scholarly journals Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification

2020 ◽  
Vol 12 (10) ◽  
pp. 1552 ◽  
Author(s):  
Veronika Döpper ◽  
Tobias Gränzig ◽  
Birgit Kleinschmit ◽  
Michael Förster
Keyword(s):  
Land Cover ◽  
Contrast Enhancement ◽  
Camera Calibration ◽  
Weather Conditions ◽  
Thermal Infrared ◽  
Temperature Correction ◽  
Image Alignment ◽  
Data Sets ◽  
Image Contrast Enhancement ◽  
The Impact

Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. Based on a valid statistical analysis of the acquired data sets, we derive general suggestions for the planning of a successful field campaign as well as recommendations for a suitable preprocessing workflow.

scholarly journals SPATIO-TEMPORAL DYNAMICS ALONG THE TERRAIN GRADIENT OF DIVERSE LANDSCAPE

2014 ◽  
Vol 22 (1) ◽  
pp. 50-63 ◽  
Author(s):  
T. V. Ramachandra ◽  
Settur Bharath ◽  
Aithal Bharath
Keyword(s):  
Land Cover ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Demographic Data ◽  
Coastal Region ◽  
Data Sets ◽  
Lulc Change ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
The Impact

Land use (LU) land cover (LC) information at a temporal scale illustrates the physical coverage of the Earth’s terrestrial surface according to its use and provides the intricate information for effective planning and management activities. LULC changes are stated as local and location specifc, collectively they act as drivers of global environmental changes. Understanding and predicting the impact of LULC change processes requires long term historical restorations and projecting into the future of land cover changes at regional to global scales. The present study aims at quantifying spatio temporal landscape dynamics along the gradient of varying terrains presented in the landscape by multi-data approach (MDA). MDA incorporates multi temporal satellite imagery with demographic data and other additional relevant data sets. The gradient covers three different types of topographic features, planes; hilly terrain and coastal region to account the signifcant role of elevation in land cover change. The seasonality is another aspect to be considered in the vegetation dominated landscapes; variations are accounted using multi seasonal data. Spatial patterns of the various patches are identifed and analysed using landscape metrics to understand the forest fragmentation. The prediction of likely changes in 2020 through scenario analysis has been done to account for the changes, considering the present growth rates and due to the proposed developmental projects. This work summarizes recent estimates on changes in cropland, agricultural intensifcation, deforestation, pasture expansion, and urbanization as the causal factors for LULC change.

Fire emissions estimates in Siberia: evaluation of uncertainties in area burned, land cover, and fuel consumption

2013 ◽  
Vol 43 (5) ◽  
pp. 493-506 ◽  
Author(s):  
Elena A. Kukavskaya ◽  
Amber J. Soja ◽  
Alexander P. Petkov ◽  
Evgeni I. Ponomarev ◽  
Galina A. Ivanova ◽  
...  
Keyword(s):  
Land Cover ◽  
Fuel Consumption ◽  
Boreal Forests ◽  
Carbon Budget ◽  
Natural Disturbance ◽  
Weather Conditions ◽  
Burned Area ◽  
Data Sets ◽  
Fire Emissions ◽  
Area Burned

Boreal forests constitute the world's largest terrestrial carbon pools. The main natural disturbance in these forests is wildfire, which modifies the carbon budget and atmosphere, directly and indirectly. Wildfire emissions in Russia contribute substantially to the global carbon cycle and have potentially important feedbacks to changing climate. Published estimates of carbon emissions from fires in Russian boreal forests vary greatly depending on the methods and data sets used. We examined various fire and vegetation products used to estimate wildfire emissions for Siberia. Large (up to fivefold) differences in annual and monthly area burned estimates for Siberia were found among four satellite-based fire data sets. Official Russian data were typically less than 10% of satellite estimates. Differences in the estimated proportion of annual burned area within each ecosystem were as much as 40% among five land-cover products. As a result, fuel consumption estimates would be expected to vary widely (3%–98%) depending on the specific vegetation mapping product used and as a function of weather conditions. Verification and validation of burned area and land-cover data sets along with the development of fuel maps and combustion models are essential for accurate Siberian wildfire emission estimates, which are central to balancing the carbon budget and assessing feedbacks to climate change.

Prospects of spring wheat cultivation in the conditions of arid Volga region

2020 ◽  
pp. 28-33
Author(s):  
Valery Genadievich Popov ◽  
Andrey Vladimirovich Panfilov ◽  
Yuriy Vyacheslavovich Bondarenko ◽  
Konstantin Mikhailovich Doronin ◽  
Evgeny Nikolaevih Martynov ◽  
...  
Keyword(s):  
Spring Wheat ◽  
Soil Layer ◽  
Weather Conditions ◽  
Volga Region ◽  
Mineral Fertilizers ◽  
Climate Conditions ◽  
Wheat Cultivation ◽  
Forest Belts ◽  
Clear Differentiation ◽  
The Impact

The article analyzes the experience of the impact of the system of forest belts and mineral fertilizers on the yield of spring wheat, including on irrigated lands. Vegetation irrigation is designed to maintain the humidity of the active soil layer from germination to maturation at the lower level of the optimum-70-75%, and in the phases of tubulation-earing - flowering - 75-80% NV. However, due to the large differences in zones and microzones of soil and climate conditions and due to the weather conditions of individual years, wheat irrigation regimes require a clear differentiation. In the Volga region in the dry autumn rainfalls give the norm of 800-1000 m3/ha, and in saline soils – 1000-1300 and 3-4 vegetation irrigation at tillering, phases of booting, earing and grain formation the norm 600-650 m3/ha. the impact of the system of forest belts, mineral fertilizers on the yield of spring wheat is closely tied to the formation of microclimate at different distances from forest edges.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Energy Performance Assessment of Innovative Building Solutions Coming from Construction and Demolition Waste Materials

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1226
Author(s):  
Beatriz Fraga-De Cal ◽  
Antonio Garrido-Marijuan ◽  
Olaia Eguiarte ◽  
Beñat Arregi ◽  
Ander Romero-Amorrortu ◽  
...  
Keyword(s):  
Thermal Insulation ◽  
Energy Demand ◽  
Energy Savings ◽  
Flow Analysis ◽  
Weather Conditions ◽  
Energy Performance ◽  
Construction And Demolition Waste ◽  
The European Union ◽  
Demolition Waste ◽  
The Impact

Prefabricated solutions incorporating thermal insulation are increasingly adopted as an energy conservation measure for building renovation. The InnoWEE European project developed three technologies from Construction and Demolition Waste (CDW) materials through a manufacturing process that supports the circular economy strategy of the European Union. Two of them consisted of geopolymer panels incorporated into an External Thermal Insulation Composite System (ETICS) and a ventilated façade. This study evaluates their thermal performance by means of monitoring data from three pilot case studies in Greece, Italy, and Romania, and calibrated building simulation models enabling the reliable prediction of energy savings in different climates and use scenarios. Results showed a reduction in energy demand for all demo buildings, with annual energy savings up to 25% after placing the novel insulation solutions. However, savings are highly dependent on weather conditions since the panels affect cooling and heating loads differently. Finally, a parametric assessment is performed to assess the impact of insulation thickness through an energy performance prediction and a cash flow analysis.

scholarly journals Earthworm Abundance Changes Depending on Soil Management Practices in Slovenian Vineyards

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1241
Author(s):  
Stanko Vršič ◽  
Marko Breznik ◽  
Borut Pulko ◽  
Jesús Rodrigo-Comino
Keyword(s):  
Management Practices ◽  
Human Impacts ◽  
Soil Layer ◽  
Soil Management ◽  
Weather Conditions ◽  
Soil Tillage ◽  
Soil Biodiversity ◽  
Straw Mulch ◽  
Green Cover ◽  
The Impact

Earthworms are key indicators of soil quality and health in vineyards, but research that considers different soil management systems, especially in Slovenian viticultural areas is scarce. In this investigation, the impact of different soil management practices such as permanent green cover, the use of herbicides in row and inter-row areas, use of straw mulch, and shallow soil tillage compared to meadow control for earthworm abundance, were assessed. The biomass and abundance of earthworms (m2) and distribution in various soil layers were quantified for three years. Monitoring and a survey covering 22 May 2014 to 5 October 2016 in seven different sampling dates, along with a soil profile at the depth from 0 to 60 cm, were carried out. Our results showed that the lowest mean abundance and biomass of earthworms in all sampling periods were registered along the herbicide strip (within the rows). The highest abundance was found in the straw mulch and permanent green cover treatments (higher than in the control). On the plots where the herbicide was applied to the complete inter-row area, the abundance of the earthworm community decreased from the beginning to the end of the monitoring period. In contrast, shallow tillage showed a similar trend of declining earthworm abundance, which could indicate a deterioration of soil biodiversity conditions. We concluded that different soil management practices greatly affect the soil’s environmental conditions (temperature and humidity), especially in the upper soil layer (up to 15 cm deep), which affects the abundance of the earthworm community. Our results demonstrated that these practices need to be adapted to the climate and weather conditions, and also to human impacts.

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