Robin M. Reich

Estimating splash pine biomass using radar backscatter

L-band HV multiple-incidence-angle aircraft synthetic aperture radar (SAR) data were analyzed in relation to average stand biomass, basal area, and tree height for 55 slash pine plantations located in northern Florida. This information was used to develop a system of equations to predict average stand biomass as a function of L-band (24.5-cm) radar backscatter. The system of equations developed in this study using three-stage least-squares and combinatorial screening accounted for 97% of the variability observed in average stand biomass per hectare. When applied to an independent data set, the biomass equations had an average bias of less than 1% with a standard error of approximately 3%. >

Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA

Fire suppression has increased fuel loadings and fuel continuity in many forested ecosystems, resulting in forest structures that are vulnerable to catastrophic fire. This paper describes the statistical properties of models developed to describe the spatial variability in forest fuels on the Black Hills National Forest, South Dakota. Forest fuel loadings (tonnes/ha) are modeled to a 30 m resolution using a combination of trend surface models to describe the coarse-scale variability in forest fuel, and binary regression trees to describe the fine-scale variability associated with site-specific variability in forest fuels. Independent variables used in the models included various Landsat TM bands, forest class, elevation, slope, and aspect. The models accounted for 55% to 72% of the variability in forest fuels. In spite of having highly skewed distributions, cross-validation showed the models to have nominal prediction bias. This paper also evaluates the feasibility of using the estimation error variance to explain estimation uncertainty. The models are allowing us to study the influence of small-scale disturbances on forest fuel loadings and diversity of resident and migratory birds on the Black Hills National Forest.

A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees

Traditional land classification techniques for large areas that use Landsat Thematic Mapper (TM) imagery are typically limited to the fixed spatial resolution of the sensors (30 m). However, the study of some ecological processes requires land cover classifications at finer spatial resolutions. We model forest vegetation types on the Kaibab National Forest (KNF) in northern Arizona to a 10-m spatial resolution with field data, using topographical information and Landsat TM imagery as auxiliary variables. Vegetation types were identified by clustering the field variables total basal area and proportion of basal area by species, and then using a decision tree based on auxiliary variables to predict vegetation types. Vegetation types modelled included pinyon-juniper, ponderosa pine, mixed conifer, spruce- and deciduous-dominated mixes, and openings. To independently assess the accuracy of the final vegetation maps using reference data from different sources, we used a post-stratified, multivariate composite estimator. Overall accuracy was 74.5% (Kappa statistic = 49.9%). Sources of error included differentiating between mixed conifer and spruce-dominated types and between openings in the forest and deciduous-dominated mixes. Overall, our non-parametric classification method successfully identified dominant vegetation types on the study area at a finer spatial resolution than can typically be achieved using traditional classification techniques.

Accounting for Heterogeneity of Public Lands in Hedonic Property Models

Open space lands, national forests in particular, are usually treated as homogeneous entities in hedonic price studies. Failure to account for the heterogeneous nature of public open spaces may result in inappropriate inferences about the benefits of proximate location to such lands. In this study the hedonic price method is used to estimate the marginal values for proximity to the Pike National Forest. The results indicate that specifying the forest as homogeneous overstates the benefits for homes within two miles relative to specifying the forest based on land use characteristics, because the significant negative effect from noise-intensive activities is omitted. (JEL H41, Q51)

Assessing the accuracy of Landsat Thematic Mapper classification using double sampling

Double sampling was used to provide a cost efficient estimate of the accuracy of a Landsat Thematic Mapper (TM) classification map of a scene located in the Rocky Mountain National Park, Colorado. In the first phase, 200 sample points were randomly selected to assess the accuracy between Landsat TM data and aerial photography. The overall accuracy and Kappa statistic were 49.5 per cent and 32.5 per cent, respectively. In the second phase, 25 sample points identified in the first phase were selected using stratified random sampling and located in the field. This information was used to correct for misclassification errors associated with the first phase samples. The overall accuracy and Kappa statistic increased to 59.6 per cent and 45.6 per cent, respectively.

Spatial cross-correlation of undisturbed, natural shortleaf pine stands in northern Georgia

In this study a cross-correlation statistic is used to analyse the spatial relationship among stand characteristics of natural, undisturbed shortleaf pine stands sampled during 1961–72 and 1972–82 in northern Georgia. Stand characteristics included stand age, site index, tree density, hardwood competition, and mortality. In each time period, the spatial cross-correlation statistic was used to construct cross-correlograms and cumulative cross-correlograms for all significant pairwise combination of stand characteristics. Both the cross-correlograms and cumulative cross-correlograms identified small-scale clustering and weak directional gradients for different stand characteristics in each time period. The cumulative cross-correlograms, which are based on inverse distance weighting were more sensitive in detecting small-scale clustering than the cross-correlograms based on a 0–1 weighting. Further analysis suggested that the significant cross-correlation observed among basal area growth and other stand characteristics were due, in a large part, on a subset of sample plots located in the northern part of the state, rather than regional or broad-scale variation as first thought. The ability to analyse the spatial relationship between two or more response surfaces should provide valuable insight in the development of ecosystem level models and assist decision makers in formulating pertinent policy on intelligent multiresource management.

Spatial Variation and Site-Specific Management Zones

Many approaches have been proposed over the last two decades for managing the spatial variation of soil and crops. In this chapter we discuss the importance of quantifying and managing spatial variation in crop production fields to implement site-specific crop management. We outline the challenges that soil and crop scientists have addressed since the inception of precision agriculture (PA) in terms of managing soil spatial variation, and the development of simple, stable and inexpensive techniques for quantifying and managing it with tools such as site-specific management zones. This chapter summarizes and cites the work of several scientists who have worked in the area of development and evaluation of site-specific management zones from around the world. Geostatistics is being applied increasingly in PA because of the need for accurate maps on which to base site-specific management. For soil and crop properties that require costly sampling and analysis, there are often insufficient data for geostatistical analyses and this chapter shows how management zones can provide an interim solution to more comprehensive site-specific management. Physical and chemical soil properties have been the most widely used properties for delineating management zones, however, intensive data from remote and proximal sensors are being used increasingly. The case study describes methods of delineating and evaluating management zones.

Climate, soils, and connectivity predict plague epizootics in black‐tailed prairie dogs (Cynomys ludovicianus)

Outbreaks of plague in wildlife are sporadic and spatially dispersed, and they depend on coincidence of susceptible hosts, flea vectors, the plague bacterium (Yersinia pestis), and environmental factors that support pathogen transmission. We fit spatial models of plague outbreaks to a long-term data set (1981–2005) of towns of black-tailed prairie dogs (Cynomys ludovicianus) on the shortgrass steppe of northeastern Colorado. We investigated the effects of spatial distribution (town area and connectivity to other prairie dog towns), climate (spring and summer precipitation and temperature), and soil moisture-holding capacity. In logistic regression models, plague epizootics were predicted by connectivity to other towns experiencing plague during periods with relatively low temperatures, in soils with high moisture-holding capacity. After accounting for connectivity between prairie dog towns and current-year climatic conditions, little additional spatial or temporal autocorrelation was detected. Spatial log...

Research article Canopy dynamics and human caused disturbance on a semi-arid landscape in the Rocky Mountains, USA

Invasion of grasslands by woody plants has been identified as a key indicator of changes in ecosystem structure and function in arid and semi-arid rangelands throughout the world. We investigated changes in the balance between woody and herbaceous components of a semi-arid landscape in western Colorado (USA) using historical aerial photography. Aerial photographs from 1937, 1965–67, and 1994 were sampled at matched locations within overlapping photographs. We modeled change in spatial pattern and heterogeneity across the entire landscape and found a small, net decrease in woody canopy cover; however means disguised normal distributions of change that demonstrated offsetting increases and decreases. We described a region of widespread canopy decline within piñon-juniper forests between 2300 and 2600 m (7500–8500 feet) and a region of predominant increase at lower elevations, between 1800 and 2250 m (5900–7400 feet). It remains unclear whether this shift was driven by climate or by human-caused or natural disturbance. Mean conifer cover decreased within coniferous forests, which counteracted a trend of increased conifer cover in mixed forests, savanna-like woodlands, and the shrub steppe. Disturbance had a significant interaction with cover change in several communities, including forests, savanna and shrublands. Anthropogenic disturbances counteracted successional trends toward canopy closure more than wildfires, but this did not entirely explain observed canopy decline. The natural dynamics in this region also caused diverse changes rather than a simple progression towards increased forest cover. Importantly, temporal change in vegetation varied spatially across the landscape illustrating the importance of landscape level, spatially explicit analyses in characterizing temporal dynamics.

Patterns of tree species richness in Jalisco, Mexico: relation to topography, climate and forest structure.

AbstactThe objective of this study was to identify the major environmental variables and components of forest structure associated with variability in tree species richness on a network of 806 permanent plots in the State of Jalisco, Mexico. Tree data recorded on the sample plots were used to characterize tree species richness by forest type and climatic conditions (temperature and precipitation) in the State. Species composition and other diversity indices were also calculated. Explanatory variables identified in a Poisson regression identified forest cover type, elevation, tree basal area, canopy closure, and winter precipitation as being important to changes in tree species richness. An “extreme quantile curve estimation” approach was then used to approximate the boundary that represented the maximum potential species richness response to the various levels of important variables. Maximum tree species richness decreased with increasing elevation. The relationships between maximum species richness and tree basal area, canopy closure, and winter precipitation followed a hump-back unimodal model, with intermediate values supporting the largest species richness. We believe that results of the current study will contribute to further development of a conservation plan for tree species in the State of Jalisco, Mexico.