SAR for Detecting and Monitoring Floods, Sea Ice, and Subsidence from Groundwater Extraction

October 24, 2023 - November 1, 2023

  • Generate subsidence maps due to groundwater extraction to inform risk and resource management

  • Detect and monitor sea ice to identify potential risks to shipping and coastal erosion

  • Detect and monitor floods in order to more closely monitor increase/recession of flood waters and better inform disaster response and management

Notebook

This assignment

Spectral Indices for Land and Aquatic Applications

October 26, 2023 - November 9, 2023

  • Recognize commonly used spectral indices in land and aquatic environments

  • Distinguish between spectral indices to select those best suited for a given land or aquatic system of interest

  • Compute spectral index calculations over appropriate areas of interest

  • Acquire spectral index products from a variety of sources

Calculating NDVI in Google Earth Engine

Index Calculation in GEE : Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Aquatic Vegetation Index (NDAVI), Floating Algal Index (FAI), Normalized Difference Turbidity Index (NDTI)

Index Calculation in GEE : Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Burn Ratio (NBR)

This assignment

GIS for Climate Action

Satellite Data for Air Quality Environmental Justice and Equity Applications

August 23, 2023 - September 6, 2023

  • Describe, at a high level, how satellite data have been combined with socioeconomic information to investigate EJ issues such as heat exposure or access to green space.

  • Identify remote sensing data products which are most relevant to assessing EJ related to air quality and pollutant exposure

  • Articulate the benefits and limitations of using remote sensing data to assess EJ concerns related to air quality

  • Import relevant air quality datasets into EJSCREEN, and use EJSCREEN to investigate and compare air quality with other environmental and demographic datasets.

  • Pair appropriate satellite datasets for environmental indicators (air quality) with demographic information using Python

  • Demonstration of satellite data visualization tools using:

Homework: Satellite Data for Air Quality Environmental Justice and Equity Applications

Assignment: Satellite Data for Air Quality Environmental Justice and Equity Applications

Sensors, tools, & software addressed: MODIS, VIIRS, GOES, OMI, TROPOMI, TEMPO

Spatial Data Science: The New Frontier in Analytics

Monitoring Water Quality of Inland Lakes using Remote Sensing

July 18, 2023 - July 25, 2023

This advanced-level training will focus on using remote sensing observations from Landsat 8 and 9, Sentinel-2, and Sentinel-3 for assessing water quality parameters, including chlorophyll-a concentration, turbidity, and Total Suspended Solids (TSS) in inland lakes. This training will also highlight the importance of in situ measurements of these parameters, coincident with satellite observations, in developing methodologies for operational water quality monitoring. Participants will perform hands-on exercises in Google Earth Engine (GEE) to access satellite data and develop methodologies to assess water quality parameters. In addition, an overview of Cyanobacteria Assessment Network (CyAN), an early warning system to assess algal blooms in freshwater lakes will be provided.

Demonstration and Exercise:

  • Calculate spectral indices as indicators of water quality from Landsat, Sentinel-2, and Sentinel-3 observations

  • Develop statistical coefficients to derive water quality parameters based on remote sensing and in situ data

  • GEE Code Links

  • CyAN Exercise

Homework: Monitoring Water Quality of Inland Lakes using Remote Sensing

This assignment: Monitoring Water Quality of Inland Lakes using Remote Sensing

Manage Successful Field Research

Reproducible Research Fundamentals

Data Science and Machine Learning: Making Data-Driven Decisions

MITx MicroMasters program in Statistics and Data Science

Fundamentals of Statistics

Probability - The Science of Uncertainty and Data

Machine Learning with Python: from Linear Models to Deep Learning

Data Analysis: Statistical Modeling and Computation in Applications

Capstone Exam in Statistics and Data Science