Leafiness

I thought it might be fun to try something different, and delve back into the world of satellite remote sensing (outside of Sentinel_bot, which isn’t a scientific tool). It’s been a while since I’ve tried anything like this, and my skills have definitely degraded somewhat, but I decided to fire up GrassGIS and give it a go with some publicly available data.

I set myself a simple task of trying to guess how ‘leafy’ streets are within an urban for urban environment from Landsat images. Part of the rationale was that whilst we could count trees using object detectors, this requires high resolution images. While I might do a blog on this at a later date, it was outside the scope of what I wanted to achieve here which is at a very coarse scale. I will be using a high resolution aerial image for ground truthing!

For the data, I found an urban area on USGS Earth Explorer with both high resolution orthoimagery and a reasonably cloud free image which were within 10 days of one another in acquisition. This turned out to be reasonably difficult to find, with the aerial imagery being the main limiting factor, but I found a suitable area in Cleveland, Ohio.

The aerial imagery is a 30 cm resolution having been acquired using a Williams ZI Digital Mapping Camera, and was orthorectified prior to download. For the satellite data, a Landsat 5 Thematic Mapper raster was acquired covering the area of interest, with a resolution of 30 m in the bands we are interested in.

This experiment sought to use the much researched NDVI, a simple index used for recovering an estimate of vegetation presence and health.

Initially, I loaded both datasets into QGIS to get an idea of the resolution differences

jezzer.png

Aerial image overlain on Landsat 5 TM data (green channel)

So a decent start, looks like our data is valid in some capacity and should be an interesting mini-experiment to run! The ground truth data is resolute enough to let us know how the NDVI is doing, and will be used farther downstream.

 

Onto GrassGIS, which I’ve always known has great features for processing satellite imagery, though I’ve never used. It’s also largely built on python, which is my coding language of choice, so I feel very comfortable troubleshooting the many errors fired at me!

The bands were loaded, DN -> reflectance conversion done (automatically, using GrassGIS routines) and a subsequent NDVI raster derived.

ndvi2.png

Aerial image overlain on NDVI values. Lighter pixels denote a higher presence of vegetation

Cool! We’ve got our NDVI band, and can ground truth it against the aerial photo as planned.

ndvi1

Lighter values were seen around areas containing vegetation

Last on the list is grabbing a vector file with street data for the area of interest so we can limit the analysis to just pixels beside or on streets. I downloaded the data from here and did a quick clip to the area of interest.

roads1.png

Vector road network (in yellow) for our aerial image. Some new roads appear to have been built.

I then generated a buffer from the road network vector file, and generated a raster mask from this so only data within 20 m of a road would be included in analyses. The result is a first stab at our leafy streets index!

map1.jpg

Visual inspection suggests it’s working reasonably well when compared with the reference aerial image, a few cropped examples are shown below.

This slideshow requires JavaScript.

Lastly, we can use this this data to scale things up, and make a map of the wider area in Cleveland. This would be simple to do for anywhere with decent road data.

map3.jpgThis might be useful for sending people on the scenic route, particularly in unfamiliar locations. Another idea might be to use it in a property search, or see if there’s a correlation with real estate prices. Right now I’ve run out of time for this post, but might return to the theme at a later date!

 

Advertisements

Mangrove canopy using photogrammetry

A good paper released in the open access journal Remote Sensing in Ecology and Conservation today detailed the use of worldview stereo pairs to estimate canopy height within mangroves in Mozambique. One of the factors making this more practical than other forest types is the relative uniformity of the canopy height (“tree height saturates and remains relatively consistent in mature and intact forests”), as well as the fact that they only occur at or around sea level, which allows for confidence to be given in ground control readings as well as the canopy height itself.

The introduction details the use of the NASA Ames stereo pipeline, which has been of some interest to me after coming across a very good description of an implementation of a semi-global matching algorithm written by an engineer who has a colleague working on Ames. SGM is particularly relevant to SfM-MVS photogrammetry (See Hirschmuller’s papers for more detail!), but is also being incorporated into big software packages such as IMAGINE. Zack’s blog is pretty amazing in general and I recommend it be given some attention!

The paper itself lists some pretty great results, and some of the maps generated are beautiful! It serves as a proof of concept for modern photogrammetry, which has come on leaps and bounds, and the potential for back-projecting and reprocessing data in this way is pretty exciting. I’ll be keeping my eye on the journal for future applications to environmental remote sensing!