What is the most important remote sensing software tools?

In recent years, remote sensing (RS) technique become more popular and important to monitor, manage, and evaluate the earth environment. This technique evaluated as the most effective method to do such activities.

Therefore, the development of user-friendly software based on the necessity  is needed.

This study began in 2011, however I don’t have enough time to produce the final result. This time I would like to   share the information. This study addresses the following question: what is the most important remote sensing software tools should be available in?

Data and method

This study employed questionnaire survey, spread over using the internet services to whole over the world during one month period, July-August 2011. The questions separated in four main groups; (1) Responder background, (2) Data preparation, (3) Data processing, and (4) Data post-processing.

Responder background

51% of responder is RS User, following by 26% of RS Analyst, 11% RS Consultant, and 11% of Business Owner. Mostly they have high level of education (40% obtained master degree, 33% have doctoral degree, 22% bachelor degree, and only 2% graduated from high school). However, the responder mostly new in the RS professional experiences with 31% have experiences under 3 years, 20% between 3-5 years, 33% between 5-10 years, 6% between 10-20 years, and only 8% of responder have experiences for more than 20 years.


The responder employer are mostly educational institution (46%), following by national government (24%), private industry (17%), and NPO (11%)

The responder are dominated from Asia countries (42%) and Southern America (35%), following Western Europe (8%), USA and Africa (4%) and from Australia/New Zealand and Eastern Europe (2%). Furthermore, it seems that the RS technology is still dominated by male (84%)


Most of the responder are using desktop paid RS software as shown in the following figure. The most popular desktop paid RS software are ERDAS Imagine (27%) and ENVI (24%), while the most popular open source RS software are ILWIS (27%) and GRASS (11%)

Primary software


Based on the questionnaire survey, the study produce the following result:

1. Data preparation

Georeferencing, visualization, and projection management become the most important tools. This result shows that the three modules mentioned above are mostly used in order to prepare the imagery before any further analysis. With many imagery comes from different sensor and format, conversion tools also appears as important module that should available in the RS software. Mosaicking, cliping raster imagery, and atmospheric correction are in the last place.

Data preparation

2. Data processing

Image classification, management of classified image, spatial multi-criteria analysis are the most important module that should be available in RS data processing software. Furthermore, vegetation analysis, vectorization, and image transformation are following in the next place.

Data processing

3. Data post-processing

This study found very important facts, that mostly the RS user are working using multiple softwares. The ‘export data to most popular format’ is the most important module in the RS data post-processing. The ‘cartographic layout composition’ is also most important, since the final result of any imagery analysis is provided in map representation to the stakeholders. While the other modules are less important but still should be available.

Data post-processing


From the explanation above, this study can conclude that before any further analysis, georeferencing, visualization, and projection management are need to be applied. Classification and image transformation are the main module that should be available in any RS software. Most RS user are working using multiple software, therefore the ‘export data to most popular format’ module is needed in RS software. Furthermore, the ‘cartographic layout composition’ is also important to produce a beautiful map, because one map can explain the phenomenon on the earth surface much better than million words.


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