Detection of Shelterbelt Density Change Using Historic APFO and NAIP Aerial Imagery

Grand Forks County, North Dakota, boasts the highest concentration of shelterbelts in the World. As trees age and reach their lifespan limits, renovations should have taken place with new trees being planted. However, in recent years, the rate of tree removal is thought to exceed the rate of replanting, which can result in a net loss of shelterbelts. Through manual digitization and geographic object-based image analysis (GEOBIA), we mapped shelterbelt densities in the Grand Forks County using historical and contemporary aerial photography, and estimated actual changes in density over 54 years. Our results showed a doubling in shelterbelt densities from 1962 to 2014, with an increase of 6402 m2/km2 over the 52 years (or 123 m2/km2/year). From 2014 to 2016, we measured 1,040,178 m2 of shelterbelt areas removed from the county, creating a density loss of −157 m2/km2/year. The total change over two years was relatively small compared with that seen over the previous 52 years. However, the fact that the rate of shelterbelt planting has slowed, and more removal is occurring, should be of concern for an increased risk of wind erosion, similar to that experienced in Midwestern U.S. during the 1930s. The reduction of shelterbelt density is likely related to changes in farming practices and a decline in the Conservation Reserve Program, resulting from the increased returns of growing other row crops. To encourage shelterbelt planting as a conservation practice, additional guidelines and financial support should be considered to balance the tradeoff between soil erosion and agricultural intensification

An Exploration of Colorectal Cancer Incidence Rates in North Dakota, USA, via Structural Equation Modeling

Purpose The state of North Dakota has one of the highest incidence rates for colorectal cancer in the USA. Its high incidence rate, coupled with a large variation in incidence rates among counties within the state, makes North Dakota a “natural laboratory” in which to investigate environmental clues to colorectal cancer. We conducted a hypothesis-generating study to explore potential determinants of colorectal cancer in North Dakota. Methods We obtained county-specific incidence rates for North Dakota’s 53 counties from the statewide cancer registry and corresponding data on county demographic, agricultural, and geophysical features from population-based sources. Candidate demographic/agricultural variables included median household income, population density, colorectal cancer screening rates, average farm size (in acres), and the percent of county fertilized. Geophysical variables included the uranium content of soil, residential radon levels, and source of drinking water (municipal or well water). Statistical analyses were performed via multivariate regression and structural equation modeling. Results Colorectal cancer incidence rates across North Dakota counties varied 3-fold. The structural equation model identified a significant role for well water use (p \u3c 0.05). This finding is consistent with studies that implicate well water in colorectal cancer. Conclusions Well water contains several agents, e.g., bacteria, disinfection by-products, and nitrates that are potent colorectal carcinogens. Studies of well water use and colorectal cancer risk at the individual level in North Dakota are warranted

Seasonal home ranges and habitat selection of three elk (Cervus elaphus) herds in North Dakota

Changes in land use have resulted in range shifts of many wildlife species, including those entering novel environments, resulting in the critical need to understand their spatial ecology to inform ecosystem effects and management decisions. Dispersing elk (Cervus elaphus) were colonizing areas of suitable habitat in the Northern Great Plains, USA, resulting in crop depredation complaints in these areas. Although state resource managers had little information on these elk herds, limited evidence suggested temporal movements into Canada. We collected and analyzed essential information on home range and habitat selection for 3 elk herds residing in North Dakota. We captured 5 adult female elk in each study area, affixed global positioning system collars, and monitored them for 1 year (2016–2017). We estimated diel period, seasonal, and hunting season home ranges using Brownian Bridge Movement Models for each individual. We analyzed habitat selection using multinomial logit models to test for differences in use of land classes, and for departures from proportionate use based on random sampling; our predictor variables included individual elk, diel period, and season. Home ranges differed between the 3 herds, seasons, and diel period; gun and winter season home ranges were both larger than in summer, as was night when compared with day. Female elk generally restricted themselves to cover during the day and entered open areas at night and during winter months. Our results also suggest that elk in our study areas tended to seek more cover, and in the case of our Turtle Mountain study area, some cross into Canada during gun season. Our study provides a better understanding of the spatial ecology of elk in the Northern Great Plains while highlighting the need for enhanced international cooperative management efforts

Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a \u3e50% probability of obtaining weekly clear observations

Homeostatic regulation of the endoneurial microenvironment during development, aging and in response to trauma, disease and toxic insult

The endoneurial microenvironment, delimited by the endothelium of endoneurial vessels and a multi-layered ensheathing perineurium, is a specialized milieu intérieur within which axons, associated Schwann cells and other resident cells of peripheral nerves function. The endothelium and perineurium restricts as well as regulates exchange of material between the endoneurial microenvironment and the surrounding extracellular space and thus is more appropriately described as a blood–nerve interface (BNI) rather than a blood–nerve barrier (BNB). Input to and output from the endoneurial microenvironment occurs via blood–nerve exchange and convective endoneurial fluid flow driven by a proximo-distal hydrostatic pressure gradient. The independent regulation of the endothelial and perineurial components of the BNI during development, aging and in response to trauma is consistent with homeostatic regulation of the endoneurial microenvironment. Pathophysiological alterations of the endoneurium in experimental allergic neuritis (EAN), and diabetic and lead neuropathy are considered to be perturbations of endoneurial homeostasis. The interactions of Schwann cells, axons, macrophages, and mast cells via cell–cell and cell–matrix signaling regulate the permeability of this interface. A greater knowledge of the dynamic nature of tight junctions and the factors that induce and/or modulate these key elements of the BNI will increase our understanding of peripheral nerve disorders as well as stimulate the development of therapeutic strategies to treat these disorders

Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability

Detection of Shelterbelt Density Change Using Historic APFO and NAIP Aerial Imagery

Grand Forks County, North Dakota, boasts the highest concentration of shelterbelts in the World. As trees age and reach their lifespan limits, renovations should have taken place with new trees being planted. However, in recent years, the rate of tree removal is thought to exceed the rate of replanting, which can result in a net loss of shelterbelts. Through manual digitization and geographic object-based image analysis (GEOBIA), we mapped shelterbelt densities in the Grand Forks County using historical and contemporary aerial photography, and estimated actual changes in density over 54 years. Our results showed a doubling in shelterbelt densities from 1962 to 2014, with an increase of 6402 m2/km2 over the 52 years (or 123 m2/km2/year). From 2014 to 2016, we measured 1,040,178 m2 of shelterbelt areas removed from the county, creating a density loss of −157 m2/km2/year. The total change over two years was relatively small compared with that seen over the previous 52 years. However, the fact that the rate of shelterbelt planting has slowed, and more removal is occurring, should be of concern for an increased risk of wind erosion, similar to that experienced in Midwestern U.S. during the 1930s. The reduction of shelterbelt density is likely related to changes in farming practices and a decline in the Conservation Reserve Program, resulting from the increased returns of growing other row crops. To encourage shelterbelt planting as a conservation practice, additional guidelines and financial support should be considered to balance the tradeoff between soil erosion and agricultural intensification