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James B. Campbell

Bio: James B. Campbell is an academic researcher from Leiden University. The author has contributed to research in topics: Land cover & Remote sensing (archaeology). The author has an hindex of 28, co-authored 133 publications receiving 6196 citations. Previous affiliations of James B. Campbell include University of Virginia & Virginia Tech College of Natural Resources and Environment.


Papers
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TL;DR: The history and scope of remote sensing is described in detail in this paper, where the authors present a detailed overview of the field of Remote Sensing and its application in agriculture, land use and land cover.
Abstract: Preface. Part I: Foundations. History and Scope of Remote Sensing. Electromagentic Radiation. Part II: Image Acquisition. Photographic Sensors. Digital Data. Image Interpretation. Land Observation Satellites. Active Microwave and Lidar. Thermal Radiation. Image Resolution. Part III: Analysis. Preprocessing. Image Classification. Field Data. Accuracy Assessment. Hyperspectral Remote Sensing. Part IV: Applications. Geographic Information Systems. Plant Sciences. Earth Sciences. Hydrospheric Sciences. Land Use and Land Cover. Global Remote Sensing.

3,445 citations

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TL;DR: Although these compounds were markedly more potent as anticonvulsants when administered orally in mice and rats, they were also more neurotoxic, which resulted in marginal protective indices.
Abstract: A series of 4(3H)-quinazolinones structurally related to 2-methyl-3-o-tolyl-4(3H)-quinazolinone (methaqualone, 3) were synthesized and evaluated for anticonvulsant activity. Preliminary screening of these compounds revealed that 2-[2-oxo-2-(4-pyridyl)ethyl]-3-aryl-4(3H)-quinazolinones 6l and 8i, 8k, and 8p-r having a single ortho substituent on the 3-aryl group had the most promising anticonvulsant activity. Compounds 6l and 8i possessing 3-o-tolyl and 3-o-chlorophenyl groups, respectively, showed good protection against MES- and scMet-induced seizures, combined with relatively low neurotoxicity after intraperitoneal administration in mice. They also exhibited low toxicity in tests for determining the mean hypnotic dose (HD50) and the median lethal dose (LD50). Although these compounds were markedly more potent as anticonvulsants when administered orally in mice and rats, they were also more neurotoxic. This neurotoxicity was particularly acute in oral tests with rats, which resulted in marginal protective indices. In drug differentiation tests, compound 6l was ineffective against seizures induced by bicuculline, picrotoxin, and strychnine, while 8i showed some protection against picrotoxin-induced seizures.

256 citations

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TL;DR: In this article, the Savitzky-Golay, asymmetric Gaussian, double-logistic, Whittaker smoother, and discrete Fourier transformation smoothing algorithms (noise reduction) applied to MODIS Normalized Difference Vegetation Index (NDVI) time-series data, to provide continuous phenology data used for land-cover (LC) classifications across the Laurentian Great Lakes Basin (GLB).

174 citations

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TL;DR: Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone.
Abstract: Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (Mw) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone.

154 citations

Journal ArticleDOI
TL;DR: In this article, a simple computer program for users to determine whether they are working in conditions where measurement of particle motion may be relevant is presented. And a supplemental tutorial and template computer code in matlab will allow users to analyse impulsive, continuous and fluctuating sounds from both pressure and particle-motion recordings.
Abstract: Summary Sound waves in water have both a pressure and a particle-motion component, yet few studies of underwater acoustic ecology have measured the particle-motion component of sound. While mammal hearing is based on detection of sound pressure, fish and invertebrates (i.e. most aquatic animals) primarily sense sound using particle motion. Particle motion can be calculated indirectly from sound pressure measurements under certain conditions, but these conditions are rarely met in the shelf-sea and shallow-water habitats that most aquatic organisms inhabit. Direct measurements of particle motion have been hampered by the availability of instrumentation and a lack of guidance on data analysis methods. Here, we provide an introduction to the topic of underwater particle motion, including the physics and physiology of particle-motion reception. We include a simple computer program for users to determine whether they are working in conditions where measurement of particle motion may be relevant. We discuss instruments that can be used to measure particle motion and the types of analysis appropriate for data collected. A supplemental tutorial and template computer code in matlab will allow users to analyse impulsive, continuous and fluctuating sounds from both pressure and particle-motion recordings. A growing body of research is investigating the role of sound in the functioning of aquatic ecosystems, and the ways in which sound influences animal behaviour, physiology and development. This work has particular urgency for policymakers and environmental managers, who have a responsibility to assess and mitigate the risks posed by rising levels of anthropogenic noise in aquatic ecosystems. As this paper makes clear, because many aquatic animals senses sound using particle motion, this component of the sound field must be addressed if acoustic habitats are to be managed effectively.

142 citations


Cited by
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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

14,171 citations

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TL;DR: This paper reviews the necessary considerations and available techniques for assessing the accuracy of remotely sensed data including the classification system, the sampling scheme, the sample size, spatial autocorrelation, and the assessment techniques.

6,747 citations

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6,278 citations

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TL;DR: It is likely that it is unlikely that a single standardized method of accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate accuracy assessment are highlighted.

3,800 citations

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TL;DR: This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting.
Abstract: A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables. This classifier has become popular within the remote sensing community due to the accuracy of its classifications. The overall objective of this work was to review the utilization of RF classifier in remote sensing. This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. It is, however, sensitive to the sampling design. The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes. Further investigations are required into less commonly exploited uses of this classifier, such as for sample proximity analysis to detect and remove outliers in the training samples.

3,244 citations