Computers & Geosciences, Volume 84, November 2015, Pages 1–9
Julia B. Curto, Tatiana Diniz, Roberta M. Vidotti, Richard J. Blakely, and Reinhardt A. Fuck
“We offer a methodology to analyze the spatial and statistical properties of the tilt derivative of magnetic anomalies, thus facilitating the mapping of the location and depth of concealed magnetic sources. This methodology uses commonly available graphical information system (GIS) software to estimate and interpolate horizontal distances between key attributes of the tilt derivative, which then are used to estimate depth and location of causative bodies.
Fig. 8. (a) Reduced-to-pole magnetic anomaly ﬁeld of the study area. (b) Main geological features of the northwest Paraná Basin. The Arenópolis magmatic arc and the Paraguay Belt represent the Neoproterozoic basement of the basin. Dashed black lines are magnetic lineaments (Curto et al., 2014): (A) Serra Negra, (B) Baliza, (C) General Carneiro.
“Application of the method to synthetic data illustrates its reliability to determine depths to magnetic contacts. We also achieved consistent depth results using real data from the northwest portion of the Paraná Basin, Brazil, where magnetic anomaly interpretations are complicated by low geomagnetic inclinations and rocks with remanent magnetization. The tilt-derivative method provides more continuous and higher resolution contact information than the 3D Euler deconvolution technique.”
Computers & Geosciences, Volume 84, November 2015, Pages 86–95
By Simon Nieland, Niklas Moran, Birgit Kleinschmit, and Michael Förster
“Semantic heterogeneity remains a barrier to data comparability and standardisation of results in different fields of spatial research. Because of its thematic complexity, differing acquisition methods and national nomenclatures, interoperability of biodiversity monitoring information is especially difficult. Since data collection methods and interpretation manuals broadly vary there is a need for automatised, objective methodologies for the generation of comparable data-sets. Ontology-based applications offer vast opportunities in data management and standardisation. This study examines two data-sets of protected heathlands in Germany and Belgium which are based on remote sensing image classification and semantically formalised in an OWL2 ontology.
Two step approach of the modiﬁed SPARK methodology. Part A shows the kernel reclassiﬁer
with an schematic 5 x 5 kernel. Input is a classiﬁcation result, which illustrates the classiﬁed
categories in different colours (see Part A and Part B(a)). The kernel includes three classes (sand
(S), forest (F), and heath (H)). The kernel reclassiﬁer performs a calculation of the class
frequency in a frequency table and compares the outcomes to the associated rule. If the kernel
corresponds to all formulated rules the centre pixel (grey) can be assigned. The interpolator
eliminates patches that have not been assigned (b) and patches that are under a certain MMU (c) and
interpolates gaps in a nearest neighbour interpolation procedure (d). (For interpretation of the
references to colour in this ﬁgure caption, the reader is referred to the web version of this
“The proposed methodology uses semantic relations of the two data-sets, which are (semi-)automatically derived from remote sensing imagery, to generate objective and comparable information about the status of protected areas by utilising kernel-based spatial reclassification. This automatised method suggests a generalisation approach, which is able to generate delineation of Special Areas of Conservation (SAC) of the European biodiversity Natura 2000 network. Furthermore, it is able to transfer generalisation rules between areas surveyed with varying acquisition methods in different countries by taking into account automated inference of the underlying semantics. The generalisation results were compared with the manual delineation of terrestrial monitoring. For the different habitats in the two sites an accuracy of above 70% was detected. However, it has to be highlighted that the delineation of the ground-truth data inherits a high degree of uncertainty, which is discussed in this study.”
By Reg A. Watson, Bridget S. Green, Sean R. Tracey, Anna Farmery, and Tony J. Pitcher
“Knowing where and how seafood is caught or farmed is central to empowering consumers, and the importers that supply them, with informed choices. Given the wide-ranging, complex and at times commercially sensitive nature of global seafood trade, it can prove very challenging to link imported seafood with information about its provenance. The databases involved are incomplete, at times vague and not harmonized.
Distributional flow of seafood from the Canary Current Large Marine Ecosystem (LME) waters for (a) 1970s and (b) 1990s, and from the Humboldt Current LME for (c) 1970s and (d) 1990s (flow rate in t year-1 is proportional to the thickness of the connection).
“Here, we present a first attempt to link all global seafood imports through a virtual marketplace to exports and map their origins. Considerable work remains to ground-truth the specific origins of all seafood commodities. We illustrate the flow of seafood and its evolution since the 1970s when supporting records began. This work allows the impact of fishing or marine farming to be associated with seafood imports.”
PLOS One, Published Online 26 August 2015
By Jonathan Aguilar, Greta G. Gramig, John R. Hendrickson, David W. Archer, Frank Forcella, and Mark A. Liebig
“Anecdotal accounts regarding reduced US cropping system diversity have raised concerns about negative impacts of increasingly homogeneous cropping systems. However, formal analyses to document such changes are lacking. Using US Agriculture Census data, which are collected every five years, we quantified crop species diversity from 1978 to 2012, for the contiguous US on a county level basis. We used Shannon diversity indices expressed as effective number of crop species (ENCS) to quantify crop diversity. We then evaluated changes in county-level crop diversity both nationally and for each of the eight Farm Resource Regions developed by the National Agriculture Statistics Service. During the 34 years we considered in our analyses, both national and regional ENCS changed. Nationally, crop diversity was lower in 2012 than in 1978. However, our analyses also revealed interesting trends between and within different Resource Regions.
Crop species diversity as effective number of species in 1978, 1987, 1997 and 2012 on a county level basis for the contiguous US. The hotter colors (red hues) indicate lower ENCS values (low crop diversity) while colder colors (blue hues) indicate higher ENCS values (high crop diversity).
“Overall, the Heartland Resource Region had the lowest crop diversity whereas the Fruitful Rim and Northern Crescent had the highest. In contrast to the other Resource Regions, the Mississippi Portal had significantly higher crop diversity in 2012 than in 1978. Also, within regions there were differences between counties in crop diversity. Spatial autocorrelation revealed clustering of low and high ENCS and this trend became stronger over time. These results show that, nationally counties have been clustering into areas of either low diversity or high diversity. Moreover, a significant trend of more counties shifting to lower rather than to higher crop diversity was detected. The clustering and shifting demonstrates a trend toward crop diversity loss and attendant homogenization of agricultural production systems, which could have far-reaching consequences for provision of ecosystem system services associated with agricultural systems as well as food system sustainability.”
PLOS One, Published 02 September 2015
By Grant Richard Woodrow Humphries
“Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori ‘muttonbirder’ diaries were obtained and used as response variables in a gridded spatial model.
Map showing GLS data from Shaffer et al. (2006) for GLS birds tracked from Whenua Hou/Codfish Island (starred on the map) from January 2005 to March 2006.
“It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as “flyways” by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest.”
PLOS One, Published online 26 August 2015
By Kevin J. Rich, Michael Ridealgh, Sarah E. West, Steve Cinderby, and Mike Ashmore
“There is increasing recognition of the importance for local biodiversity of post-mining sites, many of which lie near communities that have suffered significant social and economic deprivation as the result of mine closures. However, no studies to date have actively used the knowledge of local communities to relate the history and treatment of post-mining sites to their current ecological status. We report a study of two post-mining sites in the Yorkshire coalfield of the UK in which the local community were involved in developing site histories and assessing plant and invertebrate species composition. Site histories developed using participatory GIS revealed that the sites had a mixture of areas of spontaneous succession and technical reclamation, and identified that both planned management interventions and informal activities influenced habitat heterogeneity and ecological diversity.
(a) timeline and (b) map of the Fitzwilliam site developed using PGIS and RAP-GIS differentiating in (a) activities and site impacts and in (b) observations and active interventions.
“Two groups of informal activity were identified as being of particular importance. Firstly, there has been active protection by the community of flower-rich habitats of conservation value (e.g. calcareous grassland) and distinctive plant species (e.g. orchids) which has also provided important foraging resources for butterfly and bumblebee species. Secondly, disturbance by activities such as use of motorbikes, informal camping, and cutting of trees and shrubs for fuel, as well as planned management interventions such as spreading of brick rubble, has provided habitat for plant species of open waste ground and locally uncommon invertebrate species which require patches of bare ground. This study demonstrates the importance of informal, and often unrecorded, activities by the local community in providing diverse habitats and increased biodiversity within a post-mining site, and shows that active engagement with the local community and use of local knowledge can enhance ecological interpretation of such sites and provide a stronger basis for successful future management.”
Continental Shelf Research, Published online 17 May 2014
By Markus Diesing, Sophie L. Green, David Stephens, R. Murray Lark,
Heather A. Stewart, and Dayton Dove
“Marine spatial planning and conservation need underpinning with sufficiently detailed and accurate seabed substrate and habitat maps. Although multibeam echosounders enable us to map the seabed with high resolution and spatial accuracy, there is still a lack of fit-for-purpose seabed maps. This is due to the high costs involved in carrying out systematic seabed mapping programmes and the fact that the development of validated, repeatable, quantitative and objective methods of swath acoustic data interpretation is still in its infancy. We compared a wide spectrum of approaches including manual interpretation, geostatistics, object-based image analysis and machine-learning to gain further insights into the accuracy and comparability of acoustic data interpretation approaches based on multibeam echosounder data (bathymetry, backscatter and derivatives) and seabed samples with the aim to derive seabed substrate maps. Sample data were split into a training and validation data set to allow us to carry out an accuracy assessment.
Results of the map comparisons: A—manual-OBIA; B—manual-Random Forest; C—manual-geostatistics; D—OBIA-Random Forest; E—OBIA-geostatistics; F—Random Forest geostatistics. Green indicates agreement and red indicates disagreement.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
“Overall thematic classification accuracy ranged from 67% to 76% and Cohen׳s kappa varied between 0.34 and 0.52. However, these differences were not statistically significant at the 5% level. Misclassifications were mainly associated with uncommon classes, which were rarely sampled. Map outputs were between 68% and 87% identical. To improve classification accuracy in seabed mapping, we suggest that more studies on the effects of factors affecting the classification performance as well as comparative studies testing the performance of different approaches need to be carried out with a view to developing guidelines for selecting an appropriate method for a given dataset. In the meantime, classification accuracy might be improved by combining different techniques to hybrid approaches and multi-method ensembles.”