Depth-to-water maps as predictors of rut severity in fully mechanized harvesting operations

The preservation of the functionality of forest soil is a key aspect in planning mechanized harvesting operations. Therefore, knowledge and information about stand and soil characteristics are vital to the planning process. In this respect, depth-to-water (DTW) maps were reviewed with regard to their potential use as a prediction tool for wheel ruts. To test the applicability of open source DTW maps for prediction of rutting, the ground surface conditions of 20 clear-cut sites were recorded post harvesting, using an unmanned aerial vehicle (UAV). In total, 80 km of machine tracks were categorized by the severity of occurring rut-formations to investigate whether: i) operators intuitively avoid areas with low DTW values, ii) a correlation exists between decreasing DTW values and increasing rut severity, and iii) DTW maps can serve as reliable decision-making tool in minimizing the environmental effects of big machinery deployment. While the machine operators did not have access to these predictions (DTW maps) during the operations, there was no visual evidence that driving through these areas was actively avoided, resulting in a higher density of severe rutting within areas with DTW values <1 m. A logistic regression analysis confirmed that the probability of severe rutting rapidly increases with decreasing DTW values. However, significant differences between sites exist which might be attributed to a series of other factors such as soil type, weather conditions, number of passes and load capacity. Monitoring these factors is hence highly recommended in any further follow-up studies on soil trafficability.publishedVersio

Measuring forest machine rut depth using inexpensive remote sensing methods : A case study in Finland

Forest operations may result in rut formation detrimental to the forest environment. Affordable methods for monitoring rutting are therefore needed. In this study, three inexpensive remote sensing methods were tested for measuring rutting: a drone-based camera using photogrammetry (UAVPH); RGB-depth simultaneous localization and mapping with a mobile stereo camera (RGB-D SLAM); and mobile LiDAR scanning with an iPad (iPad). The measurements were performed at two forest operation sites (A and B) in Finland. Sufficiently reliable results were obtained with UAVPH and RGB-D SLAM on site A, which consisted of open area. Here, UAVPH and RGB-D SLAM produced rut depth estimates with a root-mean-square error (RMSE) of 4 to 7 cm. On site B, trees surrounding the ruts were present. Here, the accuracy of UAVPH was lower than on site A, with an RMSE of 12 and 14 cm for the two ruts respectively. On this site, RGB-D SLAM gave an RMSE as high as 43 and 108 cm due to lower computational power being available during measurement. Pearson’s correlation between the remote sensing measurements and reference values was over 0.90 for UAVPH and RGB-D SLAM on site A. On site B, correlation for UAVPH was over 0.70, but correlation for RGB-D SLAM was low. The iPad did not produce results of useful accuracy. With a clear view of the ruts being imaged and with sufficient computational power on site, the UAVPH and RGB-D SLAM methods appear promising approaches for monitoring rut depth in real forest operations, UAVPH being the superior of the two

A review of Sensors, Sensor-Platforms and Methods Used in 3D Modelling of Soil Displacement after Timber Harvesting

Proximal sensing technologies are becoming widely used across a range of applications in environmental sciences. One of these applications is in the measurement of the ground surface in describing soil displacement impacts from wheeled and tracked machinery in the forest. Within a period of 2–3 years, the use photogrammetry, LiDAR, ultrasound and time-of-flight imaging based methods have been demonstrated in both experimental and operational settings. This review provides insight into the aims, sampling design, data capture and processing, and outcomes of papers dealing specifically with proximal sensing of soil displacement resulting from timber harvesting. The work reviewed includes examples of sensors mounted on tripods and rigs, on personal platforms including handheld and backpack mounted, on mobile platforms constituted by forwarders and skidders, as well as on unmanned aerial vehicles (UAVs). The review further highlights and discusses the benefits, challenges, and some of the shortcomings of the various technologies and their application as interpreted by the authors. The majority of the work reviewed reflects pioneering approaches and innovative applications of the technologies. The studies have been carried out almost simultaneously, building on little or no common experience, and the evolution of standardized methods is not yet fully apparent. Some of the issues that will likely need to be addressed in developing this field are (i) the tendency toward generating apparently excessively high resolution micro-topography models without demonstrating the need for or contribution of such resolutions on accuracy, (ii) the inadequacy of conventional manual measurements in verifying the accuracy of these methods at such high resolutions, and (iii) the lack of a common protocol for planning, carrying out, and reporting this type of study

Application of UAS for Monitoring of Forest Ecosystems – A Review of Experience and Knowledge

In the last couple of years, there have been a great number of articles that cover and emphasize the advantages and possibilities that UAS (Unmanned Air System) offers in forest ecosystem research. In the available research, alongside UAS, the importance of developing sensors that are designed to be used with UAV (Unamnned Air Vehicle), a flight programming software and UAS collected data processing software have been pointed out. With the widespread use of high-precision sensors and accompanying software in forestry, it is possible to obtain accurate data in a short time that replaces long-term manpower in the field with equal or in some cases, such as windthrow calculation or wildlife counting, greater accuracy. The former practice of manual imagery processing is being partly replaced with automated approaches. The paper analyses studies that deal with some form of application of UAS in forestry, e.g. forest inventory, forest operations, ecological monitoring, forest pests and forest fires, and wildlife monitoring. In the forest inventory, a large number of studies deal with the possibilities of applying UAS in mapping vegetation and individual trees, morphological research of individual parts of trees, surface analysis, etc. The use of remote and proximal sensing technologies in forest engineering has mainly been focused on defining surface roughness and topology, road geometry, planning and maintenance, ground-based and cable-based harvesting and soil characteristics and displacement. Wildfire monitoring already relies heavily on the use of UAS and thermal cameras in operations, and it is similar to the mapping of windthrow or directions of the spread of certain insects important for forestry. In wildlife research, numerous studies deal with abundance research of individual terrestrial birds and mammals using UAS thermal imagery. With some drawbacks such as wildlife disturbance or limited UAV range, common to most of the processed studies are positive attitudes regarding the application of UAS in forestry sensing and monitoring, which is slowly becoming a common operative practice, with the scientists’ focus being on developing automated approaches in UAS imagery processing. Reducing the error by improving the technological characteristics of the sensors will in the long run reduce the number of people required to collect data important for forestry, reduce risks and in some cases increase accuracy

Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon

GIS-based decision support systems to minimise soil impacts in logging operations

Mechanised logging operations can leave negative impacts, like ruts, on forest soils. To avoid this, forestry planners and machine operators need decision support systems that can estimate soil trafficability and help to minimise soil impacts. The main objective of this thesis was to evaluate whether or how different data, stored in a geographic information system (GIS), can contribute to improved estimation of soil trafficability. Requirements for implementation of soil trafficability maps in forestry GIS applications were also described. A soil trafficability map, based on several GIS data using multi-criteria decision analysis (MCDA), was proposed in Paper I. Availability and implementation of soil trafficability maps, mainly depth-to-water (DTW) maps, in some European countries, was reviewed in Paper II. Effect of DTW map resolutions to predict soil moisture was evaluated in Paper IV, and the study showed that a spatial resolution of 1–2 m was sufficient. Risk for rutting was analysed in relation to field-measured and GIS data in Papers III, V and VI. GIS data included digital elevation models, DTW maps, hydrological data, soil type, and clay content maps. The results showed that planning forwarder trails and evaluating different alternatives can be improved by using a soil trafficability map. GIS data of high quality is required to achieve acceptable results. Easy or free access to soil trafficability maps facilitate their application in forestry operations. DTW maps, together with other data, can be used to estimate risk for rutting. Clay content maps and hydrological data, at current resolution, need further development but showed potential to predict risk for rutting. More studies are required to estimate temporal and spatial variability of soil trafficability maps. In conclusion, GIS-based decision support systems should be used for planning of logging operations to minimise risk for rutting

Estimating the Rut Depth by UAV Photogrammetry

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Silviculture of Mixed-Species and Structurally Complex Boreal Stands

Understanding structurally complex boreal stands is crucial for designing ecosystem management strategies that promote forest resilience under global change. However, current management practices lead to the homogenization and simplification of forest structures in the boreal biome. In this chapter, we illustrate two options for managing productive and resilient forests: (1) the managing of two-aged mixed-species forests; and (2) the managing of multi-aged, structurally complex stands. Results demonstrate that multi-aged and mixed stand management are powerful silvicultural tools to promote the resilience of boreal forests under global change