Radar estimates of aboveground biomass in boreal forests of interior Alaska

Rignot, Eric; Way, J.; Williams, C.; Viereck, Leslie A.

doi:10.1109/36.312903

Cited by 147 publications

(93 citation statements)

References 19 publications

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“…The best predictions were obtained using the HH channel and averaging coherence data at 5 ha scale (Table 3). In the case of backscatter, we observed a linear increase with stand age and no saturation as previously found with aboveground biomass in other boreal sites [41,42]. However, stand age showed a tighter relationship with InSAR coherence, as has been previously found for C-band studies [20].…”

Section: Discussionsupporting

confidence: 56%

Using InSAR Coherence to Map Stand Age in a Boreal Forest

2012

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Abstract:The interferometric coherence parameter γ estimates the degree of correlation between two Synthetic Aperture Radar (SAR) images and can be influenced by vegetation structure. Here, we investigate the use of repeat-pass interferometric coherence γ to map stand age, an important parameter for the study of carbon stocks and forest regeneration. In August 2009 NASA's L-band airborne sensor UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) acquired zero-baseline data over Quebec with temporal separation ranging between 45 min and 9 days. Our analysis focuses on a 66 km 2 managed boreal forest and addresses three questions: (i) Can coherence from L-band systems be used to model forest age? (ii) Are models sensitive to weather events and temporal baseline? and (iii) How is model accuracy impacted by the spatial scale of analysis? Linear regression models with 2-day baseline showed the best results and indicated an inverse relationship between γ and stand age. Model accuracy improved at 5 ha scale (R 2 = 0.75, RMSE = 5.3) as compared to 1 ha (R 2 = 0.67, RMSE = 5.8). Our results indicate that coherence measurements from L-band repeat-pass systems can estimate forest age accurately and with no saturation. However, empirical model relationships and their accuracy are sensitive to weather events, temporal baseline, and spatial scale of analysis.

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Section: Discussionsupporting

confidence: 56%

Using InSAR Coherence to Map Stand Age in a Boreal Forest

2012

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“…The adjusted 2 and RMSE of model P1 are 0.16 and 58.01 Mg/ha, respectively ( Table 8). The week correlation between SAR backscattering coefficients and forest AGB is also reported in previous studies [25,81,87]. In model P2, the adjusted 2 is increased to 0.25 and RMSE decrease to 47.0 Mg/ha (Table 8).…”

Section: Forest Agb Estimation Model Based On Corrected Datamentioning

confidence: 48%

“…Previous studies show that L-band backscattering tends to increase with increasing canopy cover, density and size of the tree [19,[24][25][26]. Both co-polarized (HH and VV) and cross-polarized (HV and VH) of L-bands are sensitive to forest biomass [19,27,28].…”

Section: Introductionmentioning

confidence: 99%

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Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Attarchi

Gloaguen

2014

Remote Sensing

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Abstract:The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R 2 = 0.59; RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R 2 = 0.76; RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest's physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions. OPEN ACCESSRemote Sens. 2014, 6 3694

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“…In addition to measuring one-time biomass densities, pol-SAR also provides the 589 capability of monitoring biomass changes resulting from clear-cutting, forest fires, insect 590 disturbance, wind damage, and to some extent more subtle changes in forest structure 591 (Saatchi et al 1997;Rignot et al 1994;Couturier et al 2001;Siegert et al 2001;Salas et 592 al. 2002;Ranson et al 2003).…”

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confidence: 99%

Characterizing 3D vegetation structure from space: Mission requirements

Hall¹,

Bergen²,

Blair³

et al. 2011

Remote Sensing of Environment

193

118

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18Human and natural forces are rapidly modifying the global distribution and 19 structure of terrestrial ecosystems on which all of life depends, altering the global carbon 20 cycle, affecting our climate now and for the foreseeable future, causing steep reductions 21 in species diversity, and endangering Earth's sustainability. 22To understand changes and trends in terrestrial ecosystems and their functioning 23 as carbon sources and sinks, and to characterize the impact of their changes on climate, 24 habitat and biodiversity, new space assets are urgently needed to produce high spatial 25 resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass 26 above ground, the carbon stored within and the implications for atmospheric green house 27 gas concentrations and climate. These needs were articulated in a 2007 National 28Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, 29DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a 30 multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives 31 of this paper are to articulate the importance of these new, multi-year, 3D vegetation 32 structure and biomass measurements, to briefly review the feasibility of radar and lidar 33 remote sensing technology to meet these requirements, to define the data products and 34 measurement requirements, and to consider implications of mission durations. The paper 35 addresses these objectives by synthesizing research results and other input from a broad 36 community of terrestrial ecology, carbon cycle, and remote sensing scientists and 37 working groups. We conclude that: 38(1) current global biomass and 3-D vegetation structure information is unsuitable 39 for both science and management and policy. The only existing global datasets of 40 biomass are approximations based on combining land cover type and representative 41 carbon values, instead of measurements of actual biomass. Current measurement attempts 42 based on radar and multispectral data have low explanatory power outside low biomass 43areas. There is no current capability for repeatable disturbance and regrowth estimates. 44(2) The science and policy needs for information on vegetation 3D structure can 45 be successfully addressed by a mission capable of producing (i) a first global inventory of 46 forest biomass with a spatial resolution 1km or finer and unprecedented accuracy (ii) 47 annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass 48 accumulation rates at resolutions of 1km or finer, and (iii) transects of vertical and 49 horizontal forest structure with 30 m along-transect measurements globally at 25 m 50 spatial resolution, essential for habitat characterization. 51 We also show from the literature that lidar profile samples together with wall-to-52 wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together 53 to satisfy these vegetation 3D structure and biomass measurement requirements. ...

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Radar estimates of aboveground biomass in boreal forests of interior Alaska

Cited by 147 publications

References 19 publications

Using InSAR Coherence to Map Stand Age in a Boreal Forest

Using InSAR Coherence to Map Stand Age in a Boreal Forest

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Characterizing 3D vegetation structure from space: Mission requirements

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