Cited by 22

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Investigating the distribution of archaeological sites: Multiparametric vs probability models and potentials for remote sensing data

Noviello, Mariangela, Cafarelli, Barbara, Calculli, Crescenza, Sarris, Apostolos, Mairota, Paola·2018·Applied Geography

Overview

Integrity signals

Registry

Crossref · 10.1016/j.apgeog.2018.04.005

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 2 references

Research works printed but not linked: 59

  1. Agapiou, 2012, Evaluation of broadband and narrowband vegetation indices for the identification of archaeological crop marks, Remote Sensing, 4, 3892, 10.3390/rs4123892 · DOI
  2. Agapiou, 2013, Optimum temporal and spectral window for monitoring crop marks over archaeological remains in the Mediterranean region, Journal of Archaeological Science, 40, 1479, 10.1016/j.jas.2012.10.036 · DOI
  3. Agapiou, 2015, Remote sensing archaeology: Tracking and mapping evolution in European scientific literature from 1999 to 2015, Journal of Archaeological Science: Report, 4, 192
  4. Alexakis, 2009, Detection of neolithic settlements in Thessaly (Greece) through multispectral and hyperspectral satellite imagery, Sensors, 9, 10.3390/s90201167 · DOI
  5. Alexakis, 2011, Integrated GIS, remote sensing and geomorphologic approaches for the reconstruction of the landscape habitation of Thessaly during the Neolithic period, Journal of Archaeological Science, 38, 89, 10.1016/j.jas.2010.08.013 · DOI
  6. Araújo, 2005, Validation of species–climate impact models under climate change, Global Change Biology, 11, 1504, 10.1111/j.1365-2486.2005.01000.x · DOI
  7. Baldwin, 2009, Use of maximum entropy modeling in wildlife research, Entropy, 11, 854, 10.3390/e11040854 · DOI
  8. Bradford, 1949, Buried landscapes in southern Italy, Antiquity, 23, 58, 10.1017/S0003598X00020056 · DOI
  9. Burnham, 2001, Kullback–Leibler information as a basis for strong inference in ecological studies, Wildlife Research, 28, 111, 10.1071/WR99107 · DOI
  10. Caldara, 2014, A multidisciplinary approach to reveal and interpret ‘missing’ archaeological features at the Masseria Pantano site in Apulia (southern Italy), Archaeological Prospection, 21, 301, 10.1002/arp.1494 · DOI
  11. Chase, 2012, Geospatial revolution and remote sensing LiDAR in Mesoamerican archaeology, Proceedings of the National Academy of Sciences, 109, 12916, 10.1073/pnas.1205198109 · DOI
  12. Chen, 1996, Evaluation of vegetation indices and a modified simple ratio for boreal applications, Canadian Journal of Remote Sensing, 22, 229, 10.1080/07038992.1996.10855178 · DOI
  13. Chen, 2017, An overview of satellite synthetic aperture radar remote sensing in archaeology: From site detection to monitoring, Journal of Cultural Heritage, 23, 5, 10.1016/j.culher.2015.05.003 · DOI
  14. Ciminale, 2007, Integrated technologies for archaeological investigation; the Celone Valley project, Archaeological Prospection, 14, 167, 10.1002/arp.312 · DOI
  15. Ciminale, 2009, A multiscale approach for reconstructing archaeological landscapes: Applications in northern Apulia (Italy), Archaeological Prospection, 16, 10.1002/arp.356 · DOI
  16. Da Silva Júnior, 2006, Correlation between vegetation indices and nitrogen leaf content and dry matter production in Brachiaria decumbens, Image Analysis for Agricultural Products and Processes, 69, 145
  17. Danese, 2014, Predictive modeling for preventive archaeology: Overview and case study, Central European Journal of Geosciences, 6, 42
  18. Demšar, 2013, Principal component analysis on spatial Data: An overview, Annals of the Association of American Geographers, 103, 106, 10.1080/00045608.2012.689236 · DOI
  19. Dormann, 2013, Collinearity: A review of methods to deal with it and a simulation study evaluating their performance, Ecography, 36, 27, 10.1111/j.1600-0587.2012.07348.x · DOI · Paper page
  20. Elith, 2006, Novel methods improve prediction of species' distributions from occurrence data, Ecography, 29, 129, 10.1111/j.2006.0906-7590.04596.x · DOI · Paper page
  21. Elith, 2011, A statistical explanation of MaxEnt for Ecologists, Diversity and Distributions, 17, 43, 10.1111/j.1472-4642.2010.00725.x · DOI · Paper page
  22. Espa, 2006, GIS based models and estimation methods for the probability of archaeological site location, Journal of Cultural Heritage, 7, 147, 10.1016/j.culher.2006.06.001 · DOI
  23. Fernandes, 2011, Deletion/Substitution/Addition (DSA) model selection algorithm applied to the study of archaeological settlement patterning, Journal of Archaeological Science, 38, 2293, 10.1016/j.jas.2011.03.035 · DOI
  24. Fielding, 1997, A review of methods for the assessment of prediction errors in conservation presence/absence models, Environmental Conservation, 01, 38, 10.1017/S0376892997000088 · DOI · Paper page
  25. Ford, 2009, Modeling settlement patterns of the late classic Maya civilization with Bayesian methods and geographic information systems. Annals of the association of American geographers, Taylor & Francis, 99, 1
  26. Franklin, 2015, Paleodistribution modeling in archaeology and paleoanthropology, Quaternary Science Reviews, 110, 1, 10.1016/j.quascirev.2014.12.015 · DOI · Paper page
  27. Galletti, 2013, Maxent modeling of ancient and modern agricultural terraces in the Troodos foothills, Cyprus. Applied Geography, 39, 46, 10.1016/j.apgeog.2012.11.020 · DOI · Paper page
  28. Gallo, 2009, Remote sensing techniques for reconstructing a vast Neolithic settlement in Southern Italy, Journal of Archaeological Science, 36, 43, 10.1016/j.jas.2008.07.002 · DOI
  29. Gallo, 2011, Susceptibility measurements, optical and X-ray analysis to explain the origin of archaeological magnetic anomalies in Tavoliere lowland (Southern Italy), Journal of Archaeological Science, 38, 399, 10.1016/j.jas.2010.09.020 · DOI
  30. Garcia, 2013, GIS-based methodology for Palaeolithic site location preferences analysis. A case study from Late Palaeolithic Cantabria (Northern Iberian Peninsula), Journal of Archaeological Science, 40, 217, 10.1016/j.jas.2012.08.023 · DOI
  31. Graves McEwan, 2012, Qualitative landscape theories and archaeological predictive modelling – a journey through No Man's land?, Journal of Archaeological Method and Theory, 19, 526, 10.1007/s10816-012-9143-6 · DOI
  32. Haralick, 1973, Textural features for image classification, IEEE Transaction on Systems, Man and Cybernetics, SMC-3, 610, 10.1109/TSMC.1973.4309314 · DOI · Paper page
  33. Hesse, 2010, LiDAR-derived Local Relief Models–a new tool for archaeological prospection, Archaeological Prospection, 17, 67, 10.1002/arp.374 · DOI
  34. Hijmans, R.J., Guarino, L., Mathur, P. (2012). DIVA-GIS Version 7.5 Manual, pp. 52–53. Portions of this computer program are owned by LizardTech, Inc., and are copyright © 1995-1998, LizardTech, Inc., and/or the University of California. U.S. Patent No. 5,710,835. All rights reserved.
  35. Howey, 2016, Geospatial modeling approach to monument construction using Michigan from AD 1000–1600 as a case study, Proceedings of the National Academy of Sciences, 113, 7443, 10.1073/pnas.1603450113 · DOI · Paper page
  36. Kotliar, 1990, Multiple scales of patchiness and patch structure: A hierarchical framework for the study of heterogeneity, Oikos, 253, 10.2307/3545542 · DOI
  37. Lasaponara, 2011, Satellite remote sensing in archaeology: Past, present and future perspectives, Journal of Archaeological Science, 38, 10.1016/j.jas.2011.02.002 · DOI
  38. Mairota, 2015, Challenges and opportunities in harnessing satellite remote-sensing for biodiversity monitoring, Ecological Informatics, 30, 207, 10.1016/j.ecoinf.2015.08.006 · DOI
  39. Mairota, 2015, Very high resolution Earth Observation features for testing the direct and indirect effects of landscape structure on local habitat quality, International Journal of Applied Earth Observation and Geoinformation, 34, 96, 10.1016/j.jag.2014.07.003 · DOI
  40. Masini, 2007, Investigating the spectral capability of QuickBird datato detect archaeological remains buried under vegetated and not vegetated areas, Journal of Cultural Heritage, 8, 53, 10.1016/j.culher.2006.06.006 · DOI
  41. Mather, 1974, Multivariate methods and geographical data, The Statistician, 23, 283, 10.2307/2987584 · DOI
  42. Meier, 2009, The BBCH system to coding the phenological growth stages of plants–history and publications, Journal für Kulturpflanzen, 61, 41
  43. Mutanga, 2012, High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm, International Journal of Applied Earth Observation and Geoinformation, 18, 399, 10.1016/j.jag.2012.03.012 · DOI
  44. Noviello, 2015, Neolithic settlements in the Tavoliere plain (Apulia, southern Italy): Predictive probability maps, Archaeologia Polona, 53, 239
  45. Noviello, 2013, Combined application of pansharpening and enhancement methods to improve archaeological cropmark visibility and identification in QuickBird imagery: Two case studies from Apulia, southern Italy, Journal of Archaeological Science, 40, 3604, 10.1016/j.jas.2013.04.013 · DOI
  46. Noviello, 2017, Advances in Reconstructing Archaeological Magnetic Signals; an algorithm for filtering the noise due to the ploughing effect, Archaeological Prospection, 24, 87, 10.1002/arp.1550 · DOI
  47. Phillips, 2008, Modeling of species distributions with maxent: New extensions and a comprehensive evaluation, Ecography, 31, 161, 10.1111/j.0906-7590.2008.5203.x · DOI · Paper page
  48. Renner, 2013, Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology, Biometrics, 69, 274, 10.1111/j.1541-0420.2012.01824.x · DOI
  49. Rocchini, 2015, Potential of remote sensing to predict species invasions: A modelling perspective, Progress in Physical Geography, 39, 283, 10.1177/0309133315574659 · DOI
  50. Štular, 2012, Visualization of LiDAR-derived relief models for detection of archaeological features, Journal of Archaeological Science, 39, 3354, 10.1016/j.jas.2012.05.029 · DOI
  51. Swets, 1988, Measuring the accuracy of diagnostic systems, Science, 240, 1285, 10.1126/science.3287615 · DOI · Paper page
  52. Tapete, 2017, SAR for landscape archaeology, Vol. 16, 101
  53. Thompson, 2002, The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA), Landscape Ecology, 17, 569, 10.1023/A:1021501231182 · DOI
  54. Tscharntke, 2007, Author sequence and credit for contributions in multiauthored publications, PLoS Biology, 5, e18, 10.1371/journal.pbio.0050018 · DOI
  55. Ullah, 2011, A GIS method for assessing the zone of human-environmental impact around archaeological sites: A test case from the late neolithic of Wadi Ziqlâb, Jordan, Journal of Archaeological Science, 38, 623, 10.1016/j.jas.2010.10.015 · DOI
  56. Vaughn, 2009, A predictive model of archaeological potential: An example from north-western Belize, Applied Geography, 29, 542, 10.1016/j.apgeog.2009.01.001 · DOI
  57. Warren, 2012, Ecological niche modeling in maxent: The importance of model complexity and the performance of model selection criteria, Ecological Applications, 21, 335, 10.1890/10-1171.1 · DOI · Paper page
  58. Williams, 2012, Which environmental variables should I use in my biodiversity model?, International Journal of Geographical Information Systems, 26, 2009, 10.1080/13658816.2012.698015 · DOI
  59. Xue, 2017, Significant remote sensing vegetation indices: A review of developments and applications, Journal of Sensors

Cited by

Sorted by citations

See all 22

Metadata

Authors
Noviello, M., Cafarelli, B., Calculli, C., Sarris, A., & Mairota, P.
Venue
Applied Geography
Publisher
Elsevier BV
Published
2018-06
Volume
95
Pages
34-44
ISSN
0143-6228
Cited by
22
References
2
Language
en
Funders
  • Ministry of Education, University and Research (PRIN10_11)
  • DiSAAT-University of Bari
  • Biodiversity Multi-SOurce Monitoring System
  • BIO SOS (263435)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review