Prediction of plot-level forest variables using TerraSAR-X stereo SAR data

Karjalainen, Mika; Kankare, Ville; Vastaranta, Mikko; Holopainen, Markus; Hyyppä, Juha

doi:10.1016/j.rse.2011.10.008

Cited by 66 publications

(50 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The very comprehensive study presented in [23] was essentially accomplished in pure deciduous forest conditions and the present study together with [24] complements [23] by covering mostly boreal forests made up of essentially pine and spruce stands. The first extended analysis showed that it was not necessary to develop species independent models as long as leaf-on conditions were supplied.…”

Section: Discussionmentioning

confidence: 99%

“…All our data sets were acquired in the late summer to late fall (in Krycklan the fall has often already passed in mid-October) and nothing can therefore be concluded about the possible effects of winter images with frozen conditions. This study does however contribute to the general knowledge base by extending the different seasons thus far investigated within radargrammetry [23,24] with another season.…”

Section: Discussionmentioning

confidence: 99%

“…No seasonal effects (frozen/unfrozen) or the influence of weather conditions could be studied as all images used in the study were acquired during April-June and no weather differences were present. This was also the case in [24] where the authors carried out Random Forest evaluation of forest using 109 field plots with 8 m radius, using only spring-time images. They found that the forest stem volume could be predicted with 34.0% relative root mean square error (RMSE) and the mean forest canopy height with 14.0%.…”

Section: Introductionmentioning

confidence: 95%

See 2 more Smart Citations

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Persson

Fransson

2014

Remote Sensing

View full text Add to dashboard Cite

Abstract: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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Persson

Fransson

2014

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Radargrammetry and interferometry are basic techniques used to derive 3D information from radar imagery. Radargrammetry is based on the stereoscopic measurement of SAR images [19] in which, analogously to photogrammetric forward intersection, two or more radar images with different viewing perspectives are used to extract 3D information from the target area. Although radargrammetry has been a well-known technique for many decades, it has gained new recognition due to the new SAR satellites with enhanced spatial resolution [20].…”

Section: Airborne and Space-borne Point Cloudsmentioning

confidence: 99%

Outlook for the Next Generation’s Precision Forestry in Finland

Holopainen

Vastaranta

Hyyppä

2014

Forests

Self Cite

161

View full text Add to dashboard Cite

During the past decade in forest mapping and monitoring applications, the ability to acquire spatially accurate, 3D remote-sensing information by means of laser scanning, digital stereo imagery and radar imagery has been a major turning point. These 3D data sets that use single-or multi-temporal point clouds enable a wide range of applications when combined with other geoinformation and logging machine-measured data. New technologies enable precision forestry, which can be defined as a method to accurately determine characteristics of forests and treatments at stand, sub-stand or individual tree level. In precision forestry, even individual tree-level assessments can be used for simulation and optimization models of the forest management decision support system. At the moment, the forest industry in Finland is looking forward to next generation's forest inventory techniques to improve the current wood procurement practices. Our vision is that in the future, the data solution for detailed forest management and wood procurement will be to use multi-source and -sensor information. In this communication, we review our recent findings and describe our future vision in precision forestry research in Finland. OPEN ACCESSForests 2014, 5 1683

show abstract

“…For example, promising results have been reported by Chen et al [21]. Furthermore, recent studies by Perko et al [22], Karjalainen et al [23], Persson and Fransson [24] and Solberg et al [25] revealed the potential of radargrammetric 3D data in forest attribute estimation. For example, Karjalainen et al [23] used TerraSAR-X radargrammetry to derive estimates for the mean height and stem volume (the relative RMSE of 34% for stem volume) at the plot level in a boreal forest zone.…”

Section: Introductionmentioning

confidence: 97%

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

et al. 2015

Self Cite

View full text Add to dashboard Cite

Abstract:It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (D g ) and Lorey's mean height (H g ) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8-6 pulses/m 2 ) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space-and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)-13.4% (2.83 m) for H g , 11.7% (3.0 cm)-20.6% (5.3 cm) for D g , 14.8% (4.0 m 2 /ha)-25.8% (6.9 m 2 /ha) for G, 15.9% (43.0 m 3 /ha)-31.2% (84.2 m 3 /ha) for VOL and 14.3% (19.2 Mg/ha)-27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for H g and D g , which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data 2015,7, seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for H g , 20.6% to 19.2% for D g , 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.

show abstract

Prediction of plot-level forest variables using TerraSAR-X stereo SAR data

Cited by 66 publications

References 28 publications

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Outlook for the Next Generation’s Precision Forestry in Finland

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

Contact Info

Product

Resources

About