Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals

Milder winters and extended wetter periods in spring and autumn limit the amount of time available for carrying out ground-based forest operations on soils with satisfactory bearing capacity. Thus, damage to soil in form of compaction and displacement is reported to be becoming more widespread. The prediction of trafficability has become one of the most central issues in planning of mechanized harvesting operations. The work presented looks at methods to model field measured spatio-temporal variations of soil moisture content (SMC, [%vol]) – a crucial factor for soil strength and thus trafficability. We incorporated large-scaled maps of soil characteristics, high-resolution topographic information – depth-to-water (DTW) and topographic wetness index – and openly available temporal soil moisture retrievals provided by the NASA Soil Moisture Active Passive mission. Time-series measurements of SMC were captured at six study sites across Europe. These data were then used to develop linear models, a generalized additive model, and the machine learning algorithms Random Forest (RF) and eXtreme Gradient Boosting (XGB). The models were trained on a randomly selected 10% subset of the dataset. Predictions of SMC made with RF and XGB attained the highest R2 values of 0.49 and 0.51, respectively, calculated on the remaining 90% test set. This corresponds to a major increase in predictive performance, compared to basic DTW maps (R2 = 0.022). Accordingly, the quality for predicting wet soils was increased by 49% when XGB was applied (Matthews correlation coefficient = 0.45). We demonstrated how open access data can be used to clearly improve the prediction of SMC and enable adequate trafficability mappings with high spatial and temporal resolution. Spatio-temporal modelling could contribute to sustainable forest management.publishedVersio

Prediction of forest soil trafficability by topography-based algorithms and in-situ test procedures

Moderne Waldbewirtschaftung bedingt den Einsatz von Forstmaschinen, da diese sichere und effiziente Erntemaßnahmen ermöglichen. Dennoch führen solche Maschinen häufig zu schwerwiegenden Bodenschäden, beispielsweise Verdichtung und Bodenumlagerung. In Zeiten sich ändernder klimatischer Bedingungen stellt die Sicherstellung einer ganzjährigen Nutzung mit minimalen negativen Auswirkungen auf den Waldboden eine anspruchsvolle Aufgabe dar. Eine Lösung für dieses Problem besteht in der Vorhersage der Bodenbefahrbarkeit durch kartographische Indizes. Diese Dissertation zeigt Möglichkeiten zur Vorhersage der Befahrbarkeit und beinhaltet mehrere Untersuchungen: (1) Werte von Depth-To-Water (DTW) und Werte des Topographic Wetness Index wurden mit Fahrspurtiefen korreliert, die während einer Spätdurchforstung in einem Laubholzbestand gemessen wurden. Zusätzlich wurden verschiedene terramechanische Testverfahren vor der Befahrung durchgeführt und mit der auftretenden Fahrspurtiefe verglichen. Der gemessene Cone-Index konnte nach einer Modifizierung zur Vorhersage auftretenden Spurtiefen verwendet werden. Daher wurde dieser Parameter für weiteren Validierungen ausgewählt. (2) Zeitreihen von Bodentragfähigkeit, quantifiziert mit dem modifizierten Cone-Index, und Bodenfeuchte wurden an sechs Untersuchungsstandorten in Europa erfasst. Die Messergebnisse wurden mit DTW-Vorhersagen validiert, was in 76 % (Cone-Index) bzw. 82 % (Bodenfeuchte) akkuraten Vorhersagen resultierte. Allerdings wich ein hoher Anteil der Messungen, die nasse oder weiche Böden anzeigten, von den Vorhersagen ab. Die von DTW angenommene jahreszeitliche Anpassung an Feuchteverhältnisse konnte nicht bestätigt werden, wahrscheinlich aufgrund von Standortseffekten, nicht-linearen hydrologischen Prozessen und dem Fehlen zuverlässiger Schätzungen der aktuellen Feuchteverhältnisse. (3) Solche Effekte und deren Interaktionen können durch leistungsfähige Methoden Maschinellen Lernens berücksichtigt werden. Ein Random-Forest-Modell sowie ein Gradienten-Boosting wurden mit zusammengeführten Daten trainiert. Dieser Datensatz enthielt dreistündliche Mittelwerte von fernerkundlich geschätzter Bodenfeuchte (Soil Moisture Active Passive Mission), Werte von DTW und TWI, sowie frei verfügbaren Bodenkarten. Das vorgeschlagene Verfahren verbesserte die Genauigkeit der Vorhersagen erheblich und reduzierte insbesondere den Klassenfehler für nasse Messungen. Mit diesem verbesserten Vorhersagemodell könnten Bodenschutzmaßnahmen umgesetzt werden, die eine umweltschonende Forstwirtschaft ermöglichen würden. Die benötigten Input-Daten sind über weite Gebiete Europas verfügbar, und wurden teilweise bereits zu Befahrungsrisikokarten verarbeitet. Eine Ausweitung der Anwendung solcher Karten in der forstlichen Praxis kann erwartet werden. (4) Die Anwendung einer bodenschonenden Technik, nämlich die Anwendung einer Traktionshilfswinde, wurde auf einem flachen Standort untersucht, wo eine solche Technologie bisher kaum untersucht wurde. Diese Arbeit untersuchte vor allem das raum-zeitlichen Verhalten der Bodenfeuchte und -tragfähigkeit an mehreren Untersuchungsstandorten und zeigte die Grenzen des DTW-Konzepts auf. Es wurde gezeigt, wie maschinelles Lernen eingesetzt werden kann, um bestehende Einschränkungen zu beheben und wie eine adäquate Einbindung offen verfügbarer Daten praxistaugliche Vorhersagewerkzeuge verbessern kann.Modern forest management entails the utilization of harvesting machinery, which enables safe and efficient forest operations. Still, such machines are frequently resulting in severe soil damage, such as compaction and displacement. To maintain or even increase year-round timber mobilization with minimal negative impacts on forest soils is a challenging task, especially in times of changing climatic conditions. One solution to address this issue is the prediction of trafficability, aiming at the reduction of traffic-induced damages. Through multiple investigations, this thesis reports on methods to predict trafficability: (1) values of the depth-to-water (DTW) index and the topographic wetness index (TWI) were related to rut depths observed during a field trial in a broad-leaved forest stand. In addition, different terramechanical test procedures were performed and related to rut depth following the fully mechanized harvesting operation. A modified Cone Index was shown to be successful in the prediction of occurring ruts. Therefore, this parameter was chosen for use in further validations. (2) Time-series data of soil strength, quantified by the modified Cone Index, and soil moisture were captured on six study sites across Europe. The measuring results were validated against DTW-derived predictions, resulting in a prediction accuracy of 76% for soil strength, and 82% for values of soil moisture. Yet, a high share of measurements indicating soft or wet soils deviated from the predictions made. Apparently, the conjectured season-adapted representation of overall levels of soil moisture by DTW map-scenarios could not be confirmed, probably owing to site-specific effects, non-linear behaviour of water accumulation across landscapes and the omission of reliable estimations of current levels of soil moisture. (3) Such effects were considered by machine learning approaches. Tree-based machine learning models were trained on merged data, containing daily retrievals of remotely-sensed soil moisture (Soil Moisture Active Passive mission), values of DTW, TWI and openly available soil maps. This procedure significantly improved the accuracy of predictions and reduced the class error for wet soil states. With this improved trafficability prediction, mitigating measures could sufficiently be implemented in forest management, potentially leading to environmentally sound forest management and lower costs for forest operations. The required in-put data for creating DTW maps is commonly available among governmental institutions of Central and Northern Europe, and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. It is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations. (4) The application of a mitigating technology, namely a traction-assist winch, was surveyed on a flat site where the application of such technology has not yet been investigated. In this dissertation, particular focus was placed on the spatio-temporal patterns of soil moisture and strength on several study sites, indicating the limitation of the basic DTW concept. A method to remedy existing constraints and promote adequate implementation of openly available data, particularly soil moisture retrievals, to further improve predictive tools applicable in forest operations was demonstrated.2022-03-1

Effects of Boom-Tip Control and a Rotating Cabin on Loading Efficiency of a Forwarder: A Pilot Study

Climate change and associated heat waves and droughts are causing enormous amounts of damaged wood in Central Europe. To face these challenges, mechanized timber harvesting systems consisting of single-grip-harvesters and forwarders are commonly employed due to their high productivity and work safety. Despite the advantages of these work systems, the operation of advanced forestry machines requires lengthy training and entails high levels of mental strain for machine operators. In recent years, operator assistance systems have been installed in forest machines with the intention of reducing mental workload of machine operators, thereby improving productivity. However, knowledge of the actual effect of operator assistance systems on productivity is still lacking. The present case study surveyed the effect of two recently released operator assitance features, Intelligent Boom Control (»IBC«) and a rotating cabin (»RC«), on productivity during loading cycles, by means of a time study. Therefore, IBC and RC were tested in different loading settings using a forwarder, John Deere 1210G. Three loading angles were tested (55°, 90° and 125° azimuthal and counterclockwise to the machine axis) in combination with five loading distances (4 m, 5.5 m, 7 m, 8.5 m, and 10 m distance from the crane pillar). The 15 loading positions were sampled using four variants (I: IBC off RC off, II: RC on IBC off, III: IBC on RC off, IV: IBC on RC on), capturing 10 replications for each position and variant, resulting in 600 loading cycles in total. When the operator was not supported by any system, mean time consumption per loading cycle amounted to 20.6 ± 0.114 sec. The utilization of IBC resulted in a significant reduction in time consumption of 2 seconds per loading cycle. Moreover, further time savings were observed when IBC was engaged in combination with a rotating cabin, leading to a mean time consumption of 17.8 ± 0.114 sec (or 14% improvement) per loading cycle. Although the lowest time consumption was observed when IBC and RC were engaged, the use of RC alone did not show any significant time improvements. Since loading activities occupy approximately 50% of the total cycle time in timber forwarding, potential time savings within this work element are crucial for further improvements of work productivity. This pilot case study quantified the time savings when IBC and RC were engaged during loading in an experimental setting. The results can be used as a basis for further investigations dealing with factors influencing the productivity of highly mechanized timber harvesting systems

Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation

Timber harvesting operations using heavy forest machinery frequently results in severe soil compaction and displacement, threatening sustainable forest management. An accurate prediction of trafficability, considering actual operating conditions, minimizes these impacts and can be facilitated by various predictive tools. Within this study, we validated the accuracy of four terramechanical parameters, including Cone Index (MPa, Penetrologger), penetration depth (cm, Penetrologger), cone penetration (cm blow−1, dual-mass dynamic cone penetrometer) and shear strength (kPa, vane meter), and additionally two cartographic indices (topographic wetness index and depth-to-water). Measurements applying the four terramechanical approaches were performed at 47 transects along newly assigned machine operating trails in two broadleaved dominated mixed stands. After the CTL thinning operation was completed, measurement results and cartographic indices were correlated against rut depth. Under the rather dry soil conditions (29 ± 9 vol%), total rut depth ranged between 2.2 and 11.6 cm, and was clearly predicted by rut depth after a single pass of the harvester, which was used for further validations. The results indicated the easy-to-measure penetration depth as the most accurate approach to predict rut depth, considering coefficients of correlation (rP = 0.44). Moreover, cone penetration (rP = 0.34) provided reliable results. Surprisingly, no response between rut depth and Cone Index was observed, although it is commonly used to assess trafficability. The relatively low moisture conditions probably inhibited a correlation between rutting and moisture content. Consistently, cartographic indices could not be used to predict rutting. Rut depth after the harvester pass was a reliable predictor for total rut depth after 2–5 passes (rP = 0.50). Rarely used parameters, such as cone penetration or shear strength, outcompeted the highly reputed Cone Index, emphasizing further investigations of applied tools

Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation

Timber harvesting operations using heavy forest machinery frequently results in severe soil compaction and displacement, threatening sustainable forest management. An accurate prediction of trafficability, considering actual operating conditions, minimizes these impacts and can be facilitated by various predictive tools. Within this study, we validated the accuracy of four terramechanical parameters, including Cone Index (MPa, Penetrologger), penetration depth (cm, Penetrologger), cone penetration (cm blow−1, dual-mass dynamic cone penetrometer) and shear strength (kPa, vane meter), and additionally two cartographic indices (topographic wetness index and depth-to-water). Measurements applying the four terramechanical approaches were performed at 47 transects along newly assigned machine operating trails in two broadleaved dominated mixed stands. After the CTL thinning operation was completed, measurement results and cartographic indices were correlated against rut depth. Under the rather dry soil conditions (29 ± 9 vol%), total rut depth ranged between 2.2 and 11.6 cm, and was clearly predicted by rut depth after a single pass of the harvester, which was used for further validations. The results indicated the easy-to-measure penetration depth as the most accurate approach to predict rut depth, considering coefficients of correlation (rP = 0.44). Moreover, cone penetration (rP = 0.34) provided reliable results. Surprisingly, no response between rut depth and Cone Index was observed, although it is commonly used to assess trafficability. The relatively low moisture conditions probably inhibited a correlation between rutting and moisture content. Consistently, cartographic indices could not be used to predict rutting. Rut depth after the harvester pass was a reliable predictor for total rut depth after 2–5 passes (rP = 0.50). Rarely used parameters, such as cone penetration or shear strength, outcompeted the highly reputed Cone Index, emphasizing further investigations of applied tools

Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals

Milder winters and extended wetter periods in spring and autumn limit the amount of time available for carrying out ground-based forest operations on soils with satisfactory bearing capacity. Thus, damage to soil in form of compaction and displacement is reported to be becoming more widespread. The prediction of trafficability has become one of the most central issues in planning of mechanized harvesting operations. The work presented looks at methods to model field measured spatio-temporal variations of soil moisture content (SMC, [%vol]) – a crucial factor for soil strength and thus trafficability. We incorporated large-scaled maps of soil characteristics, high-resolution topographic information – depth-to-water (DTW) and topographic wetness index – and openly available temporal soil moisture retrievals provided by the NASA Soil Moisture Active Passive mission. Time-series measurements of SMC were captured at six study sites across Europe. These data were then used to develop linear models, a generalized additive model, and the machine learning algorithms Random Forest (RF) and eXtreme Gradient Boosting (XGB). The models were trained on a randomly selected 10% subset of the dataset. Predictions of SMC made with RF and XGB attained the highest R2 values of 0.49 and 0.51, respectively, calculated on the remaining 90% test set. This corresponds to a major increase in predictive performance, compared to basic DTW maps (R2 = 0.022). Accordingly, the quality for predicting wet soils was increased by 49% when XGB was applied (Matthews correlation coefficient = 0.45). We demonstrated how open access data can be used to clearly improve the prediction of SMC and enable adequate trafficability mappings with high spatial and temporal resolution. Spatio-temporal modelling could contribute to sustainable forest management

Spatio-temporal prediction of soil moisture and soil strength by depth-to-water maps

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Spatio-temporal prediction of soil moisture and soil strength by depth-to-water maps

The utilization of detailed digital terrain models entails an enhanced basis for supporting sustainable forest management, including the reduction of soil impacts through predictions of site trafficability during mechanized harvesting operations. Since wet soils are prone to traffic-induced damages, soil moisture is incorporated into several systems for spatial predictions of trafficability. Yet, only few systems consider temporal dynamics of soil moisture, impeding the accuracy and practical value of predictions. The depth-to-water (DTW) algorithm calculates a cartographic index which indicates wet areas. Temporal dynamics of soil moisture are simulated by different DTW map-scenarios derived from set flow initiation areas (FIA). However, the concept of simulating seasonal moisture conditions by DTW map-scenarios was not analyzed so far. Therefore, we conducted field campaigns at six study sites across Europe, capturing time-series of soil moisture and soil strength along several transects which crossed predicted wet areas. Assuming overall dry conditions (FIA = 4.00 ha), DTW predicted 20% of measuring points to be wet. When a FIA of 1.00 ha (moist conditions) or 0.25 ha (wet conditions) were applied, DTW predicted 29% or 58% of points to be wet, respectively. De facto, 82% of moisture measurements were predicted correctly by the map-scenario for overall dry conditions – with 44% of wet measurements deviating from predictions made. The prediction of soil strength was less successful, with 66% of low values occurring on areas where DTW indicated dryer soils and subsequently a sufficient trafficability. The condition-specific usage of different map-scenarios did not improve the accuracy of predictions, as compared to static map-scenarios, chosen for each site. We assume that site-specific and non-linear hydrological processes compromise the generalized assumptions of simulating overall moisture conditions by different FIA.publishedVersio

Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry

Purpose of Review Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations