Department of Geography

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Adapting Pink Time to promote self-regulated learning across course and student types
Baird, Timothy D.; Kniola, David J.; Hartter, Joel; Carlson, Kimberly; Rogers, Sarah; Russell, Don; Tise, Joseph (2020)
To explore new opportunities to promote self-regulated learning (SRL) across a variety of contexts, this study applies a novel assignment called Pink Time in seven different courses at two universities. The assignment asks students to “skip class, do anything you want, and give yourself a grade.” In each case, instructors adapted Pink Time to fit the needs of their course. Altogether, 165 students completed 270 self-directed projects and self-assessments targeting five component behaviors of SRL. Findings show that: (1) students were more likely to perceive success in certain behaviors of SRL than in others; (2) students’ perceptions across courses were similar for some behaviors but not others; and (3) subsequent iterations of the assignment supported higher perceived measures of some SRL behaviors but not others. Together these findings illustrate the value and flexibility of this progressive assignment as well as persistent challenges in supporting students’ SRL.
Advances in tropical climatology – a review
Moraes, Flávia D. S.; Ramseyer, Craig A.; Miller, Paul W.; Trepanier, Jill C. (Informa, 2024-02-12)
Understanding tropical climatology is essential to comprehending the atmospheric connections between the tropics and extratropical latitudes weather and climate events. In this review paper, we emphasize the advances in key areas of tropical climatology knowledge since the end of the 20th century and offer a summary, assessment, and discussion of previously published literature. Among the key areas analyzed here, we explore the advances in tropical oceanic and atmospheric variability, such as El Niño – Southern Oscillation and the Madden-Julian Oscillation, and how those teleconnection events have helped us to better understand variabilities in tropical monsoons, tropical cyclones, and drought events. We also discuss new concepts incorporated into the study of tropical cyclones, such as rapid intensification, and how those studies are evolving and helping scientists to better prepare and predict hurricanes. Regarding tropical aerosols, we discuss how satellite-based dust detection has improved the comprehension of Saharan dust as a driver of drought in locations far from the dust source region while simultaneously altering tropical cyclone variability. Finally, our review shows that there have been significant advances in tropical hydroclimatic studies in order to better investigate monsoons, flooding, and drought, helping scholars of tropical climatology to better understand its extreme events.
Analysis of Model Thermal Profile Forecasts Associated with Winter Mixed Precipitation within the United States Mid-Atlantic Region
Ellis, Andrew W.; Keighton, Stephen, I; Zick, Stephanie E.; Shearer, Andrew S.; Hockenbury, Casey E.; Silverman, Anita (National Weather Association, 2022-03-04)
Winter mixed-precipitation events across the mid-Atlantic region of the United States from 2013-2014 through 2018-2019 were used to analyze common short-term model forecasts of vertical atmospheric thermal structure. Using saturated forecast soundings of the North American Mesoscale (NAM), higher-resolution nested NAM (NAMnest), and the Rapid Refresh models-corresponding with observed warm-nose precipitation events (WNPEs)-several thermal metrics formed the basis of the analysis of observed and forecast soundings. including Bourgouin positive and negative areas. While the three models accurately forecast the general thermal structure well during WNPEs, a warm bias is evident within each. Well forecast are maximum and minimum temperatures within the warm nose and surface-based cold layer, respectively, but the cold layer is commonly too thin for each of the models, and the warm nose is regularly too thick, particularly within NAM and NAMnest forecasts. Forecasts of a cold layer that is too shallow tend to coincide with observations of stronger synoptic-scale upward motion, a deeper cold surface-based layer, and a higher isentropic surface. Forecasts of a warm nose that is too thick tend to coincide with observations of weaker upward motion, a shallower cold surface-based layer, and a lower isentropic surface across the region. Two-thirds of precipitation-type estimates from model soundings agreed with those derived from observed soundings, with the remaining third predominantly representing a warm bias in precipitation type.
Anticipating and adapting to the future impacts of climate change on the health, security and welfare of low elevation coastal zone (LECZ) communities in Southeastern USA
Allen, Thomas; Behr, Joshua; Bukvic, Anamaria; Calder, Ryan S. D.; Caruson, Kiki; Connor, Charles; D'Elia, Christopher; Dismukes, David; Ersing, Robin; Franklin, Rima; Goldstein, Jesse; Goodall, Jonathon; Hemmerling, Scott; Irish, Jennifer L.; Lazarus, Steven; Loftis, Derek; Luther, Mark; McCallister, Leigh; McGlathery, Karen; Mitchell, Molly; Moore, William B.; Nichols, C. Reid; Nunez, Karinna; Reidenbach, Matthew; Shortridge, Julie; Weisberg, Robert; Weiss, Robert; Donelson Wright, Lynn; Xia, Meng; Xu, Kehui; Young, Donald; Zarillo, Gary; Zinnert, Julie C. (MDPI, 2021-10-29)
Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.
Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping
Shao, Yang; Cooner, Austin J.; Walsh, Stephen J. (MDPI, 2021-04-15)
High-spatial-resolution satellite imagery has been widely applied for detailed urban mapping. Recently, deep convolutional neural networks (DCNNs) have shown promise in certain remote sensing applications, but they are still relatively new techniques for general urban mapping. This study examines the use of two DCNNs (U-Net and VGG16) to provide an automatic schema to support high-resolution mapping of buildings, road/open built-up, and vegetation cover. Using WorldView-2 imagery as input, we first applied an established OBIA method to characterize major urban land cover classes. An OBIA-derived urban map was then divided into a training and testing region to evaluate the DCNNs’ performance. For U-Net mapping, we were particularly interested in how sample size or the number of image tiles affect mapping accuracy. U-Net generated cross-validation accuracies ranging from 40.5 to 95.2% for training sample sizes from 32 to 4096 image tiles (each tile was 256 by 256 pixels). A per-pixel accuracy assessment led to 87.8 percent overall accuracy for the testing region, suggesting U-Net’s good generalization capabilities. For the VGG16 mapping, we proposed an object-based framing paradigm that retains spatial information and assists machine perception through Gaussian blurring. Gaussian blurring was used as a pre-processing step to enhance the contrast between objects of interest and background (contextual) information. Combined with the pre-trained VGG16 and transfer learning, this analytical approach generated a 77.3 percent overall accuracy for per-object assessment. The mapping accuracy could be further improved given more robust segmentation algorithms and better quantity/quality of training samples. Our study shows significant promise for DCNN implementation for urban mapping and our approach can transfer to a number of other remote sensing applications.
Assessing Strontium and Vulnerability to Strontium in Private Drinking Water Systems in Virginia
Scott, Veronica; Juran, Luke; Ling, Erin; Benham, Brian L.; Spiller, Asa (MDPI, 2020-04-08)
A total of 1.7 million Virginians rely on private drinking water (PDW) systems and 1.3 million of those people do not know their water quality. Because most Virginians who use PDW do not know the quality of that water and since strontium poses a public health risk, this study investigates sources of strontium in PDW in Virginia and identifies the areas and populations most vulnerable. Physical factors such as rock type, rock age, and fertilizer use have been linked to elevated strontium concentrations in drinking water. Social factors such as poverty, poor diet, and adolescence also increase social vulnerability to health impacts of strontium. Using water quality data from the Virginia Household Water Quality Program (VAHWQP) and statistical and spatial analyses, physical vulnerability was found to be highest in the Ridge and Valley province of Virginia where agricultural land use and geologic formations with high strontium concentrations (e.g., limestone, dolomite, sandstone, shale) are the dominant aquifer rocks. In terms of social vulnerability, households with high levels of strontium are more likely than the average VAHWQP participant to live in a food desert. This study provides information to help 1.7 million residents of Virginia, as well as populations in neighboring states, understand their risk of exposure to strontium in PDW.
Assessing the Relationship between COVID-19, Air Quality, and Meteorological Variables: A Case Study of Dhaka City in Bangladesh
Islam, Md Sariful; Rahman, Mizanur; Tusher, Tanmoy Roy; Roy, Shimul; Razi, Mohammad Arfar (Taiwan Association for Aerosol Research, 2021-01-15)
The novel coronavirus disease 2019 (COVID-19) has become a serious health concern worldwide for almost a year. This study investigated the effects of selected air pollutants and meteorological variables on daily COVID-19 cases in Dhaka city, Bangladesh. Air pollutants and meteorological data for Dhaka city were collected from 8 April to 16 June 2020 from multiple sources. This study implied spearman’s correlation to see the correlation between daily COVID-19 cases and different air pollutants and meteorological variables. Besides, multiple linear regression and the Generalized Additive Model (GAM) were used to investigate the association between COVID-19 cases and other variables used in this study. Due to lockdown measures, significant differences between PM₂.₅, SO₂, NO₂, CO, and O₃ in 2019 and 2020 were observed in Dhaka city. We used lag-0, lag-7, lag-14, and lag-21 days on daily COVID-19 cases to look at the lag effect of different air pollutants and meteorology. The LRM results showed that the daily COVID-19 cases are significantly correlated with relative humidity (lag-0 days) and pressure (lag-14 days) (p < 0.05). Additionally, the GAM model results showed a significant nonlinear association among daily COVID-19 cases and meteorology and air quality variables on different lag days. Therefore, our results suggest that an effective public health intervention measures should be implemented to slowdown the spreading of COVID-19.
Assessing Tree Mortality in a Southern Appalachian Red Spruce Forest using UAV-Survey Derived Orthoimagery
Harris, Ryley C.; Kennedy, Lisa M.; Atkins, Maya (2020-11-06)
Assessing Urban Community Gardens’ Impact on Net Primary Production using NDVI
Parece, Tammy E.; Campbell, James B. Jr. (Access, 2017-05-25)
Community gardens are one form of urban agriculture–growing of food and non-food products for sale or consumption within urban and peri-urban areas. Urban community gardens provide many benefits, including provisioning of fresh and nutritious foods, supporting environmental education, nurturing social interaction and community building, and contributing to sustainability. In many cities worldwide, urban agriculture is now integrated within urban planning programs. Although social, community, and nutritional benefits of community gardens are well documented, few quantitative assessments of their environmental benefits exist. None have applied Normalized Difference Vegetation Index (NDVI) as an environmental metric. NDVI is widely used in forestry and agriculture to track changes in vegetation phenology, assess vegetation stress and health, and, in urban areas, to separate vegetation from impervious surfaces. NDVI has a positive relationship with net primary production. We used NDVI product from U.S. satellites–Landsats 5, 7, and 8–to assess urban community garden sites. We conducted a time series analysis over the 2007 to 2015 growing seasons (May–September) for three eastern U.S. cities–Roanoke, VA; Pittsburgh, PA; and Buffalo, NY. Our results show that establishment of community gardens alter seasonal NDVI trajectories, sometimes with initial declines, but then increasing over time. Furthermore, NDVI profiles reveal the vigorous character of urban agriculture.
Assessing Urban Landscape Variables’ Contributions to Microclimates
Parece, Tammy E.; Li, Jie; Campbell, James B. Jr.; Carroll, David F. (Hindawi, 2015-12-24)
The well-known urban heat island (UHI) effect recognizes prevailing patterns of warmer urban temperatures relative to surrounding rural landscapes. Although UHIs are often visualized as single features, internal variations within urban landscapes create distinctive microclimates. Evaluating intraurban microclimate variability presents an opportunity to assess spatial dimensions of urban environments and identify locations that heat or cool faster than other locales. Our study employs mobile weather units and fixed weather stations to collect air temperatures across Roanoke, Virginia, USA, on selected dates over a two-year interval. Using this temperature data, together with six landscape variables, we interpolated (using Kriging and Random Forest) air temperatures across the city for each collection period. Our results estimated temperatures with small mean square errors (ranging from 0.03 to 0.14); landscape metrics explained between 60 and 91% of temperature variations (higher when the previous day’s average temperatures were included as a variable). For all days, similar spatial patterns appeared for cooler and warmer areas in mornings, with distinctive patterns as landscapes warmed during the day and over successive days. Our results revealed that the most potent landscape variables vary according to season and time of day. Our analysis contributes new dimensions and new levels of spatial and temporal detail to urban microclimate research.
Assessment of Canopy Health with Drone-Based Orthoimagery in a Southern Appalachian Red Spruce Forest
Harris, Ryley C.; Kennedy, Lisa M.; Pingel, Thomas J.; Thomas, Valerie A. (MDPI, 2022-03-10)
Consumer-grade drone-produced digital orthoimagery is a valuable tool for conservation management and enables the low-cost monitoring of remote ecosystems. This study demonstrates the applicability of RGB orthoimagery for the assessment of forest health at the scale of individual trees in a 46-hectare plot of rare southern Appalachian red spruce forest on Whitetop Mountain, Virginia. We used photogrammetric Structure from Motion software Pix4Dmapper with drone-collected imagery to generate a mosaic for point cloud reconstruction and orthoimagery of the plot. Using 3-band RBG digital orthoimagery, we visually classified 9402 red spruce individuals, finding 8700 healthy (92.5%), 251 declining/dying (2.6%), and 451 dead (4.8%). We mapped individual spruce trees in each class and produced kernel density maps of health classes (live, dead, and dying). Our approach provided a nearly gap-free assessment of the red spruce canopy in our study site, versus a much more time-intensive field survey. Our maps provided useful information on stand mortality patterns and canopy gaps that could be used by managers to identify optimal locations for selective thinning to facilitate understory sapling regeneration. This approach, dependent mainly on an off-the-shelf drone system and visual interpretation of orthoimagery, could be applied by land managers to measure forest health in other spruce, or possibly spruce-fir, communities in the Appalachians. Our study highlights the usefulness of drone-produced orthoimagery for conservation monitoring, presenting a valid and accessible protocol for the monitoring and assessment of forest health in remote spruce, and possibly other conifer, populations. Adoption of drone-based monitoring may be especially useful in light of climate change and the possible displacement of southern Appalachian red spruce (and spruce-fir) ecosystems by the upslope migration of deciduous trees.
Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions
Islam, Md Sariful; Crawford, Thomas W. (MDPI, 2022-12-16)
Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally.
Atmospheric Flash Drought in the Caribbean
Ramseyer, Craig A.; Miller, Paul W. (American Meteorological Society, 2023-09-13)
Despite the intensifying interest in flash drought both within the U.S. and globally, moist tropical landscapes have largely escaped the attention of the flash drought community. Because these ecozones are acclimatized to receiving regular, near-daily precipitation, they are especially vulnerable to rapid-drying events. This is particularly true within the Caribbean basin where numerous small islands lack the surface and groundwater resources to cope with swiftly developing drought conditions. This study fills the tropical flash drought gap by examining the pervasiveness of flash drought across the pan-Caribbean region using a recently proposed criterion based on the Evaporative Demand Drought Index (EDDI). The EDDI identifies 46 instances of widespread flash drought “outbreaks” in which significant fractions of the pan-Caribbean encounter rapid drying over 15 days and then maintain this condition for another 15 days. Moreover, a self-organizing maps (SOM) classification reveals a tendency for flash drought to assume recurring typologies concentrated in either the Central American, South American, or Greater Antilles coastlines, though a simultaneous, Caribbean-wide drought is never observed within the 40-year (1981-2020) period examined. Further, three of the six flash drought typologies identified by the SOM initiate most often during Phase 2 of the Madden-Julian Oscillation. Collectively, these findings motivate the need to more critically examine the transferability of flash drought definitions into the global tropics, particularly for small water-vulnerable islands where even island-wide flash droughts may only occupy a few pixels in most reanalysis datasets.
Beaver-driven peatland ecotone dynamics: Impoundment detection using Lidar and geomorphon analysis
Swift, Troy P.; Kennedy, Lisa M. (Ecological Society of America, 2021-08-05)
Background/Question/Methods Beaver (Castor spp.) are renowned for their role as ecosystem engineers. Their ponds and vegetation consumption can greatly alter local hydrology and ratios of meadow to woodland. Beaver also actively buffer their environments against drought and wildfire susceptibility, and can influence important climate parameters like carbon retention and methanogenesis. Beaver impoundments tend to follow a multiyear cycle of construction, maintenance, degradation, and fallow. Flooding is the primary agent of destruction. This investigation focused on remotely detecting beaver impacts on the boreal peatland ecotones enmeshing Cranberry Glades Botanical Area, a National Natural Landmark in mountainous West Virginia adjacent to Cranberry Wilderness, and contained entirely by Monongahela National Forest. The Glades are perched at ~1000 m elevation and occupy ~300 ha. Literature suggests that beaver activity may have had an important role in the formation and maintenance of peatland conditions at Cranberry Glades. Aerial Lidar/photography were analyzed in tandem in order to identify and reconstruct shifting hydrological patterns associated with beaver dams and ponds. We rasterized aerial Lidar data from Nov/Dec 2018 for the entire Glades at 1-meter resolution, including a bare-earth Digital Terrain Model (RMSE vertical accuracy ~10cm) and a canopy height model sufficient to discern between trees, shrubs, and near-surface. We developed a novel method of geomorphon analysis to detect ponds and dams by exploiting their occupancy of the incised stream channels typical of these wetlands. Aerial color-infrared and RGB photography, gathered during a variety of seasons, enabled complementary identification of beaver-related infrastructure by visual inspection. Results/Conclusions Geomorphon DTM analysis successfully revealed low ridges closely bracketing inter-glade stream channels featuring free-flowing water, manifesting as a ridge/valley/ridge cross-channel sequence. This signal is conspicuously absent along stretches flooded by beaver ponds; an abrupt transition between the two states also occurs at dams. A survey using these methods counted 13 ponds in Winter 2013-14 and 17 ponds in Summer 2016. This multi-year interval worked well, allowing time for widespread changes in beaver infrastructure while conserving utility of reference imagery. Future work will include analysis of the most recent beaver activity, refinement of classification workflows, generation of more accurate physical models using drone-acquired Lidar, and more complete incorporation of historical imagery. Much remains to be understood about the full role of beaver in this rare and imperiled ‘Arctic island’ of the southern High Alleghenies.
Beaver-Driven Peatland Ecotone Dynamics: Impoundment Detection Using Lidar and Geomorphon Analysis
Swift, Troy P.; Kennedy, Lisa M. (MDPI, 2021-12-03)
This investigation focused on remotely detecting beaver impoundments and dams along the boreal-like peatland ecotones enmeshing Cranberry Glades Botanical Area, a National Natural Landmark in mountainous West Virginia, USA. Beaver (Castor spp.) are renowned for their role as ecosystem engineers. They can alter local hydrology, change the ratios of meadow to woodland, act as buffers against drought and wildfire, and influence important climate parameters such as carbon retention and methanogenesis. The Cranberry Glades (~1000 m a.s.l.) occupy ~300 ha, including ~40 ha of regionally rare, open peatlands. Given the likely historical role of beaver activity in the formation and maintenance of peatland conditions at Cranberry Glades, monitoring of recent activity may be useful in predicting future changes. We analyzed remotely sensed data to identify and reconstruct shifting patterns of surface hydrology associated with beaver ponds and dams and developed a novel application of geomorphons to detect them, aided by exploitation of absences and errors in Lidar data. We also quantified decadal-timescale dynamics of beaver activity by tallying detectable active impoundments between 1990–2020, revealing active/fallow cycles and changing numbers of impoundments per unit area of suitable riparian habitat. This research presents both a practical approach to monitoring beaver activity through analysis of publicly available data and a spatiotemporal reconstruction of three decades of beaver activity at this rare and imperiled “Arctic Island” of the southern High Alleghenies.
Capturing complexity: Environmental change and relocation in the North Slope Borough, Alaska
Garland, Anne; Bukvic, Anamaria; Maton-Mosurska, Anuszka (Elsevier, 2022-12)
This paper explores the knowledge, attitudes, and behaviors about emerging hazards, environmental change, and relocation among community groups in Utqiaġvik (Barrow) of the North Slope Borough (NSB), Alaska. This region has been experiencing accelerating erosion and warmer temperatures, permafrost thawing, more frequent and intense storm surges, and increased maritime traffic and extractive industries with ice loss, with direct or cascading effects on the mixed ethnic and indigenous communities. This paper used engagement activities (Participatory Applied Theater) and qualitative approaches (focus groups) during three consecutive summers 2016-2018 to evaluate the risk perceptions and interpretations towards coastal changes and relocation as an adaptive response in this U.S. strategic yet remote location. Each focus group session started with risk ranking activities about regional hazards to assess knowledge and perceptions of risk, followed by an interactive script reading of an Iñupiat disaster legend to facilitate discussion about risk reduction options and engagement with the survey questions. Focus groups were audio recorded, transcribed, and analyzed using qualitative data analysis software Nvivo and a hybrid coding strategy. Results indicate that relocation is considered by some participants but is not planned for nor implemented by community groups, families, or the local government to reduce the hazard risks. However, widespread recognition of accelerated hazards and environmental changes, and the need for adaptation could lead to consideration of relocation in the future. This study provides a case of disaster risk reduction in a remote place with unique place-specific characteristics (e.g., particular forms of subsistence, corporate monopolies, Traditional Ecological Knowledge, and social organizations), but also shaped by significant external influences, accompanied by a changing landscape of risk from the slow and rapid onset of environmental changes.
Catawba Sustainability Center and Catawba Hospital Renewable Energy Site Planning Process Study
Meyers, Ron; Carstensen, Laurence W.; Ford, W. Mark; Grant, Elizabeth J.; Klopfer, Scott D.; Schenk, Todd; Taylor, Adam (Virginia Tech, 2020-09-29)
The transdisciplinary Renewable Energy Facilities Siting Project produced a white paper outlining their proof-of-concept using a case study from the Catawba Valley.
Chapter 4. Value expression in decision-making
Baird, Timothy D. (2022-07-09)
Characteristics of Red Spruce (Picea rubens Sarg.) Encroachment at Two Central Appalachian Heathland Study Areas
White, Helen M.; Resler, Lynn M.; Carroll, David F. (IGI Global, 2021)
During the late 19th and early 20th centuries, intensive land use nearly eliminated red spruce (Picea rubens Sarg.) throughout portions of West Virginia (WV). Red spruce has been slow to regenerate on mountaintop heathland barrens surrounding Canaan Valley, West Virginia (WV), and little is known about the nature of encroachment. Using field surveys, geospatial data, and statistical modelling, the objectives were to 1) characterize and compare red spruce encroachment at two upland heath study areas in West Virginia (Bear Rocks and Cabin Mountain), 2) characterize percent cover of major ground cover types associated with red spruce regeneration sites in order to elucidate biotic interactions, and 3) model the biophysical correlates of red spruce encroachment using geospatial data and statistical modelling. Red spruce count was similar at both study areas, but there were substantially more seedlings and saplings at Cabin Mountain. Modelling revealed a positive relationship between red spruce count and rock cover and a negative relationship between red spruce and stand distance.
Characterizing major agricultural land change trends in the Western Corn Belt
Shao, Yang; Taff, Gregory N.; Ren, Jie; Campbell, James B. Jr. (Elsevier, 2016-12-01)
In this study we developed annual corn/soybean maps for the Western Corn Belt within the United States using multi-temporal MODIS NDVI products from 2001 to 2015 to support long-term cropland change analysis. Based on the availability of training data (cropland data layer from the USDA-NASS), we designed a cross-validation scheme for 2006–2015 MODIS data to examine the spatial generalization capability of a neural network classifier. Training data points were derived from a three-state subregion consisting of North Dakota, Nebraska, and Iowa. Trained neural networks were applied to the testing sub-region (South Dakota, Kansas, Minnesota, and Missouri) to generate corn/soybean maps. Using a default threshold value (neural network output signalP0.5), the neural networks performed well for South Dakota and Minnesota. Overall accuracy was higher than 80% (kappa > 0.55) for all testing years from 2006 to 2015. However, we observed high variation of classification performance for Kansas (overall accuracy: 0.71–0.82) and Missouri (overall accuracy: 0.65–0.77) for various testing years. We developed a threshold-moving method that decreases/increases threshold values of neural network output signals to match MODIS-derived corn/soybean acreage with the NASS acreage statistics. Over 70% of testing states and years showed improved classification performance compared to the use of a default 0.5 threshold. The largest improvement of kappa value was about 0.08. This threshold-moving method was used to generate MODIS-based annual corn/soybean map products for 2001–2015. A non-parametric Mann-Kendall test was then used to identify areas that showed significant (p < 0.05) upward/downward trends. Areas showing fast increase of corn/soybean intensities were mainly located in North Dakota, South Dakota, and the west portion of Minnesota. The highest annual increase rate for a 5-km moving window was about 6.8%.

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