Photographic quantification of water quality in mixing zones

A method was developed to quantitatively delineate waste concentrations throughout waste effluent mixing zones on the basis of densitometric measurements extracted from aerial photography. A mixing zone is the extent of a receiving water body ultilized to dilute a waste discharge to a concentration characteristic of a totally mixed condition. Simultaneously-acquired color infrared photography and suspended solids water samples were used to quantitatively delineate the mixing zone resulting from the discharge of a paper mill effluent. Digital scanning microdensitometer data was used to estimate and delineate suspended solids concentrations on the basis of a semi-empirical model. Photographic photometry, when predicated on a limited amount of ground sampling, can measure and delineate mixing zone waste distributions in more detail then conventional surface measuring techniques. The method has direct application to: (1) the establishment of definite and rational water quality guidelines; (2) the development of sampling and surveillance programs for use by governmental and private agencies; and (3) the development of design and location criteria for industrial and municipal waste effluent outfalls

Olympic Coast National Marine Sanctuary Habitat Mapping: Survey report and classification of side scan sonar data from surveys HMPR-114-2004-02 and HMPR-116-2005-01.

The Olympic Coast National Marine Sanctuary (OCNMS) continues to invest significant resources into seafloor mapping activities along Washington’s outer coast (Intelmann and Cochrane 2006; Intelmann et al. 2006; Intelmann 2006). Results from these annual mapping efforts offer a snapshot of current ground conditions, help to guide research and management activities, and provide a baseline for assessing the impacts of various threats to important habitat. During the months of August 2004 and May and July 2005, we used side scan sonar to image several regions of the sea floor in the northern OCNMS, and the data were mosaicked at 1-meter pixel resolution. Video from a towed camera sled, bathymetry data, sedimentary samples and side scan sonar mapping were integrated to describe geological and biological aspects of habitat. Polygon features were created and attributed with a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999). For three small areas that were mapped with both side scan sonar and multibeam echosounder, we made a comparison of output from the classified images indicating little difference in results between the two methods. With these considerations, backscatter derived from multibeam bathymetry is currently a costefficient and safe method for seabed imaging in the shallow (<30 meters) rocky waters of OCNMS. The image quality is sufficient for classification purposes, the associated depths provide further descriptive value and risks to gear are minimized. In shallow waters (<30 meters) which do not have a high incidence of dangerous rock pinnacles, a towed multi-beam side scan sonar could provide a better option for obtaining seafloor imagery due to the high rate of acquisition speed and high image quality, however the high probability of losing or damaging such a costly system when deployed as a towed configuration in the extremely rugose nearshore zones within OCNMS is a financially risky proposition. The development of newer technologies such as intereferometric multibeam systems and bathymetric side scan systems could also provide great potential for mapping these nearshore rocky areas as they allow for high speed data acquisition, produce precisely geo-referenced side scan imagery to bathymetry, and do not experience the angular depth dependency associated with multibeam echosounders allowing larger range scales to be used in shallower water. As such, further investigation of these systems is needed to assess their efficiency and utility in these environments compared to traditional side scan sonar and multibeam bathymetry. (PDF contains 43 pages.

An economic evaluation of using Management Zones in cotton production

This thesis examines the use of Precision Agriculture technologies to define Management Zones within a multicrop production system. It further evaluates the economic feasibility of implementing spatially variable insecticide applications against conventional blanket treatments with respect to insect pest management in cotton production. The use of geographical information systems was critical in the development of the different yield maps established to determine the level of consistency of management zones across crops over time. Several important concepts, such as data normalization, yield grid maps, inverse distance weighted and stability, were introduced throughout this research to: set the scale of measures to the same basis, facilitate comparison across crops, manipulate the data, and establish a level of confidence, respectively, concerning the use of management zones in crop production. Furthermore, the basic notion behind this study was that if fields can be divided into high/low yielding management zones, the use of variable rate technology, through an ON/OFF prescription application, offers the potential to reduce costs and increase productivity of the field. The capital recovery method was used to evaluate the per acre cost of investing in a precision farming system for gathering site-specific information and performing SVI applications. Results from this study show that the use of yield-based management zones can reveal annual cost reductions and increased profitability for the producer

Agriculture field characterization using GIS software and scanned color infrared aerial photographs

Non-Peer ReviewedThis paper addresses the potential of a color infrared aerial photograph to provide spatially distributed information for site specific management. In this process digitized color infrared aerial photographs were used to extract vegetation index information. In addition, important crop and soil information were also collected using a grid sampling technique. Crop and soil information contributing most to explain variability were determined and used in further analysis. Grain yield data obtained using combine sampling were noted along with the coordinate information of the sample points. Locational information were collected using GPS. Kriged surface were generated using soil and crop point sample information. Point information were extracted from each kriged surface using centroid of uniformly spaced grid (15 m cell). Fuzzy k-means with extragrades algorithms were used to delineate potential within-field management units based on soil and crop information and vegetation index separately. Then “goodness” of potential management zones generated using within zone variability of grain yield. Ideal number of zones were determined using the decrease in total within-zone variance. Finally, management zones determined using crop and soil information and vegetation index information were compared for similarity. The methodology is fast, can be easily automated in commercially available GIS software and has considerable advantages when comparing to other methods for delineating within-field management zones

Moran Eigenvector Filtering of Multi-year Yield Data with Application to Zone Development

A time‐series of yield monitor data may be used to identify field areas of consistently low or high yield to serve as productivity zones for site‐specific crop management. However, transient factors that affect yield in 1 yr, but not every year, detract from this approach. The objective of this study was to illustrate Moran eigenvector spatial filtering (MESF) with results from analysis of multi‐year crop yield data from two farm fields in the United States. The MESF method accounts for temporal autocorrelation within a common factor map representing the correlation across years and partitions stochastic geographic variation into spatially structured and unstructured components. Crop rotation data were utilized from a dryland field in east‐central South Dakota and an irrigated field in southwestern Georgia. A random effects (RE) model was estimated that utilized eigenfunctions of a geographic connectivity matrix to account for spatially structured random effects (SSRE) and unstructured random effects (SURE) in standardized z scores of multi‐year crop yield. The MESF method was evaluated with conventional averaging of unfiltered yield data as a reference for comparison. In South Dakota, the SSRE accounted for 26% of the yield variance shared across years. Distinct patterns appeared to be related to changes in soil type and landscape position. The Georgia field yielded similar results. The MESF is effective for revealing structured variation in a time series of yield monitor data and may be useful for defining productivity zones within fields

Farming Differentiation in the Rural-urban Interface of the Middle Mountains, Nepal: Application of Analytic Hierarchy Process (AHP)Modeling

This article investigates the dominant factors of farming differentiation in the rural-urban interface of the densely populated Kathmandu Valley, using the Analytic Hierarchy Process (AHP) modeling. The rural-urban interface in the Kathmandu Valley is an important vegetable production pocket which supplies a large amount of the vegetables in the city core. While subsistence farming in the rural area is characterized by a system which integrates livestock and forestry with agriculture, the intensification in the urban fringe is characterized by triple crop rotations and market-oriented intensive vegetable production. Seven factors which were supposed to cause farming variation in the interface were incorporated in the AHP framework and then subjected to the farmers’ judgment in distinctly delineated three farming zones. These factors played crucial yet differing roles in different farming zones. Inaccessibility and use of local resources; higher yield and accessibility and agro-ecological consideration and quality production are the key impacting factors of subsistence, commercial inorganic and smallholder organic farming respectively. The quantification of such factors of farming differentiation through AHP is an important piece of information that will contribute in modeling farming in the rural-urban interface of developing countries which are characterized by a high diversity of farming practices and are undergoing a rapid change in the land use pattern

Stochastic Estimation of Field Management Zones Using Multi-Year Yield Data and a Hidden Markov Random Field

Modern precision agriculture equipment enables treating different areas of a field differently, i.e., site specific management. This results in the concept of management zones, which are a compromise between treating a field uniformly and treating every plant individually. This work presents an algorithm for inferring the management zones for a field based on yield data from multiple years. The algorithm uses a hidden Markov random field model (HMRF) to find regions of the field which likely correspond to the same underlying yield distribution (i.e., “management zones”). These regions are assumed to be the same for each year, but their distributions are allowed to vary with time to account for year-to-year variability (from e.g., weather effects, differing crops). The zone assignments and distributions are estimated using Stochastic Expectation Maximization (SEM) and the maximizer of the posterior marginals (MPM). The underlying assumption of the model and algorithm is that the yields corresponding to a given “management zone” will behave similarly, and therefore derive from the same probability distribution. An advantage of this method is that it is able to run with only the yield data automatically collected during harvest. Also, this method requires no crop specific calibration or configuration

A bioregional classification of the continental shelf of northeastern North America for conservation analysis and planning based on representation

Understanding how well National Marine Sanctuaries and other marine protected areas represent the diversity of species present within and among the biogeographic regions where they occur is essential for assessing their conservation value and identifying gaps in the protection of biological diversity. One of the first steps in any such assessment should be the development of clearly defined and scientifically justified planning boundaries representing distinct oceanographic conditions and faunal assemblages. Here, we propose a set of boundaries for the continental shelf of northeastern North America defined by subdivisions of the Eastern Temperate Province, based on a review and synthesis (i.e. meta-analysis) of the scientific literature. According to this review, the Eastern Temperate Province is generally divided into the Acadian and Virginian Subprovinces. Broad agreement places the Scotian Shelf, Gulf of Maine, and Bay of Fundy within the Acadian Subprovince. The proper association of Georges Bank is less clear; some investigators consider it part of the Acadian and others part of the Virginian. Disparate perspectives emerge from the analysis of different groups of organisms. Further, while some studies suggest a distinction between the Southern New England shelf and the rest of the Mid-Atlantic Bight, others describe the region as a broad transition zone with no unique characteristics of its own. We suggest there exists sufficient evidence to consider the Scotian Shelf, Gulf of Maine, Georges Bank, Southern New England, and Southern Mid-Atlantic Bight as distinct biogeographic regions from a conservation planning perspective, and present a set of proposed mapped boundaries. (PDF contains 23 pages.

Mining Urban Performance: Scale-Independent Classification of Cities Based on Individual Economic Transactions

Intensive development of urban systems creates a number of challenges for urban planners and policy makers in order to maintain sustainable growth. Running efficient urban policies requires meaningful urban metrics, which could quantify important urban characteristics including various aspects of an actual human behavior. Since a city size is known to have a major, yet often nonlinear, impact on the human activity, it also becomes important to develop scale-free metrics that capture qualitative city properties, beyond the effects of scale. Recent availability of extensive datasets created by human activity involving digital technologies creates new opportunities in this area. In this paper we propose a novel approach of city scoring and classification based on quantitative scale-free metrics related to economic activity of city residents, as well as domestic and foreign visitors. It is demonstrated on the example of Spain, but the proposed methodology is of a general character. We employ a new source of large-scale ubiquitous data, which consists of anonymized countrywide records of bank card transactions collected by one of the largest Spanish banks. Different aspects of the classification reveal important properties of Spanish cities, which significantly complement the pattern that might be discovered with the official socioeconomic statistics.Comment: 10 pages, 7 figures, to be published in the proceedings of ASE BigDataScience 2014 conferenc