Airborne Laser Scanning Surveys Research Articles

Out of steady state: Tracking canopy gap dynamics across Brazilian Amazon

AbstractCanopy gaps are evidence of disturbances on forest landscapes. A forest stand is in constant flux, with long stretches of biomass accumulation punctuated by episodic disturbances. We used multitemporal airborne laser scanning data to compare the gap dynamics of four Amazon forest sites. We assessed gap dynamics over 1.9–3.8 years between 2017 and 2020 at sites in the central, central eastern, southeastern, and northeastern regions of the Brazilian Amazon, over areas ranging from 590 to 1205 ha at each site. Gap size ranged from a minimum of 10 m2 to a maximum of about 10,000 m2. We analyzed four stages of gap dynamics: formation, expansion, persistence, and recovery based on two consecutive airborne laser scanning surveys. The gap fraction at our study sites varied between 1.26% and 7.84%. All the sites have similar proportion of gaps among gap size classes. What notably differed among sites was not the gap size‐distribution, but the relative importance of stages of gap dynamics. Expansion and persistence rates ranged from 12 to 118 m2 ha−1. The gap formation rate (formation + expansion) was lower than the recovery rate for three of the four study sites. In contrast, the southeastern site has 1.44 times more area in formation and expansion compared to gap recovery. Over the 2–4 years interval of our study, no site was close to steady state. Multitemporal analyses of large areas over many years are needed to improve our understanding of tropical forest dynamics.

Analysing gap dynamics in forest canopies with landscape metrics based on multi-temporal airborne laser scanning surveys – A pilot study

For a long time gaps or openings in the forest canopy have been of considerable interest to forest ecologists and to forest managers. In the context of disturbances induced by climate change, canopy gap dynamics are of particular interest, since they can indicate imminent damage to forest resources and irreversible trends such as forest decline. Here, statistical significance is crucial for establishing whether any imminent large-scale threat to the sustainability of forest resources exists. In order to be able to assess significance, we applied the Boolean model, a null or reference model from random set statistics. The Boolean model served as a theoretical benchmark for testing the significance of the observed trends in forest canopy gap dynamics. As a pilot study we analysed airborne laser scan (ALS) data collected in the Krycklan catchment area (Northern Sweden) in 2006 and 2015. The data were analysed using eight different landscape metrics. Despite the moderate resolution of our ALS data the landscape metrics have proved to be useful tools for monitoring canopy gap dynamics of forest ecosystems. The Boolean model has been successful in ascertaining statistical significance and the model parameters indicated important trends. In the Krycklan catchment area, there was no significant trend of canopy gap dynamics indicating any harmful development between 2006 and 2015. On the contrary, we found evidence for gaps closing in and gap locations becoming more random whilst the canopy cover increased between the two survey years.

Notebook‐as‐a‐VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment

AbstractVirtual research environments (VREs) provide user‐centric support in the lifecycle of research activities, for example, discovering and accessing research assets or composing and executing application workflows. A typical VRE is often implemented as an integrated environment, including a catalog of research assets, a workflow management system, a data management framework, and tools for enabling user collaboration. In contrast, notebook environments like Jupyter allow researchers to rapidly prototype scientific code and share their experiments as online accessible notebooks. Jupyter can support several popular languages used by data scientists, such as Python, R, and Julia. However, such notebook environments do not have seamless support for running heavy computations on remote infrastructure or finding and accessing collaborative software code inside notebooks. This article investigates the gap between a notebook environment and a VRE and proposes an embedded VRE solution for the Jupyter environment called Notebook‐as‐a‐VRE (NaaVRE). The NaaVRE solution provides functional components via a component marketplace and allows users to create a customized VRE on top of the Jupyter environment. From the VRE, a user can search research assets (data, software, and algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among users in the community. We demonstrate how such a solution can enhance a legacy workflow that uses Light Detection and Ranging (LiDAR) data from country‐wide airborne laser scanning surveys for deriving geospatial data products of ecosystem structure at high resolution over broad spatial extents. This enables users to scale out the processing of multi‐terabyte LiDAR point clouds for ecological applications to more data sources in a distributed cloud environment. Similar applications could be developed for workflows producing other essential biodiversity variables.

Surface Mass-Balance Gradients From Elevation and Ice Flux Data in the Columbia Basin, Canada

The mass-balance—elevation relation for a given glacier is required for many numerical models of ice flow. Direct measurements of this relation using remotely-sensed methods are complicated by ice dynamics, so observations are currently limited to glaciers where surface mass-balance measurements are routinely made. We test the viability of using the continuity equation to estimate annual surface mass balance between flux-gates in the absence of in situ measurements, on five glaciers in the Columbia Mountains of British Columbia, Canada. Repeat airborne laser scanning surveys of glacier surface elevation, ice penetrating radar surveys and publicly available maps of ice thickness are used to estimate changes in surface elevation and ice flux. We evaluate this approach by comparing modeled to observed mass balance. Modeled mass-balance gradients well-approximate those obtained from direct measurement of surface mass balance, with a mean difference of +6.6 ± 4%. Regressing modeled mass balance, equilibrium line altitudes are on average 15 m higher than satellite-observations of the transient snow line. Estimates of mass balance over flux bins compare less favorably than the gradients. Average mean error (+0.03 ± 0.07 m w.e.) between observed and modeled mass balance over flux bins is relatively small, yet mean absolute error (0.55 ± 0.18 m w.e.) and average modeled mass-balance uncertainty (0.57 m w.e.) are large. Mass conservation, assessed with glaciological data, is respected (when estimates are within 1σ uncertainties) for 84% of flux bins representing 86% of total glacier area. Uncertainty on ice velocity, especially for areas where surface velocity is low (<10 m a−1) contributes the greatest error in estimating ice flux. We find that using modeled ice thicknesses yields comparable modeled mass-balance gradients relative to using observations of ice thickness, but we caution against over-interpreting individual flux-bin mass balances due to large mass-balance residuals. Given the performance of modeled ice thickness and the increasing availability of ice velocity and surface topography data, we suggest that similar efforts to produce mass-balance gradients using modern high-resolution datasets are feasible on larger scales.

Monitoring the understory in eucalyptus plantations using airborne laser scanning

In eucalyptus plantations, the presence of understory increases the risk of fires, acts as an obstacle to forest operations, and leads to yield losses due to competition. The objective of this study was to develop an approach to discriminate the presence or absence of understory in eucalyptus plantations based on airborne laser scanning surveys. The bimodal canopy height profile was modeled by two Weibull density functions: one to model the canopy, and other to model the understory. The parameters used as predictor in the logistic model successfully discriminated the presence or absence of understory. The logistic model composed by ℽ canopy, ℽ understory, and ℽ understory showed higher values of accuracy (0.96) and kappa (0.92), which means an adequate classification of presence of understory and absence of understory. Weibull parameters could be used as input in the logistic regression to effectively identify the presence and absence of understory in eucalyptus plantation.

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.

Persistent effects of fragmentation on tropical rainforest canopy structure after 20yr of isolation.

Assessing the persistent impacts of fragmentation on aboveground structure of tropical forests is essential to understanding the consequences of land use change for carbon storage and other ecosystem functions. We investigated the influence of edge distance and fragment size on canopy structure, aboveground woody biomass (AGB), and AGB turnover in the Biological Dynamics of Forest Fragments Project (BDFFP) in central Amazon, Brazil, after 22+ yr of fragment isolation, by combining canopy variables collected with portable canopy profiling lidar and airborne laser scanning surveys with long-term forest inventories. Forest height decreased by 30% at edges of large fragments (>10ha) and interiors of small fragments (<3ha). In larger fragments, canopy height was reduced up to 40m from edges. Leaf area density profiles differed near edges: the density of understory vegetation was higher and midstory vegetation lower, consistent with canopy reorganization via increased regeneration of pioneers following post-fragmentation mortality of large trees. However, canopy openness and leaf area index remained similar to control plots throughout fragments, while canopy spatial heterogeneity was generally lower at edges. AGB stocks and fluxes were positively related to canopy height and negatively related to spatial heterogeneity. Other forest structure variables typically used to assess the ecological impacts of fragmentation (basal area, density of individuals, and density of pioneer trees) were also related to lidar-derived canopy surface variables. Canopy reorganization through the replacement of edge-sensitive species by disturbance-tolerant ones may have mitigated the biomass loss effects due to fragmentation observed in the earlier years of BDFFP. Lidar technology offered novel insights and observational scales for analysis of the ecological impacts of fragmentation on forest structure and function, specifically aboveground biomass storage.

Comparing Airborne Laser Scanning, and Image-Based Point Clouds by Semi-Global Matching and Enhanced Automatic Terrain Extraction to Estimate Forest Timber Volume

Information pertaining to forest timber volume is crucial for sustainable forest management. Remotely-sensed data have been incorporated into operational forest inventories to serve the need for ever more diverse and detailed forest statistics and to produce spatially explicit data products. In this study, data derived from airborne laser scanning and image-based point clouds were compared using three volume estimation methods to aid wall-to-wall mapping of forest timber volume. Estimates of forest height and tree density metrics derived from remotely-sensed data are used as explanatory variables, and forest timber volumes based on sample field plots are used as response variables. When compared to data derived from image-based point clouds, airborne laser scanning produced slightly more accurate estimates of timber volume, with a root mean square error (RMSE) of 26.3% using multiple linear regression. In comparison, RMSEs for volume estimates derived from image-based point clouds were 28.3% and 29.0%, respectively, using Semi-Global Matching and enhanced Automatic Terrain Extraction methods. Multiple linear regression was the best-performing parameter estimation method when compared to k-Nearest Neighbour and Support Vector Machine. In many countries, aerial imagery is acquired and updated on regular cycles of 1–5 years when compared to more costly, once-off airborne laser scanning surveys. This study demonstrates point clouds generated from such aerial imagery can be used to enhance the estimation of forest parameters at a stand and forest compartment level-scale using small area estimation methods while at the same time achieving sampling error reduction and improving accuracy at the forest enterprise-level scale.

A poststratified ratio estimator for model-assisted biomass estimation in sample-based airborne laser scanning surveys

To estimate the aboveground biomass (AGB) for large areas, two-stage sampling designs using airborne laser scanning (ALS) as a strip sampling tool in combination with subsampling of field plots hav...

Developing an algorithm for enhancement of a digital terrain model for a densely vegetated floodplain wetland

Airborne laser scanning survey data were conducted with a scanning density of 4 points/m2 to accurately map the surface of a unique central European complex of wetlands: the lower Biebrza River valley (Poland). A method to correct a degrading effect of vegetation (so-called “vegetation effect”) on digital terrain models (DTMs) was applied utilizing remotely sensed images, real-time kinematic global positioning system elevation measurements, topographical surveys, and vegetation height measurements. Geographic object-based image analysis (GEOBIA) was performed to map vegetation within the study area that was used as categories from which vegetation height information was derived for the DTM correction. The final DTM was compared with a model obtained, where additional correction of the “vegetation effect” was neglected. A comparison between corrected and uncorrected DTMs demonstrated the importance of accurate topography through a simple presentation of the discrepancies arising in features of the flood using various DTM products. An overall map classification accuracy of 80% was attained with the use of GEOBIA. Correction factors developed for various types of the vegetation reached values from 0.08 up to 0.92 m and were dependent on the vegetation type.

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.

Characterization of Brazilian forest types utilizing canopy height profiles derived from airborne laser scanning

AbstractQuestionsThis study explores how airborne laser scanning surveys can contribute to characterizing and discriminating different Brazilian forest types based on canopy height profiles, and how sampling intensity and plot size affect the results.LocationSeven different vegetation types in Brazil – open ombrophilous Amazonian forest type in the state of Pará State, dense ombrophilous Atlantic forest in the state of São Paulo, semi‐deciduous and rupestrian field in the state of Minas Gerais, and three fast‐growing eucalyptus plantations in the state of São Paulo.MethodsCanopy height profiles were modelled for each forest type using a two‐parameter Weibull distribution. Differences in the values of the Weibull cumulative distribution parameters were studied to evaluate their discriminative capacity. Simulation was subsequently used to determine the number of cells needed to define a reliable sampling intensity for each cell size tested. Eight grid cell sizes were tested against six sampling intensities using 1000 iterations for all seven forest types.ResultsEach forest presented distinct characteristics regarding the canopy height profile. The higher presence of ground returns was remarkable in less layered structured forests (i.e. rupestrian field and eucalyptus plantations). Depending on the forest type, monotonic and sigmoidal shapes were observed for the cumulative distribution function of the canopy height profile. A sigmoidal cumulative distribution function was typical of a more layered structure (dense and open ombrophilous, and semi‐deciduous), whereas less structured typologies (i.e. Rupestrian field and forest plantation) were associated with monotonically shaped functions. Modelling convergence for the canopy height profile was achieved in most cases with data representing at least 2% cover.ConclusionCanopy height profiles generated from ALS data proved different among five different forest types: open ombrophilous, dense ombrophilous, semi‐deciduous, rupestrian field and eucalyptus plantations. Furthermore, large‐scale forest inventories can benefit from the results observed in this study, as it provides recommendations on grid cell size and sampling intensity levels.

Comparison of interferometric and stereo-radargrammetric 3D metrics in mapping of forest resources

Abstract. Accurate forest resources maps are needed in diverse applications ranging from the local forest management to the global climate change research. In particular, it is important to have tools to map changes in forest resources, which helps us to understand the significance of the forest biomass changes in the global carbon cycle. In the task of mapping changes in forest resources for wide areas, Earth Observing satellites could play the key role. In 2013, an EU/FP7-Space funded project “Advanced_SAR” was started with the main objective to develop novel forest resources mapping methods based on the fusion of satellite based 3D measurements and in-situ field measurements of forests. During the summer 2014, an extensive field surveying campaign was carried out in the Evo test site, Southern Finland. Forest inventory attributes of mean tree height, basal area, mean stem diameter, stem volume, and biomass, were determined for 91 test plots having the size of 32 by 32 meters (1024 m2). Simultaneously, a comprehensive set of satellite and airborne data was collected. Satellite data also included a set of TanDEM-X (TDX) and TerraSAR-X (TSX) X-band synthetic aperture radar (SAR) images, suitable for interferometric and stereo-radargrammetric processing to extract 3D elevation data representing the forest canopy. In the present study, we compared the accuracy of TDX InSAR and TSX stereo-radargrammetric derived 3D metrics in forest inventory attribute prediction. First, 3D data were extracted from TDX and TSX images. Then, 3D data were processed as elevations above the ground surface (forest canopy height values) using an accurate Digital Terrain Model (DTM) based on airborne laser scanning survey. Finally, 3D metrics were calculated from the canopy height values for each test plot and the 3D metrics were compared with the field reference data. The Random Forest method was used in the forest inventory attributes prediction. Based on the results InSAR showed slightly better performance in forest attribute (i.e. mean tree height, basal area, mean stem diameter, stem volume, and biomass) prediction than stereo-radargrammetry. The results were 20.1% and 28.6% in relative root mean square error (RMSE) for biomass prediction, for TDX and TSX respectively.

Are flat‐field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale

AbstractSingle or a limited number of point observations, such as from index stations, are commonly assumed to be representative for the snow cover of larger areas in many applications. This study presents a systematic investigation of the relationship between point observations and areal mean snow depths ranging from the scale of tens of metres to entire catchments. We analyse aerial snow depth information from four mountain regions in the European Alps, one in the Spanish Pyrenees and one in the Canadian Rocky Mountains, obtained from airborne laser scanning surveys. This rich data set allowed to compare point values with snow cover statistics, reflecting the real snow depth distribution of the investigation areas. We present two contrasting approaches in order to assess the representativeness of typical flat‐field snow depth measurements. In the first approach, we define potential index stations based on topographic characteristics as commonly applied for snow cover monitoring stations. The point observations of these index stations are then compared with the mean values in their vicinities. We show that most of the index stations strongly overestimate the snow depth of the catchment and of their surrounding area at distances of several hundreds of metres. Results confirm the expectation that the larger the support area, the smaller the difference to the mean of the complete catchment. The second approach was to analyse topographic characteristics of all cells with snow depths that deviated less than 10% from the catchment mean. It appears that these representative cells are rather randomly distributed and cannot be identified a priori. In summary, our results show large potential biases of index stations with respect to snow distribution and therefore also snow water equivalent. Copyright © 2014 John Wiley &amp; Sons, Ltd.

A technique of avalanche risk assessment using airborne laser scanning survey

雪崩危険度評価手法には定まった手法はなく，雪崩対策を進めるにあたり筆者らは国や地方自治体等の事業主体が独自に設定した評価手法により多くの斜面の危険度を評価してきた．これまでは斜面の傾斜や植生，積雪深，方位，見通し角などを定性的に扱い，道路や集落での危険性を評価することが多く，2004年に刊行された「2005除雪・防雪ハンドブック（防雪編）」には各機関のいくつかの例が示されている．筆者らが上記ハンドブックにより斜面の危険度を評価する際には，植生は写真判読によって疎密を判定することが多いため，判読者の個人差が現れ，雪崩危険度にも個人差が反映されることが多々あった．また，地形図の精度もまちまちであり，場合によっては，必ずしも正確な地形を再現していないものも見られ，任意の斜面における危険度の推定精度が落ちることもあった． 近年，ALS（航空機搭載型レーザスキャナ）の普及に伴い地表面や積雪面の標高データや樹高を高精度で計測して，さまざまなデータを，個人差による判断を排除し，合理的に解析していく事例が増えてきた．本報告では，ALS の計測データを雪崩の解析に応用する手法を紹介する．

POINT CLOUD METRICS FOR SEPARATING STANDING ARCHAEOLOGICAL REMAINS AND LOW VEGETATION IN ALS DATA

Abstract. The integration of Airborne Laser Scanning survey into archaeological research and cultural heritage management has substantially added to our knowledge of archaeological remains in forested areas, and is changing our understanding of how these landscapes functioned in the past. While many types of archaeological remains manifest as micro-topography, several important classes of features commonly appear as standing remains. The identification of these remains is important for archaeological prospection surveys based on ALS data, and typically represent structures from the Roman, Medieval and early Modern periods. Standing structures in mixed scenes with vegetation are not well addressed by standard classification approaches developed to identify bare earth (terrain), individual trees or plot characteristics, or buildings (roofed structures). In this paper we propose an approach to the identification of these structures in the point cloud based on multi-scale measures of local density, roughness, and normal orientation. We demonstrate this approach using discrete-return ALS data collected in the Franche-Comte region of France at a nominal point density of 8 pts/m2, a resolution which, in coming years, will become increasingly available to archaeologists through government supported mapping schemes.

Automated planimetric quality control in high accuracy airborne laser scanning surveys

With the increasing point densities of airborne laser scanning surveys, the applications of the generated point clouds have evolved from the production of digital terrain models to 3D modelling of a wide variety of objects. Likewise in quality control procedures criteria for height accuracy are extended with measures to describe the planimetric accuracy. This paper introduces a measure for the potential accuracy of outlining objects in a point cloud. It describes how this accuracy can be verified with the use of ridge lines of gable roofs in strip overlaps. Because of the high accuracy of modern laser scanning surveys, the influence of roof tiles onto the estimation of ridge lines is explicitly modelled. New selection criteria are introduced that allow an automated, reliable and accurate extraction of ridge lines from point clouds. The applicability of the procedure is demonstrated in a pilot project in an area covering 100,000ha with around 20 billion points.

Prediction of plot-level forest variables using TerraSAR-X stereo SAR data

Promising results have been obtained in recent years in the use of high-resolution X-band stereo SAR satellite images (with the spatial resolution being in order of meters) in the extraction of elevation data. In the case of forested areas, the extracted elevation values appear to be somewhere between the ground surface and the top of the canopy, depending on the forest characteristics. If the ground surface elevations are known by using a Digital Terrain Model derived from Airborne Laser Scanning surveys, it is possible to obtain information related to forest resources. To the best of our knowledge, this paper, presents the first attempt to obtain forest variables at plot level based on TerraSAR-X stereo SAR images (non-interferometric data). The study set consisted of 109 circular test plots for which forest variables were observed by performing tree-specific measurements. The statistical features were calculated for each test plot from the elevation values extracted from stereo SAR data. This was followed by predicting field-observed plot-level forest variables from the features derived from stereo SAR data using the Nearest Neighbors approach which employs the Random Forest technique in selection of the nearest neighbors. The relative errors (RMSE%) for predicting the stem volume, basal area, mean forest canopy height, and mean diameter values were 34.0%, 29.0%, 14.0%, and 19.7%, respectively. The results indicate that there was no clear saturation level in stem volume estimation. In this case study, stem volumes were predicted up to about 400 m 3/ha. In the light of these results, stereo SAR data appears to be an interesting remote-sensing technique for future forest inventories. For example, stereo SAR data could have high potential in forest inventories as the SAR-based features can be adapted to the methods currently used in inventories.

Geomorphological map of the 1944 Vesuvius lava flow (Italy)

Please click here to download the map associated with this article. The map of the 1944 lava flow shows the geomorphological characteristics of a sector of the Vesuvius volcano (Italy) covered by the products emitted during the last effusive phase. The map has been produced based on the analysis and interpretation of (a) thematic maps (slope, aspect, relative relief) derived from a high resolution (0.33 m pixel) Digital Terrain Model obtained by an Airborne Laser Scanning survey, and (b) a 1 m pixel digital colour orthophoto. Different ow structures and morphologies have been interpreted. The analytical approach proposed here can be used to characterize the smaller scale topographic/geomorphological features of volcanoes and gives constraints on the mechanism of emplacement and modelling strategies of lava flows.