scholarly journals ALS-based wood volume models of forest stands and comparison with forest inventory data

2016 ◽  
Vol 64 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Tauri Arumäe ◽  
Mait Lang
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Point Clouds ◽  
Test Site ◽  
Sample Plot ◽  
Volume Estimate ◽  
Wood Volume ◽  
Level Model ◽  
Volume Models

Abstract Airborne laser scanning (ALS) based standing wood volume models were analysed in two contrasting test sites with different forest types in Estonia. In Aegviidu test site main tree species are Scots pine and Norway spruce and Laeva test site is mainly dominated by deciduous species. ALS data measurements were carried out for Aegviidu in 2008 and for Laeva in 2013. Approximately 450 sample plots were established additionally to the forest inventory dataset in both test sites. Exclusive to the sample plots, 46 stands were measured in 2012 in Aegviidu for stand level model. The sample plot-based model standard error in Aegviidu was Se = 59.8 m3/ha (22%) and in Laeva Se = 69.2 m3/ha (29%). The stand-level model based on 46 measured stands from Aegviidu had Se = 38.4 m3/ha. Based on the models a cross-validation between the two test sites was carried out and systematic differences between the two test sites were found. The reasons are related to differences in optical properties of trees, crown shapes, flight configuration and canopy cover even though the sample plot based models included ALS-based canopy cover variable. The ALS-based wood volume estimate was also compared to forest inventory (FI) data and systematically larger estimates compared to FI dataset in both test sites were found. This average systematic error increased substantially (by 100 m3/ha) for stands with volume over 250 m3/ha. It was also detected that a model developed on small point clouds drawn for sample plots may produce systematic errors when applied to stand-level point clouds.

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 Assessment of forest thinning intensity using sparse point clouds from repeated airborne lidar measurements

2018 ◽  
Vol 68 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Mait Lang ◽  
Tauri Arumäe
Keyword(s):  
Ground Surface ◽  
Canopy Cover ◽  
Point Clouds ◽  
Test Site ◽  
Airborne Lidar ◽  
Measurement Interval ◽  
Wood Volume ◽  
Thinning Intensity ◽  
Lidar Measurements ◽  
Volume Estimates

Abstract Thinning cuttings create moderate disturbances in forest stands. Thinning intensity indicates the amount of felled wood relative to the initial standing volume. We used sparse point clouds from airborne lidar measurements carried out in 2008 and 2012 at Aegviidu test site, Estonia, to study stand level relationships of thinning intensity to the changes in canopy cover and ALS-based wood volume estimates. Thinning intensity (Kr, HRV) was estimated from forest inventory data and harvester measurements of removed wood volume. The thinning intensity ranged from 17% to 56%. By raising threshold from 1.3 m to 8.0 m over ground surface we observed less canopy cover change, but stronger correlation with thinning intensity. Correlation between ALS-based and harvester-based thinning intensity was moderate. The ALS-based thinning intensity estimate was systematically smaller than Kr, HRV. Forest height growth compensates for a small decrease in canopy cover and intensity estimates for weak thinnings are not reliable using sparse point clouds and a four-year measurement interval.

scholarly journals Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

2021 ◽  
Vol 13 (2) ◽  
pp. 257 ◽  
Author(s):  
Shaun R. Levick ◽  
Tim Whiteside ◽  
David A. Loewensteiner ◽  
Mitchel Rudge ◽  
Renee Bartolo
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Structural Diversity ◽  
Point Clouds ◽  
Large Area ◽  
Calibration And Validation ◽  
Savanna Vegetation ◽  
Landscape Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

scholarly journals Combination of Multi-Temporal Sentinel 2 Images and Aerial Image Based Canopy Height Models for Timber Volume Modelling

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 746 ◽  
Author(s):  
Johannes Schumacher ◽  
Margret Rattay ◽  
Melanie Kirchhöfer ◽  
Petra Adler ◽  
Gerald Kändler
Keyword(s):  
Forest Inventory ◽  
Point Clouds ◽  
Support Vector ◽  
Timber Volume ◽  
Volume Model ◽  
Broadleaf Tree ◽  
Multi Temporal ◽  
Conifer Trees ◽  
Volume Models ◽  
Sentinel 2

Multi-temporal Sentinel 2 optical images and 3D photogrammetric point clouds can be combined to enhance the accuracy of timber volume models on large spatial scale. Information on the proportion of broadleaf and conifer trees improves timber volume models obtained from 3D photogrammetric point clouds. However, the broadleaf-conifer information cannot be obtained from photogrammetric point clouds alone. Furthermore, spectral information of aerial images is too inconsistent to be used for automatic broadleaf-conifer classification over larger areas. In this study we combined multi-temporal Sentinel 2 optical satellite images, 3D photogrammetric point clouds from digital aerial stereo photographs, and forest inventory plots representing an area of 35,751 km2 in south-west Germany for (1) modelling the percentage of broadleaf tree volume (BL%) using Sentinel 2 time series and (2) modelling timber volume per hectare using 3D photogrammetric point clouds. Forest inventory plots were surveyed in the same years and regions as stereo photographs were acquired (2013–2017), resulting in 11,554 plots. Sentinel 2 images from 2016 and 2017 were corrected for topographic and atmospheric influences and combined with the same forest inventory plots. Spectral variables from corrected multi-temporal Sentinel 2 images were calculated, and Support Vector Machine (SVM) regressions were fitted for each Sentinel 2 scene estimating the BL% for corresponding inventory plots. Variables from the photogrammetric point clouds were calculated for each inventory plot and a non-linear regression model predicting timber volume per hectare was fitted. Each SVM regression and the timber volume model were evaluated using ten-fold cross-validation (CV). The SVM regression models estimating the BL% per Sentinel 2 scene achieved overall accuracies of 68%–75% and a Root Mean Squared Error (RMSE) of 21.5–26.1. The timber volume model showed a RMSE% of 31.7%, a mean bias of 0.2%, and a pseudo-R2 of 0.64. Application of the SVM regressions on Sentinel 2 scenes covering the state of Baden-Württemberg resulted in predictions of broadleaf tree percentages for the entire state. These predicted values were used as additional predictor in the timber volume model, allowing for predictions of timber volume for the same area. Spatially high-resolution information about growing stock is of great practical relevance for forest management planning, especially when the timber volume of a smaller unit is of interest, for example of a forest stand or a forest district where not enough terrestrial inventory plots are available to make reliable estimations. Here, predictions from remote-sensing based models can be used. Furthermore, information about broadleaf and conifer trees improves timber volume models and reduces model errors and, thereby, prediction uncertainties.

scholarly journals The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

Forests ◽  
2013 ◽  
Vol 4 (3) ◽  
pp. 518-536 ◽  
Author(s):  
Joanne White ◽  
Michael Wulder ◽  
Mikko Vastaranta ◽  
Nicholas Coops ◽  
Doug Pitt ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Airborne Laser

scholarly journals Remote-sensing support for the Estonian National Forest Inventory, facilitating the construction of maps for forest height, standing-wood volume, and tree species composition

2020 ◽  
Vol 73 (1) ◽  
pp. 77-97
Author(s):  
Mait Lang ◽  
Allan Sims ◽  
Kalev Pärna ◽  
Raul Kangro ◽  
Märt Möls ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Tree Species ◽  
Forest Inventory ◽  
Point Clouds ◽  
National Forest Inventory ◽  
National Forest ◽  
Tree Species Composition ◽  
Wood Volume ◽  
Forest Height

Abstract Since 1999, Estonia has conducted the National Forest Inventory (NFI) on the basis of sample plots. This paper presents a new module, incorporating remote-sensing feature variables from airborne laser scanning (ALS) and from multispectral satellite images, for the construction of maps of forest height, standing-wood volume, and tree species composition for the entire country. The models for sparse ALS point clouds yield coefficients of determination of 89.5–94.8% for stand height and 84.2–91.7% for wood volume. For the tree species prediction, the models yield Cohen's kappa values (taking 95% confidence intervals) of 0.69–0.72 upon comparing model results against a previous map, and values of 0.51–0.54 upon comparing model results against NFI sample plots. This paper additionally examines the influence of foliage phenology on the predictions and discusses options for further enhancement of the system.

State-of-the-art in 4D measurements

2021 ◽  
Author(s):  
Eetu Puttonen ◽  
Juha Hyyppä ◽  
Paula Litkey ◽  
Mariana Batista Campos ◽  
Heikki Hyyti ◽  
...  
Keyword(s):  
Vegetation Dynamics ◽  
Laser Scanning ◽  
Structural Changes ◽  
Structural Information ◽  
Laser Scanner ◽  
Point Clouds ◽  
Test Site ◽  
Forest Monitoring ◽  
Monitoring Technologies

<p>Light detection and ranging (lidar) has become an essential tool in mapping and change detection in different environments over the last 20 years. Laser scanners capture point clouds to create accurate digital snapshots of their surroundings. These snapshots tell about the structural information in the scene and can be readily returned to again and again to detect and measure any changes with multi-temporal measurements. However, multitemporal measurements cannot typically resolve the change events nor can they resolve more high frequency dynamics that happen on daily or weekly basis in the scene. Also, lidar systems operate still mainly with single wavelength limiting their usability in classification tasks. First multi- and hyperspectral systems have been already demonstrated, but have yet to break through in wider usage. Finnish Geospatial Research Institute (FGI) has been prototyping with different 3D measurement systems for the last 10 years to improve multitemporal mapping (4D) solutions. The prototypes include both hyperspectral and long-term multi- and hypertemporal lidar systems, and their combinations in static and mobile configurations. FGI started early on to experiment with hyperspectral laser sources (2007) and successfully demonstrated the first hyperspectral laser scanner prototype in 2012. The system was later used in detecting intraday vegetation dynamics in 2015. Multitemporal multispectral ALS measurements have been conducted since 2015 in Evo and in Espoolahti. The first long-term multitemporal studies with FGI mapping platforms were started with ALS to monitor changes in forests (1998) and built environment (2001) and with mobile laser scanning in studying the erosion of an arctic river basin (2008) annually.  Multitemporal ALS studies with vegetation started in 1998 in Kalkkinen and in 2007 in Evo followed with bi-temporal studies with TLS. Test Site Evo has been acquired with ALS. In 2020, Evo test site was granted Academy of Finland Research Infrastructure (RI) status. The RI will collect a 30-year-long time series with annual measurements using various laser scanning sensors for investigating single tree growth processes, forest dynamics, understanding cyclic forest while having variation at diurnal and annual scales and forest monitoring technologies. Vegetation dynamics monitoring was extended in 2020, when FGI started set up a permanent TLS measurement station in a boreal forest. The TLS station accurately detects structural changes of hundreds of tree crowns around it. The experiment aims to detect the changes of phenological state the trees and further link them with the environmental parameter variation. 4D measurements have successfully demonstrated their potential in extending the information available from laser scanning systems. To improve the usage of these novel information, automated pre-filtering of the vast data amounts already at sensor level will be imperative. Different lidar platforms can operate throughout the spatial scale from millimeter precision all way to national coverage. Thus, development of new scalable lidar RIs open new possibilities to complement already existing infrastructures.</p>

scholarly journals FOREST INVENTORY ATTRIBUTE ESTIMATION USING AIRBORNE LASER SCANNING, AERIAL STEREO IMAGERY, RADARGRAMMETRY AND INTERFEROMETRY–FINNISH EXPERIENCES OF THE 3D TECHNIQUES

2015 ◽  
Vol II-3/W4 ◽  
pp. 63-69 ◽  
Author(s):  
M. Holopainen ◽  
M. Vastaranta ◽  
M. Karjalainen ◽  
K. Karila ◽  
S. Kaasalainen ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Mixed Forest ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Current Status ◽  
Airborne Laser ◽  
Stereo Imagery ◽  
Sar Data ◽  
Forest Conditions

Three-dimensional (3D) remote sensing has enabled detailed mapping of terrain and vegetation heights. Consequently, forest inventory attributes are estimated more and more using point clouds and normalized surface models. In practical applications, mainly airborne laser scanning (ALS) has been used in forest resource mapping. The current status is that ALS-based forest inventories are widespread, and the popularity of ALS has also raised interest toward alternative 3D techniques, including airborne and spaceborne techniques. Point clouds can be generated using photogrammetry, radargrammetry and interferometry. Airborne stereo imagery can be used in deriving photogrammetric point clouds, as very-high-resolution synthetic aperture radar (SAR) data are used in radargrammetry and interferometry. ALS is capable of mapping both the terrain and tree heights in mixed forest conditions, which is an advantage over aerial images or SAR data. However, in many jurisdictions, a detailed ALS-based digital terrain model is already available, and that enables linking photogrammetric or SAR-derived heights to heights above the ground. In other words, in forest conditions, the height of single trees, height of the canopy and/or density of the canopy can be measured and used in estimation of forest inventory attributes. In this paper, first we review experiences of the use of digital stereo imagery and spaceborne SAR in estimation of forest inventory attributes in Finland, and we compare techniques to ALS. In addition, we aim to present new implications based on our experiences.

scholarly journals AUTOMATED STRUCTURAL FOREST CHANGES USING LIDAR POINT CLOUDS AND GIS ANALYSES

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 603-608
Author(s):  
A. Novo ◽  
H. González-Jorge ◽  
J. Martínez-Sánchez ◽  
J. M. Fernández-Alonso ◽  
H. Lorenzo
Keyword(s):  
Spatial Data ◽  
Forest Structure ◽  
Laser Scanning ◽  
Structural Model ◽  
Canopy Cover ◽  
Ground Truth ◽  
Point Clouds ◽  
Forest Community ◽  
Structural Behaviour ◽  
Ground Truth Data

Abstract. Forest spatial structure describes the relationships among different species in the same forest community. Automation in the monitoring of the structural forest changes and forest mapping is one of the main utilities of applications of modern geoinformatics methods. The obtaining objective information requires the use of spatial data derived from photogrammetry and remote sensing. This paper investigates the possibility of applying light detection and ranging (LiDAR) point clouds and geographic information system (GIS) analyses for automated mapping and detection changes in vegetation structure during a year of study. The research was conducted in an area of the Ourense Province (NWSpain). The airborne laser scanning (ALS) data, acquired in August 2019 and June of 2020, reveal detailed changes in forest structure. Based on ALS data the vegetation parameters will be analysed.To study the structural behaviour of the tree vegetation, the following parameters are used in each one of the sampling areas: (1) Relationship between the tree species present and their stratification; (2) Vegetation classification in fuel types; (3) Biomass (Gi); (4) Number of individuals per area; and (5) Canopy cover fraction (CCF). Besides, the results were compared with the ground truth data recollected in the study area.The development of a quantitative structural model based on Aerial Laser Scanning (ALS) point clouds was proposed to accurately estimate tree attributes automatically and to detect changes in forest structure. Results of statistical analysis of point cloud show the possibility to use UAV LiDAR data to characterize changes in the structure of vegetation.

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