ALS-based Estimates Research Articles

Monitoring tree occupancy and height in the Norwegian alpine treeline using a time series of airborne laser scanner data

The main objective of this study was to demonstrate a method for monitoring tree occupancy and height in the alpine treeline ecotone using a time series of ALS data. We applied data collected in a longitudinal survey, comprising three spatially consistent campaigns from the years 2008, 2012 and 2018, on 25 sites along the Scandinavian Mountain Range (60–69°N). We compared ALS-based estimates of tree occupancy and height with corresponding field-based estimates and provided ALS-based estimates of uncertainty. Cross validation of a longitudinal model for predicting tree occurrence probability from ALS data revealed an overall accuracy of 83 %. ALS data were useful for predicting the height of pioneer trees, despite sparse laser points. Both models needed to account for the time of measurement. ALS-based estimates of tree occupancy were 4.6, 6.7 and 6.0 % for the three measurement occasions, respectively, and corresponding field-based estimates were 4.3, 4.6 and 5.0 %. ALS-based estimates of tree height were 2.2, 2.1 and 2.2 m, respectively, and corresponding field-based height estimates were 2.3, 2.2 and 2.2 m. Overlapping confidence intervals of ALS-based estimates for both variables and for all three measurement occasions indicated no statistically significant changes in either of the studied variables. The proposed method can be used to monitor alpine treeline ecotones and to provide accompanying uncertainty estimates to inform whether changes are significant.

Characterizing stream morphological features important for fish habitat using airborne laser scanning data

Understanding changes in salmonid populations and their habitat is a critical issue given changing climate, their importance as a keystone species, and their cultural significance. Terrain features such as slope, gradient, and morphology, as well as forest structure attributes including canopy cover, height, and presence of on ground coarse wood, all influence the quality and quantity of salmonid habitat in forested ecosystems. The increasing availability of Airborne Laser Scanning (ALS) data for forest applications offers an opportunity to utilize these data for assessing the quality and quantity of habitat, which is often costly and difficult to characterize. ALS data provides detailed and accurate Digital Elevation Models (DEMs) under forest canopies, which in turn enable the characterization of detailed stream networks, as well as stream and terrain attributes important to salmonids. At the Nahmint watershed on Vancouver Island, British Columbia, Canada, we sampled six, 200 m long stream reaches, describing a range of terrain and stream features following standard data collection protocols. Our objective in this research was to use ALS data to estimate three attributes from the 3D point cloud and DEM that are known to be important for salmonids, including bankfull width,instream wood and discrete stream morphological units. Results indicate that ALS-based estimates had strong, significant, correlations with field-measured attributes (with Pearson's correlation of 0.80 and 0.81 for bankfull width and instream wood, respectively). Bankfull width was slightly underestimated using the ALS data (Bias = −1.01 m; MAD = 1.89 m; RMSD = 2.05 m) and 80% of instream wood pieces were detected. Using ALS-derived predictors in a Random Forest model, discrete stream morphological units (i.e. pools, riffles, glides, cascades) were classified with an overall accuracy of 85%, with pools having the highest user's class accuracy at 96%. Results presented herein indicate that ALS data can be used to provide a fine scale characterization of stream attributes that are required to identify salmonid habitat, providing critical information for sustainable forest management decision making, and providing a foundation for advanced salmonid habitat modeling.

Evaluating the accuracy of ALS-based removal estimates against actual logging data

Key messageWe examined the accuracy of the stand attribute data based on airborne laser scanning (ALS) provided by the Finnish Forest Centre. The precision of forest inventory data was compared for the first time with operative logging data measured by the harvester.ContextAirborne laser scanning (ALS) is increasingly used together with models to predict the stand attributes of boreal forests. The information is updated by growth models. Information produced by remote sensing, model prediction, and growth simulation needs field verification. The data collected by harvesters on logging sites provide a means to evaluate and verify the accuracy of the ALS-based data.AimsThis study investigated the accuracy of ALS-based forest inventory data provided by the Finnish Forest Centre at the stand level, using harvester data as the reference. Special interest was on timber assortment volumes where the quality reductions of sawlog are model predictions in ALS-based data and true realized reductions in the logging data.MethodsWe examined the accuracy of total volume and timber assortment volumes by comparing ALS-based data and operative logging data measured by a harvester. This was done both for clear cuttings and thinning sites. Accuracy of the identification of the dominant tree species of the stand was examined using the Kappa coefficient.ResultsIn clear-felling sites, the total harvest removals based on ALS and model prediction had a RMSE% of 26.0%. In thinning, the corresponding difference in the total harvested removal was 42.4%. Compared to logged volume, ALS-based prediction overestimated sawlog removals in clear cuttings and underestimated pulpwood removals.ConclusionThe study provided valuable information on the accuracy of ALS-based stand attribute data. Our results showed that ALS-based data need better methods to predict the technical quality of harvested trees, to avoid systematic overestimates of sawlog volume. We also found that the ALS-based estimates do not accurately predict the volume of trees removed in actual thinnings.

Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests

Abstract In the EU 2020 biodiversity strategy, maintaining and enhancing forest biodiversity is essential. Forest managers and technicians should include biodiversity monitoring as support for sustainible forest management and conservation issues, through the adoption of forest biodiversity indices. The present study investigates the potential of a new type of Structure from Motion (SfM) photogrammetry derived variables for modelling forest structure indicies, which do not require the availability of a digital terrain model (DTM) such as those obtainable from Airborne Laser Scanning (ALS) surveys. The DTM-independent variables were calculated using raw 3D UAV photogrammetric data for modeling eight forest structure indices which are commonly used for forest biodiversity monitoring, namely: basal area (G); quadratic mean diameter (DBHmean); the standard deviation of Diameter at Breast Height (DBHσ); DBH Gini coefficient (Gini); the standard deviation of tree heights (Hσ); dominant tree height (Hdom); Lorey’s height (Hl); and growing stock volume (V). The study included two mixed temperate forests areas with a different type of management, with one area, left unmanaged for the past 50 years while the other being actively managed. A total of 30 field sample plots were measured in the unmanaged forest, and 50 field plots were measured in the actively managed forest. The accuracy of UAV DTM-independent predictions was compared with a benchmark approach based on traditional explanatory variables calculated from ALS data. Finally, DTM-independent variables were used to produce wall-to-wall maps of the forest structure indices in the two test areas and to estimate the mean value and its uncertainty according to a model-assisted regression estimators. DTM-independent variables led to similar predictive accuracy in terms of root mean square error compared to ALS in both study areas for the eight structure indices (DTM-independent average RMSE% = 20.5 and ALS average RMSE% = 19.8). Moreover, we found that the model-assisted estimation, with both DTM-independet and ALS, obtained lower standar errors (SE) compared to the one obtained by model-based estimation using only field plots. Relative efficiency coefficient (RE) revealed that ALS-based estimates were, on average, more efficient (average RE ALS = 3.7) than DTM-independent, (average RE DTM-independent = 3.3). However, the RE for the DTM-independent models was consistently larger than the one from the ALS models for the DBH-related variables (i.e. G, DBHmean, and DBHσ) and for V. This highlights the potential of DTM-independent variables, which not only can be used virtually on any forests (i.e., no need of a DTM), but also can produce as precise estimates as those from ALS data for key forest structural variables and substantially improve the efficiency of forest inventories.

Updating stand-level forest inventories using airborne laser scanning and Landsat time series data

Vertical forest structure can be mapped over large areas by combining samples of airborne laser scanning (ALS) data with wall-to-wall spatial data, such as Landsat imagery. Here, we use samples of ALS data and Landsat time-series metrics to produce estimates of top height, basal area, and net stem volume for two timber supply areas near Kamloops, British Columbia, Canada, using an imputation approach. Both single-year and time series metrics were calculated from annual, gap-free Landsat reflectance composites representing 1984–2014. Metrics included long-term means of vegetation indices, as well as measures of the variance and slope of the indices through time. Terrain metrics, generated from a 30m digital elevation model, were also included as predictors. We found that imputation models improved with the inclusion of Landsat time series metrics when compared to single-year Landsat metrics (relative RMSE decreased from 22.8% to 16.5% for top height, from 32.1% to 23.3% for basal area, and from 45.6% to 34.1% for net stem volume). Landsat metrics that characterized 30-years of stand history resulted in more accurate models (for all three structural attributes) than Landsat metrics that characterized only the most recent 10 or 20years of stand history. To test model transferability, we compared imputed attributes against ALS-based estimates in nearby forest blocks (>150,000ha) that were not included in model training or testing. Landsat-imputed attributes correlated strongly to ALS-based estimates in these blocks (R2=0.62 and relative RMSE=13.1% for top height, R2=0.75 and relative RMSE=17.8% for basal area, and R2=0.67 and relative RMSE=26.5% for net stem volume), indicating model transferability. These findings suggest that in areas containing spatially-limited ALS data acquisitions, imputation models, and Landsat time series and terrain metrics can be effectively used to produce wall-to-wall estimates of key inventory attributes, providing an opportunity to update estimates of forest attributes in areas where inventory information is either out of date or non-existent.

Assessing forest windthrow damage using single-date, post-event airborne laser scanning data

One of many possible climate change effects in temperate areas is the increase of frequency and severity of windstorms; thus, fast and cost efficient new methods are needed to evaluate wind-induced damages in forests. We present a method for assessing windstorm damages in forest landscapes based on a two-stage sampling strategy using single-date, post-event airborne laser scanning (ALS) data. ALS data are used for delineating damaged forest stands and for an initial evaluation of the volume of fallen trees. The total volume of fallen trees is then estimated using a two-stage model-assisted approach, where variables from ALS are used as auxiliary information in the difference estimator. In the first stage, a sample of the delineated forest stands is selected, and in the second stage the within-stand damages are estimated by means of line intercept sampling (LIS). The proposed method produces maps of windthrown areas, estimates of forest damages in terms of the total volume of fallen trees, and the uncertainty of the estimates. A case study is presented for a large windstorm that struck the Tuscany Region of Italy the night of the 4th and the 5th of March 2015 and caused extensive damages to trees in both forest and urban areas. The pure field-based estimates from LIS and the ALS-based estimates of stand-level fallen wood were very similar. Our positive results demonstrate the utility of the single-date approach for a fast assessment of windthrow damages in forest stands which is especially useful when pre-event ALS data are not available.

Tree Density and Forest Productivity in a Heterogeneous Alpine Environment: Insights from Airborne Laser Scanning and Imaging Spectroscopy

We outline an approach combining airborne laser scanning (ALS) and imaging spectroscopy (IS) to quantify and assess patterns of tree density (TD) and forest productivity (FP) in a protected heterogeneous alpine forest in the Swiss National Park (SNP). We use ALS data and a local maxima (LM) approach to predict TD, as well as IS data (Airborne Prism Experiment—APEX) and an empirical model to estimate FP. We investigate the dependency of TD and FP on site related factors, in particular on surface exposition and elevation. Based on reference data (i.e., 1598 trees measured in 35 field plots), we observed an underestimation of ALS-based TD estimates of 40%. Our results suggest a limited sensitivity of the ALS approach to small trees as well as a dependency of TD estimates on canopy heterogeneity, structure, and species composition. We found a weak to moderate relationship between surface elevation and TD (R2 = 0.18–0.69) and a less pronounced trend with FP (R2 = 0.0–0.56), suggesting that both variables depend on gradients of resource availability. Further to the limitations faced in the sensitivity of the applied approaches, we conclude that the combined application of ALS and IS data was convenient for estimating tree density and mapping FP in north-facing forested areas, however, the accuracy was lower in south-facing forested areas covered with multi-stemmed trees.

Lidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observations

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95±0.45Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17±0.06Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87±0.49Gt) overestimated the national ground-based estimate by 7.5%. The comparable log-linear model result (63.29±1.36Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log-linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 ground plots. At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon, −40.8% to North Dakota, 128.6%). Log-linear models produced gross overestimates in the continental US, i.e., >2.6x, and the use of this model to predict regional biomass using GLAS data in temperate, western hemisphere forests is not appropriate. The best model form, LNI, is used to produce biomass estimates in Mexico. The average biomass density in Mexican forests is 53.10±0.88t/ha, and the total biomass for the country, given a total forest area of 688,096km2, is 3.65±0.06Gt. In Mexico, our GLAS biomass total underestimated a 2005 FAO estimate (4.152Gt) by 12% and overestimated a 2007/8 radar study's figure (3.06Gt) by 19%.

Simulation-based assessment of sampling strategies for large-area biomass estimation using wall-to-wall and partial coverage airborne laser scanning surveys

Abstract Airborne laser scanning (ALS) has been demonstrated to be an excellent source of auxiliary information for increasing the precision of estimating stand-level attributes in forest inventories. It has also been proposed to use ALS for estimating biomass and carbon stocks under the United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD). The benefits of REDD depend among other facts on the cost-efficiency of the carbon accounting systems, which should be economically feasible and highly accurate. Acquiring full-coverage ALS data would provide highly accurate estimates but might be too expensive for limited inventory budgets. As an alternative, the ALS data might be collected as a sample by acquiring data from a portion of the area of interest. However, in surveys involving complex multi-phase and multi-stage systematic sampling designs, the efficiency of ALS-based estimates is hampered by the ability of estimating the sampling variability correctly. It has been demonstrated recently that the precision of such complex analytical estimators may be largely underestimated. In order to make an informed decision, simulated sampling from artificial populations generated from empirical data may provide a means for assessing the cost-efficiency of various sampling strategies when analytical approaches fail. This study presents a simulation-based assessment of sampling strategies employing ALS with focus on large-area (27,400 km 2 ) biomass estimation. Simulated sampling mimicking the two contrasting cases “wall-to-wall” and two-phase ALS-aided surveys is exemplified using Norwegian National Forest Inventory data for creating an artificial population. The main results indicated that (1) the gain in precision (10%) when using “wall-to-wall” ALS data may not be worth the very high inventory costs, (2) using variance estimators based on higher-order successive differences produced correct confidence intervals for two-phase systematic sampling, and (3) two-phase ALS-aided systematic surveys are cost-efficient solutions for large-area biomass estimation.

Validating estimates of merchantable volume from airborne laser scanning (ALS) data using weight scale data

There is increasing interest in the use of airborne laser scanning (ALS; also referred to as airborne Light Detection and Ranging or LiDAR) for forest inventory purposes in Canada. Timber volume is one of the key inventory attributes that is commonly estimated using ALS data, and estimates of volume can be validated using post-harvest measures. ALS data and the area-based approach were used to develop an enhanced forest inventory for the Hinton Forest Management Area (FMA) in central Alberta. Weight scale measures of coniferous merchantable volume from 272 stands harvested between 2008 and 2010 were used as validation data for both conventional and ALS-based estimates. Overall, conventional estimates of coniferous merchantable volume derived from cover type adjusted volume tables were found to underestimate weight scale volumes by 19.8%. Conversely, estimates generated from the ALS data overestimated weight scale volumes by 0.6%. ALS-based estimates provide wall-to-wall, spatially explicit estimates of me...

Airborne laser scanner (LiDAR) proxies for understory light conditions

Canopy cover and canopy closure are two closely related measures of vegetation structure. They are used for estimating understory light conditions and their influence on a broad range of biological components in forest ecosystems, from the demography and population dynamics of individual species to community structure. Angular canopy closure is more closely related to the direct and indirect light experienced by a plant or an animal than vertical canopy cover, but more challenging to estimate. We used airborne laser scanner (ALS) data to estimate canopy cover for 210 5-m radius vegetation plots in semi-open habitats and forests in protected nature areas in Denmark. The method was based on the area of Thiessen (Voronoi) polygons generated from the ALS points. We also estimated angular canopy closure by transforming ALS points from Cartesian to spherical coordinates, and calculating the percentage of azimuth and zenith angle intervals which contained points. We compared these estimates with field-based estimates using densiometer for 60 vegetation plots in forest. Finally, we compared ALS-based estimates of canopy cover and canopy closure to field-based estimates of understory light, based on the average Ellenberg indicator values for light for the plant species present in a given plot. The correlations of Ellenberg values with ALS-based canopy closure were higher (r2: 0.47) than those with ALS-based canopy cover (r2: 0.26) and densiometer readings (r2: 0.41) for the forest sites. ALS-based canopy closure is thus a reasonable indicator of understory light availability and has the advantage over field-based methods that it can be rapidly estimated for extensive areas.

Assessing the accuracy of regional LiDAR-based biomass estimation using a simulation approach

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%.

Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning

In this paper two sampling and estimation strategies for regional forest inventory were investigated in detail and results were presented for various geographical scales. Airborne laser scanner (ALS) data were acquired to augment data from a systematic sample of National Forest Inventory (NFI) ground plots in Hedmark County, Norway (27,390km2). Approximately 50% of the NFI field plots were covered by the systematic ALS sample of 53 parallel flight lines spaced 6km apart. The area was stratified into eight cover classes and independent log-transformed regression models were developed for each class to predict total above-ground dry biomass (AGB). The two laser-ground estimation strategies tested were a model-dependent (MD), two-phase approach that rests on the assumption that the predictive models are correctly specified, and a model-assisted (MA) approach with a two-stage probability sampling design which utilizes design-unbiased estimators. ALS AGB estimates were reported by land cover class and compared to the NFI ground estimates. The ALS-based MA and MD mean estimates differed from the NFI AGB estimates by about 2% and 8%, respectively, for the entire County. At the county level the smallest estimated standard error (SE) for the estimates was obtained using the field data alone. However, the SEs calculated from field and ALS data were based on unequal numbers of ground plots. When considering only the NFI plots in the ALS strips, the smallest SEs were obtained using the MD framework. However, we also illustrated the sensitivity of the estimates of applying different plausible models. All the applied estimators assumed simple random sampling while the selection of flight lines as well as ground plots followed a systematic design. Thus, the estimates of SE were most likely conservative. Simulated sampling undertaken in a parallel research effort suggests that the overestimation of the SEs was probably much larger for the ALS-based estimates compared to the NFI estimates. ALS-based estimates were also derived for sub-county political units and thereby demonstrated how limited sample sizes affect the standard error of the biomass estimates.

Comparison of field inventory methods and use of airborne laser scanning for assessing coarse woody debris

The sustainable use of forests, which is one of the key principles of forest management in Finland, for instance, includes the goal of maintaining forest biodiversity. Dead wood (coarse woody debris, CWD) has been recognized as one of the main indicators of biodiversity in boreal forests and it plays a major role in nutrient cycling, for example. Much effort has therefore been put into the development of new cost-efficient methods for assessing CWD. However, if data are collected for large areas, field inventory may be expensive even when a sample-based method is used. In this thesis, sample-based CWD inventory methods were studied and since airborne laser scanning (ALS) is nowadays regarded as one of the most promising remote sensing methods and is gradually being adopted for predicting living tree characteristics, the possibilities for utilizing ALS data in CWD inventory were investigated. The material comprised data from three areas. Data for a conservation area in Koli were used to study the use of ALS data for estimating downed and standing dead wood volumes in natural forests, and data for commercially managed forests in Sonkajarvi and Juuka were used to study the efficiency of sample-based field inventory methods for assessing CWD. The sampling methods were compared in terms of the accuracy of the estimated mean CWD volume with a fixed input effort specified in fieldwork hours. Furthermore, it was studied how much the use of auxiliary information derived from ALS data or other sources could improve the sampling efficiency, i.e. reduce the standard error of the mean given the same inventory costs. The auxiliary information was used either in the design phase, for ‘probability proportional to size’ (PPS) sampling, or in the estimation phase, for ratio or regression estimation. ALS data proved useful for predicting CWD volumes in natural forests. The RMSEs for downed and standing dead wood, and for the total CWD volume estimates were 51.6%, 78.8% and 54.2%, respectively. It was also observed that ALS-based estimates for downed dead wood volume were substantially more accurate than those based on living tree characteristics measured in the field. The sample-based inventory methods developed for assessing CWD or other rare characteristics were observed to be most efficient field inventory methods, and especially the relascope-based sampling methods were highly efficient. The use of PPS sampling notably improved the efficiency of the CWD inventory, but efficiency was modest when auxiliary information was used in the estimation phase. The improvement in efficiency varied considerably between different inventory methods and CWD materials. Although the efficiency of other inventory methods could be improved more by introducing PPS sampling, relascope-based sampling methods remained the most efficient methods for assessing CWD. It was also observed that the sampling efficiency was not markedly better if ALS data were combined with either aerial photographs or standregister data, and it was usually preferable to use ALS data alone as the auxiliary data source.