scholarly journals Scale Effects on the Calculation of Ecosystem Service Values: A Comparison among Results from Different LULC Datasets

2022 ◽  
Vol 12 (2) ◽  
pp. 686
Author(s):  
Ziwen Huo ◽  
Xingdong Deng ◽  
Xuepeng Zhang ◽  
Wei Chen
Keyword(s):  
Ecosystem Service ◽  
Scale Effects ◽  
Spatial Scales ◽  
Reference Value ◽  
Coefficient Of Determination ◽  
Land Use Land Cover ◽  
Equivalent Factor ◽  
Change Trend ◽  
The Difference ◽  
Logarithmic Relationship

Land use/land cover (LULC) has an important impact on the ecological environment and is crucial for calculating ecosystem service values (ESVs). However, whether and to what extent the ESVs vary when calculated by LULC product data at different spatial scales remain unclear. Data from two LULC products were used in this study, and two datasets with different spatial scales were obtained by resampling. Then, the ESVs were calculated by the equivalent factor method. Finally, the impacts of LULC on ESVs at different scales were studied, revealing the following: (1) The ESVs calculated by LULC products and by the same products at different scales are different. (2) The difference in the ESVs calculated by the two LULC datasets is approximately 28%, and the difference tends to decrease with increasing scale. (3) With an increase in the LULC scale, the overall change trend of ESVs also increases, and the increasing trend gradually moderates. In addition, the ESVs and LULC scale conform to a logarithmic relationship, and the coefficient of determination (R2) is greater than 0.7. These results have important reference value for obtaining reliable ESVs.

scholarly journals QUANTIFYING EFFECTS OF CHANGING SPATIAL SCALE ON SPATIAL ENTROPY INDEX: USE OF FRACTAL DIMENSION

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 83-87
Author(s):  
C. J. Wang ◽  
H. R. Zhao ◽  
D. M. Huang
Keyword(s):  
Grain Size ◽  
Fractal Dimension ◽  
Spatial Scale ◽  
Scale Effects ◽  
Spatial Scales ◽  
Landscape Patterns ◽  
Coefficient Of Determination ◽  
Moderate Degree ◽  
Entropy Index ◽  
Spatial Entropy

Abstract. Quantifying landscape heterogeneity and its organization at different scales is essential for understanding ecosystems and landscapes. Among hundreds of landscape metrics, entropy-related index represents an efficient tool to quantify and characterize landscape patterns. A recent development is Spatial Entropy index (Hs), and it has been validated as flexible and effective in landscape pattern analysis. However, the effects of changing spatial scale on Hs has not been quantified. This paper applies the fractal method to measure the spatial scale (grain size) sensitivity of Hs. Using the initial land-use data of Yanhe watershed, which is located in northwest of China, eleven different spatial scales were created in order to investigate the scale effects on Hs. A linear log–log regression model was then constructed based on the power law to calculate the coefficient of determination (COD) of the model and the fractal dimension (FD) of Hs. The result indicates that Spatial Entropy index shows a robust fractal feature, and it decreases as the spatial scale (or grain size) becomes lager in a moderate degree. In total, we believe that this study will help us to get a better understanding of Hs, and to facilitate further applications of this entropy-related index.

scholarly journals Reading the bed morphology of a mountain stream: a geomorphometric study on high-resolution topographic data

2010 ◽  
Vol 14 (2) ◽  
pp. 393-405 ◽  
Author(s):  
S. Trevisani ◽  
M. Cavalli ◽  
L. Marchi
Keyword(s):  
High Resolution ◽  
Ad Hoc ◽  
Flow Direction ◽  
Spatial Scales ◽  
Mountain Stream ◽  
Topographic Data ◽  
The Difference ◽  
Morphological Differences ◽  
High Level ◽  
Local Anomalies

Abstract. High-resolution topographic data expand the potential of quantitative analysis of the earth surface, improving the interpretation of geomorphic processes. In particular, the morphologies of the channel beds of mountain streams, which are characterised by strong spatial variability, can be analysed much more effectively with this type of data. In this study, we analysed the aerial LiDAR topographic data of a headwater stream, the Rio Cordon (watershed area: 5 km2), located in the Dolomites (north-eastern Italy). The morphology of the channel bed of Rio Cordon is characterised by alternating step pools, cascades, and rapids with steps. We analysed the streambed morphology by means of ad hoc developed morphometric indices, capable of highlighting morphological features at a high level of spatial resolution. To perform the analysis and the data interpolation, we carried out a channel-oriented coordinate transformation. In the new coordinate system, the calculation of morphometric indices in directions along and transverse to the flow direction is straightforward. Three geomorphometric indices were developed and applied as follows: a slope index computed on the whole width of the channel bed, directional variograms computed along the flow direction and perpendicular to it, and local anomalies, calculated as the difference between directional variograms at different spatial scales. Directional variograms in the flow direction and local anomalies have proven to be effective at recognising morphologic units, such as steps, pools and clusters of large boulders. At the spatial scale of channel reaches, these indices have demonstrated a satisfactory capability to outline patterns associated with boulder cascades and rapids with steps, whereas they did not clearly differentiate between morphologies with less marked morphological differences, such as step pools and cascades.

scholarly journals The IG- file use to Gauge the Apical Diameter in Endodontics: An In Vitro Study

2018 ◽  
Vol 12 (1) ◽  
pp. 638-646 ◽  
Author(s):  
Massimo Amato ◽  
Alfredo Iandolo ◽  
Giuseppe Pantaleo ◽  
Dina Abtellatif ◽  
Michele Simeone ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Reference Value ◽  
In Vitro Study ◽  
Vitro Study ◽  
Root Canals ◽  
New Instrument ◽  
Clinician Experience ◽  
Incorrect Measurement ◽  
The Difference

Aim: The aim of this study was to evaluate the efficacy of the IG-file, a new instrument designed for apical diameter gauging. Materials and Methods: After shaping with F1 Universal Protaper, 60 roots were randomly divided into two groups and assigned to two operators, One Expert in Endodontics (EO) and One Unexpert (UO). In each sample, after canal curvatures have been detected, the apical diameters were measured with the IG-file and the K-NiTi. The results were compared with the reference value obtained by retrograde apical gauging. The data were statistically analyzed. Results: Among 60 samples, 10% of errors were recorded when the IG-files were used; in the K-NiTi group the incorrect measurements were 70%. In both groups (expert and unexpert) the IG-file measurements were more accurate than the K-NiTi (90 vs 33 and 90 vs 26,7). The differences were statistically significant. In curved canals, the difference between measurement rates performed with both instruments was statistically significant (85,7% IG-file vs 28,6% K-NiTi) as well as for the samples without curvatures (92,3% IG file vs 30,8% NiTi file). In root canals without curvatures overestimation errors in K-NiTi file group are more frequent than underestimation errors. This difference was statistically significant. Conclusion: A proper gauging of the apical diameter has a key role in endodontic therapy; an incorrect measurement can lead to clinical failures. This “in vitro” study highlights that IG-file improves measurement accuracy independently from clinician experience. Furthermore, in curved canals, the IG-file is more accurate than K-NiTi.

scholarly journals Single-input, multiple-output iterative algorithm for the calculation of volume, area, elevation, and shape using 3D topobathymetric models

2020 ◽  
Author(s):  
Hugo Luis Rojas-Villalobos ◽  
Blair Stringam ◽  
Zohrab Samani ◽  
Luis Carlos Alatorre Cejudo ◽  
Christopher Brown
Keyword(s):  
Water Body ◽  
Input Data ◽  
Reference Value ◽  
The Body ◽  
Digital Terrain ◽  
Terrain Models ◽  
Input Multiple Output ◽  
Single Input ◽  
The Difference ◽  
Area And Volume

Most methods for estimating the morphometric values of water bodies use equations derived from hypsographic curves or digital terrain models (DTMs) that relate depth, volume (V), and area (A) and that model the uncertainty inherent in the complex underwater morphology. This research focuses directly on the use of topobathymetric models that include the bathymetry and topography of the surrounding area next to the water body. The projection of the water surface height (H) on each DTM pixel generates a water column with intrinsic attributes such as volume and area. The process is replicated among all cells and estimates the total area and volume of the water body. If the V or A is the input data, an algorithm that iterates height values is used to generate the new data, which is compared with the entered value that functions as a reference. If the difference between the reference value and the calculated value is less than an error threshold, the iteration stops, and the maximum and average depths are calculated. The raster and the shape that represent the body of water are created. The cross comparison of H-V-A showed that there is an error between 0.0034% and 0.000039% when any of the parameters are used as input data. Performance tests determined that pixel dimensions are directly proportional to the processing time for each iteration. The results of the implementation of this algorithm were satisfactory since, for the DTM of Bustillos Lagoon, Chihuahua, Mexico, the simulation took less than 17 seconds in at most 22 iterations.

scholarly journals Scale Dependence of Radar Rainfall Uncertainty: Initial Evaluation of NEXRAD’s New Super-Resolution Data for Hydrologic Applications

2010 ◽  
Vol 11 (5) ◽  
pp. 1191-1198 ◽  
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski
Keyword(s):  
Scale Effects ◽  
Spatial Scales ◽  
Super Resolution ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
Weather Radars ◽  
Level Ii ◽  
Simple Scaling ◽  
Resolution Level ◽  
Reflectivity Data

Abstract This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008–August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data.

scholarly journals 1 Assessing the predictive adequacy of simple and complex mathematical models

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 24-24
Author(s):  
Luis O Tedeschi
Keyword(s):  
Model Development ◽  
Predictive Ability ◽  
Random Variable ◽  
Coefficient Of Determination ◽  
Skewed Data ◽  
Future Performance ◽  
Evaluation Phase ◽  
The Difference ◽  
Predicted Values ◽  
Analytical Approaches

Abstract The establishment of credibility for a mathematical model’s (MM) predictive ability is an essential component for improving the MM because it stimulates the evolutionary thinking (i.e., the next generation of the model) of mental conceptualizations, assumptions, and boundaries of the MM. Its predictive adequacy is commonly assessed through its ability to precisely or accurately predict observed (real) values. The precision component measures how closely the model predicted values are of each other or whether a defined pattern of predictions exists. The accuracy component, on the other hand, measures how closely the average of the model predicted values are to the actual (true) average. Many statistics exist to determine precision and accuracy of MM such as mean bias, resistant coefficient of determination, coefficient of determination, modeling efficiency, concordance correlation coefficient (CCC), the mean square error of prediction, Kleijnen’s statistic (regression of the difference between predicted and observed on their sum), and Altman and Bland’s limits of agreement statistics among many more. However, for complex models that use skewed data or repeated data in which the data is not independent (e.g., multiple measurements on the same subject), simple statistics may not suffice. For instance, four methods to compute CCC exist (moment, variance components, U-statistics, and generalized estimating equations—GEE), but only the last two methods are resilient to lightly skewed data. Another type of complexity arises when meta-analytical approaches are used at the model development phase or the model evaluation phase. In general, meta-analytical approaches remove errors (i.e., variation) associated with random variables that are believed to be known. Under these circumstances, MM tends to overperform (i.e., they have greater predictive adequacy) and their future performance may be deceitful when trying to forecast at scenarios in which the random variable(s) is(are) indeterminable or unquantifiable.

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