| 1. |
- Neumann, M., et al.
(författare)
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Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass
- 2012
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892 .- 1558-0644. ; 50:3, s. 714-726
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Tidskriftsartikel (refereegranskat)abstract
- Biomass estimation performance using polarimetric interferometric synthetic aperture radar (PolInSAR) data is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data are decomposed into ground and volume contributions, retrieving vertical forest structure and polarimetric layer characteristics. The sensitivity of biomass to the obtained parameters is analyzed, and a set of these parameters is used for biomass estimation, evaluating one parametric and two non-parametric methodologies: multiple linear regression, support vector machine, and random forest. The methodology is applied to airborne SAR data over the Krycklan Catchment, a boreal forest test site in northern Sweden. The average forest biomass is 94 tons/ha and goes up to 183 tons/ha at forest stand level (317 tons/ha at plot level). The results indicate that the intensity at HH-VV is more sensitive to biomass than any other polarization at L-band. At P-band, polarimetric scattering mechanism type indicators are the most correlated with biomass. The combination of polarimetric indicators and estimated structure information, which consists of forest height and ground-volume ratio, improved the root mean square error (rmse) of biomass estimation by 17%-25% at L-band and 5%-27% at P-band, depending on the used parameter set. Together with additional ground and volume polarimetric characteristics, the rmse was improved up to 27% at L-band and 43% at P-band. The cross-validated biomass rmse was reduced to 20 tons/ha in the best case. Non-parametric estimation methods did not improve the cross-validated rmse of biomass estimation, but could provide a more realistic distribution of biomass values.
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| 2. |
- Neumann, M., et al.
(författare)
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Parametric and non-parametric forest biomass estimation from PolInSAR data
- 2011
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Ingår i: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011. Vancouver, 24-29 July 2011. - 9781457710056 ; , s. 420-423, s. 420-423
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Konferensbidrag (refereegranskat)abstract
- Biomass estimation performance from model-based polarimetric SAR interferometry (PolInSAR) using generic parametric and non-parametric regression methods is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data is decomposed into ground and volume contributions, estimating vertical forest structure, and using a set of obtained parameters for biomass regression. The considered estimation methods include multiple linear regression, support vector machine and random forest. The biomass estimation performance is evaluated on DLR's airborne SAR data at L- and P-bands over Krycklan Catchment, a boreal forest test site in Northern Sweden. The combination of polarimetric indicators and estimated structure information has improved the root mean square error (RMSE) of biomass estimation up to 28% at L-band and up to 46% at P-band. The cross-validated biomass RMSE was reduced to 20 tons/ha.
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| 3. |
- Santoro, Maurizio, et al.
(författare)
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Forest growing stock volume of the northern hemisphere : Spatially explicit estimates for 2010 derived from Envisat ASAR
- 2015
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 168, s. 316-334
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degrees N) from hyper-temporal observations of Envisat Advanced Synthetic Aperture Radar (ASAR) backscattered intensity using the BIOMASAR algorithm. Approximately 70,000 ASAR images at a pixel size of 0.01 degrees were used to estimate GSV representative for the year 2010. The spatial distribution of the GSV across four ecological zones (polar, boreal, temperate, subtropical) was well captured by the ASAR-based estimates. The uncertainty of the retrieved GSV was smallest in boreal and temperate forest (<30% for approximately 80% of the forest area) and largest in subtropical forest. ASAR-derived GSV averages at the level of administrative units were mostly in agreement with inventory-derived estimates. Underestimation occurred in regions of very high GSV (>300 m(3)/ha) and fragmented forest landscapes. For the major forested countries within the study region, the relative RMSE between ASAR-derived GSV averages at provincial level and corresponding values from National Forest Inventory was between 12% and 45% (average: 29%).
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| 4. |
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| 5. |
- Nyström, Mattias, et al.
(författare)
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Detection of windthrown trees using airborne laser scanning
- 2014
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 30, s. 21-29
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Tidskriftsartikel (refereegranskat)abstract
- In this study, a method has been developed for the detection of windthrown trees under a forest canopy, using the difference between two elevation models created from the same high density (65 points/m(2)) airborne laser scanning data. The difference image showing objects near the ground was created by subtracting a standard digital elevation model (DEM) from a more detailed DEM created using an active surface algorithm. Template matching was used to automatically detect windthrown trees in the difference image. The 54 ha study area is located in hemi-boreal forest in southern Sweden (Lat. 58 degrees 29' N, Long. 13 degrees 38' E) and is dominated by Norway spruce (Picea abies) with 3.5% deciduous species (mostly birch) and 1.7% Scots pine (Pinus sylvestris). The result was evaluated using 651 field measured windthrown trees. At individual tree level, the detection rate was 38% with a commission error of 36%. Much higher detection rates were obtained for taller trees; 89% of the trees taller than 27 m were detected. For pine the individual tree detection rate was 82%, most likely due to the more easily visible stem and lack of branches. When aggregating the results to 40 m square grid cells, at least one tree was detected in 77% of the grid cells which according to the field measurements contained one or more windthrown trees. (C) 2014 Elsevier B.V. All rights reserved.
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| 6. |
- Sandberg, Gustaf, 1982, et al.
(författare)
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L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest
- 2011
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 115:11, s. 2874 - 2886
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Tidskriftsartikel (refereegranskat)abstract
- At present, the greatest source of uncertainty in the global carbon cycle is in the terrestrial ecosystems. In orderto reduce these uncertainties it is necessary to provide consistent and accurate global estimates of the worldforest biomass. One of the most promising methods for obtaining such estimates is through polarimetric SARbackscatter measurements at low frequencies. In this paper, the relation between polarimetric SAR backscatterat L- and P-bands and forest biomass is investigated using data acquired within the BioSAR-I campaign insouthern Sweden during 2007. Methods for estimating biomass on stand level using these data are developedand evaluated, and the results for the two frequency bands are compared. For L-band data, the best results wereobtained 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 thanfor L-band. Models using HV- or HH-polarized P-band backscatter give similar results, as does a modelincluding both HV and HH. The RMSEs were between 18 and 27%, and the R2 values were between 0.7 and 0.8.
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| 7. |
- Ulander, Lars, 1962, et al.
(författare)
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BIOSAR 2010 - A SAR campaign in support to the BIOMASS mission
- 2011
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Ingår i: Proceedings of IGARSS 2011 Symposium, Vancouver, Canada, 24-29 July, 2011. - 9781457710056 ; , s. 1528-1531, s. 1528-1531
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The ESA funded campaign BioSAR 2010 was carried out at the forestry test site Remningstorp in southern Sweden, in support to the BIOMASS satellite mission under study. Fully polarimetric SAR data were successfully acquired at L- and P-band using ONERA's multi-frequency system SETHI. In addition with other data types gathered, e.g. LiDAR and in-situ measurements, the compiled data set will be used for analyses and comparisons with biomass estimation results obtained at the same test site in the campaign BioSAR 2007, in which DLR's E-SAR made the SAR imaging. Detection of forest changes, robustness of biomass retrieval algorithms and long-term P-band coherence will be in focus as well as cross-validations between the two SAR sensors.
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| 8. |
- Askne, Jan, 1936, et al.
(författare)
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Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions
- 2013
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 5:11, s. 5574-5597
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Tidskriftsartikel (refereegranskat)abstract
- 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% 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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| 9. |
- Bohlin, Jonas, et al.
(författare)
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A comparison of forest inventories based on aerial image matching and Airborne Laser Scanning data
- 2014
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Forest inventories are now commonly done by Airborne Laser Scanning (ALS), especially because many countries are collecting ALS data nation-wide to produce high quality elevation data. With an accurate digital elevation model, 3D data from aerial image matching could be a more cost-effective alternative to repeated ALS acquisitions for providing updated data to forest management planning in the future. This study aims at comparing the quality of forest inventory data obtained by aerial image matching and ALS. In the study area, a mixed boreal forest situated in central Sweden, aerial images from the national acquisition program with a ground sampling distance of 0.5 m and ALS data with a point density of 1.5-2 pulses per m2 from the national ALS production were available. The aerial images were matched with three different algorithms to assess possible differences in forest information content. Two hundred field plots, located within the study area, were utilized for non-parametric prediction of forest variables using random forest. Accuracy assessment was made by leave-one-out cross-validation at plot level. The results show similar accuracy of ALS and image matching-based predictions, with ALS slightly superior. Accuracy, in terms of root mean square errors in percent of surveyed plot mean, of ALS were: 6.4% for tree height; 12.5% for tree diameter; 18.2% for basal area and 20.0% for stem volume, and of image matching: 9.5% for tree height; 15.3% for tree diameter; 21.8% for basal area and 24.8% for stem volume. Among the image matching algorithms used, SURE was found to estimate the forest variables with best accuracies. However, the other algorithms produced similar results. These results indicate that inventory data acquired by matching of aerial images have a large potential for operational use in forest management planning as a cost-effective alternative to new ALS campaigns.
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| 10. |
- Bohlin, Jonas, et al.
(författare)
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Deciduous forest mapping using change detection of multi-temporal canopy height models from aerial images acquired at leaf-on and leaf-off conditions
- 2016
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Ingår i: Scandinavian Journal of Forest Research. - : Informa UK Limited. - 0282-7581 .- 1651-1891. ; 31, s. 517-525
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Tidskriftsartikel (refereegranskat)abstract
- Discrimination of deciduous trees using spectral information from aerial images has only been partly successfully due to the complexity of the reflectance at different view angles, times of acquisition, phenology of the trees and inter-tree radiance. Therefore, the objective was to evaluate the accuracy of estimating the proportion of deciduous stem volume (P) utilizing change detection between canopy height models (CHMs) generated by digital photogrammetry from leaf-on and leaf-off aerial images instead of using spectral information. The study was conducted at a hemiboreal study area in Sweden. Using aerial images from three seasons, CHMs with a resolution of approximately 0.5 m were generated using semi-global matching. For training plots, metrics describing the change between leaf-on and leaf-off conditions were calculated and used to model the continuous variable P, using the Random Forest approach. Validated at sub-stands, the estimation accuracy of P in terms of root mean square error and bias was found to be 18% and −6%, respectively. The overall classification accuracy, using four equally wide classes, was 83% with a kappa value of 0.68. The validation plots in classes of high proportion of coniferous or deciduous stem volume were well classified, whereas the mixed forest classes showed lower classification accuracies.
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