Pansharpening in QGIS Using QTB

UPD: also you may want to check out my post on Pansharpening using R.

For a long time I wanted to play with the OrfeoToolBox instruments (and its GUI named Monteverdi) - a set of powerfull tools to process remote-sensing imagery. Finally I got the opportunity - I needed to perform pansharpening of the World-View-2 scene and I decided to make it using OTB modules available via QGIS Processing toolbox.

To make OTB modules available in QGIS you need to install it on your system. Official site provides good instructions on how to do it. When you have OTB installed you need to enable it in QGIS. Go Processing -> Options and configuration -> Providers, activate OTB and provide folder information if needed:

Pansharpening is made in two steps: 1) resampling of the low resolution raster via Superimpose sensor tool; 2) pansharpening using Pansharpening tool. Alternatively you can do it using one command line (no QGIS or other GUI needed) in your OS's console: 

otbcli_BundleToPerfectSensor -inp pan_image -inxs xs_image -out output_image.  

Performing two steps instead of just one is super-boring! Lets create a model that will allow us to perform pansharpening in QGIS in one step (virtually). Go to Processing Toolbox -> Models -> Tools and start creating new model:

In the model creation window fill the fields of the model name and group name. Then add two rasters as inputs and name them: High_resolution_panchrom_raster and Low_resolution_raster. Now add the module named Superimpose sensor (Orfeo Toolbox -> Geomentry -> Superimpose sensor) and configure it to resample the Low_resolution raster: use High_resolution_panchrom_raster as input in Reference input field and Low_resolution_raster as input in The image to reproject field. 

Add Pansharpening (rsc) (Orfeo Toolbox -> Geomentry -> Superimpose sensor) module to model. Use High_resolution_panchrom_raster as input in the Input PAN Image and output of the Superimpose sensor as the input in the Input XS Image field. Give a name (pansharpened_OTB) for the Output image to let model know that this is the final stage of the processing.

Here how our model looks like in Model builder:

And this is how it looks like when you launch it:

Also you can skip model creation process and just download model that I created. You need to paste files from archive's models folder into your /.qgis2/processing/models directory.

If you will have issues running OTB Pansharpening it is likely that they will be covered in following gis.stackexchange topics: OTB Pansharpening Error: Adapter for adaptPansharpening-bayes not found and What causes OTB pansharpening ERROR: Inputs do not occupy the same physical space? 

A Dialogue With Bot

Back in the days when ICQ was popular spam bots added you to their contact lists dozens times a day. But they were brute and ugly. At first they spew their spam links at your face when you added them, then they simply started to insert spam links in the body of the invitation. ICQ is long dead and spam-bots never bothered me in modern protocols... until some time ago.

How to Predict Where Will Next Disaster Strike?

It is amusing coincidence that another MOOC that I took this week (Geospatial Intelligence & the Geospatial revolution) mentioned [natural] disasters. About the other course see my recent Disasters: Myth or the Reality post.

In Geospatial Intelligence they gave a weird assignment: one need to mark the location on the world map where the next international natural disaster will occur O_o. This is not and easy task by any means and the lecturer suggested to use one's 'gut feeling' if one's knowledge is insufficient (I suppose it is close to impossible to find someone who can make such a prediction taking into account all the types of the disasters). Though the link to the International Disasters Database was given, so I accepted the challenge (to make a data-driven prediction). To predict the exact location of the next disaster one would need a lot of data - far more that you can get out of that database so my goal was to make prediction at the country level. (BTW the graphs from my post about disasters seems to be based on the data from this database - I saw one of them at that site)

I passed a query to the database and saved the output to process it with R. The dataframe looks like this:

Example of disasters dataset

So how to predict the country with the next disaster? I came up with the idea to calculate cumulative average occurrence of disasters per country per year and plot it on the graph to see the trends. If I would just calculate average occurrence of disasters per country for the whole time of the observations I would have significant issues choosing from countries that would have close numbers. Plus the total average disasters per year can be misleading by itself due to it can be high because of high amount of disasters in the beginning of XX century but relatively low number in XXI.  

The formula for the calculation of the cumulative average for the given year that I used was:

Cumulative_Average = Total_Occurences / ( Given_Year - (Starting_Year - 1) ) ,

where: Total_Occurrences is the sum of occurrences of disasters for given country in time interval between the starting year and the given year (inclusive).

Here is the plot I got for the short-list countries (plotting the results for all the 180 countries from the dataset makes plot unreadable):

Cumulative average number of disasters

It is clear that China and Indonesia are the two most likely candidates for the next disaster to strike, with a China having a lead. I'm not ready to provide insight on the reasons of the increasing number of natural disasters in the countries at the plot now (especially for Turkey and Iran). Maybe it is just that the events become documented more often?... It should be investigated further.

The code

Here is the code to create the plot above. 'sqldf' package was really helpful for divide data for the short list countries from the rest of 180 countries.

library(ggplot2)
library(sqldf)
library(grid)
#library(gridExtra)


# Load natural disasters data ---------------------------------------------

dis <- read.csv("~/R/Disasters/Natural_disasters.csv")

# Create data frame with average number of disasters per year -------------

average_events <- data.frame(country = character(),
                             year = numeric(),
                             disasters_per_year = numeric(),
                             stringsAsFactors = F)

countries <- unique(dis$country)

starting_year <- min(dis$year) - 1 # we subtract 1 year to have numbers greater than 0 further on

for (country in countries) {
    data <- dis[dis$country == country,] # we need data for one country at a time
    disasters_count <- 0
    years <- unique(data$year)
    
    for (year in years) {
        total_years <- year - starting_year 
        y_data <- data[data$year == year,]
        n_disasters <- sum(y_data$occurrence)
        disasters_count <- disasters_count + n_disasters
        average_disasters <- disasters_count / total_years
        row <- data.frame(country = country, year = year, disasters_per_year = average_disasters)
        average_events <- rbind(average_events, row)
    }
    
}


# Plot data about average number of disasters per country per year --------


# Data for 180 countries is hard to plot, lets filter mots affected.
# Let's use SQL to query data: subset data for countries that had more than 0.6 disasters per year
# in any year after 2000
danger <- sqldf('SELECT * FROM average_events WHERE country IN 
      (SELECT DISTINCT country FROM average_events WHERE disasters_per_year >= 0.6 AND year > 2000)')

p <- ggplot(danger, aes (x = year, y = disasters_per_year)) + 
            geom_line(size = 1.2, aes(colour = country,  linetype = country)) +
            labs(title = 'Cumulative average number of disasters per year',
                 x = 'Year',
                 y = 'Average number of disasters cumulative') +
            guides(guide_legend(keywidth = 3, keyheight = 1)) +
            theme(axis.text.x = element_text(angle=0, hjust = NULL),
                  axis.title = element_text(face = 'bold', size = 14),
                  title = element_text(face = 'bold', size = 16),
                  legend.position = 'right',
                  legend.title = element_blank(),
                  legend.text = element_text(size = 12),
                  legend.key.width = unit(1.5, 'cm'),
                  legend.key.height = unit(1, 'cm'))
           
plot(p)

Disasters: Myth or the Reality?

I enrolled a MOOC titled "Disasters and Ecosystems: Resilience in a Changing Climate" which is organised by the UNEP (and other organisations... which names I'm going to learn by heart cause they have like 2 minutes of credits after each lecture O_o ). Not that I know nothing about disasters, risks or climate change (I'm a geographer and ecologist after all), but I was curious about the product that was made by organisation of this class.

The third video (and first video that is not an introduction) they teach us about the disasters; differences between hazard and disaster; and risks. Well... the thing they told, the graphs they showed - that what inspired the title of this post.

Terminology

Here see some definitions they use.

Disaster. When they say "disaster" they mean "natural disaster" that was enhanced by human [mismanagement].

Risk - a potential losses due to disasters.

Hazard - A dangerous phenomenon, substance, human activity or condition that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage.

Exposure - People, property, systems, or other elements present in hazard zones that are thereby subject to potential losses.

Vulnerability - the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard

Fails

The risk

They presented a "great" formula for (a disaster) risk evaluation that they use in the UN:

Risk = Hazard * Exposure * Vulnerability

where: Exposure = People * ExposureTime

Vulnarability - succeptability to hazard.

Well these characteristics do correspond to the risk, but the formula is stupid! I already wrote about that: Risk = Probability * Damage. And this formula actually corresponds to the definition they give (see Terminology section). We can't get a monetary outcome from their formula. We can't get numeric numeric output out of that formula at all: can you multiply flood by people? Can you???!!!

A Disaster with Disasters

The fail with the risk evaluation is a common mistake, but the fail with disaster - that is what really cool!

Take a look at this plot (which is from reading materials from the course):

What can you conclude from this plot? That the world is doing to hell and we all will fall to disaster? Let's look closer. The exposure is growing faster for poorer countries (and it is the only conclusion they make in lecture)... but the total number of people exposed (and for each type of countries) seems to be the almost unchanged! Interesting... This means (see the definition for the exposure) that there are just a 150% increase of property value in the dangerous area of the poorer countries (and 25% for the richest) on a span of 30 years. Does this graph shows us only the economic grows? I think it does... (reminds me of my previous post).

Now to the most delicious part. Take a look at this two graphs from the lecture readings:

Deaths dynamics

Damage dynamics

This is interesting. Despite the population growth and all that questionable "climate change" staff people die less (in total numbers), see fig. 1, but the damage increases, see fig. 2. Did they take inflation into account for the damage graph? Do not know... I think they didn't, otherwise they would use "discounted damage" term instead of just "damage" and would indicate the base year. So the second graph seems to demonstrate inflation and may be the economic grows.

Clearly disasters are not that disastrous. Despite the new on the TV on the subject the nature's wrath even enhanced by human is less and less dangerous for human lives. The pockets are to suffer: the storm in port wrecking the humble fisherman's boat or a trawler - that's the difference.

Conclusion

From these graphs I can conclude one thing - it is safer to live now than in the past, a disaster should not be feared as a deadly havoc. To my mind the disaster nowadays is entirely economic issue. See, if we loose less people and (maybe) more money - we should just develop more advanced insurance techniques to cover economic damage and relax. The disasters should just be studied as phenomena to develop cheap early warning systems, let the property be destroyed (just cover the losses with insurance) and additional employment to be created (rebuilding).

This is my conclusion form the graphs I showed here: disasters are an ancient myth! Just buy insurance! LOL

Do You Know What You Show at Your Map?

As access to the GIS and mapping is becoming easier every year the more people and companies create maps. Unfortunately often they just do not know what they are actually showing at their maps. This issue is being mentioned over and over again.  

Here is the example that I discovered recently: Cyberthreat Real-Time Map by Kaspersky antivirus company. Here how it looks like:

Amongst the other info they show the Infection rank for each country... based on total threats detected.... You may have already guessed what is the fail, but I let me explain it anyway.

See, the №1 infected country is Russia, which is the home country for Kaspersky and where this antivirus is quite popular. So we can conclude that the rankings that supposed to demonstrate the severity of virus activities merely demonstrates the number of Kaspersky software installations across the globe.

Lets test this hypothesis. I don't have the data about the number of installation of Kaspersky software per country, but it is safe to assume that this number is proportional to the population of the given country. Also it is easier to get infection rankings for countries from the map than the number of the threats detected. If I had total threats data per country I would compare it to the population. Having infection rankings it is more rational to compare it to the population rankings instead. So I picked 27 random countries and compared their infection and population rankings. The result is demonstrated at the plot below:

Infection rank vs. Population rank

The linear model is fairly close to Inrection rank = Population rank. It is clear that the phenomena that is presented as an Infection rank just reflects a total software installations per country and not the severity of the 'cyberthreat'. In order to get the actual Infection rank the number of detected threats have to be normalised by the number of software installations.