An Application of Landsat-5TM Image Data for Water Quality Mapping in Lake Beysehir, Turkey

The main goal of this study was to investigate spatial patterns in water quality in Lake Beysehir, which is the largest freshwater reservoir in Turkey, by using Landsat-5TM (Thematic Mapper) data and ground surveys. Suspended sediment (SS), turbidity, Secchi disk depth (SDD), and chlorophyll-a (chl-a) data were collected from 40 sampling stations in August, 2006. Spatial patterns in these parameters were estimated using bivariate and multiple regression (MR) techniques based on Landsat-5TM multispectral data and water quality sampling data. Single TM bands, band ratios, and combinations of TM bands were estimated and correlated with the measured water quality parameters. The best regression models showed that the measured and estimated values of water quality parameters were in good agreement (0.60 < R ² < 0.71). TM3 provided a significant relationship (R ²?=?0.67, p?<?0.0001) with SS concentration. MR between chl-a and various combinations of TM bands showed that TM1, TM2, and TM4 are strongly correlated with measured chl-a concentrations (R ²?=?0.60, p?<?0.0001). MR of turbidity showed that TM1, TM2, and TM3 explain 60% (p?<?0.0001) of the variance in turbidity. MR of SDD showed a strong relationship with measured SDD, with R ²?=?0.71 (p?<?0.0001) for the ratio TM1/TM3 and TM1 band combinations. The spatial distribution maps present apparent spatial variations of selected parameters for the study area covering the largest freshwater lake and drinking water reservoir in Turkey. Interpretation of thematic water quality maps indicated similar spatial distributions for SS, turbidity, and SDD. A large area in the middle portion of the lake showed very low chl-a concentrations as it is far from point and nonpoint sources of incoming nutrients. The trophic state index values were calculated from chl-a and SDD measurements. Lake Beysehir was classified as a mesotrophic or eutrophic lake according to chl-a or SDD parameters, respectively.     

Determination of Environmental Quality of a Drinking Water Reservoir by Remote Sensing, GIS and Regression Analysis

Istanbul, housing a population over ten million and with population increase rate of approximately twice that of Turkey, is one of the greatest metropolitan cities of the world. As a consequence of rapid population growth and industrial development, Omerli watershed is highly affected by wastewater discharges from the residential areas and industrial plants. The main objective of this study is to investigate the temporal assessment of the land-use/cover of the Omerli Watershed and the water quality changes in the Reservoir. The study is mainly focused on the acquisition and analysis of the Satellite Probatoire de l'Observation de la Terre (SPOT) (1993), Indian Remote Sensing satellite (IRS) (1996 and 2000) and Landsat Thematic Mapper (TM) (2004, 2005, and 2006) satellite images that reflect the drastic land-use/cover changes utilizing the ground truth measurements. The rapid, uncontrolled, and illegal urbanization coupled with insufficient infrastructure has caused the deterioration of the water quality within the past two decades in the Omerli watershed. The water quality analysis of the drinking water Reservoir within the watershed is investigated using 2006 dated Landsat TM satellite digital data. The results are compiled and compared with the water quality measurements of parameters like total nitrogen (TN), the total phosphorus (TP), chlorophyll a (CL) and total dissolved solids (TDS). The observed reflectance shows a strong relationship with the water quality parameters and thus, the satellite data proved to provide a useful index of TN, TP, CL and TDS. Moreover, the linkage between the water quality parameters and the individual band reflectance values are supported by multiple regression analysis.     

土地混合使用制度下土壤硝态氮分布的地理空间制图研究

Mapping the spatial distribution of soil nitrate-nitrogen (NO₃-N) is important to guide nitrogen application as well as to assess environmental risk of NO₃-N leaching into the groundwater. We employed univariate and hybrid geostatistical methods to map the spatial distribution of soil NO₃-N across a landscape in northeast Florida. Soil samples were collected from four depth increments (0-30, 30-60, 60-120 and 120-180 cm) from 147 sampling locations identified using a stratified random and nested sampling design based on soil, land use and elevation strata. Soil NO₃-N distributions in the top two layers were spatially autocorrelated and mapped using lognormal kriging. Environmental correlation models for NO₃-N prediction were derived using linear and non-linear regression methods, and employed to develop NO₃-N trend maps. Land use and its related variables derived from satellite imagery were identified as important variables to predict NO₃-N using environmental correlation models. While lognormal kriging produced smoothly varying maps, trend maps derived from environmental correlation models generated spatially heterogeneous maps. Trend maps were combined with ordinary kriging predictions of trend model residuals to develop regression kriging prediction maps, which gave the best NO₃-N predictions. As land use and remotely sensed data are readily available and have much finer spatial resolution compared to field sampled soils, our findings suggested the effcacy of environmental correlation models based on land use and remotely sensed data for landscape scale mapping of soil NO₃-N. The methodologies implemented are transferable for mapping of soil NO₃-N in other landscapes.     

Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network

In remotely located watersheds or large waterbodies, monitoring water quality parameters is often not feasible because of high costs and site inaccessibility. A cost-effective remote sensing-based methodology was developed to predict water quality parameters over a large and logistically difficult area. Landsat spectral data were used as a proxy, and a neural network model was developed to quantify water quality parameters, namely chlorophyll-a, turbidity, and phosphorus before and after ecosystem restoration and during the wet and dry seasons. The results demonstrate that the developed neural network model provided an excellent relationship between the observed and simulated water quality parameters. These correlated for a specific region in the greater Florida Everglades at R ²?>?0.95 in 1998?C1999 and in 2009?C2010 (dry and wet seasons). Moreover, the root mean square error values for phosphorus, turbidity, and chlorophyll-a were below 0.03?mg?L^?1, 0.5 NTU, and 0.17?mg?m^?3, respectively, at the neural network training and validation phases. Using the developed methodology, the trends for temporal and spatial dynamics of the selected water quality parameters were investigated. In addition, the amounts of phosphorus and chlorophyll-a stored in the water column were calculated demonstrating the usefulness of this methodology to predict water quality parameters in complex ecosystems.     

应用高分辨率卫星影像提取水土保持措施信息的分类后处理技术研究

在应用高分辨率卫星影像进行水土保持措施信息的提取过程中,遥感分类图像后处理方法对不同类别的水土保持措施信息的提取精度具有重要影响。采用空间分辨率为2.5 m的卫星影像数据,针对黄土丘陵区水土保持措施的信息提取,用不同大小的聚类处理参数对各类型的措施面积精度进行了检验,在综合考虑水土保持措施的空间特征与图像特征基础上,确立了采用Spot5高分辨率卫星影像进行水土保持措施信息的遥感分类时,宜选用最小图斑为6×6个像元的参数值。     

Estimating Mo,Cu, Ni,Cd Contents in the Crop Leaves Growing on Small Land Plots Using Satellite Data

ABSTRACT

The main goal of this research was to estimate heavy metals (HMs) (molybdenum (Mo), copper (Cu), nickel (Ni), cadmium (Cd)) contents in crop leaves through multispectral satellite imagery. During the acquisition of a SPOT 7 satellite image (28 July 2017) in situ sampling (38 samples) was done from the leaves of potatoes and beans growing close to the mining town of Kajaran (Armenia). To estimate HMs contents, multivariate regression (multiple linear regression (MLR), partial least squares regression (PLSR)), and artificial neural network (ANN) were used. As input data for the models raw, atmospherically corrected (Dark Object Subtraction (DOS)) and hyperspherical direction cosines (HSDC) normalized values of SPOT 7 spectral data in combination with one or combined log10, multiplicative scatter correction (MSC), standard normal variate transform (SNV) preprocessing methods were utilized. The best results were obtained for Cu using MLR (R² _cal. = 0.79, R² _CV = 0.70, RMSE_cal. = 11.27, RMSE_CV = 13.47) and ANN (R² _Train ≈ 0.80, R² _Test ≈ 0.72, RMSE_Train ≈ 11, RMSE_Test ≈ 13) models in case of bean leaves. The results are quite optimistic, however, further research with the use of high spatial/spectral resolution satellite images is needed to improve the accuracy of models.     

风云二号静止气象卫星数据估算土壤表面水分方法研究

考虑到土壤水分会影响地表温度随太阳辐射变化的幅度,本文提出利用多时相热红外波段和可见光波段数据反演地表水分的算法,并利用第一代静止气象卫星——风云二号数据估算了2010年9月30日和10月20日西北地区范围的土壤表面水分,并与先进机械辐射扫描计(advanced mechanically scanned radiometer:AMSR)土壤水分产品进行比较验证。结果表明:该算法估算的土壤表面水分与AMSR土壤水分产品相关性为0.52,两者的均方根误差在0.025 g.cm 3以内,最大误差不超过0.07 g.cm 3。同时,利用该算法生成西北地区土壤表面水分空间分布图,与同期降水资料相比较,两者空间分布较为一致。此外,该算法可获得5 km×5 km空间尺度的土壤表面水分,提高了土壤水分遥感估算的空间分辨率。     

基于Sentinel-2卫星数据的灌区农田土壤水盐协同反演

土壤含水量（soil water content, SWC）和土壤含盐量（soil salt content, SSC）是影响作物生长和农业生产力的重要因素。光学卫星图像已成为SWC和SSC估计的主要数据源。然而,在SWC或SSC变化较大地区,土壤水分和盐分会影响对方对光谱反射率的响应,使得SSC和SWC的反演精度较差。对此,该研究提出了一个半解析性的反射率模型—RVS模型,来模拟植被光谱反射率（R_v）对作物根区土壤含水量和含盐量的响应;并通过构建的RVS模型,对植被覆盖区域的土壤含水量和土壤含盐量进行同步监测。研究表明：RVS模型在反演研究区土壤含盐量和含水量时,精度较为可靠(水分：决定系数R²为0.63～0.74,均方根误差为0.017～0.028;盐分：决定系数R²为0.68～0.75,均方根误差为0.0525～0.0617)。在作物生长过程中,植被光谱反射率对深层土壤的含水量和含盐量的响应比对浅层土壤的含水量和含盐量的响应更加明显,而且随着作物的生长,影响光谱反射率的主导因素从土壤水分慢慢转向土壤盐分和水盐相互作用。该研究在一定程度上揭示了土壤水分、盐分、水盐交互作用对作物光谱反射率的干扰过程,实现土壤水分和盐分的同步监测,对实现区域尺度上土壤含盐量和含水量的精准监测具有一定的意义。     

Estimating rice paddy areas in China using multi-temporal cloud-free normalized difference vegetation index (NDVI) imagery based on change detection

The spatial pattern of rice paddies is an essential parameter used for studies of greenhouse gas emissions, agricultural resource management, and environmental monitoring. On large spatial scales, previous studies have usually mapped rice paddies using a single vegetation index product based on a traditional classification method, or a combined analysis of various vegetation and water indices derived from the moderate resolution imaging spectroradiometer (MODIS) satellite data. However, different indices increase the computational cost and constrain the satellite data sources, and traditional classification methods (e.g., maximum likelihood classification) may be time-consuming and difficult to carry out over a large area like China. In this study, we designed an auto-thresholding and single vegetation index (normalized difference vegetation index (NDVI))-based procedure to estimate the spatial distribution of rice paddies in China. The MOD09Q1 product, which was available at MODIS''s highest spatial resolution (250 m), was taken as the input source. An auto-threshold function was also introduced into the change detection process to distinguish rice paddies from other croplands. Our MODIS-derived maps were validated with ground surveys and then compared with China national statistical data of rice paddy areas. The results indicated that the best classification result was achieved for plain regions, and that the accuracy declined for hilly regions, where the complex landscape could lead to an underestimation of the rice paddy area. A comparison between the modeled results and other analyses using 500-m MODIS data suggests that rice paddies may be identified routinely using a single vegetation index with finer resolution on large spatial scales.     

基于GIS的广东香蕉种植气候适宜性区划

以广东省86个气象站点1992-2005年的气象数据为依据、利用数理统计方法对1992-2005年香蕉平均气象产量与同期气候条件进行相关分析,确定香蕉种植的气候适宜性区划指标。根据广东86个站点1970-2009年的气候资料及对应站点的经纬度、海拔高度、坡度和坡向等基础信息数据,应用多元线性回归确定区划指标空间分析模型,推算出1km×1km的气象要素分布图。依托GIS技术空间分析功能,根据综合评分标准进行香蕉种植气候适宜性区划,得出广东省气候上最适宜种植香蕉的土地面积占17%,适宜区占18%,次适宜区占25%,不适宜区占40%。研究结果可为广东香蕉种植的合理布局提供参考。     

Soil loss estimation using rusle model to prioritize erosion control in KELANI river basin in Sri Lanka

Soil erosion contributes negatively to agricultural production, quality of source water for drinking, ecosystem health in land and aquatic environments, and aesthetic value of landscapes. Approaches to understand the spatial variability of erosion severity are important for improving landuse management. This study uses the Kelani river basin in Sri Lanka as the study area to assess erosion severity using the Revised Universal Soil Loss Equation (RUSLE) model supported by a GIS system. Erosion severity across the river basin was estimated using RUSLE, a Digital Elevation Model (15 × 15 m), twenty years rainfall data at 14 rain gauge stations across the basin, landuse and land cover, and soil maps and cropping factors. The estimated average annual soil loss in Kelani river basin varied from zero to 103.7 t ha-1 yr⁻¹, with a mean annual soil loss estimated at 10.9 t ha⁻¹ yr⁻¹. About 70% of the river basin area was identified with low to moderate erosion severity (<12 t ha−1 yr⁻¹) indicating that erosion control measures are urgently needed to ensure a sustainable ecosystem in the Kelani river basin, which in turn, is connected with the quality of life of over 5 million people. Use of this severity information developed with RUSLE along with its individual parameters can help to design landuse management practices. This effort can be further refined by analyzing RUSLE results along with Kelani river sub-basins level real time erosion estimations as a monitoring measure for conservation practices.     

The effects of land use and climate on red kite distribution in the Iberian peninsula

The main breeding populations of the red kite (Milvus milvus L.), have been declining in the Iberian peninsula during the last decade. However, there is a lack of regional assessments of habitat suitability that identifies limiting ecological factors for the species and areas with conservation problems. In this work we present a regional model for the distribution and abundance of breeding red kites in the Iberian peninsula. The occurrence and estimated abundance in 100 km² UTM squares resulting from road censuses were modelled with broad-scale explanatory variables obtained from satellite imagery, thematic digital cartography, climatic data and spatial coordinates. The occurrence model incorporated mainly climatic variables and had a good discrimination ability, while the abundance model incorporated mainly land-use variables and had a lower explanatory power (r²=0.14). The predictions somewhat overestimated the results of the censuses, and this agrees with the decline of population size and range observed for this species in the Iberian peninsula. These models are relevant in the conservation of the species: first, they suggest the limiting factors for red kite in the Iberian peninsula, and, second, they generate predictive maps pointing out both areas in which conservation problems may be acute (suitable locations that are unoccupied), and areas where no data is available but the red kite is likely to be present (thus guiding further survey and research).     

基于多年MODIS NDVI 分级的河北平原农田生产力评价

根据中等分辨率航天成像光谱仪(MODIS)遥感数据计算的归一化植被指数(NDVI)被广泛用于作物长势监测和产量预报, 但由于NDVI 数值在不同年份的同一时期变化较大, 直接用于评价农田生产力会有较大误差。本文以河北平原所在的北纬37°~39°之间连续种植的冬小麦农田为研究区域, 通过对多年冬小麦MODISNDVI 数据进行比较和分级, 尝试用每季NDVI 在区域内的高低级别评估区域农田生产力。Landsat 卫星数据用于对不同时相MODIS 图像进行精确配准, 从而实现像素尺度上长时间序列数据的统计分析。首先, 对区域内2000~2008 年间每年作物返青期到成熟期的NDVI 平均值及各生育阶段NDVI 平均值分别进行高低分级, 以了解河北平原农田生产力的空间变异, 结果显示其中高水平农田分布在太行山山前平原, 指数等级水平并没有完全按南北走向趋势分布, 表明该研究方法受纬度差异的影响较小。不同年份分析结果显示, 2008 年东部地区也出现了较高等级的田块。其次, 利用NDVI 分级结果计算出9 年间NDVI 等级的变异系数, 对采用不同生育期NDVI 可能带来的误差进行了分析, 结果显示不同小麦生育期NDVI 等级的变异系数不同, 返青期和成熟期变异系数较大, 且具有一定的地理差异。最后, 利用GIS 空间分析方法以9 年NDVI 分级结果为基础制作了以县为单元的麦田生产力等级图, 结果显示河北平原农田生产力高低分区, 同时也表明中低水平区块有较大提升空间, 为河北县级土地管理和耕地质量管理提供理论依据。     

Mapping soil erodibility in southeast China at 250 m resolution: Using environmental variables and random forest regression with limited samples

Soil erodibility (K factor) mapping has been accomplished mainly by soil map-linked or geo-statistical interpolation. However, the resulting maps usually have coarse spatial resolution at a regional scale. The objectives of this study were a) to map the K factors using a set of environmental variables and random forest (RF) model, and b) to identify the important environmental variables in the predictive mapping on a regional scale. We collected 101 surface soil samples across southeast China in the summer of 2019. For each sample, we measured the particle size distribution and organic matter content, and calculated the K factors using the nomograph equation. The hyperparameters of RF were optimized through 5-fold cross validation (m_try = 2, n_tree = 500, p = 63), and a digital map with 250 m resolution was generated for the K factor. The lower and upper limits of a 90% prediction interval were also produced for uncertainty analysis. It was found that the important environmental variables for the K factor prediction were relief, climate, land surface temperature and vegetation indexes. Since the existing K factor map has an average polygonal area of 6.8 km², our approach dramatically improves the spatial resolution of the K factor to 0.0625 km². The new method captures more distinct differences in spatial details, and the spatial distribution of the K factor derived from RF prediction followed a similar pattern with kriging interpolation. This suggests the presented approach in this study is effective for mapping the K factor with limited sampling data.     

松嫩平原盐碱化人工草地土壤与植被群落空间变异特征研究

松嫩草地由于受太平洋季风气候的影响,具有较好的水热条件,且地势平坦,非常适宜畜牧业的机械化发展。在过去的几十年间,松嫩草地物种丰富度较高,优质的牧草以多年生的羊草为主,且在植被下形成了肥力较高的黑土。然而,松嫩草地独特的地形与高矿化度的地下水,导致了盐渍土与黑土接壤,因此松嫩草地生态环境较为脆弱。此外,由于草地的过度利用,导致了松嫩草地发生退化与盐碱化,进而使得草地生产力降低。较低的草地生产力已成为限制该区域畜牧业发展的主要因素,而草地生产力与土壤水盐动态密切相关。故本研究以松嫩盐碱化人工草地为研究对象,采用经典统计学与地统计学相结合的方法,对松嫩平原西部地区的盐碱化人工草地土壤理化性质以及牧草生物学-生态学性质的空间变异特征进行研究。结果表明,0～15 cm和15 ～30 cm层土壤的pH、电导率（EC）、总碱度（TA）、以及土壤质量含水量（MWC）具有中度或强空间变异。此外,试验区域的生物多样性指数（SWI）、紫花苜蓿的株高（SH）、生物量干重（DM）与盖度（CD）均具有强烈的空间变异特征。回归分析结果表明,试验区域盐碱化人工草地紫花苜蓿产量可用公式Y(DM)=2699.73–276.496 pH（7.17< pH<9.76）预估。本研究结果可为苏打盐渍土的精细化管理与利用提供理论基础与数据支持。     

Effect of spatial scale of soil data on estimates of soil ecosystem services: Case study in 100 km2 area in France

Over the last decade, the ecosystem services (ESs) framework has been increasingly used to support mapping and assessment studies for sustainable land management purposes. Previous analysis of practical applications has revealed the significance of the spatial scale at which input data are obtained. This issue is particularly problematic with soil data that are often unavailable or available only at coarse scales or resolutions in various part of the world. In this context, four soil-based ecosystem services, namely biomass provision, water provision, global climate regulation, and water quality regulation, are assessed using three conventional soil maps at the 1:1,000,000, 1:250,000 and 1:50,000 scales. The resulting individual and joint ES maps are then compared to examine the effects of changing the spatial scale of soil data on the ES levels and spatial patterns. ES levels are finally aggregated to landforms, land use, or administrative levels in order to try to identify the determinants of the sensitivity of ES levels to change in the scale of input soil data. Whereas the three soil maps turn out to be equally useful whenever ESs levels averaged over the whole 100 km² territory are needed, the maps at the 1:1,000,000 and 1:250,000 induced biases in the assessment of ESs levels over spatial units smaller than 100 and 10 km², respectively. The simplification of the diversity and spatial distribution of soils at the two coarsest scales indeed resulted in local differences in ES levels ranging from several 10 to several 100%. Identification of the optimal representation of soil diversity and distribution to obtain a reliable representation of ESs spatial distribution is not straightforward. The ESs sensitivity to scale effect is indeed context-specific, variable among individual ESs, and not directly or simply linked with the soil typological diversity represented in soil maps. Forested and natural lands in the study area appear particularly sensitive to soil data scales as they occupy marginal soils showing very specific ESs signatures.     

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Apparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle‐mounted proximal sensors. Crop‐yield data, which is measured by harvester‐mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop‐yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop‐yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop‐yield data. This is achieved through the use of a local regression kriging approach which uses the higher‐resolution crop‐yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop‐management decisions and building more robust calibrations between field‐gathered soil ECa and primary soil properties such as clay content.     

Characterizing Spatial Variability of Soil Textural Fractions and Fractal Parameters Derived from Particle Size Distributions

Soil particle size distribution (PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of 75 soil samples, collected from a flat field in the University of Guilan, Iran, into two scaling domains using a piecewise fractal model to evaluate the relationships between fractal dimensions of scaling domains and soil clay, silt, and sand fractions and 2) to assess the potential of fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. Features of PSD of soil samples were studied using fractal geometry, and geostatistical techniques were used to characterize the spatial variability of fractal and soil textural parameters. There were two scaling domains for the PSD of soil samples. The fractal dimensions of these two scaling domains (D₁ and D₂) were then used to characterize different ranges of soil particle sizes and their relationships to the soil textural parameters. There was a positive correlation between D₁ and clay content (R² = 0.924), a negative correlation between D₁ and silt content (R² = 0.801), and a negative correlation between D₂ and sand content (R² = 0.913). The geometric mean diameter of soil particles had a negative correlation with D₁ (R² = 0.569) and D₂ (R² = 0.682). Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D₁ and D₂ and soil PSD parameters were provided using ordinary kriging. The results showed that there were also spatial correlations between D₁ and D₂ and particle size fractions. According to the semivariogram models and validation parameters, the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.     

Development of Chemical Index as a Measure of In-Stream Water Quality in Response to Land-Use and Land Cover Changes

Water pollution in response to accelerated land-use/land cover changes has drawn concerns because of public health and environmental impacts. The study was conducted to evaluate the impact of land use/land cover changes, seasonal, and location on water quality of streams within the Wheeler Lake Watershed basin in northern Alabama. Temporal water samples from 18 sheppard streams were randomly collected in 2000 and 2001, processed and analyzed for pH, and total nitrogen (TN), dissolved (Dp), particulate (Pp) and total phosphorus (Tp), dissolved oxygen (DO) and soluble lead (Pb) concentration, employing standard methods of analysis. The data were normalized and integrated into a simple index (WQC_Index) to evaluate stream water quality. Results showed that the urban proportion of the total watershed basin had increased from 2.9 to 14.7% with an associated loss of agricultural (8.9%) and wetland (4.8%) covers from 1992 to 2000. A change in land-use/land covers in association with seasonal and location variation significantly affected stream water quality. Total nitrogen concentration in stream water had a peak during the summer at 34% above the annual mean. While both Pp and Tp concentrations peaking during the summer at 24% above the annual mean and about 25% below the annual mean during spring, the DO concentrations were 46% above the annual mean during the fall and 18 to 26% below annual mean during summer. The WQC_Index had responded very seasonal and showed significant identical trends, with 21% degradation in water quality during the summer above the annual mean and improvement during the spring at 20% above the annual mean. Upstream water had a significantly greater Pp and Tp (21 to 28%) concentration than at down- and middle streams water. Location and seasonal variations had significant interactive effects on Pp, Tp and DO concentration of stream water. Total amount of seasonal rainfall significantly accounted 99.6% of the variations in WQC_Index. Increasing seasonal mean relative humidity, air and soil temperature, evaporation and solar radiation had positive relationship with the variations in WQC_Index. Among the water quality parameters, both Pp and Tp were correlated (r ² = 0.998^?*?) to each other, and accounted for more than 80% variability of the WQC_Index. Highly significant positive linear relationship between Pp and Tp concentration suggested that 99.8% of the P in stream water is in Pp form which probably transported with sediments in surface runoff. In other words, Pp is the main pollutant responsible for degradation of stream water quality in the Wheeler lake basin. Routine measurement of either Pp or Tp concentration could be used as sensitive and early indicator of temporal changes in stream water quality even when the other parameters changed negligibly or remain unchanged.     

Nitrous Oxide Emissions from Newly Created Littoral Marshes in the Drawdown Area of the Three Gorges Reservoir, China

This study aimed to understand the seasonal and spatial variations of N₂O emissions from newly created littoral marshes in the drawdown area of the Three Gorges Reservoir (TGR), China. We measured N₂O emissions at 10-day intervals during the growing season (early July to late September) in 2008. N₂O emissions were measured with static chambers in four typical vegetation stands. The results showed great spatial variations of N₂O emissions among the four stands. The greatest N₂O emissions (0.052?±?0.063 mg N₂O m^?2?h^?1) were from Scirpus triqueter stand, while the lowest N₂O emissions (0.020?±?0.020 mg N₂O m^?2?h^?1) were from Typha angustifolia stand. To such spatial variations in N₂O emissions, standing water depths and soil water content may be important explaining factors. Besides spatial variations, we also found significant temporal variations of N₂O emissions in this area. The temporal variation of N₂O emissions in the growing season was not found significantly related to any measured factor in the study. However, based on principal component analysis, we consider it partly caused by thermal conditions and the marked temporal variation of the standing water depth in the growing season, which to some degree influenced the process of denitrification and N₂O emissions. These results about TGR enable us to make a more reasonable estimate of N₂O emissions from large dam reservoirs, particularly those with a large drawdown area in the growing season in an agricultural landscape.