Mountain Scholar :: Browsing by Author "Reich, Robin M., advisor"

Browsing by Author "Reich, Robin M., advisor"

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Open Access
A landscape-scale investigation into the risk of lodgepole pine mortality caused by mountain pine beetle Dendroctonus ponderosae (Coleoptera: Curculioidae: Scolytinae)
(Colorado State University. Libraries, 2010) Johnson, Erik W., author; Reich, Robin M., advisor; Negron-Segarra, Jose F., 1960-, committee member; Jacobi, William R., committee member
Mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, is currently causing Pinus contorta Douglas (LP) mortality in several areas of western United States and Canada at high levels including portions of Colorado. For decades, researchers have developed models to help land managers predict when and where MPB infestation will develop based on forest structure, tree size, tree age and geographic characteristics; these models were developed at the stand-level for stand-level analysis. Land managers and planners have become increasingly interested in predicting MPB risk and susceptibility at the landscape-scale; however attempts at landscape-scale modeling have proven difficult as continuous forest mensuration datasets are often lacking. Techniques for producing low-cost, high-resolution, landscape-scale forest composition and forest structure Geographic Information System (GIS) layers were demonstrated by this study. These GIS layers were subsequently used to assess several existing MPB risk models, at the landscape-scale, and to derive a new empirical MPB model. The procedures outlined in this paper describe the generation of landscape-scale forest composition and structure GIS layers (predictive surfaces) based on recent and innovative remote sensing and spatial statistical techniques. These techniques transform a small field sample into a continuous GIS surface utilizing multiple linear regression and binary regression trees. Information derived from satellite imagery and digital elevation models are used as auxiliary variables to assist in the prediction of response variables (basal area, proportion of lodgepole pine basal area, diameter at breast height, quadratic mean diameter, percent canopy closure, and trees per acre). A carefully designed field sample, stratified by Landsat image spectral groupings, optimized sampling faculties by maximizing between-stratum variability while minimizing within-stratum variability. Forest composition (spatial distribution of tree species), basal area, proportion of lodgepole pine basal area, diameter at breast height, quadratic mean diameter, percent canopy closure, and trees per acre predictive surfaces were developed for Colorado's Fraser River Valley. These predictive surfaces were then used to assess the landscape-scale predictive capabilities of following MPB prediction models: Anhold et al., (1996), Amman et al. (1977), Shore and Safranyik (1992), and the USDA Forest Service National Insect and Disease Risk Map. Finally, a new MPB model is described based on geographic factors, the predictive surfaces, and recent occurrence of mountain pine beetle caused-tree mortality.
Open Access
Influence of climatic zones on the distribution and abundance of damage agents and forest types in Colorado, United States and Jalisco, Mexico
(Colorado State University. Libraries, 2012) Masoud, Moussa, author; Reich, Robin M., advisor; Jacobi, William R., committee member; Martin, Patrick H., committee member
This study investigated: the relationship between temperature, precipitation and insect abundance in the forests of Colorado, USA and Jalisco, Mexico to quantify the latitudinal effects on disease and insect population, and developed a simple climate change model to predict the influence of changes in temperature and precipitation on the abundance of forest pests in the states of Jalisco and Colorado. In Jalisco, the source of information available on the distribution and abundance of forest types and causal agents were from a set of permanent sample plots located throughout the state. In Colorado a vegetation map was available which provided detailed information of the distribution of forests types across climate zones. Aerial survey data was also available providing complete coverage of the state with respect to the area damaged by the various causal agents. Results of this study indicated that temperature and precipitation have a significant influence on the distribution and abundance of forest types and forest insects and diseases in both Jalisco and Colorado. The linear and spatial correlations observed between climate zone and the distribution and abundance of forest types and causal agents were weaker in Jalisco than those observed in Colorado. This may be due to the type of data used in the analysis.
Open Access
Optimal sampling and modeling strategies for quantifying natural resources over large geographical regions
(Colorado State University. Libraries, 2008) Pongpattananurak, Nantachai, author; Reich, Robin M., advisor
Chapter 1 evaluates a new approach of modeling the spatial distribution of soil attributes over large geographical regions. A combination of three-stage least squares (3SLS) and multivariate regression trees (MRT) was used to model the spatial variability in soil texture. In 2006, 1427 soil samples were collected as part of a state-wide inventory and monitoring program (IMRENAT) implemented in the State of Jalisco, Mexico. A two-way nested stratified design was used to allocate samples throughout the state based on the spectral variability of land cover and climatic conditions. The final set of models described 61% of the observed variability in soil pH, 62% of the variability in sand and 56% for clay. Comparison with other interpolation techniques such as ordinary kriging, suggest that the approach used in this study is far superior in terms of the accuracy and precision. Chapter 2 evaluates three sampling designs (i.e., simple random sampling, systematic sampling and two-way nested stratified design) for modeling the spatial variability in forest tree biomass in the State of Jalisco, Mexico. Monte Carlo simulations were used to implement the three sampling designs using samples of 500 and 1100 30 m x 30 m primary sampling units. Statistically, the two-way-nested stratified design outperformed the simple random and systematic sampling design. There was no significant difference between the simple random and systematic designs. Chapter 3 evaluates the statistical properties of plot size and sample intensities in estimating forest stand characteristics in seasonal dry evergreen forests in Huai Kha Khaeng Wildlife Sanctuary, Thailand. Monte Carlo simulations were used to evaluate plotsizes (5 m x 5 m, 10 m x 10 m, 20 m x 20 m, 25 m x 25 m and 50 m x 50 m) and sample intensities (0.5%, 1%, 2%, 5%, 10%, and 15%) on a 50 ha mapped dataset. All plot sizes and sampling intensities provided unbiased estimates of the population mean and variance for tree basal area and tree density. All plot sizes and sampling intensities were biased with respect to estimating the total number of tree species on the 50 ha plot.
Open Access
Sampling strategies for forest aerial detection survey in Colorado
(Colorado State University. Libraries, 2015) Ha, Anh Quang, author; Reich, Robin M., advisor; Jacobi, William R., committee member; Lundquist, John E., committee member; Khosla, Rajiv, committee member
Aerial detection survey (ADS) has been commonly employed in forest surveys in the United States for detecting forest damage and monitoring forest health. In Colorado, ADS by USDA Forest Service has conducted annual 100% census of government forested land for more than 20 years with the goal of achieving information about forest damage due to different causal agents and disorders. Sketchmapping has been commonly employed in ADS with the goal of detecting and documenting on maps mortality, defoliation and other visible forest change from aircraft. At medium and large scale, sketchmapping is a suitable technique for forest monitoring that provides valuable information in forest health. This dissertation deals with data of forest area damaged by five causal agents mountain pine beetle, spruce beetle, western spruce budworm, pin engraver, and Douglas fir beetle and two disorders subalpine fir mortality and sudden aspen decline. The combined areas damaged by all causes were also considered. Data were downloaded from ADS in Colorado from 1994 to 2013 as polygon shapefiles with associated information such as causal agents or disorders, area damaged, and type of forest. The goal of my dissertation was to identify an appropriate sampling strategies to archive good estimates of total area damaged, to decrease survey cost, and to increase safety by reducing the amount of flights. To approach this goal, four sample designs for estimating total area damaged caused by various causal agent were evaluated: simple random sampling, stratified random sampling, probability proportional to size, and non-alignment systematic sampling. A GIS layer of 150 transects covering Colorado’s forestlands was developed and represented the sample unit for my study. Each transect was 3.2 km wide and 625 km long and was numbered from 1 to 150 from south to north. Each sample design was evaluated using eight sample sizes (10, 15, 20, 25,30, 35, 50, and 70) and applied to the seven damages and the combined damaged area. The statistical properties were evaluated to determine the optimal sample design for estimating area damaged caused by different causal agents. The spatio-temporal characteristics of area damaged that influence precision and accuracy of estimate were considered. Most of the damaged forest areas by single causal agents and disorders showed aggregated spatial patterns; whereas the combined damaged areas were uniformly distributed across the landscape. A loss plus cost function was employed to determine the optimal sample size for each sample design and analyzed for the cost advantage of alternative sample designs. We found that stratified random sampling was the most optimal sample design by producing the highest percentage of unbiased estimates of total area damaged and the smallest variances. The next best sampling designs were simple random sampling and probability proportional to size. The non-alignment systematic sampling was the worst for estimating total area damaged both for individual causal agents and disorders and all causal agents combined. The optimal sample size varied by sample design and causal agents and disorders as well as the level of confidence. Optimal sample size increased with increasing variability in the population and as the desired level of confidence increased. Larger samples were required to simultaneously provide estimates for multiple causal agents and disorder with reasonable levels of precision when compared to a single causal agent. Stratified random sampling was the most cost effective when compared with other sample designs. For example, the cost advantage of stratified sampling over random sampling for estimating the damage from subalpine-fir mortality was $85,000 per year. In contrast, PPS sampling had a cost disadvantage of -$13,000 per year when compared with simple random sampling and -$95,000 per year when compared with stratified sampling for estimating the total damage from all causal agents combined at the 0.95 level of confidence.
Open Access
Spatial modeling of site productivity and plant species diversity using remote sensing and geographical information system
(Colorado State University. Libraries, 2011) Mohamed, Adel Ahmed Hassan, author; Reich, Robin M., advisor; Khosla, Rajiv, advisor; Andales, Allan, committee member; Wei, Yu, committee member
The primary objective of this study was to describe the variability in site productivity of the diverse forests found in the state of Jalisco, Mexico. This information is fundamental for the management and sustainability of the species-rich forests in the state. The study also contributes to developing conservation-management program for the plant species diversity in Elba protected area in Egypt. The objective of chapter 1 was to develop site productivity index (SPI) curves for eight major forest types in the state of Jalisco, Mexico, using the height-diameter relationship of the dominant trees. Using permanent plot data, selected height-diameter functions were evaluated for their predictive performance within each of the major forest types. An important finding of this study was that a simple linear model could be used to describe the height-diameter relationship of the dominant trees in all of the major forest types considered in this study. SPI varied significantly among forest types, which are largely determined by the trends in temperature and precipitation. SPI decreased with increasing temperature and increased with increasing precipitation. The height-diameter relationship of the dominant trees was independent of stand density, and the more productive sites are able to sustain higher levels of basal area and volume, than the less productive sites. Trees on more productive sites had less taper than trees on less productive sites; and stand density did not influence the form or taper of the dominant trees. Chapter 2 evaluates methods to model the spatial distribution of site productivity in eight major forest types found in the state of Jalisco, Mexico. A site productivity index (SPI) based on the height-diameter relationship of dominant trees was used to estimate the site productivity of 818 forests plots located throughout the state. A combination of regression analysis and a tree-based stratified design was used to describe the relationship between SPI and environmental variables which included soil attributes (pH, sand, and silt), topography (elevation, aspect, and slope), and climate (temperature and precipitation). The final model explained 59% of the observed variability in SPI. GIS layers representing SPI for each forest type, along with associated estimates of the prediction variance are developed. Chapter 3 characterizes plant species richness on four major transects in Elba protected area in Egypt. Species data recorded on 63 sample plots were used to characterize the plant species richness by species group (trees, shrubs and subshrubs). Poisson regression was used to identify explanatory variables for estimating species richness of each species group. Important variables included the location of the line transect (A, B, C, and D), soil texture (gravel, sand, silt and clay), pH, and elevation. The final model explained 23%, 58%, and 52% in the variability of species richness for shrubs, subshrubs, and trees, respectively. The results of the study will contribute to the development of an inventory and monitoring program aimed at the conservation and management of species diversity in Elba protected area of Egypt.
Open Access
Using nonlinear geostatistical models for soil salinity and yeild management
(Colorado State University. Libraries, 2013) Eldeiry, Ahmed, author; Garcia, Luis A., advisor; Reich, Robin M., advisor; Grigg, Neil S., committee member
Crop production losses associated with soil salinity on irrigated lands are significant. The genetic complexity of crops with regards to salt tolerance has limited the success of improving salt tolerance through conventional breeding programs. In the meantime, land reclamation and leaching can be expensive and sometimes impractical when fresh water sources are scarce or not readily available. This research introduces a geostatistical approach for the management of crop yield under current soil salinity conditions. It uses three nonlinear geostatistical models - disjunctive kriging (DK), indicator kriging (IK), and probability kriging (PK) - to manage soil salinity and crop yield. The nonlinear models were applied to selected irrigated fields in a study area located in the south eastern part of the Arkansas River Basin in Colorado where soil salinity is a problem in some areas. The overall objectives of this research are: 1) estimate soil salinity in irrigated fields using nonlinear gestatistical models; 2) develop conditional probability (CP) maps that divide each field into zones with different soil salinity levels; 3) estimate the expected yield potential (YP) for several crops at different zones in fields under multiple soil salinity thresholds; 4) evaluate the performance of the nonlinear geostatistical models in developing the interpolated and CP maps provide guidance to farmers and researchers by considering the output of this research as input for precision management of agriculture; and 5) provide guidance to farmers and decision makers in precision management of agriculture. The three nonlinear geostatistical models DK, IK, and PK were used to develop CP maps based on soil salinity thresholds for different crops. These CP maps were compared with actual yield data taken while conducting a soil salinity survey for two fields cultivated with alfalfa and corn. The CP maps divide each field of interest into zones with different probabilities to reach a specific YP for a given crop at a specific soil salinity threshold. Different crops were selected to represent the dominant crops grown in the study area: alfalfa, corn, sorghum, and wheat. Six fields were selected to represent the range of soil salinity levels in the area. Soil salinity data were collected in the fields using an EM-38 and the location of each soil salinity sample point was determined using a GPS unit. Datasets of soil salinity collected in irrigated fields were used to generate the CP maps and to evaluate different scenarios of the expected YP% of several crops at multiple soil salinity thresholds. These datasets were selected to represent a wide range of soil salinity conditions in order to be able to evaluate a wide variety of crops (larger set of crops than those grown in the study area) according to their soil salinity tolerances. Yield data were collected at the same fields to compare the actual data with that estimated by the models. The crops were used for evaluation were selected based on two criteria: dominant in the study area, and represent high, moderate, and low soil salinity tolerances. Different scenarios of crops and salinity levels were evaluated. Semivariograms were constructed for each scenario to represent the different classes of percent yield potential based on soil salinity thresholds of each crop. The results of this research show the nonlinear geostatistical models are efficient in assessing the impact of soil salinity on the spatial variability yield productivity. The comparison of the actual yield data with the estimated yield from the three models shows good agreement where most of the yield samples were located at the appropriate zones estimated with the models. The IK and PK models generated very similar estimates for each of the zones. However, the zones generated by both of these models are slightly different to the zones generated using the DK model. Wheat and sorghum show the highest expected yield potential based on the different soil salinity conditions that were evaluated. Expected net revenue for alfalfa and corn are the highest under the different soil salinity conditions that were evaluated. The CP maps generated using the DK technique give an accurate characterization and quantification of the different zones of the fields. Upon the knowledge of the YP% of different areas, a management decision action can be taken to manage the productivity of a field by selecting another crop or adjusting the inputs such as fertilizer, seeding rates and herbicides in low productivity areas. The information provided by the models about the variability and hotspots can be used for the precision management of agricultural resources. The IK model can be used to generate guidance maps that divide each field into areas of expected percent yield potential based on soil salinity thresholds for different crops. Zones of uncertainty can be quantified by IK and used for risk assessment of the percent yield potential.

Browsing by Author "Reich, Robin M., advisor"

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