Estimating fine-scale visibility in a temperate forest landscape using airborne laser scanning

Visibility is a key factor influencing animal behavior in forest ecosystems. Fine-scale visibility in forested areas has been measured by ground-based approaches at the plot level, using site-specific methods that have limited spatial coverage. Here we examine airborne laser scanning (ALS) as a novel tool to quantify fine-scale visibility in the temperate forests of Germany at a landscape scale. We validate the (vertically derived) ALS-derived visibility measures using proven (horizontally derived) terrestrial laser scanning (TLS) estimates of true visibility. Our results indicate that there is a good agreement between the visibility resulting from ALS and TLS with an R2 ranging from 0.53 to 0.84 and a normalized RMSE varying from 15.92% to 11.81% at various plot sizes, with the highest accuracy achieved using a plot size of 35 × 35 m. Our study demonstrates for the first time that ALS can be successfully applied to quantify fine-scale visibility in temperate forests at a landscape level. This approach holds potential for studying the spatial behavior of animals (e.g., habitat selection and predator–prey relationships) in forest ecosystems.publishedVersio

Urban aerobiological risk mapping of ornamental trees using a new index based on LiDAR and Kriging: A case study of plane trees

Ornamental trees bring benefits for human health, including reducing urban pollution. However, some species, such as plane trees (Platanus sp.), produce allergenic pollen. Consequently, urban maps are a valuable tool for allergic patients and allergists, but they often fail to include variables that contribute to the “building downwash effect”, such as the width and shape of streets and the height of buildings. Other factors that directly influence pollen dispersion (slopes and other geographical features) also have not traditionally been discussed. The LiDAR (Laser Imaging Detection and Ranging) technique enables one to consider these variables with high accuracy. This work proposes an Aerobiological Index to create Risk maps for Ornamental Trees (AIROT) and the establishment of potential areas of risk of exposure to Platanus pollen. LiDAR data from five urban areas were used to create the DEM and DSM (Digital Elevation and Surface Models) needed to perform further analysis. GIS software was used to map the points for each city and to create risk maps by Kriging, with stable (3 cases) and exponential function (2 cases) as the optimal models. In short, the AIROT index was a useful tool to map possible biological risks in cities. Since AIROT allows each city to consider its own characteristics, including geographical specifications, by using remote sensing and geostatistics techniques, the establishment of risk maps and healthy itineraries is valuable for allergic patients, allergists, architects and urban planners. This new aerobiological index provides a new decision-making tool related to urban planning and allergenicity assessment

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

A new GIS-compatible methodology for visibility analysis in digital surface models of earth sites

As a GIS tool, visibility analysis is used in many areas to evaluate both visible and non-visible places. Visibility analysis builds on a digital surface model describing the terrain morphology, including the position and shapes of all objects that can sometimes act as visibility barriers. However, some barriers, for example vegetation, may be permeable to a certain degree. Despite extensive research and use of visibility analysis in different areas, standard GIS tools do not take permeability into account. This article presents a new method to calculate visibility through partly permeable obstacles. The method is based on a quasi-Monte Carlo simulation with 100 iterations of visibility calculation. Each iteration result represents 1% of vegetation permeability, which can thus range from 1% to 100% visibility behind vegetation obstacles. The main advantage of the method is greater accuracy of visibility results and easy implementation on any GIS software. The incorporation of the proposed method in GIS software would facilitate work in many fields, such as architecture, archaeology, radio communication, and the military.Web of Science124art. no. 10110

Line of sight visibility analysis for foreign object debris detection system

It is challenging to monitor busy airports' runway through visual inspection to precisely detect foreign object debris. Currently, many technologies for the detection of foreign object debris are available. It has been investigated that millimeter-wave radar technology's detection capability can be one of the most effective techniques for detecting foreign object debris as it is weather-resilient. However, the positioning and height of a millimeter-wave radar pole covering the runway area, considering the existing runway infrastructure, are challenging. The task involves finding the appropriate placement and optimum height. This paper presents a novel method of line of sight visibility for placement and height of radar pole using human factor research to ensure that each point on the runway is visible from various heights of the millimeter-wave radar pole to the runway locations. Kuala Lumpur International Airport, Malaysia runway 32L/14R, has used a case study to test the visibility analysis. The visual analytic test's successful results for different millimeter-wave radar pole locations and viewing heights under a visible and invisible line of sight conditions on the runway have been verified in the field experiment

Wireless sensor networks for landslide monitoring: application and optimization by visibility analysis on 3D point clouds

Occurring in many geographical, geological and climatic environments, landslides represent a major geological hazard. In landslide prone areas, monitoring devices associated with Early Warning Systems are a cost-effective means to reduce the risk with a low environmental and economic impact, and in some cases, they can be the only solution. In this framework, particular interest has been reserved for Wireless Sensor Networks (WSNs), defined as networks of usually low-size and low-cost devices denoted as nodes, which are integrated with sensors that can gather information through wireless links. In this thesis, data from a new prototypical ground instability monitoring instrument called Wi-GIM (Wireless sensor network for Ground Instability Monitoring) have been analysed. The system consists in a WSN made by nodes able to measure their mutual inter-distances by calculating the time of flight of an Ultra-Wide Band impulse. Therefore, no sensors are implemented in the network, as the same signals used for transmission are also used for ranging. The system has been tested in a controlled outdoor environment and applied for the monitoring of the displacements of an actual landslide, the Roncovetro mudflow in Central Italy, where a parallel monitoring with a Robotic Total Station (RTS) allowed to validate the system. The outputs are displacement time series showing the distance of each couple of nodes belonging to the same cluster. Data retrieved from the tests revealed a precision of 2–5 cm and that measurements are influenced by the temperature. Since the correlation with this parameter has proved to be linear, a simple correction is sufficient to improve the precision and remove the effect of temperature. The campaign also revealed that measurements were not affected by rain or snow, and that the system can efficiently communicate up to 150 m with a 360° angle of view without affecting precision. Other key features of the implemented system are easy and quick installation, flexibility, low cost, real-time monitoring and acquisition frequency changeability. The comparison between Wi-GIM and RTS measurements pointed out the presence of an offset (in an order that vary from centimetric to decametric) constant for each single couple, due mainly to the presence of obstacles that can obstruct the Line Of Sight (LOS). The presence of vegetation is the main cause of the non-LOS condition between two nodes, which translates in a longer path of the signals and therefore to a less accurate distance measurements. To go further inside this issue, several tests have been carried out proving the strong influence of the vegetation over both data quantity and quality. To improve them, a MATLAB tool (R2018a, MAthWorks, Natick, MA, USA) called WiSIO (Wireless Sensor network Installation Optimizer) has been developed. The algorithm finds the best devices deployment following three criteria: (i) inter-visibility by means of a modified version of the Hidden Point Removal operator; (ii) equal distribution; (iii) positioning in preselected priority areas. With respect to the existing viewshed analysis, the main novelty is that it works directly with 3D point clouds, without rendering them or performing any surface. This lead to skip the process of generating surface models avoiding errors and approximations, that is essential when dealing with vegetation. A second installation of the Wi-GIM system has been therefore carried out considering the deployment suggested by WiSIO. The comparison of data acquired by the system positioned with and without the help of the proposed algorithm allowed to better comprehend the effectiveness of the tool. The presented results are very promising, showing how a simple elaboration can be essential to have more and more reliable data, improving the Wi-GIM system performances, making it even more usable in very complex environments and increasing its flexibility. The main left limitation of the Wi-GIM system is currently the precision. Such issue is connected to the aim of using only low-cost components, and it can be prospectively overcome if the system undergoes an industrialization process. Furthermore, since the system architecture is re-adaptable, it is prone to enhancements as soon as the technology advances and new low cost hardware enters the market

Non-destructive individual tree aboveground biomass estimation in tropical rainforest using terrestrial laser scanner

Recent methods for detailed and accurate biomass and carbon stock estimation are driven by advances in remote sensing technology. However, this method heavily relies on the availability of species and area dependent allometric equations, which has been long based on the destructive method. This study introduces a non-destructive laser-based approach for individual tree aboveground biomass estimation by developing a semiautomatic approach of individual tree measurement using the collected point cloud. Biomass of individual trees was derived from tree parameters estimated using terrestrial laser scanner (TLS) data and assessed with field collected data. This study also improvised available allometric models for aboveground biomass estimation based on tree species and individual tree properties obtained from TLS. Point cloud for this study were generated using TLS (Riegl-VZ400) representing 118 random trees from 39 plots established in Royal Belum forest reserve in the state of Perak, Malaysia. Individual tree census was carried out to collect detailed primary tree attributes such as diameter at breast height and tree height. The scanning process using TLS was done to acquire point cloud in multiple positions to ensure good visibility of individual tree. Detailed tree measurement was carried out on the point cloud generated from TLS and the results were compared with the ground collected data. The volume of tree trunk is estimated based on cylinder model fitting on point cloud. The biomass of tree trunk is calculated by multiplying the volume with the species dependent wood density values. The biomass of branches and leaves were estimated based on the same concept and the point cloud were fitted with convex-hull approach. The estimated biomass from TLS was compared with the biomass estimated using existing allometric equations. Measurements of individual tree attributes from the point cloud produced diameter at breast height estimates with of 0.06 cm root mean square error with overestimation of 0.03cm. The root mean square error value for tree height and crown base height estimates is 7.10m and 4.31m with underestimation of 3.07m and 1.05m respectively. In general, the estimated biomass of tree trunk shows strong correlation with biomass value obtained from the allometric equation with r value of 0.97. The estimated branch and leaves biomass show poor relationship with biomass estimated using existing allometric equations with r value of -0.12 and 0.24 respectively. The findings on speciesspecific non-destructive laser-based approach suggests similar correlation pattern observed for biomass of stem, branches, leaves and total aboveground biomass of all tree species with mean of r value of 0.92, -0.12, 0.24 and 0.91 respectively. The proposed methodology and results obtained in this study allow generation of species-specific allometric equations in which suitable with LiDAR-derived variables for individual trees biomass estimation which is a promising alternative approach to the destructive method

Evaluation of remote sensing methods for continuous cover forestry

The overall aim of the project was to investigate the potential and challenges in the application of high spatial and spectral resolution remote sensing to forest stands in the UK for Continuous Cover Forestry (CCF) purposes. Within the context of CCF, a relatively new forest management strategy that has been implemented in several European countries, the usefulness of digital remote sensing techniques lie in their potential ability to retrieve parameters at sub-stand level and, in particular, in the assessment of natural regeneration and light regimes. The idea behind CCF is the support of a sustainable forest management system reducing disturbance of the forest ecosystem and encouraging the use of more natural methods, e.g. natural regeneration, for which the light environment beneath the forest canopy plays a fundamental role.The study was carried out at a test area in central Scotland, situated within the Queen Elizabeth II Forest Park (lat. 56°10' N, long. 4° 23' W). Six plots containing three different species (Norway spruce, European larch and Sessile oak), characterized by their different light regimes, were established within the area for the measurement of forest variables using a forest inventory approach and hemispherical photography. The remote sensing data available for the study consisted of Landsat ETM+ imagery, a small footprint multi-return lidar dataset over the study area, Airborne Thematic Mapper (ATM) data, and aerial photography with same acquisition date as the lidar data.Landsat ETM+ imagery was used for the spectral characterisation of the species under study and the evaluation of phenological change as a factor to consider for future acquisitions of remotely sensed imagery. Three approaches were used for the discrimination between species: raw data, NDVI, and Principal Component Analysis (PCA). It can be concluded that no single date is ideal for discriminating the species studied (early summer was best) and that a combination of two or three datasets covering their phenological cycles is optimal for the differentiation. Although the approaches used helped to characterize the forest species, especially to the discrimination between spruces, larch and the deciduous oak species, further work is needed in order to define an optimum approach to discriminate between spruce species (e.g. Sitka spruce and Norway spruce) for which spectral responses are very similar. In general, the useful ranges of the indices were small, so a careful and accurate preprocessing of the imagery is highly recommended.Lidar, ATM, and aerial photographic datasets were analysed for the characterisation of vertical and horizontal forest structure. A slope-based algorithm was developed for the extraction of ground elevation and tree heights from multiple return lidar data, the production of a Digital Terrain Model (DTM) and Digital Surface Model (DSM) of the area under study, and for the comparison of the predicted lidar tree heights with the true tree heights, followed by the building of a Digital Canopy Model (DCM) for the determination of percentage canopy cover and tree crown delineation. Mean height and individual tree heights were estimated for all sample plots. The results showed that lidar underestimated tree heights by an average of 1.49 m. The standard deviation of the lidar estimates was 3.58 m and the mean standard error was 0.38 m.This study assessed the utility of an object-oriented approach for deciduous and coniferous crown delineation, based on small-footprint, multiple return lidar data, high resolution ATM imagery, and aerial photography. Special emphasis in the analysis was made in the fusion of aerial photography and lidar data for tree crown detection and classification, as it was expected that the high vertical accuracy of lidar, combined with the high spatial resolution aerial photography would render the best results and would provide the forestry sector with an affordable and accurate means for forest management and planning. Most of the field surveyed trees could be automatically and correctly detected, especially for the spruce and larch plots, but the complexity of the deciduous plots hindered the tree recognition approach, leading to poor crown extent and gap estimations. Indicators of light availability were calculated from the lidar data by calculation of laser hit penetration rates and percentage canopy cover. These results were compared to estimates of canopy openness obtained from hemispherical pictures for the same locations.Finally, the synergistic benefits of all datasets were evaluated and the forest structural variables determined from remote sensing and hemispherical photography were examined as indicators of light availability for regenerating seedlings

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Geomorphometry 2020. Conference Proceedings

Geomorphometry is the science of quantitative land surface analysis. It gathers various mathematical, statistical and image processing techniques to quantify morphological, hydrological, ecological and other aspects of a land surface. Common synonyms for geomorphometry are geomorphological analysis, terrain morphometry or terrain analysis and land surface analysis. The typical input to geomorphometric analysis is a square-grid representation of the land surface: a digital elevation (or land surface) model. The first Geomorphometry conference dates back to 2009 and it took place in Zürich, Switzerland. Subsequent events were in Redlands (California), Nánjīng (China), Poznan (Poland) and Boulder (Colorado), at about two years intervals. The International Society for Geomorphometry (ISG) and the Organizing Committee scheduled the sixth Geomorphometry conference in Perugia, Italy, June 2020. Worldwide safety measures dictated the event could not be held in presence, and we excluded the possibility to hold the conference remotely. Thus, we postponed the event by one year - it will be organized in June 2021, in Perugia, hosted by the Research Institute for Geo-Hydrological Protection of the Italian National Research Council (CNR IRPI) and the Department of Physics and Geology of the University of Perugia. One of the reasons why we postponed the conference, instead of canceling, was the encouraging number of submitted abstracts. Abstracts are actually short papers consisting of four pages, including figures and references, and they were peer-reviewed by the Scientific Committee of the conference. This book is a collection of the contributions revised by the authors after peer review. We grouped them in seven classes, as follows: • Data and methods (13 abstracts) • Geoheritage (6 abstracts) • Glacial processes (4 abstracts) • LIDAR and high resolution data (8 abstracts) • Morphotectonics (8 abstracts) • Natural hazards (12 abstracts) • Soil erosion and fluvial processes (16 abstracts) The 67 abstracts represent 80% of the initial contributions. The remaining ones were either not accepted after peer review or withdrawn by their Authors. Most of the contributions contain original material, and an extended version of a subset of them will be included in a special issue of a regular journal publication