Forest Height Estimation Research Articles

COMPARISON OF 3D POINT CLOUDS FROM AERIAL STEREO IMAGES AND LIDAR FOR FOREST CHANGE DETECTION

Abstract. This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

Combination of PolInSAR and LiDAR Techniques for Forest Height Estimation

Forests are simplified as homogeneous volumes constituted of randomly uniform particles, characterized by a constant extinction coefficient in the random volume over ground (RVoG) model, which has been extensively applied to polarimetric synthetic aperture radar (SAR) interferometry for forest height estimation. This letter takes into account the heterogeneous vertical structure reflected by the vertically varying extinction coefficient curve in the forest volume layer, and modifies the RVoG model to make it be more suitable for height inversion of forests with complex structures. For this purpose, the normalized extinction coefficient curve is fit by large-footprint light detection and ranging full waveform data using the Gaussian function. Finally, the varying extinction RVoG model is applied to forest height estimation using airborne L-band SAR data acquired by the E-SAR system. The results are compared with in situ measurements, which indicate that the varying extinction RVoG model can obtain more accurate results for forest height inversion.

Analysis of seasonal effects on forest parameter estimation of Indian deciduous forest using TerraSAR-X PolInSAR acquisitions

TerraSAR-X/TanDEM-X form the first space-borne interferometer providing full-PolInSAR acquisitions with a near-zero temporal gap. This paper explores the potential of X-band SAR data for forest height estimation over tropical Indian forests. Unique TanDEM-X data sets with varied spatial baselines are acquired over a tropical, deciduous Indian forest. Multiple dual polarimetric and fully polarimetric acquisitions are analysed and the best performance is achieved using fully polarimetric data acquired over both high- and low-biomass forests (r2=0.86andr2=0.80respectively). It is shown for the first time that, for deciduous Indian tropical forests, seasonal changes such as leaf-fall affect the PolInSAR based height inversion performance. For species exhibiting leaf-fall, acquisition during leaf-on season provides better height inversion accuracy (r2=0.75) than for leaf-fall season (r2=0.65). Results show consistent PolInSAR height inversion performance for both high- and low-biomass forests in both natural and managed forest tracts. Spatial baseline is a critical factor for selection of suitable acquisitions for PolInSAR based height estimation. The effect of spatial baseline on height inversion performance is also discussed.

Comparing Airborne Laser Scanning, and Image-Based Point Clouds by Semi-Global Matching and Enhanced Automatic Terrain Extraction to Estimate Forest Timber Volume

Information pertaining to forest timber volume is crucial for sustainable forest management. Remotely-sensed data have been incorporated into operational forest inventories to serve the need for ever more diverse and detailed forest statistics and to produce spatially explicit data products. In this study, data derived from airborne laser scanning and image-based point clouds were compared using three volume estimation methods to aid wall-to-wall mapping of forest timber volume. Estimates of forest height and tree density metrics derived from remotely-sensed data are used as explanatory variables, and forest timber volumes based on sample field plots are used as response variables. When compared to data derived from image-based point clouds, airborne laser scanning produced slightly more accurate estimates of timber volume, with a root mean square error (RMSE) of 26.3% using multiple linear regression. In comparison, RMSEs for volume estimates derived from image-based point clouds were 28.3% and 29.0%, respectively, using Semi-Global Matching and enhanced Automatic Terrain Extraction methods. Multiple linear regression was the best-performing parameter estimation method when compared to k-Nearest Neighbour and Support Vector Machine. In many countries, aerial imagery is acquired and updated on regular cycles of 1–5 years when compared to more costly, once-off airborne laser scanning surveys. This study demonstrates point clouds generated from such aerial imagery can be used to enhance the estimation of forest parameters at a stand and forest compartment level-scale using small area estimation methods while at the same time achieving sampling error reduction and improving accuracy at the forest enterprise-level scale.

Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest

The regulation of the carbon cycle is a critical ecosystem service provided by forests globally. It is, therefore, necessary to have robust techniques for speedy assessment of forest biophysical parameters at the landscape level. It is arduous and time taking to monitor the status of vast forest landscapes using traditional field methods. Remote sensing and GIS techniques are efficient tools that can monitor the health of forests regularly. Biomass estimation is a key parameter in the assessment of forest health. Polarimetric SAR (PolSAR) remote sensing has already shown its potential for forest biophysical parameter retrieval. The current research work focuses on the retrieval of forest biophysical parameters of tropical deciduous forest, using fully polarimetric spaceborne C-band data with Polarimetric SAR Interferometry (PolInSAR) techniques. PolSAR based Interferometric Water Cloud Model (IWCM) has been used to estimate aboveground biomass (AGB). Input parameters to the IWCM have been extracted from the decomposition modeling of SAR data as well as PolInSAR coherence estimation. The technique of forest tree height retrieval utilized PolInSAR coherence based modeling approach. Two techniques – Coherence Amplitude Inversion (CAI) and Three Stage Inversion (TSI) – for forest height estimation are discussed, compared and validated. These techniques allow estimation of forest stand height and true ground topography. The accuracy of the forest height estimated is assessed using ground-based measurements. PolInSAR based forest height models showed enervation in the identification of forest vegetation and as a result height values were obtained in river channels and plain areas. Overestimation in forest height was also noticed at several patches of the forest. To overcome this problem, coherence and backscatter based threshold technique is introduced for forest area identification and accurate height estimation in non-forested regions. IWCM based modeling for forest AGB retrieval showed R2 value of 0.5, RMSE of 62.73 (t ha−1) and a percent accuracy of 51%. TSI based PolInSAR inversion modeling showed the most accurate result for forest height estimation. The correlation between the field measured forest height and the estimated tree height using TSI technique is 62% with an average accuracy of 91.56% and RMSE of 2.28m. The study suggested that PolInSAR coherence based modeling approach has significant potential for retrieval of forest biophysical parameters.

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plot-level height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey’s mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALS-derived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.

Forest Vertical Parameter Estimation Using PolInSAR Imagery Based on Radiometric Correction

This work introduces an innovative radiometric terrain correction algorithm using PolInSAR imagery for improving forest vertical structure parameter estimation. The variance of radar backscattering caused by terrain undulation has been considered in this research by exploiting an iteration optimization procedure to improve the backscattering estimation for a Synthetic Aperture Radar (SAR) image. To eliminate the variance of backscatter coefficients caused by the local incident angle, a radiometric normalization algorithm has been investigated to compensate the influence of terrain on backscattering values, which hinders forest vertical parameter estimation. In vertical parameter estimation, species diversity and the spatial distribution of different vegetation have been modeled. Then, a combination of Fisher’s Alpha-Diversity model parameter estimation and the three-stage inversion method was designed for the vertical structure parameter. To demonstrate the efficiency of the proposed method in forest height estimation, the classical phase difference and three-stage inversion approach have been performed for the purpose of comparison. The proposed algorithm is tested on ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 (Radio Direction and Range Satellite 2) data sets for the Great Xing’an Mountain area and BioSAR (Biomass Synthetic Aperture Radar) 2007 data sets for the Remningstorp area. Height estimation results have also been validated using in-situ measurements. Experiments indicate the proposed method has the ability to compensate the influence of terrain undulation and improving the accuracy of forest vertical structure parameter estimation.

Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

ABSTRACTForests of the Sierra Nevada (SN) mountain range are valuable natural heritages for the region and the country, and tree height is an important forest structure parameter for understanding the SN forest ecosystem. There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution. In this study, we presented a method to map wall-to-wall forest tree height (defined as Lorey’s height) across the SN at 70-m resolution by fusing multi-source datasets, including over 1600 in situ tree height measurements and over 1600 km2 airborne light detection and ranging (LiDAR) data. Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements, and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System (GLAS) footprints. Finally, the random forest algorithm was used to model the SN tree height from these GLAS tree heights, optical imagery, topographic data, and climate data. The results show that our fine-resolution SN tree height product has a good correspondence with field measurements. The coefficient of determination between them is 0.60, and the root-mean-squared error is 5.45 m.

Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data

In this study, four models describing the interferometric coherence of the forest vegetation layer are proposed and compared with the TanDEM-X data. Our focus is on developing tools for hemiboreal forest height estimation from single-pol interferometric SAR measurements, suitable for wide area forest mapping with limited a priori information. The multi-temporal set of 19 TanDEM-X interferometric pairs and the 90th percentile forest height maps are derived from Airborne LiDAR Scanning (ALS), covering an area of 2211 ha of forests over Estonia. Three semi-empirical models along with the Random Volume over Ground (RVoG) model are examined for applicable parameter ranges and model performance under various conditions for over 3000 forest stands. This study shows that all four models performed well in describing the relationship between forest height and interferometric coherence. Use of an advanced model with multiple parameters is not always justified when modeling the volume decorrelation in the boreal and hemiboreal forests. The proposed set of semi-empirical models, show higher robustness compared to a more advanced RVoG model under a range of seasonal and environmental conditions during data acquisition. We also examine the dynamic range of parameters that different models can take and propose optimal conditions for forest stand height inversion for operationally-feasible scenarios.

Extending Airborne Lidar-Derived Estimates of Forest Canopy Cover and Height Over Large Areas Using kNN With Landsat Time Series Data

Airborne light detection and ranging (lidar) data provide an accurate and consistent means to obtain reliable forest canopy cover (CC) and height measurements, which are important in determining forest stand structure, volume, and biomass. Extending CC and height measurements over larger areas by integration with satellite imagery increases the value of airborne lidar data. A typical approach has been to use multiple regression, machine-learning, or regression tree methods to determine relationships between the forest structure variables measured in the lidar data and available single-date or multitemporal Landsat sensor reflectance data, and if the relationships are strong enough, to extend those variables over much larger areas than is typically covered by lidar. Such methods can be difficult to apply because of the complexity of the extending models and algorithms, and the long processing times, which may become prohibitive. One machine-learning approach, which uses the k-nearest neighbor (kNN) algorithm, is presented here in a British Columbia, Canada forest environment. Our goal was to simplify the estimation of lidar-derived forest structure variables with available Landsat time series data and compare the results of the kNN model to the traditional multiple regression results, and to those obtained with more complex and computationally demanding random forest (RF) methods. We develop and test the kNN model with airborne lidar-derived estimates of forest CC and height in 1846 relatively young and mature forests. The best kNN model produced estimates of airborne lidar-derived CC in validation sample ( ${\text n} = 1132$ ) of mature forest with three Landsat time series variables with an ${R}^{2} = 0.74$ and RMSE of approximately 10%. This result is comparable to the results obtained in earlier work using more complex machine-learning approaches (in approximately 1/10 the time). Younger (i.e., recently disturbed) forest CC estimation was less successful in the kNN model because of the high degree of structural variability in these forests. Extending airborne lidar estimates of forest height to larger areas was also possible though, as expected, less successful than CC estimation using the kNN approach.

Spaceborne potential for examining taiga-tundra ecotone form and vulnerability.

In the taiga-tundra ecotone (TTE), site-dependent forest structure characteristics can influence the subtle and heterogeneous structural changes that occur across the broad circumpolar extent. Such changes may be related to ecotone form, described by the horizontal and vertical patterns of forest structure (e.g., tree cover, density and height) within TTE forest patches, driven by local site conditions, and linked to ecotone dynamics. The unique circumstance of subtle, variable and widespread vegetation change warrants the application of spaceborne data including high-resolution (< 5m) spaceborne imagery (HRSI) across broad scales for examining TTE form and predicting dynamics. This study analyzes forest structure at the patch-scale in the TTE to provide a means to examine both vertical and horizontal components of ecotone form. We demonstrate the potential of spaceborne data for integrating forest height and density to assess TTE form at the scale of forest patches across the circumpolar biome by (1) mapping forest patches in study sites along the TTE in northern Siberia with a multi-resolution suite of spaceborne data, and (2) examining the uncertainty of forest patch height from this suite of data across sites of primarily diffuse TTE forms. Results demonstrate the opportunities for improving patch-scale spaceborne estimates of forest height, the vertical component of TTE form, with HRSI. The distribution of relative maximum height uncertainty based on prediction intervals is centered at ~40%, constraining the use of height for discerning differences in forest patches. We discuss this uncertainty in light of a conceptual model of general ecotone forms, and highlight how the uncertainty of spaceborne estimates of height can contribute to the uncertainty in identifying TTE forms. A focus on reducing the uncertainty of height estimates in forest patches may improve depiction of TTE form, which may help explain variable forest responses in the TTE to climate change and the vulnerability of portions of the TTE to forest structure change.

Forest Canopy Height Estimation Using Tandem-X Coherence Data

In this paper, we report results of a study aimed at assessing the potential for using X-band single-pass radar interferometric coherence for forest canopy height estimation. We use datasets from the Tandem-X satellite pair collected over Canadian forest test sites, where supporting lidar data are available for validation. We first employ dual-copolarized modes to assess the potential of polarimetric interferometry for forest canopy height retrieval. We show that for this forest type, single polarization modes have better properties, including much improved spatial coverage. We develop a new algorithm for single polarization data and validate the canopy height products against lidar. We then extend the canopy height product over a mosaic of multiple swaths to demonstrate the potential for very wide area forest height mapping using radar coherence.

Calibration and Validation of Landsat Tree Cover in the Taiga−Tundra Ecotone

Monitoring current forest characteristics in the taiga−tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover &gt;80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

Extended Three-Stage Polarimetric SAR Interferometry Algorithm by Dual-Polarization Data

Until now, most polarimetric synthetic aperture radar interferometry (PolInSAR) research has been based on full-polarization (HH + HV + VH + VV) SAR data, which can provide complete polarimetric information but usually have a smaller swath and lower spatial resolution than dual-polarization (dual-pol) data. Some existing researches concern the dual-pol PolInSAR; however, these works did not perform the coherence optimization process which may make the inversion unstable. In this paper, the PolInSAR technique based on dual-pol (HH + HV) SAR data is investigated in order to demonstrate its validity for forest height retrieval and thus show that PolInSAR is better able to meet the requirements of global-scale research. We extend the three-stage inversion process and coherence optimization algorithm to dual-pol PolInSAR. In addition, based on the random volume over ground model, a search method for finding the volume-only coherence on ambiguous line segments is proposed. Finally, the dual-pol PolInSAR technique is applied to forest height estimation using airborne L-band SAR data acquired by the E-SAR system over the Traunstein test site in Germany. The forest heights estimated by dual-pol PolInSAR are compared with those estimated using the full-pol mode and also with measurements made in situ. The results show that dual-pol PolInSAR can obtain similar estimated forest heights to the full-pol mode and also that the search method for volume-only coherence retrieval can improve the inversion accuracy. The coefficient of determination (r 2 ) for the relation between the dual-pol PolInSAR and the in situ measurements is 0.7287.

Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data

ABSTRACTMost terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R2 = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R2 = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.

Sensitivity of Pol-InSAR Measurements to Vegetation Parameters

Estimation of forest height from combined polarimetric and interferometric synthetic aperture radar (Pol-InSAR) measurements has been the focus of radar remote sensing studies in the past decade. The simplicity of the random-volume-over-ground (RVoG) model makes it one of the most widely used candidates for estimating canopy height. However, the polarization-independent extinction coefficient assumption in the RVoG model fails in some certain types of the canopies, as suggested by the oriented-volume-over-ground (OVoG) model. The sensitivity of coherence magnitude and phase to different parameters of the canopy is expressed in a closed-form formulation in this paper for the first time. In order to simplify our formulation, the forest is represented by a layer of discrete randomly distributed dielectric scatterers over ground, with azimuthal symmetry. The sensitivity analysis of this work quantifies the contribution of differential extinction due to polarization change in interferometric coherence. Therefore, we can quantitatively evaluate whether the RVoG model is accurate enough to be used for a specific kind of canopy or the OVoG model is needed for better estimation. A simple layer of leaves over ground is used to simulate the sensitivity of Pol-InSAR measurements to different parameters.

Assessing Performance of Tomo-SAR and Backscattering Coefficient for Hemi-Boreal Forest Aboveground Biomass Estimation

Tomo-SAR technique has been used for hemi-boreal forest height and further forest biomass estimation through allometric equation. Backscattering coefficient especially in longer wavelength (L- or P-band) is thought as a useful parameter for hemi-boreal forest biomass retrieval. The aim of this paper is to assess the performance of vertical backscattering power and backscattering coefficient for hemi-boreal forest aboveground biomass (AGB) estimation with airborne P-band data. The test site locates in southern Sweden called Remningstorp test site, and the in-situ forest AGB ranges from 14 t/ha to 245 t/ha at stand level. Multi-baseline P-band Pol-InSAR data in repeat-path mode collected during March and May in 2007 at Remningstorp test site was used. We found that the correlation coefficient (R) between backscattering coefficient of P-band HH polarization and the in-situ forest biomass reached 0.87. The R for P-band VV backscattering power at 5 m is 0.71 and 10 m is 0.72. Backscattering coefficient in HH polarization and vertical backscattering power at 5 m and 10 m were applied to construct a model for hemi-boreal forest AGB estimation by backward step-wise regression and cross-validation approach. The results showed that the estimated forest AGB ranges from 19 to 240 t/ha, and the constructed model obtained a higher R and smaller RMSE, the value of R is 0.91, RMSE is 30.43 t/ha at Remningstorp test site.

Forest Height Estimation by Means of Pol-InSAR Data Inversion: The Role of the Vertical Wavenumber

This paper examines the multifaceted effect of the effective spatial baseline, as expressed through the vertical (interferometric) wavenumber, on the inversion of forest height from polarimetric interferometric synthetic aperture radar (Pol-InSAR) data. First, the role of the vertical wavenumber in relating forest height to the interferometric (volume) coherence is introduced. Through the review of the forest height inversion from Pol-InSAR data, the effect of the vertical wavenumber on the inversion performance is evaluated. The selection of optimum with respect to forest height inversion performance, vertical wavenumbers is discussed. The impact of the acquisition geometry and terrain slopes on the vertical wavenumber and their consideration in the inversion methodology is addressed. The individual effects discussed are demonstrated by means of airborne repeat pass Pol-InSAR acquisitions in L- and P-band acquired over different forest conditions, including a boreal, a temperate, and a tropical forest test site. The achieved forest height inversion performance is validated against reference height data derived from airborne LIDAR acquisitions.

Estimation of Forest Biomass From Two-Level Model Inversion of Single-Pass InSAR Data

A model for aboveground biomass estimation from single-pass interferometric synthetic aperture radar (InSAR) data is presented. Forest height and canopy density estimates Δh and η <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , respectively, obtained from two-level model (TLM) inversion, are used as biomass predictors. Eighteen bistatic VV-polarized TanDEM-X (TDM) acquisitions are used, made over two Swedish test sites in the summers of 2011, 2012, and 2013 (nominal incidence angle: 41°; height-of-ambiguity: 32-63 m) . Remningstorp features a hemiboreal forest in southern Sweden, with flat topography and where 32 circular plots have been sampled between 2010 and 2011 (area: 0.5 ha; biomass: 42-242 t/ha; height: 14-32 m) . Krycklan features a boreal forest in northern Sweden, 720-km north-northeast from Remningstorp, with significant topography and where 31 stands have been sampled in 2008 (area: 2.4-26.3 ha; biomass: 23-183 t/ha; height: 7-21 m) . A high-resolution digital terrain model has been used as ground reference during InSAR processing. For the aforementioned plots and stands and if the same acquisition is used for model training and validation, the new model explains 65%-89% of the observed variance, with root-mean-square error (RMSE) of 12%-19% (median: 15%) . By fixing two of the three model parameters, accurate biomass estimation can also be done when different acquisitions or different test sites are used for model training and validation, with RMSE of 12%-56% (median: 17%) . Compared with a simple scaling model computing biomass from the phase center elevation above ground, the proposed model shows significantly better performance in Remningstorp, as it accounts for the large canopy density variations caused by active management. In Krycklan, the two models show similar performance.

Integrating Data from Discrete Return Airborne LiDAR and Optical Sensors to Enhance the Accuracy of Forest Description: A Review

Good forest management requires comprehensive and reliable inventory data spanning large areas. Forest management has increasingly relied on remote sensing, specifically light detection and ranging (LiDAR). However, due to the high costs associated with data collection and processing, wall-to-wall LiDAR data is rarely obtained for forests. In contrast, multispectral imagery from optical sensors often covers large extents but they fail to capture detail below the forest canopy and do not directly measure structural attributes. To take advantage of the complementary benefits of different sensors, active LiDAR and passive optical sensors have been combined and applied to problem-solving in a forestry context for over a decade. A review of the literature shows that fusion of different sensors has resulted in superior performance relative to individual sensors for classifying and delineating forest areas (up to 20 % accuracy improvement), identifying species (up to 21 % accuracy improvement), and estimating forest volume and biomass (up to 55 % accuracy improvement). In contrast, sensor fusion achieved only minor improvements for tree or forest height estimation (1–7 % accuracy improvement); this is likely because LiDAR alone is already so effective. This review was unable to draw conclusions on the performance of sensor fusion for forest age and productivity assessment due to the limited number of studies. The lack of results in these areas presents an opportunity for future research. The literature clearly demonstrates the utility of integrating LiDAR and optical data for many aspects of forest description. Perhaps the greatest challenge moving forward will be to operationalise the research such that forestry companies and governments can take advantage of the benefits of data fusion.