Stability and composition of terrestrially derived dissolved organic nitrogen in continental shelf surface waters

A linear decrease in dissolved organic carbon and nitrogen with increasing salinity offshore from the Georgia coast suggests that organic nitrogen compounds contributed to coastal waters by rivers are stable during the period (2–3 months) of their transfer over the continental shelf. While the C/N ratio decreased with distance from shore, total dissolved organic nitrogen (DON), total amino nitrogen, and primary amino nitrogen showed similar relative decreases, suggesting that nitrogen is associated with refractory organic compounds. Measured amino nitrogen accounted for about 20% of the total DON, leaving about 80% of the organic nitrogen undefined.     

Nutrient attenuation in a shallow,gravel-bed river. II. Spatial and temporal changes in nitrogen dynamics and community metabolism

ABSTRACT

Instream processes alter the concentration and bioavailability of nutrients as they are transported downstream. By relating primary production and periphyton composition to changes in nutrient concentration in a gravel-bed river this study made inferences about recycling and attenuation. Where dissolved inorganic nitrogen (DIN) was abundant, concentrations decreased linearly with distance but by less than required to meet the nitrogen demand of primary production. Where DIN was barely measurable photosynthesis was reduced but only by 50%. We infer that recycling sustained primary production even when DIN concentrations were negligibly small. One implication is that DIN removal underestimates attenuation. Further experimental research on recycling and improved modelling is required to better quantify the length of streams adversely affected by nutrients.     

A note on first-order normalizations of perturbed Keplerian systems

Techniques are developed to facilitate the transformation of a perturbed Keplerian system into Deláunay normal form at first order. The implicit dependence of the Hamiltonian on 1, the mean anomaly, through the explicit variable f, the true anomaly, or E, the eccentric anomaly, is removed through first order for terms of the form:

A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis.     

A bootstrap based space–time surveillance model with an application to crime occurrences

This study proposes a bootstrap-based space–time surveillance model. Designed to find emerging hotspots in near-real time, the bootstrap based model is characterized by its use of past occurrence information and bootstrap permutations. Many existing space–time surveillance methods, using population at risk data to generate expected values, have resulting hotspots bounded by administrative area units and are of limited use for near-real time applications because of the population data needed. However, this study generates expected values for local hotspots from past occurrences rather than population at risk. Also, bootstrap permutations of previous occurrences are used for significant tests. Consequently, the bootstrap-based model, without the requirement of population at risk data, (1) is free from administrative area restriction, (2) enables more frequent surveillance for continuously updated registry database, and (3) is readily applicable to criminology and epidemiology surveillance. The bootstrap-based model performs better for space–time surveillance than the space–time scan statistic. This is shown by means of simulations and an application to residential crime occurrences in Columbus, OH, year 2000.