Vacancy: Research Fellow (Postdoctoral Researcher) in Geography

Research Fellow (Postdoctoral Researcher) in Geography position available, Fixed-term Contract 16 months Full-time, Department of Geography, Trinity College Dublin, the University of Dublin

Closing Date: 12 Noon (Irish Standard Time), 19 July 2019

FREE TRAINING:Extracting Information from LiDAR Data Online

Extracting Information from LiDAR Data Online

Description

This four part training will be held for two hours each Tuesday of the month of September:

September 3, 10, 17 and 24, 2019

Class Hours: 9:00 AM – 11:00 AM MST

Rules of thumb for spatial data

While browsing the internet in an endless search for a solution to my problem…a vector data-based rule-based expert knowledge classification/decision tree (manual) plugin for GIS (or software) I came across VectorMCDAQGIS plugin, k2-tree, FRAGSTATS (see paper) but also these interesting rules of thumb, among which, the last two (particularly the last one, Murphy’s law) in the list Rules of thumb in remote sensing attracted my attention:

On this page will be placed some of the rules of thumb that have been identified by practitioners in the fields of Cartography, Remote Sensing, GPS, GIS and Spatial analysis

Rules of thumb in cartography

  • # of shades of gray distinguishable (16 max).
  • # of legend categories consistently recognized (5, 7 max).
  • Symbol sizes should not vary when mapping qualitative data.
  • Raw totals should not be mapped using a choropleth map.

Rules of thumb in remote sensing

  • The # of contiguous pixels required for object identification — 10 to 16 (source TBD).
  • McCoy (see below) recommends that, considering all of the potential variables that can affect the results, at a minimum sample units should be no smaller than a 3 x 3 cluster of pixels for training sites or accuracy assessment sites.
  • What should the size of the sample site be? A useful formula for determining the area of a sample site is: A = P(1 + 2L), where A is the minimum sample site dimension, P is the image pixel dimension, and L is the estimated lcational accuracy in number of pixels. For example, say your GPS has a locational accuracy of 15m, and you are working with TM data (30m pixels). Therefore L is equal to 0.5 (15m GPS – 30m pixel -> our locational accuracy is half a pixel). A is therefore equal to 60m X 60m. This should be considered a theoretical minimum value, since it assumes that the georegistration of the TM image is perfect. Larger sample sites should typically be used in order to allow for both greater GPS uncertainty, image georegistration uncertainties, and heterogeneity on the ground. (Source: R. M. McCoy. 2005. Field methods in remote sensing. New York: The Guilford Press., p. 23)
  • M. Mather (1987), in his “Computer Processing of Remotely-Sensed Images – An Introduction“, states that the minimum number of pixels in a training sample must be 30*p per class, where p = number of features. [Please refer to the third paragraph of section 8.4.1 on Page 290 of the above mentioned book] This may not take into account the effects of spatial autocorrelation, however.
  • The number of pixels in each training set (i.e., all of the training sites for a single land cover class) should not be less than ten times the number of bands. Thus, when you are using six bands in a classification, you should aim to have no less than 60+ pixels per training set. (Source: Dr. Michael Govorov, Malaspina University College, with attribution to IDRISI).
  • For a given level of technology, there is a fixed level of ‘total resolution’ that can be obtained:
    image detail is a function of {spatial resolution, spectral resolution, radiometric resolution}. Typically, as one increases the other decreases (thus, we often find that spatial and spectral resolutions are inversely related) (e.g., SPOT Panchromatic with its spatial resolution of 10 m; SPOT Multispectral has a spatial resolution of 20 m).
  • Ross Nelson, from the NASA Goddard Space Flight Center, has developed a rule of thumb based on careful examination of the accuracy of several studies published in the remote sensing literature. Of course exceptions can be found, but these are very useful guidelines. The underlying concept is that the more precise the class definitions are the lower the accuracy will be for the individual classes. Classification Accuracy:
    • Forest/non-forest, water/no water, soil/vegetated: accuracies in the high 90%’s
    • Conifer/hardwood: 80-90%
    • Genus: 60-70%
    • Species: 40-60%

    Note: If including a Digital Elevation Model (DEM) in the classification, add 10% (Source: Biodiversity Informatics Facility of the American Museum of Natural History’s Center for Biodiversity and Conservation).

  • Ortho-corrected images can have higher absolute accuracy, but when relative accuracy is needed it may be better to use only systematically corrected images rather than mix systematically-corrected images with ortho-corrected imagery (Source: Biodiversity Informatics Facility).
  • 50% or more of the information for a given pixel contains recorded energy from the surface area surrounding that individual pixel (Source: Biodiversity Informatics Facility).
  • Clouds will always obscure that part of the imagery that is most important to your study.

New Earth Observation Post Doc in Teagasc

We are looking for an EO Post Doc as part of the SFI funded VistaMilk center. Its a 2 year position based in Teagasc, Ashtown, Dublin (in collaboration with Insight UCD).

The project is based around habitat mapping; combining random forest classification of sentinel imagery for habitats but using terrestrial photography (say a smart phone image) as a ground truth, facilitated by a priori classification of the photography through machine learning.

This is a very innovative Post Doc with wide ranging impacts beyond producing a habitat for parts of Ireland – the emphasis is on stitching together satellite data and terrestrial photography into providing a high quality evidence base for the state of farm habitat in Ireland. Details here, closing date in June 24th.

 

 

 

Vacancies: 1 PhD position and 1 researcher position

Job vacancy at the Johann Heinrich von Thünen-Institute – PhD position improving German agricultural GHG inventory – deadline 5th July 2019

 

Vacant position – Researcher within satellite remote sensingSubmission deadline: 15th June 2019