Tropical forest biomass recovery using GeoSAR observations

Williams, Mark L.; Milne, Tony; Tapley, Ian; Jreis, J; Sanford, Mark; Kofman, B; Hensley, Scott

doi:10.1109/igarss.2009.5417346

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Postings from the X-band were 3 m; those from the P-band were 5 m. GeoSAR X-band interferometry yields a digital surface model (DSM) and P-band interferometry is used to create a digital elevation model (DEM). Previous research has used the difference between the DSM and DEM to create a canopy height model used to estimate forest biomass [20]. The provider (Fugro EarthData, Inc.) performed the preprocessing, including both the interferometry and generation of two orthorectified magnitude images: (1) the magnitude from bands X and P, expressed as the square root of the intensity values and (2) the sigma-0 (σ 0 ) or backscatter coefficient from all four looks (North, South, East, West), defined as the backscatter power per unit area on the ground.…”

Section: Geosar Datamentioning

confidence: 99%

“…The metrics derived from the ground returns class (Gr) were: frequency (count) of returns and frequency (count) of pulses ( Table 1). The metrics derived from the all returns class (All) were: frequency (count), mean height, standard deviation, coefficient of variation, minimum, maximum, percentiles (10,20,25,40,50,75, and 90; as the height or value at which a percentage-10, 20, etc.-of the total number of returns are below that value), and frequency (count) of pulses [34,37]. The metrics derived from the vegetation returns class (Veg) were the same described for the all returns class with the addition of the mode.…”

Section: Lidar Datamentioning

confidence: 99%

“…Dual-band interferometric synthetic aperture radar (DBInSAR) can now be collected using the geographic synthetic aperture radar (GeoSAR) airborne radar mapping system. GeoSAR acquires X-band (VV, 9.7 GHz) and P-band (HH, 0.35 GHz) simultaneously over 11 km swaths [20]. GeoSAR has emerged as a potential instrument to be used to estimate forest attributes, such as canopy height [21] and biomass [20,22].…”

Section: Introductionmentioning

confidence: 99%

“…GeoSAR acquires X-band (VV, 9.7 GHz) and P-band (HH, 0.35 GHz) simultaneously over 11 km swaths [20]. GeoSAR has emerged as a potential instrument to be used to estimate forest attributes, such as canopy height [21] and biomass [20,22]. Long wavelengths from the P-band (0.85 m) penetrate the upper canopy and can reach the ground; short wavelengths (0.03 m) from the X-band are scattered at the top of the canopy.…”

Section: Introductionmentioning

confidence: 99%

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Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

et al. 2012

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Abstract:The objective of this study was to determine whether leaf area index (LAI) in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone, or both in combination. In situ measurements of LAI were made using the LiCor LAI-2000 Plant Canopy Analyzer on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood; stand age ranging from 12-164 years; mean height ranging from 0.4 to 41.2 m) in the Appomattox-Buckingham State Forest, Virginia, USA. Lidar distributional metrics were calculated for all returns and for ten one meter deep crown density slices (a new metric), five above and five below the mode of the vegetation returns for each plot. GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X-and P-band interferometric heights and magnitudes for each plot. Lidar metrics alone explained 69% of the variability in LAI, while GeoSAR metrics alone explained 52%. However, combining the lidar and GeoSAR metrics increased the R 2 to 0.77 with a CV-RMSE of 0.42.This study indicates the clear potential for X-band backscatter and interferometric height (both now available from spaceborne sensors), when combined with small-footprint lidar data, to improve LAI estimation in temperate mixed forests.

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Section: Geosar Datamentioning

confidence: 99%

Section: Lidar Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

et al. 2012

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“…In this work we explore to which extent the use of a dual band SAR system may be able to overcome some of the problems mentioned above. It is well known that dual-band SAR systems are well suited for vegetation analysis [5], especially when interferometry is considered [6]. By combining backscatter from the top and the bottom of the tree canopy a better characterization of the tree volume and green mass may be achieved.…”

Section: Introductionmentioning

confidence: 99%

Road extraction in urban and rural environments exploiting a dual-band SAR system

Lisini

Gamba

Luebeck

2011

2011 IEEE International Geoscience and Remote Sensing Symposium

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In this paper we present different fusion methodologies able to exploit road extraction from an airborne dual-band SAR acquisition. All the different approaches discussed in this paper profit from the complementary backscattering characteristics of X-band and P-band radars. As a result of our research, it is shown, by comparing road network extraction using different processing chains, that fusion at the information level is the best way to combine the extraction from multi-frequency SAR data. The level of information fusion does however play a role. Specifically, the fusion of road candidates before network regularization provides the most accurate road network extraction in both the considered test areas. Additionally, the research presented in this paper highlights the differences between urban and rural environments, stressing the greater importance of dual-based acquisition outside human settlements.Index Terms-SAR data, hierarchical binary decision trees, ensemble decision fusion, class-adaptive mapping.

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Measurement of Forest Above-Ground Biomass Using Active and Passive Remote Sensing at Large (Subnational to Global) Scales

2015

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Within the global forest area, a diverse range of forest types exist with each supporting varying amounts of biomass and allocations to different plant components. At country to continental scales, remote sensing techniques have been progressively developed to quantify the above-ground biomass (AGB) of these forests, with these based on optical, radar, and/or light detection and ranging (LiDAR) (airborne and spaceborne) data. However, none have been found to be globally applicable at high (≤30 m) resolution, largely because of different forest structures (e.g., heights, covers, allocations of AGB) and varying environmental conditions (e.g., frozen, inundated). For this reason, techniques have varied between the major forest biomes. However, when combined, these estimates provide some insight into the distribution of AGB at country to global levels with associated levels of uncertainty. Comparisons of data and derived products have, in some cases, also contributed to our understanding of changes in carbon stocks across large areas. Further improvements in estimates are anticipated with the launch of new spaceborne LiDAR and SAR that have been specifically designed for better retrieval of forest structure and AGB.

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Tropical forest biomass recovery using GeoSAR observations

Cited by 8 publications

References 7 publications

Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

Road extraction in urban and rural environments exploiting a dual-band SAR system

Measurement of Forest Above-Ground Biomass Using Active and Passive Remote Sensing at Large (Subnational to Global) Scales

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