Hasil untuk "Maps"

T. Rolland, M. Tasan, B. Charloteaux et al.

Peter Henry, Michael Krainin, E. Herbst et al.

S. Kohlbrecher, O. von Stryk, Johannes Meyer et al.

M. Simard, N. Pinto, J. Fisher et al.

D. Zou, Lin Zhao, Y. Sheng et al.

Abstract. The Tibetan Plateau (TP) has the largest areas of permafrost terrain in the mid- and low-latitude regions of the world. Some permafrost distribution maps have been compiled but, due to limited data sources, ambiguous criteria, inadequate validation, and deficiency of high-quality spatial data sets, there is high uncertainty in the mapping of the permafrost distribution on the TP. We generated a new permafrost map based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs) and validated this map using various ground-based data sets. The soil thermal properties of five soil types across the TP were estimated according to an empirical equation and soil properties (moisture content and bulk density). The temperature at the top of permafrost (TTOP) model was applied to simulate the permafrost distribution. Permafrost, seasonally frozen ground, and unfrozen ground covered areas of 1.06  ×  106 km2 (0.97–1.15  ×  106 km2, 90 % confidence interval) (40 %), 1.46  ×  106 (56 %), and 0.03  ×  106 km2 (1 %), respectively, excluding glaciers and lakes. Ground-based observations of the permafrost distribution across the five investigated regions (IRs, located in the transition zones of the permafrost and seasonally frozen ground) and three highway transects (across the entire permafrost regions from north to south) were used to validate the model. Validation results showed that the kappa coefficient varied from 0.38 to 0.78 with a mean of 0.57 for the five IRs and 0.62 to 0.74 with a mean of 0.68 within the three transects. Compared with earlier studies, the TTOP modelling results show greater accuracy. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.

S. Dumoulin, B. Wandell

G. Bonham-Carter

J. Heinonen

S. Myers, L. Bottolo, C. Freeman et al.

G. Jenks, F. Caspall

Emre Aydilek

This research aims to contribute to a macro-level understanding of the intellectual foundations of the field of political science by examining it in depth through bibliometric analysis and discovering the epistemological insights hidden in it, as well as the basic dimensions of studies in the discipline. Thus, it is objectives to provide basic data and guidance to academics working on the methodology of the discipline of political science. Accordingly, the trends, topics, and general themes of academic studies published in universally respected journals with high-impact factors in the field of political science will be identified. The article's original value is the first thorough bibliometric study of the discipline in Turkish national literature and one of the first few thorough bibliometric studies in international literature. This study conducted a brief literature review to examine key scientific texts on political science methodology that reflect general trends. Subsequently, data retrieved from two selected databases (Scopus and WoS) were analyzed and interpreted using text-mining tools (WOSviewer and R Studio). As a result of this analysis, 17 datasets and meaningful patterns emerged. Word clouds, bibliometric maps, heat maps, and word maps were obtained, which include the analysis of studies according to years, fields, types, impact values, factors, themes, trends, and countries with the most studies. The findings are interpreted in the conclusion, and a general trend in the discipline is presented.

G. Lindblad

A. Toledo, L. Aragón, A. Casas

Phytophthora capsici is an aggressive pathogen in escabeche pepper on the Peruvian coast. Root rot has a strong correlation with humidity and environment. Disease behavior was evaluated epidemiologically using spatiotemporal variables. Severity was evaluated according to the advance of the secondary symptom according to grades 1 to 5. Then, coordinates of each plant were established by photogrammetric survey of a field with 1705 escabeche pepper plants. For temporal analysis, severity was adjusted to an exponential model (R2 = 0.909) and incidence to a Gompertz model (R2 = 0.921) that detected an initial delay of the disease due to temperature. For the spatial analysis, the Global Moran Index (Ii) showed a high spatial dependence of the disease reaching a peak of 0.4 and 0.7 for severity and incidence, respectively. Also, heat maps related to the Local Ii were generated from which an initial source of infestation was determined where the furrow irrigation started in random infestations. Then, the infestation spots were settled in areas of surface water accumulation. Also, rhizosphere samples were collected per plant by degree of severity on V8 or CMA whit PARB and PDA-A selective medium. As a result, significant differences were obtained between grade 1, grade 2, 3, 4 and grade 5. In addition, the effect on yield was significant for plants with grade 4 and 5 with respect to fruit weight (22.3 and 18.5g/fruit) and weight per plant (509.5 and 371.8g/plant), respectively.

Adrian Chavarro, Diego Renza, Ernesto Moya-Albor

The increasing complexity of deep learning models can make it difficult to interpret and fit models beyond a purely accuracy-focused evaluation. This is where interpretable and eXplainable Artificial Intelligence (XAI) come into play to facilitate an understanding of the inner workings of models. Consequently, alternatives have emerged, such as class activation mapping (CAM) techniques aimed at identifying regions of importance for an image classification model. However, the behavior of such models can be highly dependent on the type of architecture and the different variants of convolutional neural networks. Accordingly, this paper evaluates three Convolutional Neural Network (CNN) architectures (VGG16, ResNet50, ConvNext-T) against seven CAM models (GradCAM, XGradCAM, HiResCAM, LayerCAM, GradCAM++, GradCAMElementWise, and EigenCAM), indicating that the CAM maps obtained with ConvNext models show less variability among them, i.e., they are less dependent on the selected CAM approach. This study was performed on an image dataset for the classification of coffee leaf rust and evaluated using the RemOve And Debias (ROAD) metric.

Yuki Sato, Takeshi Tsuji, Masayuki Matsuoka

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R²) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage.

Fien Vanongeval, Jos Van Orshoven, Anne Gobin

Soil organic carbon (SOC) is central to the functioning of terrestrial ecosystems, has climate mitigation potential and provides several benefits for soil health. Understanding the spatial distribution of SOC can help formulate sustainable soil management practices. Digital soil mapping (DSM) uses advanced statistical and geostatistical methods to estimate soil properties across large areas. DSM integrates climate data, topographic features, geology, legacy soil maps, land management and remote sensing data. Bare soil spectra may reflect the presence of particular soil components, making satellite derived spectra suitable predictors of SOC. Bare soil spectra derived from Sentinel-2 were used to estimate SOC concentration (SOC%) and granulometric fractions in the plough layer (0–30 cm) of agricultural parcels in northern Belgium. Thereafter, the estimation performance of SOC% was compared for three DSM models: one with bare soil spectra, one with environmental covariates (topography, granulometry and vegetation), and a combined model with bare soil spectra and environmental covariates. The estimation performance of sand, silt and clay fractions using bare soil spectra from the spring seedbed (R2: 0.53–0.74; RPD: 1.49–2.05; RPIQ: 1.52–2.39) was higher than that of SOC% (R2: 0.16; RPD: 1.08; RPIQ: 1.32). The highest estimation performance of SOC% was obtained for a DSM model including all covariates (R2: 0.28; RPD: 1.18; RPIQ: 1.44), but the contribution of spring seedbed spectra to a model containing environmental covariates was small. The results provide valuable insights for refining soil property estimation using DSM with spectral and environmental covariates.

Kevin Dibbern, Victoria Vivtcharenko, Nacime Salomao Barbachan Mansur et al.

Abstract The early effects of progressive collapsing foot deformity (PCFD) on the ankle and syndesmotic joints have not been three-dimensionally quantified. This case-control study focused on using weight bearing CT (WBCT) distance (DM) and coverage maps (CM) and volumetric measurements as 3D radiological markers to objectively characterize early effects of PCFD on the ankle and syndesmotic joints. Seventeen consecutive patients with symptomatic stage I flexible PCFD and 20 matched controls that underwent foot/ankle WBCT were included. Three-dimensional DM and CM of the ankle and syndesmotic joints, as well volumetric assessment of the distal tibiofibular syndesmosis was performed as possible WBCT markers of early PCFD. Measurements were compared between PCFD and controls. Significant overall reductions in syndesmotic incisura distances were observed in PCFD patients when compared to controls, with no difference in the overall syndesmotic incisura volume at 1, 3, 5 and 10 cm proximally to the ankle joint. CMs showed significantly decreased articular coverage of the anterior regions of the tibiotalar joint as well as medial/lateral ankle joint gutters in PCFD patients. This study showed syndesmotic narrowing and decreased articular coverage of the anterior aspect of the ankle gutters and talar dome in stage I PCFD patients when compared to controls. These findings are consistent with early plantarflexion of the talus within the ankle Mortise, and absence of true syndesmotic overload in early PCFD, and support DM and CM as early 3D PCFD radiological markers.

P. Seaber, F. P. Kapinos, G. Knapp

SAMIR HADJ-MILOUD, Mohamed El-Amine Iddir Iddir, Tarek ASSAMI et al.

The present study consists of a valorization of a pedological database allowing to determine the Solonchaks in the north of Algeria according to the World Reference Base (WRB) classification. We studied the constituents of these Solonchaks by making different thematic maps, for this purpose a geographic information system (GIS) was created. Profiles meeting the definition of Solonchaks will be classified, spatialized in northern Algeria and grouped into reference soils and the creation of different thematic maps. The main results revealed that the Solonchaks of northern Algeria are provided with calcium carbonate (10 < CaCO3 (%) < 60) and poorly provided with gypsum with an average of 2.5% gypsum. They are also characterized by very high salinity (15 < EC (dS/m) < 40.9) and relatively high sodicity percentage of exchangeable sodium (ESP) > 15%). Statistical analysis revealed that the correlation between the EC-ESP couple is highly significant (r = 0.62; p < 0.01). Similarly, the correlation between the EC and the Ca++ of the adsorbent complex is negative and significant (r = -0.34; p < 0.05). The exploitation of the database made it possible to extract 45 profiles corresponding to the Solonchaks. The classification of these profiles revealed 13 references of Solonchaks distributed in the north of Algeria.

Magnhild H. Dagestad, Nils Vetti, Per M. Kristoffersen et al.

Abstract Background Modic Changes (MCs) in the vertebral bone marrow were related to back pain in some studies but have uncertain clinical relevance. Diffusion weighted MRI with apparent diffusion coefficient (ADC)-measurements can add information on bone marrow lesions. However, few have studied ADC measurements in MCs. Further studies require reproducible and valid measurements. We expect valid ADC values to be higher in MC type 1 (oedema type) vs type 3 (sclerotic type) vs type 2 (fatty type). Accordingly, the purpose of this study was to evaluate ADC values in MCs for interobserver reproducibility and relation to MC type. Methods We used ADC maps (b 50, 400, 800 s/mm2) from 1.5 T lumbar spine MRI of 90 chronic low back pain patients with MCs in the AIM (Antibiotics In Modic changes)-study. Two radiologists independently measured ADC in fixed-sized regions of interests. Variables were MC-ADC (ADC in MC), MC-ADC% (0% = vertebral body, 100% = cerebrospinal fluid) and MC-ADC-ratio (MC-ADC divided by vertebral body ADC). We calculated mean difference between observers ± limits of agreement (LoA) at separate endplates. The relation between ADC variables and MC type was assessed using linear mixed-effects models and by calculating the area under the receiver operating characteristic curve (AUC). Results The 90 patients (mean age 44 years; 54 women) had 224 MCs Th12-S1 comprising type 1 (n = 111), type 2 (n = 91) and type 3 MC groups (n = 22). All ADC variables had higher predicted mean for type 1 vs 3 vs 2 (p < 0.001 to 0.02): MC-ADC (10− 6 mm2/s) 1201/796/576, MC-ADC% 36/21/14, and MC-ADC-ratio 5.9/4.2/3.1. MC-ADC and MC-ADC% had moderate to high ability to discriminate between the MC type groups (AUC 0.73–0.91). MC-ADC-ratio had low to moderate ability (AUC 0.67–0.85). At L4-S1, widest/narrowest LoA were for MC-ADC 20 ± 407/12 ± 254, MC-ADC% 1.6 ± 18.8/1.4 ± 10.4, and MC-ADC-ratio 0.3 ± 4.3/0.2 ± 3.9. Difference between observers > 50% of their mean value was less frequent for MC-ADC (9% of MCs) vs MC-ADC% and MC-ADC-ratio (17–20%). Conclusions The MC-ADC variable (highest mean ADC in the MC) had best interobserver reproducibility, discriminated between MC type groups, and may be used in further research. ADC values differed between MC types as expected from previously reported MC histology.