterize the intensity and spatial distribution of fluvial geomorphic instability across the county and use the results to target and plan stormwater management and stream restoration actions. For this study, 78 stream locations were re‐surveyed in 2020, representing a time lapse of from 12 to 21 years. Data collected included XS dimensions, modified Wolman 100‐particle pebble counts, and reach‐specific soil bulk density. Land use/land cover data were compiled from the National Land Cover Dataset (NLCD), precipitation from the National Weather Service Center for Environmental Information (NCEI), and soils from the Natural Resources Conservation Service Web Soil Survey (NRCS/WSS). We calculated percent change in XS area, rates of erosion, sediment yield, and assigned geomorphic classifications, and interpreted them in the context of spatial positions relative to changes in land cover characteristics. Sediment yields among the 78 reaches exhibited a combination of those undergoing enlargement/erosion (67.9%), reduction/deposition (25.6%), and the remaining 6.4% with essentially no change over the period of record. Of the top 20 most geomorphically active reaches surveyed in the County, 12 are in the Anacostia River basin, with the other scattered among the Patuxent River and Potomac River basins.

Changes in Streamflow Statistics and Catchment Land Uses Across Select USGS Gages in Northwest and West‐central Arkansas

AbstractSince 1901, heavy rainfall events have increased in the United States in both intensity and frequency, and human population in the United States has increased, resulting in significant land use changes. Both trends contribute to an increase in observed flood magnitude and frequency. To determine if a relationship exists between land use/land cover and changing stream flows in northwest Arkansas, this study analyzed temporal changes in various flow statistics for 14 stream gages and compared the rates of change in flow statistics from gages on streams with watersheds that have varying land uses, i.e., urban, agricultural, and undeveloped. Mann‐Kendall analysis was used to determine statistically significant changes in flow statistics, which were then compared to National Land Cover Dataset (NLCD) watershed land uses from 2001 and 2019. All analyzed gages had one or more flow statistics with at least a moderately significant increase, and all analyzed flow statistics showed at least moderately significant streamflow increases at two or more gages (P < 0.100). There were no decreases of any significance in any flow statistic at any gage. In general, urban land development did not happen on native prairies and forests but on previously agricultural land. Significant positive relationships were found between maximum yearly flow and 2019 urban land use, urban land use change from 2001 to 2019, and 2019 Human Development Index (HDI). A similar relationship was found to exist between yearly minimum flow and 2019 HDI. These results highlight the importance of considering the cost of potential stream bank erosion and flooding in future land use planning, permitting, and zoning.

Green spaces in highly urbanized tracts tied to lower prevalence of chronic respiratory diseases: A nationwide study across levels of urbanicity

Previous studies have found conflicting results between green space and respiratory diseases, possibly due to the use of generalized green space measures and the diverse urbanicity levels in which they were conducted. Few nationwide studies examined how urbanicity modifies the relationship between green spaces and respiratory diseases while considering the geospatial relation between green space and human population, population density, and accessibility.This study evaluates the relationships between population-weighted forest and open space exposure (inside and outside park), chronic obstructive pulmonary disease (COPD), and asthma in the contiguous United States. The COPD and asthma data were obtained from the Centers for Disease Control and Prevention. The green space exposure within an 800 m buffer was computed using National Land-cover Dataset and WorldPop Global Population Data. The associations were estimated using spatial error models and adjusted for important covariates. We also performed stratified analyses by four urbanicity levels.Green spaces inside but not outside park were associated with a lower prevalence of COPD and asthma. We found that a 1 m2 increase in forest inside park was associated with a decrease of 3.57 and 2.97 cases per 100,000 population in COPD and asthma, and a 1 m2 increase in open space inside park was associated with a decrease of 4.82 and 7.66 cases per 100,000 population in COPD and asthma. A trend of an inverse relationship was observed across urbanicity: the beneficial associations occurred in highly urbanized tracts but not in the three less-urbanized tracts.Our results provide further evidence that nearby forests and open spaces inside park, particularly in urban areas, were tied to a lower prevalence of respiratory diseases in the US. The beneficial association suggests the necessity of ensuring access to nearby green spaces, especially those in parks, to promote respiratory health.

Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey

Timely and reliable Land Use and Cover change information is crucial to efficiently mitigate the negative impact of environmental changes. Switzerland has the ambitious objective of being a sustainable country while remaining an attractive business location with a high level of well-being. However, this aspiration is hampered by increasing pressures that are significantly impacting the environment and putting serious demands on land. In the present study, we used the national Land Cover (LC) dataset, named ArealStatistik, produced by the Federal Statistical Office, to explore the spatiotemporal patterns of Land Cover in Switzerland, providing a comprehensive assessment of land cover change at the national scale. Results indicate that, in general, Switzerland has undergone small, spatially dispersed, dynamic, and gradual change trends, with high rates of transition between low growing Brush Vegetation and forest LC classes in recent years. These pixel-level trends are more important in the lower altitude plateau and Jura regions, while greater changes in the spatial configuration of LC are observed in the alpine regions. However, findings also suggest that identifying drivers and understanding the rate of change are limited by the spatial resolution and temporal update frequency of the ArealStatistik. The ability to understand these drivers would benefit from a high-resolution annual LC dataset. Such a data product can be produced using the ArealStatistik together with dense satellite data time-series and Machine/Deep Learning techniques.

Investigation of the Microenvironment, Land Cover Characteristics, and Social Vulnerability of Heat-Vulnerable Bus Stops in Knoxville, Tennessee

The urban heat island is a climate, public health, and environmental justice issue. Sustainable urban infrastructure needs improvements in public transport to protect citizens’ health from the urban heat island. This case study investigates the local microenvironment and social vulnerability of heat-vulnerable bus stops in Knoxville, Tennessee, using publicly available data from a variety of sources. These included ground and satellite measurements of heat and humidity from the Knoxville Heat Mapping Campaign, characteristics of land surface from the National Land Cover Dataset 2019 of the United States Geological Survey, and the 2018 Social Vulnerability Index from the U.S. Centers for Disease Control and Prevention. A geographic information system and a principal component analysis were used to identify social vulnerability in areas where the bus stops are located. The results show that most heat-vulnerable bus stops are poor microenvironments without trees and shelters. The hottest bus stops are concentrated in the highly developed and densely populated areas of West Knoxville and downtown Knoxville and in South, North, Northeast, and Northwest Knoxville, which are relatively high vulnerability clustered and have poor public infrastructure. The findings provide the foundation for mitigation strategies to better prepare local communities for climate change by identifying public transportation areas negatively impacted by the urban heat island.

Cryptic tolerant fish species and their potential effect on index of biotic integrity (IBI) scores

Indices such as the Index of Biotic Integrity (IBI) are often used by management agencies to estimate the abstract property of stream health. These indices are usually predicated on the belief that certain fish species are tolerant to environmental perturbation while others are sensitive. Species are usually designated as either tolerant or sensitive in these analyses based on inherent ecological or taxonomic characteristics. However, previous literature has shown that certain species from ecological or taxonomic “sensitive” groups experience increased abundance in degraded streams. We term such species “cryptic tolerants”. Using a stream fish assemblage dataset of 433 unique sample locations across the state of Alabama and the National Landcover Dataset, our objectives were to 1) identify the most common cryptic tolerant species, 2) investigate how cryptic tolerant species might inflate indices of stream health, and 3) compare an alternative measure of stream health in which species are statistically defined rather than defined using the traditional trait-based approach. We identified cryptic tolerants using Nonmetric Multidimensional Scaling in six ecoregions. A series of regressions revealed that the proportion of cryptic tolerant species decreased in response to an increasing proportion of forested land in catchments while the proportion of true sensitives increased in all ecoregions except for the Cumberland Plateau. An index that simply used the percentage of statistically defined, non-tolerant species generally had lower p-values and higher r2 values than IBI scores when both were regressed against percentage of forest in catchment. However, both indices had low degrees of correlation with expected disturbance, indicating a univariate index may be inadequate to characterize stream health. Our results highlight a potential issue with applying the IBI to diverse southeastern systems in the United States, which may be alleviated by designating species sensitivity based on empirical response to disturbance rather than taxonomic or ecological characteristics.

Estimating the foraging range of Cerceris fumipennis (Hymenoptera: Crabronidae) using land cover data

Abstract Biosurveillance monitors the prey of the solitary buprestid-hunting wasp, Cerceris fumipennis (Hymenoptera: Crabronidae), for the presence for emerald ash borer (Agrilus planipennis, Fairmaire: Coleoptera: Buprestidae). Still unresolved is the foraging range, and thus the surveillance range, of C. fumipennis. Foraging occurs in forest canopies and wasps are difficult to track. We assumed that the proportion of conifer-feeding beetles collected at a colony would be related to the proportion of conifers in the surrounding area. If this is the case, the radius of the area around a colony which best correlates the proportion of conifers with the proportion of conifer feeding prey should reflect the foraging range of the colony. In this study, we used 7 yr of foraging data, and the National Land Cover Dataset 2016 map to estimate the foraging range of C. fumipennis. Overall, we found that the highest correlation between prey type collected, and forest type present, was between 1,000 and 1,500 m from the nest sites. We thus conclude that surveillance of a colony of C. fumipennis will yield information about the presence of non-native buprestids within a 1.0–1.5 km radius.

Fine‐scale accuracy assessment of the 2016 National Land Cover Dataset for stream‐based wildlife habitat

AbstractEfficiently and effectively identifying and assessing potential wildlife habitat and important ecological resources is essential as rapid anthropogenic land use change alters and detrimentally affects terrestrial and aquatic habitats. Accuracy assessment of remotely sensed data supports ecological planning and management decisions, and is especially important when using freely available, coarse‐resolution spatial datasets, such as the National Land Cover Dataset (NLCD). A popular dataset designed for application at larger regional to national spatial scales, the NLCD has been used in finer scale studies outside of its intended use, often without the imperative support of field‐based accuracy assessment. We ground‐truthed stratified random sampling points to assess the accuracy of the 2016 NLCD at a fine spatial scale relevant to stream‐based ecological research. Our results demonstrated an overall accuracy of <65%, less than the United States Geological Survey recommended accuracy of ≥85%. Results indicated that the NLCD may not be effective as a tool for stream‐level studies and could provide erroneous results for fine‐scale habitat assessment and planning when used as the only reference dataset. When conducting ecological research, it is important to consider the appropriate scale, resolution, and limitations of available datasets to achieve the most accurate results.

Sub-Pixel Mapping of Spectral Imagery Based on Deviation Information Measurement

Sub-pixel mapping (SPM) technology can analyze mixed pixels in spectral image and realize the transformation from abundance images to a fine sub-pixel classification image. Since the SPM belongs to an ill-posed issue, deviation information (DI) inevitably is in optimal abundance images. Due to simple structure and good robustness, most SPM approaches are according to the spatial dependence assumption; namely, the closer the spatial distance is, the more likely the subpixels belong to the same class. However, the existing SPM methods based on spatial dependence assumption cannot accurately measure the DI, which affects the accuracy of the final mapping result. To address this problem, the SPM based on DI measurement (DIM) is proposed in this work. The DIM uses a dual-scale spatial attraction model (DSAM) to process the coarse abundance images to obtain predicted abundance images. The fine prior images captured at different times from the same field of view are used to measure the deviation abundance images with the DI using a geographically weighted regression (GWR) model. The predicted abundance images and deviation abundance images are fused to derive optimal abundance images. Based on the proportion information on sub-pixels being classes in the optimal abundance images, the class labels are assigned to sub-pixels by label allocation method, yielding the final mapping result. The proposed DIM method is tested on National Land-cover Dataset, Rome Dataset, and Bastrop Fires dataset. The experimental results verify that the proposed DIM achieves the best performance with the overall accuracy of 97.26%, 88.15%, and 99.80% in the three experimental results.

Neighborhood greenspace and cognition: The cardiovascular health study.

We examined whether greenspace measures (overall percent greenspace and forest, and number of greenspace types) were associated with clinically adjudicated dementia status. In a sample of non-demented older adults (n=2141, average age=75.3 years) from the Cardiovascular Health and Cognition Study, Cox proportional hazard and logistic regression analyses were used to estimate associations of baseline greenspace with risks of incident dementia and MCI, respectively, while adjusting for demographics, co-morbidities, and other neighborhood factors. We derived quartiles of percent greenness (greenspace), forest (percent tree canopy cover), and tertiles of greenspace diversity (number of greenspace types) for 5-km radial buffers around participant's residences at study entry (1989-1990) from the 1992 National Land Cover Dataset. Dementia status and mild cognitive impairment (MCI) over 10 years was clinically adjudicated. We observed no significant association between overall percent greenspace and risk of mild cognitive impairment or dementia and mostly null results for forest and greenspace diversity. Forest greenspace was associated with lower odds of MCI (OR quartile 4 versus 1: 0.54, 95% CI: 0.29-0.98) and greenspace diversity was associated with lower hazard of incident dementia (HR tertile 2 versus 1: 0.70, 95% CI=0.50-0.99). We found divergent results for different types of greenspace and mild cognitive impairment or dementia. Improved greenspace type and diversity measurement could better characterize the association between greenspace and cognition.

Detection of land use / cover changes of the KOSH region over a period of 14 years using the South African National Land Cover datasets for 2000 and 2014

Simple algebraic change detection techniques viz. image difference and image ratio were applied to the South African national land use / cover (NLC) datasets of years 2000 and 2014, prepared in grid format covering the Klerksdorp–Orkney–Stilfontein–Hartebeestfontein (KOSH) region in order to assess land use/land cover changes. Both the 2000 and 2014 NLC datasets were generated from Landsat images using different classification schemes and the code values & attributes of the land cover classes of the two datasets were different/not comparable. In order to make these datasets comparable for change detection, the NLC2000 dataset was examined in ArcView GIS by superimposing it onto the NLC2014 dataset and similarities and differences were identified. For each cover type of the NLC2000 dataset, comparable cover type of the 2014 dataset was identified by making a query to the NLC2000 dataset and after viewing the spatial distributions of selected units in respect of the NLC2014 dataset. Suitable code values of NLC2014 dataset were identified for the NLC2000 dataset and it was later reclassified. The land use / cover change detection study reveals that increase in areas were observed for the cover types: Cultivated common fields (low), Cultivated common fields (med), Mines 2 semi-bare, Wetlands, Urban commercial and Plantations/woodlots mature. The Grassland, Thicket/dense bush, Urban residential (dense trees/bush), Mines 1 bare, and Cultivated common pivots (high) showed a decrease in places. During the 14 years, Grassland had decreased from 2,132.47 km2 (77.35% of the total area) to 1,629.78 km2 (59.11% of the total area) owing to landscape transformation to other land covers (e.g. Cultivated common fields and Urban residential) due to human activities. The percentage increase in areas observed for the Cultivated common fields (low and medium) were 8.21% and 2.96% while the Mines 2 semi-bare, Wetlands, Urban commercial, Plantations/woodlots mature showed increases of 0.67%, 0.32%, 0.28% and 0.23% respectively. The area of Thicket/dense bush decreased from 108.15 km2 to 56.71 km2 (change of 1.87%). Maps of land use/land cover changes and statistics obtained for the changed areas are very useful for identifying various changes occurring in different classes and for monitoring land use dynamics.

Land use and cover changes on the Loess Plateau: A comparison of six global or national land use and cover datasets

Image classification often produces large deviations between land use and land cover (LULC) datasets and the ‘real’ changes, leading to uncertainty in the results of LULC related assessments and the propagated impacts through modelling. LULC products are widely used as input for various large-scale climatic, ecological and hydrological models, but the accuracy and authenticity associated with data quality are rarely fully considered. In the study, six widely used global or national LULC datasets, MODIS-MCD12Q1, EAS CCI-LC, GlobeLand30, GLASS-GLC, CAS-CLUDs and ChinaCover, are used to assess the consistency and reliability of LULC on the Loess Plateau, where land cover has undergone major changes due to “Grain to Green” Project. Results show that MODIS and GLASS products have low quality, with the overall accuracy of 55.3~58.2% and 34.7~39.4% respectively, and the areal and spatial results cannot reflect the real changes of the Loess Plateau. Large areas of croplands in MODIS-MCD12Q1 are classified as natural grassland. Croplands in GLASS-GLC are overestimated in the central parts of the Loess Plateau. Both of MODIS and GLASS products are hard to separate woodlands from grasslands. ESA CCI-LC has higher classification accuracy (73.9%~74.2%) than the released MODIS and GLASS products. The woodlands in ESA CCI-LC is relatively underestimated than that of CAS-CLUD and ChinaCover, and the conversion feature from cropland to forest and grasses is almost absent on ESA CCI-LC maps. Although GlobeLand30 has a high overall accuracy at 86. 6~86.7%, it is inadequate to get the characteristic of returning of cropland to forest and grasses. The most similar land covers are CAS-CLUDs and ChinaCover, which are considered to have highest classification accuracy ranging from 89.4% to 91.6% and can reflect the actual LULC status and its changes on the Loess Plateau. A blending LULC dataset is developed and the overall accuracies for all classes can be improved by 1.63–7.49%.

Neighborhood Greenspace and Cognition: The Cardiovascular Health Study

Access to greenspace has been positively associated with cognition among older adults, however prior research has been limited in temporal and geographic scope. We evaluated associations between neighborhood greenspace and incidence of dementia and change in cognitive functioning using a longitudinal sample of non-demented adults (n=2,465) from the Cardiovascular Health and Cognition Study. Percent greenness (1-km radial buffers) was derived from the National Land Cover Dataset. Cognitive function was measured using the Mini-Mental State Examination (3MSE) and dementia status was clinically adjudicated. Cox proportional hazard and logistics regression analyses were used to examine associations of baseline greenness with risk of incident dementia and risk of mild cognitive impairment, respectively. Generalized linear mixed models accounting for within-subject correlations were used to examine the association between greenspace in the neighborhood at baseline and 3MSE score (1991-1999). Ongoing results will be presented, along with modifiers and mediators of associations.

Associations between neighborhood greenspaces and cognitive and brain volume measures in cognitively normal older adults

AbstractBackgroundLiving in neighborhoods with greater vegetation and park space has been associated with better brain health in older adults. While mechanisms relating greenspaces and brain health are not well understood, greenspaces may lead to better cognition and lower Alzheimer’s risk by providing social and physical activity opportunities and stress reduction, factors previously associated with Alzheimer’s disease. This study investigated associations between neighborhood greenspace types and multiple brain health measures for Alzheimer’s disease and related disorders, which has been examined little to date.MethodResidential addresses and magnetic resonance imaging (MRI) volumes were obtained from cognitively‐normal participants who completed Uniform Data Set assessments from Knight Alzheimer’s Disease Research Center (St. Louis, Missouri). Addresses geocoded to US Census tracts were linked to National Land Cover Dataset data to identify open spaces and areas with tree canopy (hereafter termed forest). Longitudinal clinical data were obtained from the National Alzheimer’s Coordinating Center. Outcomes included CDR® Sum of Boxes, four cognitive domain z‐scores (memory, attention, executive function, and language) constructed from ten neuropsychological tests, and hippocampal and white matter hypointensity volumes (167 had T1‐weighted MRI). Multi‐level linear regression models (clustering on Census tract and participant) controlled for demographics, comorbidities, and neighborhood disadvantage and population density. Brain volume models additionally controlled for intracranial volume.ResultParticipants (n=553) were on average 74 years old (standard deviation [SD]=7), 16% were Black, and 81% were White. Neighborhood percentage of open space ranged from 0 to 42% (mean=6.6%, SD=9.9) and forest ranged from 0‐16% (mean=0.2%, SD=1.4). Greater percentage of forest was associated with lower baseline episodic memory (estimate=‐0.79; 95% confidence interval [CI]=‐1.13, ‐0.46), slower annual episodic memory declines (estimate=0.069, 95% CI=0.002, 0.137), higher baseline executive function (0.15, 95% CI=0.03, 0.26), and fewer white matter hypointensities (estimate=‐3,521 mm3, 95% CI=‐6,443, ‐600). No other associations were observed.ConclusionLiving in neighborhoods with more tree canopy was associated with lower episodic memory but higher executive function at baseline, slower annual declines in executive function, and fewer white matter hypointensities. This study suggests mixed associations between neighborhood tree canopy and brain health that will need to be replicated in larger, diverse cohorts.

The Relationship between Urban Density and Building Energy Consumption

Neighborhood characteristics influence natural urban energy fluxes and the choices made by urban actors. This article focuses on the impact of urban density as a neighborhood physical parameter on building energy consumption profiles for seven different metropolitan areas in the United States. Primarily, 30 × 30 m2 cells were classified into five categories of settlement density using the US Geological Survey’s National Land Cover Dataset (NLCD), the US Census, and Census Block data. In the next step, linear hierarchical spatial and non-spatial models were developed and applied to building energy data in those seven metropolitan areas to explore the links between urban density (and other urban form parameters) and energy performance, using both frequentist and Bayesian statistics. Our results indicate that urban density is correlated with energy-use intensity (EUI), but its impact is not similar across different metropolitan areas. The outcomes of our analysis further show that the distance from buildings within which the influence of urban form parameters on EUI is most significant varies by city and negatively changes with urban density. Although the relationship between urban density parameters and EUI varies across cities, tree-cover area, impervious area, and neighborhood building-covered area have a more consistent impact compared to building and housing density.

Assessing natural resource change in Vhembe biosphere and surroundings.

South Africa is a custodian of an immense wealth of natural and biodiversity resources in Africa. Natural resources are continually changing in different South African biospheres based on anthropogenic and non-anthropogenic causes. Land use activities like agriculture, cultivation, livestock rearing, commercial plantations, urbanisation and mining are among the major drivers of natural resource change and transformation. In this study, land cover change assessment was used to assess natural resource change in Vhembe biosphere and surroundings. To assess natural resource change in Vhembe biosphere, land use land cover change assessment was conducted using South African's national land-cover dataset, generated from multi-seasonal Landsat 5 and Sentinel-2 images. The 72× class land cover map was re-classified into 12× classes to fit the study objectives. Eight out of twelve classes quantified in hectares: indigenous forests, thicket/dense bush, natural woodland, shrubland, grassland, water bodies and wetlands were categorised as natural resources for which the natural resource change assessment for this study was based. Assessment findings established that land use and its related activities have contributed substantially to natural resource change where cultivated commercial, natural woodland and built-up residential contributed the most significant upward change in hectarage and percentage, from 132,246.9 to 365,644.92 (ha)-percentage change of 176%; from 94,665.42 to 257,889.68 (ha)-percentage changes of 172% and from 74,070.27 to 147,701.88(ha)-percentage change of 99% respectively. Shrubland, thicket/dense bush and indigenous forests registered the highest downward changes from 263,070.6 to 977.72 (ha); from 338,723.7 to 23,166.92 and from 13,211.91 to 7402.92 (ha) with percentage changes of -100%, -93% and -44% respectively in Vhembe biosphere and the surroundings from 1990 to 2018. The study showed how natural resources are changing and the use of remote sensing for environmental monitoring and assessment in the Vhembe district.

Neighborhood Green Land Cover and Neighborhood-Based Walking in U.S. Older Adults

Greenspace exposure has been associated with physical activity, but few studies have investigated its association with physical activity in the residential neighborhood. This study investigates whether greater amounts of neighborhood open space and forest are associated with neighborhood-based walking in older adults. In 2020, cross-sectional analyses were conducted on those aged ≥65 years from the 2017 National Household Travel Survey. Minutes of neighborhood walking per day were derived from travel diaries. Green land cover measures from the 2011 National Land Cover Dataset were linked to respondent data by the U.S. census tract. Adjusted linear regression models, using weights accounting for survey sampling, tested the associations between the percentage of green land cover in the neighborhood (open space, forest) and minutes of neighborhood walking per day. Adjusted models were stratified to examine whether the associations varied by an individual- and neighborhood-level SES, sex, and race/ethnicity. Respondents (N=72,753) were aged 74 (SD=7) years on average. Greater percentage of open space was associated with more neighborhood walking in African Americans (estimate=0.069, 95% CI=0.005, 0.133). Greater percentage of forest was associated with more neighborhood walking in the overall sample (estimate=0.028, 95% CI=0.006, 0.050), women (estimate=0.025, 95% CI=0.005, 0.045), and Whites (estimate=0.034, 95% CI=0.004, 0.064). Type of neighborhood green land cover (open space versus forest) may be differentially associated with neighborhood walking depending on race/ethnicity. This study suggests a possible association between greater neighborhood open space and greater walking among African Americans that must be confirmed in future studies.

Landscape Composition Encompassing Cooper's Hawk Nest Sites in Western North Dakota

The Cooper's Hawk (Accipiter cooperii) is often classified as a woodland species, but it exhibits marked plasticity by nesting successfully in what was previously thought to be atypical habitat such as urban areas and sparsely wooded grasslands, including the Little Missouri National Grassland in North Dakota. Forest expansion into North Dakota grasslands is likely due to the reduction of natural disturbances such as wildfires and grazing by bison (Bison bison). From the 1970s to 2001, forest cover increased in McKenzie County, North Dakota, by .1000%. From 1950 to 1972 there were two recorded instances of nesting Cooper's Hawks in McKenzie County. In 2001, we recorded 18 active nests, 13 of which were successful, four that failed, and one with unknown fate. Using the 2001 National Landcover Dataset (NLCD), we calculated that the mean proportion of forest cover in circular plots (18.1 km2) surrounding nests was 0.21. We found no relationship between nest fate and proportion of forest land cover. Additionally, we found no significant difference in proportion of forest cover between nest sites and randomly selected sites centered on forest land cover, indicating that Cooper's Hawks were using forest land cover in proportion to its availability. Understanding the relationship between the shifting landscape composition of North Dakota and its avifauna is important for monitoring and managing breeding Cooper's Hawks.