Estimation of leaf area index in isolated trees with digital photography and its application to urban forestry

Accurate estimates of leaf area index (L) are strongly required for modelling ecophysiological processes within urban forests. The majority of methods available for estimating L is ideally applicable at stand scale and is therefore poorly suitable in urban settings, where trees are typically sparse and isolated. In addition, accurate measurements in urban settings are hindered by proximity of trees to infrastructure elements, which can strongly affect the accuracy of tree canopy analysis. In this study we tested whether digital photography can be used to obtain indirect estimate of L of isolated trees. The sampled species were Platanus orientalis, Liquidambar styraciflua and Juglans regia. Upward-facing photography was used to estimate gap fraction and foliage clumping from images collected in unobstructed (open areas) and obstructed (nearby buildings) settings; two image classification methods provided accurate estimates of gap fraction, based on comparison with measurements obtained from a high quality quantum sensor (LAI-2000). Leveled photography was used to characterize the leaf angle distribution of the examined tree species. L estimates obtained combining the two photographic methods agreed well with direct L measurements obtained from harvesting. We conclude that digital photography is suitable for estimating leaf area in isolated urban trees, due to its simple, fast and costeffective procedures. Use of vegetation indices allows extending significantly the applicability of the photographic method in urban settings, including green roofs and vertical greenery systems.L'articolo è disponibile sul sito dell'editore www.elsevier.com/locate/ufu

Estimation of canopy attributes in beech forests using true colourdigital images from a small fixed-wing UAV

Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but their application is limited by the spectral and temporal resolution available from these systems, which is often not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remote sensing platforms has recently gained increasing attention, but their applications in forestry are still at an experimental stage. In this study we described a methodology to obtain rapid and reliable estimates of forest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digital numbers were converted to the green leaf algorithm (GLA)and to the CIE L∗a∗b∗colour space to obtaine stimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributes were compared with in situ estimates obtained from two digital canopy photographic techniques (cover and fisheye photography).The method was tested in beech forests. UAV images accurately quantified canopy cover even in very dense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images, leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photography. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, but the accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution. We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and meaningful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage of high resolution imagery with quick turnaround series, being therefore suitable for routine forest stand monitoring and real-time applications.L'articolo è disponibile sul sito dell'editore www.elsevier.com/locate/ja

Detection in Aerial Images Using Spatial Transformer Networks

Many tasks in the field of computer vision rely on an underlying change detection algorithm in images or video sequences. Although much research has focused on change detection in consumer images, there is little work related to change detection on aerial imagery, where individual images are recorded from aerial platforms over time. This thesis presents two deep learning approaches for detection in aerial images. Both systems leverage Spatial Transformer Networks (STN) that identify the coordinate transformation for their localization capabilities. The first approach is based on a semisupervised approach which learns to locate changes within a difference image. The second is a fully-supervised approach which learns to locate and discriminate relevant targets. The supervised approach is shown to locate nearly 78% of positive samples with an Intersection Over Union (IOU) criterion of over 0.5, and nearly 94% of positive samples with an IOU over 0.3

Integrated forest management to prevent wildfires under Mediterranean environments

This review presents a multidisciplinary framework for integrating the ecological, regulatory, procedural and technical aspects of forest management for fi res prevention under Mediterranean environments. The aims are to: i) provide a foreground of wildfi re scenario; ii) illustrate the theoretical background of forest fuel management; iii) describe the available fuel management techniques and mechanical operations for fi re prevention in forest and wildland-urban interfaces, with exemplifi cation of case-studies; iv)allocate fi re prevention activities under the hierarchy of forest planning. The review is conceived as an outline commentary discussion targeted to professionals, technicians and government personnel involved in forestry and environmental management

Integrated forest management to prevent wildfi res under mediterranean environments

This review presents a multidisciplinary framework for integrating the ecological, regulatory, procedural and technical aspects of forest management for fi res prevention under Mediterranean environments. The aims are to: i) provide a foreground of wildfi re scenario; ii) illustrate the theoretical background of forest fuel management; iii) describe the available fuel management techniques and mechanical operations for fi re prevention in forest and wildland-urban interfaces, with exemplifi cation of case-studies; iv) allocate fi re prevention activities under the hierarchy of forest planning. The review is conceived as an outline commentary discussion targeted to professionals, technicians and government personnel involved in forestry and environmental managemen

Integrated forest management to prevent wildfires under Mediterranean environments

This review presents a multidisciplinary framework for integrating the ecological, regulatory, procedural and technical aspects of forest management for fi res prevention under Mediterranean environments. The aims are to: i) provide a foreground of wildfi re scenario; ii) illustrate the theoretical background of forest fuel management; iii) describe the available fuel management techniques and mechanical operations for fi re prevention in forest and wildland-urban interfaces, with exemplifi cation of case-studies; iv)allocate fi re prevention activities under the hierarchy of forest planning. The review is conceived as an outline commentary discussion targeted to professionals, technicians and government personnel involved in forestry and environmental management

Concept to Practice of Geospatial-Information Tools to Assist Forest Management and Planning under Precision Forestry Framework: a review

Precision forestry is a new direction for better forest management. Precision forestry employs information technology and analytical tools to support economic, environmental and sustainable decision; the use of geospatial information tools enables highly repeatable measurements, actions and processes to manage and harvest forest stands, simultaneously allowing information linkages between production and wood supply chain, including resource managers and environmental community. In this report, we reviewed the most recent advances in the use of geospatial information technologies in forestry, and discussed their potential opportunities and challenges towards forest management and planning in the framework of precision forestry

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Influence of Root Reinforcement on Shallow Landslide Distribution: A Case Study in Garfagnana (Northern Tuscany, Italy)

In this work, we evaluated the influence of root structure on shallow landslide distribution. Root density measurements were acquired in the field and the corresponding root cohesion was estimated. Data were acquired from 150 hillslope deposit trenches dug in areas either devoid or affected by shallow landslides within the Garfagnana Valley (northern Tuscany, Italy). Results highlighted a correlation between the root reinforcement and the location of measurement sites. Namely, lower root density was detected within shallow landslides, with respect to neighboring areas. Root area ratio (RAR) data allowed us to estimate root cohesion by the application of the revised version of the Wu and Waldron Model. Then, we propose a new method for the assimilation of the lateral root reinforcement into the infinite slope model and the limit equilibrium approach by introducing the equivalent root cohesion parameter. The results fall within the range of root cohesion values adopted in most of the physically based shallow landslide susceptibility models known in the literature (mean values ranging between ca. 2 and 3 kPa). Moreover, the results are in line with the scientific literature that has demonstrated the link between root mechanical properties, spatial variability of root reinforcement, and shallow landslide locations