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.     

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.     

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.     

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。     

Late Cretaceous seasonal palaeoclimatology and diatom palaeoecology from laminated sediments

Laminated diatom-rich marine sediments from California and the Arctic Ocean provide a window into the seasonal climate and oceanography of the mid- and high-latitude Late Cretaceous. These remarkable shallow-buried sediments constitute palaeo-sediment traps that record exceptionally well-preserved sequential biogenic and lithogenic flux events. Many of the diatom laminae are composed of a few dominant taxa, and we use a species-based approach for palaeoecological interpretation. Contrary to many earlier interpretations, results indicate that both sites preserve a major flux of taxa adapted to exploit a strongly stratified ocean. The uppermost Maastrichtian Marca Shale of California records a seasonal cycle initiated with a spring bloom flux of diatom resting spores followed by a summer lithogenic sediment input likely driven by monsoonal storms providing river runoff and aeolian input. This is followed by a dominant diatom flux of species that thrived in the summer stratification in deep chlorophyll maxima and were sedimented in the fall when this stratification broke down. Dominant taxa comprising this group include Hemiaulus, Stephanopyxis, Stellarima and Rhizosolenia. The uppermost Campanian CESAR 6 core from the Arctic Alpha Ridge, records a spring bloom flux of resting spores followed by a more dominant summer and fall flux of diatom vegetative cells which were likely concentrated in subsurface summer blooms that generated a “summer export pulse” and by the breakdown of stratification in the fall that gave massive flux of deep chlorophyll maxima species in the fall dump. The dominance and diversity of Hemiaulus in the CESAR 6 core together with widespread evidence of N₂-fixation may indicate that some diatom blooms were powered by intracellular N₂-fixing cyanobacteria as in the modern oligotrophic ocean. Thin lenses of fine lithogenic sediment that occur mostly in the spring layer represent rafting by winter sea ice and support other evidence that suggests intermittent winter freezing in the Late Cretaceous Arctic.     

Morphological and sedimentary responses to ice mass interaction during the last deglaciation

During decline of the last British–Irish Ice Sheet (BIIS) down‐wasting of ice meant that local sources played a larger role in regulating ice flow dynamics and driving the sediment and landform record. At the Last Glacial Maximum, glaciers in north‐western England interacted with an Irish Sea Ice Stream (ISIS) occupying the eastern Irish Sea basin (ISB) and advanced as a unified ice‐mass. During a retreat constrained to 21–17.3 ka, the sediment landform assemblages lain down reflect the progressive unzipping of the ice masses, oscillations of the ice margin during retreat, and then rapid wastage and disintegration. Evacuation of ice from the Ribble valley and Lancashire occurred first while the ISIS occupied the ISB to the west, creating ice‐dammed lakes. Deglaciation, complete after 18.6–17.3 ka, was rapid (50–25 m a^?1), but slower than rates identified for the western ISIS (550–100 m a^?1). The slower pace is interpreted as reflecting the lack of a calving margin and the decline of a terrestrial, grounded glacier. Ice marginal oscillations during retreat were probably forced by ice‐sheet dynamics rather than climatic variation. These data demonstrate that large grounded glaciers can display complex uncoupling and realignment during deglaciation, with asynchronous behaviour between adjacent ice lobes generating complex landform records.

     

Soil geochemical parameters influencing the spatial distribution of anthrax in Northwest Minnesota,USA

Bacillus anthracis is the pathogenic bacterium that causes anthrax, which dwells in soils as highly resilient endospores. B. anthracis spore viability in soil is dependent upon environmental conditions, but the soil properties necessary for spore survival are unclear. In this study we used a range of soil geochemical and physical parameters to predict the spatial distribution of B. anthracis in northwest Minnesota, where 64 cases of anthrax in livestock were reported from 2000 to 2013. Two modeling approaches at different spatial scales were used to identify the soil conditions most correlated to known anthrax cases using both statewide and locally collected soil data. Ecological niche models were constructed using the Maximum Entropy (Maxent) approach and included 11 soil parameters as environmental inputs and recorded anthrax cases as known presences. One ecological niche model used soil data and anthrax presences for the entire state while a second model used locally sampled soil data (n = 125) and a subset of anthrax presences, providing a test of spatial scale. In addition, simple logistic regression models using the localized soil data served as an independent measure of variable importance. Maxent model results indicate that at a statewide level, soil calcium and magnesium concentrations, soil pH, and sand content are the most important properties for predicting soil suitability for B. anthracis while at the local level, clay and sand content along with phosphorous and strontium concentrations are most important. These results also show that the spatial scale of analysis is important when considering soil parameters most important for B. anthracis spores. For example, at a broad scale, B. anthracis spores may require Ca-rich soils and an alkaline pH, but may also concentrate in microenvironments with high Sr concentrations. The study is also one of the first ecological niche models that demonstrates the major importance of soil texture for defining the ecological niche of B. anthracis. These results will help improve our understanding of the soil geochemical conditions most suitable for B. anthracis as well as more reliably identify areas where anthrax may be found to focus prevention and remediation efforts.     

过去千年太阳活动异常期的中国东部旱涝格局

根据过去千年中国东部旱涝等级资料,采用各级旱涝发生几率的比率差指标,参照新近重建的5条过去千年太阳活动序列,重建了其间11个太阳活动异常期的中国东部旱涝格局。结果发现：在11个太阳活动异常期,中国东部旱涝格局各不相同。其中在5个太阳活动极小期（1010-1050年、1280-1350年、1460-1550年、1645-1715年、1795-1823年）,中国东部旱涝格局虽不一致,但长江中下游地区（华北地区）出现偏旱（涝）的几率更高;而在2个太阳活动极大期和4个太阳辐射高值期,中世纪极大（1100-1250年）整个东部多偏旱,1845-1873年的太阳辐射高值期,整个东部多偏涝;其余4个时段（1351-1387年、1593-1612年、1756-1787年、1920-2000年）则旱涝相间出现。集合平均表明：在太阳活动极小期,中国东部呈自南向北的“涝—旱—涝”分布：长江流域偏旱,南北两侧的华南沿海和华北平原偏涝,且西北东部及西南偏旱;而在太阳活动极大期和太阳辐射高值期,长江流域及西北东部多偏涝,华南和华北多偏旱。     

Optimization of geothermal water exploitation in Xinji,Hebei Province,P. R. China

Geothermal with features of large reserves and non-pollution has been one of the most important energy. China has significant geothermal resources. There are rich hydrothermal resources in Xinji, which has been in development for 5 years. However, hot water continues to decrease because of extensive exploitation and utilization. We selected exploration area as study area, build a set of numerical models of Guantao formation on the basis of actual geological conditions. We get the distribution of hydraulic conductivity from 1.7 m/d to 1.9 m/d by parameter inversion using historical water level monitoring data, and simulation effect is good. We calculate the maximum permissible exploitation under the limitation of 200 m in depth in 50 years which is about 2.2 million m3/a. The results will provide theoretic support for plan making of geothermal exploitation.