Low-density LiDAR Research Articles

Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest

Using a combination of remote sensing data, Pinus pinaster Ait. and Pinus pinea L. were distinguished at individual tree level in mixed Mediterranean stands with over 95% accuracy. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products, stone pine nuts, and resin, and aiding forest managers to accurately predict this production. The discrimination of tree species at individual level in mixed Mediterranean forest based on remote sensing is a field which has gained greater importance. In these stands, the capacity to predict the quality and quantity of non-wood forest products is particularly important due to the very different goods the two species produce. To assess the potential of using low-density airborne LiDAR data combined with high-resolution Pleiades images to discriminate two different pine species in mixed Mediterranean forest (Pinus pinea L. and Pinus pinaster Ait.) at individual tree level. A Random Forest model was trained using plots from the pure stand dataset, determining which LiDAR and satellite variables allow us to obtain better discrimination between groups. The model constructed was then validated by classifying individuals in an independent set of pure and mixed stands. The model combining LiDAR and Pleiades data provided greater accuracy (83.3% and 63% in pure and mixed validation stands, respectively) than the models which only use one type of covariables. The automatic crown delineation tool developed allows two very similar species in mixed Mediterranean conifer forest to be discriminated using continuous spatial information at the surface: Pleiades images and open source LiDAR data. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products and aiding forest managers to accurately predict production.

Overstory-understory land cover mapping at the watershed scale: accuracy enhancement by multitemporal remote sensing analysis and LiDAR.

In forested watersheds, density, land cover, and its vertical structure are crucial factors for flood management, ecosystem monitoring, and biomass inventory. Nowadays, producing land cover maps with high accuracy has become a reality with the application of remote sensing techniques, but in some situations, it is not so easy to distinguish between the overstory and understory vegetation with only spectral information. The main goal of this study was to analyze the accuracy enhancement in overstory and understory land cover mapping at the watershed scale when using the data fusion of seasonal and annual time series of Sentinel-2 images complemented with low-density LiDAR and soil and vegetation indices. The study area was composed by two neighboring watersheds in Badajoz province (Spain). The accuracy of land cover classifications was trained in two ways: first, for each season and several soil-vegetation indices; and second, for the annual series and soil-vegetation indices. Next, LiDAR data were included in both analyses by means of a Boolean metric concerning the height. The obtained results showed that the overall accuracy was better with the annual evaluation when only spectral information was used for the classification. Additionally, if LiDAR data were included in the classification (data fusion), the overall accuracies were highly improved, especially in summer and autumn seasons. This improvement can be a significant issue in the analysis of vegetation structure and its spatial distribution as it is decisive for watershed ecosystem management.

Environmental drivers of fire severity in extreme fire events that affect Mediterranean pine forest ecosystems

The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecological and socio-economic damage. In this study, we aim to identify the main environmental factors driving fire severity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations for reducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystem dominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differenced Normalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictors included pre-fire vegetation greenness (normalized difference vegetation index and normalized difference water index), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weather conditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) and physical variables (topographic complexity, actual evapotranspiration and water deficit). We applied the Random Forest machine learning algorithm to assess the influence of these environmental factors on fire severity. Models explained 42% of the variance using a parsimonious set of five predictors: NDWI, NDVI, time since the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicated that fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-fire vegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement of vegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using only physical variables exhibited a notable association with fire severity. However, results suggested that the control exerted by the physical properties may be partially overcome by the availability and structural characteristics of fuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuel structure throughout the coefficient of variation of heights. This study provides relevant keys for decision-making on pre-fire management such as fuel treatment, which help to reduce fire severity.

Boresight Calibration of Low Point Density Lidar Sensors

Boresight Calibration of Low Point Density Lidar Sensors

Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data

We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated every 6 years (temporal cover planned for countrywide LiDAR flights in Spain). The estimation of stand diameter distribution is informative for forest managers in terms of stand structure, forest growth model inputs, and economic timber value. In this sense, airborne LiDAR may represent an adequate source of information. The objective was to evaluate the use of low-density Spanish countrywide LiDAR data for estimating diameter distributions in Pinus radiata D. Don stands in NW Spain. The empirical distributions were obtained from 25 sample plots. We applied the moment-based parameter recovery method in combination with the Weibull function to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. We evaluated the results by using the Kolmogorov–Smirnov (KS) test and a classification tree and random forest (RF) to relate the KS test result for each plot to stand-level variables. The models used to estimate average (dm) and quadratic (dg) mean diameters from LiDAR metrics, required for recovery of the Weibull parameters, explained a high percentage of the observed variance (77 and 80%, respectively), with RMSE values of 3.626 and 3.422 cm for the same variables. However, the proportion of plots accepted by the KS was low. This poor performance may be due to the strictness of the KS test and/or by the characteristics of the LiDAR flight. The results justify the assessment of this approach over different species and forest types in regional or countrywide surveys.

High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density lidar

ABSTRACTMany countries have employed recently developed technologies, such as airborne lidar, to capture nationwide three-dimensional information over the past few years. In Spain, a huge volume of lidar information is available for the majority of the territory in the Spanish National Plan of Aerial Orthophotography. In this article, a multi-source approach is taken, integrating available databases, such as nationwide lidar flights, Landsat imagery and permanent field plots from the Spanish National Forest Inventory, with good results in the generation of wall-to-wall forest inventories. Volume and basal area errors are similar to those obtained by other authors (using specific lidar flights and field plots) for similar species. Errors in stem number estimates are larger than the values found in the literature as a consequence of the great influence of variable-radius plots, as used in the Spanish National Forest Inventory, on this variable.

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m−2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m−2. For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha−1 when pulse density decreased from 12 to 0.2 pulses·m−2. The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha−1. The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data

Mapping structural changes in vegetation dynamics has, for a long time, been carried out using satellite images, orthophotos and, more recently, airborne lidar acquisitions. Lidar has established its position as providing accurate material for structure-based analyses but its limited availability, relatively short history, and lack of spectral information, however, are generally impeding the use of lidar data for change detection purposes. A potential solution in respect of detecting both contemporary vegetation structures and their previous trajectories is to combine lidar acquisitions with optical remote sensing data, which can substantially extend the coverage, span and spectral range needed for vegetation mapping. In this study, we tested the simultaneous use of a single low-density lidar data set, a series of Landsat satellite frames and two high-resolution orthophotos to detect vegetation succession related to grassland overgrowth, i.e. encroachment of woody plants into semi-natural grasslands. We built several alternative Random Forest models with different sets of variables and tested the applicability of respective data sources for change detection purposes, aiming at distinguishing unchanged grassland and woodland areas from overgrown grasslands. Our results show that while lidar alone provides a solid basis for indicating structural differences between grassland and woodland vegetation, and orthophoto-generated variables alone are better in detecting successional changes, their combination works considerably better than its respective parts. More specifically, a model combining all the used data sets reduces the total error from 17.0% to 11.0% and omission error of detecting overgrown grasslands from 56.9% to 31.2%, when compared to model constructed solely based on lidar data. This pinpoints the efficiency of the approach where lidar-generated structural metrics are combined with optical and multitemporal observations, providing a workable framework to identify structurally oriented and dynamically organized landscape phenomena, such as grassland overgrowth.

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.

Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia

The airborne lidar system (ALS) provides a means to efficiently monitor the status of remote tropical forests and continues to be the subject of intense evaluation. However, the cost of ALS acquisition can vary significantly depending on the acquisition parameters, particularly the return density (i.e., spatial resolution) of the lidar point cloud. This study assessed the effect of lidar return density on the accuracy of lidar metrics and regression models for estimating aboveground biomass (AGB) and basal area (BA) in tropical peat swamp forests (PSF) in Kalimantan, Indonesia. A large dataset of ALS covering an area of 123,000ha was used in this study. This study found that cumulative return proportion (CRP) variables represent a better accumulation of AGB over tree heights than height-related variables. The CRP variables in power models explained 80.9% and 90.9% of the BA and AGB variations, respectively. Further, it was found that low-density (and low-cost) lidar should be considered as a feasible option for assessing AGB and BA in vast areas of flat, lowland PSF. The performance of the models generated using reduced return densities as low as 1/9 returns per m2 also yielded strong agreement with the original high-density data. The use model-based statistical inferences enabled relatively precise estimates of the mean AGB at the landscape scale to be obtained with a fairly low-density of 1/4 returns per m2, with less than 10% standard error (SE). Further, even when very low-density lidar data was used (i.e., 1/49 returns per m2) the bias of the mean AGB estimates were still less than 10% with a SE of approximately 15%. This study also investigated the influence of different DTM resolutions for normalizing the elevation during the generation of forest-related lidar metrics using various return densities point cloud. We found that the high-resolution digital terrain model (DTM) had little effect on the accuracy of lidar metrics calculation in PSF. The accuracy of low-density lidar metrics in PSF was more influenced by the density of aboveground returns, rather than the last return. This is due to the flat topography of the study area. The results of this study will be valuable for future economical and feasible assessments of forest metrics over large areas of tropical peat swamp ecosystems.

Estimation of Voxel-Based Above-Ground Biomass Using Airborne LiDAR Data in an Intact Tropical Rain Forest, Brunei

The advancement of LiDAR technology has enabled more detailed evaluations of forest structures. The so-called “Volumetric pixel (voxel)” has emerged as a new comprehensive approach. The purpose of this study was to estimate plot-level above-ground biomass (AGB) in different plot sizes of 20 m × 20 m and 30 m × 30 m, and to develop a regression model for AGB prediction. Both point cloud-based (PCB) and voxel-based (VB) metrics were used to maximize the efficiency of low-density LiDAR data within a dense forest. Multiple regression model AGB prediction performance was found to be greatest in the 30 m × 30 m plots, with R2, adjusted R2, and standard deviation values of 0.92, 0.87, and 35.13 Mg∙ha−1, respectively. Five out of the eight selected independent variables were derived from VB metrics and the other three were derived from PCB metrics. Validation of accuracy yielded RMSE and NRMSE values of 27.8 Mg∙ha−1 and 9.2%, respectively, which is a reasonable estimate for this structurally complex intact forest that has shown high NRMSE values in previous studies. This voxel-based approach enables a greater understanding of complex forest structure and is expected to contribute to the advancement of forest carbon quantification techniques.

Mapping Forest Structure and Composition from Low-Density LiDAR for Informed Forest, Fuel, and Fire Management at Eglin Air Force Base, Florida, USA

Eglin Air Force Base (AFB) in Florida, in the United States, conserves a large reservoir of native longleaf pine (Pinus palustris Mill.) stands that land managers maintain by using frequent fires. We predicted tree density, basal area, and dominant tree species from 195 forest inventory plots, low-density airborne LiDAR, and Landsat data available across the entirety of Eglin AFB. We used the Random Forests (RF) machine learning algorithm to predict the 3 overstory responses via univariate regression or classification, or multivariate k-NN imputation. Ten predictor variables explained ∼ 50% of variation and were used in all models. Model accuracy and precision statistics were similar among the various RF approaches, so we chose the imputation approach for its advantage of allowing prediction of the ancillary plot attributes of surface fuels and ground cover plant species richness. Maps of the 3 overstory response variables and ancillary attributes were imputed at 30-m resolution and then aggregated to the management block level, where they were significantly correlated with each other and with fire history variables summarized from independent data. We conclude that functional relationships among overstory structure, surface fuels, species richness, and fire history emerge and become more apparent at the block level where management decisions are made.

Using low density LiDAR data to map Mediterranean forest characteristics by means of an area-based approach and height threshold analysis

<p>This study reports progress in forest inventory methods involving the use of low density airborne LiDAR data and an area-based approach (ABA). It also emphasizes the usefulness of the Spanish countrywide LiDAR dataset for mapping forest stand attributes in Mediterranean stone pine forest characterized by complex orography. Lowdensity airborne LiDAR data (0.5 first returns m<sup><span lang="EN-US">–2</span></sup>) was used to develop individual regression models for a set of forest stand variables in different types of forest. LiDAR data is now freely available for most of the Spanish territory and is provided by the Spanish National Aerial Photography Program (Plan Nacional de Ortofotografía Aérea, PNOA). The influence of height thresholds (MHT: Minimun Height Threshold and BHT: Break Height Threshold) used in extracting LiDAR metrics was also investigated. The best regression models explained 61-85%, 67-98% and 74-98% of the variability in ground-truth stand height, basal area and volume, respectively. The magnitude of error for predicting structural vegetation parameters was higher in closed deciduous and mixed forest than in the more homogeneous coniferous stands. Analysis of height thresholds (HT) revealed that these parameters were not particularly important for estimating several forest attributes in the coniferous forest; nevertheless, substantial differences in volume modelling were observed when the height thresholds (MHT and BHT) were increased in complex structural vegetation (mixed and deciduous forest). A metric-by-metric analysis revealed that there were significant differences in most of the explanatory variables computed from different height thresholds (HBT and MHT).The best models were applied to the reference stands to yield spatially explicit predictions about the forest resources. Reliable mapping of biometric variables was implemented to facilitate effective and sustainable management strategies and practices in Mediterranean Forest ecosystems.</p>

Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees.

Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods

<p>Los métodos de estimación por áreas son ya habituales para el uso de escaneo láser aerotransportado (ALS) en la predicción de atributos forestales descritos por variables continuas: biomasa, volumen, densidad, etc. No obstante, apenas se ha prestado atención a los métodos de clasificación por aprendizaje automático, que por otro lado son frecuentes en diversos campos de la teledetección, como la identificación de coberturas del suelo por sensores multiespectrales. En este artículo hemos centrado la atención en métodos estadísticos destinados a predecir variables discretas, para obtener clasificaciones supervisadas de datos ALS. Varios métodos han sido revisados: clasificadores de análisis discriminante (DA) –máxima verosimilitud, elipsoide de volumen mínimo y Bayesiano ingenuo–, máquinas de vector soporte (SVM), redes neuronales artificiales (ANN), selvas aleatorias (RF), y vecino más próximo (NN). La comparación se ha realizado en el contexto de una clasificación en las etapas de desarrollo (ED) utilizadas en la gestión forestal de los bosques de Finlandia, utilizando para ello datos de baja densidad de su vuelo nacional. RF y NN produjeron las matrices de error más equilibradas, con predicciones por validación cruzada no sesgadas para todas las EDs. Aunque SVM y ANN mostraron las mayores precisiones, obtuvieron resultados muy dispares entre las distintas EDs, siendo óptimas sólo para algunas concretas. DA obtuvo los peores resultados, y sólo se observó que pudieran ser beneficiosas en la predicción de rodales en la etapa de diseminado. Nuestros resultados muestran que, además de la confirmada capacidad del ALS para predecir variables de cuantificación de las masas forestales, también existe potencial en la clasificación de variables categóricas en general, y tipologías forestales en particular. En conclusión, consideramos que estas metodologías se pueden también adaptar a las clases naturales de edad y tipos estructurales que sean relevantes en ecosistemas mediterráneos, abriendo un abanico de posibilidades para investigación en ALS, con mucho potencial.</p>

고밀도 LiDAR 자료를 이용한 산림자원 추출에 관한 연구

이 연구의 목적은 고밀도의 LiDAR 자료를 이용하여 산림에 대한 정보를 보다 정확하게 정량화하기 위한 방법을 연구하는데 있다. 기후변화와 관련하여 온실가스 흡수량 확보를 위한 산림자원의 정량적 분석을 위해 고밀도의 LiDAR 자료 획득방법과 산림자원의 추출방안을 연구하였다. 그 결과, 고밀도의 LiDAR 자료를 이용하여 산림자원의 객체별 수고 및 수관을 추출 할 수 있었다. 산림자원의 수고 및 수관의 추출은 수치지형도 제작에 사용되는 평균 2.5points/㎡의 저밀도 LiDAR 자료를 사용할 경우 정확한 추출이 어려웠다. 지형, 산림자원의 특성, 데이터 용량 및 처리속도 등을 고려하여 7points/㎡의 고밀도 LiDAR 자료를 이용할 경우 다수의 개체를 추출할 수 있었다. 산악지형과 혼합지형이 대체적으로 평탄지형보다 점밀도가 높고 다수의 산림자원이 추출되었다. 또한 도엽별로 처리할 경우 경계지역에서의 개체를 추출 할 수 없었으며, 넓은 지역을 한번에 처리하기 보다는 처리시간과 대상지역의 특성을 고려하여 지역을 세분화하여 추출하여야 함을 알 수 있었다. 향후 산림자원 정보를 이용한 온실가스 흡수량에 대한 심도있는 연구가 이루어져야할 것이다. The objective of this study is in investigating the research for more accurately quantify the information on mountain forest by using the data on high density LiDAR. For the quantitative analysis of mountain forest resources, we investigated the method to acquire the data on high density LiDAR and extract mountain forest resources. Consequently, the height and girth of a tree each mountain forest resources could be extracted by using the data on high density LiDAR. When using the data on low density LiDAR of 2.5points/m² in average used to produce digital map, it was difficult to extract the exact height and girth of mountain forest resources. If using the data on high density LiDAR of 7points/m² by considering topography, the property of mountain forest resources, data capacity and process velocity, etc, it was found that multitudinous entities could be extracted. It was found that mountain topography and mixed topography were generally denser than plane topography and multitudinous mountain forest resources could be extracted. Furthermore, it was also found that the entity at the border could not be extracted, when each partition was individually processed and the area should be subdivided and extracted by considering the process time and property of target area rather than processing wide area at once. We expect to be studied more profoundly the absorption quantity of greenhouse gas later by using information on mountain forest resources in the future.

Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping

This paper assessed the performance of object-based supervised support vector machine (SVM) and rule-based techniques in classifying tropical vegetated floodplain using 0.6m QuickBird image and LIDAR dataset of 1.4 points per square meter (PPSM). This is particularly significant in hydraulic modelling in which vegetation roughness is a big uncertainty and largely relies on land cover classification. The supervised classification resulted in 79.40% overall accuracy whilst the results improved by 8% with rule-based classification. 40 sample plots of trees and shrubs were measured to be compared to obtain the best classification results. The results showed a linear relationship between tree diameters and NDVI with a high Pearson correlation of 0.76 and coefficient of determination (r2) of 0.58. The canopy areas of shrubs were found to be representative spatially with an even higher Pearson correlation of 0.98 and r2 of 0.95. The study concluded that the fusion of QuickBird image and low point density LIDAR in rule-based classification together with field data were useful in quantifying tropical trees and shrubs.

Automatic Classification of coarse density LiDAR data in urban area

Abstract. The classification of different objects in the urban area using airborne LIDAR point clouds is a challenging problem especially with low density data. This problem is even more complicated if RGB information is not available with the point clouds. The aim of this paper is to present a framework for the classification of the low density LIDAR data in urban area with the objective to identify buildings, vehicles, trees and roads, without the use of RGB information. The approach is based on several steps, from the extraction of above the ground objects, classification using PCA, computing the NDSM and intensity analysis, for which a correction strategy was developed. The airborne LIDAR data used to test the research framework are of low density (1.41 pts/m2) and were taken over an urban area in San Diego, California, USA. The results showed that the proposed framework is efficient and robust for the classification of objects.

On the importance of topography, site quality, stock quality and planting date in a semiarid plantation: Feasibility of using low-density LiDAR

Pine seedling survival and growth in eastern Spain have often been related to site preparation, planting date and seedling stock. However, in spite of the acknowledged importance of spatial heterogeneity in seedling performance, little is understood about how topography-related spatial patterns may modify seedling response to plantation, particularly on dry sites. We tested the hypotheses that growth and survival of Pinus halepensis seedlings are related to stock quality, plantation date and topographic conditions, as well as the spatial pattern of environmental variables using a spatially explicit design. The plantation treatment consisted of three seedling stocks, two plantation dates and two contrasted quality sites. Topographic features, such as slope, aspect, Compound Topographic Index and flow accumulation, were measured using GPS and low density LiDAR, with growth and survival monitored over a period of one and two years, respectively. The spatial pattern of the study variables was examined via spatial analysis by distance indices (SADIE). The relative importance of each topographic variable explaining the spatial pattern (local aggregation indices, ν) of seedling response was examined using ordinary least squares (OLS) regressions. P. halepensis seedlings showed higher survival and growth in better sites and early plantations, but they were very similar between seedling stocks. A significantly greater proportion of seedlings survived in early date of plantation (54%) compared to medium date (36%), and in the favorable site (51%) versus the restrictive site (38%). Seedlings also grew significantly faster for those treatments during the first year. However, stock quality had few effects on survivorship and growth. All the topographic and seedling response variables exhibited an aggregated spatial pattern. Seedling survival was clearly associated with topographic patterns, particularly to those related to water availability (CTI and flow accumulation), indicating that on topographic-spatial scales, seedling response is driven by soil–water dynamics in Mediterranean ecosystems. The topographic morphology described by LiDAR was also closely linked to seedling response, thus suggesting the potential of these data to evaluate reforestation success. Accurate maps of topographic factors may indicate whether a plantation has a higher survival and growth potential and with routine reforestation planning activities such as soil preparation.

Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities

Aims of study: To evaluate the potential use of canopy height and intensity distributions, determined by airborne LiDAR, for the estimation of crown, stem and aboveground biomass fractions.To assess the effects of a reduction in LiDAR pulse densities on model precision.Area of study: The study area is located in Galicia, NW Spain. The forests are representative of Eucalyptus globules stands in NW Spain, characterized by low-intensity silvicultural treatments and by the presence of tall shrub.Material and methods: Linear, multiplicative power and exponential models were used to establish empirical relationships between field measurements and LiDAR metrics.A random selection of LiDAR returns and a comparison of the prediction errors by LiDAR pulse density factor were performed to study a possible loss of fit in these models.Main results: Models showed similar goodness-of-fit statistics to those reported in the international literature. R2 ranged from 0.52 to 0.75 for stand crown biomass, from 0.64 to 0.87 for stand stem biomass, and from 0.63 to 0.86 for stand aboveground biomass. The RMSE/MEAN · 100 of the set of fitted models ranged from 17.4% to 28.4%.Models precision was essentially maintained when 87.5% of the original point cloud was reduced, i.e. a reduction from 4 pulses m–2 to 0.5 pulses m–2.Research highlights: Considering the results of this study, the low-density LiDAR data that are released by the Spanish National Geographic Institute will be an excellent source of information for reducing the cost of forest inventories.Key words: Eucalypt plantations; airborne laser scanning; aboveground biomass; carbon stocks; remote sensing; forest inventory.