Retrieval of Information from Different Optical 3D Remote Sensing Sources for Use in Forest Inventory (3D-FORINVENT)

U radu je dodatno istražena i evaluirana točnost fotogrametrijske procjene volumena na razini sastojine. Konkretno, na području šuma hrasta lužnjaka Pokupskog bazena (g.j. Jastrebarski lugovi) testirana je mogućnost korištenja fotogrametrijske metode bazirane na postojećim i lako dostupnim podacima (aerosnimke, digitalni model reljefa, podaci osnove gospodarenja) kao i postojećih fotogrametrijskih modela procjene sastojinskog volumena izrađenih za šume hrasta lužnjaka Spačvanskog bazena. Iz aerosnimaka i digitalnog modela reljefa izrađen je digitalni model visine krošanja (DMVK) prostorne rezolucije 5 m. Iz DMVK-a su dobiveni metrički podaci, koji su potom korišteni kao nezavisne varijable u modelima procjene volumena sastojine. Uspoređena je točnost procjene izvornih modela izrađenih za područje Spačvanskog bazena (SB modeli) te istih modela, ali s naknadno procijenjenim lokalnim parametrima za područje Pokupskog bazena (PB modeli). Fotogrametrijski procijenjeni volumeni validirani su s volumenom sastojina iz osnove gospodarenja. Dobiveni rezultati ukazuju na značajno poboljšanje točnosti fotogrametrijske procjene volumena kod PB modela u odnosu na SB modele. Korištenjem izvornih SB modela, volumen sastojine procijenjen je s korijenom srednje kvadratne pogreške od 18,47%, dok je korištenjem dodatno parametriziranih PB modela volumen procijenjen s pogreškom od 12,03%. U ovom radu prikazana fotogrametrijska metoda procjene volumena sastojina ne može zamijeniti klasične terenske metode za potrebe uređajne inventure šuma, međutim, budući da ne zahtijeva dodatna terenska mjerenja, već se u potpunosti bazira na postojećim podacima (aerosnimke, DMR, podaci osnove gospodarenja), a uz to pruža i zadovoljavajuću točnost, može poslužiti kao učinkovita i financijski isplativa metoda u slučajevima kada je u vrlo kratkom vremenu potrebno provesti inventuru nekog većeg šumskog područja.

Advances in remote sensing technologies enables increasing achievements in the field of tree species classification. In recent years, significant technological progress and improvements in the characteristics of optical sensors have been made, thus enabling improved resolution of details on satellite imagery (spatial, spectral and radiometric resolution). The increase in spatial resolution had a significant impact on the development of remote sensing techniques and methods. A new generation of very high resolution satellite imagery enables research at local and regional levels and represent a important source of forestry information. The aim of this paper is to provide an overview of global high and very high resolution satellite missions, as well as analysis and processing methodology in forest cover classification. In combination with machine learning algorithms, the application of high and very high resolution satellite imageries reduces the need for labor-intensive and time-consuming traditional field methods while increasing the quantitative and qualitative value of the information obtained in forestry.

Potrajno gospodarenje šumama zahtijeva opsežne, točne, prostorno definirane i ažurne podatke o šumskim resursima, koji se najčešće prikupljaju inventurom šuma. U većini zemalja podaci potrebni za inventuru šuma prikupljaju se metodama konvencionalne (terenske) izmjere koja se provodi na primjernim plohama. Mjere se atributi pojedinačnih stabala unutar plohe te se dalje koriste za procjenu varijabli i strukturnih karakteristika ploha i šumskih sastojina, ali i za procjenu na nacionalnoj razini. Postoje dva generalna pristupa procjeni varijabli metodama daljinskih istraživanja: pristup na razini plohe (engl. Area Based Approach – ABA) i pristup na razini pojedinačnog stabla (engl. Individual Tree Based Approach – ITBA). Lasersko skeniranje iz zraka (engl. Airborne Laser Scanning – ALS) i digitalna aerofotogrametrija (engl. Digital Aerial Photogrammetry – DAP) su u pojedinim državama uvedeni i u praktičnu primjenu pri inventuri šumskih resursa. Razvoj algoritama i minijaturizacije senzora rezultirali su razvojem bespilotnih letjelica i mobilnih senzora (kamere, laserski skeneri), odnosno sustava kojima se mogu prikupiti informacije o objektu od interesa iz neposredne blizine. Neki od takvih sustava, koji se primjenjuju u blizini objekta od interesa, su bespilotne letjelice (engl. Unmanned Aerial Vehicle – UAV) opremljene kamerom namijenjenom za fotogrametriju ili LiDAR (engl. Light Detection and Ranging) senzorima te od tuda naziv blizupredmetna daljinska istraživanja. Osim sustava temeljenih na bespilotnim letjelicama, razvijeni su i mobilni terestrički LiDAR sustavi. U ovome doktorskom radu istražene su mogućnosti fotogrametrije i LiDAR sustava temeljenih na autonomnim bespilotnim letjelicama te statičkog i mobilnoga terestričkog laserskog skenera za primjenu u inventuri šume. U sklopu ove disertacije ispitana je mogućnost primjene fotogrametrije temeljene na bespilotnim letjelicama (engl. UAV photogrammetry – UAVimage) za procjenu strukturnih parametara ABA pristupom, nadalje je evaluirana točnost UAV LiDAR-a (engl. UAV LiDAR – ULS), ručnoga laserskog skenera (engl. Hand-held Personal Laser Scanner – PLShh), te klasične izmjere za procjenu visine pristupom ITBA, odnosno na razini pojedinog stabla. DTM je podatak koji je iznimno važan pri procjeni visine stabala, bilo na razini plohe ili na razini pojedinog stabla, stoga je u sklopu ove disertacije ispitana točnost DTM-a koji se može dobiti metodama blizupredmetnih daljinskih istraživanja. Ispitana je UAV fotogrametrija, UAV LiDAR, ručni laserski skener i terestrički laserski skener. Svi su podaci evaluirani u odnosu na veliki broj točaka terena izmjerenih geodetskom mjernom stanicom. Rezultati su ukazali na to da ispitane tehnologije blizupredtmetnih daljinskih istraživanja mogu dati pouzdani DTM na šumskom području.

Digital terrain models (DTMs) are important for a variety of applications in geosciences as a valuable information source in forest management planning, forest inventory, hydrology, etc. Despite their value, a DTM in a forest area is typically lower quality due to inaccessibility and limited data sources that can be used in the forest environment. In this paper, we assessed the accuracy of close-range remote sensing techniques for DTM data collection. In total, four data sources were examined, i.e., handheld personal laser scanning (PLShh, GeoSLAM Horizon), terrestrial laser scanning (TLS, FARO S70), unmanned aerial vehicle (UAV) photogrammetry (UAVimage), and UAV laser scanning (ULS, LS Nano M8). Data were collected within six sample plots located in a lowland pedunculate oak forest. The reference data were of the highest quality available, i.e., total station measurements. After normality and outliers testing, both robust and non-robust statistics were calculated for all close-range remote sensing data sources. The results indicate that close-range remote sensing techniques are capable of achieving higher accuracy (root mean square error < 15 cm; normalized median absolute deviation < 10 cm) than airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data that are generally understood to be the best data sources for DTM on a large scale.

Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively.

Tree height is one of the most important tree attributes in forest inventory. However, using conventional field methods to measure tree height is a laborious and time-consuming process. Despite the great interest in the past to facilitate tree height measurements, new, upcoming solutions are not yet thoroughly investigated. In this study, we investigated the applicability of different close-range remote sensing options for tree height measurement in a complex lowland deciduous forest. Six sample plots in a pedunculate oak forest were measured in detail using conventional methods. Close-range remote sensing datasets used in this study represent solutions from low-cost sensors used for hand-held personal laser scanning (PLShh), unmanned–borne laser scanning (ULS) and unmanned aerial vehicle photogrammetry (UAVimage). Each tree in the sample plots was interactively measured directly from the point cloud, and correspondence of the field- and remote sensing measured trees was verified using tree positions collected during fieldwork. Cross-comparisons of different datasets were performed to evaluate the performances of different data sources in the tree height estimation with respect to crown class, tree height and species. All remote sensing data sources correlated well, e.g. biases between remote sensing sources were around ± 1%. The field-measured tree height in general correlated well with remote sensing data sources. The uncertainties and bias of the field measurements were dependent on the tree height and crown class. Field measurements tended to underestimate codominant and intermediate trees at the approximately 1 m magnitude, whilst remote sensing data sources were robust to crown classes. Low-cost ULS used in this study, and very likely in general, may not have enough penetration capability when measuring low and mostly occluded trees, causing missed treetops. PLShh gave tree height estimates closer to the real tree height than those derived from conventional field measurements for trees above 21 m height.

Growing monoculture impacts not just soil properties and biodiversity but also local hydrology including evapotranspiration (ET). The Midwest region of the U.S. is known for its monoculture trend by growing and producing corn, which commonly replaces other crop types. In addition to large areas covered with corn, the photosynthetic adaptations of corn, being the C4 crop, affects ET differently than other C3 crops such as soybean, wheat, and alfalfa. This study aims to model and compare ET for C3 and C4 crops using remote sensing (Sentinel-2 data) and the Boreal Ecosystem Productivity Simulator (BEPS) model, modified to consider C3 and C4 crops. The study explores the ET rate trend for corn and soybean in an agriculture area situated in the Western Lake Erie Basin, where the balance between evapotranspiration, groundwater level, and surface runoff may play a role in agricultural runoff and Lake Erie’s algal blooms caused by runoff pollution. The results suggest that the monthly average ET rates for both soybean (C3) and corn (C4) reach its maximum at the mid-to-late growing season. However, the ET rate for corn is higher than for soybean in the early season (June) (ET = 121 mm month−1 for corn; ET = 105 mm month−1 for soybean), while the ET rate for soybean becomes higher than for corn soon after (July) and becomes considerably higher in August (ET = 181 mm month−1 for corn; ET = 218 mm month−1 for soybean). It is surmised that the higher ET rate for corn in the early growing season is due to nitrogen-based fertilizer commonly applied to corn parcels at that time, whereas soybean growth is based on biological nitrogen fixation.

This study describes the production of high resolution maps of leaf area index (LAI) for the Hyytiala site in Finland. The maps are derived from a transfer function between the SPOT images acquired in August, September and October 2010 and in-situ measurements using PASTIS 57 (PAI Autonomous system from Transmittance Sensors at 57 degrees), an instrument capable of acquiring continuous measurements of transmittance (INRA, France). The LAI maps for three months are consistent; the average values decline from August to October in the expected manner. The study is established in support of preparation for the Sentinel-2 mission

The emergence of hand-held Personal Laser Scanning (H-PLS) systems in recent years resulted in initial research on the possibility of its application in forest inventory, primarily for the estimation of the main tree attributes (e.g. tree detection, stem position, DBH, tree height, etc.). Research knowledge acquired so far can help to direct further research and eventually include H-PLS into operational forest inventory in the future. The main aims of this review are: Þ to present the current state of the art for H-PLS systems Þ briefly describe the fundamental concept and methods for H-PLS application in forest inventory Þ provide an overview of the results of previous studies Þ emphasize pros and cons for H-PLS application in forest inventory in relation to conventional field measurements and other similar laser scanning systems Þ highlight the main issues that should be covered by further H-PLS-based forest inventory studies.

The quality and accuracy of Unmanned Aerial System (UAS) products greatly depend on the methods used to define image orientations before they are used to create 3D point clouds. While most studies were conducted in non- or partially-forested areas, a limited number of studies have evaluated the spatial accuracy of UAS products derived by using different image block orientation methods in forested areas. In this study, three image orientation methods were used and compared: (a) the Indirect Sensor Orientation (InSO) method with five irregularly distributed Ground Control Points (GCPs); (b) the Global Navigation Satellite System supported Sensor Orientation (GNSS-SO) method using non-Post-Processed Kinematic (PPK) single-frequency carrier-phase GNSS data (GNSS-SO1); and (c) using PPK dual-frequency carrier-phase GNSS data (GNSS-SO2). The effect of the three methods on the accuracy of plot-level estimates of Lorey’s mean height (HL) was tested over the mixed, even-aged pedunculate oak forests of Pokupsko basin located in Central Croatia, and validated using field validation across independent sample plots (HV), and leave-one-out cross-validation (LOOCV). The GNSS-SO2 method produced the HL estimates of the highest accuracy (RMSE%: HV = 5.18%, LOOCV = 4.06%), followed by the GNSS-SO1 method (RMSE%: HV = 5.34%, LOOCV = 4.37%), while the lowest accuracy was achieved by the InSO method (RMSE%: HV = 5.55%, LOOCV = 4.84%). The negligible differences in the performances of the regression models suggested that the selected image orientation methods had no considerable effect on the estimation of HL. The GCPs, as well as the high image overlaps, contributed considerably to the block stability and accuracy of image orientation in the InSO method. Additional slight improvements were achieved by replacing single-frequency GNSS measurements with dual-frequency GNSS measurements and by incorporating PPK into the GNSS-SO2 method.

The main goal of this study was to test the aplicability of various 3D remote sensing data (Airborne Laser Scanning, Unmanned Aerial System images, aerial images, WorldView-3 and WorldView-2 stereo images) for estimation of plot-level tree heights (Lorey’s mean height, dominant tree height). This research has been suported by : a) the Croatian Science Foundation under the project IP-2016-06-7686 “Retrieval of Information from Different Optical 3D Remote Sensing Sources for Use in Forest Inventory (3D-FORINVENT)”, b) the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776045; project “Operational sustainable forestry with satellite-based remote sensing (My Suistanable Forest)“.

Pozvano predavanje u okviru XIII. skupštinskog zasjedanja Znanstvenog vijeća za daljinska istraživanja, Hrvatska akademija znanosti i umjetnosti, Zagreb, 15. lipnja 2018.

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 Cro-atia, still rely on PHM, as they do not have complete DTM coverage by ALS (DTM ALS). The aim of this study is to investigate if the official Croatian DTM generated from PHM (DTM PHM) can be used for data normalization of UAV-based Digital Surface Model (DSM UAV) and estimating plot-level mean tree height (H L) in lowland pedunculate oak forests. For that purpose, H L estimated from DSM UAV normalized with DTM PHM and with DTM ALS were generated and compared as well as validated against field measurements. Additionally, elevation errors in DTM PHM were detected and eliminated, and the improvement by using corrected DTM PHM (DTM PHMc) was evaluated. Small, almost negligible variations in the results of the leave-one-out cross-validation were observed between H L estimated using proposed methods. Compared to field data, the relative root mean square error (RMSE %) values of H L estimated from DSM UAV normalized with DTM ALS , DTM PHM , and DTM PHMc were 5.10%, 5.14%, and 5.16%, respectively. The results revealed that in the absence of DTM ALS , the existing official Croatian DTM could be readily used in remote sensing based forest inventory of lowland forest areas. It can be noted that DTM PHMc did not improve the accuracy of H L estimates because the gross errors mainly occurred outside of the study plots. However, since the existence of the gross errors in Croatian DTM PHM has been confirmed by several studies, it is recommended to detect and eliminate them prior to using the DTM PHM in forest inventory.

Accuracy of a Digital Terrain Model (DTM) in a complex forest environment is critical and yet challenging for accurate forest inventory and management, disaster risk analysis, and timber utilization. Reducing elevation errors in photogrammetric DTM (DTMPHM), which present the national standard in many countries worldwide, is critical, especially for forested areas. In this paper, a novel automated method to detect the errors and to improve the accuracy of DTMPHM for the lowland forest has been presented and evaluated. This study was conducted in the lowland pedunculate oak forest (Pokupsko Basin, Croatia). The DTMPHM was created from three-dimensional (3D) vector data collected by aerial stereo-photogrammetry in combination with data collected from existing maps and field surveys. These data still present the national standard for DTM generation in many countries, including Croatia. By combining slope and tangential curvature values of raster DTMPHM, the proposed method developed in open source Grass GIS software automatically detected 91 outliers or 3.2% of the total number of source points within the study area. Comparison with a highly accurate LiDAR DTM confirmed the method efficiency. This was especially evident in two out of three observed subset areas where the root mean square error (RMSE) values decreased for 8% in one and 50% in another area after errors elimination. The method could be of great importance to other similar studies for forested areas in countries where the LiDAR data are not available.

Diameter at the breast height is one of the most important parameters used in the forestry. In practice, field measurement of the test plots is an intensive and time-consuming process that requires specialized equipment. Recently, the application of terrestrial laser scanner and photogrammetry in forestry, are being investigated as a fast and effective approach to forest plot measurement. In this paper, we present the algorithm for modelling and tree stem perimeter extraction based on the RANSAC (RANdom Sample Consensus) algorithm and PCA (Principle Component Analysis). The accuracy of the tree perimeters extraction is tested on the three tree models differing on the reconstruction type (Self-calibration, self-calibration with initial parameters and self-calibration without initial parameters). The smallest error is acquired by estimating perimeters on the model reconstructed with the precalibrated camera (RMSE=1.23cm), self-calibration with initial parameters (RMSE=1.35cm) and self-calibration (RMSE=1.65cm) follow. The presented algorithm indicates the great potential of photogrammetric methods application in tree perimeter estimation, with some changes in the approach.

The 15th INTERNATIONAL PHYTOTECHNOLOGY CONFERENCE and CONFERENCE SESSION ”HOW TO ADVANCE FORESTRY FOR FUTURE GENERATIONS”

Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.

Comparison of plot-level mean tree height (H) and mean diameter at breast height (DBH) estimated from UAV-based point clouds generated at different image pyramid levels.

Comparison of plot-level mean tree height (H) and mean diameter at breast height (DBH) estimated from UAV-based nDSMs generated at different spatial resolutions: 10 cm, 30 cm, and 50 cm.

This study aims to test the influence of different DTMs (ALS and PHM), in the combination with UAV DSM, on the quality of CHM (normalized point clouds) and tree height estimates at the plot-level.

We present and evaluate the fast and simple photogrammetric-based method for stand volume estimation of pedunculate oak stands in the Pokupsko basin. The method was based on the available photogrammetric materials (aerial images, digital terrain data) and existing stand volume models (equations).

Digital terrain models (DTMs) present important data source for different applications in environmental disciplines including forestry. At regional level, DTMs are commonly created using airborne digital photogrammetry or airborne laser scanning (ALS) technology. This study aims to evaluate the vertical accuracy of DTMs of different spatial resolutions derived from high-density ALS data and existing photogrammetric (PHM) data in the dense lowland even-aged pedunculate oak forests located in the Pokupsko basin in Central Croatia. As expected , the assessment of DTMs' vertical accuracy using 22 ground checkpoints shows higher accuracy for ALS-derived than for PHM-derived DTMs. Concerning the different resolutions of ALS-derived (0.5 m, 1 m, 2 m, 5 m) and PHM-derived DTMs (0.5 m, 1 m, 2 m, 5 m, 8 m) compared in this research, the ALS-derived DTM with the finest resolution of 0.5 m shows the highest accuracy. The root mean square error (RMSE) and mean error (ME) values for ALS-derived DTMs range from 0.14 m to 0.15 m and from 0.09 to 0.12 m, respectively, and the values decrease with decreasing spatial resolution. For the PHM-derived DTMs, the RMSE and ME values are almost identical regardless of resolution and they are 0.35 m and 0.17 m, respectively. The findings suggest that the 8 m spatial resolution is optimal for a given photogrammetric data, and no finer than 8 m spatial resolution is required. This research also reveals that the national digital photogrammetric data in the study area contain certain errors (outliers) specific to the terrain type, which could considerably affect the DTM accuracy. Thus, preliminary evaluation of photogrammetric data should be done to eliminate possible outliers prior to the DTM generation in lowland forests with flat terrain. In the absence of ALS data, the finding in this research could be of interests to countries, which still rely on similar photogrammetric data for DTM generation.

Digital terrain models (DTMs) present important data source for different applications in environmental disciplines including forestry. At regional level, DTMs are commonly created using airborne digital photogrammetry or airborne laser scanning (ALS) technology. This study aims to evaluate the vertical accuracy of DTMs of different spatial resolutions derived from high-density ALS data and existing photogrammetric (PHM) data in the dense lowland even-aged pedunculate oak forests located in the Pokupsko basin in Central Croatia. As expected , the assessment of DTMs' vertical accuracy using 22 ground checkpoints shows higher accuracy for ALS-derived than for PHM-derived DTMs. Concerning the different resolutions of ALS-derived (0.5 m, 1 m, 2 m, 5 m) and PHM-derived DTMs (0.5 m, 1 m, 2 m, 5 m, 8 m) compared in this research, the ALS-derived DTM with the finest resolution of 0.5 m shows the highest accuracy. The root mean square error (RMSE) and mean error (ME) values for ALS-derived DTMs range from 0.14 m to 0.15 m and from 0.09 to 0.12 m, respectively, and the values decrease with decreasing spatial resolution. For the PHM-derived DTMs, the RMSE and ME values are almost identical regardless of resolution and they are 0.35 m and 0.17 m, respectively. The findings suggest that the 8 m spatial resolution is optimal for a given photogrammetric data, and no finer than 8 m spatial resolution is required. This research also reveals that the national digital photogrammetric data in the study area contain certain errors (outliers) specific to the terrain type, which could considerably affect the DTM accuracy. Thus, preliminary evaluation of photogrammetric data should be done to eliminate possible outliers prior to the DTM generation in lowland forests with flat terrain. In the absence of ALS data, the finding in this research could be of interests to countries, which still rely on similar photogrammetric data for DTM generation.

Background and Purpose: Unmanned aerial vehicles (UAVs) are flexible to solve various surveying tasks which make them useful in many disciplines, including forestry. The main goal of this research is to evaluate the quality of photogrammetry-based digital surface model (DSM) from low-cost UAV’s images collected in non-optimal weather (windy and cloudy weather) and environmental (inaccessibility for regular spatial distribution of ground control points - GCPs) conditions. Materials and Methods: The UAV-based DSMs without (DSMP) and with using GCPs (DSMP-GCP) were generated. The vertical agreement assessment of the UAV-based DSMs was conducted by comparing elevations of 60 checkpoints of a regular 100 m sampling grid obtained from LiDAR-based DSM (DSML) with the elevations of planimetrically corresponding points obtained from DSMP and DSMP-GCP. Due to the non-normal distribution of residuals (vertical differences between UAV- and LiDAR-based DSMs), a vertical agreement was assessed by using robust measures: median, normalised median absolute deviation (NMAD), 68.3% quantile and 95% quantile. Results: As expected, DSMP-GCP shows higher accuracy, i.e. higher vertical agreement with DSML than DSMP. The median, NMAD, 68.3% quantile, 95% quantile and RMSE* (without outliers) values for DSMP are 2.23 m, 3.22 m, 4.34 m, 15.04 m and 5.10 m, respectively, whereas for DSMP-GCP amount to -1.33 m, 2.77 m, 0.11 m, 8.15 m and 3.54 m, respectively. Conclusions: The obtained results confirmed great potential of images obtained by low-cost UAV for forestry applications, even if they are surveyed in non-optimal weather and environmental conditions. This could be of importance for cases when urgent UAV surveys are needed (e.g. detection and estimation of forest damage) which do not allow careful and longer survey planning. The vertical agreement assessment of UAV-based DSMs with LiDAR-based DSM confirmed the importance of GCPs for image orientation and DSM generation. Namely, a considerable improvement in vertical accuracy of UAV-based DSMs was observed when GCPs were used.

The aim of this poster was to present the project at the first project workshop.