Course curriculum

  1. 01
    • Lesson 1: Introduction to the course

    • Lesson 2: Tools used throughout the course

    • Lesson 3: Getting started with ESA SNAP software

    • Lesson 4: Getting Started with GRASS GIS

    • Lesson 5: Different Classification Schemes for Remote Sensing Data

    • Lesson 6: First Chapter Conclusions

  2. 02
    • Lesson 7: Collecting Optical Remote Sensing Data

    • Lesson 8: Download Landsat satellite images

    • Lesson 9: Download Landsat satellite images with QGIS

    • Lesson 10: Landsat specifications

    • Lesson 11: Other satellite images

    • Lesson 12: Uses of pre-processed satellite data

    • Lesson 13: Pre-processed Outputs

    • Lesson 14: Second Chapter Conclusions

  3. 03
    • Lesson 15: Pre-processing of Optical Data

    • Lesson 16: Atmospheric Correction in R

    • Lesson 17: Stack and Unstack image bands in QGIS

    • Lesson 18: Pre-processing of Landsat data to obtain surface reflectance in QGIS

    • Lesson 19: Vegetation indices in GRASS GIS

    • Lesson 20: Tasseled CAP in GRASS GIS

    • Lesson 21: Texture Metrics in GRASS GIS

    • Lesson 22: Texture Metrics using ESA SNAP

    • Lesson 24: Third Chapter Conclusions

  4. 04
    • Lesson 25: Dimensionality Reduction Theory

    • Lesson 26: Principal Components Analysis (PCA) Dimensionality Reduction in QGIS

    • Lesson 27: Principal Components Analysis (PCA) Dimensionality Reduction in GRASS GIS

    • Lesson 28: Tasseled cap Transformation Theory

    • Lesson 29: Vegetation indices in R

    • Lesson 30: Fourth Chapter Conclusions

  5. 05
    • Lesson 31: Texture Metrics

    • Lesson 32: Supervised Classification Theory

    • Lesson 33: Unsupervised Classification Theory

    • Lesson 34: Machine Learning Theory

    • Lesson 35: Train your data in QGIS

    • Lesson 36: Semi-Automatic Classification Plugin in QGIS

    • Lesson 37: Supervised Classification in QGIS

    • Lesson 38: Unsupervised Classification in ESA SNAP software

    • Lesson 39: Machine Learning for Remote Sensing Data using R

    • Lesson 40: Fifth Chapter Conclusions

  6. 06
    • Lesson 41: Why to use active remote sensing data?

    • Lesson 42: Obtain ALOS PALSAR data

    • Lesson 43: Pre-process ALOS PALSAR data

    • Lesson 44: SAR Backscatter in R

    • Lesson 45: ALOS PALSAR Speckle Filtering

  7. 07
    • Lesson 46: Feature Selection in R

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About your Instructor

I have completed a PhD in Forest Ecology and Conservation from the University of Cambridge, UK. In my PhD I used a combination of remote sensing data such as optical data, LiDAR, aerial imagery and radar data for quantifying the impact of forest cover change on carbon stocks and biodiversity in tropical Asia. I have worked extensively in different parts of tropical Asia- Malaysia, Laos, Cambodia and the Philippines. I used extensive statistical, machine learning and image processing techniques for my research and have an intermediate level proficiency in R and Python programming languages. I am also proficient in the use of GIS softwares like QGIS and ArcGIS and remote sensing tools like ENVI. I have several peer reviewed publications in well-regarded journals to my credit. Details of publications can be found here: In January 2017, I launched Minerva's Data Lab, an endeavor hat seeks to provide information, resources and training relating to machine learning, data science, spatial data analysis, geographic information system (GIS), remote sensing (Landsat, LiDAR, hyperspectral and radar data). I am available for 1-1 consulting, tutoring, training or general discussions about topics relating to R programming language, Python, Matlab, QGIS, ArcGIS and remote sensing tools such as IDRISI and ENVI.

Minerva Singh

Data Science and Spatial Data Analysis Expert

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