On a stronger-than-best property for best prediction

The minimum mean squared error (MMSE) criterion is a popular criterion for devising best predictors. In case of linear predictors, it has the advantage that no further distributional assumptions need to be made, other then about the first- and second-order moments. In the spatial and Earth sciences, it is the best linear unbiased predictor (BLUP) that is used most often. Despite the fact that in this case only the first- and second-order moments need to be known, one often still makes statements about the complete distribution, in particular when statistical testing is involved. For such cases, one can do better than the BLUP, as shown in Teunissen (J Geod. doi: 10.1007/s00190-007-0140-6, 2006), and thus devise predictors that have a smaller MMSE than the BLUP. Hence, these predictors are to be preferred over the BLUP, if one really values the MMSE-criterion. In the present contribution, we will show, however, that the BLUP has another optimality property than the MMSE-property, provided that the distribution is Gaussian. It will be shown that in the Gaussian case, the prediction error of the BLUP has the highest possible probability of all linear unbiased predictors of being bounded in the weighted squared norm sense. This is a stronger property than the often advertised MMSE-property of the BLUP.     

Analysis of Swelling and Shrinkage Behavior of Compacted Clays

The impact of the variation in compaction condition on the swelling and shrinkage behavior of three soils has been examined. Two natural soils, namely red soil and black cotton soil, and one artificially mixed soil sample of commercial bentonite with well-graded sand, were studied. Compaction curve for Standard Proctor conditions were plotted and four compaction conditions were selected. Experimental results showed that clay mineralogy dominates over compaction conditions in influencing the swelling and shrinkage behavior of the tested soils. Monitoring of void ratio (e)−water content (w) relations during shrinkage showed that soil specimens generally shrunk in three distinct linear stages. A small reduction in void ratio occurred on reduction in water content during the first shrinkage stage and was termed as initial shrinkage. In second stage, void ratio decreased rapidly with reduction in water content and was termed as primary shrinkage. In third and final stage, reduction in water content is accompanied by a marginal change in void ratio and it’s called residual shrinkage. Irrespective of initial compaction conditions studied, the transition from primary to residual shrinkage for all the specimens occurred within a narrow range of water content (10–15%).     

Artificial Neural Network Prediction Models for Soil Compaction and Permeability

This paper presents Artificial Neural Network (ANN) prediction models which relate permeability, maximum dry density (MDD) and optimum moisture content with classification properties of the soils. The ANN prediction models were developed from the results of classification, compaction and permeability tests, and statistical analyses. The test soils were prepared from four soil components, namely, bentonite, limestone dust, sand and gravel. These four components were blended in different proportions to form 55 different mixes. The standard Proctor compaction tests were adopted, and both the falling and constant head test methods were used in the permeability tests. The permeability, MDD and optimum moisture content (OMC) data were trained with the soil’s classification properties by using an available ANN software package. Three sets of ANN prediction models are developed, one each for the MDD, OMC and permeability (PMC). A combined ANN model is also developed to predict the values of MDD, OMC, and PMC. A comparison with the test data indicates that predictions within 95% confidence interval can be obtained from the ANN models developed. Practical applications of these prediction models and the necessary precautions for using these models are discussed in detail in this paper.     

青海湖东岸末次冰期冰盛期和早全新世沙漠范围重建

基于野外沉积物调查、已报道的风成沉积物年代和重建的湖泊水位波动曲线,初步绘制了末次冰期冰盛期和全新世早期青海湖东岸的沙漠范围。初步认为,末次冰期冰盛期时青海湖大幅度萎缩,湖底沉积物暴露并遭受风蚀,在偏西风的搬运下,近岸粗颗粒砂堆积在日月山西侧和倒淌河河谷,形成流动沙丘,细颗粒沉积物被向东搬运沉积至河湟谷地和黄土高原。倒淌河源头的沙丘最晚是在末次冰期盛冰期(23~16ka BP)沙漠扩张时形成并残留至今的;青海湖东部日月山西侧山麓地带和倒淌河河谷中、晚全新世黄土、古土壤下部普遍沉积的风成砂是末次冰期冰盛期和全新世早期(11~9 ka BP)青海湖东岸地区大范围沙化的证据。     

Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure

Mangrove forests grow in intertidal zones in tropical and subtropical regions and have suffered a dramatic decline globally over the past few decades. Remote sensing data, collected at various spatial resolutions, provide an effective way to map the spatial distribution of mangrove forests over time. However, the spectral signatures of mangrove forests are significantly affected by tide levels. Therefore, mangrove forests may not be accurately mapped with remote sensing data collected during a single-tidal event, especially if not acquired at low tide. This research reports how a decision-tree −based procedure was developed to map mangrove forests using multi-tidal Landsat 5 Thematic Mapper (TM) data and a Digital Elevation Model (DEM). Three indices, including the Normalized Difference Moisture Index (NDMI), the Normalized Difference Vegetation Index (NDVI) and NDVI_L·NDMI_H (the multiplication of NDVI_L by NDMI_H, L: low tide level, H: high tide level) were used in this algorithm to differentiate mangrove forests from other land-cover and land-use types in Fangchenggang City, China. Additionally, the recent Landsat 8 OLI (Operational Land Imager) data were selected to validate the results and compare if the methodology is reliable. The results demonstrate that short-term multi-tidal remotely-sensed data better represent the unique nearshore coastal wetland habitats of mangrove forests than single-tidal data. Furthermore, multi-tidal remotely-sensed data has led to improved accuracies using two classification approaches: i.e. decision trees and the maximum likelihood classification (MLC). Since mangrove forests are typically found at low elevations, the inclusion of elevation data in the two classification procedures was tested. Given the decision-tree method does not assume strict data distribution parameters, it was able to optimize the application of multi-tidal and elevation data, resulting in higher classification accuracies of mangrove forests. When using multi-source data of differing types and distributions to map mangrove forests, a decision-tree method appears to be superior to traditional statistical classifiers.     

The early Matuyama Diatom Maximum off SW Africa: a conceptual model

An important discovery during Ocean Drilling Program Leg 175, when investigating the record of upwelling off Namibia, was the finding of a distinct Late Pliocene diatom maximum spanning the lower half of the Matuyama reversed polarity chron (MDM, Matuyama Diatom Maximum) and centered around 2.6–2.0 Ma. This maximum was observed at all sites off southwestern Africa between 20°S and 30°S, and is most strongly represented in sediments of Site 1084, off Lüderitz, Namibia. The MDM is characterized by high biogenic opal content, high numbers of diatom valves, and a diatom flora rich in Southern Ocean representatives (with Thalassiothrix antarctica forming diatom mats) as well as coastal upwelling components. Before MDM time, diatoms are rare until ca. 3.6 Ma. After the MDM, in the Pleistocene, the composition of the diatom flora points to increased importance of coastal upwelling toward the present, but is accompanied by a general decrease in opal and diatom deposition. Here we present a simple conceptual model as a first step in formalizing a possible forcing mechanism responsible for the record of opal deposition in the upwelling system off Namibia. The model takes into account Southern Ocean oceanography, and a link with deepwater circulation and deepwater nutrient chemistry which, in turn, are coupled to the evolution of North Atlantic Deep Water (NADW). The model proposes that between the MDM and the Mid-Pleistocene climate revolution, opal deposition off Namibia is not directly tied to glacial–interglacial fluctuations (as seen in the global δ¹⁸O record), but that, instead, a strong deepwater link exists with increased NADW production (as seen in the deepwater δ¹³C record) accounting for higher supply of silicate to the thermocline waters that feed the upwelling process. The opal record of Site 1084 shows affinity to eccentricity on the 400-kyr scale but not for the 100-kyr scale. This points toward long-term geologic processes for delivery of silica to the ocean.     

Late Wisconsinan glaciation and postglacial relative sea-level change on western Banks Island,Canadian Arctic Archipelago

The study revises the maximum extent of the northwest Laurentide Ice Sheet (LIS) in the western Canadian Arctic Archipelago (CAA) during the last glaciation and documents subsequent ice sheet retreat and glacioisostatic adjustments across western Banks Island. New geomorphological mapping and maximum-limiting radiocarbon ages indicate that the northwest LIS inundated western Banks Island after ~ 31 ¹⁴C ka BP and reached a terminal ice margin west of the present coastline. The onset of deglaciation and the age of the marine limit (22–40 m asl) are unresolved. Ice sheet retreat across western Banks Island was characterized by the withdrawal of a thin, cold-based ice margin that reached the central interior of the island by ~ 14 cal ka BP. The elevation of the marine limit is greater than previously recognized and consistent with greater glacioisostatic crustal unloading by a more expansive LIS. These results complement emerging bathymetric observations from the Arctic Ocean, which indicate glacial erosion during the Last Glacial Maximum (LGM) to depths of up to 450 m.     

Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco

Abstract

The study anticipated to understand sand encroachment evolution through analysis of sand contribution across space and time using remote sensing in Laâyoune-Tarfaya basin, Morocco, over the period from 1987 to 2011. The assessment based on supervised classifications of Landsat imagery orthorectified data, using Maximum Likelihood (ML), Minimum Distance (MD) and Support Vector Machine (SVM) classifiers. In order to ameliorate the information, principal components analysis (PCA) and co-occurrence measurement algorithm were used for choosing bands and data transformation. Images differencing was applied on image pairs derived from classification to analyze sand encroachment evolution. All classifiers present enhanced performances, and revealed that area covered by sand was increased by 7%, 4.66% and 4.59% for ML, MD and SVM, respectively. Consequently, images differencing results confirmed that sand material increasing arise not only from coastal area contribution but also mostly from erosion of complicated sand dunes exist in the middle part of the studied area. Evaluating of the presented phenomenon dimensions and its consequences are extremely important to increase the local authorities awareness and mainly for avoiding or minimizing the consequences of the future sand dunes threats.     

Parallelizing maximum likelihood classification on computer cluster and graphics processing unit for supervised image classification

ABSTRACT

Supervised image classification has been widely utilized in a variety of remote sensing applications. When large volume of satellite imagery data and aerial photos are increasingly available, high-performance image processing solutions are required to handle large scale of data. This paper introduces how maximum likelihood classification approach is parallelized for implementation on a computer cluster and a graphics processing unit to achieve high performance when processing big imagery data. The solution is scalable and satisfies the need of change detection, object identification, and exploratory analysis on large-scale high-resolution imagery data in remote sensing applications.     

A comparison of simulated monsoon circulations and snow accumulation in Asia during the mid-Holocene and at the Last Glacial Maximum

Tropical climatology through the last glacial cycle is believed to have ranged from colder, windier conditions at the Last Glacial Maximum (LGM) to relatively warm, stable conditions during the Holocene. Changes in strength of the South Asian monsoon have previously been determined from a variety of proxy data and have been attributed primarily to changes in radiative forcing, although tropical sea surface temperature (SST) is known to play a fundamental role in regulating monsoon strength and is also believed to have changed throughout the late Quaternary.In this study, the monsoons simulated in a coupled atmosphere–ocean general circulation model (GCM) configured for the mid-Holocene (6000 years B.P.) and for the LGM (21,000 years B.P.) are compared. The colder and windier conditions simulated for the LGM produced a summer monsoon whose westerly winds are stronger and whose precipitation and snowfall into the eastern Himalaya are increased, with drier conditions over the rest of the Indian subcontinent and over most of southwest Asia.The mid-Holocene monsoon circulation is stronger than today, and annual mean snow accumulation is increased over the northwestern Himalaya. These changes in precipitation and snow accumulation are analyzed in terms of the altered atmospheric circulations, which are in turn driven by changes in radiative forcing, sea surface temperatures, and sea surface height. All of these factors are therefore demonstrated to be important in governing the spatial distribution of snow and ice deposition in the Himalaya during the late Quaternary, and are likely to have contributed to the observed asynchroneity of Himalayan glaciation and Northern Hemisphere ice sheet volume.