scholarly journals Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus)

2018 ◽  
Vol 10 (12) ◽  
pp. 1972 ◽  
Author(s):  
Katarzyna Zielewska-Büttner ◽  
Marco Heurich ◽  
Jörg Müller ◽  
Veronika Braunisch
Keyword(s):  
Bark Beetles ◽  
Laser Scanning ◽  
Positive Relationship ◽  
Spatial Scales ◽  
Keystone Species ◽  
Remote Sensing Data ◽  
Individual Tree ◽  
Substrate Quality ◽  
Dead Trees ◽  
Negative Effect

Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44–50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management.

scholarly journals Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 905 ◽  
Author(s):  
Guerra-Hernández ◽  
Cosenza ◽  
Cardil ◽  
Silva ◽  
Botequim ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Point Clouds ◽  
Volume Estimation ◽  
P Value ◽  
Test Statistic ◽  
Individual Tree ◽  
Significant Difference

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

scholarly journals UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy

2020 ◽  
Vol 12 (22) ◽  
pp. 3829
Author(s):  
Martin Slavík ◽  
Karel Kuželka ◽  
Roman Modlinger ◽  
Ivana Tomášková ◽  
Peter Surový
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Morphological Changes ◽  
Tree Canopy ◽  
High Temporal Resolution ◽  
Human Observer ◽  
Individual Tree ◽  
Dead Trees ◽  
Multi Temporal ◽  
Cloud Metrics

High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels.

scholarly journals Predicting Occurrence, Abundance, and Fruiting of a Cultural Keystone Species to Inform Landscape Values and Priority Sites for Habitat Enhancements

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 783
Author(s):  
Scott E. Nielsen ◽  
Jacqueline M. Dennett ◽  
Christopher W. Bater
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Prescribed Burning ◽  
Spatial Scales ◽  
Keystone Species ◽  
Natural Disturbance ◽  
Fold Increase ◽  
Fruit Production ◽  
Timber Species ◽  
Environmental Niche

Environmental niche modeling is an increasingly common tool in conservation and management of non-timber species. In particular, models of species’ habitats have been aided by new advances in remote sensing and it is now possible to relate forest structure variables to understory species at a relatively high resolution over large spatial scales. Here, we model landscape responses for a culturally-valued keystone shrub, velvet-leaf blueberry (Vaccinium myrtilloides Michaux), in northeast Alberta, Canada, to better understand the environmental factors promoting or limiting its occurrence, abundance, and fruit production, and to guide regional planning. Occurrence and abundance were measured at 845 and 335 sites, respectively, with both strongly related to land cover type and topo-edaphic factors. However, their influence varied widely, reflecting differences in the processes affecting occurrence and abundance. We then used airborne laser scanning (ALS) to characterize horizontal forest canopy cover for the study area, and related this and other geospatial variables to patterns in fruit production where we demonstrated a five-fold increase in fruit production from closed to open forest stands. We then simulated forest canopy thinning across the study area to identify places where gains in fruit production would be greatest following natural disturbance or directed management (e.g., thinning, prescribed burning). Finally, we suggest this approach could be used to identify sites for habitat enhancements to offset direct (land use change) or indirect (access) losses of resources in areas impacted with resource extraction activities, or simply to increase a culturally-valued resource through management.

scholarly journals Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest

2019 ◽  
Vol 11 (22) ◽  
pp. 2614 ◽  
Author(s):  
Nina Amiri ◽  
Peter Krzystek ◽  
Marco Heurich ◽  
Andrew Skidmore
Keyword(s):  
Tree Species ◽  
Feature Selection Method ◽  
Silver Fir ◽  
Maximum Point ◽  
Individual Tree ◽  
Dead Trees ◽  
Multi Wavelength ◽  
Standing Dead ◽  
Standing Dead Trees ◽  
532 Nm

Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

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.

scholarly journals Urbanization pressures alter tree rhizosphere microbiomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carl L. Rosier ◽  
Shawn W. Polson ◽  
Vincent D’Amico ◽  
Jinjun Kan ◽  
Tara L. E. Trammell
Keyword(s):  
Organic Matter ◽  
Relative Abundance ◽  
Tree Species ◽  
High Throughput Sequencing ◽  
Regional Climate ◽  
Keystone Species ◽  
Fagus Grandifolia ◽  
Yellow Poplar ◽  
Individual Tree ◽  
Structural Formation

AbstractThe soil microbial community (SMC) provides critical ecosystem services including organic matter decomposition, soil structural formation, and nutrient cycling. Studies suggest plants, specifically trees, act as soil keystone species controlling SMC structure via multiple mechanisms (e.g., litter chemistry, root exudates, and canopy alteration of precipitation). Tree influence on SMC is shaped by local/regional climate effects on forested environments and the connection of forests to surrounding landscapes (e.g., urbanization). Urban soils offer an ideal analog to assess the influence of environmental conditions versus plant species-specific controls on SMC. We used next generation high throughput sequencing to characterize the SMC of specific tree species (Fagus grandifolia [beech] vs Liriodendron tulipifera [yellow poplar]) across an urban–rural gradient. Results indicate SMC dissimilarity within rural forests suggests the SMC is unique to individual tree species. However, greater urbanization pressure increased SMC similarity between tree species. Relative abundance, species richness, and evenness suggest that increases in similarity within urban forests is not the result of biodiversity loss, but rather due to greater overlap of shared taxa. Evaluation of soil chemistry across the rural–urban gradient indicate pH, Ca+, and organic matter are largely responsible for driving relative abundance of specific SMC members.

scholarly journals Imaging Spectroscopy for Conservation Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 292
Author(s):  
Megan Seeley ◽  
Gregory P. Asner
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Case Studies ◽  
Spatial Scales ◽  
Imaging Spectroscopy ◽  
Remote Sensing Data ◽  
Decision Makers ◽  
High Spectral Resolution ◽  
Conservation Areas ◽  
Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

scholarly journals Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

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