Low-density Light Detection And Ranging Research Articles

MAPPING 3D STRUCTURE OF URBAN TREES USING AIRBORNE LIDAR DATA FOR TOPOGRAPHIC SURVEY

Abstract. The designing of urban spaces utilizing trees is a promising approach to alleviate the severe thermal environment. Three-dimensional (3D) information on individual trees is important for their maintenance and for evaluating their microclimatic effects. Methods have been developed for estimating the 3D distribution of leaf area density (LAD) using high-density airborne light detection and ranging (LiDAR) point cloud. However, the coverage of high-density data is limited. This study sought to develop a method for estimating LAD distribution using existing low-density LiDAR data that was obtained for the nationwide topographic survey. A point cloud with the same density as the existing data was generated from the high-density data. LAD was estimated using the generated point cloud and was compared with LAD derived from the high-density data and terrestrial LiDAR to clarify the spatial scale of information obtained from the existing data. A method to improve the spatial scale was also examined. These methods were applied to the actual existing LiDAR data to generate a 3D information map at an urban scale.

Postfire damage zoning with open low-density LiDAR data sources in semi-arid forests of the Iberian Peninsula

Wildfires represent one of the major ecological disasters with significant repercussions for terrestrial ecosystems (loss of biodiversity, material damages, erosion, etc). Using LiDAR (Light Detection and Ranging) technology allows large amounts of forest measurements data to be obtained, such as size of volume, biomass, canopy cover, heights, among others. It also allows to quantify some damage caused by forest fires by comparing LiDAR data point clouds at two different times (before and after fire). By correlating in situ measurements with statistical parameters from point clouds, we aim to evaluate postfire deviations in volume, biomass and canopy integrity. The aim of this study was to estimate the losses caused by the 2012 wildfire in the Public Utility Forest No.82 “Sierra de los Donceles” (Hellín, Albacete, Spain), which devastated more than 5000 ha. We used 65 plots from the 4th National Forest Inventory (NFI4) and the LiDAR data from 2009 to develop statistical models and train Artificial Neural Networks (ANN). Another independent set of 29 plots (of 14.1-m radius with sub-metric GPS) was employed to validate the regression and ANN models. The regression results showed better performance for estimating heights (R2 = 75.03%, RMSE = 28.90%), volume (R2 = 70.32%, RMSE = 37.61%) and basal area (R2 = 68.37%, RMSE = 37.18). The total biomass showed the best fit (R2 = 93.51%), but also the maximum error (RMSE = 40.08%). The worst fit result was the estimation of number of trees per hectare (R2 = 33.76%, RMSE = 30.97%). All the models were improved with neural network implementation: number of trees per hectare (R2 = 44.41%, RMSE = 26.74%), basal area (R2 = 68.81%, RMSE = 33.49%), dominant height (R2 = 75.55%, RMSE = 15.43%), volume (R2 = 61.03%, RMSE = 14.78%) and biomass (R2 = 99.21%, RMSE = 20.81%). With our top-performing models in the 2009- and 2016-point cloud data, we measured forest reductions in terms of trees per hectare, basal area, predominant height, volume and biomass. These models provided a comprehensive view of forest losses by highlighting major decreases in vegetation coverage, tree density and tree heights.

An Automatic Lane Marking Detection Method With Low-Density Roadside LiDAR Data

Lane information is an essential part of high-resolution micro-traffic data (HRMTD). Most of the lane detection algorithms for Light Detection and Ranging (LiDAR) are applied to high-density onboard LiDAR and airborne LiDAR, which cannot be used directly to process low-density roadside LiDAR data. In this article, an algorithm for lane detection applied to low-density roadside LiDAR is proposed, which includes three steps: ground recognition, lane marking point extraction, and pavement lane marking points clustering. Firstly, an improved algorithm for ground recognition is proposed to get normal ground points (NGPs). Then, the lane marking points are extracted from the no lane marking points based on the difference of laser intensity. Finally, we divided the ground within the range of LiDAR scanning into different stripes, and search the maximum linear density of the stripe to extract the lane markings. The average distance error (ADE) of lane detection results is less than 0.1m by fusing and comparing the test results with the aerial photographs. The proposed algorithm is also proved to be robust in multiple test locations and the results can be used to assist high-precision vehicle positioning in vehicle-to-infrastructure (V2I) cooperation application within the intelligent transportation system.

Estimation of Tree Height by Combining Low Density Airborne LiDAR Data and Images Using the 3D Tree Model: A Case Study in a Subtropical Forest in China

In general, low density airborne LiDAR (Light Detection and Ranging) data are typically used to obtain the average height of forest trees. If the data could be used to obtain the tree height at the single tree level, it would greatly extend the usage of the data. Since the tree top position is often missed by the low density LiDAR pulse point, the estimated forest tree height at the single tree level is generally lower than the actual tree height when low density LiDAR data are used for the estimation. To resolve this problem, in this paper, a modified approach based on three-dimensional (3D) parameter tree model was adopted to reconstruct the tree height at the single tree level by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data. The approach was applied to two coniferous forest plots in the subtropical forest region, Fujian Province, China. The following conclusions were reached after analyzing the results: The marker-controlled watershed segmentation method is able to effectively extract the crown profile from sub meter-level resolution images without the aid of the height information of LiDAR data. The adaptive local maximum method satisfies the need for detecting the vertex of a single tree crown. The improved following-valley approach is available for estimating the tree crown diameter. The 3D parameter tree model, which can take advantage of low-density airborne LiDAR data and high resolution images, is feasible for improving the estimation accuracy of the tree height. Compared to the tree height results from only using the low density LiDAR data, this approach can achieve higher estimation accuracy. The accuracy of the tree height estimation at the single tree level for two test areas was more than 80%, and the average estimation error of the tree height was 0.7 m. The modified approach based on the three-dimensional parameter tree model can effectively increase the estimation accuracy of individual tree height by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data.

Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (Castanea sativa) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R2 value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution.

Automatic mapping of tree crowns in scattered-tree woodlands using low-density LiDAR data and infrared imagery

Accurate estimation of canopy cover (CC) and its delineation in tree-grass ecosystems (TGE) such as savannas and silvopastoral systems are crucial to analyze and model the functioning of these systems and the role of trees at different scales. At large-scale, remote sensing is a key tool, and assessments of land use and landscape elements often rely on satellite and aircraft sensor imagery and light detection and ranging (LiDAR) data. This study addresses automatic mapping of CC in TGE using high-resolution infrared orthophotographs and low-density LiDAR data from the Spanish Aerial Orthophoto National Plan (PNOA). Canopy cover mapping was performed in two areas with different structural complexity (with or without shrub layer) by an object based image analysis (OBIA) approach applied on canopy height model (CHM) generated by the LiDAR point cloud, infrared imagery, and both sources combined (LiDAR-imagery fusion). Overall accuracy (OA) was more than 91% with the two separated methods and more than 95% combining them. The results show that low-density LiDAR data is not a reliable source for the automatic mapping of canopy of scattered tree in TGE, OBIA on high-resolution infrared orthophotographs allows a more accurate automatic delineation of tree canopy, and the combined approach was the only way to obtain acceptable mapping in shrub-encroached stands, where errors were still greater than 15% with single-source based methods.

Comparison of Machine Learning Algorithms for Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne LiDAR Data and Multispectral Images

Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.

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.

Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m2) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass.

Delineation of individual deciduous trees in plantations with low-density LiDAR data

ABSTRACTDelineation of individual deciduous trees with Light Detection and Ranging (LiDAR) data has long been sought for accurate forest inventory in temperate forests. Previous attempts mainly focused on high-density LiDAR data to obtain reliable delineation results, which may have limited applications due to the high cost and low availability of such data. Here, the feasibility of individual deciduous tree delineation with low-density LiDAR data was examined using a point-density-based algorithm. First a high-resolution point density model (PDM) was developed from low-density LiDAR point cloud to locate individual trees through the horizontal spatial distribution of LiDAR points. Then, individual tree crowns and associated attributes were delineated with a 2D marker-controlled watershed segmentation. Additionally, the PDM-based approach was compared with a conventional canopy height model (CHM) based delineation. The results demonstrated that the PDM-based approach produced an 89% detection accuracy to identify deciduous trees in our study area. The tree attributes derived from the PDM-based algorithm explained 81% and 83% of tree height and crown width variations of forest stands, respectively. The conventional CHM-based tree attributes, on the other hand, could explain only 71% and 66% of tree height and crown width, respectively. Our results suggest that the application of the PDM-based individual tree identification in deciduous forests with low-density LiDAR data is feasible and has relatively high accuracy to predict tree height and crown width, which are highly desired in large-scale forest inventory and analysis.

A Comparison of LiDAR Filtering Algorithms in Vegetated Mountain Areas

Filtering of airborne light detection and ranging (LiDAR) data is a challenging task in vegetated mountain areas. Environmental features and LiDAR data characteristics have significant impacts on the performance of filtering algorithms. This study aims to determine the effects of topographic and environmental features such as slope, canopy cover, elevation variability, and LiDAR point density on five widely used filtering algorithms, including multi-scale curvature classification (MCC), interpolation-based filtering (IBF) algorithm, morphological filtering (MF) algorithm, progressive triangulated irregular network densification filtering (PTDF) algorithm, and slope-based filtering (SBF). The results show that the performances of these filtering algorithms are all significantly influenced by the chosen factors, but the dominant influential factor varies with algorithms. The MCC works well in steep and dense forests; IBF and MCC outperform the rest of filtering algorithms in areas with steep terrain but low vegetation coverage; and PTDF is more reliable for low-density LiDAR data. Our results can provide guidance for choosing the appropriate filtering algorithm based on the specific topographic and environmental features of a study area.

Evaluation of the use of low-density LiDAR data to estimate structural attributes and biomass yield in a short-rotation willow coppice: an example in a field trial

LiDAR data (low-density data, 0.5 pulses m −2 ) represent an excellent management resource as they can be used to estimate forest stand characteristics in short-rotation willow coppice (SRWC) with reasonable accuracy. The technology is also a useful, practical tool for carrying out inventories in these types of stands. This study evaluated the use of very low-density airborne LiDAR (light detection and ranging) data (0.5 pulses m−2), which can be accessed free of charge, in an SRWC established in degraded mining land. This work aimed to determine the utility of low-density LiDAR data for estimating main forest structural attributes and biomass productivity and for comparing the estimates with field measurements carried out in an SRWC planted in marginal land. The SRWC was established following a randomized complete block design with three clones, planted at two densities and with three fertilization levels. Use of parametric (multiple regression) and non-parametric (classification and regression trees, CART) fitting techniques yielded models with good predictive power and reliability. Both fitting methods were used for comprehensive analysis of the data and provide complementary information. The results of multiple regression analysis indicated close relationships (Rfit 2 = 0.63–0.97) between LiDAR-derived metrics and the field measured data for the variables studied (H, D20, D130, FW, and DW). High R 2 values were obtained for models fitted using the CART technique (R 2 = 0.73–0.94). Low-density LiDAR data can be used to model structural attributes and biomass yield in SRWC with reasonable accuracy. The models developed can be used to improve and optimize follow-up decisions about the management of these crops.

Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data

Airborne light detection and ranging (LiDAR) has been used for forest biomass estimation for the past three decades. The performance of estimation, in particular, has been of great interest. However, the difference in the performance of estimation between stem volume (SV) and total dry biomass (TDB) estimations has been a priority topic. We compared the performances between SV and TDB estimations for evergreen conifer and deciduous broadleaved forests by correlation and regression analyses and by combining height and no-height variables to identify statistically useful variables. Thirty-eight canopy variables, such as average and standard deviation of the canopy height, as well as the mid-canopy height of the stands, were computed using LiDAR point data. For the case of conifer forests, TDB showed greater correlation than SV; however, the opposite was the case for deciduous broadleaved forests. The average- and mid-canopy height showed the greatest correlation with TDB and SV for conifer and deciduous broadleaved forests, respectively. Setting the best variable as the first and no-height variables as the second variable, a stepwise multiple regression analysis was performed. Predictions by selected equations slightly underestimated the field data used for validation, and their correlation was very high, exceeding 0.9 for coniferous forests. The coefficient of determination of the two-variable equations was smaller than that of the one-variable equation for broadleaved forests. It is suggested that canopy structure variables were not effective for broadleaved forests. The SV and TDB maps showed quite different frequency distributions. The ratio of the stem part of the broadleaved forest is smaller than that of the coniferous forest. This suggests that SV was relatively smaller than TDB for the case of broadleaved forests compared with coniferous forests, resulting in a more even spatial distribution of TDB than that of SV.

Fusion of WorldView-2 and LiDAR Data to Map Fuel Types in the Canary Islands

Wildland fires are one of the factors causing the deepest disturbances on the natural environment and severely threatening many ecosystems, as well as economic welfare and public health. Having accurate and up-to-date fuel type maps is essential to properly manage wildland fire risk areas. This research aims to assess the viability of combining Geographic Object-Based Image Analysis (GEOBIA) and the fusion of a WorldView-2 (WV2) image and low density Light Detection and Ranging (LiDAR) data in order to produce fuel type maps within an area of complex orography and vegetation distribution located in the island of Tenerife (Spain). Independent GEOBIAs were applied to four datasets to create four fuel type maps according to the Prometheus classification. The following fusion methods were compared: Image Stack (IS), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), as well as the WV2 image alone. Accuracy assessment of the maps was conducted by comparison against the fuel types assessed in the field. Besides global agreement, disagreement measures due to allocation and quantity were estimated, both globally and by fuel type. This made it possible to better understand the nature of disagreements linked to each map. The global agreement of the obtained maps varied from 76.23% to 85.43%. Maps obtained through data fusion reached a significantly higher global agreement than the map derived from the WV2 image alone. By integrating LiDAR information with the GEOBIAs, global agreement improvements by over 10% were attained in all cases. No significant differences in global agreement were found among the three classifications performed on WV2 and LiDAR fusion data (IS, PCA, MNF). These study’s findings show the validity of the combined use of GEOBIA, high-spatial resolution multispectral data and low density LiDAR data in order to generate fuel type maps in the Canary Islands.

NON-TRIVIAL FEATURE DERIVATION FOR INTENSIFYING FEATURE DETECTION USING LIDAR DATASETS THROUGH ALLOMETRIC AGGREGATION DATA ANALYSIS APPLYING DIFFUSED HIERARCHICAL CLUSTERING FOR DISCRIMINATING AGRICULTURAL LAND COVER IN PORTIONS OF NORTHERN MINDANAO, PHILIPPINES

Abstract. Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m2 of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this investigation was compared to a classification based on conventional object-oriented approach promoting straight-forward functionalities of the software eCognition. A compelling rise of efficiency in the overall accuracy (74.4% to 93.4%) and kappa index of agreement (70.5% to 91.7%) is noticeable based on the initial process. Nevertheless, having low-dense LiDAR dataset could be enough in generating exponential increase of performance in accuracy.

NON-TRIVIAL FEATURE DERIVATION FOR INTENSIFYING FEATURE DETECTION USING LIDAR DATASETS THROUGH ALLOMETRIC AGGREGATION DATA ANALYSIS APPLYING DIFFUSED HIERARCHICAL CLUSTERING FOR DISCRIMINATING AGRICULTURAL LAND COVER IN PORTIONS OF NORTHERN MINDANAO, PHILIPPINES

Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m<sup>2</sup> of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this investigation was compared to a classification based on conventional object-oriented approach promoting straight-forward functionalities of the software eCognition. A compelling rise of efficiency in the overall accuracy (74.4% to 93.4%) and kappa index of agreement (70.5% to 91.7%) is noticeable based on the initial process. Nevertheless, having low-dense LiDAR dataset could be enough in generating exponential increase of performance in accuracy.

A Three-Dimensional Model-Based Approach to the Estimation of the Tree Top Height by Fusing Low-Density LiDAR Data and Very High Resolution Optical Images

Light detection and ranging (LiDAR) technology has been extensively used for estimating forest attributes. Although high-spatial-density LiDAR data can be used to accurately derive attributes at single tree level, low-density LiDAR data are usually acquired for reducing the cost. However, a low density strongly affects the estimation accuracy due to the underestimation of the tree top and the possible loss of crowns that are not hit by any LiDAR point. In this paper, we propose a 3-D model-based approach to the estimation of the tree top height based on the fusion between low-density LiDAR data and high-resolution optical images. In the proposed approach, the integration of the two remotely sensed data sources is first exploited to accurately detect and delineate the single tree crowns. Then, the LiDAR vertical measures are associated to those crowns hit by at least one LiDAR point and used together with the radius of the crown and the tree apex location derived from the optical image for reconstructing the tree top height by a properly defined parametric model. For the remaining crowns detected only in the optical image, we reconstruct the tree top height by proposing a k-nearest neighbor trees technique that estimates the height of the missed trees as the average of the k reconstructed height values of the trees having most similar crown properties. The proposed technique has been tested on a coniferous forest located in the Italian Alps. The experimental results confirmed the effectiveness of the proposed method.

Effect of point density and interpolation of LiDAR-derived high-resolution DEMs on landscape scarp identification

Recognition of geomorphic features, such as landslide scarps, is the first key step for landslide risk assessment and mitigation. Geomorphic features can be identified from high-resolution digital elevation model (DEM). Light Detection and Ranging (LiDAR) is a useful tool to collect high-density point elevation data from ground surfaces. LiDAR ground points are used to generate high-resolution DEMs. However, LiDAR sample sizes and interpolation methods are critical parameters for DEM estimation under various land cover types. To discuss the effect of the parameters, this study used a series of cases to estimate the DEMs and identify the landslide scarps, especially potential landslide scarps hidden in the forest.Results show that LiDAR sample size affects the visual identification rate of the landslide scarps. The point density of LiDAR data controls the level of detail that can be resolved in the LiDAR-derived DEM. Given low-density LiDAR ground points, the DEM accuracy is the worst, especially in dense forest. Particularly in sparse samples, the identification rate of the landslide scarp is sensitive to the interpolation method. In sparse samples, landslide scarp identification based on Kriging-estimated DEM showed the best results among the three interpolation methods. Hence, this study provides information for the assessment of the effects of sample sizes under land cover for further geomorphic monitoring, assessment and management.

Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden

Knowledge of the forest biomass and its change in time is crucial to understanding the carbon cycle and its interactions with climate change. LiDAR (Light Detection and Ranging) technology, in this respect, has proven to be a valuable tool, providing reliable estimates of aboveground biomass (AGB). The overall goal of this study was to develop a method for assessing AGB using a synergy of low point density LiDAR-derived point cloud data and multi-spectral imagery in conifer-dominated forest in the southwest of Sweden. Different treetop detection algorithms were applied for forest inventory parameter extraction from a LiDAR-derived canopy height model. Estimation of AGB was based on the power functions derived from tree parameters measured in the field, while vegetation classification of a multi-spectral image (SPOT-5) was performed in order to account for dependences of AGB estimates on vegetation types. Linear regression confirmed good performance of a newly developed grid-based approach for biomass estimation (R2 = 0.80). Results showed AGB to vary from below 1 kg/m2 in very young forests to 94 kg/m2 in mature spruce forests, with RMSE of 4.7 kg/m2. These AGB estimates build a basis for further studies on carbon stocks as well as for monitoring this forest ecosystem in respect of disturbance and change in time. The methodology developed in this study can be easily adopted for assessing biomass of other conifer-dominated forests on the basis of low-density LiDAR and multispectral imagery. This methodology is hence of much wider applicability than biomass derivation based on expensive and currently still scarce high-density LiDAR data.

Estimating Canopy Height of Deciduous Forests at a Regional Scale with Leaf-Off, Low Point Density LiDAR

. As we strive toward a more accurate understanding and quantification of carbon pools in forested ecosystems, the development of regional-scale maps of forest characteristics is essential in order to establish baselines and monitor change. Light Detection and Ranging (LiDAR) is increasingly being used to improve our understanding of forested ecosystems on a broad spatial scale, although obtaining data can be expensive and time consuming. We evaluated the effectiveness of using freely available low point density, leaf-off LiDAR collected for the entire state of Pennsylvania, in the United States, to create an accurate regional-scale dominant/codominant canopy height model for state forests in Pennsylvania. We evaluated several methodologies using an inventory dataset with over 1400 sample plots. The developed canopy height model was accurate to about 10% of the field-measured dominant/codominant tree heights for each plot, although it underestimated the field values. Root mean square error relative to the mean field height ranged between 3.5% and 12.5% across all site and forest variables such as forest community type, age, and height class. Factors that affected the accuracy of the canopy height model included tree density, slope, and percent evergreen cover.Résumé. Tout d’abord, en mettant à l’épreuve une compréhension des réserves de carbones dans l’écosystème forestiers, le développement des échelles de cartes régionales qui englobent les caractéristiques des forêts est crucial afin d’établir une base et surveiller des changements. Le « Light Detection and Ranging » (LiDAR) est utilisé de plus en plus afin d’améliorer notre compréhension par rapport à l’écosystème forestiers sur une large échelle spatiale; cependant cette cueillette de données peut être dispendieux et prendre beaucoup de temps. Nous avons évalués l’efficacité en utilisant une faible densité de points qui se nomme le « leaf-off » LiDAR, afin de créer une échelle régionale dominant/codominant précis du modèle d’hauteur de canopie pour l’état forestiers a Pennsylvania dans les États Unis. De plus, nous avons examinés plusieurs méthodologies en utilisant un terrain inventaires données avec 1400 échantillons. Le développement du modèle d’hauteur de canopie était précis jusqu’à 10% du terrain mesuré, cependant les valeurs de champs étaient sous-estimés. L’erreur de la moyenne quadratique relative à la moyenne du terrain d’hauteur était entre 3,5% et 12,5% à travers tout le site et les variables forestiers, tels que le type forestier communautaire, l’âge et la classe d’hauteur. Les facteurs qui ont affectés la précision du modèle d’hauteur de canopie incluaient la densité d’arbre, la pente et le pourcentage de couverture des feuilles persistantes.