CanopyShotNoise - An individual-based tree canopy modelling framework for projecting remote-sensing data and ecological sensitivity analysis

Very few spatially explicit tree models have so far been constructed with a view to project remote-sensing data directly. To fill this gap, we introduced the prototype of the CanopyShotNoise model, an individual-based model specifically designed for projecting airborne laser scanning (ALS) data. Given the nature of ALS data, the model focuses on the dynamics of individual-tree canopies in forest ecosystems, that is, spatial tree interaction and resulting growth, birth and death processes. In this study, CanopyShotNoise was used to analyse the long-term effects of the processes crown plasticity (C) and superorganism formation (S) on spatial tree canopy patterns that are likely to play an important role in ongoing climate change. We designed a replicated computer experiment involving the four scenarios C0S0, C1S0, C0S1 and C1S1 where 0 and 1 imply that the preceding process was switched off and on, respectively. We hypothesized that C and S are antagonistic processes, specifically that C would lead to increasing regularity of tree locations and S would result in clustering. Our simulation results confirmed that in the long run intertree distances decreased and canopy gap size increased when superorganisms were encouraged to form. At the same time, the overlap and packing of tree crowns increased. The long-term effect of crown plasticity increased the regularity of tree locations; however, this effect was much weaker than that of superorganism formation. As a result, gap patterns remained more or less unaffected by crown plasticity. In scenario C1S1, both processes interestingly interacted in such a way that crown plasticity even increased the effect of superorganism formation. Our simulation results are likely to prove helpful in recognizing patterns of facilitation with ongoing climate change

Improving Tree Crown Mapping using Airborne LiDAR with Genetic Algorithms

Landscape-scale mapping of individual trees derived from LiDAR (Light Detection And Ranging) data have been found to be valuable for a wide range of environmental analyses including carbon inventories; fuel estimations for wildfire risk assessment and management. These mapping efforts use individual tree crown (ITC) recognition algorithms applied to LiDAR point clouds, which have complex parameter sets. Genetic algorithms (GA) have been demonstrated to be excellent function optimizers for very complex search spaces and perform well for parameter tuning. Here, we use GAs to identify the best of a set of published ITC models and their optimal parameters for airborne LiDAR of forested plots in the Sierra Nevada Mountains of California. We assessed the accuracy of these ITC models in terms of the F-score and percentage bias for tree crown prediction. GA-optimization generally improved on ITC default parameters and showed that these models typically perform better for detecting overstory trees

Individual Tree Measurements From Three-Dimensional Point Clouds

This study develops and tests novel methodologies for measuring the attributes of individual trees from three-dimensional point clouds generated from an aerial platform. Recently, advancements in technology have allowed for the acquisition of very high resolution three-dimensional point clouds that can be used to map the forest in a virtual environment. These point clouds can be interpreted to produce valuable forest attributes across entire landscapes with minimal field labor, which can then aid forest managers in their planning and decision making. Biometrics derived from point clouds are often generated on a plot level, with estimates spanning many meters (rather than at the scale of individual the individual tree), a process known as area-based estimation. As the resolution of point clouds has increased however, the structural attributes of individual trees can now be distinguished and measured, which allows for tree lists including species and size metrics for individual trees. This information can be of great use to forester managers; thus, it is essential that proper methods be developed for measuring these trees. To this end, an algorithm called layer stacking, was developed to isolate points representing the shapes of individual trees from a Light Detection and Ranging (LiDAR) derived point cloud, a process called segmentation. The validity of this algorithm was assessed in a variety of forest stand types, and comparisons were made to another popular tree segmentation algorithm (i.e., watershed delineation). Results indicated that when compared to watershed delineation, layer stacking produced similar or improved detection rates in almost all forest stands, and excelled in deciduous forests, which have traditionally been challenging to segment. The algorithm was then implemented on a large scale, for individual measurements on over 200,000 trees. The species and diameter of each tree was predicted via modeling from structural and reflectance characteristics, and allometric equations were used to obtain volume and carbon content of each tree. These estimates were then compared to measurements taken in the field, and to area-based estimates. Results indicated improved accuracy of plot level basal area, volume, and carbon estimation over traditional area-based estimation, as well as moderately reliable individual tree estimates, and highly reliable species identification. Finally, because LiDAR point clouds can be expensive to acquire, point clouds generated from aerial photos via structure-from-motion (SfM) reconstruction were evaluated for their accuracy at a tree level. An analysis between tree height measurements obtained by SfM, SfM in conjunction with LiDAR, LiDAR alone, digital stereo-photo interpretation, and field measurements was conducted. Results indicated no difference between SfM in conjunction with LiDAR and LiDAR alone. We concluded that SfM represents a valid low cost means of producing a point cloud dense enough to measure individual trees. Thus, high resolution point clouds can be used to generate forest inventories containing a number of valuable biometrics, such as tree height, species, volume, biomass, and carbon mass. Such estimates may allow for the automatic development of large-scale, detailed, and precise forest inventories without the cost, effort, and safety concerns associated with extensive field inventories

Forest planning utilizing high spatial resolution data

This thesis presents planning approaches adapted for high spatial resolution data from remote sensing and evaluate whether such approaches can enhance the provision of ecosystem services from forests. The presented methods are compared with conventional, stand-level methods. The main focus lies on the planning concept of dynamic treatment units (DTU), where treatments in small units for modelling ecosystem processes and forest management are clustered spatiotemporally to form treatment units realistic in practical forestry. The methodological foundation of the thesis is mainly airborne laser scanning data (raster cells 12.5x12.5 m2), different optimization methods and the forest decision support system Heureka. Paper I demonstrates a mixed-integer programming model for DTU planning, and the results highlight the economic advances of clustering harvests. Paper II and III presents an addition to a DTU heuristic from the literature and further evaluates its performance. Results show that direct modelling of fixed costs for harvest operations can improve plans and that DTU planning enhances the economic outcome of forestry. The higher spatial resolution of data in the DTU approach enables the planning model to assign management with higher precision than if stand-based planning is applied. Paper IV evaluates whether this phenomenon is also valid for ecological values. Here, an approach adapted for cell-level data is compared to a schematic approach, dealing with stand-level data, for the purpose of allocating retention patches. The evaluation of economic and ecological values indicate that high spatial resolution data and an adapted planning approach increased the ecological values, while differences in economy were small. In conclusion, the studies in this thesis demonstrate how forest planning can utilize high spatial resolution data from remote sensing, and the results suggest that there is a potential to increase the overall provision of ecosystem services if such methods are applied

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Genetic constraints on temporal variation of airborne reflectance spectra and their uncertainties over a temperate forest

Remote sensing enhances large-scale biodiversity monitoring by overcoming temporal and spatial limitations of ground-based measurements and allows assessment of multiple plant traits simultaneously. The total set of traits and their variation over time is specific for each individual and can reveal information about the genetic composition of forest communities. Measuring trait variation among individuals of one species continuously across space and time is a key component in monitoring genetic diversity but difficult to achieve with ground-based methods. Remote sensing approaches using imaging spectroscopy can provide high spectral, spatial, and temporal coverage to advance the monitoring of genetic diversity, if sufficient relation between spectral and genetic information can be established. We assessed reflectance spectra from individual Fagus sylvatica L. (European beech) trees acquired across eleven years from 69 flights of the Airborne Prism Experiment (APEX) above the same temperate forest in Switzerland. We derived reflectance spectra of 68 canopy trees and correlated differences in these spectra with genetic differences derived from microsatellite markers among the 68 individuals. We calculated these correlations for different points in time, wavelength regions and relative differences between wavelength regions. High correlations indicate high spectral-genetic similarities. We then tested the influence of environmental variables obtained at temporal scales from days to years on spectral-genetic similarities. We performed an uncertainty propagation of radiance measurements to provide a quality indicator for these correlations. We observed that genetically similar individuals had more similar reflectance spectra, but this varied between wavelength regions and across environmental variables. The short-wave infrared regions of the spectrum, influenced by water absorption, seemed to provide information on the population genetic structure at high temperatures, whereas the visible part of the spectrum, and the near-infrared region affected by scattering properties of tree canopies, showed more consistent patterns with genetic structure across longer time scales. Correlations of genetic similarity with reflectance spectra similarity were easier to detect when investigating relative differences between spectral bands (maximum correlation: 0.40) than reflectance data (maximum correlation: 0.33). Incorporating uncertainties of spectral measurements yielded improvements of spectral-genetic similarities of 36% and 20% for analyses based on single spectral bands, and relative differences between spectral bands, respectively. This study highlights the potential of dense multi-temporal airborne imaging spectroscopy data to detect the genetic structure of forest communities. We suggest that the observed temporal trajectories of reflectance spectra indicate physiological and possibly genetic constraints on plant responses to environmental change

Terrestrial laser scanning for vegetation analyses with a special focus on savannas

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome

Real-time spatial modeling to detect and track resources on construction sites

For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin