Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data

Naidoo, Laven; Mathieu, Renaud; Main, Russell; Kleynhans, W.; Wessels, Konrad J; Asner, Gregory P.; Leblon, Brigitte

doi:10.1016/j.isprsjprs.2015.04.007

Cited by 69 publications

(70 citation statements)

References 53 publications

(3 reference statements)

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“…Hence, acquisitions of each sensor are utilized after just three iterations, underlining the potential of including multi-frequency SAR images for land cover mapping. This is in accordance with previous findings, e.g., by Naidoo et al [47], who also document slight advantages of multi-frequency integration, yet remark on limited gains over L-band data alone for the purpose of biomass mapping.…”

Section: Discussionsupporting

confidence: 93%

“…The results clearly underline the relevance of the AL2 scenes, which yield the highest accuracies when compared to the TSX and RS2 data. This reflects the general consensus in literature, which acknowledges systems of longer wavelengths to be favorable concerning vegetation mapping [25,47,54]. AL2 is in particular the most adequate sensor for mono-frequent, mono-temporal classifications (Table 2) and, thus, seems sufficient when data availability is limited.…”

Section: Discussionsupporting

confidence: 73%

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Evaluation of Multi-Frequency SAR Images for Tropical Land Cover Mapping

Hagensieker

Waske

2018

Remote Sensing

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Earth Observation (EO) data plays a major role in supporting surveying compliance of several multilateral environmental treaties, such as UN-REDD+ (United Nations Reducing Emissions from Deforestation and Degradation). In this context, land cover maps of remote sensing data are the most commonly used EO products and development of adequate classification strategies is an ongoing research topic. However, the availability of meaningful multispectral data sets can be limited due to cloud cover, particularly in the tropics. In such regions, the use of SAR systems (Synthetic Aperture Radar), which are nearly independent form weather conditions, is particularly promising.With an ever-growing number of SAR satellites, as well as the increasing accessibility of SAR data, potentials for multi-frequency remote sensing are becoming numerous. In our study, we evaluate the synergistic contribution of multitemporal L-, C-, and X-band data to tropical land cover mapping. We compare classification outcomes of ALOS-2, RADARSAT-2, and TerraSAR-X datasets for a study site in the Brazilian Amazon using a wrapper approach. After preprocessing and calculation of GLCM texture (Grey Level Co-Occurence), the wrapper utilizes Random Forest classifications to estimate scene importance. Comparing the contribution of different wavelengths, ALOS-2 data perform best in terms of overall classification accuracy, while the classification of TerraSAR-X data yields higher accuracies when compared to the results achieved by RADARSAT-2. Moreover, the wrapper underlines potentials of multi-frequency classification as integration of multi-frequency images is always preferred over multi-temporal, mono-frequent composites. We conclude that, despite distinct advantages of certain sensors, for land cover classification, multi-sensoral integration is beneficial.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 73%

Evaluation of Multi-Frequency SAR Images for Tropical Land Cover Mapping

Hagensieker

Waske

2018

Remote Sensing

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“…Despite r t were computed over vegetated plots, some negative strong and very strong significant r t were observed between r s and m v for pairs of σ • at L and X frequencies. These results partially confirm the suitability of dual frequency approach to quantify m v and biomass (F v particularly), as already reported by other authors [16,58]; further discussion about the improvements achievable through the multi-channel approach and the multi-frequency correlation will be provided in the next section.…”

Section: Temporal Single Regression Analysissupporting

confidence: 88%

Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization

et al. 2017

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Abstract:The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configuration to be used. With the launch of the ALOS Palsar, Cosmo-Skymed and Sentinel 1 sensors, a dataset of multi-frequency (X, C, L) and multi-polarization (co-and cross-polarizations) images are now available from a virtual constellation; thus, significant issues concerning the retrieval of soil-vegetation variables using SAR are: (i) identifying the more suitable SAR configuration; (ii) understanding the affordability of a multi-frequency approach. In 2006, a vast dataset of both remotely sensed images (SAR and optical/thermal) and in situ data was collected in the framework of the AgriSAR 2006 project funded by ESA and DLR. Flights and sampling have taken place weekly from April to August. In situ data included soil water content, soil roughness, fractional coverage and Leaf Area Index (LAI). SAR airborne data consisted of multi-frequency and multi-polarized SAR images (X, C and L frequencies and HH, HV, VH and VV polarizations). By exploiting this very wide dataset, this paper, explores the capabilities of SAR in describing four of the main soil-vegetation variables (SVV). As a first attempt, backscattering and SVV temporal behaviors are compared (dynamic analysis) and single-channel regressions between backscattering and SVV are analyzed. Remarkably, no significant correlations were found between backscattering and soil roughness (over both bare and vegetated plots), whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering (depending on canopy structure and SAR configuration). Most significant regressions were found between backscattering and SVV characterizing the vegetation biomass (fractional cover and LAI). Secondly, the effect of SVV changes on the spatial correlation among SAR channels (accounting for different polarization and/or frequencies) was explored. An inter-channel spatial/temporal correlation analysis is proposed by temporally correlating two-channel spatial correlation and SVV. This novel approach allowed a widening in the number of significant correlations and their strengths by also encompassing the use of SAR data acquired at two different frequencies.

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“…X-band was considered the least effective for predicting CC in savannahs 15 while C-band did yield positive results 15,17 though not as effective as L-band data.…”

mentioning

confidence: 89%