ACCURACY OF IMPERVIOUS AREA VALUES ESTIMATED USING REMOTELY SENSED DATA1

ABSTRACT: Remote sensing offers an attractive alternative to conventional data collection employed in the estimation of certain hydrologic model parameters. In this investigation, the standard error of parameters estimated from Landsat data are examined. Relationships between the standard error and the size of the spatial-modeling units are developed that allow extending results to larger areas. Based upon the investigations conducted, a generalized model of the error relationships could not be developed.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

Satellites viewing our world: The NASA landsat and the NOAA SMS/GOES

Thousands of individuals throughout the world are now users of satellite data. Hundreds of satellites have been launched—military, navigation, communications, educational, weather, and earth resources. One of the weather satellites (the SMS/GOES) and the NASA earth resources mapping satellite (Landsat) are the subjects of this article. Data from these systems have been highly cost beneficial, not only in the United States, but in developed and developing nations all over the globe as well. There is an increasing demand both for data and for training in data use.Data samples are shown and applications are discussed. Strong reference is made to the value of the digital computer in natural and man-made features mapping and monitoring. Procedures for acquiring NASA data are explained so that the reader may order data for his home region, or for other regions throughout the world which are of interest for their agriculture, forestry, hydrology, marine resources, geology, or land use. The cost of data is incredibly low; some products cost as little as three dollars.Also discussed are the remotely based data-collection platforms that acquire ground or water data daily and relay results to the NASA Landsat or to the NOAA SMS/GOES.     

ESTIMATING THE SUSPENDED SEDIMENT LOAD IN RESERVOIRS1

ABSTRACT: The total suspended sediment loads of four north Mississippi reservoirs were determined from measurements of concentrations of suspended sediment in a vertical profile at several locations on each reservoir made during the year. These data were combined with the stage-height and known stage-volume relationships for each reservoir in a numerical integration to determine the total suspended sediment in the water body. Total suspended sediments were estimated using the product of the suspended sediment concentration in the surface water by the appropriate reservoir volume. The averaged ratios of the estimated to measured suspended sediment loads for each reservoir exceeded 0.90. Since the concentration of suspended sediments in surface waters of north Mississippi reservoirs has been shown as highly correlated with spectral reflectance, estimating the total suspended sediment of these reservoirs using remotely sensed spectral reflectance data is possible.     

FLOOD HAZARD STUDIES IN THE MISSISSIPPI RIVER BASIN USING REMOTE SENSING1

ABSTRACT. The Spring 1973 Mississippi River flood was investigated using remotely sensed data from ERTS-1. Both manual and automatic analyses of the data indicate that ERTS-I is extremely useful as a regional tool for flood management. Quantitative estimates of area flooded were made in St. Charles County, Missouri and Arkansas. Flood hazard mapping was conducted in three study areas along the Mississippi River using pre-flood ERTS-1 imagery enlarged to 1:250,000 and 1:100,000 scale. The flood prone areas delineated on these maps correspond to areas that would be inundated by significant flooding (approximately the 100 year flood). The flood prone area boundaries were generally in agreement with flood hazard maps produced by the U. S. Army Corps of Engineers and U. S. Geological Survey although the latter are somewhat more detailed because of their larger scale. Initial results indicate that ERTS-1 digital mapping of flood prone areas can be performed at 1:62,500 which is comparable to some conventional flood hazard map scales.     

遥感在生态与环境监测中的主要应用领域

遥感是一种以物理手段、数学方法和地学分析为基础的综合应用技术 ,具有宏观、综合、动态和快速的特点。在解决宏观尺度的环境问题时 ,卫星遥感可重复获取多种空间、不同时相和不同波谱分辨率的地球信息 ,是适宜于调查和研究这些主题的唯一的、最有效的工具。本文通过对遥感在环境与生态监测的主要应用领域进行概要阐述 ,旨在抛砖引玉 ,推动遥感在环境与生态监测中的广泛应用。     

Calibrating a Basin‐Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ET_g) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ET_g. An iterative approach is provided that calibrates GWM to ET_g derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11‐year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ET_g to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM‐grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first‐order second moment analysis shows ET_g to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ET_g despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ET_g appears to bias ET_g during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness.     

AHLs群体感应信号分子对硝化污泥附着生长及硝化效果的影响

污水生物脱氮系统中的硝化菌生长慢、易流失,人为添加N-酰基高丝氨酸内酯类(AHLs)群体感应信号分子,可能会强化硝化菌生物膜的形成,从而有助于富集硝化菌,提高硝化效率.本研究以采用低碳氮比(C∶N=8)人工配置废水驯化100 d后的硝化活性污泥为菌源,人为外加2μmol·L-1的AHLs信号分子(C8-HSL或OHHL),分析了两种信号分子对硝化污泥静态附着、氨氮动态降解及微生物生长速率的影响.研究结果表明,信号分子OHHL能快速强化微生物附着生长,且能在一定时间内持续发挥作用,有助于硝化生物膜的形成;而信号分子C8-HSL则能明显提高硝化污泥的氨氮降解速率;两种信号分子都能促进硝化污泥生长,提高微生物生长速率,增强硝化污泥活性,加速硝化污泥生物量累积.人为添加C8-HSL或OHHL信号分子,不仅能保证氨氮降解效率还能降低出水硝氮浓度,减轻氮污染.     

大渡河上游丹巴地区地质灾害的地貌分维特征解析

地貌对地质灾害的分布、类型、规模与危害等特征具有重要的影响,宏观及微观地貌指标均能在一定程度上刻画其作用特征。应用高分辨率遥感影像详细解译崩塌和滑坡灾害,采用高精度DEM和滑动窗口技术提取地貌分维特征,并用其解析灾害分布的地貌特征。结果表明:灾害体的内部与后壁、前缘、侧壁和邻区均表现出较为明显的分异性,崩塌的微地貌分异特征较之滑坡显著,大型、厚层、老(古)崩塌及滑坡分异特征更为突出。计算地貌分维值可有效识别与其同时期的或更早的地质灾害,也可间接挖掘新发生灾害的微地貌复杂性和离散性。     

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain‐referral survey. We then used a workshop‐based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real‐time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing‐derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?