scholarly journals How to Recognize Different Types of Trees from Quite a Long Way Away: Combining UAV and Spaceborne Imagery for Stand-Level Tree Species Identification

2021 ◽  
Author(s):  
Adam H Sprott ◽  
Joseph M Piwowar
Keyword(s):  
Tree Species ◽  
Environmental Changes ◽  
Kappa Statistic ◽  
Tree Level ◽  
Landsat 8 ◽  
Individual Tree ◽  
Composite Image ◽  
Training Samples ◽  
Different Types ◽  
Colour Composite

In order to understand how a forest may respond to environmental changes or develop over time, it is necessary to examine broad, landscape level factors. With the arrival of unmanned aerial vehicles (UAVs), the combination of both spaceborne data with high resolution UAV data can provide foresters and biologists with powerful tools to classify canopies to the species level, which we illustrate here. We combine imagery from the Operational Land Imager (OLI) of the Landsat 8 satellite with aerial imagery from a Phantom 4 UAV to map canopy composition of three tree species. We manually delineated dense stands of each tree species in the UAV imagery to extract training samples from an OLI true colour composite image to perform a fuzzy membership analysis and calculate the maximum likelihood that an individual pixel represented a particular species. We verified the accuracy of our analysis finding an overall accuracy of 0.796 and a kappa statistic of 0.728. We consider these results to be a strong demonstration of the value of using UAV and satellite imagery in tandem to investigate forest-wide effects at an individual tree level.

scholarly journals A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2948 ◽  
Author(s):  
Hoang Minh Nguyen ◽  
Begüm Demir ◽  
Michele Dalponte
Keyword(s):  
Tree Species ◽  
Remote Sensing Data ◽  
Classification Problem ◽  
Learning Phase ◽  
Support Vector ◽  
Lidar Data ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

scholarly journals Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

2016 ◽  
Vol 8 (12) ◽  
pp. 1034 ◽  
Author(s):  
Songqiu Deng ◽  
Masato Katoh ◽  
Xiaowei Yu ◽  
Juha Hyyppä ◽  
Tian Gao
Keyword(s):  
Tree Species ◽  
Tree Level ◽  
Individual Tree ◽  
Rgb Images ◽  
The Individual

Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV

2020 ◽  
Vol 8 (4) ◽  
pp. 310-333
Author(s):  
Sowmya Natesan ◽  
Costas Armenakis ◽  
Udayalakshmi Vepakomma
Keyword(s):  
High Resolution ◽  
Species Identification ◽  
Tree Species ◽  
Tree Canopy ◽  
Tree Level ◽  
Eastern Canada ◽  
Convolutional Network ◽  
Individual Tree ◽  
Rgb Images ◽  
The Individual

Tree species identification at the individual tree level is crucial for forest operations and management, yet its automated mapping remains challenging. Emerging technology, such as the high-resolution imagery from unmanned aerial vehicles (UAV) that is now becoming part of every forester’s surveillance kit, can potentially provide a solution to better characterize the tree canopy. To address this need, we have developed an approach based on a deep Convolutional Neural Network (CNN) to classify forest tree species at the individual tree-level that uses high-resolution RGB images acquired from a consumer-grade camera mounted on a UAV platform. This work explores the ability of the Dense Convolutional Network (DenseNet) to classify commonly available economic coniferous tree species in eastern Canada. The network was trained using multitemporal images captured under varying acquisition parameters to include seasonal, temporal, illumination, and angular variability. Validation of this model using distinct images over a mixed-wood forest in Ontario, Canada, showed over 84% classification accuracy in distinguishing five predominant species of coniferous trees. The model remains highly robust even when using images taken during different seasons and times, and with varying illumination and angles.

scholarly journals RESNET-BASED TREE SPECIES CLASSIFICATION USING UAV IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 475-481 ◽  
Author(s):  
S. Natesan ◽  
C. Armenakis ◽  
U. Vepakomma
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Tree Species ◽  
Classification Accuracy ◽  
Forest Tree ◽  
Tree Level ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Rgb Images

<p><strong>Abstract.</strong> Tree species classification at individual tree level is a challenging problem in forest management. Deep learning, a cutting-edge technology evolved from Artificial Intelligence, was seen to outperform other techniques when it comes to complex problems such as image classification. In this work, we present a novel method to classify forest tree species through high resolution RGB images acquired with a simple consumer grade camera mounted on a UAV platform using Residual Neural Networks. We used UAV RGB images acquired over three years that varied in numerous acquisition parameters such as season, time, illumination and angle to train the neural network. To begin with, we have experimented with limited data towards the identification of two pine species namely red pine and white pine from the rest of the species. We performed two experiments, first with the images from all three acquisition years and the second with images from only one acquisition year. In the first experiment, we obtained 80% classification accuracy when the trained network was tested on a distinct set of images and in the second experiment, we obtained 51% classification accuracy. As a part of this work, a novel dataset of high-resolution labelled tree species is generated that can be used to conduct further studies involving deep neural networks in forestry.</p>

scholarly journals Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Forests ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Aaron M. Sparks ◽  
Alistair M.S. Smith
Keyword(s):  
Supply Chain ◽  
Tree Species ◽  
Coniferous Forest ◽  
Canopy Cover ◽  
Forest Products ◽  
Tree Canopy ◽  
Tree Level ◽  
Individual Tree ◽  
Tree Detection ◽  
Canopy Position

Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.

scholarly journals Allometric Equations for Estimating the Above-Ground Biomass of Five Forest Tree Species in Khangai, Mongolia

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 661 ◽  
Author(s):  
Batbaatar Altanzagas ◽  
Yongkai Luo ◽  
Batbaatar Altansukh ◽  
Chimidnyam Dorjsuren ◽  
Jingyun Fang ◽  
...  
Keyword(s):  
Tree Species ◽  
Allometric Equations ◽  
Tree Level ◽  
Pinus Sibirica ◽  
Above Ground Biomass ◽  
Individual Tree ◽  
Siberian Stone Pine ◽  
Ground Biomass ◽  
Forest Region ◽  
Species Specific

Understanding the contribution of forest ecosystems to regulating greenhouse gas emissions and maintaining the atmospheric CO2 balance requires the accurate quantification of above-ground biomass (AGB) at the individual tree species level. The main objective of this study was to develop species-specific allometric equations for the total AGB and various biomass components, including stem, branch, and foliage biomass in Khangai region, northern Mongolia. We destructively sampled a total of 183 trees of five species (22–74 trees per species), including Siberian stone pine (Pinus sibirica Du Tour.), Asian white birch (Betula platyphylla Sukacz.), Mongolian poplar (Populus suaveolens Fisch.), Siberian spruce (Picea obovata Ldb.), and Siberian larch (Larix sibirica Ldb.), across this region. The results showed that for the five species, the average biomass proportion for the stems was 75%, followed by branches at 20% and foliage at 5%. The species-specific component and total AGB models for the Khangai region were developed using tree diameter at breast height (D) and D² and tree height (H) combined ( D 2 H ); and both D and H were used as independent variables. The best allometric model was lnŶ = lna + b × lnD + c × lnH for the various components and total AGB of B. platyphylla and L. sibirica, for the stems and total AGB of P. suaveolens, and for the stem and branch biomass of P. obovata. The equation lnŶ = lna + b × ln( D 2 × H ) was best for the various components and total AGB of P. sibirica, for the branch and foliage biomass of P. suaveolens, and for AGB of P. obovata. The equation lnŶ = lna + b × ln(D) was best only for the foliage biomass of P. obovata. Our results highlight that developing species-specific tree AGB models is very important for accurately estimating the biomass in the Khangai forest region of Mongolia. Our biomass models will be used at the tree level inventories with sample plots in the Khangai forest region.

Color models used for the visual interpretation of satellite data

2021 ◽  
Vol 5 ◽  
pp. 44-51
Author(s):  
Georgi Jelev
Keyword(s):  
Satellite Imagery ◽  
False Colour Composite ◽  
Earth Surface ◽  
Visual Interpretation ◽  
Composite Image ◽  
Spectral Bands ◽  
The Earth ◽  
Different Types ◽  
Colour Composite ◽  
False Colour

This study presents the possibilities for using different colour models for visual interpretation of satellite imagery. Using the RGB model to visualise different spectral bands as a false colour composite image make it possible for different types of objects and features on the Earth surface to be highlighted and easily discerned based on their specific colour. Examples are shown based on satellite imagery from several free sources, e.g. the USGS’s Earth Explorer, the ESA’s Copernicus Open Access Hub, etc.

Enzyme Immobilization on Nanomaterials for Biosensor and Biocatalyst in Food and Biomedical Industry

2019 ◽  
Vol 25 (24) ◽  
pp. 2661-2676 ◽  
Author(s):  
Sundaresan Bhavaniramya ◽  
Ramar Vanajothi ◽  
Selvaraju Vishnupriya ◽  
Kumpati Premkumar ◽  
Mohammad S. Al-Aboody ◽  
...  
Keyword(s):  
Enzyme Activity ◽  
Enzyme Immobilization ◽  
Food Industry ◽  
Environmental Changes ◽  
Immobilized Enzymes ◽  
Food Industries ◽  
Physiological Processes ◽  
Different Types ◽  
Simple Processing ◽  
Biomedical Industry

Enzymes exhibit a great catalytic activity for several physiological processes. Utilization of immobilized enzymes has a great potential in several food industries due to their excellent functional properties, simple processing and cost effectiveness during the past decades. Though they have several applications, they still exhibit some challenges. To overcome the challenges, nanoparticles with their unique physicochemical properties act as very attractive carriers for enzyme immobilization. The enzyme immobilization method is not only widely used in the food industry but is also a component methodology in the pharmaceutical industry. Compared to the free enzymes, immobilized forms are more robust and resistant to environmental changes. In this method, the mobility of enzymes is artificially restricted to changing their structure and properties. Due to their sensitive nature, the classical immobilization methods are still limited as a result of the reduction of enzyme activity. In order to improve the enzyme activity and their properties, nanomaterials are used as a carrier for enzyme immobilization. Recently, much attention has been directed towards the research on the potentiality of the immobilized enzymes in the food industry. Hence, the present review emphasizes the different types of immobilization methods that is presently used in the food industry and other applications. Various types of nanomaterials such as nanofibers, nanoflowers and magnetic nanoparticles are significantly used as a support material in the immobilization methods. However, several numbers of immobilized enzymes are used in the food industries to improve the processing methods which not only reduce the production cost but also the effluents from the industry.

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.

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