Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

Chatziantoniou, Andromachi; Psomiadis, Emmanouil; Petropoulos, George P.

doi:10.3390/rs9121259

Cited by 148 publications

(107 citation statements)

References 66 publications

(61 reference statements)

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“…Given the promising results achieved by [26,74] with the RADARSAT-2 C-band when mapping savannah woody vegetation structure and by [75] who combined the Sentinel-1 C-band and Sentinel-2 data (with improved spatial, spectral and temporal resolutions compared to the Landsat data used in this study) in order to map land cover in a Mediterranean environment (overall accuracy =~94%, k = 0.928), it is anticipated that the synergy between the two Sentinels should provide considerable support in the efforts to accurately map savannah land cover.…”

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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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?mentioning

confidence: 99%

Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

et al. 2018

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“…Furthermore, for crop types with similar phenological cycles, only using spectral information is still challenging for reliable discrimination of crop types. As synthetic aperture radar (SAR) can reflect the structure of vegetation, and optical imagery captures the multi-spectral information of crops, it has been indicated that the synergetic use of SAR and optical data can be complementary to each other [8,9].Space-borne SAR, due to its all-day, all-weather capability, wide coverage, and strong penetrating ability, has been increasingly used in crop classification, to complement with the use of optical imagery. It was found that considerable improvement can be achieved by increasing polarization channels [10,11].…”

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confidence: 99%

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Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

et al. 2020

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Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

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“…The method was selected as it has resulted in high accuracies in monitoring wetland dynamic. 50,51 The radial basis function that fits data in a higher dimensional space to increase class separability was implemented in EnMap toolbox. 52 All optical images were classified individually using the training data after which performance metrics were assessed by computing the overall and Cohen's-Kappa coefficient.…”

Section: Classificationmentioning

confidence: 99%

Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa

Kirimi

Thiong’o

Gabiri

et al. 2018

J. Appl. Rem. Sens.

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, "Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa," J. Appl. Remote Sens. 12(2), 026027 (2018), doi: 10.1117/1.JRS.12.026027. Abstract. Increasing demand for food in East Africa has created a shift to utilize wetlands for food production. Lack of spatial information hampers sustainable use of the Kilombero Valley floodplain. We take advantage of multispectral data from RapidEye, Landsat-8, and Sentinel-2 to derive high temporal resolution maps along three hydrological zones of the Kilombero Valley, assess seasonal land cover dynamics, and relate these dynamics to groundwater measurements. The depth of groundwater increases from June and declines from December, consistent with the end and the beginning of the rainy season, respectively. Bare land cover over the study area was 45% to 57% and increases to 62% to 69% as the season shifts from rainy to dry seasons while vegetation coverage, which was 34% to 47%, decreased to 25% to 27%. During the dry season, 68% to 81% of the total vegetation is within the riparian zone indicating the hydrological conditions favor plant growth. Vegetation growth in the fringe and middle zones mainly relies on precipitation whereas that in the riparian zone relies on saturation from the river. Our findings exemplify the relationship between seasonal land cover change and hydrological conditions and contribute to improved understanding of the spatial-temporal land cover dynamic in the Kilombero floodplain, required for planning sustained use of the wetland. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

Cited by 148 publications

References 66 publications

Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

Optimisation of Savannah Land Cover Characterisation with Optical and SAR Data

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa

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