Structure-from-Motion based vegetation modeling and shade estimation

Although three-dimensional (3-D) light dimension and range (LiDAR) point cloud datasets describing the structure of vegetation have proven to be highly useful for ecological modeling, the collection of such data is expensive. However, a new technology known as Structure-from-Motion, or SfM, has become available that can be used to create 3-D point cloud datasets for far less cost. A small unmanned aerial system (UAS), point and shoot digital camera, and Agisoft PhotoScan® (http://agisoft.com) software were used to create a highly dense 3-D SfM point cloud dataset representing a short reach of the Upper South Fork of the New River in Boone, NC. The quality of the 3-D SfM point cloud dataset was evaluated with an emphasis on how accurately vegetation was represented. Also, a digital surface model (DSM) based on the 3-D SfM point cloud dataset was used in conjunction with a solar ray tracing method to predict shade cast by vegetation in the study area. Overall, the results of this study suggest that SfM based point clouds representing vegetation are of a high enough quality to be used for ecological modeling purposes

Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models

Climatic warming will increase river temperature globally, with consequences for cold water-adapted organisms. In regions with low forest cover, elevated river temperature is often associated with a lack of bankside shading. Consequently, river managers have advocated riparian tree planting as a strategy to reduce temperature extremes. However, the effect of riparian shading on river temperature varies substantially between locations. Process-based models can elucidate the relative importance of woodland and other factors driving river temperature and thus improve understanding of spatial variability of the effect of shading, but characterising the spatial distribution and height of riparian tree cover necessary to parameterise these models remains a significant challenge. Here, we document a novel approach that combines Structure-from-Motion (SfM) photogrammetry acquired from a drone to characterise the riparian canopy with a process based temperature model (Heat Source) to simulate the effects of tree shading on river temperature. Our approach was applied in the Girnock Burn, a tributary of the Aberdeenshire Dee, Scotland. Results show that SfM approximates true canopy elevation with a good degree of accuracy (R2 = 0.96) and reveals notable spatial heterogeneity in shading. When these data were incorporated into a process-based temperature model, it was possible to simulate river temperatures with a similarly-high level of accuracy (RMS

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.Peer ReviewedPostprint (published version

Lidar and true-orthorectification of infrared aerial imagery of high Pinus sylvestris forest in mountainous relief

Combination of various data sources has been demonstrated more effective than using them separately. Information retrieval is significantly improved by synergies between laser scanner and optical imagery. Digital photography relies on traditional methods for orthorectification in order to accomplish an accurate correspondence with Lidar. We investigated combinatorial techniques in a high pine forest situated in mountainous relief in the Guadarrama Range (Spain). Results have shown critical inaccuracies in the integration of these data, even when obtained simultaneously. We propose the use of Lidar-derived Digital Surface Model in the process of orthorectification of aerial imagery. We hypothesised that the use of true-orthophoto techniques for improving the planimetric accuracy of VHR can be reliable for forestry applications. The methodology slightly improved the geometrical results obtained, though radiometric results might be meaningless. Consequently, other possible solutions are also discussed

Demystifying the Differences between Structure-from-Motion Software Packages for Pre-Processing Drone Data

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows

3D Landscape Recording and Modeling of Individual Trees

The 3D city/landscape model is digital representation of real environment that can be used for planner or landscape architecture in urban development planning. That model’s more focused on building, whereas vegetation model’s also needed for urban planning simulation. This research purposes are to map existing vegetation and to generate individual trees model in some level of details. The research area is campus of ITB Jatinangor and the used data are orthophoto and DSM from UAV-Photogrammetry technology. Manual segmentation, classification, and NDSM generation process can provide tree information (position, crown diameter, species, height)-as 3D vegetation modeling input. It’s also necessary to provide classification, information, detail level, and visualization of vegetation model according to landscape architecture analysis needs. This research results are 3D vegetation models in LoD 1-3 with differents information based on appearance, geometry, semantic, and topology aspects of CityGML. Models then tested qualitatively based on visualization and sun shadow analysis. For visualization, the used data only able to generate LoD 1 and 2 vegetation model and the minimum LoD required for sun shadow analysis is LoD 2. Terrestrial data, which provide the real form and size of each tree part, is needed to generate LoD 3 vegetation model

Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models

Field data describing the height growth of trees or stands over several decades are very scarce. Consequently, our capacity of analyzing forest dynamics over large areas and long periods of time is somewhat limited. This study proposes a new method for retrospectively reconstructing plot-wise average dominant tree height based on a time series of high-resolution canopy height maps, termed canopy height models (CHMs). The absolute elevation of the canopy surface, or digital surface model (DSM), was first reconstructed by applying image-matching techniques to stereo-pairs of aerial photographs acquired in 1945, 1965, 1983, and 2003. The historical CHMs were then created by subtracting the bare earth elevation provided from a recent lidar survey from the DSMs. A method for estimating average dominant tree height from these historical CHMs was developed and calibrated for each photographic year. The accuracy of the resulting remote sensing height estimates was compared to ageheight data reconstructed based on dendrometric measurements. The height bias of the remote sensing estimates relative to the verification data ranged from 0.52 m to 1.55 m (1.16 m on average). The corresponding root-mean-square errors varied between 1.49 m and 2.88 m (2.03 m average). Despite being slightly less accurate than historical field data, the quality of the remote sensing estimates is sufficient for many types of forest dynamics studies. The procedures for implementing this method, with the exception of the calibration phase, are entirely automated such that forest height growth curves can be reconstructed and mapped over large areas for which recent lidar data and historical photographs exist

Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines

Mine sites are routinely required to rehabilitate their post-mining landforms with a safe, stable and sustainable land-cover. To assess these post-mining landforms, traditional on-ground field monitoring is generally undertaken. However, these labour intensive and time-consuming measurements are generally insufficient to catalogue land rehabilitation efforts across the large scales typical of mining sites (>100 ha). As an alternative, information derived from Unmanned Aerial Vehicles (UAV) can be used to map rehabilitation success and provide evidence of achieving rehabilitation site requirements across a range of scales. UAV based sensors have the capacity to collect information on rehabilitation sites with extensive spatial coverage in a repeatable, flexible and cost-effective manner. Here, we present an approach to automatically map indicators of safety, stability and sustainability of rehabilitation efforts, and demonstrate this framework across three coalmine sites. Using multi-spectral UAV imagery together with geographic object-based image analysis, an empirical classification system is proposed to convert these indicators into a status category based on a number of criteria related to land-cover, landform, erosion, and vegetation structure. For this study, these criteria include: mapping tall trees (Eucalyptus species); vegetation extent; senescent vegetation; extent of bare ground; and steep slopes. Converting these land-cover indicators into appropriate mapping categories on a polygon basis indicated the level of rehabilitation success and how these varied across sites and age of the rehabilitation activity. This work presents a framework and workflow for undertaking a UAV based assessment of safety, stability and sustainability of mine rehabilitation and also provides a set of recommendations for future rehabilitation assessment efforts

Airborne LiDAR and high resolution satellite data for rapid 3D feature extraction

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary?.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98% for tree feature extraction and 96% for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup

Aerial LiDAR Technology in Support to Avalanches Prevention and Risk Mitigation: AN Operative Application at "colle della Maddalena" (italy)

Abstract. Snow avalanches are the result of unstable snow masses that detach from steep slopes as consequence of changes in snowpack structure. Nowadays, remote sensing technologies can improve the knowledge of avalanches phenomenon. This work focuses on the use of high point density aerial LiDAR (Light Detection And Ranging) technology as support to avalanche events prevention and risk mitigation, by presenting an operative application at Colle della Maddalena (Italy), along the road SS n. 21, nearby the French state border. The area is often involved in intense avalanche events that adversely impact on traffic and freight transport. For this reason, regional administrations will activate the Avalanche Artificial Detachment Intervention Plan (PIDAV, 2012) in order to prevent and manage the avalanche risk in the study area, also adopting artificial detachment systems. Main aim of the present work was to generate high resolution information related to geomorphological characterization (i.e. digital elevation models, slope and aspect) of avalanche sites derived from LiDAR data processing, that will help involved authorities in the management of the avalanche control plan. Digital elevation models at 0.5 m of spatial resolution were generated together with relative tridimensional models. Secondly, a preliminary investigation about capabilities and limits of LiDAR technology was done in the identification of avalanche sites only relying on geomorphological information directly derived by LiDAR data processing. Results showed that position of avalanche sites were correctly identified while no information could be obtained about the extension of the sliding area and identification of detachment areas