Wind erosion from military training lands in the Mojave Desert, California, U.S.A.

Military training activities reduce vegetation cover, disturb crusts, and degrade soil aggregates, making the land more vulnerable to wind erosion. The objective of this study was to quantify wind erosion rates for typical conditions at the Marine Corps Air Ground Combat Center, Twentynine Palms, CA, U.S.A. Five Big Spring Number Eight (BSNE) sampler stations were installed at each of five sites. Each BSNE station consisted of five BSNE samplers with the lowest sampler at 0·05 m and the highest sampler at 1·0 m above the soil surface. Once a month, sediment was collected from the samplers for analysis. Occurrence of saltating soil aggregates was recorded every hour using Sensits, one at each site. The site with the most erosion had a sediment discharge of 311 kg m⁻¹ over a period of 17 months. Other sites eroded much less because of significant rock cover or the presence of a crust. Hourly sediment discharge was estimated combining hourly Sensit count and monthly sediment discharge measured using BSNE samplers. More simultaneously measured data are needed to better characterize the relationship between these two and reconstruct a detailed time-series of wind erosion. This measured time-series can then be used for comparison with simulation results from process-based wind erosion models such as the Wind Erosion Prediction System (WEPS), once it has been adapted to the unique aspects of military lands.     

Detection and mapping of bracken fern weeds using multispectral remotely sensed data: a review of progress and challenges

Bracken fern is one of the major invasive plants distributed all over the world currently threatening socio-economic and ecological systems due to its ability to swiftly colonize landscapes. The study aimed at reviewing the progress and challenges in detecting and mapping of bracken fern weeds using different remote sensing techniques. Evidence from literature have revealed that traditional methods such as field surveys and modelling have been insufficient in detecting and mapping the spatial distribution of bracken fern at a regional scale. The applications of medium spatial resolution sensors have been constrained by their limited spatial, spectral and radiometric capabilities in detecting and mapping bracken fern. On the other hand, the availability of most of these data-sets free of charge, large swath width and their high temporal resolution have significantly improved remote sensing of bracken fern. The use of commercial satellite data with high resolution have also proven useful in providing fine spectral and spatial resolution capabilities that are primarily essential to offer precise and reliable data on the spatial distribution of invasive species. However, the application of these data-sets is largely restricted to smaller areas, due to high costs and huge data volumes. Studies on bracken fern classification have extensively adopted traditional classification methods such as supervised maximum likelihood classifier. In studies where traditional methods performed poorly, the combination of soft classifiers such as super resolution analysis and traditional methods of classification have shown an improvement in bracken fern classification. Finally, since high spatial resolution sensors are expensive to acquire and have small swath width, the current study recommends that future research can also consider investigating the utility of the freely available recently launched sensors with a global footprint that has the potential to provide invaluable information for repeated measurement of invasive species over time and space.     

Representation of Uncertainty and Integration of PGIS-based Grazing Intensity Maps Using Evidential Belief Functions

In a project to classify livestock grazing intensity using participatory geographic information systems (PGIS), we encountered the problem of how to synthesize PGIS-based maps of livestock grazing intensity that were prepared separately by local experts. We investigated the utility of evidential belief functions (EBFs) and Dempster's rule of combination to represent classification uncertainty and integrate the PGIS-based grazing intensity maps. These maps were used as individual sets of evidence in the application of EBFs to evaluate the proposition that " This area or pixel belongs to the high, medium, or low grazing intensity class because the local expert(s) says (say) so ". The class-area-weighted averages of EBFs based on each of the PGIS-based maps show that the lowest degree of classification uncertainty is associated with maps in which "vegetation species" was used as the mapping criterion. This criterion, together with local landscape attributes of livestock use may be considered as an appropriate standard measure for grazing intensity. The maps of integrated EBFs of grazing intensity show that classification uncertainty is high when the local experts apply at least two mapping criteria together. This study demonstrates the usefulness of EBFs to represent classification uncertainty and the possibility to use the EBF values in identifying and using criteria for PGIS-based mapping of livestock grazing intensity.     

Comparative growth of juvenile white sturgeon and striped bass: Effects of temperature and hypoxia

The influences of temperature and environmental hypoxia on the growth rates of two California anadromous fishes, white sturgeon (Acipenser transmontanus) and striped bass (Morone saxatilis) were examined. Fish (0.5–0.6 g initial weight) were fedad libitum rations ofArtemia in flow-through aquaria regulated for temperature (15, 20, and 25°C) and oxygen tension (130 and 90 torr P_{o 2}). Growth of sturgeon was significantly greater at 20 °C compared with 15 °C, but there was no difference between 20 and 25 °C. Striped bass growth increased with each 5° increment of temeprature elevation to 3.2% body weight per d at 25 °C, the fastest growth rate measured. The temperature of maximum growth reflected the temperature of the native estuarine rearing area. Environmental hypoxia (90 torr P_{o 2}) reduced growth of sturgeon within each temperature level, whereas striped bass growth was reduced by hypoxia only at the upper two temperatures. Sturgeon were much more active in the growth chambers than striped bass. Sturgeon activity increased with each 5 °C temperature increase under normoxia and hypoxia, except at 25 °C (hypoxia) where activity was insignificantly different from that at 20 °C (hypoxia).     

Wind erosion climatic erosivity

A physically based wind-erosion climatic factor has been derived: $$CE = \rho \int {[u^2 } - (u_T^2 + \gamma ^l /\rho a^2 )]^{3/2} f(u)du$$ where ? is the air density, a is a constant made up of other constants (von Karman, height of wind speed observation, roughness parameter), u is the horizontal wind speed, u _T is threshold wind speed, f(u) u) is a wind speed probability density function, and γ is the cohesive resistance caused by water on the soil particles. Cohesive resistance is proportional to the square of water content relative to water content at ?1500 J kg^?1. Relative water content is approximated from the Budyko dryness ratio and the Thornthwaite PE index with similar results. CE is calculable from wind speed and other generally available meteorological data, and is usable in the wind erosion equation without some of the limitations of a previously used wind erosion climatic factor.     

Observed dependence of the water vapor and clear-sky greenhouse effect on sea surface temperature: comparison with climate warming experiments

This study presents a comparison of the water vapor and clear-sky greenhouse effect dependence on sea surface temperature for climate variations of different types. Firstly, coincident satellite observations and meteorological analyses are used to examine seasonal and interannual variations and to evaluate the performance of a general circulation model. Then, this model is used to compare the results inferred from the analysis of observed climate variability with those derived from global climate warming experiments. One part of the coupling between the surface temperature, the water vapor and the clear-sky greenhouse effect is explained by the dependence of the saturation water vapor pressure on the atmospheric temperature. However, the analysis of observed and simulated fields shows that the coupling is very different according to the type of region under consideration and the type of climate forcing that is applied to the Earth-atmosphere system. This difference, due to the variability of the vertical structure of the atmosphere, is analyzed in detail by considering the temperature lapse rate and the vertical profile of relative humidity. Our results suggest that extrapolating the feedbacks inferred from seasonal and short-term interannual climate variability to longer-term climate changes requires great caution. It is argued that our confidence in climate models' predictions would be increased significantly if the basic physical processes that govern the variability of the vertical structure of the atmosphere, and its relation to the large-scale circulation, were better understood and simulated. For this purpose, combined observational and numerical studies focusing on physical processes are needed.     

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.     

The ‘Stained Glass Procedure’, a new method to compare classification performance of images acquired with different pixel sizes

Objective comparison of classification performance of earth observation images, acquired at different spatial resolutions (e.g. NOAA-AVHRR, IRS-MOS, IRS-WiFS, Landsat-TM, IRS-LISS), is complicated because both class definition and training site selection are hampered by the inherent scale differences. This paper presents a new, generic method to compare the information content of such a set of images, the “Stained Glass Procedure”. It overcomes the stated problems by computing the scale-dependent, internal spectral variation in an image and by using this as an indicator for land cover information. The Stained Glass Procedure creates segments in the images and calculates the internal spectral variation in a high-spatial-resolution image for each segment. For each image from the set the average variance, weighted to area, is calculated. The Stained Glass Procedure can be used to predict the performance of sensors that are not available, yet, or to roughly determine the optimal spatial resolution for the classification of a specific area.The procedure was applied to images with pixel sizes ranging from 23 to 1100 m. Classification detail of Envisat-MERIS (300 m pixel size), not included in the image set, could be predicted accurately using the Stained Glass Procedure.The Stained Glass Procedure applies one procedure to all images, without any subjective decision during the analysis, thus offering a method to compare images with different pixel sizes in terms of classification detail that is truly objective.