Journal articles on the topic 'Soil Digital Twin'
To see the other types of publications on this topic, follow the link: Soil Digital Twin.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 46 journal articles for your research on the topic 'Soil Digital Twin.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Nasirahmadi, Abozar, and Oliver Hensel. "Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm." Sensors 22, no. 2 (January 10, 2022): 498. http://dx.doi.org/10.3390/s22020498.
Full textAbstract:
Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.
2
Skobelev, P. O., A. S. Tabachisnkiy, E. V. Simonova, Yu N. Zhuravel, and G. N. Miatov. "REGARDING SOME OF THE METHODS FOR CROP STATE CALCULATION IN DIGITAL TWIN OF PLANT." Izvestiya of Samara Scientific Center of the Russian Academy of Sciences 24, no. 3 (2022): 100–111. http://dx.doi.org/10.37313/1990-5378-2022-24-3-100-111.
Full textAbstract:
In this paper, a concept of digital twin of plant, which is a decision support system to implement precise farming technologies. Digital twin of plant allows to forecast and simulate real crop state and suggest agricultural measures to the fi elds based on weather and soil data. Digital twin of plant is developed with the use of multi-agent technologies and ontology-based domain formalization.
3
Nemtinov, Kirill, Maria Eruslanova, Alexander Zazulya, Yulia Nemtinova, and Haider Sabah Salih. "Creating a digital twin of an agricultural machine." MATEC Web of Conferences 329 (2020): 05002. http://dx.doi.org/10.1051/matecconf/202032905002.
Full textAbstract:
In this paper, the authors developed an approach to creating a digital twin or an electronic model of complex agricultural machinery and proposed its structure as a set of frames. It is represented by a tuple that includes: a frame describing structural composition of the technical system under consideration; a frame describing the properties that characterize it as a whole; a set of ways to determine its properties; a set of attributive characteristics; a set of parametric graphic models of elements and a set of two-dimensional drawings. Examples of digital twins of agricultural machinery are given: a combined unit for soil preparation and sowing of grain crops and a unit for cleaning and calibration of grain seeds.
4
Augustyn, Dawid, Martin D. Ulriksen, and John D. Sørensen. "Reliability Updating of Offshore Wind Substructures by Use of Digital Twin Information." Energies 14, no. 18 (September 16, 2021): 5859. http://dx.doi.org/10.3390/en14185859.
Full textAbstract:
This paper presents a probabilistic framework for updating the structural reliability of offshore wind turbine substructures based on digital twin information. In particular, the information obtained from digital twins is used to quantify and update the uncertainties associated with the structural dynamics and load modeling parameters in fatigue damage accumulation. The updated uncertainties are included in a probabilistic model for fatigue damage accumulation used to update the structural reliability. The updated reliability can be used as input to optimize decision models for operation and maintenance of existing structures and design of new structures. The framework is exemplified based on two numerical case studies with a representative offshore wind turbine and information acquired from previously established digital twins. In this context, the effect of updating soil stiffness and wave loading, which constitute two highly uncertain and sensitive parameters, is investigated. It is found that updating the soil stiffness significantly affects the reliability of the joints close to the mudline, while updating the wave loading significantly affects the reliability of the joints localized in the splash zone. The increased uncertainty related to virtual sensing, which is employed to update wave loading, reduces structural reliability.
5
Myalenko, V. I. "Development of a Digital Model of the Agricultural Tool Working Element." Agricultural Machinery and Technologies 14, no. 4 (December 18, 2020): 57–62. http://dx.doi.org/10.22314/2073-7599-2020-14-4-57-62.
Full textAbstract:
The digital twin is a virtual model of the agricultural tool working element that allows you to calculate and predict its expected behavior during the entire period of operation.(Research purpose) To develop an algorithm for consistent models that make up the digital twin of the agricultural tool working element.(Materials and methods) The fi rst component of the digital twin algorithm was determined by the method of accelerated imitation loading of the wedgelock ripper working element, which was accepted as the research object. The second component was determined by testing various soils with the specifi cation of power equivalents. The physical and mechanical properties of soils and materials (hardening) for the manufacture of working elements were taken into account during the description of the following components.(Results and discussion) The author showed that the algorithm for constructing a digital twin of the agricultural tool working element consisted from a chain of successive actions and was a system for digital description of the working element, which ensured the standard lifetime during operation. The results of a simulated immersion which registered the nature of the normal forces distribution over the friction surfaces were accepted as the fi rst component of the algorithm. The second component – the results of determining the force equivalent when loading the working element in a real soil environment. The possibility of constructing maps of the intensity of the friction surface abrasive wear, predictive calculations of structural elements was revealed. The third and fourth components were used to ensure the working element standard lifetime, based on the minimum production costs, correlated to the standard resource unit of development.(Conclusions) The resulting algorithm for constructing a digital twin is a convenient tool for creating new designs of agricultural tools working elements.
6
Kutumov, Yu D., V. E. Mizonov, A. I. Tikhonov, and T. Yu Shadrikova. "Development of digital twin model of underground electric cable: thermal part of the problem." Vestnik IGEU, no. 3 (June 30, 2021): 59–65. http://dx.doi.org/10.17588/2072-2672.2021.3.059-065.
Full textAbstract:
Innovative technologies of generative design using the concept of digital twins of the designed objects play an important role in growing digitalization trend of project activities. The digital twin of an object is the object simulation model with high accuracy of mathematical description. It is used to solve the problems of regime and structural optimization of the object. Usually, generative design technologies are implemented using 3D models of physical fields. And specialized packages which have high requirements for computer resources and user skills are used. At the same time, quite often the object for which the digital twin model is developed consists of several subsystems that allow relatively independent modeling. A one-dimensional model of the thermal process cannot provide the required accuracy, but a 2D-model is quite sufficient for this purpose. The development of such a model that combines the required accuracy, and low cost of machine time is currently topical scientific and practical problem. The method of mathematical modeling is used to solve this problem. The model uses the mathematical apparatus of the Markov chain theory. The model is two-dimensional and is adapted to the multi-layer environment representing the soil, in a separate cell of which a non-stationary heat source may be found. Heat passage through the surrounding soil is described in terms of thermal conductivity, and the heat exchange with the environment is described in terms of heat transfer. The influence of the parameters on the process flow is studied by numerical methods. At this stage of the study, experimental verification of the model is not expected. A mathematical two-dimensional model of digital twin of underground electric cable has been developed. It allows us to predict the cable temperature and its distribution in the surrounding soil. The assessment of thermal state of the power transmission line is given according to the power and the depth of the heat source location. It is found that the results of simulation modeling are consistent with the physical concepts of the process. The results obtained are of scientific novelty, since they are based on a universal modeling algorithm and allow us to describe the transients in the object under study, which is a part of the digital twin of the underground cable. The model is easy to use and requires little machine time. It can be easily used in generative design practice.
7
Silva, Letícia, Francisco Rodríguez-Sedano, Paula Baptista, and João Paulo Coelho. "The Digital Twin Paradigm Applied to Soil Quality Assessment: A Systematic Literature Review." Sensors 23, no. 2 (January 15, 2023): 1007. http://dx.doi.org/10.3390/s23021007.
Full textAbstract:
This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context.
8
Weckesser, Fabian, Michael Beck, Kurt-Jürgen Hülsbergen, and Sebastian Peisl. "A Digital Advisor Twin for Crop Nitrogen Management." Agriculture 12, no. 2 (February 21, 2022): 302. http://dx.doi.org/10.3390/agriculture12020302.
Full textAbstract:
Farmers and consultants face an unmanageable amount of diverse knowledge and information for crop management decisions. To determine optimal actions, decision makers require knowledge-based support. In this way, decisions can be improved and heuristics can be replaced over time. The study presents a digital knowledge base with an integrated decision support system (DSS), using the example of nutrient supply, specifically nitrogen (N), fertilization. Therefore, the requirements of farmers and crop consultants for DSS to inform fertilization decisions for winter wheat (Triticum aestivum L.) were elaborated using surveys, expert interviews, and a prototype test. Semantic knowledge was enriched by expert knowledge and combined in a web application, the Crop Portal. To map regional and personal decision making patterns and experiences, the tacit knowledge on the complex advisory problem of N fertilization is made digitally usable. For this purpose, 16 fuzzy variables were specified and formalized. Individual decision trees and their interactions with an integrative knowledge base were used to multiply the consulting reach of experts. Using three consultants and nine model farms from different soil–climate areas in Germany, the Crop Portal was tested under practical conditions and the perceived pragmatic and hedonic quality of the system was evaluated using a standardized questionnaire. The field test showed that the variation in fertilizer recommendations from the ‘digital advisor twin’ ranged from 5 kg N ha−1 to 16 kg N ha−1 when compared with the decisions of the experts in the field. The study presents the participatory development and evaluation of a rule-based DSS prototype in agricultural practice.
9
Laryukhin, Vladimir, Petr Skobelev, Oleg Lakhin, Sergey Grachev, Vladimir Yalovenko, and Olga Yalovenko. "Towards developing a cyber-physical multi-agent system for managing precise farms with digital twins of plants." Cybernetics and Physics, Volume 8, 2019, Number 4 (December 30, 2019): 257–61. http://dx.doi.org/10.35470/2226-4116-2019-8-4-257-261.
Full textAbstract:
The paper presents the multi-agent approach for developing cyber-physical system for managing precise farms with digital twins of plants. It discusses complexity of the problem caused by a priori incompleteness of knowledge about factors of plant growth and development, high uncertainty of crops cultivation, variety of weather, business and technical requirements, etc. The approach proposes knowledge bases and multi-agent technology in combination with machine learning methods for designing considered systems. Digital twin of plant is specified as an agent based on ontology model of objects relevant for plant cultivation (specific sort of plant, soil, etc) associated with history of operations and environment conditions. The architecture and functions of system components are designed. The expected results of system implementation and the benefits for farmers are discussed.
10
Panarin, R. N., А. А. Soloviev, and Любовь Анатольевна Хворова. "Application of Artificial Intelligence and Computer Vision Technologies in Solving Problems of Automation of Processing and Recognition of Biological Objects." Izvestiya of Altai State University, no. 1(123) (March 18, 2022): 101–7. http://dx.doi.org/10.14258/izvasu(2022)1-16.
Full textAbstract:
The article considers the application of artificial intelligence and computer vision technologies to solve the automation of processing and analysis of botanical micro and macro objects (images of fern spores). Also, there is a problem of developing software for a digital twin of an agrobot. The first problem is an interdisciplinary research aimed at solving applied and fundamental problems in botanical objects' biosystematics and studying microevolutionary processes using computer vision technologies, methods of intelligent image analysis, machine learning, and artificial intelligence. The article presents the developed software module FAST (Functional Automated System Tool) for solving the direct problem — performing measurements from images obtained by scanning electron microscopy, virtual herbaria image library, entomological collections, or images taken in a natural environment.
The second problem is software development for the digital twin of the agrorobot, designed for precise mechanical processing of plants and soil. The proposed solution includes several components: the control unit — NVIDIA Jetson NANO computing module; the actuator — 6-axis robotic arm; the machine vision unit based on an Intel RealSense camera; the chassis unit — tracked tracks and software drivers and components for their control. The digital twin of the robot considers the environmental conditions and the landscape of the operation area.
The use of ROS (Robot Operating System) allows minimal effort to transfer a digital model to a physical one (prototype and serial robot) without changing the source code. Furthermore, consideration of the environmental conditions during the programming stage provides opportunities for further development and testing of real-life mathematical models for device control.
11
SEMYACHKOV, Alexander, and Konstantin SEMYACHKOV. "Digital model of groundwater technogenesis as an element of sustainable development of the urban environment." Sustainable Development of Mountain Territories 14, no. 3 (September 30, 2022): 362–69. http://dx.doi.org/10.21177/1998-4502-2022-14-3-362-369.
Full textAbstract:
The purpose of this study is to create a digital twin of the sludge reservoir, which allows modeling and predicting the impact of technogenic formation on the sustainability of urban development, in particular, on the state of groundwater. The research methodology is to simulate groundwater based on numerical simulation using the ModTech 2.21 program. This program is designed to solve problems of various equations and partial derivatives that describe the geo-filtration of the environment, using a numerical method on a three-dimensional finite-difference grid. In order to assess the sustainable development of the urban environment in the city of Sterlitomak, a digital twin of the urban area was created in terms of groundwater. Sludge accumulator «White Sea» is a unique man-made object located in the city of Sterlitomak and affecting groundwater due to infiltration losses of distiller fluid through the bed of the sludge reservoir. According to the simulation results, the infiltration losses of the SHBM are 3078 m3/day or 1123470 m3/year. The incoming part of the balance is formed due to the attraction of river runoff, infiltration nutrition, and the outgoing part is due to the discharge of groundwater into surface water. The model assessment was carried out in relation to the conditions of migration of the main pollutant component in the territory of the «White Sea» - chlorides. With the help of the created digital model, it is possible to assess, predict and manage the situation in the urban environment in terms of groundwater. Digitalization through the use of intelligent solutions and innovations in the field of digital technologies makes it possible to understand the processes occurring in social, man-made, and natural systems at a new level. The next step in creating digital twins can be models of electronic maps of soil cover or vegetation, the state of the air basin, and more. The same models can be created on the basis of economic and social indicators. This is necessary for planning the sustainable development of the urban environment.
12
Ghahari, Farid, Niloofar Malekghaini, Hamed Ebrahimian, and Ertugrul Taciroglu. "Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements." Sensors 22, no. 3 (February 8, 2022): 1278. http://dx.doi.org/10.3390/s22031278.
Full textAbstract:
Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism.
13
Lichman, Gennadiy I., Valeriy M. Korotchenya, Igor’ G. Smirnov, and Rashid K. Kurbanov. "A Concept of Precision Farming Based on the Notions of the Ideal Field and Digital Twin." Elektrotekhnologii i elektrooborudovanie v APK, no. 3 (September 20, 2020): 81–86. http://dx.doi.org/10.22314/2658-4859-2020-67-3-81-86.
Full textAbstract:
One of the ways to increase the efficiency of crop production is to maximize the genetic potential of agricultural crops. To do this, it is necessary to create the most favorable, ideal conditions for the growth of plants. At all stages of growth and development of plants there are their own indicators, which should be ideal (optimal). (Research purpose) The research purpose is in developing an algorithm for calculating the parameters of an ideal field on the example of differential fertilization. (Materials and methods) Authors have used mathematical modeling using three basic concepts: responsiveness functions, the digital double of the agroecosystem (field), and the ideal field. The article presents the comparison of the parameters of the real field with the ideal one using a digital double of the real field. (Results and discussion) the parameters of the ideal field has been adjusted taking into account the parameters of the actual field based on the planned yield, requirements for agricultural technology, the used optimization criterion (the target function), and restrictions. In order to make optimal management decisions, along with the agrotechnical requirements for the cultivation of a particular crop, it is necessary to specify the parameters of the ideal field, which must be achieved in the actual field. The article presents the procedure for calculating the ideal field parameters using the example of solid mineral fertilizers. (Conclusions) The article presents an algorithm for calculating the optimal doses of differentiated fertilizer application based on the digital double of a specific field. The dose values are used to refine the parameters of the ideal field and adjust it during the growing season. The algorithm can be used to make optimal management decisions on differentiated seeding, tillage, and the use of plant protection products. As data on the state of soil, plants and knowledge base are accumulated in the form of plant responsiveness functions to certain agro-technical techniques, it is possible to increase the adequacy of the digital double of the actual field and obtain more reliable data for correcting the parameters of the ideal field.
14
Arai, Hironori, Kazuyuki Inubushi, and Chih-Yu Chiu. "Dynamics of Methane in Mangrove Forest: Will It Worsen with Decreasing Mangrove Forests?" Forests 12, no. 9 (September 5, 2021): 1204. http://dx.doi.org/10.3390/f12091204.
Full textAbstract:
Mangrove forests sequester a significant amount of organic matter in their sediment and are recognized as an important carbon storage source (i.e., blue carbon, including in seagrass ecosystems and other coastal wetlands). The methane-producing archaea in anaerobic sediments releases methane, a greenhouse gas species. The contribution to total greenhouse gas emissions from mangrove ecosystems remains controversial. However, the intensity CH4 emissions from anaerobic mangrove sediment is known to be sensitive to environmental changes, and the sediment is exposed to oxygen by methanotrophic (CH4-oxidizing) bacteria as well as to anthropogenic impacts and climate change in mangrove forests. This review discusses the major factors decreasing the effect of mangroves on CH4 emissions from sediment, the significance of ecosystem protection regarding forest biomass and the hydrosphere/soil environment, and how to evaluate emission status geospatially. An innovative “digital-twin” system overcoming the difficulty of field observation is required for suggesting sustainable mitigation in mangrove ecosystems, such as a locally/regionally/globally heterogenous environment with various random factors.
15
Fedorovitch, G. V. "Digital twin for the occupational safety and health. Measurement of exposure doses at the entrance." Безопасность и охрана труда, no. 2 (2022): 5–15. http://dx.doi.org/10.54904/52952_2022_2_05.
Full text
16
Truu, Murel, Ivar Annus, Janet Roosimägi, Nils Kändler, Anatoli Vassiljev, and Katrin Kaur. "Integrated Decision Support System for Pluvial Flood-Resilient Spatial Planning in Urban Areas." Water 13, no. 23 (November 25, 2021): 3340. http://dx.doi.org/10.3390/w13233340.
Full textAbstract:
Flood-resilient spatial planning in urban areas involves designing and implementing structural and nonstructural measures. For the latter, urban planners apply a precautionary principle, which is normally not grounded in the actual performance of the urban drainage system (UDS). This approach, however, fails during weather extremes with heavy precipitation. This paper presents a new concept for reducing pluvial flood risks in the urban planning process. The novelty of the developed planning support system named Extreme Weather Layer (EWL) is that it creates dynamic interlinkages between land developments, the performance of UDS, and other factors that contribute to flood risk. The EWL is built on the digital twin of the existing UDS and delivers an easy-to-use concept, where the end user can analyze hydraulic modelling results interlinked with climate scenarios using the GIS platform. This allows planning specialists to consider land use and soil types in the urban environment to simulate the response of the storm water system and the catchments to different rainfall events. This proposed approach was piloted in Haapsalu (Estonia) and Söderhamn (Sweden). The resulting planning support system, which performs as a set of layers within municipalities’ GIS, allows decision makers to understand and predict the impact of various spatial planning decisions on the pluvial flood risk.
17
Buchanan, B. P., M. Fleming, R. L. Schneider, B. K. Richards, J. Archibald, Z. Qiu, and M. T. Walter. "Evaluating topographic wetness indices across central New York agricultural landscapes." Hydrology and Earth System Sciences 18, no. 8 (August 28, 2014): 3279–99. http://dx.doi.org/10.5194/hess-18-3279-2014.
Full textAbstract:
Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWIs) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches – especially in the US. We calculated TWIs using over 400 unique formulations that considered different digital elevation model (DEM) resolutions (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA, with each field visited five to eight times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations applicable to moderate relief agricultural settings that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) lidar-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved correlations.
18
Buchanan, B. P., M. Fleming, R. L. Schneider, B. K. Richards, J. Archibald, Z. Qiu, and M. T. Walter. "Evaluating topographic wetness indices across central New York agricultural landscapes." Hydrology and Earth System Sciences Discussions 10, no. 11 (November 18, 2013): 14041–93. http://dx.doi.org/10.5194/hessd-10-14041-2013.
Full textAbstract:
Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWI) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches in the US. We calculated TWIs using over 400 unique formulations that considered different: Digital Elevation Model (DEM) resolution (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA; each field was visited 5–8 times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) LiDAR-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved the correlations.
19
Drover, D. R., C. R. Jackson, M. Bitew, and E. Du. "Effects of DEM scale on the spatial distribution of the TOPMODEL topographic wetness index and its correlations to watershed characteristics." Hydrology and Earth System Sciences Discussions 12, no. 11 (November 12, 2015): 11817–46. http://dx.doi.org/10.5194/hessd-12-11817-2015.
Full textAbstract:
Abstract. Topographic wetness indices (TWIs) calculated from digital elevation models (DEMs) are meant to predict relative landscape wetness and should have predictive power for soil and vegetation attributes. While previous researchers have shown cumulative TWI distributions shift to larger values as DEM resolution decreases, there has been little work assessing how DEM scales affect TWI spatial distributions and correlations with soil and vegetation properties. We explored how various DEM resolutions (2, 5, 10, 20, 30, and 50 m) subsampled from high definition LiDAR altered the spatial distribution of TWI values and the correlations of these values with soil characteristics determined from point samples, Natural Resources Conservation Service (NRCS) soil units, depths to groundwater, and managed vegetation distributions within a first order basin in the Upper Southeastern Coastal Plain with moderate slopes, flat valleys, and several wetlands. Point-scale soil characteristics were determined by laboratory analysis of point samples collected from riparian transects and hillslope grids. DEM scale affected the spatial distribution of TWI values in ways that affect our interpretation of landscape processes. At the finest DEM resolutions, valleys disappeared as TWI values were driven by local microtopography and not basin position. Spatial distribution of TWI values most closely matched the spatial distribution of soils, depth to groundwater, and vegetation stands for the 10, 20, and 30 m resolutions. DEM resolution affected the shape and direction of relationships between soil nitrogen and carbon contents and TWI values, but TWI values provided poor prediction of soil chemistry at all resolutions.
20
Ågren, A. M., W. Lidberg, M. Strömgren, J. Ogilvie, and P. A. Arp. "Evaluating digital terrain indices for soil wetness mapping – a Swedish case study." Hydrology and Earth System Sciences Discussions 11, no. 4 (April 11, 2014): 4103–29. http://dx.doi.org/10.5194/hessd-11-4103-2014.
Full textAbstract:
Abstract. Driving with forestry machines on wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This – in turn – can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are more susceptible to rutting. It is important to model and map the extent of these areas and associated wetness variations. This can be done with adequate reliability using high resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (TWI) and the newly developed cartographic depth-to-water index (DTW) were the best soil wetness predictors. While the TWI derivations were sensitive to scale, the DTW derivations were not and were therefore numerically fairly robust. Since the DTW derivations vary by the area threshold used for setting stream flow initiation we found that the optimal threshold values varied by landform, e.g., 1–2 ha for till-derived landforms vs. 8 –16 ha for a coarse-textured alluvial floodplain.
21
Ågren, A. M., W. Lidberg, M. Strömgren, J. Ogilvie, and P. A. Arp. "Evaluating digital terrain indices for soil wetness mapping – a Swedish case study." Hydrology and Earth System Sciences 18, no. 9 (September 12, 2014): 3623–34. http://dx.doi.org/10.5194/hess-18-3623-2014.
Full textAbstract:
Abstract. Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This – in turn – can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (TWI) and the newly developed cartographic depth-to-water index (DTW) were the best soil wetness predictors. While the TWI derivations were sensitive to scale, the DTW derivations were not and were therefore numerically robust. Since the DTW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1–2 ha for till-derived landforms versus 8–16 ha for a coarse-textured alluvial floodplain.
22
Winzeler, Hans Edwin, Phillip R. Owens, Quentin D. Read, Zamir Libohova, Amanda Ashworth, and Tom Sauer. "Topographic Wetness Index as a Proxy for Soil Moisture in a Hillslope Catena: Flow Algorithms and Map Generalization." Land 11, no. 11 (November 11, 2022): 2018. http://dx.doi.org/10.3390/land11112018.
Full textAbstract:
Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 42 locations in an agroforestry catena in Fayetteville, Arkansas, USA. We calculated TWI 546 ways using different flow algorithms and digital elevation model (DEM) preparations. We found that most TWI algorithms performed poorly on DEMs that were not first filtered or resampled, but DEM filtration and resampling (collectively called generalization) greatly improved the TWI performance. Seasonal variation of soil moisture influenced TWI performance which was best when conditions were not saturated and not dry. Pearson correlation coefficients between TWI and grand mean VWC for the 5-year measurement period ranged from 0.18 to 0.64 on generalized DEMs and 0.15 to 0.59 for on DEMs that were not generalized. These results aid management of crop fields with variable moisture characteristics.
23
Zádorová, T., D. Žížala, V. Penížek, and Š. Čejková. "Relating extent of colluvial soils to topographic derivatives and soil variables in a Luvisol sub-catchment, Central Bohemia, Czech Republic." Soil and Water Research 9, No. 2 (April 25, 2014): 47–57. http://dx.doi.org/10.17221/57/2013-swr.
Full textAbstract:
Colluvial soils, resulting from accelerated soil erosion, represent a significant part of the soil cover pattern in agricultural landscapes. Their specific terrain position makes it possible to map them using geostatistics and digital terrain modelling. A study of the relationship between colluvial soil extent and terrain and soil variables was performed at a morphologically diverse study site in a Luvisol soil region in Central Bohemia. Assessment of the specificity of the colluviation process with regard to profile characteristics of Luvisols was another goal of the study. A detailed field survey, statistical analyses, and detailed digital elevation model processing were the main methods utilized in the study. Statistical analysis showed a strong relationship between the occurrence of colluvial soil, various topographic derivatives, and soil organic carbon content. A multiple range test proved that four topographic derivatives significantly distinguish colluvial soil from other soil units and can be then used for colluvial soil delineation. Topographic wetness index was evaluated as the most appropriate terrain predictor. Soil organic carbon content was significantly correlated with five topographic derivatives, most strongly with topographic wetness index (TWI) and plan curvature. Redistribution of the soil material at the study site is intensive but not as significant as in loess regions covered by Chernozem. Soil mass transport is limited mainly to the A horizon; an argic horizon is truncated only at the steepest parts of the slope.
24
Mukhtar, Hussnain, Rainer Ferdinand Wunderlich, and Yu-Pin Lin. "Digital Twins of the Soil Microbiome for Climate Mitigation." Environments 9, no. 3 (March 9, 2022): 34. http://dx.doi.org/10.3390/environments9030034.
Full textAbstract:
Recent advances in computation power have enabled the creation of digital twins of the microbiome (DTM) to substantially curb soil greenhouse gases (GHG) emissions under global change conditions [...]
25
Silva, Bruno Montoani, Sérgio Henrique Godinho Silva, Geraldo Cesár de Oliveira, Petrus Hubertus Caspar Rosa Peters, Walbert Júnior Reis dos Santos, and Nilton Curi. "Soil moisture assessed by digital mapping techniques and its field validation." Ciência e Agrotecnologia 38, no. 2 (April 2014): 140–48. http://dx.doi.org/10.1590/s1413-70542014000200005.
Full textAbstract:
Digital techniques and tools can assist not only in the prediction of soil properties, such as soil moisture, but also in planning the use and management of areas for agriculture and, or, environmental purposes. In this sense, this work aimed to study wetness indexes methods, defining the spatial resolution and selecting the estimation method that best correlates with water content data measured in the field, evaluating even moisture at different soil depths and seasons. This study was developed in a landscape with strongly undulated relief and covered with Nitosols at the summit and upper middle third, and Argisols at the low middle third, ranging in altitude from 845 to 890 m, located in the southern state of Minas Gerais, Brazil. It were performed analyses of Pearson linear correlation between soil moisture determined in the field, at depths of 10, 20, 30, 40, 60 and 100 cm and the water storage in 0-100 cm depth, and the topographic and SAGA wetness indexes, TWI and SWI, respectively, obtained from digital elevation models at different spatial resolutions. In most studied conditions, the TWI with resolution of 10 m provided better results, particularly for the dry season. In this study, only the depth of 100 cm resulted in a significant and positive correlation, suggesting that the moisture levels are suitable for water dynamic studies in the subsurface, assisting in studies of hydrological dynamics and planning the soil use and management, especially for perennial plants with deeper root systems.
26
Nucifera, Fitria, and Sutanto Trijuni Putro. "Deteksi Kerawanan Banjir Genangan Menggunakan Topographic Wetness Index (TWI)." Media Komunikasi Geografi 18, no. 2 (January 5, 2018): 107. http://dx.doi.org/10.23887/mkg.v18i2.12088.
Full textAbstract:
Flood is the most frequent disaster occured in Indonesia. Flood events result in loss and damage to communities and the environment. Floods are triggered by several factors including hydrometeorological factors, topography, geology, soil and human activities. Topographic factor is one of the flood trigger control factors. Topographic calculation for flood inundation detection can be done by Topographic Wetness Index (TWI) method. The TWI method focuses on topographic conditions of the region, especially the upper slopes and lower slopes to assess the trend of water accumulation in a region. TWI calculations are based on the topography of an area represented by DEM (Digital Elevation Model) data in the form of DTM (Digital Terrain Model). The high value of TWI is associated with high flood vulnerability. Based on the calculation of TWI value, flood-prone areas in Kebumen District include Adimulyo Subdistrict, Puring Subdistrict, Ambal Subdistrict, Rowokele Subdistrict and Buayan Subdistrict.
27
Sørensen, R., U. Zinko, and J. Seibert. "On the calculation of the topographic wetness index: evaluation of different methods based on field observations." Hydrology and Earth System Sciences Discussions 2, no. 4 (August 31, 2005): 1807–34. http://dx.doi.org/10.5194/hessd-2-1807-2005.
Full textAbstract:
Abstract. The topographic wetness index (TWI, ln(a/tanβ)), which combines local upslope contributing area and slope, is commonly used to quantify topographic control on hydrological processes. Methods of computing this index differ primarily in the way the upslope contributing area is calculated. In this study we compared a number of calculation methods for TWI and evaluated them in terms of their correlation with the following measured variables: vascular plant species richness, soil pH, groundwater level, soil moisture, and a constructed wetness degree. The TWI was calculated by varying six parameters affecting the distribution of accumulated area among downslope cells and by varying the way the slope was calculated. All possible combinations of these parameters were calculated for two separate boreal forest sites in northern Sweden. We did not find a calculation method that performed best for all measured variables, but we were able to identify the general characteristics of the best methods for different groups of measured variables. The results provide guidelines for choosing the best method for estimating species richness, soil pH, groundwater level, and soil moisture by the TWI derived from digital elevation models.
28
Sørensen, R., U. Zinko, and J. Seibert. "On the calculation of the topographic wetness index: evaluation of different methods based on field observations." Hydrology and Earth System Sciences 10, no. 1 (February 15, 2006): 101–12. http://dx.doi.org/10.5194/hess-10-101-2006.
Full textAbstract:
Abstract. The topographic wetness index (TWI, ln(a/tanβ)), which combines local upslope contributing area and slope, is commonly used to quantify topographic control on hydrological processes. Methods of computing this index differ primarily in the way the upslope contributing area is calculated. In this study we compared a number of calculation methods for TWI and evaluated them in terms of their correlation with the following measured variables: vascular plant species richness, soil pH, groundwater level, soil moisture, and a constructed wetness degree. The TWI was calculated by varying six parameters affecting the distribution of accumulated area among downslope cells and by varying the way the slope was calculated. All possible combinations of these parameters were calculated for two separate boreal forest sites in northern Sweden. We did not find a calculation method that performed best for all measured variables; rather the best methods seemed to be variable and site specific. However, we were able to identify some general characteristics of the best methods for different groups of measured variables. The results provide guiding principles for choosing the best method for estimating species richness, soil pH, groundwater level, and soil moisture by the TWI derived from digital elevation models.
29
Guo, Jia, Ku Wang, and Shaofei Jin. "Mapping of Soil pH Based on SVM-RFE Feature Selection Algorithm." Agronomy 12, no. 11 (November 4, 2022): 2742. http://dx.doi.org/10.3390/agronomy12112742.
Full textAbstract:
The explicit mapping of spatial soil pH is beneficial to evaluate the effects of land-use changes in soil quality. Digital soil mapping methods based on machine learning have been considered one effective way to predict the spatial distribution of soil parameters. However, selecting optimal environmental variables with an appropriate feature selection method is key work in digital mapping. In this study, we evaluated the performance of the support vector machine recursive feature elimination (SVM-RFE) feature selection methods with four common performance machine learning methods in predicting and mapping the spatial soil pH of one urban area in Fuzhou, China. Thirty environmental variables were collected from the 134 samples that covered the entire study area for the SVM-RFE feature selection. The results identified the five most critical environmental variables for soil pH value: mean annual temperature (MAT), slope, Topographic Wetness Index (TWI), modified soil-adjusted vegetation index (MSAVI), and Band5. Further, the SVM-RFE feature selection algorithm could effectively improve the model accuracy, and the extreme gradient boosting (XGBoost) model after SVM-RFE feature selection had the best prediction results (R2 = 0.68, MAE = 0.16, RMSE = 0.26). This paper combines the RFE-SVM feature selection with machine learning models to enable the fast and inexpensive mapping of soil pH, providing new ideas for predicting soil pH at small and medium scales, which will help with soil conservation and management in the region.
30
Mousavi, S. R., F. Sarmadian, A. Rahmani, and S. E. Khamoshi. "DIGITAL SOIL MAPPING WITH REGRESSION TREE CLASSIFICATION APPROACHES BY RS AND GEOMORPHOMETRY COVARIATE IN THE QAZVIN PLAIN, IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 773–77. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-773-2019.
Full textAbstract:
Abstract. Digital soil mapping applies soil attributes, Remote sensing and Geomorphometrics indices to estimate soil types and properties at unobserved locations. This study carried out in order to comparison two data mining algorithms such as Random Forest (RF) and Boosting Regression tree (BRT) and two features selection principal component analysis (PCA) and variance inflation factor (VIF) for predicting soil taxonomy class at great group and subgroup levels. A total of 61 soil profile observation based on stratified random determined and digged in area with approximately 16660 hectares.19 RS indices and geomorphometrics covariates derivated from Landsate-8 imagery and DEM with 30 meters’ resolution in ERDAS IMAGINE 2014 and SAGA GIS version 7.0 software’s. Also to run four Data mining algorithms scenarios (PCA-RF, VIF-RF, PCA-BRT, VIF-BRT) from “Randomforest” and “C.5” packages were used in R studio software. 80% and 20% from soil profiles were applied for calibrating and validating. The results showed that in PCA and VIF approaches, eight covariates such as (Relative slope position, diffuse insolation, modified catchment, normalized height, RVI, Standard height, TWI, Valley depth) and six covariates (NDVI, DVI, Catchment area, DEM, Salinity index, Standard height) were selected. The validation results based on overall accuracy and kappa index for scenarios at great group level indicated that 88,93,62, 54 and 75,83,51,45 percentages and for subgroup level had 70, 77, 54, 47 and 60, 71, 43, 37 percentages, respectively. Generally, VIF-RF had accuracy rather than from other scenarios at two categorical level in this study area.
31
Larson, Johannes, William Lidberg, Anneli M. Ågren, and Hjalmar Laudon. "Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices." Hydrology and Earth System Sciences 26, no. 19 (October 5, 2022): 4837–51. http://dx.doi.org/10.5194/hess-26-4837-2022.
Full textAbstract:
Abstract. Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accurately scaled, terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluated seven different terrain indices at varying digital elevation model (DEM) resolutions and user-defined thresholds as well as two available soil moisture maps, using an extensive field dataset (398 plots) of soil moisture conditions registered in five classes from a survey covering a (68 km2) boreal landscape. We found that the variation in soil moisture conditions could be explained by terrain indices, and the best predictors within the studied landscape were the depth to water index (DTW) and a machine-learning-generated map. Furthermore, this study showed a large difference between terrain indices in the effects of changing DEM resolution and user-defined thresholds, which severely affected the performance of the predictions. For example, the commonly used topographic wetness index (TWI) performed best on a resolution of 16 m, while TWI calculated on DEM resolutions higher than 4 m gave inaccurate results. In contrast, depth to water (DTW) and elevation above stream (EAS) were more stable and performed best on 1–2 m DEM resolution. None of the terrain indices performed best on the highest DEM resolution of 0.5 m. In addition, this study highlights the challenges caused by heterogeneous soil types within the study area and shows the need of local knowledge when interpreting the modelled results. The results from this study clearly demonstrate that when using terrain indices to represent soil moisture conditions, modelled results need to be validated, as selecting an unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results.
32
Li, Xinchuan, Juhua Luo, Xiuliang Jin, Qiaoning He, and Yun Niu. "Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods." Remote Sensing 12, no. 21 (November 3, 2020): 3609. http://dx.doi.org/10.3390/rs12213609.
Full textAbstract:
Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.
33
Czarnecka, Bożenna, and Łukasz Chabudziński. "Assessment of flora diversity in a minor river valley using ecological indicator values, Geographical Information Systems and Digital Elevation Models." Open Life Sciences 9, no. 2 (February 1, 2014): 220–31. http://dx.doi.org/10.2478/s11535-013-0263-0.
Full textAbstract:
AbstractEllenberg indicator values (EIV) have been widely used to estimate habitat variables from floristic data and to predict vegetation composition based on habitat properties. Geographical Information Systems (GIS) and Digital Elevation Models (DEM) are valuable tools for studying the relationships between topographic and ecological characters of river systems. A 3-meter resolution DEM was derived for a. 3-km-long break section of the Szum River (SE Poland) from a 1:10,000 topographic map. Data on the diversity and ecological requirements of the local vascular flora were obtained while making floristic charts for 32 sections of the river valley (each 200 m long) and physical and chemical soil measurements; next, the data were translated into EIV. The correlations of the primary and secondary topographic attributes of the valley, species richness, and EIV (adapted for the Polish vascular flora) were assessed for all species recognized in each valley section. The total area and proportion of a flat area, mean slope, slope curvature, solar radiation (SRAD), and topographic wetness index (TWI) are the most important factors influencing local flora richness and diversity. The highest correlations were found for three ecological indicators, namely light, soil moisture, and soil organic content. The DEM seems to be useful in determination of correlations between topographic and ecological attributes along a minor river valley.
34
Liu, Tao, Huan Zhang, and Tiezhu Shi. "Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach." Sustainability 12, no. 22 (November 10, 2020): 9330. http://dx.doi.org/10.3390/su12229330.
Full textAbstract:
Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km2) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R2), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.
35
Yong, Bin, Li-Liang Ren, Yang Hong, Jonathan J. Gourley, Xi Chen, You-Jing Zhang, Xiao-Li Yang, Zeng-Xin Zhang, and Wei-Guang Wang. "A novel multiple flow direction algorithm for computing the topographic wetness index." Hydrology Research 43, no. 1-2 (February 1, 2012): 135–45. http://dx.doi.org/10.2166/nh.2011.115.
Full textAbstract:
The topographic wetness index (TWI), frequently used in approximately characterizing the spatial distribution of soil moisture and surface saturation within a watershed, has been widely applied in topography-related geographical processes and hydrological models. However, it is still questionable whether the current algorithms of TWI can adequately model the spatial distribution of topographic characteristics. Based upon the widely-used multiple flow direction approach (MFD), a novel MFD algorithm (NMFD) is proposed for improving the TWI derivation using a Digital Elevation Model (DEM) in this study. Compared with MFD, NMFD improves the mathematical equations of the contributing area and more precisely calculates the effective contour length. Additionally, a varying exponent strategy is adopted to dynamically determine the downslope flow-partition exponent. Finally, a flow-direction tracking method is employed to address grid cells in flat terrain. The NMFD algorithm is first applied to a catchment located upstream of the Hanjiang River in China to demonstrate its accuracy and improvements. Then NMFD is quantitatively evaluated by using four types of artificial mathematical surfaces. The results indicate that the error generated by NMFD is generally lower than that computed by MFD, and NMFD is able to more accurately represent the hydrological similarity of watersheds.
36
Kim, Jinwook, and Hosung Shin. "Soil Depth Prediction Model Using Terrain Attributes in Gangwon-do, South Korea." Applied Sciences 13, no. 3 (January 22, 2023): 1453. http://dx.doi.org/10.3390/app13031453.
Full textAbstract:
Soil depth is a crucial parameter in slope stability analysis in mountainous areas. The drilling survey is the most reliable method for determining soil depth, but it requires a high cost for the vast geographical area. Therefore, this study proposes a soil depth prediction model for mountainous areas that uses Terrain Attributes (TAs) from digital maps. Gangwon-Do, a predominantly mountainous region in South Korea, is selected as the study target area. The study area is classified by parent rock type into igneous rocks, metamorphic rocks, and sedimentary rocks. The correlation with TAs is analyzed through multi-collinearity using drilling data published in the Korea drilling information database. In addition, the most suitable combination of variables is selected through multi-collinearity analysis, and the regression model using STI, TWI, and SLOPE is found to be the most appropriate model (VIF < 10). The proposed model for soil depth shows significance at p < 0.001, and the correlation coefficient () is figured out for igneous rock (0.702), metamorphic rock (0.686), and sedimentary rock (0.693). In addition, the reliability of the proposed model was verified by using data from regions not included in the model development, and the correlation coefficients were igneous rock (0.867), metamorphic rock (0.801), and sedimentary rock (0.814). The model proposed is more suitable for Korean topography than the existing statistical models; it can help to increase the accuracy of slope stability analysis.
37
Nhu, Viet-Ha, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, Marten Geertsema, et al. "Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms." Forests 11, no. 8 (July 30, 2020): 830. http://dx.doi.org/10.3390/f11080830.
Full textAbstract:
We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.
38
Lendzioch, Theodora, Jakub Langhammer, Lukáš Vlček, and Robert Minařík. "Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning." Remote Sensing 13, no. 5 (February 28, 2021): 907. http://dx.doi.org/10.3390/rs13050907.
Full textAbstract:
One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.
39
Parsian, Saeid, Meisam Amani, Armin Moghimi, Arsalan Ghorbanian, and Sahel Mahdavi. "Flood Hazard Mapping Using Fuzzy Logic, Analytical Hierarchy Process, and Multi-Source Geospatial Datasets." Remote Sensing 13, no. 23 (November 24, 2021): 4761. http://dx.doi.org/10.3390/rs13234761.
Full textAbstract:
Iran is among the driest countries in the world, where many natural hazards, such as floods, frequently occur. This study introduces a straightforward flood hazard assessment approach using remote sensing datasets and Geographic Information Systems (GIS) environment in an area located in the western part of Iran. Multiple GIS and remote sensing datasets, including Digital Elevation Model (DEM), slope, rainfall, distance from the main rivers, Topographic Wetness Index (TWI), Land Use/Land Cover (LULC) maps, soil type map, Normalized Difference Vegetation Index (NDVI), and erosion rate were initially produced. Then, all datasets were converted into fuzzy values using a linear fuzzy membership function. Subsequently, the Analytical Hierarchy Process (AHP) technique was applied to determine the weight of each dataset, and the relevant weight values were then multiplied to fuzzy values. Finally, all the processed parameters were integrated using a fuzzy analysis to produce the flood hazard map with five classes of susceptible zones. The bi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) images, acquired before and on the day of the flood event, were used to evaluate the accuracy of the produced flood hazard map. The results indicated that 95.16% of the actual flooded areas were classified as very high and high flood hazard classes, demonstrating the high potential of this approach for flood hazard mapping.
40
Farhadi, Hadi, and Mohammad Najafzadeh. "Flood Risk Mapping by Remote Sensing Data and Random Forest Technique." Water 13, no. 21 (November 4, 2021): 3115. http://dx.doi.org/10.3390/w13213115.
Full textAbstract:
Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.
41
Kocsis, István, Ștefan Bilașco, Ioan-Aurel Irimuș, Vasile Dohotar, Raularian Rusu, and Sanda Roșca. "Flash Flood Vulnerability Mapping Based on FFPI Using GIS Spatial Analysis Case Study: Valea Rea Catchment Area, Romania." Sensors 22, no. 9 (May 7, 2022): 3573. http://dx.doi.org/10.3390/s22093573.
Full textAbstract:
The risk associated with extreme hydrological processes (flash floods, floods) is more present than ever, taking into account the global climatic changes, the expansion of inhabited areas and the changes emerging as a result of inadequate land management. Of all the hydrological risks, slope flash floods represent the processes that have the highest impact because of the high speed of their development and their place of origin, which makes them difficult to predict. This study is performed in an area susceptible to the emergence of slope flash floods, the Valea Rea catchment area, spatially located in Northwest Romania, and exposed to western circulation, which favours the development of such processes. The entire research is based on a methodology involving the integration of spatial databases, which indicate the vulnerability of the territory in the form of a weighted average equation to highlight the major impact of the most relevant factor. A number of 15 factors have been used in raster spatial databases, obtained by conversion (land use, soil type, lithology, Hydrologic Soil Group, etc.), derived from the digital elevation model (slope, aspect, TWI, etc.) or by performing spatial analysis submodels (precipitation, slope length, etc). The integration of these databases by means of the spatial analysis equation based on the weighted average led to the vulnerability of the territory to FFPI, classified on five classes from very low to very high. The final result underlines the high and very high vulnerability (43%) of the analysed territory that may have a major impact on the human communities and the territorial infrastructure. The results obtained highlight the torrential nature of the analysed catchment area, identifying several hotspots of great risk, located mainly within the built-up areas of intensely inhabited regions; a fact which involves a major risk and significant potential material damage in the territory. The model was validated by directly comparing the results obtained with locations previously affected, where the flood effects have been identified, highlighting the fact that the model may be taken into account to be applied in practice, and also to be implemented in territories that share the same features.
42
Bloch, V., T. Palosuo, H. Huitu, A. Ronkainen, J. Backman, K. Pussi, A. Suokannas, and M. Pastell. "Towards a digital twin for optimal field management." agriRxiv 2022 (January 2022). http://dx.doi.org/10.31220/agrirxiv.2022.00165.
Full textAbstract:
Abstract Software for a real-time digital twin of crop production system was developed for supporting farming decisions. Software couples the APSIM crop simulation model with data from precision farming equipment, real-time on-field and remote sensing. A field managed according to precision farming practices and equipped with underground soil temperature and moisture sensors and local weather station was used as a test case. The software automatically initialized the simulation model based on ISOBUS time log data from sowing. The field was divided into unique zones with averaged parameters implemented in the simulation. Sensor data was continuously copied to Mongo database and the model was run daily to monitor the crop status. Yield maps obtained from combine harvester and leaf area index predicted from earth observation data were used to calibrate the simulation. Further development and fitting the system to the researchers and farmers needs will be done in the future study. The digital twin will offer researchers opportunities for real time management of crops and a tool for studying spatial variability of growing conditions within fields.
43
Davis, Greg B., John L. Rayner, and Michael J. Donn. "Advancing “Autonomous” sensing and prediction of the subsurface environment: a review and exploration of the challenges for soil and groundwater contamination." Environmental Science and Pollution Research, January 13, 2023. http://dx.doi.org/10.1007/s11356-022-25125-8.
Full textAbstract:
AbstractCan we hope for autonomous (self-contained in situ) sensing of subsurface soil and groundwater pollutants to satisfy relevant regulatory criteria? Global advances in sensors, communications, digital technologies, and computational capacity offer this potential. Here we review past efforts to advance subsurface investigation techniques and technologies, and computational efforts to create a digital twin (representation) of subsurface processes. In the context of the potential to link measurement and sensing to a digital twin computation platform, we outline five criteria that might make it possible. Significant advances in sensors based on passive measurement devices are proposed. As an example of what might be achievable, using the five criteria, we describe the deployment of online real-time sensors and simulations for a case study of a petroleum site where natural source zone depletion (NSZD) is underway as a potential biodegradation management option, and where a high-quality conceptual site model is available. Multiple sensors targeting parameters (major gases and temperature influenced by soil moisture) relevant to the subsurface NSZD biodegradation processes are shown to offer the potential to map subsurface processes spatially and temporally and provide continuous estimates of degradation rates for management decisions, constrained by a computational platform of the key processes. Current limitations and gaps in technologies and knowledge are highlighted specific to the case study. More generally, additional key advances required to achieve autonomous sensing of subsurface soil and groundwater pollutants are outlined.
44
Stuyts, Bruno, Wout Weijtjens, and Christof Devriendt. "Development of a semi-structured database for back-analysis of the foundation stiffness of offshore wind monopiles." Acta Geotechnica, April 11, 2022. http://dx.doi.org/10.1007/s11440-022-01551-3.
Full textAbstract:
AbstractOffshore wind turbines founded on monopiles are highly dynamic structures in which the stiffness of the soil adjacent to the monopile controls the natural frequency of the structure. As the loading regime and ground conditions surrounding the foundation are subject to considerable uncertainty, adaptable digital twins of the offshore structures are valuable as they allow the use of in-field monitoring data for model updating. As soil conditions and water depths are rarely uniform across a wind farm site, each structure is expected to behave differently. To back-analyse structural performance, geotechnical and structural data needs to be retrieved at every foundation location. A serverless cloud-based application was developed to allow quick and reliable storage and retrieval of geotechnical and structural data. The database was combined with an API layer to allow parametric data retrieval for back-analyses and digital twin updating across an entire wind farm. As the web application is hosted in the cloud, the data can be accessed through simple HTTP requests by authenticated users working offshore, in the office or remote. The performance of this solution is illustrated with a case study in which foundation stiffness across an entire wind farm site is parametrically calculated and updated.
45
Niemi, Mikko. "Improvements to stream extraction and soil wetness mapping within a forested catchment by increasing airborne LiDAR data density – a case study in Parkano, western Finland." Silva Fennica 55, no. 5 (2021). http://dx.doi.org/10.14214/sf.10557.
Full textAbstract:
The pulse density of airborne Light Detection and Ranging (LiDAR) is increasing due to technical developments. The trade-offs between pulse density, inventory costs, and forest attribute measurement accuracy are extensively studied, but the possibilities of high-density airborne LiDAR in stream extraction and soil wetness mapping are unknown. This study aimed to refine the best practices for generating a hydrologically conditioned digital elevation model (DEM) from an airborne LiDAR -derived 3D point cloud. Depressionless DEMs were processed using a stepwise breaching-filling method, and the performance of overland flow routing was studied in relation to a pulse density, an interpolation method, and a raster cell size. The study area was situated on a densely ditched forestry site in Parkano municipality, for which LiDAR data with a pulse density of 5 m were available. Stream networks and a topographic wetness index (TWI) were derived from altogether 12 DEM versions. The topological database of Finland was used as a ground reference in comparison, in addition to 40 selected main flow routes within the catchment. The results show improved performance of overland flow modeling due to increased data density. In addition, commonly used triangulated irregular networks were clearly outperformed by universal kriging and inverse-distance weighting in DEM interpolation. However, the TWI proved to be more sensitive to pulse density than an interpolation method. Improved overland flow routing contributes to enhanced forest resource planning at detailed spatial scales.â2
46
Kolditz, Olaf, Diederik Jacques, Francis Claret, Johan Bertrand, Sergey V. Churakov, Christophe Debayle, Daniela Diaconu, et al. "Digitalisation for nuclear waste management: predisposal and disposal." Environmental Earth Sciences 82, no. 1 (January 2023). http://dx.doi.org/10.1007/s12665-022-10675-4.
Full textAbstract:
AbstractData science (digitalisation and artificial intelligence) became more than an important facilitator for many domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly usable for applications related to the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilising the best models, input data and monitoring information to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DTw) able to mirror or predict the performance of its corresponding physical twins. Therefore, the main target of the Topical Collection is exploring how the development of DTw can benefit the development of safe, efficient solutions for the pre-disposal and disposal of radioactive waste. A particular challenge for DTw in radioactive waste management is the combination of concepts from geological modelling and underground construction which will be addressed by linking structural and multi-physics/chemistry process models to building or tunnel information models. As for technical systems, engineered structures a variety of DTw approaches already exist, the development of DTw concepts for geological systems poses a particular challenge when taking the complexities (structures and processes) and uncertainties at extremely varying time and spatial scales of subsurface environments into account.