scholarly journals Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds

Silva Fennica ◽  
2021 ◽  
Vol 55 (4) ◽  
Author(s):  
Alwin Hardenbol ◽  
Anton Kuzmin ◽  
Lauri Korhonen ◽  
Pasi Korpelainen ◽  
Timo Kumpula ◽  
...  
Keyword(s):  
Tree Species ◽  
Classification Accuracy ◽  
Boreal Forests ◽  
Growing Season ◽  
Point Clouds ◽  
Image Point ◽  
Detection Accuracy ◽  
Important Species ◽  
Linear Discriminant ◽  
European Aspen

Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen ( tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests ( L., [L.] Karst., spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.PopulusPinus sylvestrisPicea abiesBetula

Relationship between wood-inhabiting fungi determined by molecular analysis (denaturing gradient gel electrophoresis) and quality of decaying logs

2010 ◽  
Vol 40 (12) ◽  
pp. 2384-2397 ◽  
Author(s):  
Tiina Rajala ◽  
Mikko Peltoniemi ◽  
Taina Pennanen ◽  
Raisa Mäkipää
Keyword(s):  
Tree Species ◽  
Gel Electrophoresis ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Lignin Content ◽  
Fruit Body ◽  
Fungal Communities ◽  
Important Species ◽  
European Aspen ◽  
Gradient Gel Electrophoresis ◽  
Decaying Wood

We investigated the fungal communities inhabiting decaying logs in a seminatural boreal forest stand in relation to host tree species, stage of decay, density, diameter, moisture, C to N ratio, Klason lignin content, and water- and ethanol-soluble extractives. Communities were profiled using denaturing gradient gel electrophoresis fingerprinting of the rDNA ITS1 region coupled with sequencing of fungal DNA extracted directly from the wood. In addition, polypore fruit bodies were inventoried. Logs from different tree species had different fungal communities and different physicochemical properties (e.g., C to N ratio, density, ethanol extractives, and diameter). Ascomycetes comprised a larger portion of communities inhabiting deciduous birch ( Betula spp.) and European aspen ( Populus tremula L.) logs compared with those living on coniferous Norway spruce ( Picea abies (L.) Karst.) and Scots pine ( Pinus sylvestris L.). A relationship between mycelial community structure and density of decaying spruce logs suggested a succession of fungi with mass loss of wood. The fruit body inventory underestimated fungal diversity in comparison with the culture-free denaturing gradient gel electrophoresis analysis that also detected inconspicuous but important species inhabiting decaying wood.

Long-term effects of prescribed burning, tree retention, and browsing on deciduous tree recruitment in European boreal forests

2020 ◽  
Author(s):  
Alwin A. Hardenbol ◽  
Michael den Herder ◽  
Jari Kouki
Keyword(s):  
Tree Species ◽  
Boreal Forests ◽  
Prescribed Burning ◽  
Structural Diversity ◽  
Deciduous Tree ◽  
Long Term Effects ◽  
Seedling Mortality ◽  
Tree Recruitment ◽  
European Aspen ◽  
Retention Forestry

Silvicultural practices, effective fire suppression, and increased browser densities have profoundly altered structural diversity in boreal forests. Prescribed burning and retention forestry may counteract losses in structural diversity in managed forests, by maintaining higher deciduous admixture. We constructed an experiment on 18 sites with three types of timber harvesting (uncut, cut with retention, and clearcut) and burned half these sites. Subsequently, we established a herbivore treatment with three compartments (unfenced, fenced excluding moose (Alces alces), and fenced excluding moose and hares (Lepus spp.)). In these compartments, we planted rowan (Sorbus aucuparia), European aspen (Populus tremula), and silver birch (Betula pendula) seedlings, and monitored these for 17 years. Birch and rowan mortality were lower on cut and burned sites, with retention further enhancing birch survival on these sites. Retention without burning did not lower seedling mortality of any tree species. While browsing resulted in greater mortality on cut sites, burning appeared to greatly reduce browsing on birch and rowan. On mature uncut sites, seedlings of all tree species exhibited high mortality. Our findings show that deciduous tree recruitment can be improved through prescribed burning, particularly for birch and rowan, and that browsing impacts on deciduous trees depend on forest age.

scholarly journals Single Tree Classification Using Multi-Temporal ALS Data and CIR Imagery in Mixed Old-Growth Forest in Poland

2021 ◽  
Vol 13 (24) ◽  
pp. 5101
Author(s):  
Agnieszka Kamińska ◽  
Maciej Lisiewicz ◽  
Krzysztof Stereńczak
Keyword(s):  
Tree Species ◽  
Classification Accuracy ◽  
Sustainable Forest Management ◽  
Point Clouds ◽  
Deciduous Tree ◽  
Spectral Information ◽  
Species Classification ◽  
Individual Tree ◽  
Mean Values ◽  
Old Growth Forest

Tree species classification is important for a variety of environmental applications, including biodiversity monitoring, wildfire risk assessment, ecosystem services assessment, and sustainable forest management. In this study we used a fusion of three remote sensing (RM) datasets including ALS (leaf-on and leaf-off) and colour-infrared (CIR) imagery (leaf-on), to classify different coniferous and deciduous tree species, including dead class, in a mixed temperate forest in Poland. We used intensity and structural variables from the ALS data and spectral information derived from aerial imagery for the classification procedure. Additionally, we tested the differences in classification accuracy of all the variants included in the data integration. The random forest classifier was used in the study. The highest accuracies were obtained for classification based on both point clouds and including image spectral information. The mean values for overall accuracy and kappa were 84.3% and 0.82, respectively. Analysis of the leaf-on and leaf-off alone is not sufficient to identify individual tree species due to their different discriminatory power. Leaf-on and leaf-off ALS point cloud features alone gave the lowest accuracies of 72% ≤ OA ≤ 74% and 0.67 ≤ κ ≤ 0.70. Classification based on both point clouds was found to give satisfactory and comparable results to classification based on combined information from all three sources (83% ≤ OA ≤ 84% and 0.81 ≤ κ ≤ 0.82). The classification accuracy varied between species. The classification results for coniferous trees were always better than for deciduous trees independent of the datasets. In the classification based on both point clouds (leaf-on and leaf-off), the intensity features seemed to be more important than the other groups of variables, especially the coefficient of variation, skewness, and percentiles. The NDVI was the most important CIR-based feature.

Aspen detection in boreal forests: Capturing a key component of biodiversity using airborne hyperspectral, lidar, and UAV data

2020 ◽  
Author(s):  
Timo Kumpula ◽  
Arto Viinikka ◽  
Janne Mäyrä ◽  
Anton Kuzmin ◽  
Pekka Hurskainen ◽  
...  
Keyword(s):  
Tree Species ◽  
Boreal Forests ◽  
Large Scale ◽  
Forest Canopy ◽  
Tree Level ◽  
Gradient Boosting ◽  
Support Vector ◽  
Species Classification ◽  
Learning Methods ◽  
European Aspen

<p>Importance of biodiversity is increasingly highlighted as an essential part of sustainable forest management. As direct monitoring of biodiversity is not possible, proxy variables have been used to indicate site’s species richness and quality. In boreal forests, European aspen (Populus tremula L.) is one of the most significant proxies for biodiversity. Aspen is a keystone species, hosting a range of endangered species, hence having a high importance in maintaining forest biodiversity. Still, reliable and fine-scale spatial data on aspen occurrence remains scarce and incomprehensive. Although remote sensing-based species classification has been used for decades for the needs of forestry, commercially less significant species (e.g., aspen) have typically been excluded from the studies. This creates a need for developing general methods for tree species classification covering also ecologically significant species.</p><p> </p><p>Our study area, located in Evo, Southern Finland, covers approximately 83km<sup>2</sup>, and contains both managed and protected southern boreal forests. The main tree species in the area are Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst), and birch (Betula pendula and pubescens L.), with relatively sparse and scattered occurrence of aspen. Along with a thorough field data, airborne hyperspectral and LiDAR data have been acquired from the study area. We also collected ultra high resolution unmanned aerial vehicle (UAV) data with RGB and multispectral sensors.</p><p> </p><p>Our aim is to gather fundamental data on hyperspectral and multispectral species classification, that can be utilized to produce detailed aspen data at large scale. For this, we first analyze species detection at tree-level. We test and compare different machine learning methods (Support Vector Machines, Random Forest, Gradient Boosting Machine) and deep learning methods (3D convolutional neural networks), with specific emphasis on accurate and feasible aspen detection. The results will show, how accurately aspen can be detected from the forest canopy, and which bandwidths have the largest importance for aspen. This information can be utilized for aspen detection from satellite images at large scale.</p>

scholarly journals Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Dominik Seidel ◽  
Peter Annighöfer ◽  
Anton Thielman ◽  
Quentin Edward Seifert ◽  
Jan-Henrik Thauer ◽  
...  
Keyword(s):  
Tree Species ◽  
Classification Accuracy ◽  
Laser Scanning ◽  
Confusion Matrix ◽  
Point Clouds ◽  
Small Sample ◽  
Training Data ◽  
Computationally Efficient ◽  
Species Classification ◽  
3D Point Clouds

Automated species classification from 3D point clouds is still a challenge. It is, however, an important task for laser scanning-based forest inventory, ecosystem models, and to support forest management. Here, we tested the performance of an image classification approach based on convolutional neural networks (CNNs) with the aim to classify 3D point clouds of seven tree species based on 2D representation in a computationally efficient way. We were particularly interested in how the approach would perform with artificially increased training data size based on image augmentation techniques. Our approach yielded a high classification accuracy (86%) and the confusion matrix revealed that despite rather small sample sizes of the training data for some tree species, classification accuracy was high. We could partly relate this to the successful application of the image augmentation technique, improving our result by 6% in total and 13, 14, and 24% for ash, oak and pine, respectively. The introduced approach is hence not only applicable to small-sized datasets, it is also computationally effective since it relies on 2D instead of 3D data to be processed in the CNN. Our approach was faster and more accurate when compared to the point cloud-based “PointNet” approach.

scholarly journals Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data

2020 ◽  
Vol 12 (16) ◽  
pp. 2610
Author(s):  
Arto Viinikka ◽  
Pekka Hurskainen ◽  
Sarita Keski-Saari ◽  
Sonja Kivinen ◽  
Topi Tanhuanpää ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Classification Accuracy ◽  
Boreal Forests ◽  
Laser Scanning ◽  
Biological Diversity ◽  
Principal Component ◽  
Airborne Laser ◽  
Red Edge ◽  
European Aspen ◽  
Management And Conservation

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455–2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers—support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.

scholarly journals TREE SPECIES CLASSIFICATION BASED ON 3D SPECTRAL POINT CLOUDS AND ORTHOMOSAICS ACQUIRED BY SNAPSHOT HYPERSPECTRAL UAS SENSOR

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 379-384 ◽  
Author(s):  
C. Iseli ◽  
A. Lucieer
Keyword(s):  
Tree Species ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Point Clouds ◽  
Sensor Calibration ◽  
Unmanned Aerial Systems ◽  
3D Point Clouds

<p><strong>Abstract.</strong> In recent years, there has been a growing number of small hyperspectral sensors suitable for deployment on unmanned aerial systems (UAS. The introduction of the hyperspectral snapshot sensor provides interesting opportunities for acquisition of three-dimensional (3D) hyperspectral point clouds based on the structure-from-motion (SfM) workflow. In this study, we describe the integration of a 25-band hyperspectral snapshot sensor (PhotonFocus camera with IMEC 600&amp;thinsp;&amp;ndash;&amp;thinsp;875&amp;thinsp;nm 5x5 mosaic chip) on a multi-rotor UAS. The sensor was integrated with a dual frequency GNSS receiver for accurate time synchronisation and geolocation. We describe the sensor calibration workflow, including dark current and flat field characterisation. An SfM workflow was implemented to derive hyperspectral 3D point clouds and orthomosaics from overlapping frames. On-board GNSS coordinates for each hyperspectral frame assisted in the SfM process and allowed for accurate direct georeferencing (&amp;lt;&amp;thinsp;10&amp;thinsp;cm absolute accuracy). We present the processing workflow to generate seamless hyperspectral orthomosaics from hundreds of raw images. Spectral reference panels and in-field spectral measurements were used to calibrate and validate the spectral signatures. This process provides a novel data type which contains both 3D, geometric structure and detailed spectral information in a single format. First, to determine the potential improvements that such a format could provide, the core aim of this study was to compare the use of 3D hyperspectral point clouds to conventional hyperspectral imagery in the classification of two Eucalyptus tree species found in Tasmania, Australia. The IMEC SM5x5 hyperspectral snapshot sensor was flown over a small native plantation plot, consisting of a mix of the <i>Eucalyptus pauciflora</i> and <i>E. tenuiramis</i> species. High overlap hyperspectral imagery was captured and then processed using SfM algorithms to generate both a hyperspectral orthomosaic and a dense hyperspectral point cloud. Additionally, to ensure the optimum spectral quality of the data, the characteristics of the hyperspectral snapshot imaging sensor were analysed utilising measurements captured in a laboratory environment. To coincide with the generated hyperspectral point cloud data, both a file format and additional processing and visualisation software were developed to provide the necessary tools for a complete classification workflow. Results based on the classification of the <i>E. pauciflora</i> and <i>E. tenuiramis</i> species revealed that the hyperspectral point cloud produced an increased classification accuracy over conventional hyperspectral imagery based on random forest classification. This was represented by an increase in classification accuracy from 67.2% to 73.8%. It was found that even when applied separately, the geometric and spectral feature sets from the point cloud both provided increased classification accuracy over the hyperspectral imagery.</p>

scholarly journals Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

2021 ◽  
Vol 13 (9) ◽  
pp. 1723
Author(s):  
Anton Kuzmin ◽  
Lauri Korhonen ◽  
Sonja Kivinen ◽  
Pekka Hurskainen ◽  
Pasi Korpelainen ◽  
...  
Keyword(s):  
Forest Management ◽  
Unmanned Aerial Vehicle ◽  
Classification Accuracy ◽  
Boreal Forests ◽  
Populus Tremula ◽  
Tree Level ◽  
Tree Crown ◽  
Individual Tree ◽  
European Aspen ◽  
Aerial Vehicle

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras: Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

scholarly journals An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1868
Author(s):  
Martina Deur ◽  
Mateo Gašparović ◽  
Ivan Balenović
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Classification Accuracy ◽  
New Technologies ◽  
Deciduous Forest ◽  
Species Classification ◽  
Tree Species Classification ◽  
Object Based ◽  
Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

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