LiDAR for Forestry

Forest inventory (FI) is the most challenging stage of forest management and planning process. Therefore, in situ surveys are often reinforced by modern remote sensing (RS) methods for collecting forestry-related data more efficiently. This study tests a state-of-the-art data collection method for practical use in the Turkish FI system for the first time. To this end, forest sampling plots were conventionally measured to collect dendrometric data from 437 trees in Artvin and Saçınka Forest Enterprises. Then, each plot was scanned using a handheld mobile laser scanning (HMLS) instrument. Finally, HMLS data were compared against ground measurements
via basic FI measures. Based on statistical tests, no apparent differences were found between the two datasets at the plot level (P < 0.05). There were also robust correlations for diameter breast height at individual tree level (r > 0.97; P < 0.01). Residual analysis showed that both positive and negative errors had a homogeneous distribution, except for plot 8 where tree stems were in irregular shapes due to
anthropogenic pressures. When all plots’ data were aggregated, average values for the number of trees, basal area, and timber volume were estimated as 535 trees/ha–1, 49.6 m2/ha–1, and 499.7 m3/ha–1, respectively. Furthermore, secondary measures such as the number of saplings and slope were successfully retrieved using HMLS method. The highest overestimation was in timber volume with less than 10% difference at the landscape level. The differences were attributed to poor data quality of conventional measurements, as well as marginal site conditions in some plots. We concluded that the HMLS method met the accuracy standards for most FI measures, except for stand height. Thus, the Turkish FI system could benefit from this novel technology, which in turn supports the implementation of sound forest management and planning.

I dati da laser scanning aereo (ALS) vengono sempre più proposti per la descrizione della struttura dei popolamenti forestali nei suoi aspetti della distribuzione verticale e orizzontale e di copertura delle chiome. Al contempo varie procedure sono state proposte per la stima del volume e della biomassa legnosa dei popolamenti forestali a partire dalla relazione tra valori di queste grandezze misurati in aree campione a terra e valori ipsometrici ottenuti dai ritorni ALS. La conoscenza sulle potenzialità dei dati ALS è però ancora relativamente modesta nell’ambiente operativo italiano
L’utilizzo dei dati ALS è stato finora finalizzato prevalentemente alla stratificazione dei popolamenti boschivi, all’identificazione di tipi forestali e colturali e alla stima delle masse legnose a scala locale. Rimangono ancora quasi inesplorate le possibilità di integrazione dei dati ALS nell’ambito di inventari forestali di ampie superfici e per la misurazione di fenomeni emergenti quali i boschi di neoformazione, le variazioni della timberline, la stima delle quantità di combustibile nelle formazioni forestali e preforestali. Anche l’integrazione di dati multispettrali e dati ALS in processi tesi all’inventariazione contestuale sia qualitativa che quantitativa delle risorse forestali rappresenta tematica di interesse così come, probabilmente in un futuro meno immediato, l’analisi della correlazione tra variazioni ipsometriche desunte da dati ALS e fattori quali l’incremento corrente di volume, il tasso di prelievo legnoso, i fenomeni di degradazione strutturale dei popolamenti forestali.

Straipsnyje pristatoma LiDAR technologija ir ją taikant gautų duomenų panaudojimas archeologijoje. Aptariami Lietuvos teritorijos LiDAR duomenys, jų panaudojimas archeologinių objektų paieškai ir analizei, parodomos skirtingos vizualizacijos galimybės.

In this study, airborne laser scanning-based and traditional field-based survey methods for tree heights estimation are assessed by using one hundred felled trees as a reference dataset. Comparisons between remote sensing and field-based methods were applied to four circular permanent plots located in the western Italian Alps and established within the Alpine Space project NewFor. Remote sensing (Airborne Laser Scanning, ALS), traditional field-based (indirect measurement, IND), and direct measurement of felled trees (DIR) methods were compared by using summary statistics, linear regression models, and variation partitioning. Our results show that tree height estimates by Airborne Laser Scanning (ALS) approximated to real heights (DIR) of felled trees. Considering the species separately, Larix decidua was the species that showed the smaller mean absolute difference (0.95 m) between remote sensing (ALS) and direct field (DIR) data, followed by Picea abies and Pinus sylvestris (1.13 m and 1.04 m, respectively). Our results cannot be generalized to ALS surveys with low pulses density (<5/m 2) and with view angles far from zero (nadir). We observed that the tree heights estimation by laser scanner is closer to actual tree heights (DIR) than traditional field-based survey, and this was particularly valid for tall trees with conical shape crowns.

Unmanned aerial vehicles (UAVs) or remotely piloted aircraft systems
are new platforms that have been increasingly used over the
last decade in Europe to collect data for forest research, thanks to
the miniaturization and cost reduction of GPS receivers, inertial
navigation system, computers, and, most of all, sensors for remote
sensing.
In this review, after describing the regulatory framework for the
operation of UAVs in the European Union (EU), an overview of
applications in forest research is presented, followed by a discussion
of the results obtained from the analysis of different case
studies.
Rotary-wing and fixed-wing UAVs are equally distributed
among the case studies, while ready-to-fly solutions are preferred
over self-designed and developed UAVs. Most adopted technologies
are visible-red, green, and blue, multispectral in visible and
near-infrared, middle-infrared, thermal infrared imagery, and lidar.
The majority of current UAV-based applications for forest
research aim to inventory resources, map diseases, classify species,
monitor fire and its effects, quantify spatial gaps, and estimate
post-harvest soil displacement.
Successful implementation of UAVs in forestry depends on UAV
features, such as flexibility of use in flight planning, low cost,
reliability and autonomy, and capability of timely provision of
high-resolution data.
Unfortunately, the fragmented regulations among EU countries,
a result of the lack of common rules for operating UAVs in
Europe, limit the chance to operate within Europe’s boundaries
and prevent research mobility and exchange opportunities.
Nevertheless, the applications of UAVs are expanding in different
domains, and the use of UAVs in forestry will increase, possibly
leading to a regular utilization for small-scale monitoring purposes
in Europe when recent technologies (i.e. hyperspectral imagery
and lidar) and methodological approaches will be consolidated.

This chapter is a case study of a prisoner of war (POW) camp in Czersk,
Poland, established by Germans during the First World War. The site of
the Czersk camp provides a context for the presentation and discussion
of three issues concerning archaeological research of the recent past. The first issue concerns how each branch of archaeology, including archaeology of the First World War, is by definition archaeology in and of the present. The second discussion is an attempt to conceptualize the First World War as an archaeological event. The third issue concerns reclaiming material memories of First World War POWs of Czersk by using the remote sensing technology of LiDAR. The chapter concludes with the observation that there are inconspicuous and forgotten material memories of the First World War that can be reclaimed only through the craft of archaeology.

The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO 2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree-or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO 2 stocks and changes to comply with the UN-REDD policies.

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection. However, as a step towards theoretically aware, impactful, and reproducible research, a more rigorous and transparent method of data processing is required. To this end, we set out to create a processing pipeline for archaeology-specific point cloud processing and derivation of products that are optimized for general-purpose data. The proposed pipeline improves on ground and building point cloud classification. The main area of innovation in the proposed pipeline is raster grid interpolation. We have improved the state-of-the-art by introducing a hybrid interpolation technique that combines inverse distance weighting with a triangulated irregular network with linear interpolation. State-of-the-art solutions for enhanced visualizations are included and essential metadata and paradata are also generated. In addition, we have introduced a QGIS plug-in that implements the pipeline as a one-step process. It reduces the manual workload by 75 to 90 percent and requires no special skills other than a general familiarity with the QGIS environment. It is intended that the pipeline and tool will contribute to the white-boxing of archaeology-specific airborne LiDAR data processing. In discussion, the role of data processing in the knowledge production process is explored.

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: an archaeology-specific DEM. Accordingly, the aim of this paper is to describe an archaeology-specific DEM in detail, provide a tool for its automatic precision assessment, and determine the appropriate grid resolution. We define an archaeology-specific DEM as a subtype of DEM, which is interpolated from ground points, buildings, and four morphological types of archaeological features. We introduce a confidence map (QGIS plug-in) that assigns a confidence level to each grid cell. This is primarily used to attach a confidence level to each archaeological feature, which is useful for detecting data bias in archaeological interpretation. Confidence mapping is also an effective tool for identifying the optimal grid resolution for specific datasets. Beyond archaeological applications, the confidence map provides clear criteria for segmentation, which is one of the unsolved problems of DEM interpolation. All of these are important steps towards the general methodological maturity of airborne LiDAR in archaeology, which is our ultimate goal.

Anahtar Kelimeler ÖZ Hava kaynaklı lazer tarama Orman meşcere yükseklik haritası DYM DAM NDYM Orman envanterinde önemli bir parametre olan meşcere yükseklik bilgisi, az gelişmiş ya da Türkiye gibi gelişmekte olan ülkelerde halen yaygın şekilde yersel tekniklerle tekil ağaç bilgisi edinme ve hata payı oldukça yüksek kestirimler ile bu bilgiyi genele yayma prensibi ile elde edilmektedir. Uzaktan algılama (UA) temelli modern ölçüm teknolojilerinin devinimi ile Amerika ve Avrupa ülkeleri başta olmak üzere gelişmiş ülkeler orman meşcere yükseklik belirleme çalışmalarında hata payı yüksek yersel yöntemlerin kullanımından vazgeçmişlerdir. Bu çalışma, hava kaynaklı lazer tarama (ALS) yoğun nokta bulutları ile orman meşcere yükseklik haritası üretimi ve üretilen haritanın mutlak konum doğruluğu potansiyelinin ortaya koyulmasını amaçlamaktadır. Amaç doğrultusunda, Houston, ABD'de orman-yoğun bir çalışma alanı seçilmiş, ALS verileri ile dijital yüzey ve arazi modelleri üretilmiş ve bu ürünler temelinde normalize dijital yüzey modeli tekniği kullanılarak üç boyutlu orman meşcere yükseklik haritası elde edilmiştir. Üretilen haritanın doğruluğu, aynı çalışma alanında yersel lazer tarama (TLS) tekniğinden elde edilen veriler ile üretilen orman meşcere yükseklik haritası kullanılarak model bazlı yaklaşımlarla değerlendirilmiştir. Değerlendirmelerde, standart sapma ve normalize medyan mutlak sapma uluslararası doğruluk metrikleri kullanılmıştır. Sonuçlar ışığında, ALS verileri ile üretilen meşcere yükseklik haritasının yatayda ±1 cm düşeyde ise ±40 cm dolayında mutlak konum doğruluğuna sahip olduğu tespit edilmiştir. Airborne laser scanning performance analysis in the forest stand map production Keywords ABSTRACT Airborne laser scanning Forest stand height map DSM DTM NDSM Stand height information, a significant parameter for forest inventory, is widely obtained with acquiring single tree information with geodetic techniques in underdeveloped or developing countries like Turkey. Acquired single tree information is generalized for whole forest stand with high error rate estimations. Developed countries, especially America and European countries, have completely excluded terrestrial methods with a high margin of error in forest stand height determination studies with the development of modern measurement technologies based on remote sensing (RS). This study aims to produce forest stand height map with airborne laser scanning (ALS) dense point clouds and to reveal the absolute location accuracy potential of the produced map. For this purpose, a forest-dominant study area was selected in Houston, USA, digital surface and terrain models were produced with ALS data, and a three-dimensional forest stand height map was obtained using a normalized digital surface model technique. The accuracy of the map produced was evaluated with model-based approaches using the forest stand height map produced with the data obtained from terrestrial laser scanning (TLS) technique in the same study area. International accuracy metrics for standard deviation and normalized median absolute deviation were used in the evaluations. In the light of the results, it has been determined that the stand height map produced with ALS data has an absolute position accuracy of ± 1 cm horizontally and ± 40 cm vertical.

Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conven- tional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and over- comes computational challenges for large-area forest assessments. However, how does lower point den- sity impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban For- est type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest dif- ference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type bio- mass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE val- ues, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional-scale forest assessment without compromis- ing the accuracy of biomass estimates, and these estimates can be further improved using development density.

The Airborne Laser Scanning (ALS) technology has been implemented in operational forest inventories in a number of countries. At the same time, as a cost-effective alternative to ALS, Digital Aerial Photogrammetry (PHM), based on aerial images, has been widely used for the past 10 years. Recently, PHM based on Unmanned Aerial Vehicle (UAV) has attracted great attention as well. Compared to ALS, PHM is unable to penetrate the forest canopy and, ultimately, to derive an accurate Digital Terrain Model (DTM), which is necessary to normalize point clouds or Digital Surface Models (DSMs). Many countries worldwide, including Croatia, still rely on PHM, as they do not have complete DTM coverage by ALS (DTMALS). The aim of this study is to investigate if the official Croatian DTM generated from PHM (DTMPHM) can be used for data normalization of UAV-based Digital Surface Model (DSMUAV) and estimating plot-level mean tree height (HL) in lowland pedunculate oak forests. For that purpose, HL estimated from DSMUAV normalized with DTMPHM and with DTMALS were generated and compared as well as validated against field measurements. Additionally, elevation errors in DTMPHM were detected and eliminated, and the improvement by using corrected DTMPHM (DTMPHMc) was evaluated. Small, almost negligible variations in the results of the leave-oneout cross-validation were observed between HL estimated using proposed methods. Compared to field data, the relative root mean square error (RMSE%) values of HL estimated from DSMUAV normalized with DTMALS, DTMPHM, and DTMPHMc were 5.10%, 5.14%, and 5.16%, respectively. The results revealed that in the absence of DTMALS, the existing official Croatian DTM could be readily used in remote sensing based forest inventory of lowland forest areas. It can be noted that DTMPHMc did not improve the accuracy of HL estimates because the gross errors mainly occurred outside of the study plots. However, since the existence of the gross errors in Croatian DTMPHM has been confirmed by several studies, it is recommended to detect and eliminate them prior to using the DTMPHM in forest inventory.

The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the cold period in winter forces individuals to optimize their trade off in searching for food and shelter. We analyzed the winter habitat selection of roe deer (Capreolus capreolus) in a montane forest landscape combining estimates of vegetation cover in three different height strata, derived from high resolution airborne Laser-scanning (LiDAR, Light detection and ranging), and activity data from GPS telemetry. Specifically, we tested the influence of temperature, snow height, and wind speed on site selection, differentiating between active and resting animals using mixed-effects conditional logistic regression models in a case-control design. Site selection was best explained by temperature deviations from hourly means, snow height, and activity status of the animals. Roe deer tended to use forests of high canopy cover more frequently with decreasing temperature, and when snow height exceeded 0.6 m. Active animals preferred lower canopy cover, but higher understory cover. Our approach demonstrates the

ABSTRACT
The Suitability of Laser Scanning of Slovenia Data for Archaeological Interpretation: Method and a Case Study
The suitability of lidar data deriving from the Laser Scanning of Slovenia Project for archaeological inter- pretation is considered. The said data has been scanned at the half or one quarter of the density suggested for archaeological interpretation. To address this issue an optimization process is proposed. It is presented as follows: point cloud data filter selection, low noise detection algorithm, manual classification docu- mentation, DEM interpolation method comparison and the selection of the most appropriate visualization method. This process is show cased on the Ajdovščina above Rodik example.
The final conclusion of the authors is that the data tested is suitable for archaeological interpretation pro- vided that the described process – or a process with similar results – is carried out.
KEY WORDS
lidar data, archaeology, archaeological interpretation, method

IZVLEČEK
Primernost podatkov projekta Lasersko skeniranje Slovenije za arheološko interpretacijo: metoda in študijski primer
V prispevku obravnavamo primernost podatkov projekta Lasersko skeniranje Slovenije za arheološko inter- pretacijo. Ti podatki so bili večinoma zajeti z gostoto, ki je dva- do štirikrat nižja od priporočene za arheologijo. Zato smo razvili postopek optimizacije podatkov za arheološke interpretacije, ki se sestoji iz naslednjih korakov: primerjavo izbranih algoritmov za samodejno filtriranje georeferenciranega oblaka točk, posto- pek odstranjevanja nizkega šuma, dokumentiranje ročnega klasificiranja, primerjavo izbranih metod interpolacije in izbor najprimernejših metod vizualizacije. Na primeru arheološkega najdišča Ajdovšči- na nad Rodikom prikažemo rezultat tega postopka in hkrati predlagamo način dokumentiranja postopka. Zaključna ugotovitev prispevka je, da podatki so primerni za arheološko interpretacijo, v kolikor je izpe- ljan celoten opisani postopek – ali postopek s primerljivimi rezultati – optimizacije podatkov.
KLJUČNE BESEDE
lidarski podatki, arheologija, arheološka interpretacija, metoda

Light detection and ranging (LiDAR) has become a common means for predicting key forest structural attributes, but comparisons of alternative statistical methods and the spatial extent of LiDAR metrics extraction on independent datasets have been minimal. The primary objective of this study was to assess the performance of local and non-local LiDAR aboveground biomass (AGB) prediction models at two locations in the Acadian Forest. Two common statistical techniques, nonlinear mixed effects (NLME) and random forest (RF), were used to fit the prediction models and compared. Finally, this study evaluated the influence of alternative plot radii for LiDAR metrics extraction on model fit and prediction accuracy. AGB models were independently developed at each forest and tested both locally (model applied to same forest used for development) and non-locally (model applied to different forest) using an extensive network of ground-based plots. In general, RF was found to outperform NLME when applied locally, but the differences between the approaches were negligible when applied to the non-local dataset. NLME was found to perform equally well locally and non-locally. LiDAR extraction radius had very little influence on model performance as well. Minimal differences between models developed using fixed- and variable-radius methods were found, while the optimal LiDAR extraction radius was not consistent among forests, statistical technique, or local vs. non-local. Overall, the results highlight the importance of a robust calibration dataset that covers the full range of observed variation for developing accurate prediction models based on remote sensing data.

Characterization of tropical forest trees has been limited to field-based techniques focused on measurement of diameter of the cylindrical part of the bole, with large uncertainty in measuring large trees with irregular shapes, and other size attributes such as total tree height and the crown size. Here, we introduce a methodology to decompose lidar point cloud data into 3D clusters corresponding to individual tree crowns (ITC) that enables the estimation of many biophysical variables of tropical forests such as tree height, crown area, crown volume, and tree number density. The ITC-based approach was tested using airborne high-resolution lidar data collected over the 50-ha Center for Tropical Forest Science (CTFS) plot in the Barro Colorado Island, Panama. The lack of tree height and crown size measurements in the field prohibits the direct validation of the ITC metrics. We assess the reliability of our method by comparing the aboveground biomass (AGB) estimated using ground and lidar individual tree measurements at multiple spatial scales, namely 1ha, 2.25 ha, 4ha, and 6.25 ha. We examined four different lidar-derived AGB models, with three based on individual tree height, crown volume, and crown area, and one with mean top canopy height (TCH) calculated at the plot level using the lidar canopy height model. Results show that the predictive power of all models based on ITC size and TCH increases with decreasing spatial resolution from 16.9% at 1ha for the worst model to 5.0% at 6.25ha for the best model. The TCH-based model performed slightly better than ITC-based models except at higher spatial scales (~4 ha) and when errors due to edge effects associated with tree crowns were reduced. Unlike the TCH models that change regionally depending on forest type and structure allometry, the ITC-based models are derived as a function of individual tree allometry and can be extended globally to all tropical forests. The method for lidar detection of individual crown size overcome some limitations of ground-based inventories such as 1) it is able to access crowns of large trees and 2) it enables the assessment of directional changes in tree density, canopy architecture and forest dynamics over large and inaccessible areas to support robust tropical ecological studies.