A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring
Abstract
:1. Introduction
- R1: What land use and land cover types, and the changes therein, have been analysed using the integration of optical and radar remote sensing data?
- R2: What combination of optical and radar sensors was most popular in studies assessing land use and land use changes, and what spatial scales were analysed?
- R3: How was the analysis of the fusion of optical and radar data conducted, and did fusion result in a more accurate assessment of land use and the changes therein?
2. Remote Sensing for LUCC Analyses
2.1. Optical Remote Sensing
2.2. Radar Remote Sensing
2.3. Limitations of Optical and Radar Products
3. Methods
TERMS | RESULTS (Articles and Reviews) |
---|---|
(radar OR scatteromet* OR microwave* OR SAR*) AND optical AND (integrat* OR synerg* OR combin* OR fus* OR compar* OR multi* OR mix*) AND (forest* OR savann* OR woodland) | 280 |
(radar OR scatteromet* OR microwave* OR SAR*) AND optical AND (integrat* OR synerg* OR combin* OR fus* OR compar* OR multi* OR mix*) AND (agricultur* OR crop* OR farm*) | 240 |
(radar OR scatteromet* OR microwave* OR SAR*) AND optical AND (integrat* OR synerg* OR combin* OR fus* OR compar* OR multi* OR mix*) AND (grazing OR pasture OR pastor* OR grass*) | 95 |
(radar OR scatteromet* OR microwave* OR SAR*) AND optical AND (integrat* OR synerg* OR combin* OR fus* OR compar* OR multi* OR mix*) AND (land use OR land cover) | 397 |
4. Results
4.1. Overview of the Characteristics of Land Use or Cover Studied
Land Use/Cover and Change Characterization | Number of Studies |
---|---|
Broad land uses | 6 |
Including land use/cover change | 0 |
Specific land uses | 37 |
Studies including change | 3 |
Continuous properties of land use/land management/land use intensity | 7 |
Studies including change | 2 |
Land use not addressed (land cover only) | 62 |
Studies including change | 6 |
Studies characterizing change as modification | 5 |
Studies characterizing change as conversion | 1 |
Total | 112 |
4.2. Characteristics of Studies Addressing Land Use
Optical Sensor | Radar Sensor | |||
---|---|---|---|---|
Number of Studies | Study IDs | Number of Studies | Study IDs | |
Very high resolution (≤4 m) | 4 | (2, 19, 35, 37) | 4 | (13, 19, 21, 37) |
High resolution (>4 and ≤15 m) | 8 | (1, 7, 13, 14, 17, 18, 22, 39) | 30 | (2, 3, 4, 6, 7, 10, 11, 12, 14, 16, 18, 20, 22, 26, 27, 29, 30, 31, 32, 33, 35, 36, 38, 39, 40, 41, 44, 46, 49, 50) |
Medium resolution (>15 and ≤100 m) | 36 | (3, 4, 5, 6, 8, 10, 11, 12, 15, 16, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 38, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50) | 16 | (1, 5, 8, 9, 15, 17, 23, 24, 25, 28, 34, 42, 43, 45, 47, 48) |
Coarse resolution (>100 m) | 2 | (9, 47) | 0 |
4.3. Specifications of Analyses in Studies Addressing Land Use
Classification Method | Number of Studies | Study IDs |
---|---|---|
Traditional | 28 | (1, 2, 3, 5, 11, 14, 15, 20, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 40, 41, 42, 43, 44, 48, 49) |
Machine learning | 17 | (2, 4, 9, 10, 12, 13, 19, 20, 24, 29, 30, 32, 33, 37, 45, 46, 50) |
Knowledge-based/decision tree | 10 | (1, 2, 8, 16, 17, 32, 34, 42, 44, 47) |
Not based on common classification methods (e.g., regression analysis is used to produce continuous output variable) | 7 | (6, 7, 18, 21, 27, 38, 39) |
Scale of Analysis | Number of Studies | Study IDs |
---|---|---|
Pixel-level | 36 | (1, 5, 6, 7, 8, 9, 10, 12, 13, 16, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 32, 33, 34, 35, 36, 38, 40, 41, 42, 43, 44, 47, 48, 49, 50) |
Neighbourhood (e.g., texture windows) | 10 | (15, 16, 22, 24, 25, 29, 39, 40, 41, 50) |
Segment-level | 15 | (2, 3, 4, 11, 13, 14, 17, 19, 26, 29, 31, 37, 38, 45, 46) |
Temporal Frequency | Number of Studies | Study IDs |
---|---|---|
Static | 23 | (1, 2, 4, 7, 9, 12, 15, 16, 19, 20, 21, 22, 23, 24, 25, 29, 36, 37, 39, 40, 41, 44, 48) |
Multi-temporal | 27 | (3, 5, 6, 8, 10, 11, 13, 14, 17, 18, 26, 27, 28, 30, 31, 32, 33, 34, 35, 38, 42, 43, 45, 46, 47, 49, 50) |
Studies that also perform change detection | 5 | (6, 14, 17, 42, 43) |
Integration Step | Number of Studies | Study IDs |
---|---|---|
Pre-classification or -modelling: fusion of input data | 37 | (1, 3, 4, 7, 10, 11, 13, 14, 15, 16, 17, 18, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 48, 50) |
Post-classification or -modelling: fusion of derived information | 16 | (2, 5, 6, 8, 9, 12, 19, 20, 23, 28, 34, 37, 42, 43, 44, 49) |
Performed at multiple or different steps of data processing | 3 | (28, 37, 44) |
Conclusion | Number of Studies | Study IDs |
---|---|---|
Fusion offers an improvement on a single data type | 28 | (3, 5, 7, 10, 11, 13, 15, 17, 19, 21, 22, 24, 26, 28, 29, 30, 33, 36, 37, 40, 41, 42, 43, 44, 45, 48, 49, 50) |
Fusion results are no different or worse than using a single data type | 4 | (4, 31, 38, 39) |
Not compared in sufficient detail | 18 | (1, 2, 6, 8, 9, 12, 14, 16, 18, 20, 23, 25, 27, 32, 34, 35, 46, 47) |
5. Discussion
- A transition in the science and application of fused remote sensing products, from traditional mapping of broad land cover or use classes to mapping the subtle intricacies of land use management or intensity and the changes therein, is urgently required in support of understanding and accurately quantifying global land use. Future research must be focussed on mapping, for example, land management aspects of cropping cycles, forest harvesting frequencies, paddy and irrigation agriculture, pasture and silvopasture classifications, shrub encroachment on grazing land, etc. Studies must be aimed at similar major global land use transitions, evaluating the most effective spatial scales and methods to fuse optical and radar data using comparable metrics of accuracy.
- In a methodological context, we urge future research to focus on the development of robust optical and radar data fusion techniques, including techniques that test how frequent time series and datasets of varying spatial resolution may be meaningfully merged with minimal information loss. The results of integrating datasets that differ fundamentally in the information they provide must be tested within the same study sites and within the same land use theme and be clearly reported as such in future studies. This research will fill a gap in understanding the discord between the chosen methodologies and their accuracies in the current literature.
- Similarly, as studies are implemented across various geographical regions and themes, systematic and standardized procedures for assessing the benefits of fusing data sources need to established. This calls for a standardization of procedures to document accuracy estimates, including uncertainty propagation applicable to the chosen methods of fusion.
- To demonstrate the feasibility of fused datasets to map and monitor global-scale land use change processes, there is an urgent need for studies to be implemented over larger spatial scales (national to continental level) compared to those in the current literature and to be supported with efficient means of data storage and computational processing. Such research will be able to identify the challenges to implementing data integration more clearly, as well as provide a better characterization of large-scale patterns of land use changes and their impacts on climate.
- In support of future airborne and satellite missions aimed at land monitoring, a permanent set of ground-based sites that are frequently monitored for calibration and validation purposes is crucial. Current research is often based on opportunistic availability of data, hence carrying a large variability in ground measurements and resulting in the incomparability of the results between studies. Permanent ground-based measurements will enable more reliable and robust accounts of whether data integration is beneficial, as well as support validating results with datasets that are truly independent from training data.
6. Conclusions
Supplementary Files
Supplementary File 1Acknowledgments
Author Contributions
Conflicts of Interest
References
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Joshi, N.; Baumann, M.; Ehammer, A.; Fensholt, R.; Grogan, K.; Hostert, P.; Jepsen, M.R.; Kuemmerle, T.; Meyfroidt, P.; Mitchard, E.T.A.; et al. A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sens. 2016, 8, 70. https://doi.org/10.3390/rs8010070
Joshi N, Baumann M, Ehammer A, Fensholt R, Grogan K, Hostert P, Jepsen MR, Kuemmerle T, Meyfroidt P, Mitchard ETA, et al. A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensing. 2016; 8(1):70. https://doi.org/10.3390/rs8010070
Chicago/Turabian StyleJoshi, Neha, Matthias Baumann, Andrea Ehammer, Rasmus Fensholt, Kenneth Grogan, Patrick Hostert, Martin Rudbeck Jepsen, Tobias Kuemmerle, Patrick Meyfroidt, Edward T. A. Mitchard, and et al. 2016. "A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring" Remote Sensing 8, no. 1: 70. https://doi.org/10.3390/rs8010070