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How to crop Geotiff image in C++ (GDAL or other possibilities)?

How to crop Geotiff image in C++ (GDAL or other possibilities)?


I am developing a code to process a geotiff image. But firsly I need to crop a geotiff image preserving its metadata (projection… ) in C++. My idea it's to provide the corners and the geotiff image to a function inside the code and then it return the subset. I thought about using GDAL but I don't know if there is any code or function.

Anybody know about this?


You could do this easily enough in C++. The way I would do it is to send the path of the file you want to crop, the path of the output/cropped raster, the top left coordinates of the cropped raster and the width and height of the cropped raster. An outline for your code could look something like this…

void crop(const char *inputPath, const char *cropPath, double topLeftX, double topLeftY, double width, double height) { GDALDataset *pInputRaster, *pCroppedRaster; GDALDriver *pDriver; pDriver = GDALGetDriverManager->getDriverByName("Name of you driver here"); pInputRaster = (GDALDataset*) GDALOpen(inputPath, GA_ReadOnly); //the affine transformation information, you will need to adjust this to properly //display the clipped raster double transform[6]; pInputRaster->getGeoTransform(transform); //adjust top left coordinates transform[0] = topLeftX; transform[3] = topLeftY; //determine dimensions of the new (cropped) raster in cells int xSize = round(width/transform[1]); int ySize = round(height/transform[1]); //create the new (cropped) dataset pCroppedRaster = pDriver->Create(cropPath, xSize, ySize, 1, GDT_Float32, NULL) //or something similar //now all you have to do is find the number of columns and rows the top left corner //of the cropped raster if offset from the original raster, and use those values to //read data from the original raster and copy/write the data to the new (cropped) raster }

It should be fairly easy to copy the projection from the input raster to the cropped raster. I usually don't worry about projections when I work in GDAL. However, it is very important to make sure you keep track of the affine transformation data. If your rasters are all in the same projection the affine transformation data will accurately describe their spatial location relative to each other.


You can always inspect the GDAL utilities for how they do it. For example, if you just want to "chip out" a section of an image, you could usegdal_translate -srcwin.

The simplest way would just be to invoke gdal_translate (e.g. with asystem()call, or maybe aCreateProcess()call if you're on Windows). Otherwise, you'd could read the code for gdal_translate.cpp to extract the bits you need.

If your needs are pretty simple, you can probably just use the RasterIO function twice - once to read the data, and again to write the data out. Examples of both are given in the GDAL API tutorial.


Apart from gdal (see other answers) and building a virtual file, you can use the OTB library. This is a C++ open source library including a large set of filters for image processing. Specifically, the otb::MultiChannelExtractROI does the trick. It is also available as an application if you want to use it directly.


I did this using python and GDAL:

#Atention with -projwin ulx uly lrx lry (upper left (ymax, xmin) - low right (xmax, ymin)) command = "gdal_translate" + " "  "-of GTiff" + " "  "-ot Float64 " +  "-co compress=LZW " +  "-projwin" + " " +  str(xmin) + " " +  str(ymax) + " " +  str(xmax) + " " +  str(ymin) + " " +  path_to_raster_tiff + " " +  output_path os.system(command)

http://gis-techniques.blogspot.com/2015/09/clip-raster-with-shapefile-using-c-and.html#.VfiVehFVhHw has example on cropping raster with feature using C# and Gdal. Hope it shed you some ideas to use GDAL with C++ to crop raster with shape.


How do I crop pictures to certain proportion in Photoshop?

For reference, I use Photoshop CS5 Extended. I would prefer to find a way to do what I want in this software.

I often want to crop my pictures so that they fit a certain proportion (either for printing or online use). Even when I want the 1.5:1 ratio my camera produces by default, it's rare that I'm not looking to crop some element out on the edge of the frame.

I know that I can hold down shift while adjusting the cropping box from one of the corners, and it will constrain the proportion there. But is there any way to set a specific proportion for the crop tool to use?

At the moment, I go through a fairly labour intensive process of getting it "about right" and then literally counting the pixels to make it correct.


2 Answers 2

Here is a way to achieve what you want with ImageMagick. Hopefully you’re comfortable using Terminal (and also use Homebrew to manage all your packages).

Either way, you must install ImageMagick so you can run convert command line in Terminal.

Open the folder you want to batch processing all the images. If your folder name called images is located on Desktop, you’ll need to enter cd

/Desktop/images/ in Terminal.

ImageMagick have a built-in option to crop images according to sizes and coordinates. The command is:


1 Answer 1

You cant do that with the add_image_size() function, after a lot of research and digging in the core files, what the Hard Crop option of the add_image_size() function actually does is resize the image using the lower dimension, then it will crop the size specified

by example if you create a new image size:

and upload an image like this one:

it will resize the image using the height 310 (since is the lower value) keeping the aspect ratio, like this:

then it will proceed to crop the image using the resized image, based in the position that was send or the default center , center , the red square overlapping is the size 475, 310 and is the area that will be cropped:

it may look like the top and bottom dont matter, but that is because the image was resized using the height , if it was the other way around, the width being the lower the image would be tall and the left and right would look like they dont matter.

To accomplish what you are trying to do, you will need a plugin or a developer who will add the necessary code to your theme, it cant be done right now with the default built-in tools of wordpress.


Captured image using RaspiCam got crop

I am working on a project using the Raspberry Pi model B and the Raspberry Pi CSI camera module (RaspiCam).

The problem is, captured image area when I'm using raspicam_test which in the raspicam library version 0.1.1 is different than when I'm using raspistill command. The captured image from raspicam_test is got crop and resized. Any idea how to solve this?

Here is the example of captured images

and this one is when im using raspistill command


Installation

Velox should work on all major operating systems (Linux, Mac OS, Windows).

Dependencies

Velox requires the rgdal, rgeos, and sf packages, which rely on the external GDAL (>= 2.0.0), GEOS (>= 3.3.0), PROJ.4 (>= 4.8.0), and UDUNITS libraries. On Debian/Ubuntu (>= 14.04), these dependencies can be installed by entering

R Package

Once the system dependencies are available, you can either install velox from CRAN

or you can install the development version using the install_github function from the devtools package:

Please note that this page refers to the development version of the package.


Include "ogr_api.h"

compilation terminated. makefile:179: recipe for target 'areadinfmn.o' failed make: *** [areadinfmn.o] Error 1

Linux: stream_reach_file warning: Layer creation failed.

StreamNet version 5.3.8 Input file stream_raster_grid.tif has geographic coordinate system. This run may take on the order of 1 minutes to complete. This estimate is very approximate. Run time is highly uncertain as it depends on the complexity of the input data and speed and memory of the computer. This estimate is based on our testing on a dual quad core Dell Xeon E5405 2.0GHz PC with 16GB RAM. Input file flow_dir_grid_d8.tif has geographic coordinate system. Input file contributing_area_grid_d8.tif has geographic coordinate system. Input file elev.tif has geographic coordinate system. Evaluating distances to outlet Creating links ERROR 10: Name was NULL in OGR_DS_CreateLayer ERROR 10: Pointer 'hLayer' is NULL in 'OGR_L_CreateField'.

stream_reach_file warning: Layer creation failed.

Error R6034

Dear all, I'm trying to use TauDEM before begin with other tools, as SedinConnect. I'm using arcGIS 10.2.1 over windows 10. I installed all the tools indicated in the download page, listed here: GDAL 201 for 64 bit Windows PC GDAL 2.1.0 for Python 2.7 Microsoft MPI v7.1 Microsoft Visual C++ 2015 Redistributable Package (x86) Microsoft Visual C++ 2015 Redistributable Package (x64) I installed Microsoft MPI v7.1 by running as administrator from the contextual menu. Following the advice of SedInConnect people (https://github.com/HydrogeomorphologyTools/SedInConnect_2.3/blob/master/Guidelines_tools_stand-alone.docx), I turned the UAC (User Account Control) to the lowest value to prevent Windows from blocking MPI activity. I installed TauDEM 5.3.7 by running as administrator from the contextual menu. I installed the arcGIS toolbox and tried to run pitremove from there. I obtain the error R6034:

And the ERROR 000732 in the arcGIS pit remove dialog, who cannot calculate statistics in the file I put as output. I tried to run from the command line, both by using the command prompt and my GIT bash: mpiexec -n 8 pitremove -z "./FromENVI/S36W072_73_DEM_RBB_UTM18S.tif" -fel "./FromTauDEM/RBB.tif"

And I obtained the same error R6034.

I don't know what to do. I'll appreciate your help, Violeta

SetRegion.cpp fails to compile on Ubuntu

I'm trying to get TauDEM compiled on Ubuntu Xenial, and after the make, I get this error:

Edit: I'm using the Develop branch from git.

Aread8 nodata value in version 5.3.8

First of all, this is really useful software.

We've been using this for many parameter accumulations following the flow direction grid. Recently, we've had some odd behavior with version 5.3.8. Regions of the weighting grid with values of -1 are returned in the output accumulated grid as areas of nodata. I think this is because -1 is hard coded into aread8 as the nodata value. I'll paste some screen shots and descriptions below. Perhaps this is related to #203 or has been fixed in v5.3.9? I've only had success getting v5.3.8 to compile on linux/HPC.

Accumulated parameter grid with -1 areas shown as nodata:

When I convert -1 to float, -0.01, the issue goes away.

Here is a snippet of what may be the offending code. I don't know c++ so I may be way off here.

It would be great to have the output nodata value not be hard-coded and instead have that value be the same as the nodata value be set as the nodata value of the input weighting grid (if properly defined).

Again, this is really great software and has saved us a ton of time.

Integration of Accelerated Flow Direction Algorithms

Hello Dr. Tarboton, Would there be an interest in integrating the accelerated flow direction algorithms developed by Survila et al (2016) into this repository? If so, I believe the accelerated O(n) algorithms are already implemented on the CyberGIS repository with a slight modification within a fork to allow for compilation. I'd be happy to try to help integrate this if desired since it is heavily used at the NWC within the Cahaba repository.


Elements

All new canvas elements need to implement the Canvas::Element interface (new elements can also sub-class existing elements, e.g. see the implementation of the Map and Window($FG_SRC/Canvas) elements), the canvas system currently supports the following primitives (see $SG_SRC/canvas/elements):

  • CanvasGroup - main element: for grouping (a group of arbitrary canvas primitives, including other groups)
  • CanvasText - for rendering texts (mapped to osgText)
  • CanvasPath - for rendering vector graphics (mapped to OpenVG, currently also used to render SVGs into groups)
  • CanvasMap - for rendering maps (automatic projection of geographic coordinates to screen coordinates, subclass of group)
  • CanvasImage - for rendering raster images (mapped to osg::Image)
  • CanvasWindow - this is part of $FG_SRC/Canvas/Window.?xx, it's a subclass of CanvasImage, used to implement windows (as of 05/2014 also to be found in simgear)

Most end-user features can be decomposed into lower-level components that need to be available in order to implement the corresponding feature in user-space.

Thus, the canvas system is based on a handful of rendering modes, each supporting different primitives - each of those modes is implemented as a so called "Canvas Element", which is a property-tree controlled subtree of a canvas texture using a certain name, that supports specific events and notifications.

According to the development philosophy outlined above, you obviously won't see new canvas elements that are highly use-case specific, such as a "night vision" or FLIR element. Instead, what is more likely to be supported are the lower level building blocks to enable end-users creating such features, i.e. by adding support for running custom effects/shaders and by rendering scenery views to canvas textures.


Question 10. When should I use what color space and compression scheme?

Due to the flexible combination of raster data with a vast amount of different compression schemes and color spaces, TIFF can be very suitable for a wide range of applications. Here is a sample of just the most popular or common applications.

ApplicationCompression scheme and color space
Bilevel dithered or very complex imagerycolorspace black and white
compression G3, G4, or perhaps JBIG
Bilevel artificial imagerycolorspace black and white
compression G3 or G4
Normal range grayscale or color photographic imagery If compression is more important than quality
colorspace Grayscale or YCbCr(*)
compression JPEG(*)
If quality is more important than compression
colorspace Grayscale, RGB, or CIE L*a*b*
compression LZW or Deflate
If quality is of utmost importance
colorspace 16bit per channel or even floating point RGB, or 16bit per channel CIE L*a*b*
compression LZW or Deflate
Normal range Grayscale or color artificial imagery If the number of colors <=256
colorspace Palette would be most suitable
compression LZW or Deflate
If the number of colors >256
colorspace Grayscale, RGB, or CIE L*a*b*
compression LZW or Deflate
Dynamic range Grayscale or color imagerycolorspace floating point Grayscale or RGB
compression LZW or Deflate

(*) The YCbCr color space and JPEG compression scheme are de facto related. Other than using JPEG as compression scheme, there is in our humble opinion no good reason for using YCbCr.


Software Sustainability in Remote Sensing

By Robin Wilson, Geography and Environment & Institute for Complex Systems Simulation, University of Southampton.

This is the third in a series of articles by the Institute's Fellows, each covering an area of interest that relates directly both to their own work and the wider issue of software's role in research.

1. What is remote sensing?

Remote sensing broadly refers to the acquisition of information about an object remotely (that is, with no physical contact). The academic field of remote sensing, however, is generally focused on acquiring information about the Earth (or other planetary bodies) using measurements of electromagnetic radiation taken from airborne or satellite sensors. These measurements are usually acquired in the form of large images, often containing measurements in a number of different parts of the electromagnetic spectrum (for example, in the blue, green, red and near-infrared), known as wavebands. These images can be processed to generate a huge range of useful information including land cover, elevation, crop health, air quality, CO2 levels, rock type and more, which can be mapped easily over large areas. These measurements are now widely used operationally for everything from climate change assessments (IPCC, 2007) to monitoring the destruction of villages in Darfur (Marx and Loboda, 2013) and the deforestation of the Amazon rainforest (Kerr and Ostrovsky, 2003).

Figure 1: Landsat 8 image of Southampton, UK shown as a false-colour composite using the Near-Infrared, Red and Green bands. Vegetation is bright red, heathland and bare ground is beige/brown, urban areas are light blue, and water is dark blue.

The field, which crosses the traditional disciplines of physics, computing, environmental science and geography, developed out of air photo interpretation work during World War II, and expanded rapidly during the early parts of the space age. The launch of the first Landsat satellite in 1972 provided high-resolution images of the majority of the Earth for the first time, producing repeat images of the same location every 16 days at a resolution of 60m. In this case, the resolution refers to the level of detail, with a 60m resolution image containing measurements for each 60x60m area on the ground. Since then, many hundreds of further Earth Observation satellites have been launched which take measurements in wavelengths from ultra-violet to microwave at resolutions ranging from 50km to 40cm. One of the most recent launches is that of Landsat 8, which will contribute more data to an unbroken record of images from similar sensors acquired since 1972, now at a resolution of 30m and with far higher quality sensors. The entire Landsat image archive is now freely available to everyone - as are many other images from organisations such as NASA and ESA - a far cry from the days when a single Landsat image cost several thousands of dollars.

2. You have to use software

Given that remotely sensed data are nearly always provided as digital images, it is essential to use software to process them. This was often difficult with the limited storage and processing capabilities of computers in the 1960s and 1970s - indeed, the Corona series of spy satellites operated by the United States from 1959 to 1972 used photographic film which was then sent back to Earth in a specialised ‘re-entry vehicle’ and then developed and processed like standard holiday photos!

However, all civilian remote-sensing programs transmitted their data to ground stations on Earth in the form of digital images, which then required processing by both the satellite operators (to perform some rudimentary geometric and radiometric correction of the data) and the end-users (to perform whatever analysis they wanted from the images).

In the early days of remote sensing, computers didn’t even have the ability to display images onscreen, and the available software was very limited (see Figure 2a). Nowadays remote sensing researchers use a wide range of proprietary and open-source software, which can be split into four main categories:

Specialist remote-sensing image processing software, such as ENVI, Erdas IMAGINE, eCogniton, Opticks and Monteverdi

Geographical Information System software which provides some remote-sensing processing functionality, such as ArcGIS, IDRISI, QGIS and GRASS

Other specialist remote-sensing software, such as SAMS for processing spectral data, SPECCHIO for storing spectral data with metadata, DART (Gastellu-Etchegorry et al., 1996) for simulating satellite images and many numerical models including 6S (Vermote et al., 1997), PROSPECT and SAIL (Jacquemoud, 1993).

General scientific software, including numerical data processing tools, statistical packages and plotting packages.

Figure 2: Remote sensing image processing in the early 1970s (a) and 2013 (b). The 1970s image shows a set of punched cards and an ‘ASCII-art’ printout of a Landsat image of Southern Italy, produced by E. J. Milton as part of his PhD. The 2013 image shows the ENVI software viewing a Landsat 8 image of Southampton on a Windows PC

I surveyed approximately forty people at the Remote Sensing and Photogrammetry Society’s Annual Student Meeting 2013 to find out how they used software in their research, and this produced a list of 19 pieces of specialist software which were regularly used by the attendees, with some individual respondents regularly using over ten specialist tools.

Decisions about which software to use are often based on experience gained through university-level courses. For example, the University of Southampton uses ENVI and ArcGIS as the main software in most of its undergraduate and MSc remote sensing and GIS courses, and many of its students will continue to use these tools significantly for the rest of their career. Due to the limited amount of teaching time, many courses only cover one or two pieces of specialist software, thus leaving students under-exposed to the range of tools which are available in the field. There is always a danger that students will learn that tool rather than the general concepts which will allow them to apply their knowledge to any software they might need to use in the future

I was involved in teaching a course called Practical Skills in Remote Sensing as part of the MSc in Applied Remote Sensing and GIS at the University of Southampton which tried to challenge this, by introducing students to a wide range of proprietary and open-source software used in remote sensing, particularly software which performs more specialist tasks than ENVI and ArcGIS. Student feedback showed that they found this very useful, and many went on to use a range of software in their dissertation projects.

2.1 Open Source Software

In the last decade there has been huge growth in open-source GIS software, with rapid developments in tools such as GIS (a GIS display and editing environment with a similar interface to ArcGIS) and GRASS (a geoprocessing system which provides many functions for processing geographic data and can also be accessed through GIS). Ramsey (2009) argues that the start of this growth was caused by the slowness of commercial vendors to react to the growth of online mapping and internet delivery of geospatial data, and this created a niche for open-source software to fill. Once it had a foothold in this niche, open-source GIS software was able to spread more broadly, particularly as many smaller (and therefore more flexible) companies started to embrace GIS technologies for the first time.

Unfortunately, the rapid developments in open-source GIS software have not been mirrored in remote sensing image processing software. A number of open-source GIS tools have specialist remote sensing functionality (for example, the i.* commands in GRASS), but as the entire tool is not focused on remotely sensed image processing they can be harder to use than tools such as ENVI. Open-source remote sensing image processing tools do exist (for example, Opticks, OrfeoToolbox, OSSIM, ILWIS and InterImage), but they tend to suffer from common issues for small open-source software projects, specifically: poor documentation complex and unintuitive interfaces and significant unfixed bugs.

In contrast, there are a number of good open-source non-image-based remote sensing tools, particularly those used for physical modelling (for example, 6S (Vermote et al., 1997), PROSPECT and SAIL (Jacquemoud, 1993)), processing of spectra (SAMS and SPECCHIO (Bojinski et al., 2003 Hueni et al., 2009)), and as libraries for programmers (GDAL, Proj4J).

Open source tools are gradually becoming more widely used within the field, but their comparative lack of use amongst some students and researchers compared to closed-source software may be attributed to their limited use in teaching. However, organisations such as the Open Source Geospatial Laboratories (OSGL: www.osgeo.org) are helping to change this, through collating teaching materials based on open-source GIS at various levels, and there has been an increase recently in the use of tools such as Quantum GIS for introductory GIS teaching.

3. Programming for remote sensing

There is a broad community of scientists who write their own specialist processing programs within the remote sensing discipline, with 80% of the PhD students surveyed having programmed as part of their research. In many disciplines researchers are arguing for increased use and teaching of software, but as there is no real choice as to whether to use software in remote sensing, it is the use and teaching of programming that has become the discussion ground.

The reason for the significantly higher prevalence of programming in remote sensing compared to many other disciplines is that much remote sensing research involves developing new methods. Some of these new methods could be implemented by simply combining functions available in existing software, but most non-trivial methods require more than this. Even if the research is using existing methods, the large volumes of data used in many studies make processing using a GUI-based tool unattractive. For example, many studies in remote sensing use time series of images to assess environmental change (for example, deforestation or increases in air pollution) and, with daily imagery now being provided by various sensors, these studies can regularly use many hundreds of images. Processing all of these images manually would be incredibly time-consuming, and thus code is usually written to automate this process. Some processing tools provide ‘macro’ functionality which allow common tasks to be automated, but this is not available in all tools (for example, ENVI) and is often very limited. I recently wrote code to automate the downloading, reprojecting, resampling and subsetting of over 7000 MODIS Aerosol Optical Thickness images, to create a dataset of daily air pollution levels in India over the last ten years: creating this dataset would have been impossible by hand!

The above description suggests two main uses of programming in remote sensing research:

Writing code for new methods, as part of the development and testing of these methods

Writing scripts to automate the processing of large volumes of data using existing methods

3.1 Programming languages: the curious case of IDL and the growth of Python

The RSPSoc questionnaire respondents used languages as diverse as Python, IDL, C, FORTRAN, R, Mathematica, Matlab, PHP and Visual Basic, but the most common of these were Matlab, IDL and Python. Matlab is a commercial programming language which is commonly used across many areas of science, but IDL is generally less well-known, and it is interesting to consider why this language has such a large following within remote sensing.

IDL’s popularity stems from the fact that the ENVI remote sensing software is implemented in IDL, and exposes a wide range of its functionality through an IDL Application Programming Interface (API). This led to significant usage of IDL for writing scripts to automate processing of remotely-sensed data, and the similarity of IDL to Fortran (particularly in terms of its array-focused nature) encouraged many in the field to embrace it for developing new methods too. Although first developed in 1977, IDL is still actively developed by its current owner (Exelis Visual Information Solutions) and is still used by many remote sensing researchers, and taught as part of MSc programmes at a number of UK universities, including the University of Southampton.

However, I feel that IDL’s time as one of the most-used languages in remote sensing is coming to an end. When compared to modern languages such as Python (see below), IDL is difficult to learn, time-consuming to write and, of course, very expensive to purchase (although an open-source version called GDL is available, it is not compatible with the ENVI API, and thus negates one of the main reasons for remote sensing scientists to use IDL).

Python is a modern interpreted programming language which has an easy-to-learn syntax - often referred to as ‘executable pseudocode’. There has been a huge rise in the popularity of Python in a wide range of scientific research domains over the last decade, made possible by the development of the numpy (‘numerical python’ (Walt et al., 2011)), scipy (‘scientific python’ (Jones et al., 2001)) and matplotlib (‘matlab-like plotting’ (Hunter, 2007)) libraries. These provide efficient access to and processing of arrays in Python, along with the ability to plot results using a syntax very similar to Matlab. In addition to these fundamental libraries, over two thousand other scientific libraries are available at the Python Package Index (http://pypi.python.org), a number of which are specifically focused on remote-sensing.

Remote sensing data processing in Python has been helped by the development of mature libraries of code for performing common tasks. These include the Geospatial Data Abstraction Library (GDAL) which provides functions to load and save almost any remote-sensing image format or vector-based GIS format from a wide variety of programming languages including, of course, Python. In fact, a number of the core GDAL utilities are now implemented using the GDAL Python interface, rather than directly in C++. Use of a library such as GDAL gives a huge benefit to those writing remote-sensing processing code, as it allows them to ignore the details of the individual file formats they are using (whether they are GeoTIFF files, ENVI files or the very complex Erdas IMAGINE format files) and treat all files in exactly the same way, using a very simple API which allows easy loading and saving of chunks of images to and from numpy arrays.

Another set of important Python libraries used by remote-sensing researchers are those originally focused on the GIS community, including libraries for manipulating and processing vector data (such as Shapely) and libraries for dealing with the complex mathematics of projections and co-ordinate systems and the conversion of map references between these (such as the Proj4 library, which has APIs for a huge range of languages). Just as ENVI exposes much of its functionality through an IDL API ArcGIS, Quantum GIS, GRASS and many other tools expose their functionality through a Python API. Thus, Python can be used very effectively as a ‘glue language’ to join functionality from a wide range of tools together into one coherent processing hierarchy.

A number of remote-sensing researchers have also released very specialist libraries for specific sub-domains within remote-sensing. Examples of this include the Earth Observation Land Data Assimilation System (EOLDAS) developed by Paul Lewis (Lewis et al., 2012), and the Py6S (Wilson, 2012) and PyProSAIL (Wilson, 2013) libraries which I have developed, which provide a modern programmatic interface to two well-known models within the field: the 6S atmospheric radiative transfer model and the ProSAIL vegetation spectral reflectance model.

Releasing these libraries - along with other open-source remote sensing code - has benefited my career, as it has brought me into touch with a wide range of researchers who want to use my code, helped me to develop my technical writing skills and also led to the publication of a journal paper. More importantly than any of these though, developing the Py6S library has given me exactly the tool that I need to do my research. I don’t have to work around the features (and bugs!) of another tool - I can implement the functions I need, focused on the way I want to use them, and then use the tool to help me do science. Of course, Py6S and PyProSAIL themselves rely heavily on a number of other Python scientific libraries - some generic, some remote-sensing focused - which have been realised by other researchers.

The Py6S and PyProSAIL packages demonstrate another attractive use for Python: wrapping legacy code in a modern interface. This may not seem important to some researchers - but many people struggle to use and understand models written in old languages which cannot cope with many modern data input and output formats. Python has been used very successfully in a number of situations to wrap these legacy codes and provide a ‘new lease of life’ for tried-and-tested codes from the 1980s and 1990s.

3.2 Teaching programming in remote sensing

I came into the field of remote sensing having already worked as a programmer, so it was natural for me to use programming to solve many of the problems within the field. However, many students do not have this prior experience, so rely on courses within their undergraduate or MSc programmes to introduce them both to the benefits of programming within the field, and the technical knowledge they need to actually do it. In my experience teaching on these courses, the motivation is just as difficult as the technical teaching - students need to be shown why it is worth their while to learn a complex and scary new skill. Furthermore, students need to be taught how to program in an effective, reproducible manner, rather than just the technical details of syntax.

I believe that my ability to program has significantly benefited me as a remote-sensing researcher, and I am convinced that more students should be taught this essential skill. It is my view that programming must be taught far more broadly at university: just as students in disciplines from Anthropology to Zoology get taught statistics and mathematics in their undergraduate and masters-level courses, they should be taught programming too. This is particularly important in a subject such as remote sensing where programming can provide researchers with a huge boost in their effectiveness and efficiency. Unfortunately, where teaching is done, it is often similar to department-led statistics and mathematics courses: that is, not very good. Outsourcing these courses to the Computer Science department is not a good solution either - remote sensing students do not need a CS1-style computer science course, they need a course specifically focused on programming within remote sensing.

In terms of remote sensing teaching, I think it is essential that a programming course (preferably taught using a modern language like Python) is compulsory at MSc level, and available at an undergraduate level. Programming training and support should also be available to researchers at PhD, Post-Doc and Staff levels, ideally through some sort of drop-in ‘geocomputational expert’ service.

4. Reproducible research in remote sensing

Researchers working in ‘wet labs’ are taught to keep track of exactly what they have done at each step of their research, usually in the form of a lab notebook, thus allowing the research to be reproduced by others in the future. Unfortunately, this seems to be significantly less common when dealing with computational research - which includes most research in remote sensing. This has raised significant questions about the reproducibility of research carried out using ‘computational laboratories’, which leads to serious questions about the robustness of the science carried out by researchers in the field - as reproducibility is a key distinguishing factor of scientific research from quackery (Chalmers, 1999).

Reproducibility is where the automating of processing through programming really shows its importance: it is very hard to document exactly what you did using a GUI tool, but an automated script for doing the same processing can be self-documenting. Similarly, a page of equations describing a new method can leave a lot of important questions unanswered (what happens at the boundaries of the image? how exactly are the statistics calculated?) which will be answered by the code which implements the method.

A number of papers in remote sensing have shown issues with reproducibility. For example, Saleska et al. (2007) published a Science paper stating that the Amazon forest increased in photosynthetic activity during a widespread drought in 2005, and thus suggested that concerns about the response of the Amazon to climate change were overstated. However, after struggling to reproduce this result for a number of years, Samanta et al. (2010) eventually published a paper showing that the exact opposite was true, and that the spurious results were caused by incorrect cloud and aerosol screening in the satellite data used in the original study. If the original paper had provided fully reproducible details on their processing chain - or even better, code to run the entire process - then this error would likely have been caught much earlier, hopefully during the review process.

I have personally encountered problems reproducing other research published in the remote sensing literature - including the SYNTAM method for retrieving Aerosol Optical Depth from MODIS images (Tang et al., 2005), due to the limited details given in the paper, and the lack of available code, which has led to significant wasted time. I should point out, however, that some papers in the field deal with reproducibility very well. Irish et al. (2000) provide all of the details required to fully implement their revised version of the Landsat Automatic Cloud-cover Assessment Algorithm (ACCA), mainly through a series of detailed flowcharts in their paper. The release of their code would have saved me re-implementing it, but at least all of the details were given.

5. Issues and possible solutions

The description of the use of software in remote sensing above has raised a number of issues, which are summarised here:

There is a lack of open-source remote-sensing software comparable to packages such as ENVI or Erdas Imagine. Although there are some tools which fulfil part of this need, they need an acceleration in development in a similar manner to Quantum GIS to bring them to a level where researchers will truly engage. There is also a serious need for an open-source tool for Object-based Image Analysis, as the most-used commercial tool for this (eCognition) is so expensive that it is completely unaffordable for many institutions.

There is a lack of high-quality education in programming skills for remote-sensing students at undergraduate, masters and PhD levels.

Many remote sensing problems are conceptually easy to parallelise (if the operation on each pixel is independent then the entire process can be parallelised very easily), but there are few tools available to allow serial code to be easily parallelised by researchers who are not experienced in high performance computing.

Much of the research in the remote sensing literature is not reproducible. This is a particular problem when the research is developing new methods or algorithms which others will need to build upon. The development of reproducible research practices in other disciplines has not been mirrored in remote sensing, as yet.

Problems 2 & 4 can be solved by improving the education and training of researchers - particularly students - and problems 1 & 3 can be solved by the development of new open-source tools, preferably with the involvement of active remote sensing researchers. All of these solutions, however, rely on programming being taken seriously as a scientific activity within the field, as stated by the Science Code Manifesto (http://sciencecodemanifesto.org).

The first piece of advice I would give any budding remote sensing researcher is learn to program! It isn’t that hard - honestly! - and the ability to express your methods and processes in code will significantly boost your productivity, and therefore your career.

Once you’ve learnt to program, I would have a few more pieces of advice for you:

Script and code things as much as possible, rather than using GUI tools. Graphical User Interfaces are wonderful for exploring data - but by the time you click all of the buttons to do some manual processing for the 20th time (after you’ve got it wrong, lost the data, or just need to run it on another image) you’ll wish you’d coded it.

Don’t re-invent the wheel. If you want to do an unsupervised classification then use a standard algorithm (such as ISODATA or K-means) implemented through a robust and well-tested library (like the ENVI API, or scikit-learn) - there is no point wasting your time implementing an algorithm that other people have already written for you!

Try and get into the habit of documenting your code well - even if you think you’ll be the only person who looks at it, you’ll be surprised what you can forget in six months!

Don’t be afraid to release your code - people won’t laugh at your coding style, they’ll just be thankful that you released anything at all! Try and get into the habit of making research that you publish fully reproducible, and then sharing the code (and the data if you can) that you used to produce the outputs in the paper.

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Marx, A.J., Loboda, T.V., 2013. Landsat-based early warning system to detect the destruction of villages in Darfur, Sudan. Remote Sens. Environ. 136, 126–134. doi:10.1016/j.rse.2013.05.006

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