Liviu Theodor Ene- PhD
- Senior Researcher at Forestry Research Institute of Sweden
Liviu Theodor Ene
- PhD
- Senior Researcher at Forestry Research Institute of Sweden
About
40
Publications
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Introduction
Current institution
Additional affiliations
August 2018 - present
August 2018 - present
Publications
Publications (40)
Key message
Enhancing the efficiency and precision of breeding programs necessitates the implementation of “high-throughput” phenotyping. By employing various sensors for rapid and frequent measurements, we can gather extensive datasets crucial for conventional breeding efforts. This approach not only holds promise for improving forest production b...
We address a Bayesian two-stage decision problem in operational forestry where the inner stage considers scheduling the harvesting to fulfill demand targets and the outer stage considers selecting the accuracy of pre-harvest inventories that are used to estimate the timber volumes of the forest tracts. The higher accuracy of the inventory enables b...
Predicting need of pre-commercial thinning of young forests using AI and GNNS-logging.
Pre-commercial thinning in the young forest phase (trees 2-4 m high) has a positive effect on profitability as early as the first extraction of commercially valuable timber (thinning). Development in older forests can be forecasted to a certain extent, but in yo...
Height is a key trait in the indices applied when selecting genotypes for use in both tree breeding populations and production populations in seed orchards. Thus, measurement of tree height is an important activity in the Swedish Norway spruce breeding program. However, traditional measurement techniques are time-consuming, expensive, and often inv...
In this study, the potential of multispectral airborne laser scanner (ALS) data to model and predict some forest characteristics was explored. Four complementary characteristics were considered, namely, aboveground biomass per hectare, Gini coefficient of the diameters at breast height, Shannon diversity index of the tree species, and the number of...
Use of data from airborne laser scanning (ALS) is a well-established practice for enhancing the accuracy of forest inventories in combination with ground-based observations. For regular monitoring of large areas, wall-to-wall ALS data is economically prohibitive. However, when data are acquired in a strip-sampling mode, ALS can support the estimati...
Forest attributes such as tree heights, diameter distribution, volumes, and biomass can be modeled utilizing the relationship between remotely sensed metrics as predictor variables, and measurements of forest attributes on the ground. The quality of the models relies on the actual relationship between the forest attributes and the remotely sensed m...
The use of image-based three-dimensional data from unmanned aerial vehicles (UAV) has proven effective for forest inventories. However, limitations in the range of UAV operations hinder their use in large scale applications. Use of partial-coverage UAV data in combination with field plots may increase precision of field-based estimates of forest re...
The current study compared two approaches for estimating change of aboveground biomass (AGB) in montane forests in Norway using field- and remotely sensed data from airborne laser scanning (ALS) from two points in time (4-yr interval). The first was an indirect method that involved modelling and prediction of AGB at two points in time using ALS met...
Airborne laser scanner (ALS) data are used operationally to support field inventories and enhance the accuracy of forest biomass estimates. Modelling the relationship between ALS and field data is a fundamental step of such applications and the quality of the model is essential for the final accuracy of the estimates. Different modelling approaches...
The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip samples of airborne lidar and Landsat-based remote sensing data products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strateg...
The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip samples of airborne lidar and Landsat-based remote sensing data products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strateg...
Post-stratified model-assisted (MA) and hybrid (HY) estimators are used with repeated airborne laser scanning (ALS) strip sampling and national forest inventory field data for stratum-wise and overall estimation of aboveground biomass (AGB) stock and change. The study area covered the southern portion of the Hedmark County in Norway. Both MA and HY...
As a non-Annex 1 Party to the United Nations Framework Convention on Climate Change (UNFCCC), Tanzania has committed to submit the results of the measurement, reporting, and verification of greenhouse gas (GHG) emissions reductions and removals by sinks under the Biennial Update Reports of national GHG inventory and mitigation actions. Thus, implem...
Airborne laser scanning (ALS) has been proposed as a reliable remote sensing technique for supporting biomass and carbon stock estimation under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in developing countries (UN-REDD). Under the United Nations Framework Convention on Climate Change (U...
To estimate the aboveground biomass (AGB) for large areas, two-stage sampling designs using airborne laser scanning (ALS) as a strip sampling tool in combination with subsampling of field plots have been successfully applied in several studies. However, the studies have pointed to problems in the proposed estimator, partly related to the unequal le...
This study proposes a method to perform spatially consistent imputations of forest data to serve simulation studies where spatial autocorrelation is expected to have an effect (e.g., sampling simulations and forest scenario analysis). Starting with a nearest neighbour imputation, an optimization process brings the spatially comprehensive data to a...
Airborne laser scanning (ALS) has been demonstrated to be an excellent source of auxiliary information for increasing the precision of estimating stand-level attributes in forest inventories. It has also been proposed to use ALS for estimating biomass and carbon stocks under the United Nations Collaborative Program on Reduced Emissions from Defores...
Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corre...
In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC...
In this study, we introduced a novel unsupervised selection method for collecting training samples for tree species classification at individual tree crown (ITC) level using hyperspectral data. The selection process is based on a distance metric defined among the spectral signatures of the pixels inside the ITCs, and a search strategy based on the...
By using more sophisticated sampling designs in forest field inventories, it is possible to select more representative field samples. When full cover auxiliary information is available at the planning stage of a forest inventory, an efficient strategy for sampling is formed by making sure that the sample is well spread in the space spanned by the a...
Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for a combined use of ALS and TLS in an inventory was tested...
The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (m...
In this study we introduced a novel unsupervised selection method for collecting training samples for tree species classification at individual tree crown (ITC) level using hyperspectral data. The selection process is based on a search strategy and a distance metric defined among the percentiles derived from the spectral distributions of the pixels...
Auxiliary information provided by airborne laser scanners (ALS) is expected to increase the accuracy of biomass estimation in large-scale forest surveys. Because acquisition of “wall-to-wall” ALS data over large areas is not economically feasible, a systematic sampling approach using ALS as a strip sampling tool was used to supplement a conventiona...
Adaptive single tree detection methods using airborne laser scanning (ALS) data were investigated and validated on 40 large plots sampled from a structurally heterogeneous boreal forest dominated by Norway spruce and Scots pine. Under the working assumption of having uniformly distributed tree locations, area-based stem number estimates were used t...
Cultural remains are laborious to register by means of field surveys. Thus, in recent years several trials using remote sensing data to detect cultural remains have been carried out. The most promising remote sensing technique for such purposes is airborne laser scanning (ALS) from which digital terrain models (DTM) that enable visual interpretatio...
A simple simulator was developed to test whether airborne laser scanning can be used as a strip sampling tool for forest inventory purposes. The simulator is based on the existing two stages, grid based laser inventory procedure. A population of trees was created using an existing forest stand structure generator. Each tree was represented by means...
The objective of this study was to investigate the use of multispectral imagery in addition to measurements from airborne laser scanning (ALS) for tree species identification. Multispectral imagery from a medium-format digital frame camera acquired simultaneously with ALS data were utilized and compared with imagery from a large-format digital fram...
The k-near neighbours technique (k-NN) combines field data from forest inventories and auxiliary information for forest resource estimation at various geographical scales. In this study, auxiliary data consisting of Landsat 5 TM satellite imagery and terrain elevations were used to perform k-NN imputations of plot-level above ground biomass. Follow...
Airborne laser scanning data and corresponding field data were acquired from boreal forests in Norway and Sweden, coniferous
and broadleaved forests in Germany and tropical pulpwood plantations in Brazil. Treetop positions were extracted using six
different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detect...
Los bosques de Araucaria araucana tienen una alta importancia ecológica y científica. Aunque existen varios estudios ecológicos llevados a cabo en bosques de A. araucana, muy pocos han producido modelos cuantitativos. Se compararon métodos estadísticos paramétricos y no paramétricos para predecir variables de rodal en función de variables derivadas...
The Araucaria araucana forests have a high level of both ecological and scientific importance, because they are long-lived and endemic. Although there have been several ecological studies conducted concerning A. araucana forests, none has produced quantitative models. We compared parametric and non-parametric statistical methods for predicting stan...
