Liviu Theodor Ene

Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates

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 to guide tree crown delineation from rasterized laser data in two ways: (1) by controlling the amount of smoothing of the canopy height model and (2) by obtaining an appropriate spatial resolution for representing the forest canopy. Single tree crowns were delineated from the canopy height models (CHMs) using a marker-based watershed algorithm, and the delineation results were assessed using a simple tree crown delineation algorithm as a reference method (‘RefMeth’). Using the proposed methods, approximately 46–50% of the total number of trees were detected, while approximately 5–6% false positives were found. The detection rate was, in general, higher for Scots pine than for Norway spruce. The accuracy of individual tree variables (total height and crown width) extracted from the laser data was compared with field-measured data. The individual tree heights were better estimated for deciduous tree species than for the coniferous species Norway spruce and Scots pine. The estimation of crown diameters for Scots pine and deciduous species achieved comparable accuracy, being better than for Norway spruce. The proposed methodology has the potential for easy integration with operational laser scanner-based stand inventories.

Simultaneously acquired airborne laser scanning and multispectral imagery for individual tree species identification

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 frame camera acquired on a separate flight mission from a higher altitude. The two acquisitions represent cost efficient methods for data collection of both three-dimensional and spectral information. The classification accuracy was assessed using 1520 segmented spruce, pine, and deciduous trees. Furthermore, ALS intensity was normalized using the range from sensor to the target (range normalization). In addition, a source of variation in intensity known as banding, is described together with a normalization procedure for diminishing this effect. The normalized intensity was better than using the raw intensity, but it did not improve the classification compared with using only ALS structural information, which provided overall classification accuracies of 74%–77%. The combined use of ALS and multispectral imagery from the medium-format imagery acquired simultaneously and the separate acquisition of large-format imagery provided overall accuracies of 87%–89% and 83%–85%, respectively. Simultaneous acquisition of ALS and medium-format digital imagery provides an efficient data acquisition strategy for tree species identification in forest inventory and will likely reduce data acquisition costs by 10%–20%.

Characterizing forest species composition using multiple remote sensing data sources and inventory approaches

Abstract 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 (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.

Relative efficiency of ALS and InSAR for biomass estimation in a Tanzanian rainforest

Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900, …, 1900 m 2 , sampled in a Tanzanian rainforest, were assessed in a model-based inference framework. Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radio detection and ranging (InSAR) were used as auxiliary information. The findings indicate that larger field plots are relatively more efficient for inventories supported by remotely sensed ALS and InSAR data. A simulation showed that a pure field-based inventory would have to comprise 3.5-6.0 times as many observations for plot sizes of 700-1900 m 2 to achieve the same

Unsupervised Selection of Training Samples for Tree Species Classification Using Hyperspectral Data

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 Sequential Forward Floating Selection algorithm. The method was developed using two kinds of samples: plots and ITCs. The experimental results indicated that the method allows reducing the amount of training samples needed for the classification process, without significantly decreasing the classification accuracy. Applying the proposed method, the kappa accuracies obtained using about half of the total number of plots (kappa accuracy=0.84) and approximately one-third of the total number of ITCs (kappa accuracy=0.83) were not statistically different from the results obtained using the full set of training samples (kappa accuracy =0.86). The proposed method demonstrates that using a priori information derived from the hyperspectral data can substantially reduce the amount of field work and, consequently, the forest inventory costs.

Model-based inference for k-nearest neighbours predictions using a canonical vine copula

Abstract The k-near neighbours (k-NN) technique 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. Following the model-based inference, a superpopulation model consisting of a canonical vine copula was constructed from the empirical data, and new samples were generated from the model and used for k-NN predictions. The method used herein allows constructing the sampling distribution for the imputation errors and for assessing the statistical properties of the k-NN estimator. Using a data-splitting procedure, the copula-based approach was assessed against pair-bootstrap resampling. The imputations were performed using k (the number of neighbours) = 1 and by using optimal k values selected according to a bias-minimizing criterion. The empirical coverage probabilities of the confidence intervals constructed using the copula-based approach were closer to the nominal coverages. The improvements were due to significant bias reduction, while the standard errors were higher compared to the bootstrap. Still, the root mean squared error was significantly reduced. The best results were obtained using the copula approach and k-NN imputations with k=1.

Métodos estadísticos paramétricos y no paramétricos para predecir variables de rodal basados en Landsat ETM+: una comparación en un bosque de Araucaria araucana en Chile

Los bosques de Araucaria araucana tienen una alta importancia ecologica y cientifica. Aunque existen varios estudios ecologicos llevados a cabo en bosques de A. araucana, muy pocos han producido modelos cuantitativos. Se compararon metodos estadisticos parametricos y no parametricos para predecir variables de rodal en funcion de variables derivadas de Landsat ETM+ para dos rodales de A. araucana en el centro-sur de Chile. Los metodos parametricos fueron regresion lineal multiple (MLR), minimos cuadrados generalizados con una estructura de correlacion no nula (GLS), modelo lineal de efectos mixtos (LME) y minimos cuadrados parciales (PLS); mientras que los metodos no parametricos fueron: k-esimo vecino mas cercano (k-NN) y vecino mas similar (MSN). En orden descendente, numero de arboles por hectarea (N), volumen bruto (V), area basal (G) y altura dominante (Hdom), fueron las variables mas complejas de modelar por todos los metodos. El modelo lineal de efectos mixtos con efectos aleatorios conocidos (LME1) tuvo el mejor desempeno, alcanzando una raiz cuadrada de las diferencias (RMSD) para N y V de 18,31 y 4,08 % versus 33,06 y 33,05 % para el segundo mejor metodo, respectivamente. Despues de LME1, GLS se comporto mejor, y tambien toma en consideracion la correlacion espacial de los datos. Las diferencias fueron mayores entre metodos no parametricos que para los parametricos, con una diferencia de 10-15 % entre k-NN y MSN. Aunque los resultados obtenidos favorecen a los metodos parametricos, se destaca que los metodos no parametricos son tambien utiles, y la eleccion entre ambos metodos depende del objetivo del estudio.

Estimation of biomass change in montane forests in Norway along a 1,200 km latitudinal gradient using airborne laser scanning: A comparison of direct and indirect prediction of change under a model-based inferential approach

ABSTRACT 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 (four-year interval). The first was an indirect method that involved modeling and prediction of AGB at two points in time using ALS metrics as predictors, estimating the change from differences between AGB predictions. The second was a direct method, where change was modeled and predicted directly using differences between corresponding ALS metrics derived at the two measurement occasions as predictors, and the estimate was based on the predicted differences. Both methods were applied over a 1500 km long and 250 m wide transect from south to north in Norway comprising 250 m2 grid cells. The results showed that the indirect method was more precise than the direct method. The indirect method estimated 0.65 Mg ha−1 change in AGB over the observation period, with a corresponding 95% confidence interval of ±0.27 Mg ha−1. The corresponding figures for the direct method was 0.54 and ±0.51 Mg ha−1. The direct method has been recommended previously. We conclude that the indirect method is both more precise and versatile.

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

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 trees per hectare. Multispectral ALS data were acquired with an Optech Titan sensor, which consists of three scanners, called channels, working in three wavelengths (532 nm, 1064 nm, and 1550 nm). Standard ALS data acquired with a Leica ALS70 system were used as a reference. The study area is located in Southern Norway, in a forest composed of Scots pine, Norway spruce, and broadleaf species. ALS metrics were extracted for each plot from both elevation and intensity values of the ALS points acquired with both sensors, and for all three channels of the ALS multispectral sensor. Regression models were constructed using different combinations of metrics. The results showed that all four characteristics can be accurately predicted with both sensors (the best R2 being greater than 0.8), but the models based on the multispectral ALS data provide more accurate results. There were differences regarding the contribution of the three channels of the multispectral ALS. The models based on the data of the 532 nm channel seemed to be the least accurate.

Forest species and biomass estimation using airborne laser scanning and hyperspectral images

Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas offers the possibility to combine data from different sensors. In this study high resolution airborne hyperspectral and ALS data at 0.4m resolution were acquired during summer 2012 in a complex forest ecosystem in the Alps, characterized by different tree species and difficult morphology. Using tree crown polygons from ALS and classification map from hyperspectral images, a species-specific tree canopy map was obtained. Then, height distribution of dominant tree species in three habitat strata were analyzed. Our initial experiments show the potential of the mix-sensors approach for further forest biophysical parameters estimation which is a vital part of forest inventory.