Introduction to Microwave Remote Sensing

Woodhouse, Iain

doi:10.1201/9781315272573

Cited by 208 publications

(195 citation statements)

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“…First, we computed reference AGB from the field inventory, and then used it to predict AGB based on three TLS-derived canopy height metrics-canopy height, canopy cover, and their product as used by [14]; secondly, we derived AGB from L-band SAR backscatter intensities with TLS-derived AGB in the first stage as reference data. The final stage involved the use of the multi-temporal and dual polarization L-band SAR data, which was acquired during the dry season and processed after methodologies adopted from [31,39,41] SAR data, which was acquired during the dry season and processed after methodologies adopted from [31,39,41] to estimate the change in biomass over a four-year period between 2007 to 2010. …”

Section: Summary On Methodologymentioning

confidence: 99%

“…The wavelength (λ), relative to the size of the scatterer affects interactions between the two. Longer wavelengths are more relevant in the detection of vegetation geometry due to their penetrative effects, resulting in volume scattering [30,31]. The wavelength interaction with canopy elements results in diffuse scattering, hence more energy returning to the sensor [32].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo

Nickless

Berger

et al. 2016

Forests

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Abstract:The use of optical remote sensing data for savanna vegetation structure mapping is hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud cover and unpredictable weather conditions. Longwave microwave data, with its low sensitivity to clouds addresses some of these problems, but many space borne studies are still limited by low quality structural reference data. Terrestrial laser scanning (TLS) derived canopy cover and height metrics can improve aboveground biomass (AGB) prediction at both plot and landscape level. To date, few studies have explored the strength of TLS for vegetation structural mapping, and particularly few focusing on savannas. In this study, we evaluate the potential of high resolution TLS-derived canopy cover and height metrics to estimate plot-level aboveground biomass, and to extrapolate to a landscape-wide biomass estimation using multi-temporal L-band Synthetic Aperture Radar (SAR) within a 9 km 2 area savanna in Kruger National Park (KNP). We inventoried 42 field plots in the wet season and computed AGB for each plot using site-specific allometry. Canopy cover, canopy height, and their product were regressed with plot-level AGB over the TLS-footprint, while SAR backscatter was used to model dry season biomass for the years 2007, 2008, 2009, and 2010 for the study area. The results from model validation showed a significant linear relationship between TLS-derived predictors with field biomass, p < 0.05 and adjusted R 2 ranging between 0.56 for SAR to 0.93 for the TLS-derived canopy cover and height. Log-transformed AGB yielded lower errors with TLS metrics compared with non-transformed AGB. An assessment of the backscatter based on root mean square error (RMSE) showed better AGB prediction with cross-polarized (RMSE = 6.6 t/ha) as opposed to co-polarized data (RMSE = 6.7 t/ha), attributed to volume scattering of woody vegetation along river valleys and streams. The AGB change analysis showed 32 ha (3.5%) of the 900 ha experienced AGB loses above an average of 5 t/ha per annum, which can mainly be attributed to the falling of trees by mega herbivores such as elephants. The study concludes that SAR data, especially L-band SAR, can be used in the detection of small changes in savanna vegetation over time.

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Section: Summary On Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo

Nickless

Berger

et al. 2016

Forests

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“…and are responsible for the high scattering of microwave signal back to the SAR sensor (Woodhouse, 2006). The backscatter response of superimposed ice is dependent on air bubble content and size, where high frequency of bubbles typically causes higher backscatter values.…”

Section: Identifying Glacier Faciesmentioning

confidence: 99%

“…In addition to the fact that we use repeat paths for the timeseries of images, the ASTER GDEM is shown to be more than sufficient for terrain correction. Generally, geometric distortions due to layover, foreshortening and shadowing effects will degrade the SAR images in certain regions, especially in slopes facing towards the satellite sensor (Woodhouse, 2006). 10 We assessed combinations of optical and SAR data to analyze image time-series using 1) chronological gap-fill; i.e using SAR data to supplement optical time-series that suffer from heavy cloud cover or missing data due to the dark season.…”

Section: Processing Of Sar Imagerymentioning

confidence: 99%

Using SAR satellite data time-series for regional glacier mapping

Winsvold¹,

Kääb²,

Nuth³

et al. 2017

Preprint

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With dense SAR satellite data time-series it is possible to map surface and subsurface glacier properties that vary in time. On 10 Sentinel-1A and Radarsat-2 backscatter images over mainland Norway and Svalbard, we have used descriptive methods for outlining the possibilities of using SAR time-series for mapping glaciers. We present five application scenarios, where the first shows potential for tracking transient snow lines with SAR backscatter time-series, and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time-series representation of glacier facies corresponding to SAR glacier zones shows 15 potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time-series together with a coupled energy balance and multilayer firn model. We find strong correlation between backscatter signals with both the modeled firn air-content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR timeseries, revealing important information about the area extent of internal accumulation. Finally, in the last application 20 scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time-series data. Our results reveal the potential of satellite imagery for automatically derived products as important input in modeling assessments and glacier change analysis. 25

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“…Roughness and permittivity are the governing factors in the case of bare soil (Oh et al, 1992). The dielectric constant highly depends on moisture content, leading to higher backscatter values in the microwave range under wet soil conditions (Woodhouse, 2006). Regions with soil conditions close to saturation near the surface can be therefore identified using SAR data.…”

Section: Introductionmentioning

confidence: 99%

Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra?

et al. 2016

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Abstract.A new approach for the estimation of soil organic carbon (SOC) pools north of the tree line has been developed based on synthetic aperture radar (SAR; ENVISAT Advanced SAR Global Monitoring mode) data. SOC values are directly determined from backscatter values instead of upscaling using land cover or soil classes. The multi-mode capability of SAR allows application across scales. It can be shown that measurements in C band under frozen conditions represent vegetation and surface structure properties which relate to soil properties, specifically SOC. It is estimated that at least 29 Pg C is stored in the upper 30 cm of soils north of the tree line. This is approximately 25 % less than stocks derived from the soil-map-based Northern Circumpolar Soil Carbon Database (NCSCD). The total stored carbon is underestimated since the established empirical relationship is not valid for peatlands or strongly cryoturbated soils. The approach does, however, provide the first spatially consistent account of soil organic carbon across the Arctic. Furthermore, it could be shown that values obtained from 1 km resolution SAR correspond to accounts based on a high spatial resolution (2 m) land cover map over a study area of about 7 × 7 km in NE Siberia. The approach can be also potentially transferred to medium-resolution C-band SAR data such as ENVISAT ASAR Wide Swath with ∼ 120 m resolution but it is in general limited to regions without woody vegetation. Global Monitoring-mode-derived SOC increases with unfrozen period length. This indicates the importance of this parameter for modelling of the spatial distribution of soil organic carbon storage.

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Introduction to Microwave Remote Sensing

Cited by 208 publications

References 0 publications

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Using SAR satellite data time-series for regional glacier mapping

Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra?

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