Forestry

 

Reese Hall on South Campus.
Faculty, Staff and Students gather for a get acquainted supper.
Every year our students volunteer to collect money for St. Jude's Children's Hospital.
Faculty assist students with advising.
School of Forestry Faculty and Staff.
Students are encouraged to create posters and enter them in poster competitions.
Conclave 2013: Men's Bow Saw, Isaac Moore.
Graduating seniors who are SAF members receive a free ring from Society of American Foresters.
Xi Sigma Pi Forestry National Honor Society Initiates.
Forestry Hosts FFA Career Development Event Every Fall.

Forestry (BSF)

 

Quantifying the dynamics of mixed stands from southern forest region

 

Project coordinator: Bogdan Strimbu

Assistant Professor - Louisiana Tech University

 

 

1.       Introduction

 

 

By the middle of the 21 century the US demand for forest is projected to increase by almost 70% while timber harvests are only expected to increase by less than 40%. High demand for forest related products could place a significant stress on the US forests as the timber removal from National Forests has decreased more than 50% in the last 30 years. Therefore, there is concern regarding the land that could be used to meet both the increased demand and the reduced harvest. Possible solutions consist of using timber supplied by areas that currently are not covered with forest (such as farmland) or by areas that have reduced priority in the forest planning process. The low impact of these areas in the resource planning process is usually associated with low present values for growing traditional forest products on the respective sites (such as bottomland hardwoods).  However, tactical and strategic planning used to accommodate increased demand with decreased land availability faces challenges associated with the lack of accurate growth and yield models of the more complex forest ecosystems in the southern US.

 

2.       Justification

 

 

 

Louisiana forests cover approximately 14 million acres (47% of the state). The majority of these forests are located on the western side of the State on sites favoring pine but with many interspersed hardwood areas. However, mixed hardwood stands account for more than 50% of Louisiana forests. Approximately 70 hardwood species have been identified as having commercial importance at regional and national level. A large array of studies has investigated development of most of species with a significant impact on the ecology and the economy of the region. The main topics of the studies investigating the southern hardwoods focused either on silvicultural regeneration or economical aspect of these species.  A reduced number of studies have investigated the quantitative aspects of the hardwood species with an even smaller number addressing the mixed-species stands dynamics. The limited availability of quantitative assessments of mixed-stand dynamics impacts forest management decisions, especially for commercial species. Therefore this study targets the development of detailed quantitative assessments of the main commercial species growing preponderantly in mixed-stands. The assessment would occur on two levels: stand level and landscape level. 

 

 

3.       Approach

 

 

To develop a quantitative assessment of mixed-stands growing in the southern forest region, a serial modular approach is proposed. Therefore, a series of four modules organized in a successive manner would be used to build the quantitative assessment. The first module would concentrate on developing the sampling and experimental design that would be used by the subsequent modules. The next three modules will address the main aspects of stand quantification, specifically stand description (develop a measure of stand growth and yield), stand evolution (develop growth and yield models) and stand integration into landscape level planning (landscape management).

Impact of size, shape and layout of sampling units on estimates

 

Project coordinator: Bogdan Strimbu

Assistant Professor - Louisiana Tech University

 

Co-PI: David Long

  

Overview

 

Foresters routinely estimate basal area of a stand by measuring diameters at breast height of trees within a set of sample plots since measuring every tree in the forest is impractical.  The central limit theorem provides the expansion framework from sample based estimates to stand (i.e., population) parameters.  The expansion from sample to population is complicated as trees are seldom uniformly random distributed through the stand, often showing a clumping pattern.  Similarly, tree diameters do not follow a uniform random pattern either; different diameter curves being developed to represent the number of trees in each diameter class, namely the stand table.  Furthermore, the forest structure varies according to the management goals from uneven-aged, for which the diameter distribution can be described in the extreme case by the exponential distribution, to even-aged, for which the diameter distribution follow a curve close to the normal distribution, commonly the Weibull distribution. Finally, the stand density can play a significant role in the expansion from the sample to population, as stand density seems to directly impacts stand homogeneity, some studies indicating that the increase in number of trees increases the stand variability. Consequently, one could ask whether or not the variation of diameter and tree spatial distribution have an effect on the accuracy of the sample based estimates. Furthermore, are sample based estimates influenced by the shape or size of sample plots, besides the trees biometry and location? 

The traditional method to answer the above questions would be to find representative stands (i.e., from spatial, density, structural and diameter distribution perspective), establish a sampling scheme, measure the trees and compare the results to a complete inventory of the stand.  However, natural stands do not have a homogeneous tree distribution throughout the stand consequently the sample could supply evidence for a different distribution than the actual stand distribution.  Also, measuring tree locations and mapping the spatial pattern is difficult and time consuming. Finally, finding representative stands from both distributional and spatial perspective can be challenging and could make difficult the assessment of all the density/spatial association/diameter combinations of practical interest. With the advent of high speed computers having large memory, the development of spatially explicit tree dependent forest representations has become possible on personal computers, without the need of accessing large mainframes. Virtual forests can be created to represent and simulate any natural forest situation, specifically diameter distribution and spatial association.  Overlaying the virtual forests with polygons representing the shape and size of the sampling unit one can perform a virtual sampling on the computer generated forests. Therefore, to investigate the effect of the shape and size of the sampling plot on parameter estimates and the impact of the density, spatial and diameter distribution of trees on these estimates the authors embraced the computer generation of stands. The objective of the project is to assess the impact of sampling shape and size, as well as layout of the sampling design. Additionally the project would investigate whether or not the spatial arrangements of the trees has a significant effect on sampling estimates.

 

 

Publications

 

Long, W.D. and Strimbu, B.M. (2010) Generating 3D forests for resources inventory.  Journal of Forests 125-3:32-37


Plant level carbon and water exchange in four elite loblolly pine families subject to a range of nutrient availability

 

PROJECT COLLABORATORS

 

Michael C. Tyree, Ph.D.  

Assistant Professor, Forest Ecophysiology  

School of Forestry, Louisiana Tech University  

P.O. Box 10138, Ruston, LA 71272-0045  

Louisiana Tech University, Ruston, LA

 

Michael Blazier, Ph.D.  

Forestry Research Project Leader 

LSU AgCenter, Hill Farm Research Station 

11959 Hwy 9, Homer, LA 71040

 

Mary Anne Sword Sayer, Ph.D.  

Plant Physiologist  

USDA-Forest Service  

Restoring Longleaf Pine Ecosystems (4158)  

Alexandria Forestry Center  

2500 Shreveport Highway, Pineville, LA 71360

 

 

 

Goals/Obj/Expected outputs 

The objective of this study is to understand the physiological characteristics that underlie productivity of a loblolly pine plantation in north Louisiana in response to: genetics, and fertilizer regime.  Specifically, this research will focus on differences in plant level C and water exchange in four rapid-growing families subject to a range of nutrient regimes.

 

 

Methods 

This research involves block plots of four elite loblolly clones replicated three times.  We will use annual growth and canopy gas exchange data to compare growth strategies among clones which represent distinctly different ideotypes.  Gas exchange data will be collected monthly using a portable photosynthesis system (Li-Cor 6400).  Net CO2 assimilation-light response curves will be measured three times annually at times that represent distinct phenological stages throughout the year.  Environmental variables such as temperature, relative humidity, precipitation, and photosynthetically active radiation (PAR) will be monitored continuously on site.  These data will be used to model total canopy C capture and water loss for 2.5 years.  A fertilization treatment will be implemented one year into the study to determine how each clone responds immediately following fertilization.

 

 

 

Non-Technical Summary 

Results from this work will be important in determining the potential role of using elite loblolly pine clones in intensively managed pine plantations in the western gulf coast region.  Most clonal material is of eastern origin and no studies to our knowledge have looked at how they will respond to environmental conditions of the western gulf coast, specifically, hot dry summers. 

 

 

 

Keywords 

Clones  

Fertilization 

 

Pinus taeda 

photosynthesis  

transpiration  

water use efficiency 

 

 

 

 

An analytical platform for cumulative impact assessment

 

Project coordinators: Bogdan Strimbu

Assistant Professor -  Louisiana Tech University                                                          

   

Overview

The impact of human activities on the environment ranges from relatively innocuous industries such as tourism, to more invasive extractive industries such as forestry, mining or oil and gas exploration. The harmful consequences of industrial development have become notable as environmental effects associated with these activities challenge the capacity of an ecosystem to incorporate naturally occurring disturbances. Over time, the accumulation of stresses resulting from human activities could transform an ecosystem so profoundly that recovery to a preexisting desirable state is no longer possible using existing conventional processes. To prevent the environment changing beyond socially acceptable limits, the potential effects of human-induced perturbations are generally evaluated by environmental impact assessment studies. The main difficulty in assessing the environmental response to human developments lies in the way that most economic activities have limited impacts when assessed individually, but generally have additive or synergistic effects when considered in the context of all past, present and foreseeable future activities. The combined influence of a suite of projects occurring in a predefined area is therefore evaluated using cumulative impact assessments studies (CIA). These consider the consequences of multiple projects, each insignificant on its own, yet important when considered collectively. 

  

Objectives

To address the lack of confidence in the techniques currently used for CIA, the present research proposes a change in the focus of the environmental assessment when the prediction of future activities is of interest. In the case when potential developments could impact the environment, the CIA should focus on identifying patterns in the environmental attributes that could lead to undesirable environmental outcomes. To identify the patterns associated with unacceptable states of the environment, the research presented here promotes the development of a set of future environments, each of them possible and equally likely. The set of futures is similar to the states of the molecules in a fluid, with each future resembling the trajectory of a molecule). As in statistical thermodynamics, the set of futures would be used to represent globally the state (i.e., the possible evolution of the environment during the length of the future) but, because the futures are objects with the probability of existence asymptotically compared with the particles from a fluid that are physical entities (i.e., probability of existence is 1), the CIA investigation will focus on describing the similarities or differences existing within the state, rather than quantifying a specific attribute of the state.  Therefore, the objectives of the present research are to identify the patterns describing significantly different environmental states and the environmental attributes associated with the respective patterns.

Spatial Analysis of Water Quality Resulting from Louisiana Forestry Best Management Practices

 

PROJECT LEADER

William B. Patterson, Ph.D.

Assistant Professor, Forest Soils and Watershed Management

School of Forestry, Louisiana Tech University

P.O. Box 10138

Ruston, LA 71272-0045

 

 

Goals/Objectives/Expected Outputs

 

The goal of this study is to relate geospatial characteristics of the nine tracts used in the Louisiana Tech Forestry BMP Effectiveness project to observed hydrology and water quality characteristics. The objectives are to: delineate watersheds of streams used in the BMP Effectiveness study, using DEMs and ArcGIS 9; determine distances from stream in tract, to possible disturbances, such as roads and pipelines; model stream flow or runoff within each tract, assuming an equal amount of precipitation, at the same time; Visualize watersheds in two and three dimensions; compare and contrast soils of tracts and watersheds; use calculated geospatial variables as covariates in analysis of covariance (ANCOVA), with observed storm discharge and water quality data; Investigate applications relating to GIS such as the following for modeling hydrology and water quality, if appropriate at our project watershed scales. Expected outputs will include: table of spatial characteristics, contrasting the nine treatment watersheds;

 

 

means of spatial characteristics by treatment, USGS Watershed (Dugdemona, Castor);

 

 

detailed map of each watershed, with watershed boundary, tract boundary, streams, Streamside Management Zones, upstream and downstream sampling stations;

 

 

quantitative spatial characteristics symbolized by value of  measured variables; Analysis of Covariance using SAS Version 8.1 (by period, pre-harvest or post-harvest), predicting downstream storm discharge (or water quality variable) with upstream storm discharge and selected quantitative spatial characteristics as covariates;  improved prediction of water quantity and quality effects over existing results of analysis of variance and analysis of covariance (with only upstream discharge as covariate); increased understanding of harvesting effects on storm discharge and water quality for intermittent stream watersheds in Louisiana; more sensitive analysis of the effectiveness of forestry BMPs in Louisiana.

 

 

Methods

Nine tracts with upstream and downstream monitoring and sampling stations were established in the Upper Dugdemona and Castor Creek USGS Hydrologic Units (watersheds) in Bienville, Jackson, and Winn Parishes in 2004-5. Three of the tracts had the Control/No Harvest treatment; three tracts were the Harvest with BMPs treatment; and three tracts received the Harvest with no BMPs treatment. Storm discharge was recorded for six storms in 2006 before the fall 2006 harvest, and six storms after harvest, in 2007. The following water quality parameters were measured on storm composite samples for each station: total suspended solids, total phosphorus, nitrate-nitrite nitrogen, and total Kjehldal nitrogen. The following data layers will be used within ArcGIS Version 9 and ArcMapto calculate geospatial characteristics: USGS Digital Elevation Model (DEM) raster files from the Atlas: The Louisiana Statewide GIS website; Digital Orthophoto Quarter Quadrangles (DOQQs), with contour lines, from the above website; contour lines, 10 foot interval, vector; from Atlas; stream center-line vector data from Atlas; NRCS 12 digit Watershed Boundary Dataset, from Louisiana NRCS State Office; GPS point data of upstream and downstream stations; GPS line data of stream paths; GPS line data of actual Streamside Management Zones in Harvest with BMPs treatment tracts; ArcMap 9.3 generated buffers around streams (idealized 35 ft SMZs). Predict downstream water quality and quantity levels, using paired upstream variables and geospatial characteristics as covariates in ANCOVA.  Calculate reductions in nonpoint source pollution variables, as a result of implementation of

Louisiana

forestry BMPs, based on results of the ANCOVA. Model storm discharge and water quality using Arc GIS Hydrology tools and BASINS.

 

 

Non-Technical Summary

Spatial characteristics of watersheds such as watershed area, tracts area, and stream length will be measured using Geographic Information Systems (GIS).  This information will be used, along with previously measured stormwater flow and  water quality, to better predict the water quantity and quality resulting from application of

Louisiana

forestry best management practices (BMPs).

 

Keywords

 

best management practices

 

 

forestry BMPs

 

 

BMP effectiveness

 

 

storm discharge

 

 

water quality

 

 

stream temperature

 

 

total suspended solids

 

 

total phosphorus

 

 

nitrate-nitrite

 

 

total Kjeldahl nitrogen

 

 

total nitrogen

 

 

BACI

 

 

upstream downstream

 

 

before after

 

 

GIS

 

spatial analysis