Analysis of microtopography, vegetation, and active-layer thickness using terrestrial LIDAR and kite photography, Barrow, AK

Arctic regions underlain by permafrost are among the most vulnerable to impacts from climate change. This study examined changes in the active layer of permafrost near Barrow, Alaska at very fine scale to capture subtle changes related to microtopography and landcover. In 2010, terrestrial LIDAR was used to collect high-resolution elevation data for four 10 m × 10 m plots where maximum active-layer thickness (ALT) and elevation have been monitored on an annual basis since the mid-1990s and had been monitored in the 1960s as well. The raw LIDAR point cloud was analyzed and processed into four 10 cm resolution digital elevation models (DEMs). Elevation data, collected using differential global positioning system (DGPS) to assess heave and subsidence, has been gathered annually since 2004 and was used to assess the accuracy of the DEMs generated for August 2010. Higher-resolution DEMs did not have higher accuracy compared to the DGPS control points due to artifacts inherent in the LIDAR data. The four DEMs were used to classify each plot based on microtopographical variations derived from terrain attributes including elevation, slope, and Melton’s Ruggedness Number (MRN). Landcover at each plot was classified using the Visible Vegetation Index (VVI), calculated from a series of high-resolution (~10 cm) kite photographs obtained in August 2012 by researchers from the University of Texas – El Paso. The microtopography and land-cover classifications were then used to analyze ALT and elevation data from a range of years. Analysis revealed little difference in either dataset based upon microtopography and landcover. The high amount of interclass and interannual variation made it difficult to draw any conclusions about temporal trends. The results suggest that while microtopography and vegetation are important factors within the complex interaction which determines ALT, the scale of analysis made possible by the high-resolution data utilized in this study did not significantly enhance understanding of the main controlling mechanisms. While terrestrial LIDAR is excellent for many applications, particularly those with substantial vertical variability, for future research at this scale on relatively flat topography, airborne LIDAR may be more suitable

Place-level urban-rural indices for the United States from 1930 to 2018

Rural-urban classifications are essential for analyzing geographic, demographic, environmental, and social processes across the rural-urban continuum. Most existing classifications are, however, only available at relatively aggregated spatial scales, such as at the county scale in the United States. The absence of rurality or urbanness measures at high spatial resolution poses significant problems when the process of interest is highly localized, as with the incorporation of rural towns and villages into encroaching metropolitan areas. Moreover, existing rural-urban classifications are often inconsistent over time, or require complex, multi-source input data (e.g., remote sensing observations or road network data), thus, prohibiting the longitudinal analysis of rural-urban dynamics. Here, we develop a set of distance- and spatial-network-based methods for consistently estimating the remoteness and rurality of places at fine spatial resolution, over long periods of time. We demonstrate the utility of our approach by constructing indices of urbanness for 30,000 places in the United States from 1930 to 2018 and further test the plausibility of our results against a variety of evaluation datasets. We call these indices the place-level urban-rural index (PLURAL) and make the resulting datasets publicly available (https://doi.org/10.3886/E162941) so that other researchers can conduct long-term, fine-grained analyses of urban and rural change. In addition, due to the simplistic nature of the input data, these methods can be generalized to other time periods or regions of the world, particularly to data-scarce environments.</jats:p

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R-2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t center dot ha(-1)). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management

Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R-2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t center dot ha(-1)). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management

Water balance modeling in the Eastern United States

The thesis adresses the water balance modeling issue by studying and comparing the performances of 5 water balance models at seasonal and annual time scale on 39 small/medium catchments spreaded throughout the eastern US. Two PET datasets are compared as well as the implication of inculuding the surface runoff by means of rainfall filtering. The final results is to obtain streamflow PDF in udgauged section by coupling WB models to a geomorphic model able estimate streamflow recessionsopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e/o informazioni sensibil

Mapping Forest Canopy Structure with On-Demand Fusion of Remotely Sensed Data

Current methods of mapping forest canopy structure often result in data products that are limited in resolution, coverage, or ease of access. On-demand processing introduces several new ways in which existing data products can be combined and re-purposed, mitigating some of these limitations. In this research, we investigate several methods of extending the spatial and temporal resolution, coverage, and accessibility of existing forest canopy datasets by processing them on demand. These methods include downscaling coarse-resolution canopy height data dynamically to estimate height at 30 m and 1 m resolution for any location within the contiguous United States. A related method involves sampling individual trees from field measurements on demand to estimate local forest canopy characteristics, using globally-available remotely sensed data and field data from across the United States. Canopy height profiles, which are highly sensitive to horizontal canopy variability, are generated on demand for any location within North America using new methods that account for this variability. Trends in canopy coverage and above-ground biomass are generated for any location globally using methods sensitive to local conditions. Each of the techniques developed as part of this research extends the resolution, coverage, or ease-of-access of existing remote sensing datasets, by combining multiple existing resources on demand

Data Science, Data Visualization, and Digital Twins

Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. Chapters cover such topics as deep learning techniques, web and dashboard-based visualisations during the COVID pandemic, 3D modelling of trees for mobile communications, digital twinning in the mining industry, data science libraries, and potential areas of future data science development

Maximum Entropy Modeling of the Iron Age Settlement Distributions in River Valleys of Turku Region, Southwest Finland

Species distribution models (SDM) are predictive modeling tools widely used in analytical biology that have also found applications in archaeological research. They can be used to quickly produce predictive maps for a variety of use cases like conservation and to guide field surveys. Modern SDMs take advantage of advances in computing like machine learning and artificial intelligence to achieve better predictions. In this study Maximum Entropy, or MaxEnt, machine learning SDM algorithm was used to create predictive models of the Iron Age settlement around Turku region in Southwest Finland, focusing on Aurajoki, Savijoki, and Vähäjoki river valleys. MaxEnt is the most popular SDM algorithm, largely due to its ability to create predictions based on presence-only data and consistently good performance. Only open access -data was used, and the selection of variables was based on availability and previous studies. The results show that MaxEnt can create in some cases surprisingly accurate models based on archaeological information, but the results were limited by the quality of existing data. The most influential variable was distance to water, which was the majority contributor whenever present. Even without the variable, the predicted distributions followed the waterways closely due to the influence of other variables. It was concluded that to improve the accuracy of the results the quality of the data should be a major focus. The results should also be tested through field surveys. Additionally, attention should be based on the model conception