30 research outputs found
Estimation of canopy structure and individual trees from laser scanning data
During the last fifteen years, laser scanning has emerged as a data source for forest inventory. Airborne laser scanning (ALS) provides 3D data, which may be used in an automated analysis chain to estimate vegetation properties for large areas. Terrestrial laser scanning (TLS) data are highly accurate 3D ground-based measurements, which may be used for detailed 3D modeling of vegetation elements.
The objective of this thesis is to further develop methods to estimate forest information from laser scanning data. The aims are to estimate lists of individual trees from ALS data with accuracy comparable to area-based methods, to collect detailed field reference data using TLS, and to estimate canopy structure from ALS data. The studies were carried out in boreal and hemi-boreal forests in Sweden.
Tree crowns were delineated in three dimensions with a model-based clustering approach. The model-based clustering identified more trees than delineation of a surface model, especially for small trees below the dominant tree layer. However, it also resulted in more erroneously delineated tree crowns. Individual trees were estimated with statistical methods from ALS data based on field-measured trees to obtain unbiased results at area level. The accuracy of the estimates was similar for delineation of a surface model (stem density root mean square error or RMSE 32.0%, bias 1.9%; stem volume RMSE 29.7%, bias 3.8%) as for model-based clustering (stem density RMSE 33.3%, bias 1.1%; stem volume RMSE 22.0%, bias 2.5%).
Tree positions and stem diameters were estimated from TLS data with an automated method. Stem attributes were then estimated from ALS data trained with trees found from TLS data. The accuracy (diameter at breast height or DBH RMSE 15.4%; stem volume RMSE 34.0%) was almost the same as when trees from a manual field inventory were used as training data (DBH RMSE 15.1%; stem volume RMSE 34.5%).
Canopy structure was estimated from discrete return and waveform ALS data. New models were developed based on the Beer-Lambert law to relate canopy volume to the fraction of laser light reaching the ground. Waveform ALS data (canopy volume RMSE 27.6%) described canopy structure better than discrete return ALS data (canopy volume RMSE 36.5%). The methods may be used to estimate canopy structure for large areas
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Estimating Forest Inventory Attributes Using Airborne LiDAR in Southwestern Oregon
ï»żThis thesis mainly consists of two parts: (1) comparing statistical modeling methods based on the area-based approach (ABA) for predicting forest inventory attributes using airborne light detection and ranging (LiDAR) data (Chapter 2), and (2) suggesting a new methodology fusing the individual tree detection (ITD) approach and the ABA for generating tree-lists using airborne LiDAR data (Chapter 3).
Chapter 2 compared selected modeling methods used to predict five forest attributes, basal area (BA), stem volume (VOL), Loreyâs height (LOR), quadratic mean diameter (QMD), and tree density (DEN), from airborne LiDAR metrics in southwestern Oregon, USA. The selected methods included most similar neighbor (MSN) imputation, gradient nearest neighbor (GNN) imputation, Random Forest (RF) based imputation, BestNN imputation, Ordinary least square (OLS) regression, spatial linear model (SLM), and geographically weighted regression (GWR). Several performances of each method were assessed by 500 simulations with different ï»żnumbers of training data. No modeling methods was always superior to the others in prediction of the forest attributes. The best method varied according to response variable, prediction type, and performance measures, even though there was a leading group (SLM, OLS, BestNN, and GWR) that always outperformed the other methods in root mean squared prediction error (RMSPE). Modelâs performance was quite affected when a small number of training data was used in modeling procedure. The optimal sizes of training data were 100-150 for point prediction and 200-250 for total prediction. SLM showed its applicability to wider conditions in that it produced better performance in most cases. RF imputation produced poorer performances than the other methods, particularly with lower prediction interval coverage. This might be because RF imputation had some bias and smaller prediction standard error; RFâs poor performance did not stem from the smaller number of predictor variables.
In Chapter 3, a new approach, combining ITD and ABA, was proposed to generate tree-lists using airborne LiDAR data. ITD based on the Canopy Height Model (CHM) was applied for overstory trees, while ABA based on nearest neighbor (NN) imputation was applied for understory trees. The approach is intended to compensate for the weakness of LiDAR data and ITD in estimating understory trees, keeping the strength of ITD in estimating overstory trees in tree-level. We investigated the effects of three parameters on the performance of our proposed approach: smoothing of CHM, resolution of CHM, and height cutoff (a specific height that classifies trees into overstory and understory). There was no single combination of those parameters that produced the best performance for estimating stems per ha, mean tree height, basal area, diameter distribution and height distribution. The trees in the lowest LiDAR height class yielded the largest relative bias and relative root mean squared error. Although ITD and ABA showed limited explanatory powers to estimate stems per hectare and basal area, there could be improvements from methods such as using LiDAR data with higher density, applying better algorithms for ITD and decreasing distortion of the structure of LiDAR data. Automating the procedure of finding optimal combinations of those parameters is essential to expedite forest management decisions across forest landscapes using remote sensing data
Comparison of Airborne Laser Scanning Methods for Estimating Forest Structure Indicators Based on Lorenz Curves
The purpose of this study was to compare a number of state-of-the-art methods in airborne laser scan- ning (ALS) remote sensing with regards to their capacity to describe tree size inequality and other indi- cators related to forest structure. The indicators chosen were based on the analysis of the Lorenz curve: Gini coefficient ( GC ), Lorenz asymmetry ( LA ), the proportions of basal area ( BALM ) and stem density ( NSLM ) stocked above the mean quadratic diameter. Each method belonged to one of these estimation strategies: (A) estimating indicators directly; (B) estimating the whole Lorenz curve; or (C) estimating a complete tree list. Across these strategies, the most popular statistical methods for area-based approach (ABA) were used: regression, random forest (RF), and nearest neighbour imputation. The latter included distance metrics based on either RF (NNâRF) or most similar neighbour (MSN). In the case of tree list esti- mation, methods based on individual tree detection (ITD) and semi-ITD, both combined with MSN impu- tation, were also studied. The most accurate method was direct estimation by best subset regression, which obtained the lowest cross-validated coefficients of variation of their root mean squared error CV(RMSE) for most indicators: GC (16.80%), LA (8.76%), BALM (8.80%) and NSLM (14.60%). Similar figures [CV(RMSE) 16.09%, 10.49%, 10.93% and 14.07%, respectively] were obtained by MSN imputation of tree lists by ABA, a method that also showed a number of additional advantages, such as better distributing the residual variance along the predictive range. In light of our results, ITD approaches may be clearly inferior to ABA with regards to describing the structural properties related to tree size inequality in for- ested areas
Individual tree detection and modelling aboveground biomass and forest parameters using discrete return airborne LiDAR data
Individual tree detection and modelling forest parameters using Airborne Laser
Scanner data (Light Detection and Ranging (LiDAR) is becoming increasingly
important for the monitoring and sustainable management of forests. Remote sensing
has been a useful tool for individual tree analysis in the past decade, although
inadequate spatial resolution from satellites means that only airborne systems have
sufficient spatial resolution to conduct individual tree analysis. Moreover, recent
advances in airborne LiDAR now provide high horizontal resolution as well as
information in the vertical dimension. However, it is challenging to fully exploit and
utilize small-footprint LiDAR data for detailed tree analysis. Procedures for forest
biomass quantification and forest attributes measurement using LiDAR data have
improved at a rapid pace as more robust and sophisticated modelling used to improve
the studies.
This thesis contains an evaluation of three approaches of utilizing LiDAR data for
individual tree forest measurement. The first explores the relationship between
LiDAR metrics and field reference to assess the correlation between LiDAR and field
data at the individual-tree level. The intention was not to detect trees automatically,
but to develop a LiDAR-AGB model based on trees that were mapped in the field so
as to evaluate the relationships between LiDAR-type metrics under controlled
conditions for the study sites, and field-derived AGB. A non-linear AGB model based
on field data and LiDAR data was developed and LiDAR height percentile h80 and
crown width measurement (CW) was found to best fit the data as evidenced by and
Adj-R2 value of 0.63, the root mean squared error of the model of 14.8% and analysis
of the residuals. This paper provides the foundation for a predictive LiDAR-AGB
model at tree level over two study sites, Pasoh Forest Reserve and FRIM Forest
Reserve.
The second part of the thesis then takes this AGB-LiDAR relationship and combines
it with individual tree crown delineation. This chapter shows the contribution of
performing an automatic individual tree crown delineation over the wider forest areas.
The individual tree crown delineation is composed of a five-step framework, which is
unique in its automated determination of dominant crown sizes in a forest area and its
adaption of the LiDAR-AGB model developed for the purpose of validation the
method. This framework correctly delineated 84% and 88% of the tree crowns in the
two forest study areas which is mostly dominated with lowland dipterocarp trees.
Thirdly, parametric and non-parametric modelling approaches are proposed for
modelling forest structural attributes. Selected modelling methods are compared for
predicting 4 forest attributes, volume (V), basal area (BA), height (Ht) and
aboveground biomass (AGB) at the species level. The AGB modelling in this paper is
extracted using the LiDAR derived variables from the automated individual tree crown
delineation, in contrast to the earlier AGB modelling where it is derived based on the
trees that were mapped in the field. The selected non-parametric method included, k-nearest
neighbour (k-NN) imputation methods: Most Similar Neighbour (MSN) and
Gradient Nearest Neighbour (GNN), Random Forest (RF) and parametric approach:
Ordinary Least Square (OLS) regression. To compare and evaluate these approaches
a scaled root mean squared error (RMSE) between observed and predicted forest
attribute sampled from both forest site was computed. The best method varied
according to response variable and performance measure. OLS regression was to found
to be the best performance method overall evidenced by RMSE after cross validation
for BA (1.40 m2), V (1.03 m3), Ht (2.22 m) and AGB (96 Kg/tree) respectively, showed
its applicability to wider conditions, while RF produced best overall results among the
non-parametric methods tested.
This thesis concludes with a discussion of the potential of LiDAR data as an
independent source of important forest inventory data source when combined with
appropriate designed sample plots in the field, and with appropriate modelling tools
Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)
Obtaining low vegetation data is important in order to quantify the structural characteristics of a forest. Dense three-dimensional (3D) laser scanning data can provide information on the vertical profile of a forest. However, most studies have focused on the dominant and subdominant layers of the forest, while few studies have tried to delineate the low vegetation. To address this issue, we propose a framework for individual tree crown (ITC) segmentation from laser data that focuses on both overstory and understory trees. The framework includes 1) a new algorithm (SSD) for 3D ITC segmentation of dominant trees, by detecting the symmetrical structure of the trees, and 2) removing points of dominant trees and mean shift clustering of the low vegetation. The framework was tested on a boreal forest in Sweden and the performance was compared 1) between plots with different stem density levels, vertical complexities, and tree species composition, and 2) using airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data, and merged ALS and TLS data (ALS + TLS data). The proposed framework achieved detection rates of 0.87 (ALS + TLS), 0.86 (TLS), and 0.76 (ALS) when validated with field inventory data (of trees with a diameter at breast height >= 4 cm). When validating the estimated number of understory trees by visual interpretation, the framework achieved 19%, 21%, and 39% root-mean-square error values with ALS + TLS, TLS, and ALS data, respectively. These results show that the SSD algorithm can successfully separate laser points of overstory and understory trees, ensuring the detection and segmentation of low vegetation in forest. The proposed framework can be used with both ALS and TLS data, and achieve ITC segmentation for forests with various structural attributes. The results also illustrate the potential of using ALS data to delineate low vegetation
Metsien kartoitus ja seuranta aktiivisella 3D-kaukokartoituksella
The main aim in forest mapping and monitoring is to produce accurate information for forest managers with the use of efficient methodologies. For example, it is important to locate harvesting sites and stands where forest operations should be carried out as well as to provide updates regarding forest growth, among other changes in forest structure. In recent years, remote sensing (RS) has taken a significant technological leap forward. It has become possible to acquire three-dimensional (3D), spatially accurate information from forest resources using active RS methods. In practical applications, mainly 3D information produced by airborne laser scanning (ALS) has opened up groundbreaking potential in natural resource mapping and monitoring. In addition to ALS, new satellite radars are also capable of acquiring spatially accurate 3D information. The main objectives of the present study were to develop 3D RS methodologies for large-area forest mapping and monitoring applications. In substudy I, we aim to map harvesting sites, while in substudy II, we monitor changes in the forest canopy structure. In studies III-V, efficient mapping and monitoring applications were developed and tested.
In substudy I, we predicted plot-level thinning maturity within the next 10-year planning period. Stands requiring immediate thinning were located with an overall accuracy of 83%-86% depending on the prediction method applied. The respective prediction accuracy for stands reaching thinning maturity within the next 10 years was 70%-79%.
Substudy II addressed natural disturbance monitoring that could be linked to forest management planning when an ALS time series is available. The accuracy of the damaged canopy cover area estimate varied between -16.4% to 5.4%. Substudy II showed that changes in the forest canopy structure can be monitored with a rather straightforward method by contrasting bi-temporal canopy height models.
In substudy III, we developed a RS-based forest inventory method where single-tree RS is used to acquire modelling data needed in area-based predictions. The method uses ALS data and is capable of producing accurate stand variable estimates even at the sub-compartment level. The developed method could be applied in areas with sparse road networks or when the costs of fieldwork must be minimized. The method is especially suitable for large-area biomass or stem volume mapping.
Based on substudy IV, the use of stereo synthetic aperture radar (SAR) satellite data in the prediction of plot-level forest variables appears to be promising for large-area applications. In the best case, the plot-level stem volume (VOL) was predicted with a relative error (RMSE%) of 34.9%. Typically, such a high level of prediction accuracy cannot be obtained using spaceborne RS data. Then, in substudy V, we compared the aboveground biomass and VOL estimates derived by radargrammetry to the ALS estimates. The difference between the estimation accuracy of ALS based and TerraSAR X based features was smaller than in any previous study in which ALS and different kinds of SAR materials have been compared.
In this thesis, forest mapping and monitoring applications using active 3D RS were developed. Spatially accurate 3D RS enables the mapping of harvesting sites, the monitoring of changes in the canopy structure and even the making of a fully RS-based forest inventory. ALS is carried out at relatively low altitudes, which makes it relatively expensive per area unit, and other RS materials are still needed. Spaceborne stereo radargrammetry proved to be a promising technique to acquire additional 3D RS data efficiently as long as an accurate digital terrain model is available as a ground-surface reference.Metsien kartoitus ja seuranta aktiivisella 3D-kaukokartoituksella.
MetsĂ€varoista kerĂ€tÀÀn mahdollisimman tarkkaa tietoa metsĂ€nomistajan pÀÀtöksenteon tueksi. Tietoa kerĂ€tÀÀn puustotunnusten lisĂ€ksi toimenpidekohteista ja metsĂ€ssĂ€ tapahtuvista muutoksista, kuten kasvusta ja luonnontuhoista. Laajojen metsĂ€alueiden kartoituksessa kĂ€ytetÀÀn apuna lentokoneesta tai satelliiteista tehtĂ€vÀÀ kaukokartoitusta. Metsien kaukokartoitus on viime vuosina ottanut merkittĂ€vĂ€n kehitysaskeleen, kun aktiiviset 3D-kaukokartoitusmenetelmĂ€t ovat yleistyneet. Aktiivisessa kaukokartoituksessa, kuten laserkeilauksessa ja tutkakuvauksessa instrumentti vastaanottaa lĂ€hettĂ€mÀÀnsĂ€ sĂ€teilyĂ€. Laserkeilaus tuottaa kohteesta 3D-havaintoja, jotka metsĂ€alueilla kuvaavat suoraan puuston pituutta ja metsĂ€n tiheyttĂ€. Laserkeilauksella kohteesta saadaan tĂ€llĂ€ hetkellĂ€ tyypillisesti 0,5â20 havaintoa/m2. Laserkeilaus tehdÀÀn lentokoneesta 500â3000 m korkeudesta, jolloin aineiston hankinta laajoilta alueilta on kallista verrattuna satelliittikuviin. Myös satelliittitutkakuvilta voidaan tuottaa spatiaalisesti tarkkaa 3D-tietoa, jonka pistetiheys on tosin huomattavasti harvempaa kuin laserkeilauksella.
Tutkimuksessa kehitettiin sovelluksia metsien kartoitukseen ja seurantaan hyödyntĂ€en aktiivisia 3D-kaukokartoitusmenetelmiĂ€. Metsiköiden toimenpidetarvetta ennustettiin onnistuneesti laserkeilausaineiston avulla. Harvennettaviksi luokitellut metsiköt pystyttiin kartoittamaan 70%â86% tarkkuudella.
Kahden ajankohdan laserkeilausaineistoja kÀytettiin lumituhojen vuoksi vaurioituneiden puiden kartoittamiseen. Tuhoutuneen latvuspinta-alan kartoitus perustui laserkeilausaineistosta tuotettujen latvusmallien erotuskuviin. Kehitetty menetelmÀ soveltuu latvusrakenteessa tapahtuneiden muutosten, kuten lumi- ja tuulituhojen, kartoittamiseen ja seurantaan.
Laajojen metsÀalueiden kartoitus perustuu yleensÀ kaksivaiheeseen inventointimenetelmÀÀn, jossa kÀytetÀÀn maastomittauksia ja tiedon yleistyksessÀ kaukokartoitusaineistoa. Kartoitusta voidaan tehostaa joko maastomittauksia vÀhentÀmÀllÀ tai hyödyntÀmÀllÀ mahdollisimman halpaa kaukokartoitusaineistoa. Tutkimuksessa kehitettiin tÀysin kaukokartoitukseen perustuva kaksivaiheinen metsien inventointimenetelmÀ. Tarvittava maastotieto mitattiin suoraan laserkeilausaineistosta. MenetelmÀ soveltuu puuston tilavuuden tai biomassan kartoitukseen erityisesti alueille, joilla maastomittausten kustannukset ovat merkittÀvÀt.
Satelliittitutkakuvat ovat potentiaalinen aineisto etenkin laajojen alueiden metsÀvarojen seurannassa. Synteettisen apertuurin tutka (SAR)-stereokuvilta mitattiin automaattisesti 3D-pisteitÀ, joita kÀytettiin puustotunnusten ennustamisessa. Keskitilavuus ennustettiin parhaimmillaan lÀhes samalla tarkkuudella kuin laserkeilauksella.
Tutkimus osoitti aktiivisen 3D-kaukokartoitustiedon mahdollistavan entistÀ yksityiskohtaisemman metsien kartoituksen ja seurannan
Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data
Multispectral airborne laser scanning (MS-ALS) provides information about 3D structure as well as the intensity of the reflected light and is a promising technique for acquiring forest information. Data from MS-ALS have been used for tree species classification and tree health evaluation. This paper investigates its potential for individual tree detection (ITD) when using intensity as an additional metric. To this end, rasters of height, point density, vegetation ratio, and intensity at three wavelengths were used for template matching to detect individual trees. Optimal combinations of metrics were identified for ITD in plots with different levels of canopy complexity. The F-scores for detection by template matching ranged from 0.94 to 0.73, depending on the choice of template derivation and raster generalization methods. Using intensity and point density as metrics instead of height increased the F-scores by up to 14% for the plots with the most understorey trees
Flygburen laserskanning för skogliga skattningar
Flygburen laserskanning har under de senaste 15 Ären blivit en datakÀlla för skogsbruksplanering. Data frÄn flygburen laserskanning Àr noggranna tredimensionella mÀtningar av mark och vegetation med laserljus frÄn ett flygplan eller helikopter.Flygburna laserdata kan kombineras med referensdata frÄn fÀltobservationer för att automatiskt ta fram heltÀckande skogsskattningar.SkogsÀgare och skogsförvaltare kan anvÀnda resultaten för bÀttre planering av avverkningar och naturvÄrd.Om man mÀter tillrÀckligt tÀtt kan enskilda trÀd identifieras i laserdata. Detta ger nya möjligheter att effektivt beskriva bÄde diameterfördelningar och trÀdslag för stora omrÄden