Digital Canopy Model Research Articles

COMPARISON ASSESSMENT OF DIGITAL 3D MODELS OBTAINED BY DRONE-BASED LIDAR AND DRONE IMAGERY

Abstract. The purpose of this study is to assess the potential of drone airborne LiDAR technology in Morocco in comparison with drone photogrammetry. The cost and complexity of the equipment which includes a laser scanner, an inertial measurement unit, a positioning system and a platform are among the causes limiting its use. Furthermore, this study was motivated by the following reasons: (1) Limited number of studies in Morocco on drone-based LiDAR technology applications, (2) Lack of study on the parameters that influence the quality of drone-based LiDAR surveys as well as on the evaluation of the accuracy of derived products. In this study, the evaluation of LiDAR technology was carried out by an analysis of the geometric accuracy of the 3D products generated: Digital Terrain Model (DTM), Digital Surface Model (DSM) and Digital Canopy Model (DCM). We conduct a comparison with the products generated by drone photogrammetry and GNSS surveys. Several tests were carried out to analyse the parameters that influence the mission results namely height, overlap, drone speed and laser pulse frequency. After data collection, the processing phase was carried out. It includes: the cleaning, the consolidation then the classification of point clouds and the generation of the various digital models. This project also made it possible to propose and validate a workflow for the processing, the classification of point clouds and the generation of 3D digital products derived from the processing of LiDAR data acquired by drone.

Methods for tree cover extraction from high resolution orthophotos and airborne LiDAR scanning in Spanish dehesas

<p>Dehesas are high value agroecosystems that benefit from the effect tree cover has on pastures. Such effect occurs when tree cover is incomplete and homogeneous. Tree cover may be characterized from field data or through visual interpretation of remote sensing data, both time-consuming tasks. An alternative is the extraction of tree cover from aerial imagery using automated methods, on spectral derivate products (i.e. NDVI) or LiDAR point clouds. This study focuses on assessing and comparing methods for tree cover estimation from high resolution orthophotos and airborne laser scanning (ALS). RGB image processing based on thresholding of the ‘Excess Green minus Excess Red’ index with the Otsu method produced acceptable results (80%), lower than that obtained by thresholding the digital canopy model obtained from the ALS data (87%) or when combining RGB and LiDAR data (87.5%). The RGB information was found to be useful for tree delineation, although very vulnerable to confusion with the grass or shrubs. The ALS based extraction suffered for less confusion as it differentiated between trees and the remaining vegetation using the height. These results show that analysis of historical orthophotographs may be successfully used to evaluate the effects of management changes while LiDAR data may provide a substantial increase in the accuracy for the latter period. Combining RGB and Lidar data did not result in significant improvements over using LIDAR data alone.</p>

Weak Environmental Controls of Tropical Forest Canopy Height in the Guiana Shield

Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping. Making use of high-resolution maps of LiDAR-derived Digital Canopy Model (DCM) and environmental covariates from a Digital Elevation Model (DEM) acquired over 30,000 ha of tropical forest in French Guiana, we first show that forest canopy height is spatially correlated up to 2500 m. Forest canopy height is significantly associated with environmental variables, but the degree of correlation varies strongly with pixel resolution. On the whole, bottomland forests generally have lower canopy heights than hillslope or hilltop forests. However, this global picture is very noisy at local scale likely because of the endogenous gap-phase forest dynamic processes. Forest canopy height has been predictively mapped across a pixel resolution going from 6 m to 384 m mimicking a low resolution case of 3 points·km − 2 . Results of canopy height mapping indicated that the error for spatial model with environment effects decrease from 8.7 m to 0.91 m, depending of the pixel resolution. Results suggest that, outside the calibration plots, the contribution of environment in shaping the global canopy height distribution is quite limited. This prevents accurate canopy height mapping based only on environmental information, and suggests that precise canopy height maps, for local management purposes, can only be obtained with direct LiDAR monitoring.

Canopy gaps do not help establish pioneer species in a South Florida dry forest

Abstract:Canopy gaps create a temporary spatial heterogeneity, often allowing pioneer species to establish and grow in mature forests. In this study, we asked whether the above model holds for tropical dry forests in the Florida Keys. Six hundred and forty-eight canopy gaps in an extensive Key Largo forest were identified with a LiDAR digital canopy model. The structure and composition of juvenile trees were examined in 45 selected gaps in three stands of known age, and weighted averaging calibration and regression were applied to the data to determine the successional age optimum for each tree species, and the inferred age for each gap based on its sapling composition. Less than 1% of the forest area was recorded as canopy gaps in the LiDAR model. The inferred stand ages were about 70 y greater in canopy gaps in young forest than in the surrounding, unimpacted forest. This suggested that gap formation advanced succession rather than reversing or resetting it. The apparent lack of recruitment by early-successional species may be due to the small size of canopy gaps in this forest, and the minimal contrast between gap and understorey environments; light and water conditions in the small gaps may favour late-successional rather than pioneer species. Establishment of pioneer species may not take place without intense, large-scale disturbances such as fires and hurricanes that remove the entire canopy and consume or erode soils.

High-resolution forest canopy height estimation in an African blue carbon ecosystem.

Mangrove forests are one of the most productive and carbon dense ecosystems that are only found at tidally inundated coastal areas. Forest canopy height is an important measure for modeling carbon and biomass dynamics, as well as land cover change. By taking advantage of the flat terrain and dense canopy cover, the present study derived digital surface models (DSMs) using stereo‐photogrammetric techniques on high‐resolution spaceborne imagery (HRSI) for southern Mozambique. A mean‐weighted ground surface elevation factor was subtracted from the HRSI DSM to accurately estimate the canopy height in mangrove forests in southern Mozambique. The mean and H100 tree height measured in both the field and with the digital canopy model provided the most accurate results with a vertical error of 1.18–1.84 m, respectively. Distinct patterns were identified in the HRSI canopy height map that could not be discerned from coarse shuttle radar topography mission canopy maps even though the mode and distribution of canopy heights were similar over the same area. Through further investigation, HRSI DSMs have the potential of providing a new type of three‐dimensional dataset that could serve as calibration/validation data for other DSMs generated from spaceborne datasets with much larger global coverage. HSRI DSMs could be used in lieu of Lidar acquisitions for canopy height and forest biomass estimation, and be combined with passive optical data to improve land cover classifications.

Using a Multistructural Object-Based LiDAR Approach to Estimate Vascular Plant Richness in Mediterranean Forests With Complex Structure

A multistructural object-based LiDAR approach to predict plant richness in complex structure forests is presented. A normalized LiDAR point cloud was split into four height ranges: 1) high canopies (points above 16 m); 2) middle-high canopies (8-16 m); 3) middle-low canopies (2-8 m); and 4) low canopies (0-2 m). A digital canopy model (DCM) was obtained from the full normalized LiDAR point cloud, and four pseudo-DCMs (pDCMs) were obtained from the split point clouds. We applied a multiresolution segmentation algorithm to the DCM and the four pDCMs to obtain crown objects. A partial least squares path model (PLS-PM) algorithm was applied to predict total vascular plant richness using object-based image analysis (OBIA) variables, derived from the delineated crown objects, and topographic variables, derived from a digital terrain model. Results showed that the object-based model was able to predict the total richness with an r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.64 and a root-mean-square error of four species. Topographic variables showed to be more important than the OBIA variables to predict richness. Furthermore, high-medium canopies (8-16 m) showed the biggest correlation with the total plant richness within the structural segments of the forest.

Extraction of individual tree crown using hyperspectral image and LiDAR data

Tree information such as tree height, tree type, diameter at breast height, and number of trees are critical for effective forest analysis and management. In this regard, this paper presents an individual tree extraction method that uses airborne LiDAR (Light Detection and Ranging) and hyperspectral data. The Support Vector Machine (SVM) classifier was first used to extract tree areas from hyperspectral imagery. Then, Principal Components Analysis (PCA) was applied to the hyperspectral imagery to derive a PCA image consisting of five bands. A segmentation process was performed on the four datasets, which are 1) hyperspectral image, 2) LiDAR-based DCM (Digital Canopy Model), 3) Fusion of PCA image and LiDAR-DCM, and 4) Fusion of PCA image, LiDAR-based DCM and NDVI (Normalized Difference Vegetation Index). Finally, individual tree parameters were estimated based on the segmentation results. The field data were then compared with the final results. The result shows that using fusion data set our method can detect 70% and 92% of reference trees in the forest areas with high and low density, respectively. Conclusively, the tree extraction approach based on the fusion of different data sources provided better results than ones using the single data sources.

Characterization of the horizontal structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis

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.

Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties

The distribution of mangroves and other tropical and subtropical vegetation in the Greater Everglades Ecosystem is largely dependent on subtle variations in elevation, with mangroves occupying the lowest elevations. Combining a digital terrain model (DTM) derived from last-return light detection and ranging (LiDAR) data with IKONOS multispectral imagery in a maximum likelihood supervised classification resulted in a 7.1% increase in overall classification accuracy among seven classes (red mangrove, black mangrove, tropical hardwood hammock, coastal rock barren vegetation, mudflat, sand/rock and asphalt) compared with using the multispectral imagery alone, and the classification accuracy was improved for all four spectrally similar vegetation classes. A digital canopy model (DCM) was created by subtracting the digital terrain model from a digital surface model derived from LiDAR first returns. The DCM-recorded heights well correlated with mangrove canopy heights measured in the field but were systematically lower, by up to 2 m, for the tallest canopy. LiDAR has been documented to underestimate vegetation heights but the presence of water beneath some of the red mangrove canopy probably exacerbated this effect. The DCM and empirical allometric algorithms were used to estimate stem density and biomass for the classified red and black mangroves.

Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.

Area-based assessment of forest standing volume by field measurements and airborne laser scanner data

Airborne laser scanning (ALS) is increasingly applied as a tool for extracting forest inventory data. In recent years most applications for the assessment of forest standing volume rely on a single tree recognition approach, which generally gives satisfactory results in coniferous forests. The aim of this paper is to apply a raster-based approach for the assessment of forest standing volume based on field measurements and a Digital Canopy Model (DCM) derived from ALS data. In addition, we explore the potential of hot spot analysis of DCM data for automatic forest gap detection, as a means to improve the accuracy of the estimation of forest standing volume by traditional estimation methods.

Airborne laser scanning in forest management: Individual tree identification and laser pulse penetration in a stand with different levels of thinning

This study examined the ability of an airborne laser scanner to identify individual trees in the canopy of a Chamaecyparis obtusa stand and investigated the relationship between the penetration rate of the laser pulses and stand attributes under different canopy conditions caused by different levels of thinning. Individual tree crowns were identified from a digital canopy model (DCM) derived from airborne laser scanner data by the watershed segmentation method. The identification rate of individual trees in blocks with heavy thinning (ratio of the basal area of the felled trees to the total basal area, hereinafter thinning ratio of the basal area, 38.0%), moderate thinning (30.4%), and no thinning was 95.3%, 89.2%, and 60.0%, respectively. Individual tree heights were estimated from the DCM values by local maximum filtering within identified individual crowns. Tree height in the three blocks was estimated with a root-mean-square error of 0.95, 0.65, and 0.68m, respectively. Tree heights determined in a field survey were regressed against those estimated from the DCM, yielding coefficients of determination (r2) of 0.71, 0.87, and 0.85, respectively, for the blocks with heavy thinning, moderate thinning, and no thinning, respectively, and 0.86 overall. The respective penetration rates of the laser pulses through the canopy to the ground were 50.6%, 43.1%, and 9.2%. Regression of the laser pulse penetration rate against the thinning ratio of the basal area and against the total basal area of the remaining trees in 25 quadrats established in the blocks, yielded r2 values of 0.89 and 0.74, respectively.

Detection of individual trees and estimation of tree height using LiDAR data

For estimation of tree parameters at the single-tree level using light detection and ranging (LiDAR), detection and delineation of individual trees is an important starting point. This paper presents an approach for delineating individual trees and estimating tree heights using LiDAR in coniferous (Pinus koraiensis, Larix leptolepis) and deciduous (Quercus spp.) forests in South Korea. To detect tree tops, the extended maxima transformation of morphological image-analysis methods was applied to the digital canopy model (DCM). In order to monitor spurious local maxima in the DCM, which cause false tree tops, different h values in the extended maxima transformation were explored. For delineation of individual trees, watershed segmentation was applied to the distance-transformed image from the detected tree tops. The tree heights were extracted using the maximum value within the segmented crown boundary. Thereafter, individual tree data estimated by LiDAR were compared to the field measurement data under five categories (correct delineation, satisfied delineation, merged tree, split tree, and not found). In our study, P. koraiensis, L. leptolepis, and Quercus spp. had the best detection accuracies of 68.1% at h = 0.18, 86.7% at h = 0.12, and 67.4% at h = 0.02, respectively. The coefficients of determination for tree height estimation were 0.77, 0.80, and 0.74 for P. koraiensis, L. leptolepis, and Quercus spp., respectively.