scholarly journals Climate change in a typical Apulian region for table grape production: spatialisation of bioclimatic indices, classification and Future Scenarios

OENO One ◽  
2021 ◽  
Vol 55 (3) ◽  
pp. 317-336
Author(s):  
Vittorio Alba ◽  
Giovanni Gentilesco ◽  
Luigi Tarricone
Keyword(s):  
Classification System ◽  
Time Window ◽  
Interpolation Method ◽  
Table Grape ◽  
Future Scenarios ◽  
Bioclimatic Indices ◽  
Window Period ◽  
Climatic Classification ◽  
Maximum Temperatures ◽  
Elevation Model

The present research focused on the characterisation of climate evolution in a typical Apulian region for table grape production under the protected geographical indication, “Uva di Puglia I.G.P.”Two thirty-year time window period (TW) were analysed: 1961-1990 and 1991-2020. Georeferenced maps for both TWs were produced to delimit homogeneous zones and to evaluate the climate variability within the investigated area by means of the two bioclimatic indices, Heliothermal Index (HI) and Winkler Index (WI). Spatial analysis of HI and WI was performed using the regression-kriging (RK) interpolation method and the Digital Elevation Model/DEM (10 x 10 m) as a prediction attribute.An increase in both the minimum and maximum temperatures was observed, and locations above 300 m a.s.l. shifted from HI+1 “temperate warm” to HI+2 “warm” according to the Geoviticulture Multicriteria Climatic Classification System. WI values similarly increased between the periods 1961–1990 and 1991–2020, shifting all the sites grouped in the Elevation Classes defined as being below 300 m a.s.l. from Region IV to Region V of the Winkler Classification.According to HI and WI, presumed maturity was calculated as being reached 9 to 15 (HI) and 12 to 28 days (WI) earlier in 1991–2020 than in 1961–1990, taking into account the heat requirements of cv. Italia table grape (representative of Apulian table grape production), were set at 2200 for both indices on the basis of literature data.Moreover, three table grape vineyards, located in the three main producing provinces of Apulia (Bari, Taranto and Barletta-Andria-Trani (BAT)), were considered for future scenarios analysis on the basis of two different Representative Concentration Pathways (RCPs), 4.5 and 8.5, and classified according to the Geoviticulture Multicriteria Climatic Classification System (MCC). Future scenarios scored WI values that exceeded the threshold of 2700 in the BAT and TA provinces in the 2061–2090 time window period for RCP 8.5. In contrast, RCP 4.5 led to a mitigating effect, which was not noticeable until 2040, with a consequent reclassification of the investigated areas on the basis of HI and Cool Night Index (CI).These findings suggest that in order to prevent or overcome heat stress, it will be necessary to implement strategies, such as vineyard relocation to unexplored elevations or latitudes and/or the exploitation of new table grape varieties able to fulfill the optimal maturity parameters, even when the duration of the phenological phases is shorter.

scholarly journals Effect of DEM Interpolation Neighbourhood on Terrain Factors

2019 ◽  
Vol 8 (1) ◽  
pp. 30 ◽  
Author(s):  
Ying Zhu ◽  
Xuejun Liu ◽  
Jing Zhao ◽  
Jianjun Cao ◽  
Xiaolei Wang ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Simulated Data ◽  
Topographic Features ◽  
Structure And Morphology ◽  
Digital Elevation ◽  
Different Types ◽  
Slope Error ◽  
Elevation Model ◽  
The Impact ◽  
Mls Method

Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, mean aspect, and RMSEs of slope and aspect from the interpolated DEM. As the moving least squares (MLS) method can maintain the inherent properties and other characteristics of a surface, this method is chosen for DEM interpolation. Three areas containing different types of topographic features are selected for study. Simulated data from a Gauss surface is also used for comparison. First, the impact of the number of points on the DEM root mean square error (RMSE) is analysed. The DEM RMSE in the three study areas decreases gradually with the number of points in the neighbourhood. In addition, the effect of the number of points in the neighbourhood on mean slope and mean aspect was studied across varying topographies through regression analysis. The two variables respond differently to changes in terrain. However, the RMSEs of the slope and aspect in all study areas are logarithmically related to the number of points in the neighbourhood and the values decrease uniformly as the number of points in the neighbourhood increases. With more points in the neighbourhood, the RMSEs of the slope and aspect are not sensitive to topography differences and the same trends are observed for the three studied quantities. Results for the Gauss surface are similar. Finally, this study analyses the spatial distribution of slope and aspect errors. The slope error is concentrated in ridges, valleys, steep-slope areas, and ditch edges while the aspect error is concentrated in ridges, valleys, and flat regions. With more points in the neighbourhood, the number of grid cells in which the slope error is greater than 15° is gradually reduced. With similar terrain types and data sources, if the calculation efficiency is not a concern, sufficient points in the spatial autocorrelation range should be analysed in the neighbourhood to maximize the accuracy of the slope and aspect. However, selecting between 10 and 12 points in the neighbourhood is economical.

scholarly journals A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 698 ◽  
Author(s):  
Chen Liu ◽  
Nanshan Zheng ◽  
Kefei Zhang ◽  
Junyu Liu
Keyword(s):  
Water Vapor ◽  
Interpolation Method ◽  
Geographical Location ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Virtual Sample ◽  
Weighted Mean Temperature ◽  
Sample Points ◽  
Elevation Model ◽  
A Minor

Abstract: The objective of the study was to put forth an interpolation method (the LZ method) for refining the GNSS-derived precipitable water vapor (PWV) map. We established a regional weighted mean temperature (Tm) model for this experiment, which introduced a minor difference into the resultant GNSS-derived PWV compared to the previous Tm models. The kernel of the LZ method consists of increasing the sample density via the virtual sample points. These virtual sample points originated from the digital elevation model (DEM) were constructed on the basis of the statistically significant correlation between PWV and geographical location (i.e., geographical coordinates and elevation). The LZ method was validated and compared to the conventional interpolation approach only accounting for the original sample points. The results reflect that the PWV maps generated by the LZ method showed more details than through conventional one. In addition, the prediction performance of the LZ method was better than that of the conventional method by using cross-validation.

scholarly journals Creating long-term gridded fields of reference evapotranspiration in Alpine terrain based on a recalibrated Hargreaves method

2016 ◽  
Vol 20 (3) ◽  
pp. 1211-1223 ◽  
Author(s):  
Klaus Haslinger ◽  
Annett Bartsch
Keyword(s):  
High Resolution ◽  
Reference Evapotranspiration ◽  
Grid Point ◽  
Temperature Fields ◽  
Time Step ◽  
Digital Elevation ◽  
Maximum Temperatures ◽  
Elevation Model ◽  
Relative Errors ◽  
Mean Square Errors

Abstract. A new approach for the construction of high-resolution gridded fields of reference evapotranspiration for the Austrian domain on a daily time step is presented. Gridded data of minimum and maximum temperatures are used to estimate reference evapotranspiration based on the formulation of Hargreaves. The calibration constant in the Hargreaves equation is recalibrated to the Penman–Monteith equation in a monthly and station-wise assessment. This ensures, on one hand, eliminated biases of the Hargreaves approach compared to the formulation of Penman–Monteith and, on the other hand, also reduced root mean square errors and relative errors on a daily timescale. The resulting new calibration parameters are interpolated over time to a daily temporal resolution for a standard year of 365 days. The overall novelty of the approach is the use of surface elevation as the only predictor to estimate the recalibrated Hargreaves parameter in space. A third-order polynomial is fitted to the recalibrated parameters against elevation at every station which yields a statistical model for assessing these new parameters in space by using the underlying digital elevation model of the temperature fields. With these newly calibrated parameters for every day of year and every grid point, the Hargreaves method is applied to the temperature fields, yielding reference evapotranspiration for the entire grid and time period from 1961–2013. This approach is opening opportunities to create high-resolution reference evapotranspiration fields based only temperature observations, but being as close as possible to the estimates of the Penman–Monteith approach.

scholarly journals DEM Void Filling Based on Context Attention Generation Model

2020 ◽  
Vol 9 (12) ◽  
pp. 734
Author(s):  
Chunsen Zhang ◽  
Shu Shi ◽  
Yingwei Ge ◽  
Hengheng Liu ◽  
Weihong Cui
Keyword(s):  
Interpolation Method ◽  
Digital Simulation ◽  
Generation Model ◽  
Generative Adversarial Network ◽  
Cloud Data ◽  
Adversarial Network ◽  
Network Training ◽  
Digital Elevation ◽  
Elevation Model ◽  
Feature Expression

The digital elevation model (DEM) generates a digital simulation of ground terrain in a certain range with the usage of 3D point cloud data. It is an important source of spatial modeling information. Due to various reasons, however, the generated DEM has data holes. Based on the algorithm of deep learning, this paper aims to train a deep generation model (DGM) to complete the DEM void filling task. A certain amount of DEM data and a randomly generated mask are taken as network inputs, along which the reconstruction loss and generative adversarial network (GAN) loss are used to assist network training, so as to perceive the overall known elevation information, in combination with the contextual attention layer, and generate data with reliability to fill the void areas. The experimental results have managed to show that this method has good feature expression and reconstruction accuracy in DEM void filling, which has been proven to be better than that illustrated by the traditional interpolation method.

scholarly journals APPLICABILITY OF VARIOUS INTERPOLATION APPROACHES FOR HIGH RESOLUTION SPATIAL MAPPING OF CLIMATE DATA IN KOREA

2018 ◽  
Vol XLII-3 ◽  
pp. 703-710 ◽  
Author(s):  
A. Jo ◽  
J. Ryu ◽  
H. Chung ◽  
Y. Choi ◽  
S. Jeon
Keyword(s):  
Interpolation Method ◽  
Training Data ◽  
Spatial Mapping ◽  
Observation Data ◽  
Climate Data ◽  
Weather System ◽  
Validation Data ◽  
Instantaneous Temperature ◽  
Elevation Model ◽  
Total Point

The purpose of this study is to create a new dataset of spatially interpolated monthly climate data for South Korea at high spatial resolution (approximately 30m) by performing various spatio-statistical interpolation and comparing with forecast LDAPS gridded climate data provided from Korea Meterological Administration (KMA). Automatic Weather System (AWS) and Automated Synoptic Observing System (ASOS) data in 2017 obtained from KMA were included for the spatial mapping of temperature and rainfall; instantaneous temperature and 1-hour accumulated precipitation at 09:00 am on 31th March, 21th June, 23th September, and 24th December. Among observation data, 80 percent of the total point (478) and remaining 120 points were used for interpolations and for quantification, respectively. With the training data and digital elevation model (DEM) with 30 m resolution, inverse distance weighting (IDW), co-kriging, and kriging were performed by using ArcGIS10.3.1 software and Python 3.6.4. Bias and root mean square were computed to compare prediction performance quantitatively. When statistical analysis was performed for each cluster using 20 % validation data, co kriging was more suitable for spatialization of instantaneous temperature than other interpolation method. On the other hand, IDW technique was appropriate for spatialization of precipitation.

scholarly journals EVALUATION DIGITAL ELEVATION MODEL GENERATED BY SYNTHETIC APERTURE RADAR DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 57-62
Author(s):  
H. B. Makineci ◽  
H. Karabörk
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Interpolation Method ◽  
Digital Elevation Models ◽  
European Space Agency ◽  
Mountain Area ◽  
Synthetic Aperture Radar Data ◽  
Radar Images ◽  
Digital Elevation ◽  
Elevation Model

Digital elevation model, showing the physical and topographical situation of the earth, is defined a tree-dimensional digital model obtained from the elevation of the surface by using of selected an appropriate interpolation method. DEMs are used in many areas such as management of natural resources, engineering and infrastructure projects, disaster and risk analysis, archaeology, security, aviation, forestry, energy, topographic mapping, landslide and flood analysis, Geographic Information Systems (GIS). Digital elevation models, which are the fundamental components of cartography, is calculated by many methods. Digital elevation models can be obtained terrestrial methods or data obtained by digitization of maps by processing the digital platform in general. Today, Digital elevation model data is generated by the processing of stereo optical satellite images, radar images (radargrammetry, interferometry) and lidar data using remote sensing and photogrammetric techniques with the help of improving technology. <br><br> One of the fundamental components of remote sensing radar technology is very advanced nowadays. In response to this progress it began to be used more frequently in various fields. Determining the shape of topography and creating digital elevation model comes the beginning topics of these areas. <br><br> It is aimed in this work , the differences of evaluation of quality between Sentinel-1A SAR image ,which is sent by European Space Agency ESA and Interferometry Wide Swath imaging mode and C band type , and DTED-2 (Digital Terrain Elevation Data) and application between them. The application includes RMS static method for detecting precision of data. Results show us to variance of points make a high decrease from mountain area to plane area.

scholarly journals Solar Radiation Estimation Using Data Mining Techniques for Remote Areas—A Case Study in Ethiopia

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5714
Author(s):  
Bizuayehu Abebe Worke ◽  
Hans Bludszuweit ◽  
José A. Domínguez-Navarro
Keyword(s):  
Data Mining ◽  
Solar Radiation ◽  
Digital Elevation Model ◽  
Interpolation Method ◽  
Inference System ◽  
Remote Areas ◽  
Radiation Data ◽  
Digital Elevation ◽  
Elevation Model

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km × 10 km to a resolution of 1 km × 1 km and are validated with data from the PVGIS and SWERA projects.

A Proactive Defense Strategy to Enhance Situational Awareness in Computer Network Security

2013 ◽  
pp. 1569-1591
Author(s):  
Yi Luo ◽  
Ferenc Szidarovszky
Keyword(s):  
Network Security ◽  
Control Problem ◽  
Adaptive Learning ◽  
Situational Awareness ◽  
Computer Network ◽  
Time Window ◽  
Dynamic Game ◽  
Future Scenarios ◽  
Computer Network Security ◽  
On Line

With the development of situational awareness in intrusion defense, a proactive response is a realistic and effective approach against the attackers. It is assumed that each player can update knowledge of the opponent and assess possible future scenarios of the dynamic game based on their previous interactions. Therefore, finding the best current move of the defender is modeled as a discrete-time stochastic control problem. An on-line, convergent, scenario based proactive defense (SPD) algorithm considering adaptive learning is developed based on differential dynamic programming (DDP) to solve the associated optimal control problem. Numerical experiment shows that the new algorithm can help the defender in finding the best dynamic strategies quickly and efficiently. Moreover, the SPD algorithm can provide optimal defensive efforts against possible future attacks within an appropriate time window, so the success of the attack in the possible future interactions can be assessed, improving situational awareness in computer network security.

scholarly journals Effective Identification of Terrain Positions from Gridded DEM Data Using Multimodal Classification Integration

2018 ◽  
Vol 7 (11) ◽  
pp. 443 ◽  
Author(s):  
Ling Jiang ◽  
Dequan Ling ◽  
Mingwei Zhao ◽  
Chun Wang ◽  
Qiuhua Liang ◽  
...  
Keyword(s):  
Classification System ◽  
Hydrological Modeling ◽  
Northern China ◽  
Assessment Method ◽  
Classification Performance ◽  
Topographic Features ◽  
Multimodal Classification ◽  
Object Oriented Methods ◽  
Elevation Model ◽  
Terrain Attribute

Terrain positions are widely used to describe the Earth’s topographic features and play an important role in the studies of landform evolution, soil erosion and hydrological modeling. This work develops a new multimodal classification system with enhanced classification performance by integrating different approaches for terrain position identification. The adopted classification approaches include local terrain attribute (LA)-based and regional terrain attribute (RA)-based, rule-based and supervised, and pixel-based and object-oriented methods. Firstly, a double-level definition scheme is presented for terrain positions. Then, utilizing a hierarchical framework, a multimodal approach is developed by integrating different classification techniques. Finally, an assessment method is established to evaluate the new classification system from different aspects. The experimental results, obtained at a Loess Plateau region in northern China on a 5 m digital elevation model (DEM), show reasonably positional relationship, and larger inter-class and smaller intra-class variances. This indicates that identified terrain positions are consistent with the actual topography from both overall and local perspectives, and have relatively good integrity and rationality. This study demonstrates that the current multimodal classification system, developed by taking advantage of various classification methods, can reflect the geographic meanings and topographic features of terrain positions from different levels.

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