Haze correction for Landsat imagery

This correction is optional. It can be used when considered that there is some influence of haze on the imagery. The example has been prepared using pre-loaded routines in ERDAS Imagine 2011.

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Atmospheric correction of Landsat imagery

Corrección atmosférica de imágenes Landsat

When an image is downloaded, besides the corresponding bands (which contain the data), you will notice that there are two txt files that accompany the data. Such files contain the Metadata necessary for the calibration of the image. The procedure you should follow is described in several papers, among which Chander et al. (2009) is highly recommended when using Landsat imagery. Next we describe the procedure we recommend to accomplish this task. The following is an example with a Landsat ETM scene, prepared by the ecologist and agricultural engineer C. Buitrón, under the assessment of the geographic engineer J. Fernández. Both are independent consultants in environmental projects.

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Image processing with ERDAS Part 2: Creation of an AOI (Area of Interest)

The next step in the unsupervised classification process is the selection of an Area of Interest, a.k.a. AOI in Erdas Imagine. The reasons to select an AOI are:

- To reduce the file size (very important when dealing with several images).

- To reduce the range of spatial variability, which is important when we need to apply a technique that considers ranges of variation of the values that characterize an area.

The tutorial presented in this post has been elaborated by the ecologist and agricultural engineer Carola Buitron, with the support of Jose Fernandez. The upcoming posts will show you the remaining steps to accomplish the task I mentioned. Enjoy and learn.

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Image processing Part I: Stacking multispectral bands using ERDAS

Satellite imagery can be used for several purposes. In environmental studies, its use has obvious advantages: low cost, large spatial coverage, and historical snapshots of a certain phenomena. In this series of posts, I am uploading a very well constructed tutorial on how to carry an unsupervised classification, for the study of multi temporal trends of a given geomorphological feature.

In this post, I am showing you how to stack multispectral bands in Erdas 2011. The tutorial has been elaborated by the ecologist and agricultural engineer Carola Buitron, supported by J. Fernandez and our team. The upcoming posts will show you the remaining steps to accomplish the task I mentioned. Enjoy and learn.

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Monte Carlo and Sensitivity Analysis II: On the spatial representativeness of point measurements

Monte Carlo y análisis de sensibilidad II: Acerca de la representatividad espacial de las mediciones puntuales

Nature may be seen as a complex array of point data, because of which, environmental modellers should always assess the uncertainty contained in the representativeness of our point-field measurements. The following is an alternative to assess such problematic, through a relatively simple analysis of sensitivity under a Monte Carlo approach.

Next I provide the link to the ppt presented at the IUGG meeting in Australia in 2011. I expect that the material will give you some ideas on practical applications of Monte Carlo based approaches. If useful, you may want to cite it as:

Soria F., Kazama, S. Monte Carlo experiments for uncertainty investigation of glacier melt discharge predictions through surface energy balance analysis. IAHS Publ. 346, 2011. 

I hope you find it interesting.

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C++ Example 2: Reading data in text (.txt) format.

Ejemplo 2 para C++: Lectura de datos en formato .txt

After more than a month of not having updated this section of the blog (the others are being updated regularly though), this entry will show how to read .txt data using C++.

The input data for rainfall-runoff and environmental models is commonly provided by external sources, such as data loggers or the responsible of the IT department at the meteorological institutions, which rewrite the collected data in a certain format. Such data is often saved into binary (.bin) or text (.txt) format.  The former (.bin) is more efficient because it is the mother langauge of our computers. Data written in .bin format can be stored using less space than any other format, and can be retrieved at high speed; however, I have faced some situations where the writers of the .bin file forgot to write some details about the format of the data (e.g., is the data type Integer, Float, Double Long?), which made the data retrieval very difficult to accomplish . That is why throughout this example I am presenting some lines written in C++ that can be used to read a .txt file, a format that is popular among some groups of users.

Thus, the example shows:

i) how to read .txt data in C++, and

ii) how to format the input data into the format that our model uses.

A detailed explanation accompanies each line of the sample program, because I want to provide a practical example for those engineers interested in learning the C++ programming language.

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C++ Example 1: Reading GCM output data (IPCC Data Distribution Centre).

Ejemlo 1 para C++: Lectura de salidas de modelos de circulación global (Datos del IPCC)

Do you know how do climate change studies are carried?

From an engineering perspective, a fundamental element for climate change studies is provided by the outputs from Global Circulation Models, GCM, whose numerical experiments are run for several "climate change scenarios".

Why is it important to learn some programming language? Practitioners (especially young ones) may tend to think that commercial software packages have "the solution". Unfortunately, that is not true, and most of the time we will need to build our own tools. Then, postgraduate students will see the advantages, because those obstacles will enrich their experiences throughout a process where the aim is to understand the basis behind a phenomena.

I am posting a practical example where are described the elements in a C++ program that can be used when dealing with matrices. Why matrices?, because from my perspective, those elements are perhaps the most useful resource in problems where environmental models are involved. You will notice that my programs are not as elaborated as a program written by an IT expert, but they work. Details on the language may be found elsewhere.

At the same time the example shows how to read data obtained from the IPCC Data Distribution Centre.

Glossary:

GCM: Global Circulation Model.

IPCC: Intergovernmental Panel on Climate Change.

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