Scholarly Works, Forest Resources and Environmental Conservation
Permanent URI for this collection
Research articles, presentations, and other scholarship
Browse
Browsing Scholarly Works, Forest Resources and Environmental Conservation by Title
Now showing 1 - 20 of 429
Results Per Page
Sort Options
- Above- and Below-Ground Carbon Sequestration in Shelterbelt Trees in Canada: A ReviewMayrinck, Rafaella C.; Laroque, Colin P.; Amichev, Beyhan Y.; Van Rees, Ken (MDPI, 2019-10-19)Shelterbelts have been planted around the world for many reasons. Recently, due to increasing awareness of climate change risks, shelterbelt agroforestry systems have received special attention because of the environmental services they provide, including their greenhouse gas (GHG) mitigation potential. This paper aims to discuss shelterbelt history in Canada, and the environmental benefits they provide, focusing on carbon sequestration potential, above- and below-ground. Shelterbelt establishment in Canada dates back to more than a century ago, when their main use was protecting the soil, farm infrastructure and livestock from the elements. As minimal-and no-till systems have become more prevalent among agricultural producers, soil has been less exposed and less vulnerable to wind erosion, so the practice of planting and maintaining shelterbelts has declined in recent decades. In addition, as farm equipment has grown in size to meet the demands of larger landowners, shelterbelts are being removed to increase efficiency and machine maneuverability in the field. This trend of shelterbelt removal prevents shelterbelt’s climate change mitigation potential to be fully achieved. For example, in the last century, shelterbelts have sequestered 4.85 Tg C in Saskatchewan. To increase our understanding of carbon sequestration by shelterbelts, in 2013, the Government of Canada launched the Agricultural Greenhouse Gases Program (AGGP). In five years, 27 million dollars were spent supporting technologies and practices to mitigate GHG release on agricultural land, including understanding shelterbelt carbon sequestration and to encourage planting on farms. All these topics are further explained in this paper as an attempt to inform and promote shelterbelts as a climate change mitigation tool on agricultural lands.
- Above-ground tree carbon storage in response to nitrogen deposition in the US is heterogeneous and may have weakenedClark, Christopher M.; Thomas, R. Quinn; Horn, Kevin J. (Springer Nature, 2023-02-14)Long-term nitrogen deposition may not provide sustained stimulation of tree carbon storage, suggest analyses of a tree inventory and growth for the contiguous US between 2000 and 2016, compared to data for the 1980s and 1990s. Changes in nitrogen (N) availability affect the ability for forest ecosystems to store carbon (C). Here we extend an analysis of the growth and survival of 94 tree species and 1.2 million trees, to estimate the incremental effects of N deposition on changes in aboveground C (dC/dN) across the contiguous U.S. (CONUS). We find that although the average effect of N deposition on aboveground C is positive for the CONUS (dC/dN = +9 kg C per kg N), there is wide variation among species and regions. Furthermore, in the Northeastern U.S. where we may compare responses from 2000-2016 with those from the 1980s-90s, we find the recent estimate of dC/dN is weaker than from the 1980s-90s due to species-level changes in responses to N deposition. This suggests that the U.S. forest C-sink varies widely across forests and may be weakening overall, possibly necessitating more aggressive climate policies than originally thought.
- Accuracy of Visible and Ultraviolet Light for Estimating Live Root Proportions with MinirhizotronsWang, Z. Q.; Burch, W. H.; Mou, P.; Jones, R. H.; Mitchell, R. J. (Ecological Society of America, 1995-10)
- Advancing lake and reservoir water quality management with near-term, iterative ecological forecastingCarey, Cayelan C.; Woelmer, Whitney M.; Lofton, Mary E.; Figueiredo, Renato J.; Bookout, Bethany J.; Corrigan, Rachel S.; Daneshmand, Vahid; Hounshell, Alexandria G.; Howard, Dexter W.; Lewis, Abigail S. L.; McClure, Ryan P.; Wander, Heather L.; Ward, Nicole K.; Thomas, R. Quinn (2021-01-18)Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, real-time freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential open-source option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.
- Alternate Trait-Based Leaf Respiration Schemes Evaluated at Ecosystem-Scale Through Carbon Optimization Modeling and Canopy Property DataThomas, R. Quinn; Williams, M.; Cavaleri, M. A.; Exbrayat, J. -F.; Smallman, T. L.; Street, L. E. (American Geophysical Union, 2019-12-25)Leaf maintenance respiration (Rleaf,m) is a major but poorly understood component of the terrestrial carbon cycle (C). Earth systems models (ESMs) use simple sub-models relating Rleaf,m to leaf traits, applied at canopy scale. Rleaf,m models vary depending on which leaf N traits they incorporate (e.g., mass or area based) and the form of relationship (linear or nonlinear). To simulate vegetation responses to global change, some ESMs include ecological optimization to identify canopy structures that maximize net C accumulation. However, the implications for optimization of using alternate leaf-scale empirical Rleaf,m models are undetermined. Here we combine alternate well-known empirical models of Rleaf,m with a process model of canopy photosynthesis. We quantify how net canopy exports of C vary with leaf area index (LAI) and total canopy N (TCN). Using data from tropical and arctic canopies, we show that estimates of canopy Rleaf,m vary widely among the three models. Using an optimization framework, we show that the LAI and TCN values maximizing C export depends strongly on the Rleaf,m model used. No single model could match observed arctic and tropical LAI-TCN patterns with predictions of optimal LAI-TCN. We recommend caution in using leaf-scale empirical models for components of ESMs at canopy-scale. Rleaf,m models may produce reasonable results for a specified LAI, but, due to their varied representations of Rleaf,mfoliar N sensitivity, are associated with different and potentially unrealistic optimization dynamics at canopy scale. We recommend ESMs to be evaluated using response surfaces of canopy C export in LAI-TCN space to understand and mitigate these risks.
- Analysis of a lidar voxel-derived vertical profile at the plot and individual tree scales for the estimation of forest canopy layer characteristicsSumnall, Matthew; Peduzzi, Alicia; Fox, Thomas R.; Wynne, Randolph H.; Thomas, Valerie A. (2016)The goal of the current study was to develop methods of estimating the height of vertical components within plantation coniferous forest using airborne discrete multiple return lidar. In the summer of 2008, airborne lidar and field data were acquired for Loblolly pine forest locations in North Carolina and Virginia, USA, which comprised a variety of stand conditions (e.g. stand age, nutrient regime, and stem density). The methods here implement both field plot-scale analysis and an automated approach for the delineation of individual tree crown (ITC) locations and horizontal extents through a marker-based region growing process applied to a lidar derived canopy height model. The estimation of vertical features was accomplished through aggregating lidar return height measurements into vertical height bins, of a given horizontal extent (plot or ITC), creating a vertical 'stack' of bins describing the frequency of returns by height. Once height bins were created the resulting vertical distributions were smoothed with a regression curve-line function and canopy layers were identified through the detection of local maxima and minima. Estimates from Lorey's mean canopy height was estimated from plot-level curve-fitting with an overall accuracy of 5.9% coefficient of variation (CV) and the coefficient of determination (R-2) value of 0.93. Estimates of height to the living canopy produced an overall R-2 value of 0.91 (11.0% CV). The presence of vertical features within the sub-canopy component of the fitted vertical function also corresponded to areas of known understory presence and absence. Estimates from ITC data were averaged to the plot level. Estimates of field Lorey's mean canopy top height from average ITC data produced an R-2 value of 0.96 (7.9% CV). Average ITC estimates of height to the living canopy produced the closest correspondence to the field data, producing an R-2 value of 0.97 (6.2% CV). These results were similar to estimates produced by a statistical regression method, where R-2 values were 0.99 (2.4% CV) and 0.98 (4.9% CV) for plot average top canopy height and height to the living canopy, respectively. These results indicate that the characteristics of the dominant canopy can be estimated accurately using airborne lidar without the development of regression models, in a variety of intensively managed coniferous stand conditions.
- Analyzing Trade-Offs, Synergies, and Drivers among Timber Production, Carbon Sequestration, and Water Yield in Pinus elliotii Forests in Southeastern USACademus, Ronald; Escobedo, Francisco J.; McLaughlin, Daniel L.; Abd-Elrahman, Amr (MDPI, 2014-06-20)Managing Pinus elliotii forests for timber production and/or carbon sequestration is a common management objective, but can negatively affect water yield due to high losses from evapotranspiration. Thus, understanding the trade-offs and potential synergies among multiple ecosystem goods services, as well as the drivers influencing these interactions, can help identify effective forest management practices. We used available data from 377 permanent plots from the USDA Forest Service Forest Inventory and Analysis Program for 2002–2011, and a forest water yield model to quantify provision levels and spatial distribution and patterns of carbon sequestration, timber volume and water yield for Pinus elliotii ecosystems in North Florida, USA. A ranking-classification framework and statistical analyses were used to better understand the interactions among ecosystem services and the effect of biophysical drivers on ecosystem service bundles. Results indicate that increased biomass reduced water yield but this trade-off varied across space. Specific synergies, or acceptable provision levels, among carbon sequestration, timber volume and water yield were identified and mapped. Additionally, stand age, silvicultural treatment, and site quality significantly affected the provision level of, and interactions among, the three ecosystem goods and services. The framework developed in this study can be used to assess, map, and manage subtropical forests for optimal provision of ecosystem services.
- Approximating Prediction Uncertainty for Random Forest Regression ModelsCoulston, John W.; Blinn, Christine E.; Thomas, Valerie A.; Wynne, Randolph H. (2016-03)Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as inputs to other modeling applications such as fire modeling. Here we use a Monte Carlo approach to quantify prediction uncertainty for random forest regression models. We test the approach by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors. Our approach produced conservative prediction intervals across most of the range of predicted values. However, because the Monte Carlo approach was data driven, prediction intervals were either too wide or too narrow in sparse parts of the prediction distribution. Overall, our approach provides reasonable estimates of prediction uncertainty for random forest regression models.
- ArcGIS Pro, Python, and R-Bridge Support Small Area Estimation for ForestsBell, David M.; Blinn, Christine E.; Peery, Stephen S.; Wynne, Randolph H.; Radtke, Philip J.; Thomas, Valerie A.; Oswalt, Christopher M.; Wilson, B. Ty (2023-07-12)
- Assessing Biotic and Abiotic Effects on Biodiversity Index Using Machine LearningBayat, Mahmoud; Burkhart, Harold E.; Namiranian, Manouchehr; Hamidi, Seyedeh Kosar; Heidari, Sahar; Hassani, Majid (MDPI, 2021-04-10)Forest ecosystems play multiple important roles in meeting the habitat needs of different organisms and providing a variety of services to humans. Biodiversity is one of the structural features in dynamic and complex forest ecosystems. One of the most challenging issues in assessing forest ecosystems is understanding the relationship between biodiversity and environmental factors. The aim of this study was to investigate the effect of biotic and abiotic factors on tree diversity of Hyrcanian forests in northern Iran. For this purpose, we analyzed tree diversity in 8 forest sites in different locations from east to west of the Caspian Sea. 15,988 trees were measured in 655 circular permanent sample plots (0.1 ha). A combination of machine learning methods was used for modeling and investigating the relationship between tree diversity and biotic and abiotic factors. Machine learning models included generalized additive models (GAMs), support vector machine (SVM), random forest (RF) and K-nearest–neighbor (KNN). To determine the most important factors related to tree diversity we used from variables such as the average diameter at breast height (DBH) in the plot, basal area in largest trees (BAL), basal area (BA), number of trees per hectare, tree species, slope, aspect and elevation. A comparison of RMSEs, relative RMSEs, and the coefficients of determination of the different methods, showed that the random forest (RF) method resulted in the best models among all those tested. Based on the results of the RF method, elevation, BA and BAL were recognized as the most influential factors defining variation of tree diversity.
- Assessing Ecosystem State Space Models: Identifiability and EstimationSmith, John W.; Johnson, Leah R.; Thomas, R. Quinn (Springer, 2023-03)Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.
- Assessing methods for comparing species diversity from disparate data sources: the case of urban and peri-urban forestsStaudhammer, Christina L.; Escobedo, Francisco J.; Blood, Amy (Ecological Society of America, 2018-10)Multi-scale forest inventory and monitoring data are increasingly being used in studies assessing forest diversity, structure, disturbance, and carbon dynamics. Also, local-level urban forest inventories are providing plot data and protocols to study tree diversity and ecosystem services in urban forests worldwide. But, differences in the sampling methods underlying these disparate protocols and data sources is a non-trivial concern in formulating comparative analyses. We assess commonly used methods for comparing tree diversity in peri-urban and urban forests when available data have different sample sizes, plot sizes, and sampling intensities. We present methods for appropriately evaluating species richness, as well as methods for comparing species distributions via community data matrices. Using permanent plot data from the southeastern United States, we present a case study comparing urban and peri-urban forests along a north-south gradient, and assessing species richness and the ecological homogenization hypothesis. Our findings indicate that comparisons of tree species richness among communities, or forest types, are often inconclusive since commonly used sample sizes do not provide precise estimates of the number of species present. While the ecological homogenization hypotheses can be tested under conditions of unequal sampling effort, we suggest robust methods such as PERMANOVA and the Raup-Crick dissimilarity index. A framework for selecting appropriate methods is also discussed. As forests are increasingly being altered by anthropogenic drivers, future studies using disparate data sources must account for differences in measurements and sampling protocols in order to produce results that are both statistically defensible and useful for science-based management.
- Assessing opportunities and inequities in undergraduate ecological forecasting educationWillson, Alyssa M.; Gallo, Hayden; Peters, Jody A.; Abeyta, Antoinette; Watts, Nievita Bueno; Carey, Cayelan C.; Moore, Tadhg N.; Smies, Georgia; Thomas, R. Quinn; Woelmer, Whitney M.; McLachlan, Jason S. (Wiley, 2023-05)Conducting ecological research in a way that addresses complex, real-world problems requires a diverse, interdisciplinary and quantitatively trained ecology and environmental science workforce. This begins with equitably training students in ecology, interdisciplinary science, and quantitative skills at the undergraduate level. Understanding the current undergraduate curriculum landscape in ecology and environmental sciences allows for targeted interventions to improve equitable educational opportunities. Ecological forecasting is a sub-discipline of ecology with roots in interdisciplinary and quantitative science. We use ecological forecasting to show how ecology and environmental science undergraduate curriculum could be evaluated and ultimately restructured to address the needs of the 21(st) century workforce. To characterize the current state of ecological forecasting education, we compiled existing resources for teaching and learning ecological forecasting at three curriculum levels: online resources; US university courses on ecological forecasting; and US university courses on topics related to ecological forecasting. We found persistent patterns (1) in what topics are taught to US undergraduate students at each of the curriculum levels; and (2) in the accessibility of resources, in terms of course availability at higher education institutions in the United States. We developed and implemented programs to increase the accessibility and comprehensiveness of ecological forecasting undergraduate education, including initiatives to engage specifically with Native American undergraduates and online resources for learning quantitative concepts at the undergraduate level. Such steps enhance the capacity of ecological forecasting to be more inclusive to undergraduate students from diverse backgrounds and expose more students to quantitative training.
- Assessing patterns of oak regeneration and C storage in relation to restoration-focused management, historical land use, and potential trade-offsCarter, David R.; Fahey, Robert T.; Dreisilker, Kurt; Bialecki, Margaret B.; Bowles, Marlin (2015-01-29)Restoration of composition, structure, and function in oak dominated ecosystems is the focus of management in temperate forests around the world. Land managers focused on oak ecosystem restoration are challenged by the legacy effects of complex land-use histories, urbanization, climate change, and potential stakeholder response to management. Trade-offs may exist between managing forests for climate mitigation (e.g., maximizing C storage or sequestration) and promoting shade-intolerant species historically associated with frequent or high-severity disturbances. This study assessed the potentially conflicting goals of sustained live biomass accrual and increased oak regeneration in the East Woods Natural Area at The Morton Arboretum in Lisle, IL, USA. We evaluated how biomass trends and oak regeneration were related to management regimes, land-use history, current stand structure and composition, and topoedaphic factors. Our results indicated no significant trade-off between sustained live biomass accrual and oak regeneration. Live biomass was increasing across the landscape (biomass increment averaged 18,186 kg ha-1 yr-1) and was not strongly related to differences in management or land-use history. Oak regeneration was rare, especially beyond the seedling stage (~226 seedlings and 9 saplings ha-11) and was also not strongly related to recent management. Our results indicate that even 20+ years of annual prescribed burning combined with understory thinning has failed to produce the open canopy conditions and high light availability that are necessary for successful oak recruitment. The absence of any trade-offs between biomass accrual and oak regeneration may, therefore, be largely related to the ineffectiveness of current management for promoting oak regeneration. More intensive management utilizing canopy manipulations could produce greater trade-offs, but is likely necessary to establish and release oak regeneration.
- Assessing the extent and drivers of forest plantation establishment in Andhra PradeshWynne, Randolph H.; Thomas, Valerie A.; Gundimeda, Haripriya; Amacher, Gregory S.; Cobourn, Kelly M.; Köhlin, Gunnar (2017-07)
- Assessing the transferability of statistical predictive models for leaf area index between two airborne discrete return LiDAR sensor designs within multiple intensely managed Loblolly pine forest locations in the south-eastern USASumnall, Matthew; Peduzzi, Alicia; Fox, Thomas R.; Wynne, Randolph H.; Thomas, Valerie A.; Cook, Bruce (2016-04)Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R-2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 ( LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R-2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.
- Assessing the utility of NAIP digital aerial photogrammetric point clouds for estimating canopy height of managed loblolly pine plantations in the southeastern United StatesRitz, Alison L.; Thomas, Valerie A.; Wynne, Randolph H.; Green, P. Corey; Schroeder, Todd A.; Albaugh, Timothy J.; Burkhart, Harold E.; Carter, David R.; Cook, Rachel L.; Campoe, Otavio C.; Rubilar, Rafael A.; Rakestraw, Jim (Elsevier, 2022-09)Remote sensing offers many advantages to supplement traditional, ground-based forest measurements, such as limiting time in the field and fast spatial coverage. Data from airborne laser scanning (lidar) have provided accurate estimates of forest height, where, and when available. However, lidar is expensive to collect, and wall-to-wall coverage in the United States is lacking. Recent studies have investigated whether point clouds derived from digital aerial photogrammetry (DAP) can supplement lidar data for estimating forest height due to DAP's lower costs and more frequent acquisitions. We estimated forest heights using point clouds derived from the National Agricultural Imagery Program (NAIP) DAP program in the United States to create a predicted height map for managed loblolly pine stands. For 534 plots in Virginia and North Carolina, with stand age ranging from 1 year to 42 years old, field-collected canopy heights were regressed against the 90th percentile of heights derived from NAIP point clouds. Model performance was good, with an R2 of 0.93 and an RMSE of 1.44 m. However, heights in recent heavily thinned stands were consistently underestimated, likely due to between-row shadowing leading to a poor photogrammetric solution. The model was applied to non-thinned evergreen areas in Virginia, North Carolina, and Tennessee to produce a multi-state 5 m x 5 m canopy height map. NAIP-derived point clouds are a viable means of predicting canopy height in southern pine stands that have not been thinned recently.
- Assessment of Canopy Health with Drone-Based Orthoimagery in a Southern Appalachian Red Spruce ForestHarris, 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 Land Cover, Tree Canopy, and Plantable Space on Virginia Tech CampusByers, Alexander M.; Wiseman, P. Eric (2020-05-20)To better understand the extent, distribution, and value of Virginia Tech’s tree canopy cover, students in a senior-level urban forestry course in the Department of Forest Resources and Environmental Conservation conducted a land cover and tree canopy cover assessment of campus during the spring 2020 semester. The assessment was performed using a software application called i-Tree Canopy. The application combines aerial photographs of the landscape with ecosystem models to derive estimates of land cover types and the ecosystem services provided by tree canopy cover. The land cover data is created through a process called sample-based photo interpretation. In this process, an analyst views randomly sampled scenes of the landscape using Google Earth imagery and classifies each scene into predetermined cover types. For this assessment, the imagery was dated June 2019. The campus area included in the assessment comprised most all of central campus east of US Route 460, south of Prices Fork Road, north of Virginia Tech Airport, and west of Main Street. The study area was subdivided using two different geographic schemes, one that used Campus Districts (16 total subdivisions) and one that used Tree Maintenance Zones (42 total subdivisions). The largest existing land cover type was impervious surface at 24.4%, totaling 257 acres. Turf and building were nearly identical at 11.8% (124 acres) and 11.2% (118 acres), respectively. Existing tree canopy cover was estimated at 16.9%, totaling 177 acres. Existing tree canopy cover in the Campus Districts geographic scheme mitigates 8,790 pounds of air pollution annually (95% confidence interval: 8,546 lb – 9,034 lb). Mitigation includes removal of harmful pollutants that are by-products of fossil fuel combustion: carbon monoxide, ozone, nitrogen dioxide, sulfur dioxide, and particulate matter (PM2.5 and PM10). Total value of air pollution mitigation across Campus Districts subdivisions is $4,630 per year (95% confidence interval: $4,495–$4,764).
- Automated Mapping of Typical Cropland Strips in the North China Plain Using Small Unmanned Aircraft Systems (sUAS) PhotogrammetryZhang, Jianyong; Zhao, Yanling; Abbott, A. Lynn; Wynne, Randolph H.; Hu, Zhenqi; Zou, Yuzhu; Tian, Shuaishuai (MDPI, 2019-10-10)Accurate mapping of agricultural fields is needed for many purposes, including irrigation decisions and cadastral management. This paper is concerned with the automated mapping of cropland strips that are common in the North China Plain. These strips are commonly 3–8 m in width and 50–300 m in length, and are separated by small ridges that assist with irrigation. Conventional surveying methods are labor-intensive and time-consuming for this application, and only limited performance is possible with very high resolution satellite images. Small Unmanned Aircraft System (sUAS) images could provide an alternative approach to ridge detection and strip mapping. This paper presents a novel method for detecting cropland strips, utilizing centimeter spatial resolution imagery captured by sUAS flying at low altitude (60 m). Using digital surface models (DSM) and ortho-rectified imagery from sUAS data, this method extracts candidate ridge locations by surface roughness segmentation in combination with geometric constraints. This method then exploits vegetation removal and morphological operations to refine candidate ridge elements, leading to polyline-based representations of cropland strip boundaries. This procedure has been tested using sUAS data from four typical cropland plots located approximately 60 km west of Jinan, China. The plots contained early winter wheat. The results indicated an ability to detect ridges with comparatively high recall and precision (96.8% and 95.4%, respectively). Cropland strips were extracted with over 98.9% agreement relative to ground truth, with kappa coefficients over 97.4%. To our knowledge, this method is the first to attempt cropland strip mapping using centimeter spatial resolution sUAS images. These results have demonstrated that sUAS mapping is a viable approach for data collection to assist in agricultural land management in the North China Plain.