基于高分数据的村级地块面向对象分类方法研究

针对农作物对地抽样调查工作中村级抽样单元野外调查工作量大的问题,研究了一种基于高分数据的村级农作物种植面积提取方法。以辽宁省北镇市孟家村SPOT5遥感影像为实验数据(主要包括农田、裸地、道路、大棚、水体、房屋等类型),基于eCognition平台,根据局部方差法筛选地物最优分割尺度,按照最优分割尺度从大到小组织实验区地物分割次序,结合影像对象的光谱、形状等特征,建立分类规则,完成了村级农作物种植面积的提取。该方法有效避免了在影像分割时参数反复试错带来的复杂性和随机性,提高了农作物种植面积面向对象分类精度和效率。通过野外调查样地进行精度验证表明,精度达到90.4%,为提高农作物种植面积对地抽样调查工作效率,减少野外调查的工作量提供技术支撑。     

基于高分辨率遥感影像的城市土地利用景观格局分析

以IKONOS卫星遥感影像为基础数据源,在GIS技术的支持下,获取了泸州市区的土地利用类型。借鉴景观生态学的基本原理和方法,基于Fragstats 3.3软件,用景观格局指数定量分析了泸州市区土地利用景观格局的特征。分析结果表明,泸州市区土地利用景观类型丰富,景观多样性程渡高,景观类型均匀程度也较高,但优势景观类型不明显;由于人类干扰程度较大,土地利用景观破碎,稳定性较差。     

Modeling Residential Lawn Fertilization Practices: Integrating High Resolution Remote Sensing with Socioeconomic Data

This article investigates how remotely sensed lawn characteristics, such as parcel lawn area and parcel lawn greenness, combined with household characteristics, can be used to predict household lawn fertilization practices on private residential lands. This study involves two watersheds, Glyndon and Baisman’s Run, in Baltimore County, Maryland, USA. Parcel lawn area and lawn greenness were derived from high-resolution aerial imagery using an object-oriented classification approach. Four indicators of household characteristics, including lot size, square footage of the house, housing value, and housing age were obtained from a property database. Residential lawn care survey data combined with remotely sensed parcel lawn area and greenness data were used to estimate two measures of household lawn fertilization practices, household annual fertilizer nitrogen application amount (N_yr) and household annual fertilizer nitrogen application rate (N_ha_yr). Using multiple regression with multi-model inferential procedures, we found that a combination of parcel lawn area and parcel lawn greenness best predicts N_yr, whereas a combination of parcel lawn greenness and lot size best predicts variation in N_ha_yr. Our analyses show that household fertilization practices can be effectively predicted by remotely sensed lawn indices and household characteristics. This has significant implications for urban watershed managers and modelers.     

浙江城镇空间扩展与经济发展的遥感研究

近年来。浙江省经济飞速发展。城镇规模迅速扩大。但有关全省城镇建成区扩展面积的具体数量和扩展速度的客观数据还很缺乏。利用多时相的Landsat卫星遥感图像数据。采用目视解译与计算机自动分类方法，对1998～2001年浙江省城镇时空扩展进行了调查。调查结果显示，1998年和2001年浙江省县级以上城市建成区面积分别为977．85km^2和1499．06km^。3年内全省县级以上城市建成区面积扩展了521．21km^2，年增长速率达15．31％。在此基础上，根据区域社会经济统计数据，对城镇经济发展与城市空间扩展的相关性进行了分析。     

高分辨率遥感影像在城市绿地信息提取中的应用研究

高分辨率卫星遥感图像数据量大、空间分辨率高、结构信息复杂、地物同物异谱现象更为突出等特征给专题信息提取技术带来了新的挑战。基于植被的光谱特征,利用监督分类、植被指数分类和目视解译等方法对QuickBird高分辨率卫星遥感影像的绿地信息进行提取,并对分类精度作了比对分析。研究结果表明,监督分类方法不能得到令人满意的结果,运用植被指数分类方法则有明显改善,其中归一化植被指数(NDVI)精度最高,因此NDVI能有效地对植被进行分类与识别。     

洪水灾害遥感监测研究综述

洪水灾害是最严重的自然灾害之一,洪灾造成的损失十分严重,对其进行科学的监测是防灾减灾的基础。在洪水灾害遥感监测研究进展的介绍基础上,着重对中分辨率、高时相、微波、高精度DEM、多源数据遥感洪水监测原理及方法的研究进展进行比较和总结,分析各种数据特点。在此基础上提出了洪水遥感监测向高分辨率、高时相性方向,遥感影像相互订正和利用3S技术是洪涝灾害动态监测发展的方向,为洪水灾害的快速反应和防洪辅助决策提供依据。     

遥感监测的土地利用覆被数据质检流程研究

文章从生态环境遥感监测的土地利用覆被数据入库质检的现实要求出发,重点讨论利用地理信息系统工具,建立土地利用覆被数据精度评价的业务流程,可较好的满足本部门所生产数据入库质量检查与控制的要求。     

浅谈水质在线监控中遥感技术的应用

空间信息技术以及计算机技术的发展，给环境监控提供了先进、快速和科学的方法。其中，遥感技术由于能够快速、宏观地获得所监控区域的数据，近年来已逐渐成为环境监控系统中的重要技术手段。遥感信息是被控区域的电磁辐射能量及其结构特征与时空状态在遥感图像上的表现。今后，遥感技术、在线监控系统和地理信息系统等多种信息处理工具的结合将是环境信息监控系统发展的主要方向。     

基于贝叶斯网络分类的土壤盐渍化遥感监测

贝叶斯网络是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的概率模型.采用贝叶斯网络分类对具有典型干旱特征的库车县土壤盐渍化情况进行监测,首先应用条件独立性测试原理建立贝叶斯网络结构,把研究区遥感数据进行离散化,然后应用贝叶斯定理作为分类原则,将每个像元分为像元最大概率的类别.研究结果表明该方法来分类6种地类的整体分类精度达到96%,并为该区盐渍地面积,空间分布等特征监测提供较好的依据.     

低温雪灾后莽山保护区森林植被恢复遥感评价

以2008年1月的低温雪灾为背景,选择反映植被长势的植被覆盖度为研究对象,以受人类活动干扰最少、最能体现植被自然恢复的莽山保护区为研究区,以像元二分模型为基础,通过计算样区植被遥感直方图的拐点来确定NDYI(soil)和NDVI(veg),通过植被覆盖的变化情况来衡量低温雨雪后森林植被的恢复状况,并对不同地形条件下、不同林龄和树种的森林植被恢复情况进行了分析,以期为整个莽山乃至南方受灾地区的受灾程度评价提供方法支持,为灾后重建决策提供数据参考.     

Use of Remote Sensing Techniques to Determine the Effects of Grazing on Vegetation Cover and Dune Elevation at Assateague Island National Seashore: Impact of Horses

The effects of grazing by feral horses on vegetation and dune topography at Assateague Island National Seashore were investigated using color-infrared imagery, lidar surveys, and field measurements. Five pairs of fenced and unfenced plots (300 m²) established in 1993 on sand flats and small dunes with similar elevation, topography, and vegetation cover were used for this study. Color-infrared imagery from 1998 and field measurements from 2001 indicated that there was a significant difference in vegetation cover between the fenced and unfenced plot-pairs over the study period. Fenced plots contained a higher percentage of vegetation cover that was dominated by American beachgrass (Ammophila breviligulata). Lidar surveys from 1997, 1999, and 2000 showed that there were significant differences in elevation and topography between fenced and unfenced plot-pairs. Fenced plots were, on average, 0.63 m higher than unfenced plots, whereas unfenced plots had generally decreased in elevation after establishment in 1993. Results demonstrate that feral horse grazing has had a significant impact on dune formation and has contributed to the erosion of dunes at Assateague Island. The findings suggest that unless the size of the feral horse population is reduced, grazing will continue to foster unnaturally high rates of dune erosion into the future. In order to maintain the natural processes that historically occurred on barrier islands, much larger fenced exclosures would be required to prevent horse grazing.     

湖泊水面温度遥感定量反演研究综述

湖泊水面温度与藻类生长密切相关,通过获取水体表层温度信息来监测湖泊水面温度时空动态变化,对于蓝藻水华预警监控具有重要意义。湖泊水面温度遥感定量反演的理论方法与反演地表、海洋表面温度基本相同。本文在介绍遥感反演地表温度原理的基础上,总结了国内外基于热红外波段反演水体表面温度的单窗算法、劈窗算法、多通道算法等几种常用方法,重点分析了各算法的优缺点和反演的主要难点,并对今后在内陆水体藻华预测顸警等方面的应用趋势进行了展望。     

An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China

Monitoring the dynamics of forest biomass at various spatial scales is important for better understanding the terrestrial carbon cycle as well as improving the effectiveness of forest policies and forest management activities. In this article, field data and Landsat image data acquired in 1999 and 2007 were utilized to quantify spatiotemporal changes of forest biomass for Dongsheng Forestry Farm in Changbai Mountain region of northeastern China. We found that Landsat TM band 4 and Difference Vegetation Index with a 3 × 3 window size were the best predictors associated with forest biomass estimations in the study area. The inverse regression model with Landsat TM band 4 predictor was found to be the best model. The total forest biomass in the study area decreased slightly from 2.77 × 10⁶ Mg in 1999 to 2.73 × 10⁶ Mg in 2007, which agreed closely with field-based model estimates. The area of forested land increased from 17.9 × 10³ ha in 1999 to 18.1 × 10³ ha in 2007. The stabilization of forest biomass and the slight increase of forested land occurred in the period following implementations of national forest policies in China in 1999. The pattern of changes in both forest biomass and biomass density was altered due to different management regimes adopted in light of those policies. This study reveals the usefulness of the remote sensing-based approach for detecting and monitoring quantitative changes in forest biomass at a landscape scale.     

A Comparison of Data Sets Varying in Spatial Accuracy Used to Predict the Occurrence of Wildlife-Vehicle Collisions

Wildlife-vehicle collisions (WVCs) pose a significant safety and conservation concern in areas where high-traffic roads are situated adjacent to wildlife habitat. Improving transportation safety, accurately planning highway mitigation, and identifying key habitat linkage areas may all depend on the quality of WVC data collection. Two common approaches to describe the location of WVCs are spatially accurate data derived from global positioning systems (GPS) or vehicle odometer measurements and less accurate road-marker data derived from reference points (e.g., mile-markers or landmarks) along the roadside. In addition, there are two common variable types used to predict WVC locations: (1) field-derived, site-specific measurements and (2) geographic information system (GIS)-derived information. It is unclear whether these different approaches produce similar results when attempting to identify and explain the location of WVCs. Our first objective was to determine and compare the spatial error found in road-marker data (in our case the closest mile-marker) and landmark-referenced data. Our second objective was to evaluate the performance of models explaining high- and low-probability WVC locations, using congruent, spatially accurate (<3-m) and road-marker (<800-m) response variables in combination with field- and GIS-derived explanatory variables. Our WVC data sets were comprised of ungulate collisions and were located along five major roads in the central Canadian Rocky Mountains. We found that spatial error (mean ± SD) was higher for WVC data referenced to nearby landmarks (516 ± 808 m) than for data referenced to the closest mile-marker data (401 ± 219 m). The top-performing model using the spatially accurate WVC locations contained all explanatory variable types, whereas GIS-derived variables were only influential in the best road-marker model and the spatially accurate reduced model. Our study showed that spatial error and sample size, using road-marker data for ungulate species, are important to consider for model output interpretation, which will impact the appropriate scale on which to apply modeling results. Using road-marker references <1.6 km or GPS-derived data locations may represent an optimal compromise between data acquisition costs and analytical performance. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data1

Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313‐1327. Abstract: Large‐scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (?) 8% to (+) 20% in the Ganges basin, by (?) 15 to (+) 12% in the Brahmaputra basin, and by (?) 15 to (+) 19% in the Meghna basin. Our large‐scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs.     

四川盆地西部气溶胶空间分布现状的遥感研究初报

根据连续2天卫星遥感气溶胶光学厚度(AOD,Aerosol Optical Depth)实例,2011年4月26日AOD的高值区位于成都市区附近,27日迁移至德阳中部;27日绵阳城区及其附近的AOD次高值区是由当地累积、扩散而成;高值区的迁移与大气流场和地形特征密切相关。气溶胶颗粒物不属于四川盆地之外输入;较之以往更详细地展示了当地AOD的空间分布状况。     

Impact of Watershed Subdivision and Soil Data Resolution on SWAT Model Calibration and Parameter Uncertainty1

Abstract: Impact of watershed subdivision and soil data resolution on Soil Water Assessment Tool (SWAT) model calibration and parameter uncertainty is investigated by creating 24 different watershed model configurations for two study areas in northern Indiana. SWAT autocalibration tool is used to calibrate 14 parameters for simulating seven years of daily streamflow records. Calibrated parameter sets are found to be different for all 24 watershed configurations, however in terms of calibrated model output, their effect is minimal. In some cases, autocalibration is followed by manual calibration to correct for low flows, which were underestimated during autocalibration. In addition to normal validation using four years of streamflow data for each calibrated parameter set, cross‐validation (using a calibrated parameter set from one model configuration to validate observations using another configuration) is performed to investigate the effect of different model configurations on streamflow prediction. Results show that streamflow output during cross‐validation is not affected, thus highlighting the non‐unique nature of calibrated parameters in hydrologic modeling. Finally, parameter uncertainty is investigated by extracting good parameter sets during the autocalibration process. Parameter uncertainty analysis suggests that significant parameters show very narrow range of uncertainty across different watershed configurations compared with nonsignificant parameters. Results from recalibration of some configurations using only six significant parameters were comparable to that from calibration using 14 parameters, suggesting that including fewer significant parameters could reduce the uncertainty arising from model parameters, and also expedite the calibration process.     

The Impact of Map and Data Resolution on the Determination of the Agricultural Utilisation of Organic Soils in Germany

Due to its nature, agricultural land use depends on local site characteristics such as production potential, costs and external effects. To assess the relevance of the modifying areal unit problem (MAUP), we investigated as to how a change in the data resolution regarding both soil and land use data influences the results obtained for different land use indicators. For the assessment we use the example of the greenhouse gas (GHG) emissions from agriculturally used organic soils (mainly fens and bogs). Although less than 5 % of the German agricultural area in use is located on organic soils, the drainage of these areas to enable their agricultural utilization causes roughly 37 % of the GHG emissions of the German agricultural sector. The abandonment of the cultivation and rewetting of organic soils would be an effective policy to reduce national GHG emissions. To assess the abatement costs, it is essential to know which commodities, and at what quantities, are actually produced on this land. Furthermore, in order to limit windfall profits, information on the differences of the profitability among farms are needed. However, high-resolution data regarding land use and soil characteristics are often not available, and their generation is costly or the access is strictly limited because of legal constraints. Therefore, in this paper, we analyse how indicators for land use on organic soils respond to changes in the spatial aggregation of the data. In Germany, organic soils are predominantly used for forage cropping. Marked differences between the various regions of Germany are apparent with respect to the dynamics and the intensity of land use. Data resolution mainly impairs the derived extent of agriculturally used peatland and the observed intensity gradient, while its impact on the average value for the investigated set of land-use indicators is generally minor.     

Techniques to Reduce the Impact of Asulam Drift from Helicopter Sprayers on Native Vegetation

Asulam is often applied from the air to control bracken. This herbicide affects other ferns and spray drift could affect their survival. This paper discusses the use of bioassays to assess drift (a) spatially around a single bracken patch, (b) downwind from sprayed areas, and (c) spatially in undulating terrain and with different vegetation cover types. The aims were to develop policies to protect sensitive habitats. It is suggested that “no-spray” buffer zones in excess of 160–180 m are needed to minimise risk. Protection of rare ferns from overstorey vegetation or from steep slopes did not occur. Although less drift was found upwind there was significant damage at the end of the helicopter runs because of errors in switching the sprayer on and off.     

Synthetic Analysis for Extracting Information on Soil Salinity Using Remote Sensing and GIS: A Case Study of Yanggao Basin in China

/ This paper reports the experience of extracting information on the salinity of soil and offers a method of synthetic analysis. The experimental areas for analysis are located in Yanggao Basin, Shanxi Province, China. The types of soil are mainly meadow soil and salinized meadow soil. The method of synthetic analysis of salinity uses a geographic information system (GIS) as a tool, building a basic saltwater analysis model of saline soil and adjusting the result with expert experience after computer processing. The method of feature extraction has been used for remotely sensed data. An optimum combination of features has been determined and, after comparing several combinations in the Yanggao region, an improved result has been obtained after Kauth-Thomas (K-T) transformation. For precise quantitative analysis of the salinization, not only Thematic Mapper (TM) remote sensing data, but also two forms of non-remote-sensing data are needed: depth of groundwater and mineralization rate of groundwater according to the theory of genesis of soil. For the analysis of synthetic compounded multisources, a generalized Bayes classification is used after overlay, matching, and related coefficients have been determined. On the premise that various information sources are independent, global membership functions with probability are used to combine various pieces of information in order to apply them directly to the pixels and classifications of soil salinity. The experiment indicates that this analytical method is sound because of the increased speed of processing and its simplicity and improved precision of classification of salinity. Finally, it is necessary to examine and adjust the factors using expert intelligence. The experiment shows that synthetic analysis using the geographic information system can raise the precision of quantitative analysis of salinity, which has advantages for environmental monitoring and management.KEY WORDS: Salinity; Remote sensing; Thematic Mapper; Geographic information system; Classification