Light Detection And Ranging Acquisitions Research Articles

Improving the application of UAV-LiDAR for erosion monitoring through accounting for uncertainty in DEM of difference

Successful control of erosion requires effective monitoring and an in-depth understanding of erosion processes over large areas. Data from repeated unmanned aerial vehicles (UAV) light detection and ranging (LiDAR) acquisitions have been one important option to derive erosion-related information across large areas for example by calculating a digital elevation model of difference (DoD). However, the lack of an efficient and practical DoD uncertainty derivation method constrains the use of repeated UAV- LiDAR acquisitions for erosion monitoring, particularly for erosion that induces relatively small changes (i.e. field scale erosion). This study employed repeated UAV-LiDAR acquisitions to promote the understanding of DoD uncertainties and integrate the gained understanding in an erosion monitoring approach. The uncertainty of DoD was derived based on the impacting factors (point density and topography) using the fuzzy inference system (FIS), with the rules established according to the empirical relationships between DoD uncertainty and impacting factors for one sloping field. The proposed method was then validated on another sloping field by using terrestrial laser scanning (TLS) data. Results showed that the absolute value of DoD uncertainty (DoDua) linearly increased with local roughness and decreased exponentially with increasing point density. The established DoD uncertainty derivation method was found to be able to account for over 96% of raw DoD uncertainty. A validation with TLS results showed that our method slightly underestimated erosion and deposition volume, respectively, (-0.29 m3 and 0.74 m3 compared to −0.94 m3 and 1.34 m3 measured by TLS) but the trends were comparable. The distribution of deposition (volume and area) in different parts of the validation slope was well captured by our method, while the distribution of detected erosion was less satisfactory. This may be explained by the fact that our method did not well capture subtle erosion, suggesting a direction for future improvement.

Assessing post-fire forest structure recovery by combining LiDAR data and Landsat time series in Mediterranean pine forests

Understanding post-fire recovery dynamics is critical for effective management that enhance forest resilience to fire. Mediterranean pine forests have been largely affected by wildfires, but the impacts of both changes in land use and climate endanger their capacity to naturally recover. Multispectral imagery is commonly used to estimate post-fire recovery, yet changes in forest structure must be considered for a comprehensive evaluation of forest recovery. In this research, we combine Light Detection And Ranging (LiDAR) with Landsat imagery to extrapolate forest structure variables over a 30-year period (1990–2020) to provide insights on how forest structure has recovered after fire in Mediterranean pine forests. Forest recovery was evaluated attending to vegetation cover (VC), tree cover (TC), mean height (MH) and heterogeneity (CVH). Structure variables were derived from two LiDAR acquisitions from 2016 and 2009, for calibration and independent spatial and temporal validation. A Support Vector Regression model (SVR) was calibrated to extrapolate LiDAR-derived variables using a series of Landsat imagery, achieving an R2 of 0.78, 0.64, 0.70 and 0.63, and a relative RMSE of 24.4%, 30.2%, 36.5% and 27.4% for VC, TC, MH and CVH, respectively. Models showed to be consistent in the temporal validation, although a wider variability was observed, with R2 ranging from 0.51 to 0.74. A different response to fire was revealed attending to forest cover and height since vegetation cover recovered to a pre-fire state but mean height did not 26-years after fire. Less than 50% of the area completely recovered to the pre-fire structure within 26 years, and the area subjected to fire recurrence showed signs of greater difficulty in initiating the recovery. Our results provide valuable information on forest structure recovery, which can support the implementation of mitigation and adaptation strategies that enhance fire resilience.

Application of allometric systems for compatible area-based LiDAR-assisted estimation in the Province of Nova Scotia

Light detection and ranging (LiDAR) is used to estimate tree, stand, and forest characteristics across large geographic areas. In the province of Nova Scotia, an enhanced forest inventory (EFI) was developed to provide high-resolution spatial forest inventory estimates across the landscape. For various forest attributes, independent LiDAR-based relationships were built leading to mathematical and biological inconsistency among forest attribute estimates. A systems approach, composed of allometric equations describing the relationships between volume per unit area, Lorey’s average height, basal area, quadratic mean diameter, and density, is developed to address these inconsistencies. Previous results showed that applying the systems approach provided reasonable and compatible estimates and eliminated inconsistency issues among forest attributes. This study evaluates application of the systems approach applied to eastern Nova Scotia using field data from a network of permanent sample plots and recent LiDAR acquisitions. The independent EFI estimates had inconsistencies of greater than 100% for basal area and implied stand-level form factor. These inconsistencies were eliminated using the systems approach. Results show that the systems approach can be scaled to larger landscape areas and that long-term field data can be leveraged to fit the allometric systems producing mathematically and biologically consistent estimates.

How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study

ABSTRACTAerial discrete return LiDAR (Light Detection And Ranging) technology (ALS – Aerial Laser Scanner) is now widely used for forest characterization due to its high accuracy in measuring vertical and horizontal forest structure. Random and systematic errors can still occur and these affect the native point cloud, ultimately degrading ALS data accuracy, especially when adopting datasets that were not natively designed for forest applications. A detailed understanding of how uncertainty of ALS data could affect the accuracy of derivable forest metrics (e.g. tree height, stem diameter, basal area) is required, looking for eventual error biases that can be possibly modelled to improve final accuracy. In this work a low-density ALS dataset, originally acquired by the State of Minnesota (USA) for non-forestry related purposes (i.e. topographic mapping), was processed attempting to characterize forest inventory parameters for the Cutfoot Sioux Experimental Forest (north-central Minnesota, USA). Since accuracy of estimates strictly depends on the applied species-specific dendrometric models a first required step was to map tree species over the forest. A rough classification, aiming at separating conifers from broadleaf, was achieved by processing a Landsat 8 OLI (Operational Land Imager) scene. ALS-derived forest metrics initially greatly overestimated those measured at the ground in 230 plots. Conversely, ALS-derived tree density was greatly underestimated. To reduce ALS uncertainty, trees belonging to the dominated plane were removed from the ground dataset, assuming that they could not properly be detected by low-density ALS measures. Consequently, MAE (Mean Absolute Error) values significantly decreased to 4.0 m for tree height and to 0.19 cm for diameter estimates. Remaining discrepancies were related to a bias affecting the native ALS point cloud, which was modelled and removed. Final MAE values were 1.32 m for tree height, 0.08 m for diameter, 8.5 m2 ha−1 for basal area, and 0.06 m for quadratic mean diameter. Specifically focusing on tree height and diameter estimates, the significance of differences between ground and ALS estimates was tested relative to the expected ‘best accuracy’. Results showed that after correction: 94.35% of tree height differences were lower than the corresponding reference value (2.86 m); 70% of tree diameter differences were lower than the corresponding reference value (4.5 cm for conifers and 6.8 cm for broadleaf). Finally, forest parameters were computed for the whole Cutfoot Sioux Experimental Forest. Main findings include: 1) all forest estimates based on a low-density ALS point cloud can be derived at plot level and not at a tree level; 2) tree height estimates obtained by low-density ALS point clouds at the plot level are highly reasonably accurate only after testing and modelling eventual error bias; 3) diameter, basal area, and quadratic mean diameter estimates have large uncertainties, suggesting the need for a higher point density and, probably, a better mapping of tree species (if possible) than achieved with a remote sensing-based approach.

Mapping and Monitoring Fractional Woody Vegetation Cover in the Arid Savannas of Namibia Using LiDAR Training Data, Machine Learning, and ALOS PALSAR Data

Namibia is a very arid country, which has experienced significant bush encroachment and associated decreased livestock productivity. Therefore, it is essential to monitor bush encroachment and widespread debushing activities, including selective bush thinning and complete bush clearing. The aim of study was to develop a system to map and monitor fractional woody cover (FWC) at national scales (50 m and 75 m resolution) using Synthetic Aperture Radar (SAR) satellite data (Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics, 2009, 2010, 2015, 2016) and ancillary variables (mean annual precipitation—MAP, elevation), with machine learning models that were trained with diverse airborne Light Detection and Ranging (LiDAR) data sets (244,032 ha, 2008–2014). When only the SAR variables were used, an average R2 of 0.65 (RSME = 0.16) was attained. Adding either elevation or MAP, or both ancillary variables, increased the mean R2 to 0.75 (RSME = 0.13), and 0.79 (RSME = 0.12). The inclusion of MAP addressed the overestimation of FWC in very arid areas, but resulted in anomalies in the form of sharp gradients in FWC along a MAP contour which were most likely caused by to the geographic distribution of the LiDAR training data. Additional targeted LiDAR acquisitions could address this issue. This was the first attempt to produce SAR-derived FWC maps for Namibia and the maps contain substantially more detailed spatial information on woody vegetation structure than existing national maps. During the seven-year study period the Shrubland–Woodland Mosaic was the only vegetation structural class that exhibited a regional net gain in FWC of more than 0.2 across 9% (11,906 km2) of its area that may potentially be attributed to bush encroachment. FWC change maps provided regional insights and detailed local patterns related to debushing and regrowth that can inform national rangeland policies and debushing programs.

An Approach to Tree Detection Based on the Fusion of Multitemporal LiDAR Data

The repetitive acquisition of airborne light detection and ranging (LiDAR) data for forest surveys is rapidly increasing, thus making possible the forest dynamic analysis. Moreover, the availability of multitemporal data enables the possibility to improve the forest attribute estimates performed at single date, especially when one LiDAR acquisition has a lower pulse density with respect to the other. This letter presents a novel approach that exploits the bitemporal data information to: 1) improve the tree detection at both dates and 2) identify forest changes at single tree level. This is done by using a novel compound approach to the detection of trees in bitemporal data based on the Bayes rule for minimum error. Significant geometric features are extracted for each candidate tree-top and are used to estimate statistical terms employed in the compound approach. The multitemporal information is considered by estimating (in an iterative way) the probabilities of transition, which takes into account the temporal dependence between the LiDAR acquisitions. The proposed approach is evaluated on multitemporal LiDAR data acquired in a coniferous forest located in the Southern Italian Alps. Experimental results confirm the effectiveness of the compound detection that increases the overall accuracy (OA) up to 8.6% with respect to the single-date detection.

UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy)

Airborne remote sensing systems are increasingly used in engineering geology and geomorphology for studying and monitoring natural hazardous scenarios and events. In this study, we used two remote sensing monitoring techniques, i.e., light detection and ranging (LiDAR) and unmanned aerial vehicles (UAV) to analyze the kinematic evolution of the Montescaglioso landslide (Basilicata, Southern Italy), a large rain-triggered landslide that occurred in December 2013. By comparing pre- and post-event LiDAR and UAV DEMs and UAV orthomosaics, we delineated landslide morphological features and measured horizontal displacements and elevation change differences within landslide body. Analysis of two subsequent post-events digital terrain models (DTMs) also allowed the evaluation of the evolutionary behavior of the slope instability, highlighting no signs of reactivation. The UAV-derived digital surface models (DSMs) were found consistent with the LiDAR-DTMs, but their use was in addition highlighted as highly effective to support geomorphic interpretations and complement LiDAR and field-based data acquisitions. This study shows the effectiveness of combining the two UAV-LiDAR methodologies to evaluate geomorphological features indicative of the failure mechanism and to interpret the evolutionary behavior of the instability process

Geoarchaeological Characterisation of Sites of Iberian and Roman Cordoba Using LiDAR Data Acquisitions

The National Geographic Institute of Spain (IGN) carried out a Light Detection and Ranging (LiDAR) flight for the entire region of Andalusia between 2013 and 2014, which completed the general LiDAR acquisitions obtained for all of Spain since 2010. Recently, successive updates of orthoimages of Spain and Andalusia have also been acquired. This geographic documentation allows several applications for the aerial detection of archaeological sites. In recent years, numerous discoveries have been made in Spain, especially in non-built-up areas located outside urban enclaves. Less attention, however, has been paid to the use of this geographical information in historic cities because, apparently, they do not preserve their historical micro relief. This study analyses the city of Cordoba (southern Spain) by processing LiDAR data of the Plan Nacional de Ortofotografía Aérea (PNOA-LiDAR). Digital Surface Models (DSM) obtained for the entire city, in combination with geological and archaeological records, provide evidence of the geomorphological reconstruction of the city in ancient times. Using Cordoba as an example, the main purpose of this article is to highlight the fact that LiDAR data are also useful for the diachronic analysis of ancient urban structures buried some metres deep in current historic cities.

Estimation of Changes of Forest Structural Attributes at Three Different Spatial Aggregation Levels in Northern California using Multitemporal LiDAR

Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key to quantify greenhouse gas (GHG) emissions/sequestration. We compared two different modeling strategies to estimate changes in V, BA and B, at three different spatial aggregation levels using auxiliary information from two light detection and ranging (LiDAR) flights. The study area is Blacks Mountains Experimental Forest, a ponderosa pine dominated forest in Northern California for which two LiDAR acquisitions separated by six years were available. Analyzed strategies consisted of (1) directly modeling the observed changes as a function of the LiDAR auxiliary information ( δ -modeling method) and (2) modeling V, BA and B at two different points in time, including a term to account for the temporal correlation, and then computing the changes as the difference between the predicted values of V, BA and B for time two and time one. We analyzed predictions and measures of uncertainty at three different level of aggregation (i.e., pixels, stands or compartments and the entire study area). Results showed that changes were very weakly correlated with the LiDAR auxiliary information. Both modeling alternatives provided similar results with a better performance of the δ -modeling for the entire study area; however, this method also showed some inconsistencies and seemed to be very prone to extrapolation problems. The y -modeling method, which seems to be less prone to extrapolation problems, allows obtaining more outputs that are flexible and can outperform the δ -modeling method at the stand level. The weak correlation between changes in structural attributes and LiDAR auxiliary information indicates that pixel-level maps have very large uncertainties and estimation of change clearly requires some degree of spatial aggregation; additionally, in similar environments, it might be necessary to increase the time lapse between LiDAR acquisitions to obtain reliable estimates of change.

High‐resolution habitat modeling with airborne LiDAR for red tree voles

ABSTRACTHabitat suitability maps are important tools for conservation planning and species investigations, but maps at resolutions that are biologically meaningful and useful for local‐level decision‐making are lacking for many species that use resources at fine scales. Airborne light detection and ranging (LiDAR) describes 3‐dimensional structure of forests and other habitats at high resolution, which can improve habitat models for many species and facilitate investigations otherwise impossible with field data or other remote‐sensing techniques. We used an information‐theoretic approach and logistic regression to model habitat of red tree voles (Arborimus longicaudus) at multiple spatial scales with predictors derived from airborne LiDAR in southwestern Oregon. We developed a priori models to evaluate habitat associations and identify good models useful for high‐resolution mapping of habitat suitability across a large spatial extent. Our best models that compared habitats around nest trees of red tree voles to available sites performed well (i.e., accuracy = 0.83) on an independent test data set. We found fair performance in comparisons of habitat around nest trees to unused locations within areas previously identified as red tree vole habitat (i.e., accuracy = 0.71). Red tree vole nests were often in the largest trees in the stand and away from forest edges. Both analyses suggested that fine‐ and broad‐scale predictors are important for modeling habitat of red tree voles. Habitat suitability for red tree voles should be predicted effectively by LiDAR acquisitions even with low point densities because maps were robust to reductions in point densities as low as 1 point/m2. We found that LiDAR was effective for modeling habitat of red tree voles and expect it to improve performance of models for other species with similar habitat requirements. © 2016 The Wildlife Society.

Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes

Satellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals.

Challenges and lessons from a wetland LiDAR project: a case study of the Okefenokee Swamp, Georgia, USA

Airborne LiDAR (Light Detection and Ranging) provides opportunities to generate high-quality digital elevation models (DEMs) even in wetland environments. Our project, performed over the Okefenokee Swamp in Georgia during the spring of 2010, shows that several, distinctive factors must be considered to ensure successful wetland LiDAR projects. Some of the challenges include selecting optimal flight times in accordance with weather variability and water levels, having effective and quality control protocols, applying and developing filtering and interpolation algorithms, breaklines in swamps and accounting for data striping and noise. While some of these issues are faced in any airborne LiDAR acquisition, many of these require special consideration in a low-slope wetland environment with water saturated soils, widespread shallow water, and sediments and extensive vegetation. An examination of these issues and how they were handled will help in ensuring the success of future LiDAR acquisitions and, in particular, will advance knowledge of producing quality DEMs in wetland environments.