A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.     

风云三号卫星气溶胶光学厚度产品的适用性验证

利用长三角地区浦东、东滩、太湖3个测站的太阳光度计CE318地基遥感观测得到的气溶胶光学厚度(AOD)数据对风云三号气象卫星FY-3A/B MERSI反演的550 nm波长AOD进行有效性验证。结果表明，FY-3A的反演结果相关系数高于0.96,仅有20%的样本表现出较大的偏差。FY-3B的相关系数最高为0.77,均方根误差(RMSE)最大为0.35。太湖站点的相关系数比浦东大，说明MERSI的反演算法在太湖更适用。FY-3A/B MERSI反演结果总体上偏小，存在一定的系统偏差，主要由气溶胶模型的假设、设备标定、选择像素比例等原因造成。     

Comparison of a Subjective and a Physical Approach for Identification of Priority Areas for Soil and Water Management in a Watershed – A Case Study of Nagwan Watershed in Hazaribagh District of Jharkhand, India

The present investigation was an attempt to compare the within-watershed prioritization capabilities of a physical model based SDSS with the SYI and RPI model based subjective-SDSS, conventionally devised for between-watershed prioritizations, by All India Soil Survey and Land Use Planning Division of Ministry of Agriculture, Government of India. Application of these two approaches on a test watershed situated in Damodar-Barakar catchment in India, the second most seriously eroded area in the world, demonstrated that the proposed physical model based SDSS was capable of realistically and logically mimicking the sub-watershed-scaled water and soil losses thereby suggesting its immense application potential for priority area identification within the test watershed. In contrast to the proposed physical method, the subjective approaches, which assigned totally reverse priorities to about 67–93% of the test-sub-watersheds, were observed to be incapable of realistically assessing the impact of topography and varied land use and soil types in the test watershed on their sub-watershed scaled run-off and soil loss generating potential. Besides, the physical approach could be used for assessing the annual dynamics of the total water and sediment yields, under prevailing resource management systems in the test watershed with good to moderately good correlation coefficients of 0.83 and 0.65; model efficiency coefficients of 0.54 and 0.70; mean relative errors of –4.28% and –17.97% and root mean square prediction errors of 71.8 mm and 9.63 t/ha, respectively.     

Geostatistical analysis as applied to two environmental radiometric time series

This article details the results of an investigation into the application of geostatistical data analysis to two environmentalradiometric time series. The data series employed consist of ⁹⁹Tc values for seaweed (Fucus vesiculosus) and seawater samples taken as part of a marine monitoring program conducted on the coast of northern Norway by the Norwegian Radiation Protection Authority. Geostatistical methods were selected in order to provide information on values of the variables at unsampled times and to investigate the temporalcorrelation exhibited by the data sets. This information is ofuse in the optimisation of future sampling schemes and for providing information on the temporal behaviour of the variablesin question that may not be obtained during a cursory analysis.The results indicate a high degree of temporal correlation withinthe data sets, the correlation for the seawater and seaweed databeing modelled with an exponential and linear function,respectively. The semi-variogram for the seawater data indicatesa temporal range of correlation of approximately 395 days with noapparent random component to the overall variance structure and was described best by an exponential function. The temporal structure of the seaweed data was best modelled by a linear function with a small nugget component. Evidence of drift was present in both semi-variograms. Interpolation of the data setsusing the fitted models and a simple kriging procedure were compared, using a cross-validation procedure, with simple linearinterpolation. Results of this exercise indicate that, for theseawater data, the kriging procedure outperformed the simpleinterpolation with respect to error distribution andcorrelation of estimates with actual values. Using theunbounded linear model with the seaweed data produced estimatesthat were only marginally better than those produced by thesimple interpolation.     

Assessing monthly average solar radiation models: a comparative case study in Turkey

Solar radiation data are required by solar engineers, architects, agriculturists, and hydrologists for many applications such as solar heating, cooking, drying, and interior illumination of buildings. In order to achieve this, numerous empirical models have been developed all over the world to predict solar radiation. The main objective of this study is to examine and compare 147 solar radiation models available in the literature for the prediction of monthly solar radiation at Ankara (Turkey) based on selected statistical measures such as percentage error, mean percentage error, root mean square error, mean bias error, and correlation coefficient. Our results showed that Ball et al. (Agron J 96:391?C397, 2004) model and Chen et al. (Energy Convers Manag 47:2859?C2866, 2006) model performed best in the estimation of solar radiation on a horizontal surface for Ankara.     

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. Water sampling works were conducted on 15 July 2007 and 13 September 2008 concurrent with the Indian Remote-Sensing Satellite (IRS-P6) overpass of the Shitoukoumen Reservoir. Both empirical regression and back-propagation artificial neural network (ANN) models were established to estimate Chl-a and TSM concentration with both in situ and satellite-received radiances signals. It was found that empirical models performed well on the TSM concentration estimation with better accuracy (R ² = 0.94, 0.91) than their performance on Chl-a concentration (R ² = 0.62, 0.75) with IRS-P6 imagery data, and the models accuracy marginally improved with in situ spectra data. Our results indicated that the ANN model performed better for both Chl-a (R ² = 0.91, 0.82) and TSM (R ² = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R ² = 0.75 and 0.90 for Chl-a; R ² = 0.97 and 0.95 for TSM). The relative root mean square errors (RMSEs) from the empirical model for TSM (Chl-a) were less than 15% (respectively 27.2%) with both in situ and IRS-P6 imagery data, while the RMSEs were less than 7.5% (respectively 18.4%) from the ANN model. Future work still needs to be undertaken to derive the dynamic characteristic of Shitoukoumen Reservoir water quality with remotely sensed IRS-P6 or Landsat-TM data. The algorithms developed in this study will also need to be tested and refined with more imagery data acquisitions combined with in situ spectra data.     

Isoprene Emission from the Forest of Haryana State

Tropical and sub tropical regions are regarded as dominant source of biogenic volatile organic compounds emission (BVOC). However, measurement studies from these regions are limited and largely confined to South Africa and Amazonia. Consequently, global BVOC estimates are mainly based on modeling studies. Moreover, BVOC emission estimate is altogether lacking for any region of the Indian sub continent. This study attempts to estimate isoprene emission capacity of forest of Haryana state. Individual plant species isoprene emission capacity is found to vary from below detection limit (BDL) to 12.01 mg Cm^{− 2} h^{− 1}. Maximum emission capacity (12.01 mg Cm^{− 2} h^{− 1}) is noticed in case of Dalbergia sissoo. The area average isoprene emission capacity for the Haryana forest is found to be 19.98 mg Cm^{− 2} h^{− 1}, which is significantly (2.4 times) higher than the reported isoprene emission value of 8.2 mg Cm^{− 2} h^{− 1} for the Kalahari woodland of Africa.     

Evaluation of Terrestrial and Streamside Salamander Monitoring Techniques at Shenandoah National Park

In response to concerns about amphibian declines, a study evaluating and validating amphibian monitoring techniques was initiated in Shenandoah and Big Bend National Parks in the spring of 1998. We evaluate precision, bias, and efficiency of several sampling methods for terrestrial and streamside salamanders in Shenandoah National Park and assess salamander abundance in relation to environmental variables, notably soil and water pH. Terrestrial salamanders, primarily redback salamanders (Plethodon cinereus), were sampled by searching under cover objects during the day in square plots (10 to 35 m²). We compared population indices (mean daily and total counts) with adjusted population estimates from capture-recapture. Analyses suggested that the proportion of salamanders detected (p) during sampling varied among plots, necessitating the use of adjusted population estimates. However, adjusted population estimates were less precise than population indices, and may not be efficient in relating salamander populations to environmental variables. In future sampling, strategic use of capture-recapture to verify consistency of p's among sites may be a reasonable compromise between the possibility of bias in estimation of population size and deficiencies due to inefficiency associated with the estimation of p. The streamside two-lined salamander (Eurycea bislineata) was surveyed using four methods: leaf litter refugia bags, 1 m² quadrats, 50 × 1 m visual encounter transects, and electric shocking. Comparison of survey methods at nine streams revealed congruent patterns of abundance among sites, suggesting that relative bias among the methods is similar, and that choice of survey method should be based on precision and logistical efficiency. Redback and two-lined salamander abundance were not significantly related to soil or water pH, respectively.     

A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration

The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and daily maximum concentrations of ambient CO, NO₂, NO, O₃, SO₂ and PM_2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009 to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations of CO, NO₂, NO, O₃, SO₂ and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network design may be employed successfully for air quality forecasting.     

The water quality of the Vrgorska Matica River

The article presents the results of investigations carried out on the 42 km long Vrgorska Matica River, which flows through the 15 km long Vrgorsko polje (polje = field) which covers an areaof 3000 ha, and is at 24 m a.s.l., located in Southern Croatia.It covers the years 1997–2000 after this field had been reclaimed for agricultural use.The purpose of the investigations was to evaluate the influence of the Vrgorska Matica River which is part of the catchment areaof the Trebizat River, on the water quality in Modro Oko Lakeand Prud Spring, which are used for water supply and are locateddownstream of the Vrgorska Matica River on the right bank of theNeretva River.The water quality was evaluated by using the quality index basedon the following nine parameters: temperature, mineralization, corrosion coefficient, K = (Cl + SO₄)/HCO₃, dissolvedoxygen, BOD₅, total N, protein N, total phosphorus and totalcoliform bacteria (100 mL)^-1 (MPN coli (100 mL)^-1) for which concentrations C ₉₅ are calculated. After completingthe nine parameters the results of C ₉₅ were recorded andtransferred to the score table to obtain the q-value. The q-value used is an attempt to quantify environmental factors which would otherwise be qualitative. For each parameter the q-value was multiplied by a weighting factor based upon the relative significance of the parameter. The nine resulting scores values were then added to arrive at an overall water quality index (S₉₅).According to this index the water can be classified into four categories. The first category, according to the Croatian Water Classification Act (Official Bulletin No. 77,1998), includes ground and surface waters used for drinking or in the food industry either in its natural state or after disinfection, and surface water used for raising high-quality species of fish, ranging from 85–100 scores; the second category includes waterused in its natural state for swimming and recreation, sports orfor other species of fish and the treated water used for drinkingand other industrial purposes ranging between 70–85 scores. Thethird category includes water used in industry with no specific requests upon water quality and in agriculture ranging from 50–70 scores; the fourth category includes water used only afterbeing treated in areas with water shortage which is less than 50 scores.According to results obtained by investigations, the water of theVrgorska Matica River and Prud Spring falls into the second (II)category, while water from the Modro Oko Lake belongs to the first (I) category. This means that the Matica River water does not influence the water quality of the Modro Oko Lake. This lakewater quality is influenced by the Rastoka Matica River from theRastoko polje which is located upstream from the Vrgorska MaticaRiver. This has been proved by dyeing tests.