A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Deriving pseudo-vertical waveforms from small-footprint full-waveform LiDAR data

This is an author's accepted manuscript of an article published in “Remote Sensing Letters", Volume 5, Issue 4, 2014; copyright Taylor & Francis; available online at: http://www.tandfonline.com/doi/abs/10.1080/2150704X.2014.903350[EN] When processing scanning LiDAR data, it is commonly assumed that the extracted full-waveform LiDAR pulse registers truly vertical information of forest canopies. This assumption may lead to uncertain results for the spatiotemporal analysis of the waveforms due to off-nadir scanning angles and varying trajectories travelled by the pulses in overlapping strips. In this letter, we investigate these assumptions and undertake some preliminary analysis to overcome their impacts on forest-based LiDAR analyses. Our results demonstrate that for a standard LiDAR forest acquisition programme in Oregon, USA, most of the hits (83%) are acquired off-nadir, which leads to positional displacements on the ground of the full-waveforms of about 0.20 m for each 1-m height increment. We propose an approach to synthetize multiple waveform data into composite waveforms containing the vertical profile of vegetation for a given location. This approach is based on partitioning the aboveground vertical space into voxels and using the maximum full-waveform intensity value to construct new full-waveforms comprising the vertical information of the various waveforms crossing over a location. Our initial results indicate that deriving spatiotemporal metrics from the composite pseudo-vertical full-waveforms produces a more consistent response across adjacent height levels, which in turn enables a more complete characterization and more vegetation structure to be retrieved. We conclude that this type of pseudo-vertical full-waveform analysis is necessary to more fully understand the impact of the return signals from tree crownsThis paper was developed as a result of a visiting scholar grant funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering – TEE Project. The authors also wish to thank the Generalitat Valenciana for the mobility grant [BEST/2012/235] and the Panther Creek Remote Sensing and Research cooperative programme for the data provided for this research.Hermosilla, T.; Coops, N.; Ruiz Fernández, LÁ.; Moskal, M. (2014). Deriving pseudo-vertical waveforms from small-footprint full-waveform LiDAR data. Remote Sensing Letters. 5(4):332-341. https://doi.org/10.1080/2150704X.2014.903350S33234154Baltsavias, E. . (1999). Airborne laser scanning: basic relations and formulas. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 199-214. doi:10.1016/s0924-2716(99)00015-5Blair, J. B., Rabine, D. L., & Hofton, M. A. (1999). The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 115-122. doi:10.1016/s0924-2716(99)00002-7BOUDREAU, J., NELSON, R., MARGOLIS, H., BEAUDOIN, A., GUINDON, L., & KIMES, D. (2008). Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. Remote Sensing of Environment, 112(10), 3876-3890. doi:10.1016/j.rse.2008.06.003Bretar, F., M. Pierrot-Deseilligny, and M. Roux. 2004. “Solving the Strip Adjustment Problem of 3D Airborne Lidar Data.” IEEE International Geoscience and Remote Sensing Symposium Proceedings, Anchorage, AK, September 20–24, 4734–4737.Buddenbaum, H., Seeling, S., & Hill, J. (2013). Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands. International Journal of Remote Sensing, 34(13), 4511-4524. doi:10.1080/01431161.2013.776721Carabajal, C. C., & Harding, D. J. (2005). ICESat validation of SRTM C-band digital elevation models. Geophysical Research Letters, 32(22), n/a-n/a. doi:10.1029/2005gl023957Drake, J. B., Dubayah, R. O., Clark, D. B., Knox, R. G., Blair, J. B., Hofton, M. A., … Prince, S. (2002). Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79(2-3), 305-319. doi:10.1016/s0034-4257(01)00281-4Ferraz, A., G. Goncalves, P. Soares, M. Tome, C. Mallet, S. Jacquemoud, F. Bretar, and L. Pereira. 2012. “Comparing Small-footprint LiDAR and Forest Inventory Data for Single Strata Biomass Estimation – A Case Study over a Multi-layered Mediterranean Forest.” IEEE Geoscience and Remote Sensing Symposium (IGARSS), Munich, July 22–27, 6384–6387.Hall, S. A., Burke, I. C., Box, D. O., Kaufmann, M. R., & Stoker, J. M. (2005). Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208(1-3), 189-209. doi:10.1016/j.foreco.2004.12.001Harding, D. J. (2005). ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophysical Research Letters, 32(21). doi:10.1029/2005gl023471Heinzel, J., & Koch, B. (2011). Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 13(1), 152-160. doi:10.1016/j.jag.2010.09.010Hermosilla, T., Ruiz, L. A., Kazakova, A. N., Coops, N. C., & Moskal, L. M. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire, 23(2), 224. doi:10.1071/wf13086Höfle, B., Hollaus, M., & Hagenauer, J. (2012). Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 134-147. doi:10.1016/j.isprsjprs.2011.12.003HYDE, P., DUBAYAH, R., PETERSON, B., BLAIR, J., HOFTON, M., HUNSAKER, C., … WALKER, W. (2005). Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems. Remote Sensing of Environment, 96(3-4), 427-437. doi:10.1016/j.rse.2005.03.005Kim, Y., Yang, Z., Cohen, W. B., Pflugmacher, D., Lauver, C. L., & Vankat, J. L. (2009). Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment, 113(11), 2499-2510. doi:10.1016/j.rse.2009.07.010Koetz, B., Morsdorf, F., Sun, G., Ranson, K. J., Itten, K., & Allgower, B. (2006). Inversion of a Lidar Waveform Model for Forest Biophysical Parameter Estimation. IEEE Geoscience and Remote Sensing Letters, 3(1), 49-53. doi:10.1109/lgrs.2005.856706Kronseder, K., Ballhorn, U., Böhm, V., & Siegert, F. (2012). Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 18, 37-48. doi:10.1016/j.jag.2012.01.010Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests. Remote Sensing of Environment, 70(3), 339-361. doi:10.1016/s0034-4257(99)00052-8Lefsky, M. A., Harding, D. J., Keller, M., Cohen, W. B., Carabajal, C. C., Del Bom Espirito-Santo, F., … de Oliveira, R. (2005). Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32(22), n/a-n/a. doi:10.1029/2005gl023971Mallet, C., & Bretar, F. (2009). Full-waveform topographic lidar: State-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 1-16. doi:10.1016/j.isprsjprs.2008.09.007Ni-Meister, W., Jupp, D. L. B., & Dubayah, R. (2001). Modeling lidar waveforms in heterogeneous and discrete canopies. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1943-1958. doi:10.1109/36.951085Pang, Y., Lefsky, M., Sun, G., & Ranson, J. (2011). Impact of footprint diameter and off-nadir pointing on the precision of canopy height estimates from spaceborne lidar. Remote Sensing of Environment, 115(11), 2798-2809. doi:10.1016/j.rse.2010.08.025Reitberger, J., Krzystek, P., & Stilla, U. (2008). Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing, 29(5), 1407-1431. doi:10.1080/01431160701736448Reitberger, 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.2009.04.002Sarrazin, M. J. D., van Aardt, J. A. N., Asner, G. P., McGlinchy, J., Messinger, D. W., & Wu, J. (2012). Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems. Canadian Journal of Remote Sensing, 37(6), 653-665. doi:10.5589/m12-007SUN, G., RANSON, K., KIMES, D., BLAIR, J., & KOVACS, K. (2008). Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data. Remote Sensing of Environment, 112(1), 107-117. doi:10.1016/j.rse.2006.09.036Van Leeuwen, M., & Nieuwenhuis, M. (2010). Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129(4), 749-770. doi:10.1007/s10342-010-0381-4Wu, J., van Aardt, J. A. N., McGlinchy, J., & Asner, G. P. (2012). A Robust Signal Preprocessing Chain for Small-Footprint Waveform LiDAR. IEEE Transactions on Geoscience and Remote Sensing, 50(8), 3242-3255. doi:10.1109/tgrs.2011.2178420Yang, W., Ni-Meister, W., & Lee, S. (2011). Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model. Remote Sensing of Environment, 115(11), 2810-2822. doi:10.1016/j.rse.2010.02.02

Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set - DISCOVER-AQ 2011

© Author(s) 2014. This open access work is distributed under the Creative Commons Attribution 3.0 License (https://creativecommons.org/licenses/by/3.0/).Retrievals of aerosol microphysical properties (effective radius, volume and surface-area concentrations) and aerosol optical properties (complex index of refraction and single-scattering albedo) were obtained from a hybrid multiwavelength lidar data set for the first time. In July 2011, in the Baltimore-Washington DC region, synergistic profiling of optical and microphysical properties of aerosols with both airborne (in situ and remote sensing) and ground-based remote sensing systems was performed during the first deployment of DISCOVER-AQ. The hybrid multiwavelength lidar data set combines ground-based elastic backscatter lidar measurements at 355 nm with airborne High-Spectral-Resolution Lidar (HSRL) measurements at 532 nm and elastic backscatter lidar measurements at 1064 nm that were obtained less than 5 km apart from each other. This was the first study in which optical and microphysical retrievals from lidar were obtained during the day and directly compared to AERONET and in situ measurements for 11 cases. Good agreement was observed between lidar and AERONET retrievals. Larger discrepancies were observed between lidar retrievals and in situ measurements obtained by the aircraft and aerosol hygroscopic effects are believed to be the main factor in such discrepancies.Peer reviewe

Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data

The aim of this study is to analyze methodologies based on airborne LiDAR (light detection and ranging) technology of low pulse density points (0.5m(-2)) for height and volume quantification of olive trees in Viver (Spain). A total of 29 circular plots, each with a radius of 20m, were sampled and their volumes and heights were obtained by dendrometric methods. For these estimations, several statistics derived from LiDAR data were calculated in each plot. Regression models were used to predict volume and height. The results showed good performance for estimating volume (R-2=0.70) and total height (R-2=0.67).The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion (Ministry for Science & Innovation) within the framework of the project AGL2010-15334 and by the Vice-Rectorate for Research of the Universitat Politecnica de Valencia [Grant PAID-06-12-3297; SP20120534].Estornell Cremades, J.; Velázquez Martí, B.; López Cortés, I.; Salazar Hernández, DM.; Fernández-Sarría, A. (2014). Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data. GIScience and Remote Sensing. 51(1):17-29. https://doi.org/10.1080/15481603.2014.883209S1729511Estornell, 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.2011.07.026García, M., Riaño, D., Chuvieco, E., & Danson, F. M. (2010). Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment, 114(4), 816-830. doi:10.1016/j.rse.2009.11.021Hyyppa, J., Kelle, O., Lehikoinen, M., & Inkinen, M. (2001). A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing, 39(5), 969-975. doi:10.1109/36.921414Kim, Y., Yang, Z., Cohen, W. B., Pflugmacher, D., Lauver, C. L., & Vankat, J. L. (2009). Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment, 113(11), 2499-2510. doi:10.1016/j.rse.2009.07.010Moorthy, I., Miller, J. R., Berni, J. A. J., Zarco-Tejada, P., Hu, B., & Chen, J. (2011). Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agricultural and Forest Meteorology, 151(2), 204-214. doi:10.1016/j.agrformet.2010.10.005Næsset, 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:10.1080/02827580410019544Popescu, S. C. (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655. doi:10.1016/j.biombioe.2007.06.022Popescu, S. C., Wynne, R. H., & Nelson, R. F. (2002). Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37(1-3), 71-95. doi:10.1016/s0168-1699(02)00121-7Velázquez-Martí, B., Estornell, J., López-Cortés, I., & Martí-Gavilá, J. (2012). Calculation of biomass volume of citrus trees from an adapted dendrometry. Biosystems Engineering, 112(4), 285-292. doi:10.1016/j.biosystemseng.2012.04.011Velázquez-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.2009.11.027Velázquez-Martí, B., Fernández-González, E., López-Cortés, I., & Salazar-Hernández, D. M. (2011). Quantification of the residual biomass obtained from pruning of trees in Mediterranean olive groves. Biomass and Bioenergy, 35(7), 3208-3217. doi:10.1016/j.biombioe.2011.04.042Yu, X., Hyyppä, J., Kaartinen, H., & Maltamo, M. (2004). Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sensing of Environment, 90(4), 451-462. doi:10.1016/j.rse.2004.02.00

Construction of New Forest Roads in Malaysia Using a GIS-Based Decision Support System

This paper describes how a Geographical Information System (GIS)-based Decision Support System (DSS) was applied in selecting the most compatible block or compartment to construct a new forest road for a sustainable timber harvesting purposes. The study area was located in Gunong Stong Permanent Forest Reserve, Kelantan, Malaysia between latitude 5o00’ N and 5o25’ N and longitude 101o20’ E and 102o05’ E. Four criteria have been considered in this paper which is timber volume (m3), slope (degree), ground condition and distance from the primary and secondary forest road that previously exists (m). ArcView and ArcMap softwares were used to evaluate all the criteria using remote sensing and field data. Results indicated that the best block that fulfill all the criteria chosen for the new forest road construction is Block Nos. 9 and 11

Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: a possible contribution for updating forest management plans

This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units within private forest inventories in the Autonomous Province of Trento (PAT), using currently available remote sensing solutions. In particular, data from LiDAR and hyperspectral surveys of 2014 made available by PAT were acquired and processed. Such studies are very important in the context of forest management scenarios. The method includes defining tree species ground-truth by outlining single tree crowns with polygons and labeling them. Successively two supervised machine learning classifiers, K-Nearest Neighborhood and Support Vector Machine (SVM) were used. The results show that, by setting specific hyperparameters, the SVM methodology gave the best results in classification of tree species. Biomass was estimated using canopy parameters and the Jucker equation for the above ground biomass (AGB) and that of Scrinzi for the tariff volume. Predicted values were compared with 11 field plots of fixed radius where volume and biomass were field-estimated in 2017. Results show significant coefficients of correlation: 0.94 for stem volume and 0.90 for total aboveground tree biomass

Applications using estimates of forest parameters derived from satellite and forest inventory data

From the combination of optical satellite data, digital map data, and forest inventory plot data, continuous estimates have been made for several forest parameters (wood volume, age and biomass). Five different project areas within Sweden are presented which have utilized these estimates for a range of applications. The method for estimating the forest parameters was a ”k-Nearest Neighbor” algorithm, which used a weighted mean value of k spectrally similar reference plots. Reference data were obtained from the Swedish National Forest Inventory. The output was continuous estimates at the pixel level for each of the variables estimated. Validation results show that accuracy of the estimates for all parameters was low at the pixel level (e.g., for total wood volume RMSE ranged from 58-80%), with a tendency toward the mean, and an underestimation of higher values while overestimating lower values. However, when the accuracy of the estimates is assessed over larger areas, the errors are lower, with best results being 10% RMSE over a 100 ha aggregation, and 17% RMSE over a 19 ha aggregation. Applications presented in this paper include moose and bird habitat studies, county level planning activities, use as input information to prognostic programs, and computation of statistics on timber volume within drainage basins and smaller land holdings. This paper provides a background on the kNN method and gives examples of how end users are currently applying satellite-produced estimation data such as these

Assessing water availability in Mediterranean regions affected by water conflicts through MODIS data time series analysis

Water scarcity is a widespread problem in arid and semi-arid regions such as the western Mediterranean coastal areas. The irregularity of the precipitation generates frequent droughts that exacerbate the conflicts among agriculture, water supply and water demands for ecosystems maintenance. Besides, global climate models predict that climate change will cause Mediterranean arid and semi-arid regions to shift towards lower rainfall scenarios that may exacerbate water conflicts. The purpose of this study is to find a feasible methodology to assess current and monitor future water demands in order to better allocate limited water resources. The interdependency between a vegetation index (NDVI), land surface temperature (LST), precipitation (current and future), and surface water resources availability in two watersheds in southeastern Spain with serious difficulties in meeting water demands was investigated. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI and LST products (as proxy of drought), precipitation maps (generated from climate station records) and reservoir storage gauging information were used to compute times series anomalies from 2001 to 2014 and generate regression images and spatial regression models. The temporal relationship between reservoir storage and time series of satellite images allowed the detection of different and contrasting water management practices in the two watersheds. In addition, a comparison of current precipitation rates and future precipitation conditions obtained from global climate models suggests high precipitation reductions, especially in areas that have the potential to contribute significantly to groundwater storage and surface runoff, and are thus critical to reservoir storage. Finally, spatial regression models minimized spatial autocorrelation effects, and their results suggested the great potential of our methodology combining NDVI and LST time series to predict future scenarios of water scarcity.Published versio

Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation.Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086S224233232Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Andersen, 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.2004.10.013Arroyo, L. A., Pascual, C., & Manzanera, J. A. (2008). Fire models and methods to map fuel types: The role of remote sensing. Forest Ecology and Management, 256(6), 1239-1252. doi:10.1016/j.foreco.2008.06.048Ashworth, A., Evans, D. L., Cooke, W. H., Londo, A., Collins, C., & Neuenschwander, A. (2010). Predicting Southeastern Forest Canopy Heights and Fire Fuel Models using GLAS Data. Photogrammetric Engineering & Remote Sensing, 76(8), 915-922. doi:10.14358/pers.76.8.915Buddenbaum, H., Seeling, S., & Hill, J. (2013). Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands. International Journal of Remote Sensing, 34(13), 4511-4524. doi:10.1080/01431161.2013.776721Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29(2), 147-159. doi:10.1016/0034-4257(89)90023-0CHUVIECO, E., & SALAS, J. (1996). Mapping the spatial distribution of forest fire danger using GIS. International journal of geographical information systems, 10(3), 333-345. doi:10.1080/02693799608902082Chuvieco, E., Riaño, D., Aguado, I., & Cocero, D. (2002). Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. International Journal of Remote Sensing, 23(11), 2145-2162. doi:10.1080/01431160110069818Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martı́nez-Vega, J., de la Riva, J., & Pérez, F. (2004). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92(3), 322-331. doi:10.1016/j.rse.2004.01.019Cruz, M. G., Alexander, M. E., & Wakimoto, R. H. (2003). Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire, 12(1), 39. doi:10.1071/wf02024Drake, J. B., Dubayah, R. O., Clark, D. B., Knox, R. G., Blair, J. B., Hofton, M. A., … Prince, S. (2002). Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79(2-3), 305-319. doi:10.1016/s0034-4257(01)00281-4Erdody, 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.2009.11.002Falkowski, M. J., Gessler, P. E., Morgan, P., Hudak, A. T., & Smith, A. M. S. (2005). Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. Forest Ecology and Management, 217(2-3), 129-146. doi:10.1016/j.foreco.2005.06.013Flannigan, M. ., Stocks, B. ., & Wotton, B. . (2000). Climate change and forest fires. 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Estimation of forest variables using airborne laser scanning

Airborne laser scanning can provide three-dimensional measurements of the forest canopy with high efficiency and precision. There are presently a large number of airborne laser scanning instruments in operation. The aims of the studies reported in this thesis were, to develop and validate methods for estimation of forest variables using laser data, and to investigate the influence of laser system parameters on the estimates. All studies were carried out in hemi-boreal forest at a test area in southwestern Sweden (lat. 58°30’N, long. 13°40’ E). Forest variables were estimated using regression models. On plot level, the Root Mean Square Error (RMSE) for mean tree height estimations ranged between 6% and 11% of the average value for different datasets and methods. The RMSE for stem volume estimations ranged between 19% and 26% of the average value for different datasets and methods. On stand level (area 0.64 ha), the RMSE was 3% and 11% of the average value for mean tree height and stem volume estimations, respectively. A simulation model was used to investigate the effect of different scanning angles on laser measurement of tree height and canopy closure. The effect of different scanning angles was different within different simulated forest types, e.g., different tree species. High resolution laser data were used for detection of individual trees. In total, 71% of the field measurements were detected representing 91% of the total stem volume. Height and crown diameter of the detected trees could be estimated with a RMSE of 0.63 m and 0.61 m, respectively. The magnitude of the height estimation errors was similar to what is usually achieved using field inventory. Using different laser footprint diameters (0.26 to 3.68 m) gave similar estimation accuracies. The tree species Norway spruce (Picea abies L. Karst.) and Scots pine (Pinus sylvestris L.) were discriminated at individual tree level with an accuracy of 95%. The results in this thesis show that airborne laser scanners are useful as forest inventory tools. Forest variables can be estimated on tree level, plot level and stand level with similar accuracies as traditional field inventories