set of bands for a specific location) is organized into its own directory (e.g. cloud cover and shadows).Įach Landsat scene (i.e. red, near-infrared) and one with the quality assurance information (e.g. Recall that Landsat 8 data is provided as a series of GeoTIFF files - one for each band (e.g. Get a list of the data files for one Landsat scene.Begin With the Workflow for One Landsat Scene The steps below will walk you through beginning this process of writing pseudocode. Save NDVI values to pandas dataframe with the associated date and site name for plotting.īegin your process by pseudocoding - or writing out steps in plain English - everything that you will need to do in order to get to your end goal.You know you will need to do the following: Your next step is to write out the steps that you need to follow to get to your end goal plot. You have been given Landsat 8 data and a study area boundary for each site. Harvard Forest (HARV) in the Eastern United States.San Joaquin Experimental Range (SJER) in Southern California, United States.You have now identified your challenge - to calculate and plot average normalized difference vegetation index (NDVI) for every Landsat scene (individually) across one year for two study locations: Be able to approach a coding task with a modular, systematic approach.ĭesign an Efficient Data Workflow Using Pseudocode.SECTION 7 INTRODUCTION TO API DATA ACCESS IN OPEN SOURCE PYTHON.

SECTION 6 INTRODUCTION TO HIERARCHICAL DATA FORMATS IN PYTHON.

Chapter 11: Calculate Vegetation Indices in Python.

Chapter 7: Intro to Multispectral Remote Sensing Data.

SECTION 5 MULTISPECTRAL REMOTE SENSING DATA IN PYTHON.

Chapter 6: Uncertainty in Remote Sensing Data.

SECTION 4 SPATIAL DATA APPLICATIONS IN PYTHON.

Chapter 5: Processing Raster Data in Python.

Chapter 4: Intro to Raster Data in Python.

SECTION 3 INTRODUCTION TO RASTER DATA IN PYTHON.

Chapter 3: Processing Spatial Vector Data in Python.

SECTION 2 INTRO TO SPATIAL VECTOR DATA IN PYTHON.

Chapter 1.5: Flood Returns Period Analysis in Python.

Write a pseudocode