The United States Forest Service (USFS) Forest Inventory and Analysis (FIA) program provides a diverse selection of data used to assess the status of the nation’s forested areas using sample locations dispersed throughout the country. Airborne, and more recently, terrestrial lidar (light detection and ranging) systems are capable of producing accurate measurements of individual tree dimensions and also possess the ability to characterize three-dimensional vertical forest structure. This study investigates the potential of airborne and terrestrial scanning lidar systems for modeling forest volume and aboveground biomass on FIA subplots in the Malheur National Forest, eastern Oregon. A methodology for the creation of five airborne lidar metric sets (four point cloud-based and one individual tree based) and four terrestrial lidar metric sets (three height-based and one distance-based) is presented. Metrics were compared to estimates of subplot aboveground biomass and gross volume derived from FIA data using national and regional allometric equations respectively. Simple linear regression models from the airborne lidar data accounted for 15 percent of the variability in subplot biomass and 14 percent of the variability in subplot volume, while multiple linear regression models increased these amounts to 29 percent and 25 percent, respectively. When subplot estimates of biophysical parameters were scaled to the plot-level and compared with plot-level lidar metrics, simple linear regression models were able to account for 60 percent of the variability in biomass and 71 percent of the variation in volume. Terrestrial lidar metrics produced moderate results with simple linear regression models accounting for 41 percent of the variability in biomass and 46 percent of the variability in volume, with multiple linear regression models accounting for 71 percent and 84 percent, respectively. Results show that: (1) larger plot sizes help to mitigate errors and produce better models; and (2) a combination of height-based and distance-based terrestrial lidar metrics has the potential to estimate biomass and volume on FIA subplots.

L'importance de mesurer les paramètres biophysiques de la forêt pour la surveillance de la santé des écosystèmes et la gestion forestière encourage les chercheurs à trouver des méthodes précises et à faible coût en particulier sur les zones étendues et montagneuses. Dans la présente étude, le lidar satellitaire GLAS embarqué à bord du satellite ICESat (Ice Cloud and land Elevation Satellite) a été utilisé pour estimer trois caractéristiques biophysiques des forêts situées dans le nord de l'Iran:1) hauteur maximale de la canopée (Hmax),2)hauteur de Lorey (HLorey), et 3)le volume du bois (V). Des régressions linéaires multiples (RLM), des modèles basés sur les Forêts Aléatoires (FA : Random Forest) et aussi des réseaux de neurones (ANN) ont été développés à l'aide de deux ensembles différents de variables incluant des métriques obtenues à partir des formes d’onde GLAS et des composantes principales (CP) produites à partir de l'analyse en composantes principales (ACP) des données GLAS. Pour valider et comparer les modèles, des critères statistiques ont été calculées sur la base d'une validation croisée. Le meilleur modèle pour l’estimation de la hauteur maximale a été obtenu avec une régression RLM (RMSE=5.0m) qui combine deux métriques extraites des formes d'onde GLAS (H50, Wext), et un paramètre issu du modèle numérique d'élévation (Indice de relief TI). L'erreur moyenne absolue en pourcentage (MAPE) sur les estimations de la hauteur maximale est de 16.4%. Pour la hauteur de Lorey, un modèle basé sur les réseaux de neurones et utilisant des CPs et le Wext fournit le meilleur résultat avec RMSE=3.4m et MAPE=12.3%. Afin d'estimer le volume du bois, deux approches ont été utilisées:(1)estimation du volume à l'aide d’une relation volume-hauteur avec une hauteur estimée à partir de données GLAS et (2)estimation du volume du bois directement à partir des données GLAS en développant des régressions entre le volume in situ et les métriques GLAS. Le résultat de la première approche (RMSE=116.3m3/ha) était légèrement meilleur que ceux obtenus avec la seconde approche. Par exemple, le réseau de neurones basé sur les PCs donnait un RMSE de 119.9m3/ha mais avec des meilleurs résultats que l’approche basée sur la relation volume-hauteur pour les faibles (<10m3/ha) et les forts (>800m3/ha) volumes. Au total, l'erreur relative sur le volume de bois est estimée à environ 26%. En général, les modèles RLM et ANN avaient des meilleures performances par rapport aux modèles de FA. En outre, la précision sur l’estimation de la hauteur à l'aide de métriques issues des formes d'onde GLAS est meilleure que celles basées sur les CPs.Compte tenu des bons résultats obtenus avec les modèles de hauteur GLAS, la production de la carte des hauteurs d’étude par une utilisation combinée de données de télédétection lidar, radar et optique et de données environnementales a été effectuée à l’intérieur de notre zone. Ainsi, des régressions RLM et FA ont été construites entre toutes les hauteurs dérivées des données GLAS, à l'intérieur de la zone d'étude, et les indices extraits des données de télédétection et des paramètres environnementaux. Les meilleurs modèles entrainés pour estimer Hmax (RMSE=7.4m et R_a^2=0.52) et HLorey (RMSE=5.5m et R_a^2=0.59) ont été utilisées pour produire les cartes de hauteurs. La comparaison des Hmax de la carte obtenue avec les valeurs de Hmax in situ à l'endroit de 32 parcelles produit un RMSE de 5.3m et un R2 de 0.71. Une telle comparaison pour HLorey conduit à un RMSE de 4.3m et un R2 de 0.50. Une méthode de régression-krigeage a également été utilisée pour produire une carte des hauteurs en considérant la corrélation spatiale entre les hauteurs. Cette approche, testée dans le but d'améliorer la précision de la carte de la hauteur du couvert fournie par la méthode non-spatiale, a échouée due à l'hétérogénéité de la zone d'étude en termes de la structure forestière et de la topographie. / The importance of measuring forest biophysical parameters for ecosystem health monitoring and forest management encourages researchers to find precise, yet low-cost methods especially in mountainous and large areas. In the present study Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice Cloud and land Elevation Satellite) was used to estimate three biophysical characteristics of forests located in the north of Iran: 1) maximum canopy height (Hmax), 2) Lorey’s height (HLorey), and 3) Forest volume (V). A large number of Multiple Linear Regressions (MLR), Random Forest (RF) and also Artificial Neural Network regressions were developed using two different sets of variables including waveform metrics and Principal Components (PCs) produced from Principal Component Analysis (PCA). To validate and compare models, statistical criteria were calculated based on a five-fold cross validation. Best model concerning the maximum height was an MLR (RMSE=5.0m) which combined two metrics extracted from waveforms (waveform extent "Wext" and height at 50% of waveform energy "H50"), and one from Digital Elevation Model (Terrain Index: TI). The mean absolute percentage error (MAPE) of maximum height estimates was 16.4%. For Lorey’s height, an ANN model using PCs and waveform extent “Wext” outperformed other models (RMSE=3.4m, MAPE=12.3%). In order to estimate forest volume, two approaches was employed: First, estimating volume using volume-height relationship while height is GLAS estimated height; Second, estimation of forest volume directly from GLAS data by developing regressions between in situ volume and GLAS metrics. The result from first approach (116.3m3/ha) was slightly better than the result obtained by the second approach that is a PCs-based ANN model (119.9 m3/ha). But the ANN model performed better in very low (<10 m3/ha) and very high (> 800 m3/ha) volume stands. In total, the relative error of estimated forest volume was about 26%. Generally, MLR and ANN models had better performance when compared to the RF models. In addition, the accuracy of height estimations using waveform metrics was better than those based on PCs.Given the suitable results of GLAS height models (maximum and Lorey’s heights), production of wall to wall height maps from synergy of remote sensing (GLAS, PALSAR, SPOT5 and Landsat-TM) and environmental data (slope, aspect, classified elevation map and also geological map) was taken under consideration. Thus, MLR and RF regressions were built between all GLAS derived heights, inside of the study area, and indices extracted from mentioned remotely sensed and environmental data. The best resulted models for Hmax (RMSE=7.4m and R_a^2=0.52) and HLorey (RMSE=5.5m and R_a^2=0.59) were used to produce a wall to wall maximum canopy height and Lorey’ height maps. Comparison of Hmax extracted from the resulted Hmax map with true height values at the location of 32 in situ plots produced an RMSE and R2 of 5.3m and 0.71, respectively. Such a comparison for HLorey led to an RMSE and R2 of 4.3m and 0.50, respectively. Regression-kriging method was also used to produce canopy height map with considering spatial correlation between canopy heights. This approach, with the aim of improving the precision of canopy height map provided from non-spatial method, was unsuccessful which could be due to the heterogeneity of the study area in case of forest structure and topography.

Att underhålla infrastruktur med hög standard är viktigt för alla länder, och att misslyckas med detta innebär allvarliga logistiska och ekonomiska konsekvenser. Kraftledningsinspektion är en betydelsefull del i detta. Denna uppsats har sökt svar på vad för- och nackdelarna är med att använda satellitövervakning av kraftledningar, samt svar på ifall teknik för satellitövervakning av kraftledningar också kan tillämpas på volymberäkningar av skog. Metoden har utgått från intervjuer med experter och relevanta företag samt litteratur som underlag. Att undersöka vilka för- och nackdelar som finns med satellitövervakning av kraftledningar var viktigt eftersom satellitövervakning är ett snabbt växande fält, men inte särskilt väl undersökt. Att undersöka huruvida teknik för kraftledningsövervakning med satelliter är tillräcklig för att estimera skogsvolym bedömdes vara värdefullt eftersom skogsvolym idag estimeras med luftburen LiDAR, medan luftburen LiDAR påstods vara signifikant mycket dyrare överlag än satellitmätningar. Det fanns alltså en eventuell ekonomisk fördel med att estimera skogsvolym med satelliter istället för dagens luftburna mätningar. Det förväntade resultatet var att tekniken för satellitövervakning av kraftledningar är tillräcklig för att estimera skogsvolym. De största nackdelarna med satellitövervakning av kraftledningar berör problemen med att nå tillräckligt hög noggrannhet i processerna för trädidentifiering, samt att utveckla effektiva tillvägagångssätt för att utvärdera detta då underlaget gällande föreslagna och utvärderade metoder är glest. En annan nackdel visade sig vara att satellitmetoderna är svåra att göra konkurrenskraftiga i jämförelse med de etablerade luftburna LiDAR-metoderna i fråga om kostnader. Anledningen är att de högupplösta satellitbilder som ofta krävts för att nå hög noggrannhet fortfarande är dyra, även om en fördel som också identifierades var att ny och billigare satellitteknik just nu utvecklas i hög takt. Gällande denna fråga visade sig den största fördelen vara den snabba utvecklingen av nya satelliter med högre upplösning, som öppnar upp möjligheten för att komma ikapp de konventionella metoderna. Det förväntade resultatet kring huruvida satellitövervakning har ekonomiska fördelar jämfört med luftburen övervakning motsägs alltså av resultatet i denna rapport, med avseende på kraftledningsövervakning. Däremot indikerar resultatet att tekniken för satellitövervakning av kraftledningar är tillräcklig för att estimera skogsvolym, vilket överensstämmer med det förväntade resultatet. / Maintaining infrastructure of high standard is important for all countries. Failing this means severe logistical and economic consequences. Power line inspection is an important part of this. This thesis has searched for an answer to what the advantages and disadvantages are of inspecting power lines by using satellites, as well as an answer to if the technology of satellite surveillance of power lines is sufficient to estimate forest volume. The methodology of the thesis has been to turn to companies and experts in the field and to use relevant literature. Examining what the advantages and disadvantages of satellite inspection of power lines are was important since satellite surveillance is a growing field, but not very well researched. To analyze whether technology of satellite surveillance of power lines is enough to estimate forest volume was thought to be valuable since forest volume today is estimated by airborne LiDAR, while airborne LiDAR was claimed to be significantly more expensive in general than 3 satellite measurements. Thus, there was a potential economic advantage to estimate forest volume with satellites instead of airborne measurements. The expected result was that the technology of satellite surveillance of power lines is sufficient to estimate forest volume. The biggest disadvantages of satellite surveillance of power lines involve the problems of achieving high enough accuracy in the processes of tree identification, as well as developing effective formulas to evaluate this when the research material of proposed methods is sparse. Another disadvantage turned out to be that the satellite methods are hard to compete with, in comparison to the established airborne LiDAR methods and in regard to cost. The reason is that the high-resolution satellite images that often are demanded still are expensive, even though an advantage that also was identified in this thesis is that new and cheaper satellite technology is being developed at a quick rate. The biggest advantage of satellite surveillance of power lines turned out to be the quick development of new satellites with higher resolution, which enables the possibility to catch up with the conventional methods. The expected result in regard to whether satellite surveillance has economic advantages compared to airborne surveillance is contradicted in the result of this thesis, in regard to power line inspection. However, the result indicates that the technology of satellite surveillance of power lines is sufficient to estimate forest volume, which concurs with the expected result.

This study assessed the utility of an object-oriented approach to deciduous and coniferous forest volume and above ground biomass estimation, based solely on small-footprint, multiple return lidar data. The study area is located in Appomattox Buckingham State Forest in the Piedmont physiographic province of Virginia, U.S.A, at 78°41’ W, 37°25’ N. Vegetation is composed of various coniferous, deciduous, and mixed forest stands. The eCognition segmentation algorithm was used to derive objects from a lidar-based canopy height model (CHM). New segment selection criteria, based on between- and within-segment CHM variance, and average field plot size, were developed. Horizontal point samples were used to measure in-field volume and biomass, for 2-class (deciduous-coniferous) and 3-class (deciduous-coniferous-mixed) forest schemes. Per-segment lidar distributional parameters, e.g., mean, range, and percentiles, were extracted from the lidar data and used as input to volume and biomass regression analysis. Discriminant classification was performed using lidar point height and CHM distributions. There was no evident difference between the two-class and three-class approaches, based on similar adjusted R2 values. Two-class forest definition was preferred due to its simplicity. Two-class adjusted R2 and root mean square error (RMSE) values for deciduous volume (0.59; 51.15 m3/ha) and biomass (0.58; 37.41 Mg/ha) were improvements over those found in another plot-based study for the same study area. Although coniferous RMSE values for volume (38.03 m3/ha) and biomass (17.15 Mg/ha) were comparable to published results, adjusted R2 values (0.66 and 0.59) were lower. This was attributed to more variability and a narrower range (6.94 - 350.93 m3/ha) in measured values. Classification accuracy for discriminant classification based on lidar point height distributions (89.2%) was a significant improvement over CHM-based classification (79%). A lack of modeling and classification differences between average segment sizes was attributed to the hierarchical nature of the segmentation algorithm. However, segment-based modeling was distinctly better than modeling based on existing forest stands, with values of 0.42 and 62.36 m3/ha (volume) and 0.46 and 41.18 Mg/ha (biomass) for adjusted R2 and RMSE, respectively. Modeling results and classification accuracies indicated that an object-oriented approach, based solely on lidar data, has potential for full-scale forest inventory applications. / Ph. D.

Darbo objektas - Telšių miškų urėdijos, Žarėnų girininkijos brandūs mišrūs eglės su lapuočiais medynai. Darbo tikslas – nustatyti dažniausiai miško inventorizacijos praktikoje naudojamų tūrio nustatymo metodų tikslumą ir išanalizuoti jį lemiančias priežastis. Darbo metodai – tūrio paklaidų, gautų lyginant medienos tūrius, nustatytus pagal sklypinės miškų inventorizacijos ir biržių atrėžimo ir įvertinimo duomenimis su pagamintos medienos duomenimis, analizė. Darbo rezultatai. Atlikus tų pačių medynų tūrio įvertinimą įvairiais metodais ir visų matavimo rezultatų analizę, paaiškėjo, kad sklypinė miškų inventorizacija tūrį Žarėnų miško masyve vidutiniškai didina 10,2 %, lyginant su ištisiniu medžių apmatavimo metodu. Įvertinę brandžių medynų tūrį, pagal pagamintą medienos produkciją, gauti rezultatai mišriuose eglės medynuose svyruoja nuo -3,0 % iki 9,9 %, bendra paklaida yra 4,9 % didesnė lyginant su ištisiniu medžių matavimo metodu. Raktažodžiai: Sklypinė miškų inventorizacija, ištisinis medžių matavimas, atrankinis medžių matavimas, medynų tūris, paklaidos. / OBJECT OF RESEARCH-mature mixed spruce - broadlives forests in Telšiai forest enterprise, Žarėnai forest. AIM OF RESEARCH- to determine the accuracy of most frequently applied methods to rate the volume in the practice of forest inventory and to analyze the causes that influence it. METHODS OF RESEARCH- the analysis of volume errors, got by comparing timber volumes, set according to the data of inventory of forest plots and their delimitation and the data of their evaluation with the data of manufactured timber acceptance. RESULTS OF RESEARCH-after having done the evaluation of the same timber volume using different methods and the analysis of all the results of measurement, it emerged that stand-wise forest inventory increases the volume in the array of Žarėnai forests by 10.2% when compared to the continuous forest measurement. After evaluating the volume of mature trees according to the made timber production the results in mixed fir trees range from -3,0 % to 9,9 %, a common error 4,9 % better compared to the continuous forest measurement.