Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data

Tropical forests are huge reservoirs of terrestrial carbon and are experiencing rapid degradation and deforestation. Understanding forest structure proves vital in accurately estimating both forest biomass and also the natural disturbances and remote sensing is an essential method for quantification of forest properties and structure in the tropics. Our objective is to examine canopy vegetation profiles formulated from discrete return LIght Detection And Ranging (lidar) data and examine their usefulness in estimating forest structural parameters measured during a field campaign. We developed a modeling procedure that utilized hypothetical stand characteristics to examine lidar profiles. In essence, this is a simple method to further enhance shape characteristics from the lidar profile. In this paper we report the results comparing field data collected at La Selva, Costa Rica (10° 26′ N, 83° 59′ W) and forest structure and parameters calculated from vegetation height profiles and forest structural modeling. We developed multiple regression models for each measured forest biometric property using forward stepwise variable selection that used Bayesian information criteria (BIC) as selection criteria. Among measures of forest structure, ranging from tree lateral density, diameter at breast height, and crown geometry, we found strong relationships with lidar canopy vegetation profile parameters. Metrics developed from lidar that were indicators of height of canopy were not significant in estimating plot biomass (p-value = 0.31, r2 = 0.17), but parameters from our synthetic forest model were found to be significant for estimating many of the forest structural properties, such as mean trunk diameter (p-value = 0.004, r2 = 0.51) and tree density (p-value = 0.002, r2 = 0.43). We were also able to develop a significant model relating lidar profiles to basal area (p-value = 0.003, r2 = 0.43). Use of the full lidar profile provided additional avenues for the prediction of field based forest measure parameters. Our synthetic canopy model provides a novel method for examining lidar metrics by developing a look-up table of profiles that determine profile shape, depth, and height. We suggest that the use of metrics indicating canopy height derived from lidar are limited in understanding biomass in a forest with little variation across the landscape and that there are many parameters that may be gleaned by lidar data that inform on forest biometric properties

Tropical Peatland Vegetation Structure and Biomass: Optimal Exploitation of Airborne Laser Scanning

Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB and small-footprint airborne Light Detection and Ranging (LiDAR) discrete return (DR) and full waveform (FW) derived metrics, with a view to establishing the optimal use of this technology in this environment. The study was undertaken in North Selangor peat swamp forest (NSPSF) reserve, Peninsular Malaysia. Plot-based multiple regression analysis was performed to established the strongest predictive models of PSF AGB using DR metrics (only), FW metrics (only), and a combination of DR and FW metrics. Overall, the results demonstrate that a Combination-model, coupling the benefits derived from both DR and FW metrics, had the best performance in modelling AGB for tropical PSF (R2 = 0.77, RMSE = 36.4, rRMSE = 10.8%); however, no statistical difference was found between the rRMSE of this model and the best models using only DR and FW metrics. We conclude that the optimal approach to using airborne LiDAR for the estimation of PSF AGB is to use LiDAR metrics that relate to the description of the mid-canopy. This should inform the use of remote sensing in this ecosystem and how innovation in LiDAR-based technology could be usefully deployed

Analyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics

[EN] LiDAR full-waveform (LFW) pulse density is not homogeneous along study areas due to overlap between contiguous flight stripes and, to a lesser extent, variations in height, velocity and altitude of the platform. As a result, LFW-derived metrics extracted at the same spot but at different pulse densities differ, which is called ¿side-lap effect¿. Moreover, this effect is reflected in forest stand estimates, since they are predicted from LFW-derived metrics. This study was undertaken to analyze LFW-derived metric variations according to pulse density, voxel size and value assignation method in order to reduce the side-lap effect. Thirty LiDAR samples with a minimum density of 16 pulses.m¿2 were selected from the testing area and randomly reduced to 2 pulses.m¿2 with an interval of 1 pulse.m¿2, then metrics were extracted and compared for each sample and pulse density at different voxel sizes and assignation values. Results show that LFW-derived metric variations as a function of pulse density follow a negative exponential model similar to the exponential semivariogram curve, increasing sharply until they reach a certain pulse density, where they become stable. This value represents the minimum pulse density (MPD) in the study area to optimally minimize the side-lap effect. This effect can also be reduced with pulse densities lower than the MPD modifying LFW parameters (i.e. voxel size and assignation value). Results show that LFW-derived metrics are not equally influenced by pulse density, such as number of peaks (NP) and ROUGHness of the outermost canopy (ROUGH) that may be discarded for further analyses at large voxel sizes, given that they are highly influenced by pulse density. In addition, side-lap effect can be reduced by either increasing pulse density or voxel size, or modifying the assignation value. In practice, this leads to a proper estimate of forest stand variables using LFW data.This research has been funded by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R. The authors also thank the Bureau of Land Management and the Panther Creek Remote Sensing and Research Cooperative Program for the data provided.Crespo-Peremarch, P.; Ruiz Fernández, LÁ.; Balaguer-Beser, Á.; Estornell Cremades, J. (2018). Analyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics. ISPRS Journal of Photogrammetry and Remote Sensing. 146:453-464. https://doi.org/10.1016/j.isprsjprs.2018.10.012S45346414

A full-waveform airborne laser scanning metric extraction tool for forest structure modelling. Do scan angle and radiometric correction matter?

[EN] In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0°¿5° and 15°¿20°) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.This research was funded by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the projects ForeStructure (CGL2013-46387-C2-1-R) and FIRMACARTO (CGL2016-80705-R).Crespo-Peremarch, P.; Ruiz Fernández, LÁ. (2020). A full-waveform airborne laser scanning metric extraction tool for forest structure modelling. Do scan angle and radiometric correction matter?. Remote Sensing. 12(2):1-17. https://doi.org/10.3390/rs12020292S11712

Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data

[EN] The use of laser scanning acquired from the air, or ground, holds great potential for the assessment of forest structural attributes, beyond conventional forest inventory. The use of full-waveform airborne laser scanning (ALSFW) data allows for the extraction of detailed information in different vertical strata compared to discrete ALS (ALSD). Terrestrial laser scanning (TLS) can register lower vertical strata, such as understory vegetation, without issues of canopy occlusion, however is limited in its acquisition over large areas. In this study we examine the ability of ALSFW to characterize understory vegetation (i.e. maximum and mean height, cover, and volume), verified using TLS point clouds in a Mediterranean forest in Eastern Spain. We developed nine full-waveform metrics to characterize understory vegetation attributes at two different scales (3.75¿m square subplots and circular plots with a radius of 15¿m); with, and without, application of a height filter to the data. Four understory vegetation attributes were estimated at plot level with high R2 values (mean height: R2¿=¿0.957, maximum height: R2¿=¿0.771, cover: R2¿=¿0.871, and volume: R2¿=¿0.951). The proportion of explained variance was slightly lower at 3.75¿m side cells (mean height: R2¿=¿0.633, maximum height: R2¿=¿0.470, cover: R2¿=¿0.581, and volume R2¿=¿0.651). These results indicate that Mediterranean understory vegetation can be estimated and accurately mapped over large areas with ALSFW. The future use of these types of predictions includes the estimation of ladder fuels, which drive key fire behavior in these ecosystems.This research was developed mainly in the Integrated Remote Sensing Studio (IRSS) of University of British Columbia (UBC) (Canada) as a result of the Erasmus + KA-107 mobility grant. The authors thank the financial support provided by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R.Crespo-Peremarch, P.; Tompalski, P.; Coops, N.; Ruiz Fernández, LÁ. (2018). Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data. Remote Sensing of Environment. 217:400-413. https://doi.org/10.1016/j.rse.2018.08.033S40041321

Assessing the use of discrete, full-waveform LiDAR and TLS to classify Mediterranean forest species composition

Revista oficial de la Asociación Española de Teledetección[EN] LiDAR technology –airborne and terrestrial- is becoming more relevant in the development of forest inventories, which are crucial to better understand and manage forest ecosystems. In this study, we assessed a classification of species composition in a Mediterranean forest following the C4.5 decision tree. Different data sets from airborne laser scanner full-waveform (ALSFW), discrete (ALSD) and terrestrial laser scanner (TLS) were combined as input data for the classification. Species composition were divided into five classes: pure Quercus ilex plots (QUI); pure Pinus halepensis dense regenerated (HALr); pure P. halepensis (HAL); pure P. pinaster (PIN); and mixed P. pinaster and Q. suber (mPIN). Furthermore, the class HAL was subdivided in low and dense understory vegetation cover. As a result, combination of ALSFW and TLS reached 85.2% of overall accuracy classifying classes HAL, PIN and mPIN. Combining ALSFW and ALSD, the overall accuracy was 77.0% to discriminate among the five classes. Finally, classification of understory vegetation cover using ALSFW reached an overall accuracy of 90.9%. In general, combination of ALSFW and TLS improved the overall accuracy of classifying among HAL, PIN and mPIN by 7.4% compared to the use of the data sets separately, and by 33.3% with respect to the use of ALSD only. ALSFW metrics, in particular those specifically designed for detection of understory vegetation, increased the overall accuracy 9.1% with respect to ALSD metrics. These analyses show that classification in forest ecosystems with presence of understory vegetation and intermediate canopy strata is improved when ALSFW and/or TLS are used instead of ALSD.[ES] La tecnología LiDAR, tanto en sus versiones aerotransportada como terrestre, ha adquirido relevancia en los últimos años en la realización de inventarios forestales que permiten entender y adecuar la gestión de los ecosistemas forestales. En este estudio, se evaluó la clasificación por composición de especies en un bosque mediterráneo mediante el árbol de decisión C4.5. Para ello, se emplearon diferentes conjuntos de datos derivados de LiDAR discreto (ALSD ), LiDAR de retorno de onda completa (full-waveform, ALSFW) y láser escáner terrestre (TLS) como datos de entrada de la clasificación. La composición de especies se dividió en cinco clases: parcelas puras de Quercus ilex (QUI); puras de Pinus halepensis regenerado (HALr); puras de P. halepensis (HAL); puras de P. pinaster (PIN); y mixta de P. pinaster y Q. suber (mPIN). Además, se realizó una subdivisión de la clase HAL en cobertura de sotobosque escasa y densa. Como resultado se obtuvo una fiabilidad del 85,2% en la clasificación de las clases HAL, PIN y mPIN combinando ALSFW y TLS. En la clasificación de las cinco composiciones de especies, la fiabilidad alcanzada empleando ALSFW y ALSD fue del 77,0%. Finalmente, en la clasificación de las subclases de cobertura de sotobosque se logró un 90,9% de fiabilidad con ALSFW. En general, la combinación de ALSFW y TLS mejoró los resultados en un 7,4% en la clasificación de las clases HAL, PIN y mPIN en comparación con el uso de los datos de los sensores por separado, y en un 33,3% con respecto al uso de ALSD. 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