Challenges and solutions of remote sensing at offshore wind energy developments

Radar is becoming an important tool used to gather data on bird and bat activity at proposed and existing land-based wind energy sites. Radar will likely play an even more important role at the increasing development of wind energy offshore, given both the lack of knowledge about bird and bat activity offshore and the increased difficultly in obtaining offshore information. Most radar studies to date have used off-the-shelf or modified marine radars. However, there are several issues that continue to hinder the potential usefulness of radar at wind energy sites, with offshore sites providing a particular suite of challenges. We identify these challenges along with current or developing solutions.     

基于多源遥感数据的南极冰架与海岸线变化监测

综合使用光学与微波遥感数据,提出了南极冰架变化连续监测的系统方法,包括基于MOA的冰架基准图生成,基于相似性测度的影像匹配,及基于阈值与分水岭变换的图像分割方法.使用该方法获取了2002—2011年初全南极18个主要冰架的变化数据,并归纳了南极冰架变化的三种类别.进一步得出,近年间崩解变化为主的冰架均处于西南极,并主要集中在南极半岛;扩展变化为主的冰架集中在东南极;南极三大冰架的扩展变化明显,其中Amery冰架将在近年发生较大崩解.本研究首次获取了2002年初至2011年初每年一幅的动态的全南极海岸线数据,并得出近10年间南极海岸线扩展增加总量为5878 km2.     

探地雷达应用概述

以ASTM标准规范为基础对探地雷达（Ground Penetrating Radar 以下简称GPR）的地下探测方法的应用做了比较系统的概述,主要内容包括：部分专业术语的解释;测试过程中雷达探测深度及其中心频率、垂直分辨率、水平分辨率等的关系,测试过程中常见的天线移动方式,以及雷达波速的预测几种方法,数据显示方式;雷达数据解释和数据处理的一般过程及方法.目前我国尚没有关于GPR的国家规范或行业标准,文章对GPR的使用及规范的编制具有一定的参考意义.     

探地雷达检测公路结构层隐含裂缝实用方法研究

本文在简要介绍探地雷达基本原理的基础上,详细介绍了作者最近完成的“多层均匀层状介质中的垂直裂缝对雷达波场响应的数值模拟和物理模拟实验研究”成果,通过与实际的公路工程检测结果对比,三者表现出很好的一致性. 研究结果表明,采用探地雷达检测公路结构层中的隐含裂缝,理论条件是充分的,实践上是可行的,检测方法本身实用性较大,检测结果直观、可靠.     

探地雷达在LNAPL污染土壤探测中的应用进展研究

轻非水相液体(LNAPL)的不当使用、渗漏及不当处置等会造成严重的土壤和地下水污染,威胁环境和公共卫生安全.探地雷达(GPR)作为一项重要的浅表地球物理观测技术已在LNAPL污染土壤探测中发挥重要作用.本文对近年来国内外学者利用GPR探测LNAPL污染土壤方面的理论和应用研究进行梳理,结合实例主要从以下几个方面开展评述.这些方面包括LNAPL污染土壤电性特征、基于GPR探测的LNAPL污染土壤建模、GPR信号响应、GPR的测量方式等.这些理论与应用研究为如何从场地的地质和水文背景中提取与污染有关的GPR信号做出了指导与成功的示范.现场和实验室的大量的研究工作表明,所有成功的案例都不可能倚赖单一的手段或方法.直接(例如,钻孔)和间接(例如,GPR)调查结合,多手段、多方法的有效配合,才有可能最大程度的减小探测结果的非唯一性,达到全面准确了解污染场地的目的.     

遥感技术在防震减灾领域中的应用

为了有效地组织救灾和震后重建,快速地获取地震造成的破坏程度、破坏范围成为至关重要的一环。遥感技术本身所具有的宏观性、时效性、经济高效性使其被广泛应用于防震减灾事业中。本文简单介绍了遥感技术的原理、特点及其在防震减灾领域中的应用历史及现状。在借鉴已有成果的基础上较系统地阐述了遥感技术在地震防灾方面的应用及展望。     

太湖秋季水体遥感反射比的简单经验估测模型

2004年10月现场测量了太湖水体的遥感反射比、后向散射系数以及其他必要参数,实验室测量了水体的悬浮颗粒物质浓度.首先把太湖划分为3个大区,即遥感反射比受湖底影响的区域(影响区)、不受湖底影响的区域(非影响区)以及可能受湖底影响的区域(可能影响区),然后再把太湖分为高混浊、中混浊、低清澈以及高清澈等4类水体类型,分别列出了各种水体区域类型的相关属性数据.在整个太湖区域范围内,建立了后向散射和悬浮颗粒物质浓度的经验回归模型;仅在非影响区内,建立了遥感反射比和后向散射的经验回归模型,并可以利用悬浮颗粒物质浓度直接估测遥感反射比.最后详细分析了模型的精度.     

南极冰盖物质平衡变化的卫星遥感监测现状与发展趋势

南极冰盖物质平衡对全球海平面变化影响巨大,提高南极冰盖物质平衡估算精度对实现全球海平面变化趋势准确预测至关重要.卫星遥感技术的发展,特别是极地卫星对南极冰盖系统监测能力的提升,有助于认识、保护和利用南极,解决极地科学国际前沿难题.揭示南极冰盖对全球气候环境的响应及其反馈机制,对深化人类对南极的科学认知也具有重要意义.本文首先系统总结了用于南极冰盖监测卫星的发展现状及现有的卫星遥感数据产品序列,然后讨论了运用遥感卫星监测南极物质平衡的主要方法和研究进展,并对南极卫星遥感技术的最新进展进行了分析.最后在我国极地遥感卫星发展方面提出了建议,旨在进一步提升我国对极地冰盖变化和全球海平面上升的长期观测及预测能力,为相关应对策略的制定提供有力的科学支撑.

     

Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain

Climate change significantly impact on agriculture in recent year, the accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C₄ crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS–P–YEC (Remote-Sensing–Photosynthesis–Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS–P–YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002–2011. The 111 statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (p < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002 to 2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale.     

Analysis of radar and raingauge measurements for a critical meteorological event in Tuscany

In this paper, some considerations are given to the employment of C-band polarimetric weather radars for rainfall estimates. The most common error sources are discussed, such as ground clutter and propagation attenuation effects, together with decorrelation in the sampling at the ground between radar and raingauge measurements, which can be quite significant in radar systems located in hilly regions, as is the case of the Arno basin in Tuscany. Since the main objective from a hydrological point of view is the estimate of rainfall at ground, integrations and comparisons are needed between radar and raingauge data, which are characterized by different time and space sampling. The paper is then focussed mainly on this problem and a technique is presented in order to improve radar based rainfall estimates through the integration with raingauge data, in order to enhance the correlation between the two types of measurements. Such a method is finally applied to a serious meteorological event which affected the Arno basin on October 1992.     

Time series analysis of optical remote sensing data for the mapping of temporary surface water bodies in sub-Saharan western Africa

A map of temporary small water bodies (TSWB) at 1 km resolution was derived for the arid, semi-arid and dry sub-humid regions of sub-Saharan western Africa where the spatio-temporal distribution of actual surface water occurrence exhibits high inter- and intra-annual variability. Water bodies and humid areas have been mapped and characterized by the analysis of 10 daily small water bodies (SWB) maps based on SPOT VEGETATION (VGT) data spanning the period January 1999–September 2007. Further analysis of the SWB time series provided additional information about the seasonal recurrence of water bodies as well as their hydrological function. A map derived from a continuous time series assures the inclusion of temporary features, a clear advantage compared to other datasets, which are based on several single date observations. The method described in this paper targets at a rapid creation of TSWB maps based on the SWB time series for different time intervals and regions.An accuracy assessment has been carried out with a stratified random sampling approach and a one-stage cluster analysis that relies on high-resolution satellite data to verify the detected water bodies. The overall accuracy, considering only the commission error, is 95.4% for the whole study region, with best results in the arid and semi-arid climate zone. The method to map water bodies delivers satisfactory results, particularly for sparsely vegetated areas as well as flat areas of the study region. In more humid, more vegetated areas and in mountainous areas, the possibility of false detections increases due to surface characteristics.     

Assessment of Hurricane Ivan impact on chlorophyll-a in Pensacola Bay by MODIS 250 m remote sensing

The impact of Hurricane Ivan on water quality in Pensacola Bay was investigated by MODIS 250 m remote sensing of chlorophyll-a concentrations at different time slots before and after the hurricane event. Before the hurricane, the mean chlorophyll-a in the Bay was 5.3 μg/L. Heavy rainfall occurred during the hurricane landfall. The 48 h rainfall reached 40 cm and the peak storm surge reached 3 m on 9/16. After the rainstorm and during the storm surge on 9/17/2004, the mean chlorophyll-a concentration substantially increased to 14.7 μg/L. 26.3% water area was in the poor-water-quality condition (chl-a > 20 μg/L). This indicates that heavy nutrient loads from urban stormwater runoff and storm-surge inundation simulated chlorophyll bloom. After the end of the storm surge on 9/18/2004, the mean chlorophyll dropped to 2.0 μg/L, suggesting the effective flushing of polluted water from the bay to the Gulf of Mexico by the storm-surge. The good water quality condition lasted for almost several weeks after the storm surge. The peak river flow, arriving on the 4th day after the peak storm surge, did not alter the good water quality situation in the bay. This indicates that urban stormwater runoff rather than the river inflow is the major pollutant source for water quality in Pensacola Bay during the hurricane.     

基于非分裂CFS-PML边界条件的频散介质GPR时域有限元模拟

通过数值模拟研究探地雷达（GPR）高频电磁波在频散介质中的传播规律,对提高实测资料的解释精度具有重要意义.复频移完全匹配层边界条件（CFS-PML）以其优越的吸收特性被广泛用于一阶电磁波动方程的GPR时域有限差分数值模拟中,其实现过程大都涉及电磁场的卷积计算,辅助变量较多,降低计算效率.为此,本文从复拉伸坐标系下的Debye频散介质电磁波动方程出发,通过合理构造辅助微分方程,推导了二阶Debye频散介质电磁波动方程的非分裂CFS-PML边界条件实现公式,避免了电磁波场的分裂和卷积计算.在此基础上,利用Galerkin法和Newmark-β差分法推导了基于非分裂CFS-PML边界条件的GPR有限元方程及其时域差分离散格式.两个GPR模型的模拟结果表明：本文提出的基于辅助微分方程的非分裂CFS-PML边界条件实现方法可有效地吸收大角度入射的低频虚假反射波,提高模拟精度;相比于非频散介质,高频电磁波在频散介质中传播衰减更强、子波持续时间增大、分辨率和传播速度降低、直达波和反射波的主频更小,分析结果有助于提高实测GPR资料的解译精度.

     

月球冷海高铝玄武岩遥感识别及其地质特征分析

高铝玄武岩是美国Apollo和前苏联Luna任务采集的月球样品中极其特殊且具有重要意义的一类样品,对月幔的不均匀性分异和月球早期热演化活动具有重要的指示性意义.本文以地形和成因极其独特的月球冷海中小型撞击坑坑壁或者近端溅射物中新鲜裸露的岩石（岩石丰富>0.5%）为突破口,利用中心和临近像元之间的Al₂O₃含量和镁指数（Mg#）间的线性关系消除了月海-高地混合物影响,将实验室岩性分类标准应用于Lunar Prospector（LP）钍（Th）和Lunar Reconnaissance Orbiter（LRO）Diviner主要元素氧化物反演产品中,获得月球表面新鲜裸露的高铝玄武岩质（Al₂O₃>11 wt%）岩石位置信息.以此为基础,通过目视解译的方法识别了埋藏在冷海表面风化层以下的高铝玄武岩斑块（10个）和单元（1个）.在LRO宽角（WAC）相机和数字地形图影像（LOLA GLD100）中对高铝玄武岩形态学地质特征统计表明,冷海中的高铝玄武岩总面积和体积为3694 km²和1160 km³,岩层的最大埋藏深度和厚度为331 m和207 m.研究结果表明,冷海高铝玄武岩岩层一般具有不一致且不连续的厚度和较大的成分变化范围,并且不是广泛地分布于冷海之中,只是零散地分布于中西部,以及集中于东部的两个表层月海玄武岩地质单元之下.这些分布特征意味着月球冷海早期的高铝型火山活动可能以随机的小型（面积小于500 km²或者体积小于100 km³）喷发为主,偶尔会出现大型的喷发.

     

The hydrology of sand rivers in Zimbabwe and the use of remote sensing to assess their level of saturation

Sand rivers are ephemeral watercourses containing sand that are occasionally flooded with rainwater runoff during the rainy season. Although the riverbed appears dry for most of the year, there is perennial groundwater flow within the sand. This water flowing beneath the surface is a valuable resource for local communities; nonetheless our understanding of such river systems is limited. Hence, this paper aims to improve our understanding of the hydrology of sand rivers and to examine the potential use of remote sensing to detect the presence of water in the sand. The relationship between rainfall events and changes in the water level of two sand rivers in the Matabeleland South Province of Zimbabwe was investigated. A lagged relationship was observed for the Manzamnyama River but for the Shashani River the relationship was seen only when considering cumulative rainfall events. The comparison of the modelled flow as simulated by a water balance model with observations revealed the important influence of the effective sediment depth on the recharge and recession of the alluvial channels in addition to the length of the channel. The possibility of detecting water in the alluvial sands was investigated using remote sensing. During the wet season, optical images showed that the presence of water on the riverbed was associated with a smooth signal, as it tends to reflect the incident radiation. A chronological analysis of radar images for different months of the year demonstrates that it is possible to detect the presence of water in the sand rivers. These results are a first step towards the development of a methodology that would aim to use remote sensing to help reducing survey costs by guiding exploratory activities to areas showing signs of water abstraction potential.     

Assessing the feasibility of integrating remote sensing and in-situ measurements in monitoring water quality status of Lake Chivero,Zimbabwe

This work investigates the likelihood of integrating the cheap and readily-available broadband multispectral MODIS data and in-situ measurements in quantifying and monitoring water quality status of an inland lake within Upper Manyame Catchment in Zimbabwe. Specifically we used MODIS images to quantify inland lake chlorophyll_a concentrations, as a proxy for predicting lake pollution levels. The findings of this study show a high chlorophyll_a concentration of 0.101 ± 0.128 μg/L within the Lake. The results further demonstrated that the chlorophyll_a concentration levels did not significantly vary (p = 0.788) between sites, except among depths (p = 0.05). Further, prediction results based on the relationship between observed and predicted chlorophyll_a produced a high R² value of 0.89 and a root mean square error (RMSE) value of 0.003 μg/L. Moreover, the derived landuse maps of Upper Manyame Catchment indicated a significant variation in the percentage settlement in 1985, 1994 and 2010 change from 1985 to 2010. For instance, 8% increase in settlement in the period between 1994 and 2010 and over 12% increase from 1985 to 2010 and a decline in percent forest coverage (i.e. 9.8% in 1985 to 2.0% in the year 2010) in the catchment was observed. Overall, the findings of this study highlights the importance of free and readily-available satellite datasets (such as the multispectral MODIS and Landsat) in quantifying and monitoring water quality across inland lakes especially in data-scarce areas like Sub-Saharan Africa.     

Development and application of a remote sensing-based salinity prediction model for a large estuarine lake in the US Gulf of Mexico coast

Salinity in estuaries is highly variable due to river discharge, tidal motion, and winds. Information on the spatial and temporal changes in salinity can provide important ecological indications, but accurate monitoring of the space–time variability for a large estuary is often costly and time-consuming. This study applied remote sensing techniques to develop a salinity prediction model for Lake Pontchartrain, a large estuarine lake located in the Northern Gulf of Mexico, USA. “Ground truth” salinity was measured along two transects across the lake and near the shoreline. Water-leaving reflectance from the measurement locations was extracted from Landsat Thematic Mapper (TM) images pre-processed through “banding” noise reduction and radiometrical correction approaches. Ordinary least square and ridge regression methods were performed to identify model parameters and to determine relationships between salinity and reflectance. Salinity in the lake on eight dates was predicted with the developed model. Difference in salinity level and patterns, and impacts of Hurricanes Katrina and Rita on salinity were assessed with ANOVA and Fuzzy Similarity methods. The results showed that the model achieved a high power in prediction of the lake salinity (R² = 0.89 and RMSE of validation = 0.27). Reflectance from TM bands 1, 2, and 4 was positively correlated to salinity levels and explained 1.9%, 20.3%, and 10.2% variance in salinity levels. Reflectance from bands 3 and 5 was negatively correlated to salinity and explained 34.1% and 31.2% variance. Under normal circumstances without the impacts of hurricanes, the lake salinity presented two patterns with average salinity level of 5.5 ppt. After Katrina’s landfall, the average was significantly increased by 1.1 ppt and the spatial patterns were altered. The pattern on 30 August 2005 was the most dissimilar one as compared to the two normal patterns, and then followed by the patterns on 9 and 25 October, and 7 September 2005. This study demonstrated that satellite remote sensing techniques can be applied to monitor salinity in coastal environments, and that freshwater discharge not only affects salinity levels and patterns under normal conditions but also is crucial for the return of salinity patterns to normal conditions after hurricane disturbance.     

Modelling runoff using optical satellite remote sensing data in a high mountainous alpine catchment of Italy

Development of hydrological models for seasonal and real-time runoff forecast in rivers of high alpine catchments is useful for management of water resources. The conceptual models for this purpose are based on a temperature index and/or energy budget and can be either lumped or distributed over the catchment area. Remote sensing satellite data are most useful to acquire near real-time geophysical parameters in order to input to the distributed forecasting models. In the present study, integration of optical satellite remote sensing-derived information was made with ground meteorological and hydrological data, and predetermined catchment morphological parameters, to study the feasibility of application of a distributed temperature index snowmelt runoff model to one of the high mountainous catchments in the Italian Alps, known as Cordevole River Basin. Five sets of Landsat Multispectral Scanning System (MSS) and Thematic Mapper (TM) computer-compatible tapes (CCTs) were processed using digital image processing techniques in order to evaluate the snow cover variation quantitatively. Digital elevation model, slope and aspect parameters were developed and used during satellite data processing. The satellite scenes were classified as snow, snow under transition and snow free areas. A second-order polynomial fit has been attempted to approximate the snow depletion and to estimate daily snow cover areal extent for three elevation zones of the catchment separately. Model performance evaluation based on correlation coefficient, Nash–Sutcliffe coefficient and percentage volume deviation indicated very good simulation between measured and computed discharges for the entire snowmelt period. The use of average temperature values computed from the maximum and minimum temperatures into the model was studied and a suitable algorithm was proposed. © 1997 John Wiley & Sons, Ltd.     

A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland

The concentrations of chlorophyll-a (chl-a), total suspended solids (TSS) and the absorption coefficient of colored dissolved organic matter (a_CDOM(400)) are estimated in Case II waters using medium resolution imaging spectrometer (MERIS) satellite (full resolution [FR] level 1b, 300 m resolution) and AISA airborne spectrometer data acquired during a spring bloom in the Gulf of Finland, Baltic Sea on April 27, 2004. The accuracy of the estimation is analyzed using empirical band-ratio algorithms together with in situ observations that include water samples analyzed in a laboratory (variation ranges: 22–130 μg/l, 2.9–20 mg/l, and 1.29–2.61 m⁻¹ for chl-a, TSS and a_CDOM(400), respectively). Additional in situ estimates (transects) on these characteristics are available through absorption and scattering coefficients measured with an ac-9 absorption and attenuation meter installed in a flow-through system. The retrieval accuracy (R²) of all three water quality characteristics with MERIS data is close to or above 0.9, while the RMSE is 7.8 μg/l (22%), 0.74 mg/l (16%) and 0.08 m⁻¹ (5%), for chl-a, TSS and a_CDOM(400), respectively. The validity of the chl-a algorithm is tested using nine additional data points. The BIAS-error for these points is 5.2 μg/l and the RMSE is 10.6 μg/l. The effects of changes in the atmospheric characteristics on band-ratio algorithms in cases where no concurrent in situ reference data are available are analyzed using the MODerate spectral resolution atmospheric TRANSmittance algorithm and computer model (MODTRAN). The additional error due to these changes is estimated to be below 20% for the applied ratio algorithms. The water quality data available in the level 2 MERIS-product distributed by the European Space Agency did not include valid results for the date investigated here.