Remotely sensed (RS) data are becoming increasingly important as sources of auxiliary information in forest resource assessments. Data from several satellites providing moderate image resolution are ...freely available (e.g. Sentinel-2). In addition, very-high-resolution three-dimensional data are available due to the advent of unmanned aerial vehicles (UAV). The increasing availability of auxiliary data offers new opportunities for large-scale forest surveys using UAVs. A recently developed hierarchical model-based mode of inference makes it possible to use hierarchically nested auxiliary data in estimating population properties, such as total or mean biomass or volume, and their corresponding uncertainties in a statistically appropriate manner. In this study, hierarchical model-based inference was used to estimate growing stock volume (GSV; m3ha−1) and its variance using a small sample of field data, a larger sample of UAV data, and wall-to-wall Sentinel-2 data in a study area in SE Norway. The main objective of the study was to compare the performance, in terms of precision, of hierarchical model-based inference (denoted Case C) against two alternative cases. These were (1) model-based inference based on field data and wall-to-wall data, collected either with airborne laser scanning (Case A.1) or Sentinel-2 data (Case A.2), and (2) hybrid inference using a small sample of field data and a larger sample of UAV data (Case B). A second objective was to assess the possibility of reducing the UAV sampling intensity when adopting Case C rather than B, without decreasing the precision of the GSV estimates. The results, calculated as standard error as percentage of the mean (SÊ%), indicated that in case C the precision was of similar magnitude (SÊ%=3.44%) as for Case A.1 (SÊ%=3.69%) and for Case B (SÊ%=3.58%). The standard error of Case A.2 was nearly twice as large (SÊ%=5.81%) as the rest of the cases. The results also indicated possibilities of reducing the UAV sampling intensity without losing precision in cases where wall-to-wall Sentinel-2 data are available (Case C). The same precision for Case C with only five UAV samples was achieved as for Case B with 55 UAV samples. Thus, the study highlights the cost-efficiency of applications of UAV as in Case C and also provides first insights in the use of Sentinel-2 data for GSV estimation in boreal conditions.
•Forest growing stock volume was estimated using field, UAV, and Sentinel-2 data.•Hierarchical model-based inference was adopted.•Adding Sentinel-2 data in UAV based estimation was cost-efficient.•Similar results for the hierarchical inference and model-based estimation with ALS.•First insights in using Sentinel-2 data for forest growing stock volume estimation.
NASA's Global Ecosystem Dynamics Investigation (GEDI) mission will collect waveform lidar data at a dense sample of ∼25 m footprints along ground tracks paralleling the orbit of the International ...Space Station (ISS). GEDI's primary science deliverable will be a 1 km grid of estimated mean aboveground biomass density (Mg ha−1), covering the latitudes overflown by ISS (51.6 °S to 51.6 °N). One option for using the sample of waveforms contained within an individual grid cell to produce an estimate for that cell is hybrid inference, which explicitly incorporates both sampling design and model parameter covariance into estimates of variance around the population mean. We explored statistical properties of hybrid estimators applied in the context of GEDI, using simulations calibrated with lidar and field data from six diverse sites across the United States. We found hybrid estimators of mean biomass to be unbiased and the corresponding estimators of variance appeared to be asymptotically unbiased, with under-estimation of variance by approximately 20% when data from only two clusters (footprint tracks) were available. In our study areas, sampling error contributed more to overall estimates of variance than variability due to the model, and it was the design-based component of the variance that was the source of the variance estimator bias at small sample sizes. These results highlight the importance of maximizing GEDI's sample size in making precise biomass estimates. Given a set of assumptions discussed here, hybrid inference provides a viable framework for estimating biomass at the scale of a 1 km grid cell while formally accounting for both variability due to the model and sampling error.
Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The ...Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.
Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions, but how these effects scale up to influence ...competition, a key driver of community assembly in terrestrial vegetation, has remained unclear. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits--wood density, specific leaf area and maximum height--consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.
To meet the increasing need for reliable and timely timber resources and carbon stock estimates at intermediate and local decision levels, a sampling approach using airborne laser scanning (ALS) as a ...strip sampling tool has been proposed as a supplement to the conventional field-based National Forest Inventory system. This idea led to a large-scale biomass survey project undertaken in Hedmark County, Norway, an area encompassing 27390km2. The field biomass estimates were provided by the Norwegian NFI, and the ALS measurements were acquired in parallel strips using a systematic (SYS) design. The ALS-based biomass estimation was performed using regression estimators under design-based and the model-based inferential frameworks. A possible approach to assess the validity of inference when complex designs are involved is to use a sampling simulator where an artificial population represents the ‘ground truth’ and the properties of the estimators are investigated via simulated sampling. To create the artificial population, a large multivariate dataset containing NFI field observations and ALS metrics was generated using a copula function fitted to the empirical observations, and then it was generalized over the study area using satellite imagery and nearest neighbor imputations. The properties of several design-based model-assisted and model-based variance estimators were investigated using simulated sampling and the accuracy of ALS-based and ground-based estimates under simple random sampling without replacement (SRSwoR) and SYS designs were compared. The simulation results indicated that the ALS-based survey produced valid inference under design-based and model-based frameworks. The variance estimators performed well under two-phase SRSwoR, but the real standard errors were overestimated approximately 4.7 times under two-phase SYS. Compared to the pure ground-based inventories, the estimated standard errors of the ALS-based estimates were approximately 1.8 times larger, while the real accuracy (in terms of root mean squared error) improved with 59%.
► A simulation approach for assessing design- and mode-based estimators was proposed. ► An artificial population was created using empirical data and a copula function. ► A two-phase ALS-aided biomass survey with limited auxiliaries was simulated. ► Using two-phase systematic sampling greatly overestimated the real standard error. ► The systematic ALS-aided survey was more efficient than the ground-based inventory.
Airborne Light Detection and Ranging (LiDAR) and Landsat data were evaluated as auxiliary information with the intent to increase the precision of growing stock volume estimates in field-based forest ...inventories. The aim of the study was to efficiently utilize both wall-to-wall Landsat data and a sample of LiDAR data using model-assisted estimation. Variables derived from the Landsat 7 ETM+ satellite image were spectral values of blue, green, red, near infra-red (IR), and two shortwave IR (SWIR) bands. From the LiDAR data twenty-six height and density based metrics were extracted. Field plots were measured according to a design similar to the 10th Finnish National Forest Inventory, although with an increased number of plots per area unit. The study was performed in a 30000ha area of Kuortane, Western Finland. Three regression models based on different combinations of auxiliary data were developed, analysed, and applied in the model-assisted estimators. Our results show that adding auxiliary Landsat and LiDAR data improves estimates of growing stock volume. Very precise results were obtained for the case where wall-to-wall Landsat data, LiDAR strip samples, and field plots were combined; for simple random sampling of LiDAR strips the relative standard errors (RSE) were in the range of 1–4%, depending on the size of the sample. With only LiDAR and field data the RSEs ranged from 4% to 25%. We also showed that probability-proportional-to-size sampling of LiDAR strips (utilizing predicted volume from Landsat data as the size variable) led to more precise results than simple random sampling.
•We examined RS auxiliary information sources in sample based forest inventories.•LiDAR data from sample strips considerably improved the precision of the estimators.•Additional inclusion of wall-to-wall Landsat data led to further improvements.•Use of auxiliary information in the sample selection also improved the results.
Field inventoried data are often used as references (ground truth) in forest remote sensing studies. However, the reference values are affected by various kinds of errors, which tend to make the ...reported accuracies of the remote sensing-based predictions worse than they are. The more accurate the remote sensing techniques are becoming, the more pronounced this problem will be. This paper addresses the impact of uncertainties in field reference data due to measurement errors, model errors, and position errors when evaluating the accuracy of biomass predictions from airborne laser scanning at plot level. We present novel theoretical analysis methods that take the interactions of the error sources into account. Further, an error characterization model (ECM) is used to describe the error structure of the remote sensing-based predictions, and we show how the parameters of the ECM can be adjusted when field references contain errors. We also show how root mean square error (RMSE) estimates can be adjusted. Based on data from Scandinavian forests, we conclude that the field reference errors have an impact on the remote sensing-based predictions. By accounting for these errors the RMSE of the remote sensing-based predictions was reduced by 6–18%. The most influential sources of error in the field references were found to be the residual errors of the allometric biomass model and the field plot position errors. Together, these two sources accounted for 97% of the variance while measurement errors and biomass model parameter uncertainties were negligible in our study.
•A framework to characterize and correct for field reference errors in remote sensing estimates.•The field reference errors constituted up to 18% of the root mean square error.•Position and residual model errors were more severe than measurement and parameter errors.•An error characterization model provides more details about the error structure.
This discussion paper addresses (1) the challenge of concisely reporting uncertainties in forest remote sensing (RS) studies, primarily conducted at plot and stand level, and (2) the influence of ...reference data errors and how corrections for such errors can be made. Different common ways of reporting uncertainties are discussed, and a parametric error model is proposed as a core part of a comprehensive approach for reporting uncertainties (compared to, e.g., conventional reporting of root mean square error (RMSE)). The importance of handling reference data errors is currently increasing since estimates derived from RS data are becoming increasingly accurate; in extreme cases the accuracies of RS- and field-based estimates are of equal magnitude and there is a risk that reported RS accuracies are severely misjudged due to inclusion of errors from the field reference data. Novel methods for correcting for some types of reference data errors are proposed, both for the conventional RMSE uncertainty metric and for the case when a parametric error model is applied. The theoretical framework proposed in this paper is demonstrated using real data from a typical RS study where airborne laser scanning and synthetic aperture radar (SAR) data are applied for estimating biomass at the level of forest stands. With the proposed correction method, the RMSE for the RS-based estimates from laser scanning was reduced from 50.5 to 49.5 tons/ha when errors in the field references were properly accounted for. The RMSE for the estimates from SAR data was reduced from 28.5 to 26.1 tons/ha.
Airborne laser scanning (ALS) has been proposed as a reliable remote sensing technique for supporting biomass and carbon stock estimation under the United Nations Collaborative Program on Reducing ...Emissions from Deforestation and Forest Degradation in developing countries (UN-REDD). Under the United Nations Framework Convention on Climate Change (UNFCCC), developing countries can receive financial benefits from REDD+ activities upon the implementation of reliable measuring, verification, and reporting mechanisms. As a UN-REDD country, Tanzania has implemented the National Forestry Resources Monitoring and Assessment (NAFORMA) program as a cost-efficient solution for providing an appropriate level of precision required for sustainable forest management practices, and for international reporting on carbon pools and carbon pools change estimates at national and regional scales. The main objective of the study was to investigate various design- and model-based sampling strategies incorporating ALS measurements for estimation of aboveground biomass (AGB) in miombo woodlands. The field data consisted of 65 clusters containing 513 circular NAFORMA ground plots located in Liwale District (15,867km2), southeastern Tanzania, on which the aboveground tree biomass was estimated using locally developed allometric models. The ALS measurements were acquired along 32 parallel flight lines oriented in the east–west direction, covering nearly 26% of Liwale District. The flight lines were spaced 5km apart and were distributed over the ground plots. Compared to the uncertainty (standard error) of the field-based estimate (4.79Mgha−1), the uncertainties of the ALS-assisted AGB estimates were consistently lower, varying from 1.73Mgha−1 up to 2.52Mgha−1 under two-stage cluster sampling, and 1.96Mgha−1 for double sampling with regression estimation. Finally, strengths and shortcomings of using the NAFORMA inventory for ALS-assisted biomass estimation were discussed, underlining the implications of the field inventory design. Importantly, this study reveals the difficulty of accommodating a double sampling for stratification design, which was employed for NAFORMA, with an ALS survey having the flight lines systematically positioned over the landscape.
•Tanzanian NFI data from miombo forests•ALS data acquired along systematically positioned strips•Biomass estimation under design-and model-based inference•ALS-based estimation nearly three times more efficient•The NAFORMA design suboptimal for large-area ALS inventories
The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides profile samples over the terrain. While primarily designed for snow and ice ...monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good spatial coverage of boreal forests.
The aim of this study is to evaluate the estimation of mean AGBD from ICESat-2 data using a hierarchical modeling approach combined with rigorous statistical inference. We propose a hierarchical hybrid inference approach for uncertainty quantification of the average AGBD of the area of interest estimated directly from a sample of ICESat-2 lidar profiles. Our approach models the errors coming from the multiple modeling steps, including the allometric models used for predicting tree-level AGB. For testing the procedure, we have data from two adjacent study sites, denoted Valtimo and Nurmes, of which Valtimo site is used for model training and Nurmes for validation.
The ICESat-2 estimated mean AGBD in the Nurmes validation area was 65.7 ± 1.9 Mg/ha (relative standard error of 2.9%). The local reference hierarchical model-based estimate obtained from wall-to-wall airborne lidar data was 63.9 ± 0.6 Mg/ha (relative standard error of 1.0%). The reference estimate was within the 95% confidence interval of the ICESat-2 hierarchical hybrid estimate. The small standard errors indicate that the proposed method is useful for AGBD assessment. However, some sources of error were not accounted for in the study and thus the real uncertainties are probably slightly larger than those reported.
•ICESat-2 data was used to estimate biomass of a boreal forest with good results.•The ICESat-2 model was validated using data from a neighboring area and year.•Variance of the estimated biomass was quantified using a novel statistical approach.
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