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index.html
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta http-equiv="Content-Style-Type" content="text/css" />
<meta name="generator" content="pandoc" />
<meta name="author" content="Jan Verbesselt, Dainius Masiliūnas" />
<title>MODIS NDVI time series analysis using BFAST</title>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
<!-- jQuery -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.10.3/jquery-ui.min.js"></script>
<!-- bootstrap -->
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<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
<!-- highlight.js -->
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hljs.highlightBlock(e);
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$('#toc').generate_TOC();
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});
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$('.theme-switch').closest('li').removeClass('active');
$(this).closest('li').addClass('active');
return false;
});
/* code style switch */ //TODO use same function for both of these?
$('.highlight-switch').click(function(){
var css = $('link[title="' + $(this).attr('title') + '"]');
$('#highlight[rel=stylesheet]').attr('href', css.attr('href'));
$('.highlight-switch').closest('li').removeClass('active');
$(this).closest('li').addClass('active');
return false;
});
//TODO refresh on show/hide
$window.on('load', function () {
$body.scrollspy('refresh');
})
});
</script>
<style>
/* Knitr_bootstrap styles */
#header {
display: none !important;
visibility: hidden !important;
}
#wrap .container-fluid {
padding: 0;
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}
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text-transform: capitalize;
}
.toggle-global{
text-transform: capitalize;
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margin:0;
}
html,
body {
height: 100%;
padding:0 !important;
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/*overflow-x: hidden;*/
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height: auto !important;
height: 100%;
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height: 120px;
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font-size: 20px;
}
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font-size: 12px;
}
button.thumbnails {
margin-left:0px;
}
/*
* Side navigation
*
* Scrollspy and affixed enhanced navigation to highlight sections and secondary
* sections of docs content.
*/
/* By default it's not affixed in mobile views, so undo that */
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position: static;
}
/* First level of nav */
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margin-top: 30px;
margin-bottom: 30px;
padding-top: 10px;
padding-bottom: 10px;
border-radius: 5px;
}
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display: block;
padding: 5px 20px;
}
.sidebar .nav > li > a:hover,
.sidebar .nav > li > a:focus {
text-decoration: none;
border-right: 1px solid;
}
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.sidebar .nav > .active:hover > a,
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margin-bottom: 8px;
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font-size: 90%;
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padding-left: 50px;
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display: block;
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.sidebar.affix,
.sidebar.affix-bottom {
width: 213px;
}
.sidebar.affix-top,
.sidebar.affix {
position: fixed; /* Undo the static from mobile first approach */
top: 30px;
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.sidebar.affix-bottom {
position: absolute; /* Undo the static from mobile first approach */
}
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.sidebar.affix .sidenav {
margin-top: 0;
margin-bottom: 0;
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@media screen and (min-width: 1200px) {
/* Widen the fixed sidebar again */
.sidebar.affix-bottom,
.sidebar.affix {
width: 263px;
}
}
#toc {
padding: 10px 0px;
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border:0;
}
.panel pre {
margin: 0;
padding: 0;
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button + pre {
margin: 0;
padding: 0;
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pre code {
border-radius: 0;
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filter: alpha(opacity=80); }
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left: 0;
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z-index: 1043;
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box-sizing: border-box; }
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content: '';
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vertical-align: middle; }
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display: none; }
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position: relative;
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text-align: left;
z-index: 1045; }
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<h1 class="title">MODIS NDVI time series analysis using BFAST</h1>
<h2 class="author">Jan Verbesselt, Dainius Masiliūnas</h2>
<h3 class="date">22 March, 2022</h3>
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<h1 id="modis-based-time-series-analysis-using-bfast-monitor-and-bfast-lite">MODIS based time series analysis using BFAST Monitor and BFAST Lite</h1>
<p><img src="https://www.wur.nl/upload/99f85964-d53b-4dc7-b3ff-fb62abc648ce_WUR_RGB_standard_2021-site.svg" alt="WUR logo" style="height: 35px;"/></p>
<p><a href="http://www.wur.eu/changemonitor">Jan Verbesselt, Dainius Masiliūnas</a></p>
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<p>22-03-2022</p>
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<p>This document explains how to use the R scripting language for downloading MODIS data and analyzing its time series within R. By the end of the tutorial, you will be able to download and preprocess MODIS data, and apply a time series break detection algorithm to automatically determine changes in time series. For this time series analysis demonstration it is not required to know R details, we only use R for some practical demonstration of its great potential. In this exercise we will automatically download MODIS data for specific locations around the world. You can then apply, at your own choice, one of the two algorithms for break detection: BFAST Monitor or BFAST Lite.</p>
<h2 id="introduction-to-modis-data-and-online-analysis">Introduction to MODIS data and online analysis</h2>
<p>The MODIS satellite data that we will download for this time series analysis exercise is available from the <a href="https://modis.ornl.gov/sites/">MODIS Subsetting Website</a>. MODIS data is made available for specific locations.</p>
<p>More specifically, we will look at the MODIS product called MOD13Q1 which are <em>global 16-day images at a spatial resolution of 250 m</em>. Each image contains several bands; i.e. blue, red, and near-infrared reflectances, centered at 469 nanometers, 645 nanometers, and 858 nanometers, respectively. These bands are used to determine the MODIS vegetation indices.</p>
<p>The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA’s Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for applications requiring long time series. MODIS also includes a new Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke, sub-pixel and thin clouds. The MODIS NDVI and EVI products are computed from atmospherically corrected surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows.</p>
<p>Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and its changes. We will work with the MODIS NDVI band of the MOD13Q1 product. More information about this MODIS data set can be found via the <a href="https://lpdaac.usgs.gov/products/mod13q1v006/">MODIS product table</a>.</p>
<p>Now go to the <a href="https://modis.ornl.gov/sites/">MODIS Land Products</a>:</p>
<ul>
<li>Search for ‘Netherlands’ and select the ‘Gelderland Loobos’ Site.</li>
<li>Select the MODIS 13Q1 product by clicking on it.There is no immediate need to create an account unless you want to manually download the MODIS data via this website. In the below explanation, we will download MODIS NDVI data via an R script.</li>
<li>Under ‘Download data’ tab and table, via the website, you can see a small link named <strong>Pixel Numbering Scheme</strong>, Click it to see that the blue pixel (i.e. pixel number 545) is the “site pixel” (i.e. the middle of the spatial subset). This is where a flux tower is located.</li>
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<strong>Question 1</strong>: What are the three main land cover types close to the center of the site? (See the integrated legend in the maps below.) Which land cover types are correct and which are not?
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<h2 id="preprocessing-automated-modis-ndvi-download-and-analysis-via-r">Preprocessing: automated MODIS NDVI download and analysis via R</h2>
<p>We will download the MODIS data for the Loobos Site via R and process the data for one location to detect changes within the time series.</p>
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<h3 id="getting-started-install-packages-and-load-the-necessary-functions-for-modis-data-analysis">Getting started: install packages and load the necessary functions for MODIS data analysis</h3>
<p>Now we are ready to get started with the MODIS time series analysis exercise in R!</p>
First, choose your working directory (i.e. a folder on your hard drive) to which the MODIS data will be downloaded and where you will save your R script.
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<strong>Protip</strong>: Do not hard code <code>setwd()</code> in your R script as the path might be different on another computer, and therefore your script will not be fully reproducible by others.
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<p>In RStudio you can set your working directory this way, if you have saved your R script to your working directory: <img src="figs/setwd_rstudiotip.png" /></p>
<p>Check your working directory by</p>
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<pre style=""><code class="source r">getwd() # check if your working directory is correctly set</code></pre>
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<p>The necessary add-on packages need to be installed within R before loading the package using the <code>library()</code> function. Below we define a helper function that installs the R package if it is not installed yet, and then also loads it using the <code>library</code> function:</p>
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<pre style=""><code class="source r"># pkgTest is a helper function to load packages and install packages only when they are not installed yet.
pkgTest <- function(x)
{
if (x %in% rownames(installed.packages()) == FALSE) {
install.packages(x, dependencies= TRUE)
}
library(x, character.only = TRUE)
}
neededPackages <- c("zoo", "bfast", "terra", "raster", "leaflet", "MODISTools")
for (package in neededPackages){pkgTest(package)}</code></pre>
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<p>Loading extra function <code>timeser()</code> to create a time series object in R:</p>
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<pre style=""><code class="source r"># Utility function to create time series object from a numeric vector
# val_array: data array for one single pixel (length is number of time steps)
# time_array: array with Dates at which raster data is recorded (same length as val_array)
timeser <- function(val_array, time_array) {
z <- zoo(val_array, time_array) # create zoo object
yr <- as.numeric(format(time(z), "%Y")) # extract the year numbers
jul <- as.numeric(format(time(z), "%j")) # extract the day numbers (1-365)
delta <- min(unlist(tapply(jul, yr, diff))) # calculate minimum time difference (days) between observations
zz <- aggregate(z, yr + (jul - 1) / delta / 23) # aggregate into decimal year timestamps
(tso <- as.ts(zz)) # convert into timeseries object
return(tso)
}</code></pre>
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<h3 id="downloading-modis-data-using-the-modistools-package">Downloading MODIS data using the MODISTools package</h3>
<p>First we download the MODIS data via the <code>mt_subset</code> function:</p>
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<pre style=""><code class="source r"># Downloading the NDVI data, starting from 2000-01-01
VI <- mt_subset(product = "MOD13Q1",
site_id = "nl_gelderland_loobos",
band = "250m_16_days_NDVI",
start = "2000-01-01",
end = "2022-03-22",
km_lr = 2,
km_ab = 2,
site_name = "testsite",
internal = TRUE,
progress = TRUE)</code></pre>
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<pre style=""><code class="message r">## Downloading chunks:
</code></pre>
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