太湖湖泛现象的卫星遥感监测

2010年8月20日太湖地区Landsat ETM影像显示,太湖西部沿岸带存在湖泛黑水团现象,对该景遥感影像进行了大气校正,提取了湖泛样区、其他水体样区的ETM各波段光谱反射率数据统计特征。结果表明,湖泛样区在可见光波长的ETM波段1、2、3具有很低的反射率,水色暗黑,与人眼观察一致,而在反射红外波长的ETM波段4则有比波段3高的反射率,差异植被指数DVI>0,其原因为湖泛黑水团中,虽然大量蓝藻死亡分解,然而水中还残留有一定数量的活体蓝藻,残余叶绿素及细胞造成了虽然较弱、但仍较为稳定的反射红外波长处的光谱反射能力。提出了识别湖泛现象的遥感判据为ρ0.485<0.05 andρ0.56<0.08 andρ0.66<0.065 and(ρ0.83-ρ0.66)>0 andρ0.83<0.1。     

基于NDVI指数的骆马湖水生植被分级研究

基于典型时段骆马湖LandsatTM遥感影像,利用归一化植被指数（NDVI）法对骆马湖水生植被进行分级研究.采用标准差分级方法,将骆马湖水生植被分为5个等级.研究结果表明,利用这种分级方法处理的图像能够较准确地反映骆马湖水生植被区域分布和等级分区,能够更准确地给出采砂区和生态保护区域的位置,增强湖区水生态环境保护工作的有效性和针对性.     

Small farm ponds: overlooked features with important impacts on watershed sediment transport

Despite their size, small farm ponds are important features in many landscapes. Yet hydrographical databases often fail to capture these ponds, and their impacts on watershed processes remain unclear. For a 230‐km² portion of central Texas, United States (U.S.), we created a historical inventory of ponds and quantified the accuracy of automated detection methods under varying drought conditions. In addition, we documented pond dredging/enlargement events and identified sites that had been abandoned. We also analyzed sediment cores from downstream reservoirs to track changes in watershed sediment transport. Over 75 years, pond densities increased more than 350% — to among the highest documented in the U.S. — and the ability of automated methods to detect these ponds was highly dependent on drought severity (R² = 0.96). Approximately 5% of ponds present in the 1950s were no longer present in 2012, while 33% were dredged between 1937 and 2012. Downstream reservoir sedimentation has decreased by an average of 55% as ponds have increased in number. These findings suggest that small ponds and the maintenance of trapping efficiency have large‐scale impacts on sediment dynamics. Accurately accounting for these storage effects is vital to water resource planning efforts.     

Sense and Influence: Environmental Monitoring Tools and the Power of Citizen Science

Automated monitoring devices are useful technologies for communities seeking to document and solve environmental problems. However, without deeper scrutiny of their design and deployment, there is a risk that they will fail to have the impact that many of their promoters intend. We develop a rubric for analysing how different kinds of monitoring devices help environmental advocates influence public debates. We apply this rubric in a study of environmental organizations in Pennsylvania that are choosing between recruiting volunteer citizen scientists and using automated sensor-based devices to gather water quality data in streams threatened by hydraulic fracturing for natural gas. Many organizations rely on volunteers using simple monitoring tools because they are affordable and produce easily managed data sets. An argument for this method of monitoring is that volunteering in the field also fosters citizen engagement in environmental debates. By comparison, we find the increased use of automated devices tends to reinforce hierarchies of expertise and constrains the agendas of nonprofessionals who participate in monitoring projects. We argue that these findings suggest that automated technologies, however effective they may be in gathering data on environmental quality, are not well designed to support broad public participation in environmental science and politics.     

Assessing Satellite and Ground‐Based Potential Evapotranspiration for Hydrologic Applications in the Colorado River Basin

This study evaluates a remotely sensed and two ground‐based potential evapotranspiration (PET) products for hydrologic application in the Upper Colorado River Basin (UCRB). The remotely sensed Moderate Resolution Imaging Spectroradiometer product (MODIS‐PET) is a continuous, daily time series with 250 m resolution derived using the Priestley‐Taylor (P‐T) equation. The MODIS‐PET is evaluated against regional flux tower data as well as a synthetic pan product (Epan; 0.125°, daily) derived from the North American Land Data Assimilation System (NLDAS) and a Hargreaves PET derived from DAYMET variables (DAYMET‐PET; 1 km, daily). Compared to point‐scale PET computed using regional flux tower data, the MODIS‐PET had lower errors, with RMSE values ranging from 2.24 to 2.85 mm/day. Epan RMSE values ranged from 3.70 to 3.76 mm/day and DAYMET‐PET RMSE values ranged from 3.55 to 4.58 mm/day. Further investigation showed biases in temperature and radiation data contribute to uncertainty in the MODIS‐PET values, while bias in NLDAS temperature, downward shortwave (SW↓), and downward longwave (LW↓) propagate in the Epan estimates. Larger discrepancies between methods were observed in the warmer, drier regions of the UCRB, however, the MODIS‐PET was more responsive to landcover transitions and better captured basin heterogeneity. Results indicate the satellite‐based MODIS product can serve as a viable option for obtaining spatial PET values across the UCRB.     

天水市大气SO2浓度时空变化特征的卫星遥感监测与影响分析

大气卫星遥感监测作为一种新型监测手段,具有范围广、速度快、成本低等优势,对环境应急保护及其预警都具有非常重要的意义。选取天水市2006—2013年各年12月每日OMI level-2数据产品,利用Aura卫星技术和Arc GIS等技术平台,对天水市大气中SO_2的时空分布规律和污染原因进行了分析研究。结果表明:2006—2013年天水市SO_2柱浓度及其总量呈现出明显增加的趋势,但在2008年有小幅降低;在2008年以前,天水市SO_2浓度呈现出由东南向西北逐渐减少的趋势,但自2009年后污染重心发生迁移并且出现了数个集中化的SO_2高值区;研究区SO_2垂直柱浓度有自然因素、人类活动等多方面的复合影响,其中能源消耗及机动车尾气排放是主要影响因素。研究进一步讨论了遥感数据产品的应用前景。     

Identifying Urban Features from LiDAR for a High‐Resolution Urban Hydrologic Model

Light Detection and Ranging (LiDAR), is relatively inexpensive, provides high spatial resolution sampling at great accuracy, and can be used to generate surface terrain and land cover datasets for urban areas. These datasets are used to develop high‐resolution hydrologic models necessary to resolve complex drainage networks in urban areas. This work develops a five‐step algorithm to generate indicator fields for tree canopies, buildings, and artificial structures using Geographic Resources Analysis Support System (GRASS‐GIS), and a common computing language, Matrix Laboratory. The 54 km² study area in Parker, Colorado consists of twenty‐four 1,500 × 1,500 m LiDAR subsets at 1 m resolution with varying degrees of urbanization. The algorithm correctly identifies 96% of the artificial structures within the study area; however, application success is dependent upon urban extent. Urban land use fractions below 0.2 experienced an increase in falsely identified building locations. ParFlow, a three‐dimensional, grid‐based hydrological model, uses these building and artificial structure indicator fields and digital elevation model for a hydrologic simulation. The simulation successfully develops the complex drainage network and simulates overland flow on the impervious surfaces (i.e., along the gutters and off rooftops) made possible through this spatial analysis process.     

近十年长三角地区陆地净第一性生产力时空变化

陆地净第一性生产力（NPP）是陆地生态系统的一项重要指示性指标，与碳循环过程和全球气候变化密切相关，不仅已成为地学研究的热点问题，更受到各国政府的高度重视。长三角地区是我国经济最发达、最具综合竞争力的地区之一，该地区的陆地生态系统同时承受着自然因素及高强度人为活动的双重影响。因此，利用CASA模型，在RS和GIS技术支撑下，探讨近十年来长三角地区陆地NPP的时空演变规律及控制因子，以期为进一步开展高强度人为活动对陆地系统碳循环过程的影响研究提供基础参数。主要结论如下：（1）在时间上呈下降趋势：近十年长三角地区陆地NPP呈现3 a周期式波动下降；（2）在空间上分异规律明显：陆地NPP分布为浙江＞江苏＞上海，呈现南北高中间低的凹槽状；（3）控制NPP的自然要素是NDVI和太阳辐射，而城市化是NPP变化的主要社会经济要素     

基于SPOT VGT/NDVI的陕西省关中地区冬小麦遥感估产

冬小麦是关中地区的主要粮食作物之一。对冬小麦产量估算的研究可使国家有关部门及时掌握粮食生产状况,对国家制定粮食政策和经济计划,及时进行宏观调控有着重要的意义。以陕西省关中地区为研究区域,利用1999—2007年的SPOT VGT/NDVI遥感数据对冬小麦产量进行预测。首先,选取冬小麦关键生长期内0.2—0.8范围内的旬NDVI数据,利用最大值合成法合成后的月NDVI数据建立与冬小麦产量之间的关系;其次,使用相关分析方法筛选出冬小麦关键期内与产量相关性较好的月份和旬,分别建立这些月份、旬与冬小麦产量之间的回归关系模型,最后对模型进行精度验证。结果显示,NDVI与产量之间有很好的相关性,其中4月上旬的相关性最好,其相关系数R2为0.605,即利用4月份上旬的数据预测冬小麦产量最好。在预测产量与实际产量对比的基础上,得出估产的相对误差为-5.2%—-7.9%,表明估产精度良好,可运用于冬小麦的实际估产。     

汤逊湖水体叶绿素浓度遥感估测研究

内陆水体叶绿素浓度遥感监测的各种经验模型在模型的方法和参数上都有一定的时空限制。为了实现武汉汤逊湖叶绿素浓度的遥感监测,利用同步实测的光谱数据和水质数据进行了该湖泊叶绿素浓度的遥感定量反演研究。首先分析了汤逊湖水体的反射率光谱曲线;其次通过相关分析的方法,发现了汤逊湖水体反射率在单波段法、一阶微分法和比值法建模中的最优波段或组合,并进行了一元线性建模;最后对3种模型的反演精度进行了比较分析。结果表明,对于汤逊湖叶绿素浓度遥感反演,一阶微分法和比值法优于单波段法,一阶微分法的最优波段在446.9 nm附近,比值法的最优波段组合为R861.1/R865.7,二者回归R2都在0.86以上。本项研究可为汤逊湖叶绿素浓度的遥感估测提供了参考。