Gallagher, S.J., Duddy, I.R., Quilt, P.G., Smith, A.J., Wallance, M.W., Holdgate, G.R. and Boult, P.J. (2004) The Use of Foraminiferal Colouration Index (FCI) as a Thermal Indicator and Correlation with Vitrinite Reflectance in the Sherbrook Group, Otway Basin, Victoria. Eastern Australian Basins Symposium II, Adelaide, 5, 643-653.
https://www.researchgate.net/profile/Stephen_Gallagher/publication/

has been cited by the following article:

• TITLE: Land Suitability Evaluation for Agricultural Cropland in Mongolia Using the Spatial MCDM Method and AHP Based GIS

  AUTHORS: Munkhdulam Otgonbayar, Clement Atzberger, Jonathan Chambers, D. Amarsaikhan, Sebastian Böck, Jargaltulga Tsogtbayar

  KEYWORDS: Land Suitability, MCDM, Boolean and Fuzzy Analysis, AHP, RF and PLS Regression

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.5 No.9, September 26, 2017

  ABSTRACT: The purpose of this study was to prepare a cropland suitability map of Mongolia based on comprehensive landscape principles, including topography, soil properties, vegetation, climate and socio-economic factors. The primary goal was to create a more accurate map to estimate vegetation criteria (above ground biomass AGB), soil organic matter, soil texture, and the hydrothermal coefficient using Landsat 8 satellite imagery. The analysis used Landsat 8 imagery from the 2016 summer season with a resolution of 30 meters, time series MODIS vegetation products (MOD13, MOD15, MOD17) averaged over 16 days from June to August 2000-2016, an SRTM DEM with a resolution of 30 meters, and a field survey of measured biomass and soil data. In total, 6 main factors were classified and quality evaluation criteria were developed for 17 criteria, each with 5 levels. In this research the spatial MCDM (multi-criteria decision-making) method and AHP based GIS were applied. This was developed for each criteria layer’s value by multiplying parameters for each factor obtained from the pair comparison matrix by the weight addition, and by the suitable evaluation of several criteria factors affecting cropland. General accuracy was 88%, while PLS and RF regressions were 82.3% and 92.8%, respectively.