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

Sandberg, Gustaf; Ulander, Lars M. H.; Fransson, Johan E. S.; Holmgren, Johan; Toan, Thuy Le

doi:10.1016/j.rse.2010.03.018

Cited by 170 publications

(160 citation statements)

References 20 publications

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“…Interferometric SAR datasets acquired at C-band with one-day temporal separation were evaluated to retrieve forest stem volume in [13] achieving RMSE of 27%. In [27] L-and P-band SAR backscatter was used for biomass estimation with RMSE between 31% and 46% for L-band and 18% and 27% for P-band. In [42] a biomass model for P-band with training data from Krycklan, a test site in northern Sweden, was used, and validation data from Remningstorp resulted in RMSE 22-33%.…”

Section: Discussionmentioning

confidence: 99%

“…A local value of BEF i.e., 0.512 was used, due to lack of a more general value. In the literature somewhat higher values can be seen, see e.g., [46], and when testing BEF = 0.58 (determined from proportions of tree species of the seven 80 m × 80 m stands and BEF-factors for pine, spruce and deciduous according to [27]) the RMSE values were slightly changed (varying from 16.4% to 31.1%) but the mean value of RMSE for IWCM was unchanged at 23.1%. Since BEF is used for training as well as validation stands, the effect of BEF is small.…”

Section: Discussionmentioning

confidence: 99%

“…[27] and [28], it was assumed that a single and approximate value was sufficient for the purpose of this investigation.…”

Section: Field Observationsmentioning

confidence: 99%

See 2 more Smart Citations

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

et al. 2013

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Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accuracy of biomass; in this respect, it is important to examine the potential offered by new sensors. To assess the contribution of the TanDEM-X mission, eighteen interferometric Synthetic Aperture Radar (SAR) image pairs acquired over the hemi-boreal test site of Remningstorp in Sweden were investigated. Three models were used for interpretation of TanDEM-X signatures and above-ground biomass retrieval: Interferometric Water Cloud Model (IWCM), Random Volume over Ground (RVoG) model, and a simple model based on penetration depth (PD). All use an allometric expression to relate above-ground biomass to forest height measured by TanDEM-X. The retrieval was assessed on 201 forest stands with a minimum size of 1 ha, and ranging from 6 to 267 Mg/ha (mean biomass of 105 Mg/ha) equally divided into a model training dataset and a validation test dataset. Biomass retrieved using the IWCM resulted in a Root Mean Square Error (RMSE) between 17% OPEN ACCESS Remote Sens. 2013, 5 5575 and 33%, depending on acquisition date and image acquisition geometry (angle of incidence, interferometric baseline, and orbit type). The RMSE in the case of the RVoG and the PD models were slightly higher. A multitemporal estimate of the above-ground biomass using all eighteen acquisitions resulted in an RMSE of 16% with R 2 = 0.93. These results prove the capability of TanDEM-X interferometric data to estimate forest aboveground biomass in the boreal zone.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

et al. 2013

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“…Nichol and Sarker (2011) recently presented a study where texture feature ratios extracted from AVNIR-2 and HRG data were successfully employed in modelling biomass with R 2 up to 0.939. LiDAR, SAR, and even Landsat data have been also employed in biomass estimation studies (Kronseder et al 2012;Banskota et al 2011;Vastaranta et al 2014;Langner et al 2012;Sandberg et al 2011). Stem volume and basal area have been estimated through a synergy of ALS with airborne colour infrared (CIR) and AVNIR-2 data (Hou et al 2011).…”

Section: Forestry Monitoringmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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“…A series of studies suggested that a widely applicable relationship exists between biomass and backscatter from L-band SAR for woody vegetation with lower levels of biomass (≤150 Mg·ha −1 ) in tropical [6][7][8], temperate and boreal biomes [9][10][11][12][13]. Both airborne and spaceborne systems were involved in these studies, including airborne instruments such as AIRborne SAR (AIRSAR) and the Uninhabited Aerial Vehicle SAR (UAVSAR) developed by the National Aeronautics and Space Administration (NASA), Experimental-SAR (ESAR) operated by the German Aerospace Center (DLR), as well as spaceborne instruments such as the first Earth-orbiting satellite SEASAT, Spaceborne Imaging Radar-C and X-B and SAR (SIR-C/XSAR), Japanese Earth Resources Satellite 1 (JERS-1), and Phased Array type L-band SAR (PALSAR) on board the Advanced Land Observing Satellite (ALOS) operated by the Japan Aerospace Exploration Agency (JAXA).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass

Huang

Sun

et al. 2015

Remote Sensing

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Accurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the effect of incidence angle on SAR backscatter could be reduced to less than 1 dB by the correction model for airborne SAR data; (2) over 50% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha

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

Cited by 170 publications

References 20 publications

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass

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