The Phil-LiDAR 2 program aims to extract the natural resources of the Philippines from the available two points per square meter LiDAR data. Mangroves, being coastal resources, were one of the foci of this program under the Aquatic Resources Extraction from LiDAR Surveys (CoastMap). The object-based image analysis (OBIA) approach, and support vector machine (SVM) algorithm were utilized to classify three major classes from the LiDAR data, namely: mangrove, other vegetation, and non-vegetation. Object feature values used in the classification include the mean, standard deviation, mode, and texture values from the generated LiDAR derivatives. These derivatives include the Digital Surface Model (DSM), Digital Terrain Model (DTM), Canopy Height Model (CHM), Intensity, Number of Returns, Normalized DSM (NDSM), Slope, and Slope of Slope. Moreover, field data collection and validation provided key references in the supervised SVM classification and contextual editing of the extracted mangrove areas. From the implemented classification, an overall accuracy of above 90% was achieved. Focusing with the final classified mangrove coverage, management of the mangrove resources can be made proper and efficient. Furthermore, high resolution or detailed spatial information can support programs like Reducing Emissions from Deforestation and forest Degradation Plus (REDD+) and biodiversity studies.

Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-Earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to de-bias the global-extent datasets. Recent advances in satellite platforms coupled with increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Among the more challenging landscapes to quantify surface elevations are densely forested mountain catchments, where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms have resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and a 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing deforested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.

Abstract. Light Detection and Ranging (LiDAR) technology has become a significant factor in producing up-to-date and accurate topographic data in the current world. LiDAR technology has been used for years for many applications, including the efficient creation of digital model for large scale, high accuracy mapping. This technology offers fast, accurate, expedient and cost-effective ways of capturing wide area elevation information to produce highly detailed digital model of the earth. LiDAR is based on airborne laser scanners enables to acquire dense and accurate 3D data of the surveyed area, i.e., the Digital Surface Model (DSM). This paper presents an exploratory study to assess the accuracy of constructed DTM (Digital Terrain Model) and evaluating ground height without surface features using LiDAR Digital Surface Model (DSM). The study area comprised of an undulated area situated at Jinjang in the Klang Valley region, Malaysia covering an area of one kilometre square. LiDAR DSM and DTM constructed and derived from LiDAR were critically assessed with reference to the USGS Map Accuracy Standards. The accuracy of derived DSM and DTM were evaluated using ground control points derived from conventional surveying technique. The constructed models were accessed quantitatively and qualitatively.

Abstract The retrieval of Light Detection and Ranging (LiDAR) data is a complex procedure that necessitates extensive processing in order to develop terrain and surface models and forest structure applications. The gradual acquisition of LiDAR information is required to create Digital Elevation Models (DEM) and Digital Surface Models (DSM). The purpose of the study was to generate topographic DEM and normalized DSM (nDSM) data from LiDAR point cloud and to outline the canopy height extraction procedure in the New Forest region of the United Kingdom. Later, under 21 random enclosures, a demonstration of how the nDSM can be used in forest inventory mapping was discussed. The results show that, of the various interpolation techniques used to generate DEM, IDW had the lowest RMSE value of 0.382. The Digital Terrain Model (DTM) was created using two neighborhood settings (3×3) and (30×30), with the last one showing higher accuracy. In the comparison of different interpolation techniques, Inverse Distance Weighting (IDW) was found to have the lowest RMSE value of 0.382. Finally, within the enclosures, the percentage of no trees (mostly shrubs), canopy height ranged 2-10m, 10-15m, and > 15 was mapped. Each enclosure with 40% of its area covered by trees taller than 15 m was assumed to be harvestable. The study demonstrated detailed algorithm-based LiDAR data extraction and processing, which can be used to explore and forecast terrestrial ecosystems with advanced longitudinal orientation potentialities.

ABSTRACTThe accuracy of digital surface models (DSMs) derived from TanDEM-X interferograms of a dense and mostly evergreen boreal forest area was evaluated across 5 datasets acquired under various geometrical and phenological conditions. For each, an interferometric synthetic aperture radar (InSAR) canopy height model (CHM) was produced by subtracting a LiDAR digital terrain model from the TanDEM-X DSM. These InSAR CHMs were compared to a LiDAR CHM at a resolution of 25 m and led to biases from 0.77 to 1.56 m, r2 from 0.68 to 0.38, and root-mean-square errors (RMSEs) from 2.06 to 3.67 m. Two datasets acquired in similar conditions differed by 1.27 m (RMSE). Differences in the interferometric baseline had the strongest effect on the DSMs (RMSE of 3.27 m between short and long baseline DSMs). The height of ambiguity therefore had a significant effect on the resulting canopy height. The effect of phenological changes on canopy height estimations was lower (RMSE of 2.30 m between leaf-on and leaf-off DSMs) and not highly significant. These results indicate that, despite variations in the acquisition conditions, a continuous TanDEM-X mosaic acquired with proper baselines could produce a reliable estimate of canopy surface elevations of evergreen closed-canopy boreal forests.

Abstract. The digital terrain model (DTM) represents the bare ground earth's surface without any objects like vegetation and buildings. In contrast to a DTM, Digital surface model (DSM) represents the earth's surface including all objects on it. The DTM mostly does not change as frequently as the DSM. The most important changes of the DSM are in the forest areas due to the vegetation growth. Using the LIDAR technology the canopy height model (CHM) is obtained by subtracting the DTM and the corresponding DSM. The DSM is calculated from the first pulse echo and DTM from the last pulse echo data. The main problem of the DSM and CHM data using is the actuality of the airborne laser scanning. This paper describes the method of calculating the CHM and DSM data changes using the relations between the canopy height and age of trees. To get a present basic reference data model of the canopy height, the photogrammetric and trigonometric measurements of single trees were used. Comparing the heights of corresponding trees on the aerial photographs of various ages, the statistical sets of the tree growth rate were obtained. These statistical data and LIDAR data were compared with the growth curve of the spruce forest, which corresponds to a similar natural environment (soil quality, climate characteristics, geographic location, etc.) to get the updating characteristics.

Accurately detecting single trees from LiDAR data requires generating a high-resolution Digital Surface Model (DSM) that faithfully represents the uppermost layer of the forest canopy. A high-resolution DSM raster is commonly generated by interpolating all first LiDAR returns through a Delaunay TIN. The first-return 2D surface interpolation struggles to produce a faithful representation of the canopy when there are first returns that have very similar x-y coordinates but very different z values. When triangulated together into a TIN, such constellations will form needle-shaped triangles that appear as spikes that geometrically disrupt the DSM and negatively affect treetop detection and subsequent extraction of biophysical parameters. We introduce a spike-free algorithm that considers all returns (e.g. also second and third returns) and systematically prevents spikes formation during TIN construction by ignoring any return whose insertion would result in a spike. Our algorithm takes a raw point cloud (i.e., unclassified) as input and produces a spike-free TIN as output that is then rasterized onto a corresponding pit-free DSM grid. We evaluate the new algorithm by comparing the results of treetop detection using the pit-free DSM with those achieved using a common first-return DSM. The results show that our algorithm significantly improves the accuracy of treetop detection, especially for small trees.

The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.

This article's goal is to explore the benefits of using Digital Surface Model (DSM) and Digital Terrain Model (DTM) derived from LiDAR acquisitions for characterizing the horizontal structure of different facies in forested areas (primary forests vs. secondary forests) within the framework of an object-oriented classification. The area under study is the island of Mayotte in the western Indian Ocean. The LiDAR data were the data originally acquired by an airborne small-footprint discrete-return LiDAR for the “Litto3D” coastline mapping project. They were used to create a Digital Elevation Model (DEM) at a spatial resolution of 1m and a Digital Canopy Model (DCM) using median filtering. The use of two successive segmentations at different scales allowed us to adjust the segmentation parameters to the local structure of the landscape and of the cover. Working in object-oriented mode with LiDAR allowed us to discriminate six vegetation classes based on canopy height and horizontal heterogeneity. This heterogeneity was assessed using a texture index calculated from the height-transition co-occurrence matrix. Overall accuracy exceeds 90%. The resulting product is the first vegetation map of Mayotte which emphasizes the structure over the composition.

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.