L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

Naidoo, Laven; Mathieu, Renaud; Main, Russell; Wessels, Konrad J; Asner, Gregory P.

doi:10.1016/j.jag.2016.05.006

Cited by 38 publications

(54 citation statements)

References 45 publications

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“…This is in agreement with Naidoo et al [7] who found that the combination of multi-sensor data produces the highest overall accuracies, reporting an improvement of 8% and 17% from the PALSAR-only and Landsat-only models, respectively. Laurin et al [73] also compared PALSAR and optical (Landsat and AVNIR-2) land cover classifications in a wet tropical area and reported an improvement from the multi-sensor integration.…”

Section: Landsat or Palsar? Or Both?supporting

confidence: 92%

“…This is in support of a number of studies who tested the performance of SAR data when mapping woody vegetation or the percentage of woody cover. Naidoo et al [7] compared L-band PALSAR dry-season data with Random Forest models incorporating Landsat bands and vegetation and textural indices from different seasons, in order to map a woody canopy cover in an area within the Kruger National Park in South Africa. They found that the SAR-only models outperformed the best Landsat-only model by 9% (R 2 = 0.81 and R 2 = 0.72, respectively).…”

Section: Landsat or Palsar? Or Both?mentioning

confidence: 99%

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Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

et al. 2018

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Accurately mapping savannah land cover at the regional scale can provide useful input to policy decision making efforts regarding, for example, bush control or overgrazing, as well as to global carbon emissions models. Recent attempts have employed Earth observation data, either from optical or radar sensors, and most commonly from the dry season when the spectral difference between woody vegetation, crops and grasses is maximised. By far the most common practice has been the use of Landsat optical bands, but some studies have also used vegetation indices or SAR data. However, conflicting reports with regards to the effectiveness of the different approaches have emerged, leaving the respective land cover mapping community with unclear methodological pathways to follow. We address this issue by employing Landsat and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data to assess the accuracy of mapping the main savannah land cover types of woody vegetation, grassland, cropland and non-vegetated land. The study area is in southern Africa, covering approximately 44,000 km 2 . We test the performance of 15 different models comprised of combinations of optical and radar data from the dry and wet seasons. Our results show that a number of models perform well and very similarly. The highest overall accuracy is achieved by the model that incorporates both optical and synthetic-aperture radar (SAR) data from both dry and wet seasons with an overall accuracy of 91.1% (±1.7%): this is almost a 10% improvement from using only the dry season Landsat data (81.7 ± 2.3%). The SAR-only models were capable of mapping woody cover effectively, achieving similar or lower omission and commission errors than the optical models, but other classes were detected with lower accuracies. Our main conclusion is that the combination of metrics from different sensors and seasons improves results and should be the preferred methodological pathway for accurate savannah land cover mapping, especially now with the availability of Sentinel-1 and Sentinel-2 data. Our findings can provide much needed assistance to land cover monitoring efforts to savannahs in general, and in particular to southern African savannahs, where a number of land cover change processes have been related with the observed land degradation in the region.

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Section: Landsat or Palsar? Or Both?supporting

confidence: 92%

Section: Landsat or Palsar? Or Both?mentioning

confidence: 99%

Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

et al. 2018

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“…Because of the data availability and technically less complex analysis compared to InSAR, PolInSAR and TomoSAR, most studies on the estimation of forest structure parameters are based on SAR backscatter analysis (e.g., for AGB estimation [26]). For instance, L-band backscatter was successfully applied to map fractional woody cover [27][28][29][30][31] as well as regional or global forests [32][33][34][35]. Moreover, L-band backscatter was used to predict vegetation height in various biomes from boreal [36] and temperate [4,37,38] to tropical forests [39].…”

Section: Introductionmentioning

confidence: 99%

Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico

et al. 2018

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Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic model's predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the model's predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter.

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“…The inclusion of Synthetic Aperture Radar (SAR) data as additional explanatory variables which are sensitive to woody vegetation structure (cover height and biomass) [75,76] may improve the discrimination between the natural vegetation classes "Grasslands", "Medium Bush", "Dense Thicket and Bush" and "Forest (indigenous)" which made a significant contribution to overall error. Optical and SAR data are increasingly being combined as complementary features in land cover change detection and mapping systems with enhanced results [38,77].…”

Section: Discussionmentioning

confidence: 99%

Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

et al. 2016

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Abstract:The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million) in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%-67% using 22 detailed classes and 72%-74% using 12 aggregated national classes. "Water", "Plantations", "Plantations-clearfelled", "Orchards-trees", "Sugarcane", "Built-up/dense settlement", "Cultivation-Irrigated" and "Forest (indigenous)" had user's accuracies above 70%. Other detailed classes (e.g., "Low density settlements", "Mines and Quarries", and "Cultivation, subsistence, drylands") which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land cover mapping.

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L-band Synthetic Aperture Radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs

Cited by 38 publications

References 45 publications

Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico

Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

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