Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion
Abstract
:1. Introduction
2. Materials and Methods
2.1. Software and Libraries
- RSGISLib
- KEA
- Pandas/GeoPandas
- GeoPackage
- Docker/Singularity
2.1.1. Remote Sensing and GIS Library (RSGISLib)
2.1.2. KEA
2.1.3. Pandas/GeoPandas
2.1.4. GeoPackage
2.1.5. Docker/Singularity
2.2. Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA)
2.2.1. Image Segmentation
2.2.2. Image Tiling
2.2.3. Image Statistics
3. Example Studies
3.1. Remote Sensing Imagery
3.1.1. WorldView
3.1.2. Sentinel-1 Time-Series
3.1.3. Sentinel-3 Time-Series
3.1.4. Sentinel-2 Time-Series
3.2. Senegal River Valley
3.2.1. Senegal Study Site A: Broad Scale Photosynthetic Cover Mapping
3.2.2. Senegal Study Site B: High-Resolution Agriculture Monitoring
3.3. Mekong Delta, Vietnam
4. Discussion
4.1. Comparison to Other Methods
- FARMA provides an efficient and scalable method for the fusion of multi-resolution data. High- and moderate-resolution time-series imagery was populated into the VHR derived objects, but the segmentation and imagery used for analysis can be any combination of sizes. Furthermore, multiple resolution pixel values can be populated into the same image object, allowing a user to analyze multiple raster datasets per object at their native resolution. In addition, the system is fully open source and does not require proprietary software at any stage of the process.
- It has the ability to tile the imagery so that smaller portions of data can be processed more efficiently even with modest computing resources. However, within a large distributed computing environment, this enables the advantage of splitting the data volume across a large number of CPUs. The run time subsequently becomes trivial as all tiles can be run simultaneously. A primary advantage of FARMA is that the tile size is entirely customizable, so a user can trade tile size for number of tiles, depending on the computing architecture available. FARMA can be fully operated on a single thread or it can be distributed across as many CPUs as possible.
- It is able to tile polygons without creating artificial boundaries between them. While the benefit of processing large data volumes as individual tiled datasets is known, this has not been readily demonstrated with image objects which do not conform to neat tile boundaries. FARMA enables objects to be assigned to a tile using a mode value, subsequently enabling a segmentation to be tiled using soft tile boundaries. Furthermore, by creating these tiles as vector datasets, this has the benefit of enabling zonal statistics to be populated into objects irrespective of spatial resolution while being processed in parallel.
- FARMA reads each vector layer into memory before calculating and assigning zonal statistics. While this may pose an issue for very large vectors (>10,000 ha), typically not associated with agriculture fields, this has the benefit of reducing the vector I/O and thus is more computationally efficient. This contributes to the framework being fully scalable.
4.2. Performance
4.3. Framework Expansion
4.4. Data Processing Considerations and Limitations
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Thomas, N.; Neigh, C.S.R.; Carroll, M.L.; McCarty, J.L.; Bunting, P. Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion. Remote Sens. 2020, 12, 3459. https://doi.org/10.3390/rs12203459
Thomas N, Neigh CSR, Carroll ML, McCarty JL, Bunting P. Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion. Remote Sensing. 2020; 12(20):3459. https://doi.org/10.3390/rs12203459
Chicago/Turabian StyleThomas, Nathan, Christopher S. R. Neigh, Mark L. Carroll, Jessica L. McCarty, and Pete Bunting. 2020. "Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion" Remote Sensing 12, no. 20: 3459. https://doi.org/10.3390/rs12203459