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This tutorial paper presents a simplified view of one of the more recently published multivariatecalibration methods particularly suited to dealing with non-linear data sets.The method is referred toas MARS and stands for multivariate adaptive regression splines.Simple examples are provided toexplain the workings of the method. 相似文献
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Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series. 相似文献
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In high mountainous areas, the development and distribution of alpine permafrost is greatly affected by macro- and micro-topographic factors. The effects of latitude, altitude, slope, and aspect on the distribution of permafrost were studied to understand the distribution patterns of permafrost in Wenquan on the Qinghai-Tibet Plateau. Cluster and correlation analysis were performed based on 30 m Global Digital Elevation Model (GDEM) data and field data obtained using geophysical exploration and borehole drilling methods. A Multivariate Adaptive Regression Spline model (MARS) was developed to simulate permafrost spatial distribution over the studied area. A validation was followed by comparing to 201 geophysical exploration sites, as well as by comparing to two other models, i.e., a binary logistic regression model and the Mean Annual Ground Temperature model (MAGT). The MARS model provides a better simulation than the other two models. Besides the control effect of elevation on permafrost distribution, the MARS model also takes into account the impact of direct solar radiation on permafrost distribution. 相似文献
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The aim of the study was the development of habitat models for Nephtys species (Polychaeta: Nephtyidae). The investigation area was the German Bight, the southeastern part of the North Sea. Models were developed based on field data collected between 2000 and 2006. In addition, data on environmental variables were retrieved from long-term monitoring data sets and from the sediment map by Figge [Figge, K., 1981. Nordsee. Sedimentverteilung in der Deutschen Bucht. Map No. 2900. Publisher: Deutsches Hydrographisches Institut, Hamburg]. The statistical modelling technique used was multivariate adaptive regression splines (MARS). Models were fitted individually for each species. Evaluation of predictive discrimination and predictive accuracy of the developed models was by calculation of the area under the receiver operating curve (AUC) or sensitivity and specificity, respectively. Habitat models with best predictive fit were selected for the presentation of habitat suitability maps.Six Nepthys species were found: Nephtys assimilis, N. caeca, N. cirrosa, N. hombergii, N. incisa and N. longosetosa. N. hombergii was most common whereas N. incisa and N. longosetosa were rare. Habitat preferences varied considerably among the species. For all investigated Nephtys species except N. longosetosa a habitat model could be developed based on four predictor variables. The habitat models with best predictive fit were those for N. cirrosa and N. hombergii. The N. caeca habitat model was of limited predictive accuracy and only accept predictive discrimination. The number of predictors as well as the relative importance of the respective predictors in the model varied among the different species. Direct comparison of most suitable habitats for the different species based on modelling revealed that in the mostly sandy regions parallel to the German coast in water depths up to 20 m an overlap between N. caeca, N. hombergii and N. cirrosa exists. In the deeper central German Bight with mostly fine sands with increased mud contents N. hombergii, N. assimilis and, at least partially and rare in numbers, N. incisa co-occur. It can be concluded that important sediment characteristics like grain size median and mud content as well as water depth and mean salinity are useful parameters to describe the habitat requirements of most Nepthys species in the German Bight. However, additional variables need to be incorporated into such analyses. 相似文献
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论述了利用数量不同的格网数据构建的样条函数DEM模型和Coons曲面DEM模型的传递误差不同,指出同一插值方法的DEM模型随着构建模型所用的格网数据数目的增多,传递误差增大;当格网数据数目同样多时,减弱相邻格网单元上格网数据权重比例,传递误差就会相应地减少。 相似文献
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