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This article evaluates different spatial interpolation methods for mapping submerged aquatic vegetation (SAV) in the Caloosahatchee Estuary, Florida. Data used for interpolation were collected by the Submersed Aquatic Vegetation Early Warning System (SAVEWS). The system consists of hydro-acoustic equipment, which operates from a slow-moving boat and records bottom depth, seagrass height, and seagrass density. This information is coupled with geographic location coordinates from a Global Positioning System (GPS) and stored together in digital files, representing SAV status at points along transect lines. Adequate spatial interpolation is needed to present the SAV information, including density, height, and water depth, as spatially continuous data for mapping and for comparison between seasons and years. Interpolation methods examined in this study include ordinary kriging with five different semivariance models combined with a variable number of neighboring points, the inverse distance weighted (IDW) method with different parameters, and the triangulated irregular network (TIN) method with linear and quintic options. Interpolation results were compared with survey data at selected calibration transects to examine the suitability of different interpolation methods. Suitability was quantified by the determination coefficient (R2) and the root-mean-square error (RMSE) between interpolated and observed values. The most suitable interpolation method was identified as the one yielding the highest R2 value and/or the lowest RMSE value. For different geographic conditions, seasons, and SAV parameters, different interpolation methods were recommended. This study identified that kriging was more suitable than the IDW or TIN method for spatial interpolation of all SAV parameters measured. It also suggested that transect data with irregular spatial distribution patterns such as SAV parameters are sensitive to interpolation methods. An inappropriate interpolation method such as TIN can lead to erroneous spatial representation of the SAV status. With a functional geographic system and adequate computing power, the evaluation and selection of interpolation methods can be automated and quantitative, leading to a more efficient and accurate decision. 相似文献
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基于GIS空间分析的海底表层沉积物粒度分布特征插值研究 总被引：3，自引：0，他引：3
为了对比研究不同空间插值方法在沉积物粒度空间分布表述上的精度及适用性,在2004年11月荣成宁津小型海湾-黑泥湾综合调查的基础上,探讨了表层沉积物粒度参数的空间插值,比较了IDW(反距离加权法)、Kriging(克里金插值法)、Spline(样条插值法)与NN(自然邻域法)4种GIS空间分析方法的特征、差异及实效性,并对影响插值结果准确性的因素进行分析。结果显示,在黑泥湾表层沉积物粒度结果的空间插值计算中,从插值准确性和空间表达能力两方面考虑,IDW,Spline,Kriging和NN中以IDW法较为适宜,但要考虑到"牛眼效应"的出现会与局部实际情况存在差异;Kriging法和NN法的插值结果准确性较高,但其空间表达能力稍逊;Spline法在近岸带表层沉积物粒度特征插值中的应用性相对较差。海洋调查要素空间分布的内在规律性是控制插值结果的主要因素,表层沉积物分布以长期稳定的潮流、地形特征为主导因素,呈现由岸向海条带状分布的特征。数据均匀分布区域的插值结果要优于边界区和突变区;另外,在选取的3个观察尺度上,不同插值方法的误差均与野外取样网格间距呈显著线性正相关。 相似文献
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黄海渔业资源密度空间插值方法的比较研究 总被引：2，自引：0，他引：2
空间插值是渔业生态学研究,特别是海洋生态系统模型构建中的常用工具。为找到适合黄海渔业资源密度的插值方法,本文对四种常用的空间插值方法进行了比较研究。四种插值方法分别为反距离加权（Inverse distance weighted,IDW）、全局多项式（global polynomial interpolation,GPI）、局部多项式（local polynomial interpolation,LPI）和普通克里格（ordinary kriging,OK）。采用交叉验证分析（cross-validation diagnostic）确定不同插值方法的准确度。同时,结合渔业资源密度空间分布的可视化表达,比较不同插值方法结果的优劣。结果表明,原始的生物学数据具有典型的非正态分布特征,对数转化方法能让其显著地接近或符合正态分布。四个调查阶段中,指数模型在2014年8月和10月为最佳半方差函数,2015年1月和5月数据具有明显的纯块金效应。配对样本T检验表明,预测数据和实测数据之间没有显著差异（P>0.05）。交叉验证结果表明,OK在2014年10月插值效果最好,IDW在剩余的三个航次表现最佳,GPI和LPI表现效果不佳。OK在空间分布的可视化表达方面效果最好,既不像IDW一样有较多的“牛眼”效应,也不像GPI和LPI一样具有过多的平滑效果。然而,纯块金效应的存在有时会影响OK的应用。综上所述,我们推荐操作简单且处理快速的IDW作为黄海渔业资源密度常规的插值方法。 相似文献
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利用插值试验分析采样网格对粒度趋势分析的影响 总被引：10，自引：0，他引：10
利用地理信息系统软件ArcView3．2的空间分析功能，采用三次样条插值方法，对采自山东半岛月湖的表层沉积物的粒度参数进行插值。应用“粒度趋势分析”方法，选取一系列的特征距离值进行试验，从原始数据和插值后数据中提取净输运方向的信息。结果显示，原始的不规则采样点所得粒度趋势对于特征距离的选择很敏感，而插值后的规则格网在不同的特征距离下均表现出比较统一的粒度趋势信息。研究表明，规则格网较不规则格网采样点有许多优势，应用插值方法可有效地提高原始数据的信息含量及其质量。 相似文献
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Optimizing interpolation of shoot density data from a Posidonia oceanica seagrass bed 总被引：1，自引：0，他引：1
Michele Scardi Lorenzo A. Chessa Eugenio Fresi Antonio Pais & Simone Serra 《Marine Ecology》2006,27(4):339-349
A case study on the optimization of Posidonia oceanica density interpolation, using a data set from a large meadow at Porto Conte Bay (NW Sardinia, Italy), is presented. Ordinary point kriging, cokriging and a weighted average based on inverse square distance were used to interpolate density data measured in 36 sampling stations. The results obtained from different methods were then compared by means of a leave‐one‐out cross‐validation procedure. The scale at which interpolation was carried out was defined on the basis of the Hausdorff dimension of the variogram. Optimizing spatial scale and data points search strategy allowed obtaining more accurate density estimates independently of the interpolation method. 相似文献
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Shaoyuan Pan Siquan Tian Xuefang Wang Libin Dai Chunxia Gao Jianfeng Tong 《海洋学报(英文版)》2021,40(8):119-132
Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors. To obtain a more continuous distribution of these variables usually measured by stationary sampling designs, spatial interpolation methods(SIMs) is usually used. However, different SIMs may obtain varied estimation values with significant differences, thus affecting the prediction of fish spatial distribution. In this study, different SIMs were used to obtain continuous environmental variables(water depth, water temperature, salinity, dissolved oxygen(DO), p H,chlorophyll a and chemical oxygen demand(COD)) in the Changjiang River Estuary(CRE), including inverse distance weighted(IDW) interpolation, ordinary Kriging(OK)(semivariogram model: exponential(OKE),Gaussian(OKG) and spherical(OKS)) and radial basis function(RBF)(regularized spline function(RS) and tension spline function(TS)). The accuracy and effect of SIMs were cross-validated, and two-stage generalized additive model(GAM) was used to predict the distribution of Coilia nasus from 2012 to 2014 in CRE. DO and COD were removed before model prediction due to their autocorrelation coefficient based on variance inflation factors analysis. Results showed that the estimated values of environmental variables obtained by the different SIMs differed(i.e., mean values, range etc.). Cross-validation revealed that the most suitable SIMs of water depth and chlorophyll a was IDW, water temperature and salinity was RS, and p H was OKG. Further, different interpolation results affected the predicted spatial distribution of Coilia nasus in the CRE. The mean values of the predicted abundance were similar, but the differences between and among the maximum value were large. Studies showed that different SIMs can affect estimated values of the environmental variables in the CRE(especially salinity).These variations further suggest that the most applicable SIMs to each variable will also differ. Thus, it is necessary to take these potential impacts into consideration when studying the relationship between the spatial distribution of fishes and environmental changes in the CRE. 相似文献
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Mark S. Fonseca W. Judson Kenworthy Emily Griffith Margaret O. Hall Mark Finkbeiner Susan S. Bell 《Estuarine, Coastal and Shelf Science》2008
The scale of landscape pattern formation of an ecological community may provide clues as to the processes influencing its spatial and temporal dynamics. We conducted an examination of the spatial organization of an annual seagrass (Halophila decipiens) in an open ocean setting at two spatial scales and growing seasons to identify the relative influence of external (hurricanes) versus internal (clonal growth) factors. Visual surveys of seagrass cover were conducted over 2 years within three replicate 1 km2 study areas each separated by ∼25 km in an inshore–offshore transect along the southwest coast of Florida at depths between ∼10 and 30 m. A towed video sled allowed observations of seagrass cover of 1 m2 areas approximately every 6 m over thousands of meters of evenly spaced transects within the study areas (coarse scale). The towed video revealed that 17.5% of the seafloor was disturbed irrespective of location or sample time. Randomly selected 10 × 10 m quadrats within the larger, 1 km2 study areas were completely surveyed for seagrass cover by divers at 0.625 m2 resolution (fine scale). The coarse-scale observations were tested using both conventional geostatistics and an application of a time-series technique (Runs test) for scale of seagrass cover contiguity. Fine-scale observations were examined using conventional geostatistics and a least squares approach (cumulative logistic). 相似文献