Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

Abstract This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates. Keywords: Airborne laser scanning, area-based approaches, individual tree crown approaches, forest management, timber volume estimation, multitemporal ALS surveys. Introduction Information about the state and changes to forest stands is important for environmental and timber assessment on various levels of forest ecosystem planning and management and for the global change science community [Corona and Marchetti, 2007]. Standing volume and above-ground tree biomass are key parameters in this respect. Actually, fine-scale studies have demonstrated the influence of structural characteristics on ecosystem functioning: characterization of forest attributes at fine scales is necessary to manage resources in a manner that replicates, as closely as possible, natural ecological conditions. To apply this knowledge at broad scales is problematical because information on broad-scale patterns of vertical canopy structure has been very difficult to be obtained. Passive remote sensing tools cannot help for detailed height, total biomass, or leaf biomass estimates beyond early stages of succession in forests with high leaf area or biomass [Means et al., 1999]. Over the last decades, survey methods and techniques for assessing such biophysical attributes have greatly advanced [Corona, 2010]. Among others, laser scanning techniques from space o

An alternative approach to using LiDAR remote sensing data to predict stem diameter distributions across a temperate forest landscape

© 2017 by the authors. We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R2 ≥ 0.75, and another 23% had 0.5 ≤ R2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m2/ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution

Delineation of individual tree crowns from ALS and hyperspectral data: A comparison among four methods

In this paper four different delineation methods based on airborne laser scanning (ALS) and hyperspectral data are compared over a forest area in the Italian Alps. The comparison was carried out in terms of detected trees, while the ALS based methods are compared also in terms of attributes estimated (e.g. height). From the experimental results emerged that ALS methods outperformed hyperspectral one in terms of tree detection rate in two of three cases. The best results were achieved with a method based on region growing on an ALS image, and by one based on clustering of raw ALS point cloud. Regarding the estimates of the tree attributes all the ALS methods provided good results with very high accuracies when considering only big trees

Two-phase forest inventory using very-high-resolution laser scanning

In this study, we compared a two-phase laser-scanning-based forest inventory of stands versus a traditional field inventory using sample plots. The two approaches were used to estimate stem volume (VOL), Lorey's mean height (HL), Lorey's stem diameter (DL), and VOL per tree species in a study area in Sweden. The estimates were compared at the stand level with the harvested reference values obtained using a forest harvester. In the first phase, a helicopter acquired airborne laser scanning (ALS) data with >500 points/m2 along 50-m wide strips across the stands. These strips intersected systematic plots in phase two, where terrestrial laser scanning (TLS) was used to model DL for individual trees. In total, phase two included 99 plots across 10 boreal forest stands in Sweden (lat 62.9 degrees N, long 16.9 degrees E). The single trees were segmented in both the ALS and TLS data and linked to each other. The very-high-resolution ALS data enabled us to directly measure tree heights and also classify tree species using a convolutional neural network. Stem volume was predicted from the predicted DBH and the estimated height, using national models, and aggregated at the stand level. The study demonstrates a workflow to derive forest variables and stand-level statistics that has potential to replace many manual field inventories thanks to its time efficiency and improved accuracy. To evaluate the inventories, we estimated bias, RMSE, and precision, expressed as standard error. The laser-scanning-based inventory provided estimates with an accuracy considerably higher than the field inventory. The RMSE was 17 m3/ha (7.24%), 0.9 m (5.63%), and 16 mm (5.99%) for VOL, HL, and DL respectively. The tree species classification was generally successful and improved the three species-specific VOL estimates by 9% to 74%, compared to field estimates. In conclusion, the demonstrated laser-scanning-based inventory shows potential to replace some future forest inventories, thanks to the increased accuracy demonstrated empirically in the Swedish forest study area

Assessment of factors affecting shrub volume estimations using airborne discrete-return LiDAR data in Mediterranean areas

Shrub vegetation is a key element of Mediterranean forest areas and it is necessary to develop tools that allow a precise knowledge of this vegetation. This study aims to predict shrub volume and analyze the factors affecting the accuracy of these estimations in small stands using airborne discrete-return LiDAR data. The study was performed over 83 circular stands with 0.5 m radius located in Chiva (Spain) mainly occupied by Quercus coccifera. The vegetation inside each area was clear cut, and the height and the diameter of each plant was measured to compute the volume of shrub vegetation per stand. Volume values were related with maximum height values derived from LiDAR data reaching a coefficient of determination value R2=0.26. Afterwards, factors affecting the quality of volume estimations were analyzed, i.e., vegetation type, LiDAR density, and accuracy of the digital terrain model (DTM). Significant accuracy improvements (R2=0.71) were detected for stands with 0.5 m, LiDAR data density greater than 8 points/m2, vegetation Q. coccifera, and error associated to the DTM less than 0.20 m. These results show the feasibility of using LiDAR data to predict shrub volume under certain conditions, which can contribute to improved forest management and characterization.The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion in the framework of the project CGL2010-19591.Estornell Cremades, J.; Ruiz Fernández, LÁ.; Velázquez Martí, B.; Hermosilla Gómez, T. (2012). Assessment of factors affecting shrub volume estimations using airborne discrete-return LiDAR data in Mediterranean areas. Journal of Applied Remote Sensing. 6:1-10. https://doi.org/10.1117/1.JRS.6.063544S1106Velázquez-Martí, B., Fernández-González, E., Estornell, J., & Ruiz, L. A. (2010). Dendrometric and dasometric analysis of the bushy biomass in Mediterranean forests. Forest Ecology and Management, 259(5), 875-882. doi:10.1016/j.foreco.2009.11.027Mundt, J. T., Streutker, D. R., & Glenn, N. F. (2006). Mapping Sagebrush Distribution Using Fusion of Hyperspectral and Lidar Classifications. Photogrammetric Engineering & Remote Sensing, 72(1), 47-54. doi:10.14358/pers.72.1.47Riaño, D., Chuvieco, E., Ustin, S. L., Salas, J., Rodríguez-Pérez, J. R., Ribeiro, L. M., … Fernández, H. (2007). Estimation of shrub height for fuel-type mapping combining airborne LiDAR and simultaneous color infrared ortho imaging. International Journal of Wildland Fire, 16(3), 341. doi:10.1071/wf06003Riaño, D., Chuvieco, E., Condés, S., González-Matesanz, J., & Ustin, S. L. (2004). Generation of crown bulk density for Pinus sylvestris L. from lidar. Remote Sensing of Environment, 92(3), 345-352. doi:10.1016/j.rse.2003.12.014Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94(4), 441-449. doi:10.1016/j.rse.2004.10.013Erdody, T. L., & Moskal, L. M. (2010). Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment, 114(4), 725-737. doi:10.1016/j.rse.2009.11.002Næsset, E. (2004). Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project. Scandinavian Journal of Forest Research, 19(6), 554-557. doi:10.1080/02827580410019544Breidenbach, J., Nothdurft, A., & Kändler, G. (2010). Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data. European Journal of Forest Research, 129(5), 833-846. doi:10.1007/s10342-010-0384-1Gonzalez, P., Asner, G. P., Battles, J. J., Lefsky, M. A., Waring, K. M., & Palace, M. (2010). Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. Remote Sensing of Environment, 114(7), 1561-1575. doi:10.1016/j.rse.2010.02.011Wulder, M. A., White, J. C., Stinson, G., Hilker, T., Kurz, W. A., Coops, N. C., … Trofymow, J. A. (Tony). (2009). Implications of differing input data sources and approaches upon forest carbon stock estimation. Environmental Monitoring and Assessment, 166(1-4), 543-561. doi:10.1007/s10661-009-1022-6Dubayah, R. O., Sheldon, S. L., Clark, D. B., Hofton, M. A., Blair, J. B., Hurtt, G. C., & Chazdon, R. L. (2010). Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. Journal of Geophysical Research: Biogeosciences, 115(G2), n/a-n/a. doi:10.1029/2009jg000933(2010). Journal of Ecology, 98(3). doi:10.1111/jec.2010.98.issue-3Bater, C. W., Wulder, M. A., Coops, N. C., Nelson, R. F., Hilker, T., & Nasset, E. (2011). Stability of Sample-Based Scanning-LiDAR-Derived Vegetation Metrics for Forest Monitoring. IEEE Transactions on Geoscience and Remote Sensing, 49(6), 2385-2392. doi:10.1109/tgrs.2010.2099232Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X., & Maltamo, M. (2008). Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29(5), 1339-1366. doi:10.1080/01431160701736489Popescu, S. C. (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655. doi:10.1016/j.biombioe.2007.06.022Holopainen, M., Mäkinen, A., Rasinmäki, J., Hyyppä, J., Hyyppä, H., Kaartinen, H., … Kangas, A. (2009). Effect of tree-level airborne laser-scanning measurement accuracy on the timing and expected value of harvest decisions. European Journal of Forest Research, 129(5), 899-907. doi:10.1007/s10342-009-0282-6Dalponte, M., Bruzzone, L., & Gianelle, D. (2011). A System for the Estimation of Single-Tree Stem Diameter and Volume Using Multireturn LIDAR Data. IEEE Transactions on Geoscience and Remote Sensing, 49(7), 2479-2490. doi:10.1109/tgrs.2011.2107744Hill, R. A., & Thomson, A. G. (2005). Mapping woodland species composition and structure using airborne spectral and LiDAR data. International Journal of Remote Sensing, 26(17), 3763-3779. doi:10.1080/01431160500114706Mutlu, M., Popescu, S. C., & Zhao, K. (2008). Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps. Forest Ecology and Management, 256(3), 289-294. doi:10.1016/j.foreco.2008.04.014Su, J. G., & Bork, E. W. (2007). Characterization of diverse plant communities in Aspen Parkland rangeland using LiDAR data. Applied Vegetation Science, 10(3), 407-416. doi:10.1111/j.1654-109x.2007.tb00440.xEstornell, J., Ruiz, L. A., Velázquez-Martí, B., & Fernández-Sarría, A. (2011). Estimation of shrub biomass by airborne LiDAR data in small forest stands. Forest Ecology and Management, 262(9), 1697-1703. doi:10.1016/j.foreco.2011.07.026Takhtajan, A. ,Floristic regions of the world, University of California Press, Los Angeles (1986).Gómez, F. ,Los bosques ibéricos, Editorial Planeta, Barcelona (1998).Estornell, J., Ruiz, L. A., Velázquez-Martí, B., & Hermosilla, T. (2011). Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area. International Journal of Digital Earth, 4(6), 521-538. doi:10.1080/17538947.2010.533201Reitberger, J., Schnörr, C., Krzystek, P., & Stilla, U. (2009). 3D segmentation of single trees exploiting full waveform LIDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 561-574. doi:10.1016/j.isprsjprs.2009.04.002Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery. Photogrammetric Engineering & Remote Sensing, 72(7), 799-811. doi:10.14358/pers.72.7.79

An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

This study developed an IPCC (Intergovernmental Panel on Climate Change) compliant method for the estimation of above-ground carbon (AGC) in forest stands using remote sensing technology. A multi-level morphological active contour (MMAC) algorithm was employed to obtain tree-level metrics (tree height (LH), crown radius (LCR), competition index (LCI), and stem diameter (LDBH)) from an airborne LiDAR-derived canopy height model. Seven biomass-based AGC models and 13 volume-based AGC models were developed using a training dataset and validated using a separate validation dataset. Four accuracy measures, mean absolute error (MAE), root-mean-square error (RMSE), percentage RMSE (PRMSE), and root-mean-square percentage error (RMSPE) were calculated for each of the 20 models. These measures were transformed into a new index, accuracy improvement percentage (AIP), for post hoc testing of model performance in estimating forest stand AGC stock. Results showed that the tree-level AGC models explained 84% to 91% of the variance in tree-level AGC within the training dataset. Prediction errors (RMSEs) for these models ranged between 15 ton/ha and 210 ton/ha in mature forest stands, which is equal to an error percentage in the range 6% to 86%. At the stand-level, several models achieved accurate and reliable predictions of AGC stock. Some models achieved 90% to 95% accuracy, which was equal to or superior to the R-squared of the tree-level AGC models. The first recommended model was a biomass-based model using the metrics LDBH, LH, and LCI and the others were volume-based models using LH, LCI, and LCR and LDBH and LH. One metric, LCI, played a critical role in upgrading model performance when banded together with LH and LCR or LDBH and LCR. We conclude by proposing an IPCC-compatible method that is suitable for calculating tree-level AGC and predicting AGC stock of forest stands from airborne LiDAR data

A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space

In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchmarked and investigated. The methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types, and structures. This is the first benchmark ever performed for different forests within the Alps. The evaluation of the detection results was carried out in a reproducible way by automatically matching them to precise in situ forest inventory data using a restricted nearest neighbor detection approach. Quantitative statistical parameters such as percentages of correctly matched trees and omission and commission errors are presented. The proposed automated matching procedure presented herein shows an overall accuracy of 97%. Method based analysis, investigations per forest type, and an overall benchmark performance are presented. The best matching rate was obtained for single-layered coniferous forests. Dominated trees were challenging for all methods. The overall performance shows a matching rate of 47%, which is comparable to results of other benchmarks performed in the past. The study provides new insight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions.The European Commissio

Analyses of the feasibility of participatory REDD+ MRV approaches to Lidar assisted carbon inventories in Nepal

Forests are estimated to sequester and emit respectively 15% and 20% of the CO2 emissions. REDD+ aims at establishing a financial framework to compensate developing countries for reducing Green House Gasses emissions due to decreased deforestation and land degradation. An accurate Monitoring, Reporting and Verification (MRV) of the forest carbon pools is needed. The adoption of State-Of-The-Art remote sensing technologies, such as Lidar in combination with participatory approaches can potentially produce an accurate assessment of the forest resources, ensuring the sustainability of the process. The study aims at defining the feasibility of Lidar assisted Above Ground Biomass (AGB) assessment with a participatory approach. The study compares AGB regression models built with wall-to-wall, low density (0.8 points m-2) laser scanning data and two field datasets collected by professionals and Community Forest User Groups (CFUGs) teams. The models were built using ArboLiDAR©, a tool-box developed in ESRI environment by Arbonaut Oy, that uses a Sparse Bayesian approach to define a set of weights for each independent variable based on the variance of the field measured AGB and the Lidar metrics. Finally the models were validated with Leave-One-Out Cross Validation (LOOCV). The adjusted R2, relative RMSE and BIAS as well as the analyses of the residuals were used to compare the models. In addition the study also analyzed the reliability of the models across different forest structures. The professional model described a greater part of the variability of the AGB (adj.R2=0.75) compared the CFUG model (adj. R2=0.55), moreover the first was slightly more accurate (professional: rel. RMSE= 45.6 %; CFUG: rel. RMSE= 47.2 %). Although both of the models proved to have the mean of the error term not equal to zero and did not follow a normal distribution, the CUFG model showed heteroschedastic residuals. The accuracy improved when applying the models to forests characterized by a more uniform height distribution (rel. RMSE= 32.1 – 45.2 %), whereas it drastically decreased for sparse forests (rel. RMSE= 91.4 -130.5 %). The study concludes that with the limitation of having different sampling designs and measuring techniques the CFUGs models were slightly worst than the professional ones. However, it is likely that with a more accurate retrieval of the GPS plot center and increase of plot size the results can be as good as the ones obtained with professionally collected data

Assessing the Orange Tree Crown Volumes Using Google Maps as a Low-Cost Photogrammetric Alternative

The accurate assessment of tree crowns is important for agriculture, for example, to adjust spraying rates, to adjust irrigation rates or even to estimate biomass. Among the available methodologies, there are the traditional methods that estimate with a three-dimensional approximation figure, the HDS (High Definition Survey), or TLS (Terrestrial Laser Scanning) based on LiDAR technology, the aerial photogrammetry that has re-emerged with unmanned aerial vehicles (UAVs), as they are considered low cost. There are situations where either the cost or location does not allow for modern methods and prices such as HDS or the use of UAVs. This study proposes, as an alternative methodology, the evaluation of images extracted from Google Maps (GM) for the calculation of tree crown volume. For this purpose, measurements were taken on orange trees in the south of Spain using the four methods mentioned above to evaluate the suitability, accuracy, and limitations of GM. Using the HDS method as a reference, the photogrammetric method with UAV images has shown an average error of 10%, GM has obtained approximately 50%, while the traditional methods, in our case considering ellipsoids, have obtained 100% error. Therefore, the results with GM are encouraging and open new perspectives for the estimation of tree crown volumes at low cost compared to HDS, and without geographical flight restrictions like those of UAVs