ALBERT

Description: A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Québec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.3×106 km2 were mapped with a 0.01° pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1° and 0.5° was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5° was consistently within a magnitude of 20–30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available information.

Description: Between 2006 and 2011, the Advanced Land Observing Satellite (ALOS) Phased Array L-type Synthetic Aperture Radar (PALSAR) instrument acquired multi-temporal datasets under several environmental conditions and multiple configurations of look angle and polarization. The extensive archive of SAR backscatter observations over the forest test sites of Krycklan (boreal) and Remningstorp (hemi-boreal), Sweden, was used to assess the retrieval of stem volume at stand level. The retrieval was based on the inversion of a simple Water Cloud Model with gaps; single estimates of stem volume are then combined to obtain the final multi-temporal estimate. The model matched the relationship between the SAR backscatter and the stem volume under all configurations. The retrieval relative Root Mean Square Error (RMSE) differed depending upon environmental conditions, polarization and look angle. Stem volume was best retrieved in Krycklan using only HV-polarized data acquired under unfrozen conditions with a look angle of 34.3° (relative RMSE: 44.0%). In Remningstorp, the smallest error was obtained using only HH-polarized data acquired under predominantly frozen conditions with a look angle of 34.3° (relative RMSE: 35.1%). The relative RMSE was below 30% for stands >20 ha, suggesting high accuracy of ALOS PALSAR estimates of stem volumes aggregated at moderate resolution.

Description: Data assimilation techniques were used to estimate forest stand data in 2011 by sequentially combining remote sensing based estimates of forest variables with predictions from growth models. Estimates of stand data, based on canopy height models obtained from image matching of digital aerial images at six different time-points between 2003 and 2011, served as input to the data assimilation. The assimilation routines were built on the extended Kalman filter. The study was conducted in hemi-boreal forest at the Remningstorp test site in southern Sweden (lat. 13°37′ N; long. 58°28′ E). The assimilation results were compared with two other methods used in practice for estimation of forest variables: the first was to use only the most recent estimate obtained from remotely sensed data (2011) and the second was to forecast the first estimate (2003) to the endpoint (2011). All three approaches were validated using nine 40 m radius validation plots, which were carefully measured in the field. The results showed that the data assimilation approach provided better results than the two alternative methods. Data assimilation of remote sensing time series has been used previously for calibrating forest ecosystem models, but, to our knowledge, this is the first study with real data where data assimilation has been used for estimating forest inventory data. The study constitutes a starting point for the development of a framework useful for sequentially utilizing all types of remote sensing data in order to provide precise and up-to-date estimates of forest stand parameters.

Description: Remote Sensing, Vol. 9, Pages 1253: Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data Remote Sensing doi: 10.3390/rs9121253 Authors: Henrik Persson Håkan Olsson Maciej Soja Lars Ulander Johan Fransson This paper report experiences from the processing and mosaicking of 518 TanDEM-X image pairs covering the entirety of Sweden, with two single map products of above-ground biomass (AGB) and forest stem volume (VOL), both with 10 m resolution. The main objective was to explore the possibilities and overcome the challenges related to forest mapping extending a large number of adjacent satellite scenes. Hence, numerous examples are presented to illustrate challenges and possible solutions. To derive the forest maps, the observables backscatter, interferometric phase height and interferometric coherence, obtained from TanDEM-X, were evaluated using empirical robust linear regression models with reference data extracted from 2288 national forest inventory plots with a 10 m radius. The interferometric phase height was the single most important observable, to predict AGB and VOL. The mosaics were evaluated on different datasets with field-inventoried stands across Sweden. The root mean square error (RMSE) was about 21%–25% (27–30 tons/ha and 52–65 m3/ha) at the stand level. It was noted that the most influencing factors on the observables in this study were local temperature and geolocation errors that were challenging to robustly compensate against. Because of this variability at the scene-level, determinations of AGB and VOL for single stands are recommended to be used with care, as an equivalent accuracy is difficult to achieve for all different scenes, with varying acquisition conditions. Still, for the evaluated stands, the mosaics were of sufficient accuracy to be used for forest management at the stand level.

Description: Most poplar plantations are planted on marginal agricultural land, but poplar plantations also hold the potential for increased profits compared to plantations of other species on non-agricultural, previously forested land. To date, the establishment of poplar plantations on previously forested land is limited by the production of suitable containerized poplar stock for planting. The objective of this study is to investigate how different cutting quality and fertilizer treatments influence height, diameter, and root biomass growth and root-to-shoot ratio, all important variables for plant establishment. Our results show that fertilization increases plant growth and that single-bud and two-bud cuttings with cutting diameters of 5 to 10 mm can be used in the production of containerized plants. Root biomass was similar between these plant types but the number of roots per plant was higher if two-bud cuttings were used. In contrast to fertilized plants, only one cutting type (two-bud 10 mm) grew to a sufficient height and diameter for use in poplar plantation establishment. Interestingly, the root-to-shoot ratio for this cutting type was 0.16 while the ratio for the same cutting type is 0.11 if fertilized. Together, these results suggest that most types of poplar cuttings can be used to establish poplar plantations if fertilizer is used and that the largest cutting type (two-bud 10 mm) might be more suitable to establish poplar plantations at harsh sites, thus reducing the cost of poplar plant production.

Description: The last decade has seen launches of radar satellite missions operating in X-band with the sensors acquiring images with spatial resolutions on the order of 1 m. This study uses digital surface models (DSMs) extracted from stereo synthetic aperture radar images and a reference airborne laser scanning digital terrain model to calculate the above-ground biomass and tree height. The resulting values are compared to in situ data. Analyses were undertaken at the Swedish test sites Krycklan (64°N) and Remningstorp (58°N), which have different site conditions. The results showed that, for 459 forest stands in Remningstorp, biomass estimation at the stand level could be performed with 22.9% relative root mean square error, while the height estimation showed 9.4%. Many factors influenced the results and it was found that the topography has a significant effect on the generated DSMs and should therefore be taken into consideration when the stand level mean slope is above four degrees. Different tree species did not have a major effect on the models during leaf-on conditions. Moreover, correct estimation within young forest stands was problematic. The intersection angles resulting in the best results were in the range 8–16°. Based on the results in this study, radargrammetry appears to be a promising potential remote sensing technique for future forest applications.

Description: 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 R2 = 0.93. These results prove the capability of TanDEM-X interferometric data to estimate forest aboveground biomass in the boreal zone.