Gisela

Created Tuesday 10 March 2020

Tasks

Training on processing hyperspectral imagery

Envi tutorial (https://folk.uio.no/inf160/tutorial.pdf (12 - 17))
- 12 Introduction to Hyperspectral Data and Analysis (compare TOA and atmospherically-corrected spectra)
- 13 Basic Hyperspectral Analysis (Visualization of spectra and spectral libraries, and 2-D scatterplots)
- 14 Selected Mapping Methods Using Hyperspectral Data
  - EFFORT
  - SAM of EFF image with (previously calculated) EM
  - SAM of EFF image with library
    - apply classification threshold
  - Spectral Feature Fitting
- 15 Hyperspectral Analysis
  - MNF
  - Interactive EM selection in MNF space
  - Spectral unmixing (aka Spectral Mixture Analysis)
  - SPI
  - n-Dimensional visualization
  - Mixture-Tuned Matched Filtering
EnGeoMap tutorial (Feature-based classification)

Spectral classification vs. Feature-based classification for the identification of geological materials

(or Mineral identification and ore estimation from hyper-spectral imagery in Sn – W deposits)

Enmapbox tutorials
Transfer panel imagery and data
Classification with
- RF
- SVM (named SVC in enmapbox)
- Gaussian
- Short presentation
Apply Envi tutorial to panel image (SAM and Spectral Unmixing
Apply EnGeoMap (Feature-based classification) to panel image

A satellite exploration of the Río Tinto mining district

Detecting changes in the mine district of Rio Tinto using time series of Sentinel-1 and Sentinel-2 imagery
- Download S2
- Download S1
- LCU change analysis

https://raster-timeseries-manager.readthedocs.io/en/latest/content.html

Satellite-based analysis of surface materials in the Rio Tinto mine district (Spain)

(Sotiel case of study)

ASTER
S2
Airborne?

Other alternatives

Satellite-based analysis of surface materials in the Rӧssing Uranium Mine District (Namibia)
ASTER
WV3

Satellite-based analysis of surface materials in the Cuprite District (Nevada, USA)
ASTER
AVIRIS
WV3

GeoInformation et al

AsterRT

Creado miércoles 18 marzo 2020

He hecho proyecto RioTinto en https://search.earthdata.nasa.gov/
En el informe de Greenland usan L1T + atcor
(compara en RT l1T y L2)
usan fechas previas al desastre de 2009: 1999 and 2007
AST_07 and AST_07XT should identical (no swir, thus corsstalk corr irrelevant)
AST14OHT better geom corr, use to fine tune the other products
RT_AST.qgz

Bib

Creado jueves 23 abril 2020

https://serc.carleton.edu/NAGTWorkshops/data_models/toolsheets/google_earth.html

Bib

Greenberger, Rebecca N, John F Mustard, Edward A Cloutis, Paul Mann, Janette H. Wilson, Roberta L Flemming, Kevin Robertson, Mark R Salvatore, and Christopher Edwards. 2015. ‘Hydrothermal Alteration and Diagenesis of Terrestrial Lacustrine Pillow Basalts: Coordination of Hyperspectral Imaging with Laboratory Measurements’. Geochimica et Cosmochimica Acta 171: 174200. https://doi.org/10.1016/j.gca.2015.08.024. (zotero)
Baissa, Rachid, Kamal Labbassi, Patrick Launeau, Anne Gaudin, and Brahim Ouajhain. 2011. ‘Using HySpex SWIR-320m Hyperspectral Data for the Identification and Mapping of Minerals in Hand Specimens of Carbonate Rocks from the Ankloute Formation (Agadir Basin, Western Morocco)’. Journal of African Earth Sciences 61 (1): 1–9. https://doi.org/10.1016/j.jafrearsci.2011.04.003.
https://pdfs.semanticscholar.org/1e02/a41e9ff88cdf80bbe41bd81ecde71e779548.pdf
Chapter 5 Spectral Geology: Discriminating Rocks and Soils https://h2oexplore.wordpress.com/chapter-5-spectral-geology-discriminating-rocks-and-soils/
Van der Meer et al. 2012. Multi- and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation,14: 112–128. zotero
Van der Meer et al. 2014. Potential of ESA’s Sentinel-2 for geological applications. Remote Sensing of Environment 148: 124–133. zotero
Buzzi, Jorge, Asunción Riaza, Eduardo García-Meléndez, Sebastian Weide, and Martin Bachmann. 2014. ‘Mapping Changes in a Recovering Mine Site with Hyperspectral Airborne HyMap Imagery (Sotiel, SW Spain)’. Minerals 4 (2): 313–29. https://doi.org/10.3390/min4020313.
REMOTE SENSING FOR THE MINING INDUSTRY https://www.oulu.fi/sites/default/files/36/RESEM_EOReview.pdf
USE OF REMOTE SENSING FOR MAPPING AND EVALUATION OF MINING WASTE ANOMALIES

AT NATIONAL TO MULTI-COUNTRY SCALE
A CASE STUDY TO INTEGRATE REMOTE SENSING INFORMATION WITH THEMATIC DATA LAYERS AND NATIONAL INVENTORIES
ON MINING FEATURES IN PRE-ACCESSION COUNTRIES
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=16&ved=2ahUKEwjb_u75pcLpAhWpxoUKHc2BCx04ChAWMAV6BAgGEAE&url=https%3A%2F%2Fpublications.jrc.ec.europa.eu%2Frepository%2Fbitstream%2FJRC27636%2FEUR%252021185%2520EN.pdf&usg=AOvVaw0iAj6fLht0VJaSZs-MHiMD

Gomez Cécile, Adeline K., Bacha S., Driessen B., Gorretta N., Lagacherie P., Roger J. M., Briottet X. (2018). Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios. Remote Sensing of Environment, 204, 18-30. ISSN 0034-4257

Remote sensing for mineral exploration https://www.sciencedirect.com/science/article/pii/S0169136899000074
https://www.mdpi.com/journal/remotesensing/special_issues/hyperspectral_mining
Mapping of mineral resources and lithological units: a review of remote sensing techniques https://www.tandfonline.com/doi/full/10.1080/19479832.2019.1589585
Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information https://www.sciencedirect.com/science/article/pii/S0098300413002720

Teledetección y Geología: mitos y realidades. José Manuel Lattus
https://www.youtube.com/watch?v=GHCXMP6wfgA
Crippen and Bloom. 2001. unveiling the lithology of veetated terrains in remotely sensed imagery
Landsat RGB: 3/1, 5/4, 5/7
741

Airborne Thermal Infrared Hyperspectral Imaging for Mineral Mapping.pdf

Spectral geology Background

libro gupta (otros en pdf?)
https://www.nrsc.gov.in/sites/default/files/pdf/ebooks/Chap_7_Geosciences.pdf
ISSSR Tutorial 1: Introduction to Spectral Remote Sensing
- https://www.researchgate.net/publication/235055785_ISSSR_Tutorial_1_Introduction_to_Spectral_Remote_Sensing
- https://apps.dtic.mil/dtic/tr/fulltext/u2/a284268.pdf
[1]Geoimage, “SWIR_Solutions_ebook.” (zotero)

https://www.geoimage.com.au/SWIR%20Series/Landing%20Page/160411_SWIR_Solutions_ebook.pdf

Aster

Geological application of ASTER remote sensing within sparsely outcropping terrain, Central New South Wales, Australia
- https://www.cogentoa.com/article/10.1080/23312041.2017.1319259.pdf
https://www.researchgate.net/publication/336231686_ASTER_data_analysis_applied_to_mineral_and_geological_mapping_in_North_East_Greenland_Documentation_of_the_NEG_ASTER_Project
https://www.geoimage.com.au/services/aster-spectral-processing MOLT IMPORTANT
[1]A. B. Pour and M. Hashim, “The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits,” Ore Geology Reviews, vol. 44, pp. 1–9, Feb. 2012, doi: 10.1016/j.oregeorev.2011.09.009.
[1]M. El Janati, “Application of remotely sensed ASTER data in detecting alteration hosting Cu, Ag and Au bearing mineralized zones in Taghdout area, Central Anti-Atlas of Morocco,” Journal of African Earth Sciences, vol. 151, pp. 95–106, Mar. 2019, doi: 10.1016/j.jafrearsci.2018.12.002. zotero falta pdf, bajar con PAPI

roads

https://www.researchgate.net/publication/237532807_Mapping_asphalt_road_conditions_with_hyperspectral_remote_sensing
gogle hyperespectral roads
https://www.semanticscholar.org/paper/ROAD-CONDITION-MAPPING-WITH-HYPERSPECTRAL-REMOTE-Herold-Roberts/2915524151e10185e75125a533816b1b2beead55

Molt important Road pavement condition mapping andassessment using remote sensing data based onMESMA
- https://iopscience.iop.org/article/10.1088/1755-1315/34/1/012023/pdf

Seguir draft con metodos hyper
S1+S2 rio tinto
Aster rio tinto
Decepcion?
tutorial R

Hyper

tutoriales Enmap toolbox
- background en RF, SVM,
  - /k-fold-cross-validation: https://machinelearningmastery.com/k-fold-cross-validation/
  - https://www.guru99.com/r-random-forest-tutorial.html
tutoriales envi
- https://folk.uio.no/inf160/tutorial.pdf (12 - 17)
- http://ftpearth.bu.edu/public/amerb/IDL_Intro/IDL_coursefiles/data/
  - CUP95_RD.INT and the CUP95CAL.SL.
- https://lost-contact.mit.edu/afs/enea.it/software/rsi/envi_3.5/spec_lib/
- https://www.harrisgeospatial.com/Support/Self-Help-Tools/Tutorials
tutoriales Engeomap
- [1]C. Mielke, C. Rogass, N. Boesche, K. Segl, and U. Altenberger, “EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission,” Remote Sensing, vol. 8, no. 2, p. 127, Feb. 2016, doi: 10.3390/rs8020127. artic + supp material
- ‘EnGeoMAP - Tutorial for Application: Basic Minerals and Rare Earth Elements Mapping. EnMAP Technical Report’. n.d., 18. (zotero)
  - http://gfzpublic.gfz-potsdam.de/pubman/item/escidoc:1504014/component/escidoc:1504016/EnMAP_EnGeoMAP_2016_003.pdf
- Presentacion: Spacebornehyperspectralimaging:Applicationsfortheminingindustry (zotero)
  - https://workshop.copernicus.eu/sites/default/files/content/attachments/form-ZpdWWke1Pspaj6_KXzsXFbLNWJoJ_DnBIyREBgIXAsY/boesche_-_gfz_potsdam.pdf
https://eo-college.org/now-online-hyperedu-educational-resources-on-imaging-spectroscopy/
[1]G. Camps-Valls, “Hyperspectral Image Processing.” (ppt zotero)
[1]F. A. Kruse, S. L. Perry, and A. Caballero, “District-level mineral survey using airborne hyperspectral data, Los Menucos, Argentina,” p. 10. zotero
[1]R. A. Davis Jr., A. T. Welty, J. Borrego, J. A. Morales, J. G. Pendon, and J. G. Ryan, “Rio Tinto estuary (Spain): 5000 years of pollution,” Environmental Geology, vol. 39, no. 10, pp. 1107–1116, Sep. 2000, doi: 10.1007/s002549900096. zotero
interessant per ella: [1]B. Lottermoser, Mine Wastes: Characterization, Treatment and Environmental Impacts. Springer Science & Business Media, 2013.
info hyper RT
- [1]S. Lorenz et al., “Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops,” Remote Sensing, vol. 10, no. 2, p. 176, Feb. 2018, doi: 10.3390/rs10020176.
- Jakob 2017 The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data
Hyperspectral Imaging Network D6.1 – Algorithm Theoretical Basis Document
- List of methods, minimally explained, but with refeences and application examples
- http://www.geo-informatie.nl/courses/grs60312/spectroscopy/lectures/HYPERINET_D6.1.pdf

WV3

https://www.geoimage.com.au/SWIR%20Series/case-study-mineral-exploration
https://www.geoimage.com.au/SWIR%20Series/mining-dataset
160411_SWIR_Solutions_ebook.pdf (en drive RSEducMaterial)
- Case study Namibia: extract and georeference geological info
- https://www.tandfonline.com/doi/pdf/10.1071/EG997185?casa_token=_DlVw1N9ibEAAAAA:6EpONdi_ZceJsIGf8HWVmyF3ZfUYg__zkLvi1F75bBFmhbFjnH1BEJwmqnQCVS-0o3kCa_EATmq0cg
  - [1]H. Anderson and C. Nash, “Integrated Lithostructural Mapping of the Rössing Area, Namibia using High Resolution Aeromagnetic, Radiometric, Landsat Data and Aerial Photographs,” Exploration Geophysics, vol. 28, no. 1–2, pp. 185–191, Mar. 1997, doi: 10.1071/EG997185. (en zotero)

Sentinel-2

[1]H. Van der Werff and F. Van der Meer, “Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing,” Remote Sensing, vol. 8, no. 11, p. 883, Nov. 2016, doi: 10.3390/rs8110883.

Landsat
Crippen and Bloom. 2001. unveiling the lithology of veetated terrains in remotely sensed imagery
[1]R. E. Crippen and R. G. Blom, “Unveiling the Lithology of Vegetated Terrains in Remotely Sensed Imagery,” p. 9.

Time Series

RSTimeSeries:TemporalMosaics

GEE

GEARS - Geospatial Ecology and Remote Sensing en GEE:GEETraining
- Guided tutorial covering how to filter the Sentinel-1 image collection in Google Earth Engine, and explaining how to map different polarisations. Temporal RGB composites are covered.
Time composites using GEE https://developers.google.com/earth-engine/ic_visualization

Land cover change

Breaks For Additive Season and Trend (BFAST) https://www.nature.com/articles/s41467-019-13452-3
- Zhu, Z. Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS J. Photogramm. Remote Sens 130, 370–384 (2017).
- Verbesselt, J., Hyndman, R., Newnham, G. & Culvenor, D. Detecting trend and seasonal changes in satellite images time series. Remote Sens. Environ. 114, 106–115 (2010).

Anomalias

https://github.com/twitter/AnomalyDetection

S1

http://step.esa.int/docs/tutorials/S1TBX%20Synergetic%20use%20of%20S1%20(SAR)%20and%20S2%20(optical)%20data%20Tutorial.pdf

Mining

Engineering Tools for Environmental Risk Management 1.
Chpt 5 Environmental risk of mining

Aznalcollar

Geochemistry of Rio Guadiamar Sediments Following the April1998 Spanish Aznalcollar Mine Tailings Dam Failure andSubsequent Clean-up 1pag (NERC funding)
Kemper, T., & Sommer, S. 2003. Mapping and monitoring of residual heavy metal contamination and acidification risk after the Aznalcollar mining accident (Andalucia, Spain) using field and airborne hyperspectral data. Procs. of 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, Germany, pp. 333-343.
Riaza, A., Ong, C., & Müller, A. 2006. Dehydration and oxidation of pyrite mud and potential acid mine drainage using hyperspectral DAIS 7915 data (Aznalcollar, Spain). Procs. of ISPRS Commission VII Mid-term Symposium “Remote Sensing: from pixels to processes”, Enschede, The Netherlands.

Other sites

[1]G. M. Llewellyn, “Remote sensing of grassland with contaminated soil using the spectral red-edge.”
https://openjicareport.jica.go.jp/pdf/11816691_03.pdf ?
Estudio de la Influencia de las Actividades Mineras de Mercurio en la Comarca de Almadén Aplicando Técnicas deTeledetección http://documenta.ciemat.es/bitstream/123456789/123/1/42725_IC1213.pdf
Mapping Changes in a Recovering Mine Site with Hyperspectral Airborne HyMap Imagery (Sotiel, SW Spain) https://www.mdpi.com/2075-163X/4/2/313

Sotiel: Utilizar para TFM: simular espectros ASTER y S2, ver si se ve algo con S2. Georeferenciar figuras con Landsat 2005
Qué se puede discriminar con S2? (simular espectros) Se ven los índices? Cómo es ahora?
Acabar de comprobar con una imagen ASTER del 2005 y simular S2 del 2005

MachineLearningMethods

Creado sábado 23 mayo 2020

http://maths-people.anu.edu.au/~johnm/courses/dm/math3346/2008/pdf/ buscar si hay algo, no esta claro

‘Caret Package - A Complete Guide to Build Machine Learning in R’. 2018. Machine Learning Plus. 11 March 2018. https://www.machinelearningplus.com/machine-learning/caret-package/. zotero
- muy practico, no hay conceptos

Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information https://www.sciencedirect.com/science/article/pii/S0098300413002720
- muy resumido

[1]A. E. Maxwell, T. A. Warner, and F. Fang, “Implementation of machine-learning classification in remote sensing: an applied review,” International Journal of Remote Sensing, vol. 39, no. 9, pp. 2784–2817, May 2018, doi: 10.1080/01431161.2018.1433343. https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1433343
- recommended in http://eo.hu-geomatics.de/ at the end of session 04 as Reading for Session 05 on Machine learning for image classification
- buena comparativa pero flojo en conceptos

Turner, Darren, Arko Lucieer, Zbyněk Malenovský, Diana King, and Sharon A. Robinson. “Assessment of Antarctic Moss Health from Multi-Sensor UAS Imagery with Random Forest Modelling.” International Journal of Applied Earth Observation and Geoinformation 68 (June 1, 2018): 168–79. https://doi.org/10.1016/j.jag.2018.01.004. zotero
- buen ejemplo de uso (incl cómo afinar los parámetros) pero no hay conceptos

RStoolbox https://bleutner.github.io/RStoolbox/rstbx-docu/superClass.html
- Models: los de Caret::Train
- http://topepo.github.io/caret/train-models-by-tag.html

Maxwell et al. (2018): Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing 39(9), 2784-2817

SVM

Revisit SVM

R Random Forest Tutorial with Example https://www.guru99.com/r-random-forest-tutorial.html
- muy practico, pocos conceptos
- pero hay un parrafo muy claro: "In earlier tutorial, you learned how to use Decision trees to make a binary prediction. To improve our technique, we can train a group of Decision Tree classifiers, each on a different random subset of the train set. To make a prediction, we just obtain the predictions of all individuals trees, then predict the class that gets the most votes. This technique is called Random Forest."
file:///C:/Users/1/Google%20Drive/SYNCED/TutorialsDocs/MachineLearning/Earth%20Observation%20(2020-05-18%201_18_25%20PM).html (http://eo.hu-geomatics.de/) conceptos

HyperRT

Creado martes 31 marzo 2020
http://flight-finder.ceda.ac.uk/
alobo y el de siempre con numero

Hay que clicar en cada linea de vuelo para ver los datos de 1 en 1 ...
Parece que el L1b debe ser procesado con azgcorr y no lo encuentro (antiguo)?
L3b siempre vacio
La mayoria de 2006. "c" en el filename es casi, "a" es ATM
Parece que tb hay Eagle y Hawk
~~Imposible descargar, siempre se aborta.~~
enviado email a los del NERC https://mail.google.com/mail/u/0/?pli=1#sent/QgrcJHsNhNhjHhqQHfznXcTtMKWbjnmWTKB

ARSF Data Processing
http://arsf.nerc.ac.uk/data/index.asp?cookieConsent=A
https://www.bas.ac.uk/polar-operations/sites-and-facilities/nerc-arf-data-processing/
https://help.ceda.ac.uk/article/4537-airborne-research-and-survey-facility-arsf
NEODAAS Airborne Data Analysis Node https://nerc-arf-dan.pml.ac.uk/

Tests with http://data.ceda.ac.uk/neodc/arsf/2006/WM06_11/WM06_11-2006_142_Rio_Tinto

WV3RT

Creado lunes 06 abril 2020

https://imagehunter.apollomapping.com/search/6d3caf47385c48289217ee0fb8638d4c
https://www.geoimage.com.au/SWIR%20Series
https://www.geoimage.com.au/services/SWIR
160411_SWIR_Solutions_ebook.pdf en RSEducMaterial
RSGEO:CompositesGEO:WV3