Comparing different spatial interpolation methods to predict the distribution of fishes: A case study of Coilia nasus in the Changjiang River Estuary

Although DEM occupies an important basic position in spatial analysis, so far, the quality of DEM modeling has still not reached a satisfactory accuracy. This research mainly discusses the influence of interpolation parameters in the inverse distance-weighted interpolation algorithm on the DEM interpolation error. The interpolation parameters to be studied in this paper are the number of search points, the search direction, and the smoothness factor. In order to study the optimization of IDW parameters, the parameters that have uncertain effects on DEM interpolation are found through analysis, such as the number of search points and smoothing factor. This paper designs an experiment for the optimization of the interpolation parameters of the polyhedral function and finds the optimal interpolation parameters through experimental analysis. Of course, the “optimum” here is not the only one, but refers to different terrain areas, which makes the interpolation results relatively good. The selection of search points will be one of the research focuses of this article. After determining the interpolation algorithm, the kernel function is also one of the important factors that affect the accuracy of DEM. The value of the smoothing factor in the kernel function has always been the focus of DEM interpolation research. Different terrains, different interpolations, and functions will have different optimal smoothing factors. The search direction is to ensure that the sampling points are distributed in all directions when the sampling points are sparse and to improve the contribution rate of the sampling points to the interpolation points. The selection of search shape is to improve computing efficiency and has no effect on DEM accuracy; the search radius is mainly controlled by the number of search points, and there are two methods: adaptive search radius and variable length search radius. When the weight coefficient k = 1 , 2 , 3 , 4 , the number of sampling points involved in the interpolation calculation is different, and the error in the residual varies greatly, and both increase with the increase of the number of sampling points in the parameter interpolation calculation. This research will help improve the quality evaluation of DEM.

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“…The lengths of the three sides are approximately equal. The expression of the plane equation is follows [12]:…”

Section: Essence and Characteristics Of Dem Generationmentioning

confidence: 99%

[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

Liu

Cheng

et al. 2021

Journal of Sensors

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“…Although the fixed-station survey used has bias in capturing the abundance of fishery species in the YRE, we think that it is feasible to obtain the relationship between density and environmental factors in each season using fishery data and the corresponding environmental data collected at each station. Meanwhile, the predicted density distribution of fishery resources in 2017 showed a pattern of high density in the north and low density in the south, which has also has been indicated in other studies (Dai 2019;Wu et al 2019;Pan et al 2021).…”

Section: Selection Of the Quantity Of Trawl Survey Stations For Fishe...supporting

confidence: 73%

Optimization of Sampling Effort for Different Fishery Groups in the Yangtze River Estuary, China

Zhao

Yang

2022

Mar Coast Fish

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The Yangtze River Estuary (YRE) plays an important role in the conservation of fishery resources in the Yangtze River basin, and the Chinese government has established a special fishing ban management area at the YRE. To assess effects of the fishing ban, survey design and appropriate effort need to be determined and implemented. This study used long-term trawl survey data of the YRE (2004YRE ( -2017 to explore the effects of survey effort (8-30 continuous survey stations selected by systematic and stratified sampling) on precision and accuracy of estimates of abundance of different fishery groups in the YRE. Estimated precision for a given number of survey stations varied by season and fishery group. Precision of systematic sampling fluctuated with the number of stations. Precision of stratified sampling with a proportional allocation scheme gradually increased with the number of survey stations. We suggest including 15-20 survey stations for all fishery groups sampled using the examined sampling methods.This study provides a research reference for selecting the survey effort (number of survey stations) for a fishery-independent survey in the YRE under the backgroud of conservation.

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“…Part 5: Marine ecology of China (HY/T 147.5-2013). At present, there are many spatial interpolation methods for discrete points, such as Radial Basis Function fitting method (RBF, Franke, 1982;Nie et al, 2020;Guo et al, 2020), Kriging interpolation method (Nurhadiyatna et al, 2016;Kleijnen, 2017;Du et al, 2018;Saulquin et al, 2019;Pan et al, 2021), Inverse Distance Weight interpolation method (IDW, Lam, 1983;Nurhadiyatna et al, 2016;Badlowski et al, 2021;Pan et al, 2021) and so on. In the subsequent study, we will select the most suitable method for Chl-a concentration interpolation in Bohai Sea by comparing RBF, IDW and Kriging methods.…”

Section: Observation Datamentioning

confidence: 99%

Temporal and spatial variation of chlorophyll a concentration and ecological environment assessment in Bohai Sea

Zhao

Xiao

Guo

et al. 2023

Preprint

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Chlorophyll-a (Chl-a) is an important indicator of phytoplankton bloom and water eutrophication, as well as a basic parameter to assess coastal ecosystem health. This study utilizes a significant amount of observational data regarding Chl-a concentration. The linear Radial Basis Function (RBF-Linear) is a fitting method used to reconstruct the in-situ observation data of Chl-a concentration in the Bohai Sea in August 2017, with two advantages of small comprehensive error and reasonable spatial distribution. This method is selected through 10-fold cross-validation, correlation analysis and spatial distribution comparison. This will provide a potential application prospect for evaluating satellite products and improving the accuracy of ecological models. Then we use this method to obtain the complete three-dimensional spatial field of Chl-a concentration in the Bohai Sea during March, May, August and October from 2016 to 2018. The spatial and temporal distribution of Chl-a concentration in the Bohai Sea shows obvious spatial and temporal distribution characteristics: spatially, the high Chl-a concentration is mainly distributed in coastal waters, especially near estuaries and mariculture area; Temporally, there are two obvious peaks in March and August. Additionally, two indicators of total Chl-a and area with high Chl-a concentration are given in order to assess the marine ecological environment in four sub-regions of the Bohai Sea (Bohai Bay, Liaodong Bay, Laizhou Bay, and Central Bohai Sea), with a focus on the impacts of special weather events and bottom water quality. Along with restoring the marine natural environment, attention should be given to balancing urban development.

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Comparing different spatial interpolation methods to predict the distribution of fishes: A case study of Coilia nasus in the Changjiang River Estuary

Cited by 13 publications

References 45 publications

[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

[Retracted] Interpolation Parameters in Inverse Distance‐Weighted Interpolation Algorithm on DEM Interpolation Error

Optimization of Sampling Effort for Different Fishery Groups in the Yangtze River Estuary, China

Temporal and spatial variation of chlorophyll a concentration and ecological environment assessment in Bohai Sea

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