scholarly journals Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements

Silva Fennica ◽  
2021 ◽  
Vol 55 (1) ◽  
Author(s):  
Mikko Kukkonen ◽  
Eetu Kotivuori ◽  
Matti Maltamo ◽  
Lauri Korhonen ◽  
Petteri Packalen
Keyword(s):  
Tree Species ◽  
Small Area ◽  
Laser Scanning ◽  
Point Clouds ◽  
Unmanned Aircraft ◽  
Remotely Sensed Data ◽  
Alternative Source ◽  
Species Specific ◽  
Species Proportions ◽  
Sentinel 2

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by nearest neighbor (-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.kk

scholarly journals Fusing Sentinel-2 Imagery and ALS Point Clouds for Defining LULC Changes on Reclaimed Areas by Afforestation

2019 ◽  
Vol 11 (5) ◽  
pp. 1251 ◽  
Author(s):  
Marta Szostak ◽  
Kacper Knapik ◽  
Piotr Wężyk ◽  
Justyna Likus-Cieślik ◽  
Marcin Pietrzykowski
Keyword(s):  
Laser Scanning ◽  
Canopy Cover ◽  
Point Clouds ◽  
Test Site ◽  
Vegetation Growth ◽  
Landsat 7 ◽  
Forested Areas ◽  
Primary Focus ◽  
Year 2000 ◽  
Sentinel 2

The study was performed on two former sulphur mines located in Southeast Poland: Jeziórko, where 216.5 ha of afforested area was reclaimed after borehole exploitation and Machów, where 871.7 ha of dump area was reclaimed after open cast strip mining. The areas were characterized by its terrain structure and vegetation cover resulting from the reclamation process. The types of reclamation applied in these areas were forestry in Jeziórko and agroforestry in the Machów post-sulphur mine. The study investigates the possibility of applying the most recent Sentinel-2 (ESA) satellite imageries for land cover mapping, with a primary focus on detecting and monitoring afforested areas. Airborne laser scanning point clouds were used to derive precise information about the spatial (3D) characteristics of vegetation: the height (95th percentile), std. dev. of relative height, and canopy cover. The results of the study show an increase in afforested areas in the former sulphur mines. For the entire analyzed area of Jeziórko, forested areas made up 82.0% in the year 2000 (Landsat 7, NASA), 88.8% in 2009 (aerial orthophoto), and 95.5% in 2016 (Sentinel-2, ESA). For Machów, the corresponding results were 46.1% in 2000, 57.3% in 2009, and 60.7% in 2016. A dynamic increase of afforested area was observed, especially in the Jeziórko test site, with the presence of different stages of vegetation growth.

scholarly journals National mapping and estimation of forest area by dominant tree species using Sentinel-2 data

2020 ◽  
Author(s):  
Johannes Breidenbach ◽  
Lars T. Waser ◽  
Misganu Debella-Gilo ◽  
Johannes Schumacher ◽  
Johannes Rahlf ◽  
...  
Keyword(s):  
Tree Species ◽  
Deciduous Forest ◽  
National Level ◽  
National Forest Inventory ◽  
Forest Area ◽  
Forest Resource ◽  
Species Specific ◽  
Minimum Numbers ◽  
Generate Model ◽  
Sentinel 2

Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) plot data for modelling and subsequent mapping of spruce-, pine- and deciduous-dominated forest in Norway at a 16m×16m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42,000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by approximately 10%. The produced map was subsequently used to generate model-assisted (MA) and post stratified (PS) estimates of species-specific forest area. At the national level, efficiencies of the estimates increased by 20% to 50% for MA and up to 90% for PS. Greater minimum numbers of observations constrained the use of PS. For MA estimates of municipalities, efficiencies improved by up to a factor of 8 but were sometimes also less than 1. PS estimates were always equally as or more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.

scholarly journals Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands

2020 ◽  
Vol 12 (20) ◽  
pp. 3328
Author(s):  
Mohammad Imangholiloo ◽  
Ninni Saarinen ◽  
Markus Holopainen ◽  
Xiaowei Yu ◽  
Juha Hyyppä ◽  
...  
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Sustainable Forest Management ◽  
Single Channel ◽  
Point Clouds ◽  
Tree Density ◽  
Airborne Laser Scanning ◽  
Tree Species Composition ◽  
Coniferous Trees ◽  
Airborne Laser

Information from seedling stands in time and space is essential for sustainable forest management. To fulfil these informational needs with limited resources, remote sensing is seen as an intriguing alternative for forest inventorying. The structure and tree species composition in seedling stands have created challenges for capturing this information using sensors providing sparse point densities that do not have the ability to penetrate canopy gaps or provide spectral information. Therefore, multispectral airborne laser scanning (mALS) systems providing dense point clouds coupled with multispectral intensity data theoretically offer advantages for the characterization of seedling stands. The aim of this study was to investigate the capability of Optech Titan mALS data to characterize seedling stands in leaf-off and leaf-on conditions, as well as to retrieve the most important forest inventory attributes, such as distinguishing deciduous from coniferous trees, and estimating tree density and height. First, single-tree detection approaches were used to derive crown boundaries and tree heights from which forest structural attributes were aggregated for sample plots. To predict tree species, a random forests classifier was trained using features from two single-channel intensities (SCIs) with wavelengths of 1550 (SCI-Ch1) and 1064 nm (SCI-Ch2), and multichannel intensity (MCI) data composed of three mALS channels. The most important and uncorrelated features were analyzed and selected from 208 features. The highest overall accuracies in classification of Norway spruce, birch, and nontree class in leaf-off and leaf-on conditions obtained using SCI-Ch1 and SCI-Ch2 were 87.36% and 69.47%, respectively. The use of MCI data improved classification by up to 96.55% and 92.54% in leaf-off and leaf-on conditions, respectively. Overall, leaf-off data were favorable for distinguishing deciduous from coniferous trees and tree density estimation with a relative root mean square error (RMSE) of 37.9%, whereas leaf-on data provided more accurate height estimations, with a relative RMSE of 10.76%. Determining the canopy threshold for separating ground returns from vegetation returns was found to be critical, as mapped trees might have a height below one meter. The results showed that mALS data provided benefits for characterizing seedling stands compared to single-channel ALS systems.

scholarly journals Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 279 ◽  
Author(s):  
Ernest William Mauya ◽  
Joni Koskinen ◽  
Katri Tegel ◽  
Jarno Hämäläinen ◽  
Tuomo Kauranne ◽  
...  
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Small Scale ◽  
Remotely Sensed Data ◽  
Weighted Mean ◽  
Alos Palsar ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

scholarly journals Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement

2020 ◽  
Vol 12 (8) ◽  
pp. 1236 ◽  
Author(s):  
Karel Kuželka ◽  
Martin Slavík ◽  
Peter Surový
Keyword(s):  
Cross Sections ◽  
Hough Transform ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Bias Error ◽  
Bias Error ◽  
Least Trimmed Squares ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Tree Stem

Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results.

scholarly journals VALIDATING A WORKFLOW FOR TREE INVENTORY UPDATING WITH 3D POINT CLOUDS OBTAINED BY MOBILE LASER SCANNING

2018 ◽  
Vol XLII-2 ◽  
pp. 1163-1168
Author(s):  
J. Wang ◽  
R. Lindenbergh
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Spatial Information ◽  
Open Data ◽  
Point Clouds ◽  
Urban Trees ◽  
Biomass Estimation ◽  
Species Classification ◽  
3D Point Clouds ◽  
Tree Inventory

Urban trees are an important component of our environment and ecosystem. Trees are able to combat climate change, clean the air and cool the streets and city. Tree inventory and monitoring are of great interest for biomass estimation and change monitoring. Conventionally, parameters of trees are manually measured and documented in situ, which is not efficient regarding labour and costs. Light Detection And Ranging (LiDAR) has become a well-established surveying technique for the acquisition of geo-spatial information. Combined with automatic point cloud processing techniques, this in principle enables the efficient extraction of geometric tree parameters. In recent years, studies have investigated to what extend it is possible to perform tree inventories using laser scanning point clouds. Give the availability of a city of Delft Open data tree repository, we are now able to present, validate and extend a workflow to automatically obtain tree data from tree location until tree species. The results of a test over 47 trees show that the proposed methods in the workflow are able to individual urban trees. The tree species classification results based on the extracted tree parameters show that only one tree was wrongly classified using k-means clustering.

scholarly journals EVALUATING THE POSSIBILITY OF TREE SPECIES CLASSIFICATION WITH DUAL-WAVELENGTH ALS DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1097-1103
Author(s):  
M. Pilarska ◽  
W. Ostrowski
Keyword(s):  
Spatial Data ◽  
Tree Species ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Tree Canopy ◽  
Species Classification ◽  
Airborne Laser ◽  
Simultaneous Acquisition

<p><strong>Abstract.</strong> Airborne laser scanning (ALS) plays an important role in spatial data acquisition. One of the advantages of this technique is laser beam penetration through vegetation, which makes it possible to not only obtain data on the tree canopy but also within and under the canopy. In recent years, multi-wavelength airborne laser scanning has been developed. This technique consists of simultaneous acquisition of point clouds in more than one band. The aim of this experiment was to examine and assess the possibilities of tree segmentation and species classification in an urban area. In this experiment, point clouds registered in two wavelengths (532 and 1064&amp;thinsp;nm) were used for tree segmentation and species classification. The data were acquired with a Riegl VQ-1560i-DW laser scanner over Elblag, Poland, during August 2018. Tree species collected by a botanist team within terrain measurements were used as a reference in the classification process. Within the experiment segmentation and classification process were performed. Regarding the segmentation, TerraScan software and Li et al.’s algorithm, implemented in LidR package were used. Results from both methods are clearly over-segmented in comparison to the manual segments. In Terrasolid segmentation, single reference segments are over-segmented in 28% of cases, whereas, for LidR, over-segmentation occurred in 73% of the segments. According the classification results, Thuja, Salix and Betula were the species, for which the highest classification accuracy was achieved.</p>

Tropical Tree Species 3D Modelling and Classification Based on LiDAR Technology

2020 ◽  
pp. 1-22
Author(s):  
Panagiotis Barmpoutis ◽  
Tania Stathaki ◽  
Jonathan Lloyd ◽  
Magna Soelma Bessera de Moura
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Tensor Decomposition ◽  
Singular Values ◽  
Point Clouds ◽  
Tropical Tree ◽  
Species Classification ◽  
Tropical Tree Species ◽  
Sensing Technology ◽  
3D Information

Over the last decade or so, laser scanning technology has become an increasingly popular and important tool for forestry inventory, enabling accurate capture of 3D information in a fast and environmentally friendly manner. To this end, the authors propose here a system for tropical tree species classification based on 3D scans of LiDAR sensing technology. In order to exploit the interrelated patterns of trees, skeleton representations of tree point clouds are extracted, and their structures are divided into overlapping equal-sized 3D segments. Subsequently, they represent them as third-order sparse structure tensors setting the value of skeleton coordinates equal to one. Based on the higher-order tensor decomposition of each sparse segment, they 1) estimate the mode-n singular values extracting intra-correlations of tree branches and 2) model tropical trees as linear dynamical systems extracting appearance information and dynamics. The proposed methodology was evaluated in tropical tree species and specifically in a dataset consisting of 26 point clouds of common Caatinga dry-forest trees.

scholarly journals Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

2018 ◽  
Vol 10 (9) ◽  
pp. 1403 ◽  
Author(s):  
Jianwei Wu ◽  
Wei Yao ◽  
Przemyslaw Polewski
Keyword(s):  
Tree Species ◽  
Urban Areas ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Scanning System ◽  
Urban Road ◽  
Road Corridors ◽  
Laser Scanning System

To meet a growing demand for accurate high-fidelity vegetation cover mapping in urban areas toward biodiversity conservation and assessing the impact of climate change, this paper proposes a complete approach to species and vitality classification at single tree level by synergistic use of multimodality 3D remote sensing data. So far, airborne laser scanning system(ALS or airborne LiDAR) has shown promising results in tree cover mapping for urban areas. This paper analyzes the potential of mobile laser scanning system/mobile mapping system (MLS/MMS)-based methods for recognition of urban plant species and characterization of growth conditions using ultra-dense LiDAR point clouds and provides an objective comparison with the ALS-based methods. Firstly, to solve the extremely intensive computational burden caused by the classification of ultra-dense MLS data, a new method for the semantic labeling of LiDAR data in the urban road environment is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. These priors encode geometric primitives found in the scene through sample consensus segmentation. Then, single trees are segmented from the labelled tree points using the 3D graph cuts algorithm. Multinomial logistic regression classifiers are used to determine the fine deciduous urban tree species of conversation concern and their growth vitality. Finally, the weight-of-evidence (WofE) based decision fusion method is applied to combine the probability outputs of classification results from the MLS and ALS data. The experiment results obtained in city road corridors demonstrated that point cloud data acquired from the airborne platform achieved even slightly better results in terms of tree detection rate, tree species and vitality classification accuracy, although the tree vitality distribution in the test site is less balanced compared to the species distribution. When combined with MLS data, overall accuracies of 78% and 74% for tree species and vitality classification can be achieved, which has improved by 5.7% and 4.64% respectively compared to the usage of airborne data only.

scholarly journals FEATURE RELEVANCE ASSESSMENT OF MULTISPECTRAL AIRBORNE LIDAR DATA FOR TREE SPECIES CLASSIFICATION

2018 ◽  
Vol XLII-3 ◽  
pp. 31-34 ◽  
Author(s):  
N. Amiri ◽  
M. Heurich ◽  
P. Krzystek ◽  
A. K. Skidmore
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Species Classification ◽  
Tree Species Classification ◽  
3D Point Clouds ◽  
Segmentation Approach ◽  
Species Mapping ◽  
Spectral Channels

The presented experiment investigates the potential of Multispectral Laser Scanning (MLS) point clouds for single tree species classification. The basic idea is to simulate a MLS sensor by combining two different Lidar sensors providing three different wavelngthes. The available data were acquired in the summer 2016 at the same date in a leaf-on condition with an average point density of 37&amp;thinsp;points/m<sup>2</sup>. For the purpose of classification, we segmented the combined 3D point clouds consisiting of three different spectral channels into 3D clusters using Normalized Cut segmentation approach. Then, we extracted four group of features from the 3D point cloud space. Once a varity of features has been extracted, we applied forward stepwise feature selection in order to reduce the number of irrelevant or redundant features. For the classification, we used multinomial logestic regression with <i>L<sub>1</sub></i> regularization. Our study is conducted using 586 ground measured single trees from 20 sample plots in the Bavarian Forest National Park, in Germany. Due to lack of reference data for some rare species, we focused on four classes of species. The results show an improvement between 4&amp;ndash;10&amp;thinsp;pp for the tree species classification by using MLS data in comparison to a single wavelength based approach. A cross validated (15-fold) accuracy of 0.75 can be achieved when all feature sets from three different spectral channels are used. Our results cleary indicates that the use of MLS point clouds has great potential to improve detailed forest species mapping.

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