Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia

To evaluate the use of multi-frequency, polarimetric Synthetic Aperture Radar (SAR) data for quantifying the above ground biomass (AGB) of open forests and woodlands, NASA JPL AIRSAR (POLSAR) data were acquired over a 37 × 60 km area west of Injune, central Queensland, Australia. From field measurements recorded within 32 50 × 50 m plots, AGB was estimated by applying species-specific allometric equations to stand measurements. AGB was then scaled-up to the larger area using relationships established with Light Detection and Ranging (LiDAR) data acquired over 150 (10 columns, 15 rows) 500 × 150 m cells (or Primary Sampling Units, PSUs) spaced 4 × 4 km apart in the north- and east-west directions. Large-scale (1 : 4000) stereo aerial photographs were also acquired for each PSU to assess species composition. Based on the LiDAR extrapolations, the median AGB for the PSU grid was 82 Mg ha^− 1 (maximum 164 Mg ha^− 1), with the higher levels associated with forests containing a high proportion of Angophora and Callitris species. Empirical relationships between AGB and SAR backscatter confirmed that C-, L- and P-band saturated at different levels and revealed a greater strength in the relationship at higher incidence angles and a larger dynamic range and consistency of relationships at HV polarizations. A higher level of saturation (above ∼50 Mg ha^− 1) was observed at C-band HV compared to that reported for closed forests which was attributable to a link between foliage projected cover (FPC) and AGB. The study concludes that L-band HV backscatter data acquired at incidence angles approaching or exceeding 45° are best suited for estimating the AGB up to the saturation level of ∼80-85 Mg ha^− 1. For regional mapping of biomass below the level of saturation, the use of the Japanese Space Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phase Arrayed L-band SAR (PALSAR) is advocated.     

Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest

At present, the greatest source of uncertainty in the global carbon cycle is in the terrestrial ecosystems. In order to reduce these uncertainties it is necessary to provide consistent and accurate global estimates of the world forest biomass. One of the most promising methods for obtaining such estimates is through polarimetric SAR backscatter measurements at low frequencies. In this paper, the relation between polarimetric SAR backscatter at L- and P-bands and forest biomass is investigated using data acquired within the BioSAR-I campaign in southern Sweden during 2007. Methods for estimating biomass on stand level using these data are developed and evaluated, and the results for the two frequency bands are compared. For L-band data, the best results were obtained using HV-polarized backscatter only, giving estimation errors in terms of root mean square errors (RMSE) between 31% and 46% of the mean biomass for stands with biomass ranging from 10 to 290 t/ha, and an (adjusted) coefficient of determination (R²) between 0.4 and 0.6. For P-band data, the results are better than for L-band. Models using HV- or HH-polarized P-band backscatter give similar results, as does a model including both HV and HH. The RMSEs were between 18 and 27%, and the R² values were between 0.7 and 0.8.     

Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha^-1) and σ° (L-HH) and σ° (L-HV) for the data collected on each of the three dates, with the highest correlation found using the L-HV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band σ° and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur.     

Decomposition of polarimetric synthetic aperture radar backscatter from upland and flooded forests

The goal of this research was to decompose polarimetric Synthetic Aperture Radar (SAR) imagery of upland and flooded forests into three backscatter types: single reflection, double reflection, and cross-polarized backscatter. We used a decomposition method that exploits the covariance matrix of backscatter terms. First we applied this method to SAR imagery of dihedral and trihedral corner reflectors positioned on a smooth, dry lake bed, and verified that it accurately isolated the different backscatter types. We then applied the method to decompose multi-frequency Jet Propulsion Laboratory (JPL) airborne SAR (AIRSAR) backscatter from upland and flooded forests to explain scattering components in SAR imagery from forested surfaces. For upland ponderosa pine forest in California, as SAR wavelength increased from C-band to P-band, scattering with an odd number of reflections decreased and scattering with an even number of reflections increased. There was no obvious trend with wavelength for cross-polarized scattering. For a bald cypress-tupelo floodplain forest in Georgia, scattering with an odd number of reflections dominated at C-band. Scattering power with an even number of reflections from the flooded forest was strong at L-band and strongest at P-band. Cross-polarized scattering may not be a major component of total backscatter at all three wavelengths. Various forest structural classes and land cover types were readily distinguishable in the imagery derived by the decomposition method. More importantly, the decomposition method provided a means of unraveling complex interactions between radar signals and vegetated surfaces in terms of scattering mechanisms from targets. The decomposed scattering components were additions to the traditional HH and V V backscatter. One cautionary note: the method was not well suited to targets with low backscatter and a low signal-to-noise ratio.     

Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass

Understanding the spatial variability of tropical forest structure and its impact on the radar estimation of aboveground biomass (AGB) is important to assess the scale and accuracy of mapping AGB with future low frequency radar missions. We used forest inventory plots in old growth, secondary succession, and forest plantations at the La Selva Biological Station in Costa Rica to examine the spatial variability of AGB and its impact on the L-band and P-band polarimetric radar estimation of AGB at multiple spatial scales. Field estimation of AGB was determined from tree size measurements and an allometric equation developed for tropical wet forests. The field data showed very high spatial variability of forest structure with no spatial dependence at a scale above 11 m in old-growth forest. Plot sizes of greater than 0.25 ha reduced the coefficients of variation in AGB to below 20% and yielded a stationary and normal distribution of AGB over the landscape. Radar backscatter measurements at all polarization channels were strongly positively correlated with AGB at three scales of 0.25 ha, 0.5 ha, and 1.0 ha. Among these measurements, PHV and LHV showed strong sensitivity to AGB < 300 Mg ha^− 1 and AGB < 150 Mg ha^− 1 respectively at the 1.0 ha scale. The sensitivity varied across forest types because of differences in the effects of forest canopy and gap structure on radar attenuation and scattering. Spatial variability of structure and speckle noise in radar measurements contributed equally to degrading the sensitivity of the radar measurements to AGB at spatial scales less than 1.0 ha. By using algorithms based on polarized radar backscatter, we estimated AGB with RMSE = 22.6 Mg ha^− 1 for AGB < 300 Mg ha^− 1 at P-band and RMSE = 23.8 Mg ha^− 1 for AGB < 150 Mg ha^− 1 at L-band and with the accuracy optimized at 1-ha scale within 95% confidence interval. By adding the forest height, estimated from the C-band Interferometry data as an independent variable to the algorithm, the AGB estimation improved beyond the backscatter sensitivity by 20% at P-band and 40% at L-band. The results suggested the estimation of AGB can be improved substantially from the fusion of lidar or InSAR derived forest height with the polarimetric backscatter.     

Laboratory and field measurements of the modification of radar backscatter by sand

Over the last two decades, the use of synthetic aperture radar (SAR) to address geologic problems has expanded as new applications for radar have been developed. One of the earliest and perhaps most surprising results from orbital SAR images of the Sahara was that, under certain conditions, radar signals penetrated up to several meters of sand to reveal subsurface features such as ancient river channels. Subsequent studies of radar penetration of arid sand deposits have dealt with factors that govern the ability of radar to penetrate a sand cover. This paper presents results from a laboratory experiment in which radar backscatter from a surface of rocks was measured under controlled conditions as a function of frequency, polarization, incidence angle, and sand cover thickness. The sand used in the experiment had a moisture content of 0.28 vol.% and caused calculated average attenuations of 4.2±1 dB/m for C-band and ∼11±2 dB/m for X-band. Results from the experiment were compared to field measurements of sand thickness during acquisition of airborne radar images. In AIRSAR images, the extent of dry sand in a dune field appears best in C-band because longer wavelength L- and P-band signals penetrate thinner sand deposits. Images of wet sand (4.9 vol.%) suggest that L-band was able to penetrate thin sand even though that sand was wet. Together, these laboratory and field measurements contribute towards a better understanding of how a sand cover modifies the radar backscatter of a surface.     

Relationships between polarimetric SAR backscatter and forest canopy and sub-canopy biophysical properties

This paper highlights the potential of multiwaveband polarimetric SAR data for the estimation of both canopy (percentage canopy closure) and sub-canopy (stem biomass) biophysical variables of a Sitka spruce forest in upland Wales. Stand stem biomass was estimated using forest survey data on diameter at breast height (DBH) and tree height from 0.01 ha plots. Photographs of the forest canopy were taken using a camera fitted with a wide-angle fisheye lens from a number of locations within a stand. The photographs were later digitized and estimates of stand percentage canopy closure were derived using image processing software. It was found that C-band HV and VV, and L-band HV and VV polarization backscatter were significantly related to stem biomass. There was no sensitivity to percentage canopy closure using single polarization backscatter but highly significant relationships were obtained using ratios of single polarization backscatter and variables derived from the polarization signatures. The strong correlations between C-band backscatter and stem biomass indicated a relationship between the structure of the top crown layer and sub-canopy biomass.     

Measuring biomass changes due to woody encroachment and deforestation/degradation in a forest-savanna boundary region of central Africa using multi-temporal L-band radar backscatter

Satellite L-band synthetic aperture radar backscatter data from 1996 and 2007 (from JERS-1 and ALOS PALSAR respectively), were used with field data collected in 2007 and a back-calibration method to produce biomass maps of a 15 000 km² forest-savanna ecotone region of central Cameroon. The relationship between the radar backscatter and aboveground biomass (AGB) was strong (r² = 0.86 for ALOS HV to biomass plots, r² = 0.95 relating ALOS-derived biomass for 40 suspected unchanged regions to JERS-1 HH). The root mean square error (RMSE) associated with AGB estimation varied from ~ 25% for AGB < 100 Mg ha^− 1 to ~ 40% for AGB > 100 Mg ha^− 1 for the ALOS HV data. Change detection showed a significant loss of AGB over high biomass forests, due to suspected deforestation and degradation, and significant biomass gains along the forest-savanna boundary, particularly in areas of low population density. Analysis of the errors involved showed that radar data can detect changes in broad AGB class in forest-savanna transition areas with an accuracy > 95%. However, quantitative assessment of changes in AGB in Mg ha^− 1 at a pixel level will require radar images from sensors with similar characteristics collecting data from the same season over multiple years.     

Effects of soil moisture and water depth on ERS SAR backscatter measurements from an Alaskan wetland complex

We conducted a preliminary investigation of the response of ERS C-band SAR backscatter to variations in soil moisture and surface inundation in wetlands of interior Alaska. Data were collected from 5 wetlands over a three-week period in 2007. Results showed a positive correlation between backscatter and soil moisture in sites dominated by herbaceous vegetation cover (r = 0.74, p < 0.04). ERS SAR backscatter was negatively correlated to water depth in all open (non-forested) wetlands when water table levels were more than 6 cm above the wetland surface (r = − 0.82, p < 0.001). There was no relationship between backscatter and soil moisture in the forested (black spruce-dominated) wetland site. Our preliminary results show that ERS SAR data can be used to monitor variations in hydrologic conditions in high northern latitude wetlands (including peatlands), particularly sites with sparse tree cover.     

Surface displacement of the Mw 7 Machaze earthquake (Mozambique): Complementary use of multiband InSAR and radar amplitude image correlation with elastic modelling

In this paper we investigate the surface displacement related to the 2006 Machaze earthquake using Synthetic Aperture Radar Interferometry (InSAR) and sub-pixel correlation (SPC) of radar amplitude images. We focus on surface displacement measurement during three stages of the seismic cycle. First, we examined the co-seismic stage, using an Advanced SAR (ASAR) sensor onboard the Envisat satellite. Then we investigated the post-seismic stage using the Phase Array L-band SAR sensor (PALSAR) onboard the ALOS satellite. Lastly, we focussed on the inter-seismic stage, prior to the earthquake by analysing the L-band JERS-1 SAR data. The high degree of signal decorrelation in the C-band co-seismic interferogram hinders a correct positioning of the surface rupture and correct phase unwrapping. The post-seismic L-band interferograms reveal a time-constant surface displacement, causing subsidence of the surface at a ∼ 5 cm/yr rate. This phenomenon continued to affect the close rupture field for at least two years following the earthquake and intrinsically reveals a candidate seismogenic fault trace that we use as a proxy for an inversion against an elastic dislocation model. Prior to the earthquake, the JERS interferograms do not indicate any traces of pre-seismic slip on the seismogenic fault. Therefore, slip after the earthquake is post-seismic, and it was triggered by the Machaze earthquake. This feature represents a prominent post-seismic slip event rarely observed in such a geodynamic context.     

Correlation Analysis between L-band SAR and ForestStem Volume in Northeast China

Because Synthetic Aperture Radar(SAR)can penetrate into forest canopy and interact with the primary stem volume contents of the trees (trunk and branch),SAR data are widely used for forest stem volume estimation.This paper investigated the correlation between SAR data and forest stem volume in Xunke,Heilongjiang using the stand-wise forest inventory data in 2003 and ALOS PALSAR data for five dates in 2007.The influences of season and polarizations on the relationship between stem volume and SAR data were studied by analyzing the scatterplots;that was followed by interpretation of the mechanisms primarily based on a forest radar backscattering model-water cloud model.The results showed that the relationship between HV polarization backscatter and stem volume is better than HH polarization,and SAR data in summer dry conditions are more correlated to stem volume than the data acquired in other conditions.The interferometric coherence with 46-day temporal baseline is negatively correlated to the stem volume.The correlation coefficients from winter coherence are higher than those from summer coherence and backscatter.The study results suggest using the interferometric coherence in winter as the best choice for forest stem volume estimation with L-band SAR data.     

Comparative flood area analysis of C-band VH,VV, and L-band HH polarizations SAR data

The role of synthetic aperture radar (SAR)-image-based flood area mapping is proved beyond the doubts. It is also well known that different wavelength, polarization SAR reacts in varying ways over the same land-use/land-cover region. In line to this, this article mainly brings out the significance of comparing and analysing different wavelength, polarization SAR data of the same inundated region against the land-use classes of the study area. The C-band ENVISAT advanced synthetic aperture radar data of vertically transmitted horizontally received (VH), vertically transmitted vertically received (VV) polarizations data, and L-band ALOS-1 PALSAR data of horizontally transmitted horizontally received (HH) polarization data has been obtained as both these satellites captured the same flood event of Andhra Pradesh state of India. Initially, the SAR images are classified with the help of digital elevation model of the disaster region which supports in mapping the fully submerged, partially submerged and non-flooded pixels of disaster region. The fully submerged regions includes the natural waterbodies, adjacent flood plain regions which are completely submerged, as well as not accessible, whereas the partially submerged regions are spatially discontinuous and scattered regions which are inundated due to recent disaster but accessible. In this study, much emphasis has been given in comparing and analysing the fully submerged, partially submerged, and non-flooded regions of classified SAR images against each land use of the disaster region by which the response of individual land-use units of the disaster region at different wavelength, polarization has been brought out. From this comparative assessment, it has been observed that the areal extent of fully submerged regions is considerably more in L-band HH image than in the C-band polarization images. It is also been noticed that C-band VH polarization image is able to map and quantify considerable part of the land-use classes as partially submerged regions than the L-band HH polarization image. In addition to this, the proposed technique is able to rectify in classifying mangrove regions as non-flooded regions due to the land-use/land-cover-based approach.     

Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery

This article presents for the first time the combination of dual-polarimetric C-band Sentinel-1 synthetic aperture radar (SAR) data and quad-polarimetric L-band ALOS-2/PALSAR-2 imagery for mapping of flooded areas with a special focus on flooded vegetation. L-band SAR data is well suited for mapping of flooded vegetation, while C-band enables an accurate extraction open water areas. Polarimetric decomposition-based unsupervised Wishart classification is combined with object-based post-classification refinement and the integration of spatial contextual information and global auxiliary data. In eight different scenarios, focusing on single datasets or fusion of classification results of several ones, respectively, different polarimetric decomposition and classification principles, including the entropy/anisotropy/alpha and the Freeman–Durden–Wishart classification, were investigated. The helix scattering component of the Yamaguchi decomposition, derived from ALOS-2 imagery, showed high suitability to refine the Sentinel-1-based detection of flooded vegetation. A test site at the Evros River (Greek/Turkish border region) was chosen, which was affected by a flooding event that occurred in spring 2015. The validation was based on high spatial resolution optical WorldView-2 imagery acquired with short temporal delay to the SAR data.     

The cross-scattering component of polarimetric SAR in urban areas and its application to model-based scattering decomposition

After the work of Freeman, Durden, Pottier, and Yamaguchi, many decomposition techniques have been proposed for urban areas, mainly to resolve the overestimation problem of volume scattering. Since it has been validated that the cross-polarized (HV) scattering is caused not only by forests but also by rotated dihedrals, in this paper, we propose a cross-scattering coherency matrix to model the HV component from orientated and complex buildings and then demonstrate its performance on model-based scattering decomposition. The building orientation angle is considered in this coherency matrix, making it flexible and adaptive in the decomposition. Therefore, the HV components from forests and orientated urban areas can be modelled. Two decomposition procedures are applied in this paper. The first one is to validate the effectiveness of this scattering model. We regard the HV component from urban areas as cross-scattering, which is an independent scattering component added to the Yamaguchi’s four-component decomposition. Another one is the urban area decomposition application using this scattering model. Decomposition is implemented for urban and natural areas, and the HV component from urban areas is regarded as their volume scattering. This procedure is similar to many other state-of-the-art methods for urban areas and needs to discriminate the urban and natural areas before decomposition. Spaceborne Radarsat-2 C-band, the airborne synthetic aperture radar (AIRSAR) L-band, and uninhabited aerial vehicle synthetic aperture radar (UAVSAR) L-band full polarimetric SAR data are used to validate the performance of this cross-scattering coherency matrix. The HV component of orientated buildings is generated, leading to a better decomposition result for urban areas.     

Assessment of an analytical model for sea surface wind speed retrieval from spaceborne SAR

An analytical model based on radar backscatter theory was utilized to retrieve sea surface wind speeds from C-band satellite synthetic aperture radar (SAR) data at either vertical (VV) or horizontal (HH) polarization in transmission and reception. The wind speeds were estimated from several ENVISAT Advanced SAR (ASAR) images in Hong Kong coastal waters and from Radarsat-1 SAR images along the west coast of North America. To evaluate the accuracy of the analytical model, the estimated wind speeds were compared to coincident buoy measurements, as well as winds retrieved by C-band empirical algorithms (CMOD4, CMOD_IRF2 and CMOD5). The comparison shows that the accuracy of the analytical model is comparable to that of the C-band empirical algorithms. The results indicate the capability of the analytical model for sea surface wind speed retrieval from SAR images at both VV and HH polarization.     

Exploring LiDAR-RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR

Scanning Light Detecting and Ranging (LiDAR), Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) were analyzed to determine (1) which of the three sensor systems most accurately predicted forest biomass, and (2) if LiDAR and SAR/InSAR data sets, jointly considered, produced more accurate, precise results relative to those same data sets considered separately. LiDAR ranging measurements, VHF-SAR cross-sectional returns, and X- and P-band cross-sectional returns and interferometric ranges were regressed with ground-estimated (from dbh) forest biomass in ponderosa pine forests in the southwestern United States. All models were cross-validated. Results indicated that the average canopy height measured by the scanning LiDAR produced the best predictive equation. The simple linear LiDAR equation explained 83% of the biomass variability (n = 52 plots) with a cross-validated root mean square error of 26.0 t/ha. Additional LiDAR metrics were not significant to the model. The GeoSAR P-band (λ = 86 cm) cross-sectional return and the GeoSAR/InSAR canopy height (X-P) captured 30% of the forest biomass variation with an average predictive error of 52.5 t/ha. A second RaDAR-FOPEN collected VHF (λ ∼ 7.8 m) and cross-polarized P-band (λ = 88 cm) cross-sectional returns, none of which proved useful for forest biomass estimation (cross-validated R² = 0.09, RMSE = 63.7 t/ha). Joint consideration of LiDAR and RaDAR measurements produced a statistically significant, albeit small improvement in biomass estimation precision. The cross-validated R² increased from 83% to 84% and the prediction error decreased from 26.0 t/ha to 24.9 t/ha when the GeoSAR X-P interferometric height is considered along with the average LiDAR canopy height. Inclusion of a third LiDAR metric, the 60th decile height, further increased the R² to 85% and decreased the RMSE to 24.1 t/ha. On this 11 km² ponderosa pine study area, LiDAR data proved most useful for predicting forest biomass. RaDAR ranging measurements did not improve the LiDAR estimates.     

Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100 t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r² = 0.53 with an RMSE of 79 t/ha. The model is valid up to 307 t/ha with an accuracy requirement of 50 t/ha and up to 614 t/ha with an accuracy requirement of 100 t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.     

Radar modelling of coniferous forest using a tree growth model

The use of a tree growth model to provide statistical information about the microwave scattering components of boreal-type forests (in this case, Scots pine and Norwegian spruce), as an alternative to data obtained through intensive fieldwork, is described. The total backscatter from six test stands at C- and L-band frequency for three polarization combinations (HH, VV and HV) was predicted. Differences between measured C- and L-band data from a polarimetric airborne Synthetic Aperture Radar (EMISAR) and simulated backscatter values compare favourably with previous studies, with like- and cross-polarization differences generally less than 2.5 dB. Modelled backscatter values were consistently less than those observed. A likely explanation for such a discrepancy is the unrealistic manner in which the model incorporates the spatial distribution of tree needles.     

Radar observations of basaltic lava flows,Craters of the Moon,Idaho

Abstract

Radar images were assessed to determine the backscatter characteristics of basaltic lava flows of predominantly pahoehoe textures and the ability to detect fissure vents. The images were obtained from synthetic aperture side-looking airborne radar systems—X-band HH, X-band HV, L-band HH and L-band H V. Smooth, collapsed blisters of shelly pahoehoe have weak returns in all four radar images. These returns are identical to those from pahoehoe surfaces covered with smooth mantles of windblown sediments. Hummocky pahoehoe flows have strong backscatter in all four images, most likely due to the large range in surface roughness causing multiple scattering at both radar wavelengths. Aa lava flows show the greatest variation in backscatter intensities—strong XHH, weak XHV, strong LHH and very strong LHV returns. This variation is due to an increase in multiple scattering at the L-band scale. Although smooth and rough surface textures can be differentiated in the radar images, there are constraints in tracing textural changes back to a particular fissure vent, in part because near-vent flows do not have unique radar signatures. Eruptive fissures are detectable in the radar images by virtue of associated parallel spatter ramparts which have diagnostic, strong backscatter in the X-band images that are in contrast to the weak backscatter of the surrounding shelly pahoehoe lava. However, spatter ramparts are not delineated in the L-band images. The centimetre-scale relief of the agglutinate spatter may cause scattering of the X-band energy more than the L-band energy. Although the structures are several metres high, the look directions for both imaging systems are approximately parallel to the trend of the ramparts. The rampart walls do not serve as reflectors. Such findings emphasize the importance of look direction in the use of radar images to characterize terrains.