Relationships between particle-grazing zooplankton and vertical phytoplankton distributions on the Texas continental shelf

This study compares vertical distributions of Penilia avirostris and Temora turbinata with vertical distributions of chlorophyll a over short time periods at a station in the north-western Gulf of Mexico in the summer and early fall of 1978. In June and July 1978 chlorophyll a and turbidity increased below the thermocline. In September 1978 chlorophyll a was concentrated in the upper mixed layer while turbidity was greatest near the bottom.In June and July P. avirostris and copepodites of T. turbinata were concentrated within the subsurface chlorophyll a layer. Adult T. turbinata were more widely distributed than the copepodites in June but apparently migrated diurnally in July. In September, abundance of all animals generally decreased slightly with depth. This change in animal distribution reflected changes in biotic and abiotic varlables between this cruise and previous cruises. In all cruises, animal distributions were positively correlated with chlorophyll a suggesting that food was the primary influence on distributions of these animals.     

Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry,object-based classification,and geological features: a case study from Mayo,Yukon Territory,Canada

Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of ?2 to ?4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data.     

评航天飞机大像幅摄像机对中国西部地区的摄影

1984年10月,美国“挑战者”号航天飞机携带的大像幅摄影机,对中国西部部分地区拍摄了一套分辨率极高的照片。本文对这次实验的结果以及所拍摄的中国西部地区照片的特点进行了认真细致的研究和评价,并探讨了这些照片的应用前景。     

Geocoding crime and a first estimate of a minimum acceptable hit rate

The Fuzzy Kappa statistic expresses the agreement between two categorical raster maps. The statistic goes beyond cell‐by‐cell comparison and gives partial credit to cells based on the categories found in the neighborhood. When matching categories are found at shorter distances the agreement is higher. Like the well‐established Kappa statistic the Fuzzy Kappa statistic expresses the mean agreement relative to the expected agreement. The model underlying the expected agreement assumes absence of spatial autocorrelation in both compared maps. In reality however, spatial autocorrelation does lower the expected agreement as matching categories become less likely to be found close‐by. Since most maps have some degree of spatial autocorrelation, the calculated expected agreement is generally higher than the true expected agreement. This leads to counterintuitive results when maps that appear to have considerable agreement obtain negative Fuzzy Kappa values. Furthermore, the Fuzzy Kappa may be biased, as it systematically attributes lower agreement to maps with stronger spatial autocorrelation. This paper proposes an improved Fuzzy Kappa statistic that is based on the same local agreement and has the same attractive properties as the original Fuzzy Kappa. The novelty is that the new statistic accounts for spatial autocorrelation, such that the expected Fuzzy Kappa for maps that are not cross‐correlated is equal to zero. The improved statistic is applied on two cases to demonstrate its properties.     

Two‐dimensional discrete Fourier transform analysis of karst and coral reef morphologies

Fourier transforms have been used in the analysis of landscapes that exhibit the influence of cyclic structures or other morphogenetic controls. Two‐dimensional Fourier transforms have been most successful when modeling features with a high frequency over the sample space. This research focuses on applications of 2D discrete Fourier transforms for karst and spur and groove coral reefs, using ArcGIS geoprocessing tools extended with Python NumPy numerical methods. Ten‐meter digital elevation data from Puerto Rico and Kentucky holokarst landscapes and five‐meter bathymetry from more unidirectional spur and groove coral reefs at Midway Atoll were analyzed. Our method identifies the dominant contributing waves in frequency space, and analyzed power contributions by 5° and 15° azimuth bins. A limiting factor in this analysis is the spatial extent of consistent morphology in the landscape. In contrast to time‐domain Fourier analysis, dominant landform frequencies can thus be of low magnitude, creating an imprecise estimate of wave morphometry and direction since this is derived from the combination of inverted x and y frequency values, and the limited frequency grain inherent in the discrete model degrades precision in the solution. Simulated karst and spur & groove landscapes were used to evaluate the grain of waveform orientation solutions.     

The influence of Atlantic hurricanes on Southern Ontario’s precipitation extremes

Little is known about the influence of hurricanes on precipitation extremes (PEs) in Southern Ontario, Canada. We examine PEs and their spatial–temporal link with hurricanes events in Southern Ontario during the period of 1950–2000. On average, 5.4 PEs or 11 % of the 50 wettest days in the selected five locations occurred under the influence of hurricanes within this 51-year period. Our results indicate hurricane-influenced PEs are most frequent in September and derive from storms that had reached major hurricane status (>50 m/s) at some point during their lifetime. An absence of landfalling hurricanes in Southern Ontario during the 1960s to 1980s suggests either that the direct impact of hurricanes occurs on a multidecadal time scale or that recent years are experiencing unprecedented change.     

Analysis of climate policy targets under uncertainty

Although policymaking in response to the climate change threat is essentially a challenge of risk management, most studies of the relation of emissions targets to desired climate outcomes are either deterministic or subject to a limited representation of the underlying uncertainties. Monte Carlo simulation, applied to the MIT Integrated Global System Model (an integrated economic and earth system model of intermediate complexity), is used to analyze the uncertain outcomes that flow from a set of century-scale emissions paths developed originally for a study by the U.S. Climate Change Science Program. The resulting uncertainty in temperature change and other impacts under these targets is used to illustrate three insights not obtainable from deterministic analyses: that the reduction of extreme temperature changes under emissions constraints is greater than the reduction in the median reduction; that the incremental gain from tighter constraints is not linear and depends on the target to be avoided; and that comparing median results across models can greatly understate the uncertainty in any single model.