Special issue in the journal “Open Geospatial Data, Software and Standards”
Published: October 2017
Open Science for Earth Remote Sensing: latest developments in software and data
Significant changes have taken place over the past few years in remote sensing technology. Quantity, quality and diversity of sensors have increased exponentially and so have related data. Open source software packages capable of processing digital imagery have improved in terms of number of available programs, implemented algorithms, and stability. Internet speed increases by 50% every year (Nielsen’s Law), and this fact, along with a growing awareness of the importance of collaboration and sharing through open access to data, has boosted the public availability of remote sensing datasets.
The trends outlined above indicate that the remote sensing science community has easier access to data which are growing exponentially in terms of volume, velocity and variety. Every part of Earth’s surface has been – and will be – covered frequently by numerous active and passive imaging sensors, which differ in resolution (spatial, spectral and radiometric), in revisit time and in the sensing mode (passive, active). The above characteristics can lead to consider a “big data” approach to image analysis in some cases.
For decades, the open source community has provided tremendous contributions to remote sensing in terms of tools and solutions which has led to support and inspiration to research, development and end-users. The evolution in open access of data and tools and the corresponding advantages in science for Earth Observation has motivated this special issue, whose objective is to report on past, current and future scenarios regarding the “open” parts of the remote sensing analysis process, from data acquisition, analysis to a final deliverable.
Dr. Francesco Pirotti, University of Padova, Italy
Dr. Markus Neteler, mundialis GmbH & Co. KG, Bonn, Germany
Dr. Duccio Rocchini, Fondazione Edmund Mach, Italy
- Preface to the special issue “Open Science for earth remote sensing: latest developments in software and data“
- Remote sensing of burned areas via PCA, Part 1; centering, scaling and EVD vs SVD
- Remote sensing of burned areas via PCA, Part 2: SVD-based PCA using MODIS and Landsat data
- Open source R for applying machine learning to RPAS remote sensing images
- Orfeo ToolBox: open source processing of remote sensing images
- MicMac – a free, open-source solution for photogrammetry
- Improving FOSS photogrammetric workflows for processing large image datasets
- A combined change detection procedure to study desertification using opensource tools
- Generalized 3D fragmentation index derived from lidar point clouds
- OpenDragon: software and a programmer’s toolkit for teaching remote sensing and geoinformatics
- Fusion of high-resolution DEMs for water flow modeling