From ef85d1bd752b06724fc98385f138814f40d3c8ba Mon Sep 17 00:00:00 2001
From: Ollie Ballinger <58981760+oballinger@users.noreply.github.com>
Date: Thu, 17 Nov 2022 09:26:50 +0000
Subject: [PATCH] test
---
.DS_Store | Bin 8196 -> 8196 bytes
RojavaRefineries.qmd | 16 ----------------
_book/ch1.html | 3 ++-
_book/ch4.html | 20 +++++++++++++++-----
_book/ch5.html | 5 +++++
_book/search.json | 2 +-
ch1.qmd | 4 +++-
7 files changed, 26 insertions(+), 24 deletions(-)
diff --git a/.DS_Store b/.DS_Store
index a7e63b58c788188c519445eac9a5563545bf8fe6..420a89fe33f60adb374b98931e3d7218a571ae5f 100644
GIT binary patch
delta 14
VcmZp1XmQx^Lx7QC^G^Y8egG>?1sDJT
delta 14
VcmZp1XmQx^Lx7QS^G^Y8egG>|1sMPU
diff --git a/RojavaRefineries.qmd b/RojavaRefineries.qmd
index 7cda823..8c9ab00 100644
--- a/RojavaRefineries.qmd
+++ b/RojavaRefineries.qmd
@@ -34,22 +34,6 @@ Below is an Earth Engine application that automates the detection of makeshift r
You can draw an Area of Interest (AOI) and get the total number of contaminated points as well as the total number of contaminated square meters within the AOI. drawing multiple AOIs will show a running total of these statistics. It's not perfect-- it misses some refineries and falsely identifies some others-- but it is generally quite accurate; you can visually verify the results of the prediction by zooming in using the "+" button. You can toggle different layers using the "layers" tab as well. This tool could be used to get an estimate of oil production in a user-defined area, and eventually to direct cleanup efforts. The fullscreen version of the application can be found [here](https://ollielballinger.users.earthengine.app/view/rojavaoil), and the source code [here](https://code.earthengine.google.com/7a80f10412e1eb2a4d2c5d95989e70bd). This tutorial will first cover the basics of multispectral remote sensing, before moving into a step-by-step guide in the construction of this model.
-## Multispectral Remote Sensing
-
-Different materials reflect light differently. An apple absorbs shorter wavelengths (e.g. blue and green), and reflects longer wavelengths (red). Our eyes use that information-- the color-- to distinguish between different objects. But our eyes can only see a relatively small sliver of the electromagnetic spectrum covering blue, yellow, and red; we can't see UV or infrared wavelengths, for example, though the extent to which different materials reflect or absorb these wavelengths is just as useful for distinguishing between them. For example, Astroturf (fake plastic grass) and real grass will both look green to us, espeically from a satellite image. But living plants absorb radiation from the sun in a part of the light spectrum that we can't see. There's a spectral index called the Normalized Difference Vegetation Index (NDVI) which exploits this fact to isolate vegetation in multispectral satellite imagery. So if we look at [Gilette Stadium](https://en.wikipedia.org/wiki/Gillette_Stadium) near Boston, we can tell that the three training fields south of the stadium are real grass (they generate high NDVI values, showing up red), while the pitch in the stadium itself is astroturf (generating low NDVI values, showing up blue).
-
-
-
-In other words, even though these fields are all green and indistinguishable to the human eye, their *spectral profiles* beyond the visible light spectrum differ, and we can use this information to distinguish between them. Below is a plot of the spectral profiles of different materials, including oil.
-
-
-
-The European Space Agency's Sentinel-2 satellite collects spectral information well beyond the visible light spectrum, enabling this sort of analysis. It chops the electromagnetic spectrum up into "bands", and measures how strongly wavelengths in each of those bands is reflected:
-
-
-
-We'll be using this satellite to distinguish between oil and other materials, similar to the way we were able to distinguish between real and fake grass at Gilette Stadium. First, we'll have to do a bit of pre-processing on the Sentinel-2 imagery after which we'll train a machine learning model to identify oil.
# Machine Learning Workflow
diff --git a/_book/ch1.html b/_book/ch1.html
index feb4721..c052438 100644
--- a/_book/ch1.html
+++ b/_book/ch1.html
@@ -261,7 +261,8 @@ ul.task-list li input[type="checkbox"] {
-
One of the main advantages of GEE is that it hosts several Petabytes of satellite imagery and other spatial data sets, all in one place. Among these are a many that could prove useful to those investigating illegal mining and logging, estimating conflict-induced damage, monitoring pollution from extractive industries, conducting maritime surveillance without relying on ship transponders, verifying the locations of artillery strikes, tracking missile defense systems, and many other topics. This section highlights ten categories of geospatial data available natively in the GEE catalogue ranging from optical satellite imagery, to atmospheric data, to building footprints. Each sub-section provides an overview of the given data type, suggests potential applications, and lists the corresponding datasets in the GEE catalogue. The datasets listed under each heading are not an exhaustive list– there are over 500 in the whole catalogue, and the ones listed in this section are simply the ones with the most immediate relevance to open source investigations. If a particular geospatial dataset you want to work with isn’t hosted in the GEE catalog, you can upload your own data. We’ll cover that in the next section.
+
One of the main advantages of GEE is that it hosts several Petabytes of satellite imagery and other spatial data sets, all in one place. Among these are a many that could prove useful to those investigating illegal mining and logging, estimating conflict-induced damage, monitoring pollution from extractive industries, conducting maritime surveillance without relying on ship transponders, verifying the locations of artillery strikes, tracking missile defense systems, and many other topics.
+
This section highlights ten categories of geospatial data available natively in the GEE catalogue ranging from optical satellite imagery, to atmospheric data, to building footprints. Each sub-section provides an overview of the given data type, suggests potential applications, and lists the corresponding datasets in the GEE catalogue. The datasets listed under each heading are not an exhaustive list– there are over 500 in the whole catalogue, and the ones listed in this section are simply the ones with the most immediate relevance to open source investigations. If a particular geospatial dataset you want to work with isn’t hosted in the GEE catalog, you can upload your own data. We’ll cover that in the next section.