Статті в журналах з теми "Global field root mean square"
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Apeh, O. I., E. C. Moka, and V. N. Uzodinma. "Evaluation of Gravity Data Derived from Global Gravity Field Models Using Terrestrial Gravity Data in Enugu State, Nigeria." Journal of Geodetic Science 8, no. 1 (December 1, 2018): 145–53. http://dx.doi.org/10.1515/jogs-2018-0015.
Повний текст джерелаАнотація:
Abstract Spherical harmonic expansion is a commonly applied mathematical representation of the earth’s gravity field. This representation is implied by the potential coeffcients determined by using elements/parameters of the field observed on the surface of the earth and/or in space outside the earth in the spherical harmonic expansion of the field. International Centre for Gravity Earth Models (ICGEM) publishes, from time to time, Global Gravity Field Models (GGMs) that have been developed. These GGMs need evaluation with terrestrial data of different locations to ascertain their accuracy for application in those locations. In this study, Bouguer gravity anomalies derived from a total of eleven (11) recent GGMs, using sixty sample points, were evaluated by means of Root-Mean-Square difference and correlation coeficient. The Root-Mean-Square differences of the computed Bouguer anomalies from ICGEMwebsite compared to their positionally corresponding terrestrial Bouguer anomalies range from 9.530mgal to 37.113mgal. Additionally, the correlation coe_cients of the structure of the signal of the terrestrial and GGM-derived Bouguer anomalies range from 0.480 to 0.879. It was observed that GECO derived Bouguer gravity anomalies have the best signal structure relationship with the terrestrial data than the other ten GGMs. We also discovered that EIGEN-6C4 and GECO derived Bouguer anomalies have enormous potential to be used as supplements to the terrestrial Bouguer anomalies for Enugu State, Nigeria.
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ARKIN, HANDAN. "STUDY OF ELASTIN SEQUENCES WITH SOLVENT INDUCED FORCE FIELD." International Journal of Modern Physics C 16, no. 09 (September 2005): 1339–46. http://dx.doi.org/10.1142/s0129183105007959.
Повний текст джерелаАнотація:
Conformational structures of two common repeat motifs Val1-Pro2-Gly3-Val4-Gly5 and Gly1-Leu2-Gly3-Gly4 of tropoelastin are investigated by using the multicanonical simulation procedure with solvation effects included energy force field. The effects of solvation energy term on the conformations are determined by analyzing Ramachandran plots. By minimizing the energy structures along the trajectory, the thermodynamically most stable low-energy microstates of the molecule in aqueous solution are determined and the root mean square deviations of these structures with respect to the global minimum are calculated.
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Arbuzov, Andrej B., and Alexander E. Pavlov. "Reduced conformal geometrodynamics." International Journal of Modern Physics A 35, no. 02n03 (January 30, 2020): 2040023. http://dx.doi.org/10.1142/s0217751x20400230.
Повний текст джерелаАнотація:
The global time in geometrodynamics is defined in a covariant under diffeomorphisms form. An arbitrary static background metric is taken in the tangent space. The global intrinsic time is identified with the mean value of the logarithm of the square root of the ratio of the metric determinants. The procedures of the Hamiltonian reduction and deparametrization of dynamical systems are implemented. The reduced Hamiltonian equations of motion of gravitational field in semi-geodesic coordinate system are written.
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Udompant, Kannapat, Ricardo Ospina, Yong-Joo Kim, and Noboru Noguchi. "Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester." Agronomy 11, no. 3 (March 5, 2021): 483. http://dx.doi.org/10.3390/agronomy11030483.
Повний текст джерелаАнотація:
The purpose of this study is to evaluate the performance of a robot combine harvester by comparing the Centimeter Level Augmentation Service (CLAS) and the Multi-Global Navigation Satellite System (GNSS) Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) from the Quasi-Zenith Satellite System (QZSS) by using the Real Time Kinematic (RTK) positioning technique as a reference. The first section of this study evaluates the availability and the precision under static conditions by measuring the activation time, the reconnection time, and obtaining a Twice Distance Root Mean Square (2DRMS) of 0.04 m and 0.10 m, a Circular Error Probability (CEP) of 0.03 m and 0.08 m, and a Root Mean Square Error (RMSE) of 0.57 m and 0.54 m for the CLAS and MADOCA, respectively. The second section evaluates the accuracy under dynamic conditions by using a GNSS navigation-based combine harvester running in an experimental field. The results show that the RMSE of the lateral deviation is between 0.04 m and 0.69 m for MADOCA and between 0.03 m and 0.31 m for CLAS; which suggest that the CLAS positioning augmentation system can be utilized for the robot combine harvester if the user considers these accuracy and dynamic characteristics.
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Li, Zhongbing, Wei Pang, Haibo Liang, Guihui Chen, Hongming Duan, and Chuandong Jiang. "Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares." Energies 15, no. 4 (February 12, 2022): 1325. http://dx.doi.org/10.3390/en15041325.
Повний текст джерелаАнотація:
Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology.
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Mäkinen, Antti, Ilkka Korpela, Timo Tokola, and Annika Kangas. "Effects of imaging conditions on crown diameter measurements from high-resolution aerial images." Canadian Journal of Forest Research 36, no. 5 (May 1, 2006): 1206–17. http://dx.doi.org/10.1139/x06-011.
Повний текст джерелаАнотація:
Imaging geometry, the structure of the forest, and certain tree properties can cause inaccuracy in image measurements of the crown dimensions of individual trees. Measurement error of the crown diameter was studied in relation to various factors to explain this error. A secondary aim was to generate calibration models for improving the accuracy of crown diameter image measurements. The crown diameters of a total of 715 sample trees in southern Finland were measured in the field and from aerial photographs at scales 1:6000, 1 : 12 000, and 1 : 16 000. The photo grammetric image measurement seemed to systematically underestimate the true crown diameter, and the major factor affecting the bias was tree species. The mean underestimation varied from 0.30 to 0.80 m, with root mean square errors of 0.951.10 m depending on the tree species. Linear regression analysis was employed to define the factors that had an effect on the image measurements, and calibration models in the form of linear regression models were generated. The calibration models worked reasonably well, and the root mean square error for the calibrated observations decreased by 22% for Scots pine (Pinus sylvestris L.), 53% for Norway spruce (Picea abies (L.) Karst), and 47% for silver birch (Betula pendula Roth).
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Wallerman, Jörgen, Steve Joyce, Coomaren P. Vencatasawmy, and Håkan Olsson. "Prediction of forest stem volume using kriging adapted to detected edges." Canadian Journal of Forest Research 32, no. 3 (March 1, 2002): 509–18. http://dx.doi.org/10.1139/x01-214.
Повний текст джерелаАнотація:
The modern techniques of the global positioning system and geographic information system enable many new approaches to forestry planning problems. Using these it is possible to efficiently geoposition, store, and analyze each field measurement in a spatial context. This work is directed towards the application of a dynamic forestry planning system based on a forest map with very high spatial resolution created from geopositioned field plot data, instead of the traditional forest stand map. The new dynamic system is dependent on accurate methods to create a high-resolution map from a set of field measurements. This problem may be solved using the kriging spatial prediction (interpolation) method. The aim of this paper is to present and empirically evaluate a new kriging method side-by-side with global and stratified kriging. The new method uses the output from an edge-detection algorithm, here applied to Landsat TM image data, to increase the prediction accuracy. Prediction evaluation was made in terms of mean forest stem volume per hectare measured on circular field plots of 10 m radius. The new method showed a prediction root mean square error of 41% of the mean volume, compared with corresponding results of global, 58%, and stratified kriging, 45%.
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Wang, Sijia, Yunhao Chen, Mingguo Wang, Yifei Zhao, and Jing Li. "SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions." Remote Sensing 11, no. 8 (April 23, 2019): 967. http://dx.doi.org/10.3390/rs11080967.
Повний текст джерелаАнотація:
The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies.
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Okpoko, J. S., and H. A. P. Audu. "Geostatistical Modelling and Mapping of the Concentration of Gaseous Pollutants." October 2018 2, no. 2 (October 2018): 219–32. http://dx.doi.org/10.36263/nijest.2018.02.0084.
Повний текст джерелаАнотація:
In this study, the prediction of the concentration of gaseous pollutants around Ughelli West gas flow station in Delta State of Nigeria was carried out using Geostatistical technique in GIS environment. Since air pollutants negatively affect quality of air, lives and the environment, there is therefore the need to frequently monitor air quality, have thorough understanding of the pollutants’ concentration and their spatial distribution in an environment. The gaseous pollutants data of volatile organic compounds (VOCs), methane (CH4), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3), were obtained using Multi-parameter gas monitor while that of fine particulate matter (PM2.5) was obtained with SPM meter for a period of three months. Thermo Anemometer was used to obtain the values of wind speed, ambient temperature, atmospheric pressure and relative humidity. Artificial Neural Network designer software (Pythia) was used to validate the acquired field data; predict the concentration of the gaseous pollutants at selected distances from the flow station. The geospatial coordinates of the flow station were obtained using Global Navigation Satellite System (GNSS) receivers; the geospatial modelling and analysis were performed with ArcGIS software and ordinary kriging method of Geostatistical techniques. The results of the maximum concentration for the gaseous pollutants in the study area were 28.17 µg/m3, 19.44 µg/m3, 0.37 µg/m3, 49.81 µg/m3, 0.061 µg/m3 and 0.047µg/m3 for VOCs, CH4, NO2, PM2.5, O3 and SO2 respectively. The root mean square error for the concentration of the gaseous pollutants, ozone and sulphur (IV) oxide in the study area were 0.01618 and 0.008417 indicating a good interpolation model, while their root mean square standard errors, which show the reliability of the predicted values, were 0.70513551 and 0.8459251 respectively. These results conform with the report of other researchers that a better kriging method yields a smaller root mean square and a standard root mean square closer to one. The developed prediction maps for the gaseous pollutants in this study revealed that the study area will experience lower concentration of gaseous pollutants at a distance of 400 m and above.
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Li, Qi, and Ding Jun Wu. "Simulation and Field Test for Local Acceleration of an U-Shaped Girder." Advanced Materials Research 250-253 (May 2011): 2016–23. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.2016.
Повний текст джерелаАнотація:
The local vibration of a bridge under moving train is the source of low frequency noise radiated from the structure. Local vibration of bridges has not been widely studied compared to global vibration that leads to serviceability and safety problems. The local vibration of an urban rail transit U-shaped girder is simulated with the proposed mode superposition based train-track-bridge dynamic interaction model. The computed local acceleration responses under various train speeds are then verified by those obtained from field test, in terms of time-histories, spectra, peak-to-peak values and root mean square (RMS) values. It is founded that the main frequencies of the local acceleration of this girder are below 100 Hz. The proposed method and the computed local vibration could be further applied to predict the bridge-borne noise.
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Ross, Robert, and Rahinul Hoque. "Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications." Applied Sciences 10, no. 1 (December 23, 2019): 146. http://dx.doi.org/10.3390/app10010146.
Повний текст джерелаАнотація:
In recent decades Global Positioning Systems (GPS) have become a ubiquitous tool to support navigation. Traditional GPS has an error in the order of 10–15 m, which is adequate for many applications (e.g., vehicle navigation) but for many robotics applications lacks required accuracy. In this paper we describe a technique, FAGPS (Fiducial Augmented Global Positioning System) to periodically use fiducial markers to lower the GPS drift, and hence for a small time-period have a more accurate GPS determination. We describe results from simulations and from field testing in open-sky environments where horizontal GPS accuracy was improved from a twice the distance root mean square (2DRMS) error of 5.5 m to 2.99 m for a period of up-to 30 min.
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Anttila, Perttu. "Nonparametric estimation of stand volume using spectral and spatial features of aerial photographs and old inventory data." Canadian Journal of Forest Research 32, no. 10 (October 1, 2002): 1849–57. http://dx.doi.org/10.1139/x02-108.
Повний текст джерелаАнотація:
The forest management planning inventories for private forests in Finland are currently carried out in stand-level field inventories. To decrease the amount of fieldwork, aerial photographs and old inventory data could be utilized. The main objectives were to test the accuracy of a method based on these data sources and the effect of stand shape on the accuracy. Median pixel values, semivariances, and old inventory data were extracted for each of the 577 stands in the study. These data were applied as indicator attributes in k-nearest-neighbor estimation of stand volume. Stand-level estimates were computed as weighted means of k most similar neighbors. When all the stands were used, a root mean square error of 29.9% was obtained. Old inventory data proved to be valuable auxiliary information. It was also found that exclusion of stands with tortuous boundaries and small area decreased the error. The accuracy of mean volume estimation just met the requirements for stand-level inventory, but the method still needs further research before the final conclusion of the applicability for management planning.
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Correa Muñoz, Nixon Alexander, and Liliana Alejandra Cerón-Calderón. "Precision and accuracy of the static GNSS system for surveying networks used in Civil Engineering." Ingeniería e Investigación 38, no. 1 (January 1, 2018): 52–59. http://dx.doi.org/10.15446/ing.investig.v38n1.64543.
Повний текст джерелаАнотація:
We implemented a field check for calibrating surveying equipment. It was geo-referenced with a Total Station Theodolite and by implementing procedures concerning repeatability and reproducibility. We carried out GNSS (Global Navigation Satellite System) static positioning with double frequency equipment, sensitizing occupation times, day times, uncorrected coordinates subjected to a differential correction procedure and type of coordinates obtained. This facilitated an evaluation of precision and accuracy for the GNSS system with the static method, which gave a global RMS (root mean square) error of 1 cm for conditions with no multi-path effect and 4cm for field calibration points close to buildings. Additionally, we found optimal results for occupation times of 30 minutes, and the need to use planar Cartesian coordinates to ensure compatibility with the surveys using electronic measurement of distances, which allows for the use of the static GNSS system in precise surveys for different applications in Civil Engineering.
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Ghiasi, Yusof, Claude R. Duguay, Justin Murfitt, Joost J. van der Sanden, Aaron Thompson, Hugo Drouin, and Christian Prévost. "Application of GNSS Interferometric Reflectometry for the Estimation of Lake Ice Thickness." Remote Sensing 12, no. 17 (August 23, 2020): 2721. http://dx.doi.org/10.3390/rs12172721.
Повний текст джерелаАнотація:
Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.
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Härkönen, Sanna, Timo Tokola, Petteri Packalén, Lauri Korhonen, and Annikki Mäkelä. "Predicting forest growth based on airborne light detection and ranging data, climate data, and a simplified process-based model." Canadian Journal of Forest Research 43, no. 4 (April 2013): 364–75. http://dx.doi.org/10.1139/cjfr-2012-0295.
Повний текст джерелаАнотація:
Increasing use of airborne light detection and ranging (LiDAR) in forest inventories offers new possibilities to apply process-based forest models (PBM) in practice. We present a new approach, where a simplified PBM is run using inputs derived from the LiDAR data. The PBM was built by combining several existing models together, and it was tested with 52 Scots pine (Pinus sylvestris L.) dominated sample plots in Finland with the LiDAR (PBM_LIDAR) and field (PMB_FIELD) inputs. The results were compared with empirical growth predictions (EM_FIELD) and field reference growth. LiDAR-based stand variables (mean height of tree and crown base and leaf area index) were, on average, well in line with the field measurements. Basal area growth was slightly underestimated with the PBM_LIDAR (bias 4.1%, root mean square prediction error (RMSPE, 26.7%) and overestimated with the PBM_FIELD (bias –10.2%, RMSPE 33.3%), the EM_FIELD being the least biased (bias –1.9%, RMSPE of 24.6%). The bias varied with stand age and fertility. The dependence on field reference growth was highest with EM_FIELD and PBM_LIDAR (R2 = 0.47 and 0.34, respectively), and lowest with PBM_FIELD (R2 = 0.18). Despite several development needs, the approach is promising for easy incorporation of canopy and weather data into forest growth predictions.
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Hameed, Khurram, Wasim Khan, Yasser S. Abdalla, Fatemah F. Al-Harbi, Ammar Armghan, Muhammad Asif, Muhammad Salman Qamar, et al. "Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm." Electronics 11, no. 4 (February 12, 2022): 558. http://dx.doi.org/10.3390/electronics11040558.
Повний текст джерелаАнотація:
For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).
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Li, Zhen, Jinyun Guo, Bing Ji, Xiaoyun Wan, and Shengjun Zhang. "A Review of Marine Gravity Field Recovery from Satellite Altimetry." Remote Sensing 14, no. 19 (September 25, 2022): 4790. http://dx.doi.org/10.3390/rs14194790.
Повний текст джерелаАнотація:
Marine gravity field recovery relies heavily on satellite altimetry. Thanks to the evolution of altimetry missions and the improvements in altimeter data processing methods, the marine gravity field model has been prominently enhanced in accuracy and resolution. However, high-accuracy and high-resolution gravity field recovery from satellite altimeter data remains particularly challenging. We provide an overview of advances in satellite altimetry for marine gravity field recovery, focusing on the impact factors and available models of altimetric gravity field construction. Firstly, the evolution of altimetry missions and the contribution to gravity field recovery are reviewed, from the existing altimetry missions to the future altimetry missions. Secondly, because the methods of altimeter data processing are of great significance when obtaining high-quality sea surface height observations, these improved methods are summarized and analyzed, especially for coastal altimetry. In addition, the problems to be resolved in altimeter data processing are highlighted. Thirdly, the characteristics of gravity recovery methods are analyzed, including the inverse Stokes formula, the inverse Vening Meinesz formula, Laplace’s equation, and least squares collocation. Furthermore, the latest global marine gravity field models are introduced, including the use of altimeter data and processing methods. The performance of the available global gravity field model is also evaluated by shipboard gravity measurements. The root mean square of difference between the available global marine gravity model and shipboard gravity from the National Centers for Environmental Information is approximately 5.10 mGal in the low-middle latitude regions, which is better than the result in high-latitude regions. In coastal areas, the accuracy of models still needs to be further improved, particularly within 40 km from the coastline. Meanwhile, the SDUST2021GRA model derived from the Shandong University of Science and Technology team also exhibited an exciting performance. Finally, the future challenges for marine gravity field recovery from satellite altimetry are discussed.
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Maltamo, Matti, Petteri Vartiainen, Petteri Packalen, and Lauri Korhonen. "Estimation of periodic annual increment of tree ring widths by airborne laser scanning." Canadian Journal of Forest Research 52, no. 4 (April 2022): 644–51. http://dx.doi.org/10.1139/cjfr-2021-0267.
Повний текст джерелаАнотація:
Most forest growth studies using airborne laser scanning (ALS) consider how the changes in forest attributes are observed in repeated ALS data acquisitions, but the prediction of future forest growth from ALS data is still a rarely discussed topic. This study examined the prediction of the periodic annual increment (PAI) of the width of tree rings over a period of 10 years. The requirement for this approach is that ALS data are acquired at the beginning of the growth period. This is followed by field measurements of growth by drilling after a given growth period. The PAI was modelled in terms of ALS metrics by using the principle of the area-based approach. The metrics related to intensity were particularly significant as predictors, whereas the effective leaf area index was not. The root-mean-square error (RMSE) of the predictions was slightly over 21%. Additional field information (soil type, management operations) improved the RMSE by 2.7 percentage units.
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Li, Jiwei, David E. Knapp, Mitchell Lyons, Chris Roelfsema, Stuart Phinn, Steven R. Schill, and Gregory P. Asner. "Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine." Remote Sensing 13, no. 8 (April 10, 2021): 1469. http://dx.doi.org/10.3390/rs13081469.
Повний текст джерелаАнотація:
Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs).
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Ganagina, I. G., V. F. Kanushin, D. N. Goldobin, and I. V. Zverev. "Analysis of data on pure and mixed gravity anomalies obtained from the results of the space gravity missions GRACE, GOCE." Interexpo GEO-Siberia 1 (May 18, 2022): 130–37. http://dx.doi.org/10.33764/2618-981x-2022-1-130-137.
Повний текст джерелаАнотація:
The article analyzes the data of pure and mixed gravity anomalies on the territory of the Novosibirsk region, obtained from the results of the space gravimetric missions GRACE, GOCE. Gravimetric and geodetic measurements carried out in the study area made it possible to obtain mixed and simulate pure gravity anomalies. The information obtained was the starting point for the analysis of gravity anomalies calculated from the coefficients of the global geopotential models XGM2019e_2159, GOCO01S. The results of the study led to the conclusion that when calculating mixed gravity anomalies according to the combined geopotential model XGM2019e_2159 and pure gravity anomalies according to the GOCO01S satellite model data, equal root-mean-square errors were obtained (for mixed anomalies - 3.45 mGal, for pure - 3.44 mGal). Therefore, modern combined global gravity field models can be used to obtain pure anomalies on the earth's surface.
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Huang, Zhi-Cheng, Cheng-Yang Yeh, Kuo-Hsin Tseng, and Wen-Yang Hsu. "A UAV–RTK Lidar System for Wave and Tide Measurements in Coastal Zones." Journal of Atmospheric and Oceanic Technology 35, no. 8 (August 2018): 1557–70. http://dx.doi.org/10.1175/jtech-d-17-0199.1.
Повний текст джерелаАнотація:
AbstractA lightweight and low-cost unmanned aerial vehicle (UAV) system for coastal wave and tide measurements is developed. This system is based on an assembly of a multirotor UAV, a robotic lidar, an altitude and heading reference system (AHRS), and a real-time kinematic (RTK) Global Navigation Satellite System (GNSS). A great advantage of the system is that it can be operated at low altitude in a few meters; the accuracy and spatial resolution can therefore be increased. When the system was moved up and down in 2–12 m, the root-mean-square error (RMSE) was approximately 5 cm compared to the ground truth value measured by a manual RTK GNSS. The system was operated in a hover mode to measure the tide and waves in the field. The results of using the UAV–RTK lidar system were consistent with those of in situ measurements using a pressure sensor. The root-mean-square errors between the two techniques for measurements of tidal elevation, significant wave height, and wave period were 4.9 cm, 4.8 cm, and 0.028 s, respectively. This finding suggests the system could be applied to measure instantaneous sea surface displacement. The system provides the potential for using a low-cost, extremely portable, and efficient tool for monitoring wave properties, topographic changes, and water-level gradients in coastal zones.
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Pfau, Maximilian, Moritz Lindner, Julia S. Steinberg, Sarah Thiele, Christian K. Brinkmann, Monika Fleckenstein, Frank G. Holz, and Steffen Schmitz-Valckenberg. "Visual field indices and patterns of visual field deficits in mesopic and dark-adapted two-colour fundus-controlled perimetry in macular diseases." British Journal of Ophthalmology 102, no. 8 (November 16, 2017): 1054–59. http://dx.doi.org/10.1136/bjophthalmol-2017-311012.
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Background/AimsTo analyse the retest reliability of visual field indices and to describe patterns of visual field deficits in mesopic and dark-adapted two-colour fundus-controlled perimetry (FCP) in macular diseases.MethodsSeventy-seven eyes (30 eyes with macular diseases and 47 normal eyes) underwent duplicate mesopic and dark-adapted two-colour FCP (Scotopic Macular Integrity Assessment, CenterVue). Non-weighted (mean defect, loss variance), variability-weighted (mean deviation, pattern standard deviation (PSD)) and graphical (cumulative defect (Bebie) curves) indices were computed. Reproducibility (coefficient of repeatability, CoR) of these indices was assessed. Cluster analysis was carried out to identify patterns of visual field deficits.ResultsThe intrasession reproducibility was lower for the mean defect as compared with the mean deviation (CoR (dB) 2.67 vs 2.57 for mesopic, 1.71 vs 1.45 for dark-adapted cyan, 1.94 vs 1.87 for dark-adapted red testing) and lower for the square-root loss variance as compared with the PSD (CoR (dB) 1.48 vs 1.34, 0.77 vs 0.65, 1.23 vs 1.03). Hierarchical cluster analysis of the indices disclosed six patterns of visual field deficits (approximately unbiased P value>0.95) with varying degrees of global versus focal defect and rod versus cone dysfunction. These were also reflected by the cumulative defect curves.ConclusionFCP with mesopic and dark-adapted two-colour testing allows for reproducible assessment of different types of retinal sensitivity, whereby mean deviation and PSD exhibited the better retest reliability of the tested indices. Distinct patterns of retinal dysfunction can be identified using this setup, reflecting variable degrees of rod and cone dysfunction in different macular diseases. Dark-adapted two-colour FCP provides additional diagnostic information and allows for refined structure–function correlation in macular diseases.
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Tennant, Warren J., Glenn J. Shutts, Alberto Arribas, and Simon A. Thompson. "Using a Stochastic Kinetic Energy Backscatter Scheme to Improve MOGREPS Probabilistic Forecast Skill." Monthly Weather Review 139, no. 4 (April 1, 2011): 1190–206. http://dx.doi.org/10.1175/2010mwr3430.1.
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Abstract An improved stochastic kinetic energy backscatter scheme, version 2 (SKEB2) has been developed for the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Wind increments at each model time step are derived from a streamfunction forcing pattern that is modulated by a locally diagnosed field of likely energy loss due to numerical smoothing and unrepresented convective sources of kinetic energy near the grid scale. The scheme has a positive impact on the root-mean-square error of the ensemble mean and spread of the ensemble. An improved growth rate of spread results in a better match with ensemble-mean forecast error at all forecast lead times, with a corresponding improvement in probabilistic forecast skill from a more realistic representation of model error. Other examples of positive impact include improved forecast blocking frequency and reduced forecast jumpiness. The paper describes the formulation of the SKEB2 and its assessment in various experiments.
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Wang, Gen, Dongyong Wang, Wei Han, and Jian Yin. "Typhoon Cloud System Identification and Forecasting Using the Feng-Yun 4A/Advanced Geosynchronous Radiation Imager Based on an Improved Fuzzy Clustering and Optical Flow Method." Advances in Meteorology 2019 (July 31, 2019): 1–11. http://dx.doi.org/10.1155/2019/5890794.
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This study adopted an improved fuzzy clustering and optical flow method for the multiscale identification and forecasting of a cloud system based on the cloud images from a 10.8-micron infrared channel of the Advanced Geosynchronous Radiation Imager. First, we used the locally constrained fuzzy c-means (FCM) clustering method to identify typhoon-dominant cloud systems. Second, we coupled the background field-constrained optical flow method with the semi-Lagrangian scheme to forecast typhoon-dominant cloud systems. The experimental results for Typhoon Maria showed that the improved FCM method was able to effectively identify changes in the cloud system while retaining its edge information through the effective removal of the offset field. The identified dominant cloud system was consistent with the precipitation field of the Global Precipitation Measurement mission. We optimized the semi-Lagrangian nonlinear extrapolation of the optical flow field by introducing background field information, thus improving the forecast accuracy of the optical flow field. Based on the assessment indicators of structural similarity, normalized mutual information, peak signal-to-noise ratio, relative standard deviation, and root mean square error, the forecast results demonstrated that the forecast effect of the background field-constrained optical flow method was better than that of the standard optical flow method.
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May, Jackie C., Clark Rowley, and Neil Van de Voorde. "The Naval Research Laboratory Ocean Surface Flux (NFLUX) System: Satellite-Based Turbulent Heat Flux Products." Journal of Applied Meteorology and Climatology 55, no. 5 (May 2016): 1221–37. http://dx.doi.org/10.1175/jamc-d-15-0187.1.
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AbstractThe Naval Research Laboratory ocean surface flux (NFLUX) system provides satellite-based surface state parameter and surface turbulent heat flux fields operationally over the global ocean. These products are presented as an alternative to using numerical weather prediction models—namely, the U.S. Navy Global Environmental Model (NAVGEM)—to provide the surface forcing to operational ocean models. NFLUX utilizes satellite sensor data records from the Special Sensor Microwave Imager/Sounder (SSMIS), the Advanced Microwave Sounding Unit-A (AMSU-A), the Advanced Technology Microwave Sounder (ATMS), and the Advanced Microwave Scanning Radiometer-2 (AMSR-2) sensors as well as satellite environmental data records from WindSat, the Advanced Scatterometers (ASCAT), and the Oceansat scatterometer (OSCAT). The satellite data are processed and translated into estimates of surface specific humidity, surface air temperature, and 10-m scalar wind speed. Two-dimensional variational analyses of quality-controlled satellite data, in combination with an atmospheric-model field, form global gridded surface state parameter fields. Bulk formulas are then applied to produce surface turbulent heat flux fields. Six-hourly analysis fields are created from 1 January 2013 through 31 December 2013. These fields are examined and validated against in situ data and NAVGEM. Overall, the NFLUX fields have a smaller bias, lower or similar root-mean-square error, and increased skill score relative to those of NAVGEM.
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Kug, Jong-Seong, June-Yi Lee, and In-Sik Kang. "Systematic Error Correction of Dynamical Seasonal Prediction of Sea Surface Temperature Using a Stepwise Pattern Project Method." Monthly Weather Review 136, no. 9 (September 1, 2008): 3501–12. http://dx.doi.org/10.1175/2008mwr2272.1.
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Abstract Every dynamical climate prediction model has significant errors in its mean state and anomaly field, thus degrading its performance in climate prediction. In addition to correcting the model’s systematic errors in the mean state, it is also possible to correct systematic errors in the predicted anomalies by means of dynamical or statistical postprocessing. In this study, a new statistical model has been developed based on the pattern projection method in order to empirically correct the dynamical seasonal climate prediction. The strength of the present model lies in the objective and automatic selection of optimal predictor grid points. The statistical model was applied to systematic error correction of SST anomalies predicted by Seoul National University’s (SNU) coupled GCM and evaluated in terms of temporal correlation skill and standardized root-mean-square error. It turns out that the statistical error correction improves the SST prediction over most regions of the global ocean with most forecast lead times up to 6 months. In particular, the SST predictions over the western Pacific and Indian Ocean are improved significantly, where the SNU coupled GCM shows a large error.
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Dr. Vrushali Y. Kulkarni, Pranali K. Kosamkar ,. "ANALYSIS AND PREDICTION OF GREENHOUSE GAS EMISSION USING FEEDFORWARD NEURAL NETWORK." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 1, 2021): 304–13. http://dx.doi.org/10.17762/itii.v9i1.133.
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Greenhouse Gas (GHG) emission is caused by decomposition of biomass and dead plant residues, livestock enteric fermentation in ruminants, and burning of crop residues. As the concentration of GHG rises it raises the temperature on the globe causing the Global Warming. Alterations in agriculture management practices may reduce the GHG emission. Therefore, there is a need to analyze and forecast the GHG emission from Agriculture. We have built the Feedforward Neural Network using sequential neural network in keras for predicting CO2 and CH4 emission for Onion crop from open farm and poly house. We selected Onion for our study because Onion is one of the second most important commercial crops of the India. The GHG emission may vary in open farm and poly house for onion crop because the environment is controlled in poly house as compared to open farm. For this study we collected the field data of soil attributes, climatic attributes and CO2, CH4 greenhouse gases from the experiment field. We hyper tune the model with 3, 4 and 5 layers with different epoch. We have used Root mean squared error (RMSE), Mean squared error (MSE) and R-square as a coefficient of correlation for model prediction accuracy. Model predicted that Nitrogen, Moisture, Pressure, Humidity and Temperature are major affecting factors for emission of GHG for onion crop from open farm and poly house. The model indicates good prediction response for GHG emission with major influencing attribute for onion crop.
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Toohey, M., and T. von Clarmann. "Climatologies from satellite measurements: the impact of orbital sampling on the standard error of the mean." Atmospheric Measurement Techniques Discussions 5, no. 6 (November 9, 2012): 8241–69. http://dx.doi.org/10.5194/amtd-5-8241-2012.
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Abstract. Climatologies of atmospheric observations are often produced by binning measurements according to latitude, and calculating zonal means. The uncertainty in these climatological means is characterized by the standard error of the mean (SEM). However, the usual estimator of the SEM, i.e. the sample standard deviation divided by the square root of the sample size, holds only for uncorrelated randomly sampled measurements. Measurements of the atmospheric state along a satellite orbit cannot always be considered as independent because (a) the time-space interval between two nearest observations is often smaller than the typical scale of variations in the atmospheric state, and (b) the regular time-space sampling pattern of a satellite instrument strongly deviates from random sampling. We have developed an experiment where global chemical fields from a chemistry climate model are sampled according to real sampling patterns of satellite-borne instruments. As case studies, sampling patterns of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and Atmospheric Chemistry Experiment Fourier-Transform Spectrometer (ACE-FTS) satellite instruments are used to iteratively subsample the model O3 fields and produce empirical estimates of the standard error of monthly mean zonal mean model O3 in 5° latitude bins. We find that generally the classic SEM estimator is a conservative estimate of the SEM, i.e. the empirical SEM is often less than the classic estimate. Exceptions occur in instances where the zonal sampling distribution shows non-uniformity with a similar zonal structure as variations in the sampled field, leading to maximum sensitivity to arbitrary phase shifts between the sample distribution and sampled field. The occurrence of such instances is thus very sensitive to slight changes in the sampling distribution, and to the variations in the measured field. This study highlights the need for caution in the interpretation of the oft-used classically computed SEM, and outlines a relatively simple methodology that can be used to assess one component of the uncertainty in monthly mean zonal mean climatologies produced from measurements from satellite-borne instruments.
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Curry, Charles, Kurusch Ebrahimi–Fard, Simon J. A. Malham, and Anke Wiese. "Algebraic structures and stochastic differential equations driven by Lévy processes." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2221 (January 2019): 20180567. http://dx.doi.org/10.1098/rspa.2018.0567.
Повний текст джерелаАнотація:
We construct an efficient integrator for stochastic differential systems driven by Lévy processes. An efficient integrator is a strong approximation that is more accurate than the corresponding stochastic Taylor approximation, to all orders and independent of the governing vector fields. This holds provided the driving processes possess moments of all orders and the vector fields are sufficiently smooth. Moreover, the efficient integrator in question is optimal within a broad class of perturbations for half-integer global root mean-square orders of convergence. We obtain these results using the quasi-shuffle algebra of multiple iterated integrals of independent Lévy processes.
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Huang, Shengli, Carlos Ramirez, Scott Conway, Kama Kennedy, Tanya Kohler, and Jinxun Liu. "Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA." Canadian Journal of Forest Research 47, no. 1 (January 2017): 113–24. http://dx.doi.org/10.1139/cjfr-2016-0209.
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High-resolution site index (SI) and mean annual increment (MAI) maps are desired for local forest management. We integrated field inventory, Landsat, and ecological variables to produce 30 m SI and MAI maps for the Tahoe National Forest (TNF) where different tree species coexist. We converted species-specific SI using adjustment factors. Then, the SI map was produced by (i) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, (ii) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and (iii) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an R2 of 0.7637, a root-mean-square error (RMSE) of 3.60, and a mean absolute error (MAE) of 3.07 m. The MAI map was similarly produced with an R2 of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m3·ha−1·year−1. Spatial patterns and trends of SI and MAI were analyzed to be related to elevation, aspect, slope, soil productivity, and forest type. The 30 m SI and MAI maps can be used to support decisions on fire, plantation, biodiversity, and carbon.
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Wang, Meimei, and Jiayuan Lin. "Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)." Canadian Journal of Forest Research 50, no. 10 (October 2020): 1012–24. http://dx.doi.org/10.1139/cjfr-2019-0418.
Повний текст джерелаАнотація:
Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.
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Karjalainen, Tomi, Lauri Mehtätalo, Petteri Packalen, Terje Gobakken, Erik Næsset, and Matti Maltamo. "Field calibration of merchantable and sawlog volumes in forest inventories based on airborne laser scanning." Canadian Journal of Forest Research 50, no. 12 (December 2020): 1352–64. http://dx.doi.org/10.1139/cjfr-2020-0033.
Повний текст джерелаАнотація:
In many countries, airborne laser scanning (ALS) inventories are implemented to produce predictions for stand-level forest attributes. Nevertheless, mature stands are usually field-visited prior to clear-cutting, so some measurements can be conducted on these stands to calibrate the ALS-based predictions. In this paper, we developed a seemingly unrelated multivariate mixed-effects model system that includes component models for basal area, merchantable volume, and sawlog volume for 225 m2 cells. We used ALS data and accurately positioned cut-to-length harvester observations from clear-cut stands dominated by Norway spruce (Picea abies (L.) Karst.). Our aim was to study the effect of 1–10 local angle-gauge basal area measurements on the accuracy of predicted merchantable and sawlog volumes. A seemingly unrelated mixed-effect model system was fitted to estimate cross-model correlations in residuals and random effects, which were then utilized to predict all the random effects of the system for calibrated stand-level predictions. The 10 angle-gauge plots decreased the relative root mean square error (RMSE%) of the basal area and merchantable volume predictions from 16.8% to 10.5% and from 15.8% to 11.9%, respectively. Cross-model correlations of the stand effects of sawlog volume with the other responses were low; therefore, the initial RMSE% of ∼22% was decreased only marginally by the calibration.
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Barrett, T. M. "Optimizing efficiency of height modeling for extensive forest inventories." Canadian Journal of Forest Research 36, no. 9 (September 1, 2006): 2259–69. http://dx.doi.org/10.1139/x06-128.
Повний текст джерелаАнотація:
Although critical to monitoring forest ecosystems, inventories are expensive. This paper presents a generalizable method for using an integer programming model to examine tradeoffs between cost and estimation error for alternative measurement strategies in forest inventories. The method is applied to an example problem of choosing alternative height-modeling strategies for 1389 plots inventoried by field crews traveling within an 82.5 × 106 ha region of the west coast of North America during one field season. In the first part of the application, nonlinear regional height models were constructed for 38 common species using a development data set of 137 374 measured tree heights, with root mean square error varying from 6.7 to 2.1 m. In the second part of the application, alternative measurement strategies were examined using a minimal cost objective subject to constraints on travel time and estimation error. Reduced travel time for field crews can be a significant portion of the cost savings from modeling tree heights. The optimization model was used to identify a height-modeling strategy that, given assumptions made, resulted in <10% of maximum average plot volume error, >33% of potential measurement cost savings, and small bias for estimates of regional volume and associated sampling error (0.1% and 0.4%, respectively).
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Stenberg, Pauline, Lauri Korhonen, and Miina Rautiainen. "A relascope for measuring canopy cover." Canadian Journal of Forest Research 38, no. 9 (September 2008): 2545–50. http://dx.doi.org/10.1139/x08-081.
Повний текст джерелаАнотація:
Angle count or relascope sampling has traditionally been used in forestry to estimate stand basal area. In this paper, we present an extension to the basal area relascope, the crown relascope, which differs from the normal relascope in that the relascope’s slot is very high and wide. We describe the theoretical basis of the instrument and present results from a field test in which a crown relascope with a basal area factor of 250 m2/ha (0.025) was used to estimate canopy cover of 73 sample plots in northern Finland. The crown relascope estimates had a root mean square difference of 9.3% and an average difference of –3.1% when compared with estimates obtained with the control method, line intersect sampling using the Cajanus tube. The results indicated that the crown relascope is a quick and fairly reliable instrument for canopy cover estimation, especially in relatively sparse forests where crown overlap is insignificant and visibility does not limit an efficient use of the instrument.
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Gregoire, T. G., S. M. Zedaker, and N. S. Nicholas. "Modeling relative error in stem basal area estimates." Canadian Journal of Forest Research 20, no. 5 (May 1, 1990): 496–502. http://dx.doi.org/10.1139/x90-065.
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The computed estimate of basal area was the focus of this investigation into the accuracy of tree diameter measurements. Diameter at breast height was measured on 101 trees, the breast height disc was removed from the stem, and each disc outline was then digitized to determine its actual area. Actual areas were compared with the basal areas computed with the field measurements of diameter by expressing the deviations as a proportion of the actual areas. These relative errors averaged 3.1% when basal area was computed with a taped diameter at breast height, and they averaged −2.5% when the mean of two calipered, perpendicular diameter at breast height measurements were used. Noncircularity accounted for about 2.6% of the bias, on average, in basal areas from taped diameters. Root mean square error of relative basal area errors ranged from 7.3 to 7.9% among these methods. The difference between basal areas computed with a taped diameter at breast height and with calipered diameter at breast height was computed and expressed as a proportion of the actual basal area. On average, a difference of 5% was observed. Interval estimates for these differences are presented.
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Jaafar, Hadi, and Roya Mourad. "GYMEE: A Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine Based on Landsat, Weather, and Soil Data." Remote Sensing 13, no. 4 (February 20, 2021): 773. http://dx.doi.org/10.3390/rs13040773.
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In this study, we used Landsat Earth observations and gridded weather data along with global soil datasets available in Google Earth Engine (GEE) to estimate crop yield at 30 m resolution. We implemented a remote sensing and evapotranspiration-based light use efficiency model globally and integrated abiotic environmental stressors (temperature, soil moisture, and vapor deficit stressors). The operational model (Global Yield Mapper in Earth Engine (GYMEE)) was validated against actual yield data for three agricultural schemes with different climatic, soil, and management conditions located in Lebanon, Brazil, and Spain. Field-level crop yield data on wheat, potato, and corn for 2015–2020 were used for assessment. The performance of GYMEE was statistically evaluated through root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), relative error (RE), and index of agreement (d). The results showed that the absolute difference between the modeled and predicted field-level yield was within ±16% for the analyzed crops in both Brazil and Lebanon study sites and within ±15% in the Spain site (except for two fields). GYMEE performed best for wheat crop in Lebanon with a low RMSE (0.6 t/ha), MAE (0.5 t/ha), MBE (−0.06 t/ha), and RE (0.83%). A very good agreement was observed for all analyzed crop yields, with an index of agreement (d) averaging at 0.8 in all studied sites. GYMEE shows potential in providing yield estimates for potato, wheat, and corn yields at a relative error of ±6%. We also quantified and spatialized the soil moisture stress constraint and its impact on reducing biomass production. A showcasing of moisture stress impact on two emphasized fields from the Lebanon site revealed that a 12% difference in soil moisture stress can decrease yield by 17%. A comparison between the 2017 and 2018 seasons for the potato culture of Lebanon showed that the 2017 season with lower abiotic stresses had higher light use efficiency, above-ground biomass, and yield by 5%, 10%, and 9%, respectively. The results show that the model is of high value for assessing global food production.
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Nasiri, Vahid, Ali A. Darvishsefat, Hossein Arefi, Marc Pierrot-Deseilligny, Manochehr Namiranian, and Arnaud Le Bris. "Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (case study: Hyrcanian mixed forest)." Canadian Journal of Forest Research 51, no. 7 (July 2021): 962–71. http://dx.doi.org/10.1139/cjfr-2020-0125.
Повний текст джерелаАнотація:
Tree height and crown diameter are two common individual tree attributes that can be estimated from unmanned aerial vehicle (UAV) images thanks to photogrammetry and structure from motion. This research investigates the potential of low-cost UAV aerial images to estimate tree height and crown diameter. Two successful flights were carried out in two different seasons corresponding to leaf-off and leaf-on conditions to generate a digital terrain model and a digital surface model, which were further employed in calculation of a canopy height model (CHM). The CHM was used to estimate tree height using low pass and local maximum filters, and crown diameter was estimated based on an inverse watershed segmentation algorithm. UAV-based tree height and crown diameter estimates were validated against field measurements and resulted in 3.22 m (10.1%) and 0.81 m (7.02%) root mean square errors, respectively. The results showed high agreement between our estimates and field measurements, with an R2 of 0.808 for tree height and an R2 of 0.923 for crown diameter. Generally, the accuracy of the results was considered acceptable and confirmed the usefulness of this approach for estimating tree heights and crown diameter.
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Gonzalez Musso, Romina F., Facundo J. Oddi, Matías G. Goldenberg, and Lucas A. Garibaldi. "Applying unmanned aerial vehicles (UAVs) to map shrubland structural attributes in northern Patagonia, Argentina." Canadian Journal of Forest Research 50, no. 7 (July 2020): 615–23. http://dx.doi.org/10.1139/cjfr-2019-0440.
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Unmanned aerial vehicles (UAVs) have gained attention for forestry applications in recent years. These technologies provide ultrahigh-resolution spatial data for detailed mapping of forest structure, among other forestry applications. UAVs have mainly been tested in high-value timber stands, but little is known about their performance in other woody ecosystems such as shrublands that also provide key ecosystem services. Field measurements in shrublands are time-consuming, so UAVs could be used instead to provide data for shrubland management and conservation. We tested whether UAVs could map common structural attributes in shrublands of northern Patagonia. We specifically evaluated the capability of UAV point clouds for mapping (i) canopy height, (ii) stand density, (iii) basal area, and (iv) volume. The agreement with the field measurements was satisfactory (R2 was up to 0.95 and relative root mean square error (rRMSE) ranged between 12% and 39%) and comparable with those found for coniferous forests in similar studies. This study is a first attempt to characterize the structure of Patagonian shrublands using UAV data. Despite the challenges and methodological aspects that need to be solved, our results encourage the use of UAVs in these types of ecosystems.
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Wong, Charissa J., Daniel James, Normah A. Besar, Kamlisa U. Kamlun, Joseph Tangah, Satoshi Tsuyuki, and Mui-How Phua. "Estimating Mangrove Above-Ground Biomass Loss Due to Deforestation in Malaysian Northern Borneo between 2000 and 2015 Using SRTM and Landsat Images." Forests 11, no. 9 (September 22, 2020): 1018. http://dx.doi.org/10.3390/f11091018.
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Mangrove forests are highly productive ecosystems and play an important role in the global carbon cycle. We used Shuttle Radar Topography Mission (SRTM) elevation data to estimate mangrove above-ground biomass (AGB) in Sabah, Malaysian northern Borneo. We developed a tree-level approach to deal with the substantial temporal discrepancy between the SRTM data and the mangrove’s field measurements. We predicted the annual growth of diameter at breast height and adjusted the field measurements to the SRTM data acquisition year to estimate the field AGB. A canopy height model (CHM) was derived by correcting the SRTM data with ground elevation. Regression analyses between the estimated AGB and SRTM CHM produced an estimation model (R2: 0.61) with a root mean square error (RMSE) of 8.24 Mg ha−1 (RMSE%: 5.47). We then quantified the mangrove forest loss based on supervised classification of multitemporal Landsat images. More than 25,000 ha of mangrove forest had disappeared between 2000 and 2015. This has resulted in a significant decrease of about 3.96 million Mg of mangrove AGB in Sabah during the study period. As SRTM elevation data has a near-global coverage, this approach can be used to map the historical AGB of mangroves, especially in Southeast Asia, to promote mangrove carbon stock conservation.
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Omar Jr., Dawin Maghanoy. "SMS-OPTIMIZED CONSUMER-GRADE TRACKING SYSTEM FOR LOW-COST MONITORING." ASEAN Engineering Journal 11, no. 4 (October 26, 2021): 29–44. http://dx.doi.org/10.11113/aej.v11.17862.
Повний текст джерелаАнотація:
Consumer grade tracking systems exist but are not optimally designed for low-cost operation in the Philippines setting. Tracking devices typically use Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies. Commonly, for device management and real-time tracking, data are transmitted as General Packet Radio Service (GPRS) to a proprietary or third-party cloud service. This method is costly in the Philippines context as well as power consuming. This study was conducted to design a tracking system that is optimized for short messaging service (SMS) mode of data transmission. This study covered the design of a tracking device using consumer-grade hardware components, development of system interface to enable remote operation using commands sent as short message(s), development of a simple desktop client monitoring service and actual field-testing using land and water vehicles. Tests results showed a 100% tracking data delivery efficiency and small difference between Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for each route indicating that there is a small variance in distance errors. The tracking device also performed well in a sea route which is comparable to a commercial standard GPS data logger. Test results also showed that the device can be used for tracking applications i.e., vehicle security, inland fleet management, monitoring of tourist boats, and research or field surveys, where GPS and GSM are available.
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Tereszchuk, Keith A., Yves J. Rochon, Chris A. McLinden, and Paul A. Vaillancourt. "Optimizing UV Index determination from broadband irradiances." Geoscientific Model Development 11, no. 3 (March 27, 2018): 1093–113. http://dx.doi.org/10.5194/gmd-11-1093-2018.
Повний текст джерелаАнотація:
Abstract. A study was undertaken to improve upon the prognosticative capability of Environment and Climate Change Canada's (ECCC) UV Index forecast model. An aspect of that work, and the topic of this communication, was to investigate the use of the four UV broadband surface irradiance fields generated by ECCC's Global Environmental Multiscale (GEM) numerical prediction model to determine the UV Index. The basis of the investigation involves the creation of a suite of routines which employ high-spectral-resolution radiative transfer code developed to calculate UV Index fields from GEM forecasts. These routines employ a modified version of the Cloud-J v7.4 radiative transfer model, which integrates GEM output to produce high-spectral-resolution surface irradiance fields. The output generated using the high-resolution radiative transfer code served to verify and calibrate GEM broadband surface irradiances under clear-sky conditions and their use in providing the UV Index. A subsequent comparison of irradiances and UV Index under cloudy conditions was also performed. Linear correlation agreement of surface irradiances from the two models for each of the two higher UV bands covering 310.70–330.0 and 330.03–400.00 nm is typically greater than 95 % for clear-sky conditions with associated root-mean-square relative errors of 6.4 and 4.0 %. However, underestimations of clear-sky GEM irradiances were found on the order of ∼ 30–50 % for the 294.12–310.70 nm band and by a factor of ∼ 30 for the 280.11–294.12 nm band. This underestimation can be significant for UV Index determination but would not impact weather forecasting. Corresponding empirical adjustments were applied to the broadband irradiances now giving a correlation coefficient of unity. From these, a least-squares fitting was derived for the calculation of the UV Index. The resultant differences in UV indices from the high-spectral-resolution irradiances and the resultant GEM broadband irradiances are typically within 0.2–0.3 with a root-mean-square relative error in the scatter of ∼ 6.6 % for clear-sky conditions. Similar results are reproduced under cloudy conditions with light to moderate clouds, with a relative error comparable to the clear-sky counterpart; under strong attenuation due to clouds, a substantial increase in the root-mean-square relative error of up to 35 % is observed due to differing cloud radiative transfer models.
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Mahmud, Md Sultan, Qamar U. Zaman, Travis J. Esau, Young K. Chang, G. W. Price, and Balakrishnan Prithiviraj. "Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System." Agronomy 10, no. 7 (July 16, 2020): 1027. http://dx.doi.org/10.3390/agronomy10071027.
Повний текст джерелаАнотація:
Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (>8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.
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Herbert, Tata, Nzelibe Ifechukwu Ugochukwu, and Raufu Ibrahim Olatunji. "Assessing the accuracy of online GNSS processing services and commercial software on short baselines." South African Journal of Geomatics 9, no. 2 (September 7, 2022): 321–32. http://dx.doi.org/10.4314/sajg.v9i2.21.
Повний текст джерелаАнотація:
With the rapid establishment of free online processing services to provide users with reliable solutions, it is important to determine the reliability of using free online processing software for the Global Navigation Satellite System post-processing. The study aim at assessing the accuracy of two (2) free online processing software, AUSPOS, and CSRS-PPP and two (2) commercial software, compass post-processing, and GNSS solutions. Field observations were carried out on seven (7) control points using static GNSS observation techniques with an observation period of 1hr for three (3) consecutive days and conventional surveying using total station instruments to establish a closed traverse. The acquired data were post-processed using both online and commercial software. The co-ordinates generated from each software were then compared with the ones obtained using total station instruments to determine their relative discrepancies and accuracies. Root mean square error and T-test were used for the analysis of the result. The result obtained is (0.004m, 0.003m and 0.007m) for compass post-processing software and (0.015m, 0.012m and 0.016m) for GNSS solutions software and the online software had the Root Mean Square Error (RMSE) values of (0.025m, 0.023m and 0.027m) for AUSPOS and (0.034m, 0.037m and 0.041m) for CSRS-PPP both in X, Y, and Z direction i.e. UTM East, North and ellipsoidal height respectively. Analysis at a 5% level of significance shows no significant difference between the two methods. Online GNSS processing services are easy to use, do not require the knowledge of GNSS data processing and can be adopted for engineering and geodetic applications.
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Lu, Jinwen, Yanjin Zhang, Hua Yan, Lei Zhang, and Hongcheng Yin. "Global Scattering Center Representation of Target Wide-Angle Single Reflection/Diffraction Mechanisms Based on the Multiple Manifold Concept." Electronics 11, no. 24 (December 16, 2022): 4209. http://dx.doi.org/10.3390/electronics11244209.
Повний текст джерелаАнотація:
Many radar applications, such as system design and testing and target detection and recognition, need to compress and rapidly reconstruct the target-scattering characteristics data through some suitable sparse representations, while the scattering center (SC) model resulting from different scattering mechanisms is just a prospective candidate. For the target scattering characteristics data with single reflection/diffraction mechanisms, the multimanifold structures of wide-angle SCs were revealed in light of asymptotic high-frequency theory and ray theory of electromagnetic field. Then the multimanifold clustering and curve/surface fitting algorithms are introduced to construct the target global SC (GSC) model. Compared with simulation data of sphere-cone target, the RCS at elevation 90°, azimuth 0–180° can be accurately reconstructed by only 77 parameters, the compress rate and root mean square error are 0.66 and 1.17 dB respectively. Simulation results showed that the GSC model could greatly compress the wide-angle scattering data while ensuring a suitable reconstruction accuracy. The proposed multimanifold GSC representation is convenient to implement and can effectively replace the redundant original scattering characteristics data.
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Long, Xunjian, and Yaokui Cui. "Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product." Remote Sensing 14, no. 3 (January 29, 2022): 658. http://dx.doi.org/10.3390/rs14030658.
Повний текст джерелаАнотація:
High spatiotemporal resolution evapotranspiration (ET) data are very important for end users to manage water resources. The global ET product always has a high temporal resolution, but the spatial resolution is too low to meet the requirements of most end users. In this study, we developed a deep neural network (DNN)-based global ET product downscaling algorithm by combining remotely sensed and meteorological data sets as the input data. The relationship between global ET product and input data was built at a low spatial resolution using the DNN. Then, this relationship was applied at high spatial resolution to generate high spatial resolution ET derived from the input data with high spatial resolution. Taking the Global Land Evaporation Amsterdam Model (GLEAM) ET product as an example, downscaled ET was found to be highly consistent with the original GLEAM ET product, but to have high spatial resolution. Field validations showed that the overall coefficient of correlation and root mean square error (bias, Nash–Sutcliffe efficiency coefficient) of the downscaled GLEAM ET is 0.90 and 0.87 mm/d (−0.32 mm/d, 0.62), respectively, indicating high quality. The proposed method bridged the gaps between the global ET product and the requirements of local end users. This will benefit end users in charge of water resources management.
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Raufu, Ibrahim Olatunji, and Herbert Tata. "Comparison of Two Corrector Surface Models of Orthometric Heights from GPS/Levelling Observations and Global Gravity Model." JGISE: Journal of Geospatial Information Science and Engineering 5, no. 1 (June 30, 2022): 15. http://dx.doi.org/10.22146/jgise.72531.
Повний текст джерелаАнотація:
The advent of space-based measurement systems such as the Global Positioning System (GPS) offers a new alternative in orthometric height determination over conventional spirit levelling. The ellipsoidal height (h) obtained from GPS observations can be transformed into orthometric height if the geoid undulation (N) is known from a national gravimetric geoid model. However, the lack of a national geoid model in Nigeria hinders the use of the method. This study compares two corrector surface models of orthometric heights from GPS/levelling observations and the Global Gravity Model. Model A (7-parameter) and Model B (8-parameter) are based on the general 7-parameter similarity datum shift transformation. A network of twenty-one (21) GPS/levelling benchmarks within the study area were used and their geoidal heights were computed using GeoidEval utility software with reference to Global Gravitational Model (EGM08). Least squares adjustment was used to compute the coefficients of the models. Root mean square error (RMSE) was used to assess the accuracy of the models with model A having RMSE=0.171m and model B having RMSE=0.169m. Model B with the lowest RMSE is hence the better of the two models. The t-test and hypothesis test conducted at a 95% confidence level, however, revealed that the two models did not differ significantly. The study shows that the use of corrective surface to combine the gravity field model EGM08 and GPS/levelling significantly improves the determination of heights as observed from GPS in the study area.
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PATIL, N. G., G. S. RAJPUT, R. K. NEMA, and R. B. SINGH. "Predicting hydraulic properties of seasonally impounded soils." Journal of Agricultural Science 148, no. 2 (October 8, 2009): 159–70. http://dx.doi.org/10.1017/s002185960999030x.
Повний текст джерелаАнотація:
SUMMARYAgricultural crop management decisions often require data on hydraulic properties of soils. Little information is available on hydraulic properties of clay soils that are impounded by rainwater (known as ‘Haveli’ lands) every year during the monsoon season in large tracts of Madhya Pradesh in India. Estimating hydraulic properties using global pedotransfer functions (PTFs) is one possible way to collect such information. Rules in the widely used global PTF Rosetta were executed to obtain estimates of two important hydraulic properties, namely soil water retention characteristics (SWRC) and saturated hydraulic conductivity (Ks). SWRC estimates obtained with maximum input (particle size distribution, bulk density, field capacity and permanent wilting point) in Rosetta were relatively closer to the laboratory-measured data as compared with the estimates obtained with lower levels of input. Root mean square error (RMSE) of estimates ranged from 0·01 to 0·05 m3/m3. Hierarchical PTFs to predictKsfrom basic soil properties were derived using statistical regression and artificial neural networks. Evaluation of these indicated that neural PTFs were acceptable and hence could be used without loss of accuracy.
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Toohey, M., and T. von Clarmann. "Climatologies from satellite measurements: the impact of orbital sampling on the standard error of the mean." Atmospheric Measurement Techniques 6, no. 4 (April 10, 2013): 937–48. http://dx.doi.org/10.5194/amt-6-937-2013.
Повний текст джерелаАнотація:
Abstract. Climatologies of atmospheric observations are often produced by binning measurements according to latitude and calculating zonal means. The uncertainty in these climatological means is characterised by the standard error of the mean (SEM). However, the usual estimator of the SEM, i.e., the sample standard deviation divided by the square root of the sample size, holds only for uncorrelated randomly sampled measurements. Measurements of the atmospheric state along a satellite orbit cannot always be considered as independent because (a) the time-space interval between two nearest observations is often smaller than the typical scale of variations in the atmospheric state, and (b) the regular time-space sampling pattern of a satellite instrument strongly deviates from random sampling. We have developed a numerical experiment where global chemical fields from a chemistry climate model are sampled according to real sampling patterns of satellite-borne instruments. As case studies, the model fields are sampled using sampling patterns of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and Atmospheric Chemistry Experiment Fourier-Transform Spectrometer (ACE-FTS) satellite instruments. Through an iterative subsampling technique, and by incorporating information on the random errors of the MIPAS and ACE-FTS measurements, we produce empirical estimates of the standard error of monthly mean zonal mean model O3 in 5° latitude bins. We find that generally the classic SEM estimator is a conservative estimate of the SEM, i.e., the empirical SEM is often less than or approximately equal to the classic estimate. Exceptions occur only when natural variability is larger than the random measurement error, and specifically in instances where the zonal sampling distribution shows non-uniformity with a similar zonal structure as variations in the sampled field, leading to maximum sensitivity to arbitrary phase shifts between the sample distribution and sampled field. The occurrence of such instances is thus very sensitive to slight changes in the sampling distribution, and to the variations in the measured field. This study highlights the need for caution in the interpretation of the oft-used classically computed SEM, and outlines a relatively simple methodology that can be used to assess one component of the uncertainty in monthly mean zonal mean climatologies produced from measurements from satellite-borne instruments.
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Feng, Jiandi, Baomin Han, Zhenzhen Zhao, and Zhengtao Wang. "A New Global Total Electron Content Empirical Model." Remote Sensing 11, no. 6 (March 24, 2019): 706. http://dx.doi.org/10.3390/rs11060706.
Повний текст джерелаАнотація:
Research on total electron content (TEC) empirical models is one of the important topics in the field of space weather services. Global TEC empirical models based on Global Ionospheric Maps (GIMs) TEC data released by the International GNSS Service (IGS) have developed rapidly in recent years. However, the accuracy of such global empirical models has a crucial restriction arising from the non-uniform accuracy of IGS TEC data in the global scope. Specifically, IGS TEC data accuracy is higher on land and lower over the ocean due to the lack of stations in the latter. Using uneven precision GIMs TEC data as a whole for model fitting is unreasonable. Aiming at the limitation of global ionospheric TEC modelling, this paper proposes a new global ionospheric TEC empirical model named the TECM-GRID model. The model consists of 5183 sections, corresponding to 5183 grid points (longitude 5°, latitude 2.5°) of GIM. Two kinds of single point empirical TEC models, SSM-T1 and SSM-T2, are used for TECM-GRID. According to the locations of grid points, the SSM-T2 model is selected as the sub-model in the Mid-Latitude Summer Night Anomaly (MSNA) region, and SSM-T1 is selected as the sub-model in other regions. The fitting ability of the TECM-GRID model for modelling data was tested in accordance with root mean square (RMS) and relative RMS values. Then, the TECM-GRID model was validated and compared with the NTCM-GL model and Center for Orbit Determination in Europe (CODE) GIMs at time points other than modelling time. Results show that TECM-GRID can effectively describe the Equatorial Ionization Anomaly (EIA) and the MSNA phenomena of the ionosphere, which puts it in good agreement with CODE GIMs and means that it has better prediction ability than the NTCM-GL model.
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Melton, Joe R., Ed Chan, Koreen Millard, Matthew Fortier, R. Scott Winton, Javier M. Martín-López, Hinsby Cadillo-Quiroz, Darren Kidd, and Louis V. Verchot. "A map of global peatland extent created using machine learning (Peat-ML)." Geoscientific Model Development 15, no. 12 (June 20, 2022): 4709–38. http://dx.doi.org/10.5194/gmd-15-4709-2022.
Повний текст джерелаАнотація:
Abstract. Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed by Earth system models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here, we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning (ML) techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of peatland formation and include spatially distributed climate, geomorphological and soil data, and remotely sensed vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualitative comparisons to other maps in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r2 of 0.73 with a root-mean-square error and mean bias error of 9.11 % and −0.36 %, respectively. Our second error estimate was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping products generated through more traditional approaches, at least for boreal peatlands.