Rick L. Day

Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested Riparian Buffers During Leaf-Off and Leaf-On Conditions

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available.

Regression modeling of streamflow, baseflow, and runoff using geographic information systems

Regression models for predicting total streamflow (TSF), baseflow (TBF), and storm runoff (TRO) are needed for water resource planning and management. This study used 54 streams with >20 years of streamflow gaging station records during the period October 1971 to September 2001 in Pennsylvania and partitioned TSF into TBF and TRO. TBF was considered a surrogate of groundwater recharge for basins. Regression models for predicting basin-wide TSF, TBF, and TRO were developed under three scenarios that varied in regression variables used for model development. Regression variables representing basin geomorphological, geological, soil, and climatic characteristics were estimated using geographic information systems. All regression models for TSF, TBF, and TRO had R(2) values >0.94 and reasonable prediction errors. The two best TSF models developed under scenarios 1 and 2 had similar absolute prediction errors. The same was true for the two best TBF models. Therefore, any one of the two best TSF and TBF models could be used for respective flow prediction depending on variable availability. The TRO model developed under scenario 1 had smaller absolute prediction errors than that developed under scenario 2. Simplified Area-alone models developed under scenario 3 might be used when variables for using best models are not available, but had lower R(2) values and higher or more variable prediction errors than the best models.

Quantifying land use effects on forested riparian buffer vegetation structure using LiDAR data

Quantifying variability of forested riparian buffer (FRB) vegetation structure with variation in adjacent land use supports an understanding of how anthropogenic disturbance influences the ability of riparian systems to perform ecosystem services. However, quantifying FRB structure over large regions is a challenge and requires efficient data collection and processing methods that integrate conventional in situ vegetation sampling with remote sensing data. This study uses automated algorithms to process airborne light detection and ranging (LiDAR) data for mapping of riparian vegetation height, canopy cover and corridor width along 5,900 transects using methods validated in 80 mensuration plots in central Pennsylvania, USA. The key objective of this study was to use airborne LiDAR data to quantify differences in edge vs interior vegetation structure as influenced by buffer width and adjacent land use type, continuously throughout a watershed. Riparian vegetation height, canopy cover and buffer width were ...

AnnGIS: Integration of GIS and a Continuous Simulation Model for Non‐Point Source Pollution Assessment

Non-Point Source (NPS) models and monitoring data are often used to evaluate management practices and develop NPS pollution control plans. Application of a dynamic NPS model requires efficient input data acquisition, storage, organization, reduction, and analysis accompanied by manipulation, interpretation, reporting, and display of model outputs. A Geographic Information System (GIS) helps extract, store, and organize input data as well as manipulate and display model outputs. This paper illustrates the development of an integrated GIS system for a continuous simulation, pollutant-loading model, AnnAGNPS (Annualized AGricultural Non-Point Source Pollution). The integrated system, called AnnGIS, was developed using the ArcView GIS and related program extensions. Using AnnGIS, modeling studies and management plans can be efficiently and easily developed. AnnGIS helps store, organize, and manipulate spatial and tabular data, extract spatial input parameters, develop analysis scenarios, and visualize input and output data in spatial, tabular, and graphical forms. AnnGIS is generic in nature (not limited to a particular geographic location) and can be successfully used in regions for which AnnAGNPS is designed. AnnGISs powerful graphical user interface and reference data sets facilitate efficient and informed decision-making concerning agricultural non-point pollution control and management.

Uncertainty and Sensitivity Analyses of Visual PRZM-3 Using Monte Carlo Simulation Technique

Uncertainty in chemical movement predictions of the Pesticide Root Zone Model-3 due to variability in model parameter estimation for atrazine, metolachlor, and acetochlor applications in Hagerstown clay, Cookport sandy clay loam, Tilsit silt loam, and Washington clay loam soils are identified through a stochastic sensitivity analysis. Due to model complexity and system variability, a stochastic sensitivity analysis based on Monte Carlo simulation in PRZM-3 is chosen. Monte Carlo simulation provides a criterion by which to judge the uncertainties in model predictions due to errors in parameter estimation when the system variability is expressed in probabilistic terms. A stepwise regression approach with P<0.05 yielded organic carbon, pesticide partition coefficient and decay rates of the surface horizons to be the most sensitive parameters for the maximum concentration (Cmax) and annual flux of the herbicides at 1.57 m depth in the soils for 18,000 Monte Carlo simulations. The decay rate of the surface horizon had a negative effect on atrazine Cmax (i.e. a standard deviation increase in decay rate would reduce Cmax by a standard deviation change of 0.4583) in Hagerstown clay soil. The day of occurrence of maximum concentration (Dmax) was more sensitive to the time dependent parameters, which were kept constant in this study, and did not show much sensitivity to the parameters considered in the study. PRZM-3 model outputs showed more sensitivity to soil parameters (top 5 soil parameters ranked among top 10) than to chemical parameters (top 5 chemical parameters ranked among top 15) than to crop/environmental parameters (top 5 crop/environmental parameters ranked among top 35) for the specific combinations of soils and herbicides studied.