Traditional and well-established applications of airborne remote sensing to archaeology involve standard aerial photographic recording, either oblique or vertical, of archaeological phenomena visible as shadow, soil or cropmarks, all of which require particular conditions before they become visible. Cropmarks have made the most important contribution to archaeological site discovery and are the most complex of the phenomena, whose occurrence relies upon the differential availability of moisture to a crop growing over buried archaeological remains, reflecting different soil depths and precipitation, to create differential growth patterns in times of moisture stress. Consistently successful aerial photographic reconnaissance for cropmarks relies on dry weather and well-drained soils in arable agriculture. There is, thus, in Scotland a bias in the discovery of archaeological sites in favour of drier eastern districts, supporting arable agriculture, compared to the west with its wetter climate focussed on pastureland. Because cropmarks are linked to moisture stress in growing plants they are potentially detectable in bands outside the visible part of the electro-magnetic spectrum. Although historically cropmark detection has used film, whose sensitivity closely approximates the human eye, hyperspectral scanning allows consideration of a wider range of different wavelengths, beyond the visible spectrum, many of which are more sensitive to changes in vegetation status. The main objectives of this research are to test to what extent hyperspectral and multispectral imagery can reveal otherwise invisible archaeological sites surviving as cropmarks; to assess the relative usefulness of the different sensors employed; and to investigate the potential of hyperspectral and multispectral imagery to augment cropmark detection rates in areas less conducive to their production. It uses a range of imagery (CASI 2, ATM and digital vertical photographic data) acquired by NERC ARSF from two case study sites in Lowland Scotland, one in the east and one in the west, selected to facilitate comparison between areas of good and poor cropmark production. Following processing, comparison and detailed analysis of this data, this thesis has demonstrated the high efficacy of this imagery in the identification of archaeological cropmarks; has established the most appropriate range of bandwidths and processing methods applicable to that imagery; has demonstrated its potential for the discovery of previously unrecognised archaeological sites in areas of lowland pasture; and has reinforced the value of systematic block coverage as compared to traditional ‘observer-directed’ archaeological aerial reconnaissance.

The use of active and passive remote sensing systems for relating forest spatial patterns to fire history was tested over one of the Arizona Sky Islands. Using Landsat Thematic Mapper (TM), Shuttle Imaging Radar (SIR-C), and data fusion I examined the relationship between landscape metrics and a range of fire history characteristics. Each data type (TM, SIR-C, and fused) was processed in the following manner: each band, channel, or derived feature was simplified to a thematic layer and landscape statistics were calculated for plots with known fire history. These landscape metrics were then correlated with fire history characteristics, including number of fire-free years in a given time period, mean fire-free interval, and time since fire. Results from all three case studies showed significant relationships between fire history and forest spatial patterns. Data fusion performed as well or better than Landsat TM alone, and better than SIR-C alone. These comparisons were based on number and strength of significant correlations each method achieved. The landscape metric that was most consistent and obtained the greatest number of significant correlations was Shannon's Diversity Index. Results also agreed with field-based research that has linked higher fire frequency to increased landscape diversity and patchiness. An additional finding was that the fused data seem to detect fire-related spatial patterns over a range of scales.

The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat. By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data, effective models of habitat preference were created. Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition. Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data. Such relationships, once verified and established, can be used to help indirectly monitor the population. Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations. The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.

The general objective of this research is to develop a methodology that will allow mapping and quantifying shrub encroachment with remote sensing. The multitemporal properties of the Moderate Resolution Imaging Spectroradiometer (MODIS) -250m, 16-day vegetation index products were combined with the hyperspectral and high spatial resolution (3.6m) computation of the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) to detect the dynamics of mesquite and grass/soil matrix at two sites of high (19.5%) and low (5.7%) mesquite cover in the Santa Rita Experimental Range (SRER). MODIS results showed separability between grassland and mesquite based on phenology. Mesquite landscapes had longer green peak starting in April through February, while the grassland only peaked during the monsoon season (July through October). AVIRIS revealed spectral separability, but high variation in the data implicated high heterogeneity in the landscape. Nonetheless, the methodology for larger data was developed in this study and combines ground, air and satellite data.

Concern for environmental management of our natural resources is most often focused on the human impacts upon these resources. Minor stresses on surface materials in sensitive desert landscapes can greatly increase the rate and character of erosion. The National Training Center, Ft. Irwin, located in the middle of the Mojave Desert, California, provides a study area of intense off-road vehicle (ORV) activity spanning a 50-year period. This study documents a case of concentrated ORV activity on sensitive desert environments, and the resulting environmental impacts. Geomorphic surfaces from two study sites within the Ft. Irwin area were mapped from 1:28,400 scale black and white aerial photographs taken in 1947. Surface disruption attributed to military activity was then mapped for the same areas from 1993, 1:12,000, black and white aerial photographs. Several field checks were conducted to verify this mapping. Images created from SPOT panchromatic and Landsat Thematic Mapper (TM) multispectral data acquired during the spring of 1987 and 1993 were analyzed to assess both the extent of disrupted surfaces and the surface geomorphology discernable from satellite data. Classified and merged images were then created from these data and demonstrate the capabilities of satellite data to aid in the delineation of disrupted geomorphic surfaces. Correlations were also established between highly disrupted surfaces and soil surface conditions on selected geomorphic surfaces. Disruption maps produced from the air photos indicate that the amount of disrupted surfaces within the study sites grew from a combined total of 1.3 km² in 1947 to 33.4 km² by 1993. A combination of 6 bands of Landsat TM data with a seventh band of SPOT panchromatic data yielded a product that delineated broad geomorphic surfaces that closely correlate with those mapped from the aerial photography. An error matrix between these two products resulted in an overall accuracy of 83.36% and a Kappa Index of Agreement of 77.28%. A 15-class unsupervised classification of the SPOT panchromatic data produced the representation of the extent and levels of disruption present in the study areas that closely matched field observations. Field sampling of soil strength and clay/silt percentages on disturbed and undisturbed surfaces reveals that these arid land surfaces react to intense ORV activity by becoming more compact and exhibiting higher percentages of clays and silts.

A three-year randomized field study was undertaken to evaluate the effects of prescribed burning and the intense rainfall events associated with the summer monsoon of southern Arizona, specifically examining the interchange between the plant, soil and surface hydrologic processes within a savanna. The effects of fire and rainfall treatments were evaluated by examining their specific effects on vegetation, arbuscular mycorrhizal fungi (AMF), soil structure, soil nutrient capital, surface runoff and sediment. In addition to the field study, two factorial greenhouse studies were completed in conjunction with the field study. The objectives of the greenhouse studies were: (1) to test the effects of prescribed fire and high intensity rainfall on AMF infectivity potential, and (2) to examine the effect of mycotrophy on the biomass production of native and introduced grasses grown in pasteurized and native soils. A third study examined the development of an empirical model to estimate sediment production from the field study's plots. The objectives of this third study were: (1) to develop the empirical model using two years of collected sediment, and (2) to compare model estimates with the commonly used Revised Universal Soil Loss Equation (RUSLE) model. The results of these studies indicate that a integrated mechanism exists between the plant community and biotic and physical soil processes, which when disturbed affect the hydrology of the watershed.

No description available.

Accelerated soil erosion in Malawi, Southern Africa, increasingly threatens agricultural productivity, given current and projected population growth trends. Previous attempts to document soil erosion potential have had limited success, lacking appropriate information and diagnostic tools. This study utilized geomatics technologies and the latest available information from topography, soils, climate, vegetation, and land use of a watershed in southern Malawi. The Soil Loss Estimation Model for Southern Africa (SLEMSA), developed for conditions in Zimbabwe, was evaluated and used to create a soil erosion hazard map for the watershed under Malawi conditions. The SLEMSA sub-models of cover, soil loss, and topography were computed from energy interception, rainfall energy, and soil erodibility, and slope length and steepness, respectively. Geomatics technologies including remote sensing and Geographic Information Systems (GIS) provided the tools with which land cover/land use, a digital elevation model, and slope length and steepness were extracted and integrated with rainfall and soils spatial information. Geomatics technologies enable rapid update of the model as new and better data sets become available. Sensitivity analyses of the SLEMSA model revealed that rainfall energy and slope steepness have the greatest influence on soil erosion hazard estimates in this watershed. Energy interception was intermediate in sensitivity level, whereas slope length and soil erodibility ranked lowest. Energy interception and soil erodibility were shown by parameter behavior analysis to behave in a linear fashion with respect to soil erosion hazard, whereas rainfall energy, slope steepness, and slope length exhibit non-linear behavior. When SLEMSA input parameters and results were compared to alternative methods of soil erosion assessment, such as drainage density and drainage texture, the model provided more spatially explicit information using 30 meter grid cells. Results of this study indicate that more accurate soil erosion estimates can be made when: (1) higher resolution digital elevation models are used; (2) data from improved precipitation station network are available, and; (3) greater investment in rainfall energy research.

Pandora moth (Coloradia pandora Blake) is a phytophagous insect, defoliating ponderosa pine trees in the western United States. However, long-term studies of this insect and its effects on the forest ecosystem have not been conducted. Using dendrochronological techniques, I examined past timing and intensity of defoliation through its effects on radial growth of trees in the forests of south central Oregon. Pandora moth leaves a distinctive ring-width "signature" that was easily identifiable in the wood. The growth for the first year of the signature was half the normal ring-width with narrow latewood. The following two years produced extremely narrow rings, with the entire suppression lasting from 4 to 18 years. Twenty-two individual outbreaks were reconstructed from this 620 year chronology. I found that pandora moth outbreaks were episodic on individual sites, with a return interval of 9 to 156 years. Conversely, on the regional scale of south central Oregon, outbreaks demonstrated a 37-year periodicity. On average, pandora moth defoliation caused a 29% mean periodic growth reduction in defoliated ponderosa pine trees. Spread maps of the first year that sites demonstrated suppression were plotted revealing an apparent annual spread of the outbreaks. Examination of a fire history on one pandora moth outbreak site suggested that pandora moth outbreaks delay fire by interrupting the needle fall needed for fire spread. Superposed epoch analysis showed that the year that the outbreak was first recorded was significantly dry and the fourth year prior was significantly wet. Therefore, climate may be a triggering factor in pandora moth outbreaks. The stem analysis demonstrated that the percent volume reduction was greatest at the base of the tree and declined further up the bole. The percent volume reduction in the canopy of the trees was variable with outlying high and low values. The mean volume reduction per outbreak was .053 m³ per tree. Although this insect is considered a forest pest and causes inconvenience for people living nearby, pandora moth is not as widespread and damaging as some other phytophagous insects. However, its very distinctive ring-width signature and the length of the ponderosa pine record enables reconstruction of very long outbreak histories, which may deepen our understanding of the interaction between defoliating insects and their ecosystem.

This thesis examines the temporal characteristics and climatology of wintertime carbon monoxide (CO) concentrations in central Phoenix (AZ). Variations in CO concentrations on several temporal scales are explored to determine the relationship between atmospheric conditions, CO concentrations, and motor vehicle traffic levels as well as to spotlight the timing of high CO concentrations. Typical surface atmospheric conditions that are related to high and low CO mornings are revealed through compositing. Finally, a set of CO forecasting models are developed that predict 3 AM 8 hour average CO concentrations. Important results are as follows. The first three weeks of December have the most CO exceedance days. High CO concentrations are associated with anticyclonic conditions while low CO concentrations are linked to the passage of a cold front or trough. Final models had little systematic mean squared error and predictions that were within 1 ppm of the observed CO concentration.