国家基本资源与环境遥感数据库集成中的面积汇总技术

主要论述了在遥感和地理信息系统技术支持下的自然资源宏观调查中数据汇总各主要方面的技术问题。对于以卫星影像为主要信息源进行的资源调查工作 ,文中分析了结果数据中的误差来源并详细说明了其面积平差技术及面积校正方案的抽样原理和方法。     

环渤海湾-莱州湾地区湿地现状遥感调查

应用Landsat-7 遥感数据和GIS技术对环渤海湾-莱州湾地区湿地现状进行了调查，调查该区湿地类型、面积和分布现状，并对湿地现状遥感调查结果进行了分析。     

基于GIS的滩涂资源面积量算方法研究

滩涂资源面积量算是滩涂调查中的一项重要工作内容,其结果数据是调查成果的总体表现与直观反映,为滩涂围垦规划调整、围垦工程合理建设提供了数据支持。本文首先分析了矢量形式滩涂资源数据预处理主要内容,对滩涂分布类型进行归纳整理,其次比较了先整体后局部与自下而上基于标准图幅的滩涂资源面积量算方法,阐述了两者关键实现技术,最后提出了一种可快速实现的滩涂资源面积量算方法。该方法以GIS软件空间操作为主、通过编程计算水深平均值判断滩涂资源类型为辅,实现过程简单易于推广使用,并在2011年浙江省滩涂资源面积量算中得到使用与验证。     

CORSRTD技术在土地利用数据调查中的应用

土地利用问题,对于协调人地关系、发展国民经济有着重要的作用。因此,为了合理利用土地,保证以集约利用为主与适量的耕地面积相结合,对土地利用数据进行调查有着至关重要的作用。本文利用CORS RTD技术对土地利用数据调查中的应用进行研究,对CORS RTD测量结果相对于RTK测量结果作为已知值的点位误差、测量中误差。并且认为随着测量面积的增大CORS RTD的面积测量精度逐渐提高。由此可见,利用CORS RTD技术进行面积测量保证了土地资源调查的面积精度要求。     

湿地资源调查更新若干问题探讨

对古浪县第二次全国湿地资源调查的技术、结果进行了系统分析,探索了研究区内湿地资源调查更新的方法,通过"天空地"协同和"互联网+"的调查更新技术全面更新研究区内湿地资源调查数据库,以全面掌握湿地资源状况。研究结果:1)古浪县第二次湿地资源调查范围内的非湿地类型面积达4 615.35 hm2,占全县第二次湿地调查总面积的59.31%。2)变更后县域内湿地资源总面积4 131.54 hm2,以河流水面、内陆滩涂、水库水面、沟渠为主要用地类型,较第二次调查湿地面积净减少3 650.25 hm2。3)"天空地"协同遥感监测技术为湿地资源变更监测提供高精度多源数据,保证了调查、监测的精度和准确性。     

利用高斯投影地图反算图斑椭球面积的误差

对于调查中计算图斑的椭球面积误差,普遍认为边长是造成最终面积误差的主要因素,本文指出了存在问题。使用不同节点分配的图斑数据,本文对国土调查面积公式的计算误差进行了分析,提出了图斑节点对称性是影响面积公式误差主要因素的观点,通过几何推证和实例说明了该结论的正确性。根据图斑节点对称性,提出了面积误差影响因子概念,给出了对图斑进行误差预判和按规定精度控制面积误差的方法,该方法对图斑具有通用性。通过编程验证了该方法的可操作性和实用性。     

GPS/PDA数据采集系统在第二次土地调查中的应用研究

土地利用现状调查是一门交叉学科,与很多学科和技术有紧密联系,随着测量学、计算机制图、3S技术、计算机技术和网络技术的发展,土地利用现状调查理论和技术取得了长足的进步和重大突破,集成了3S技术的PDA数据采集系统作为一种新的调查手段正在应用于土地利用现状调查.主要从GPS/PDA数据采集系统的精度、功能、调查流程等方面对新的调查手段进行闸述.     

全国土地利用格网数据生产技术方法探讨

本文在全国多尺度土地利用格网数据需求分析的基础上,基于土地利用矢量网格化生产工程实践,分析和阐述了国家土地利用格网参数选择依据、格网内地类面积的算法、格网综合和批量化处理等主要技术方法。研究结果表明:利用本研究开发的土地利用矢量网格化软件,对县级土地利用矢量数据进行网格化处理,可快速生产合格的全国土地利用地理格网数据,并将极大的促进全国土地调查数据成果的社会化共享应用。     

MAPGIS在县市界线数据库建设中的应用

MAPGIS软件二次开发技术在数据加工处理中有广泛的应用,在第二次土地调查工作中,落实行政界线,对各县市面积做控制,是协调各级土地调查工作的依据,因此建立县市级行政界线数据库,保证各县市使用行政界线的一致性。基于项目建设,阐述了界线数据库的建立及控制面积计算的关键技术,探讨了基于MAPGIS软件二次开发技术在项目中的关键技术应用。     

城镇地籍调查数据库建设

城镇地籍调查主要是全面查清调查区内部土地利用现状,确定每宗土地的位置、界线、权属、面积、用途等,编绘相关图件,形成数据、图件、表册等调查资料,建立城镇地籍数据库。国土局地籍部门以地籍调查形成的数据库为基础,进行日常的地籍管理工作,如发证、变更以及面积统计等。综上所述,地籍调查数据库建设在整个调查过程中的重要性不言而喻了。     

遥感图像空间分辨率改进值计算方法

为了克服超分辨重建后的遥感图像空间分辨率的界定还采用人工对比判读存在误差和结论不统一的缺点,利用调制传递函数、奈奎斯特采样定理和人眼极限频率,建立了一种新的空间分辨率客观评价方法。利用该方法计算超分辨率重建后的遥感图像相比原始遥感图像空间分辨率的提高倍数,从而推断出重建后遥感图像的空间分辨率的大小。在数值测试中,利用不同的超分辨率方法对分级变频矩形光栅图像进行重建,采用提出的空间分辨率评价方法,与归一化均方误差、峰值信噪比、信息熵、灰度平均梯度进行客观评价的结果一致。此方法为遥感图像空间分辨率改进值的计算提供了一种可行的量化模型。     

基于对地抽样总量控制下的玉米种植面积提取

提出了一种基于统计抽样总量控制下的中高分辨率遥感影像玉米种植面积信息提取方法, 该方法首先利用分层抽样技术对调查目标总体(玉米)进行分层抽样；然后对抽样小区进行目视解译, 反推区域总量真值；最后在总量控制下进行区域目标作物的空间分布提取。以河北省三河市中部地区的部分影像为研究区, 以该区2006-08-21的10m分辨率的SPOT 5多光谱影像为基础数据进行了试验研究。结果表明该方法基于群样本检验的总体精度达到93.8%, Kappa系数达到0.88, 均高于最大似然监督分类结果的精度。另外, 所提出的方     

Identification and mapping of winter wheat by integrating temporal change information and Kullback–Leibler divergence

Crop acreage and its spatial distribution are a base for agriculture related works. Current research combining medium and low spatial resolution images focuses on data fusion and unmixing methods. The purpose of the former is to generate synthetic fine spatial resolution data instead of directly solving the problem. In the latter, high-resolution data is only used to provide endmembers and the result is usually an abundance map rather than the true spatial distribution data. To solve this problem, this paper designs a conceptual model which divides the study area into different types of pixels at a MODIS 250 m scale. Only three types of pixels contain winter wheat, i.e., pure winter wheat pixels (P_A), the mixed pixels comprising winter wheat and other vegetation (M_A) and the mixed pixels comprising winter wheat and other crops (M_B). Different strategies are used in processing them. (1) Within the pure cultivated land pixels, the Kullback–Leibler (KL) divergence is employed to analyze the similarity between unknown pixels and the pure winter wheat samples on the temporal change characteristics of NDVI. Further P_A is identified. (2) For M_A, a proposed reverse unmixing method is firstly used to extract the temporal change information of cultivated land components, after which winter wheat is identified from the cultivated land components as previously described. (3) For M_B which only appears on the border of P_A, a mask is created by expanding the P_A and temporal difference is utilized to identify winter wheat under the mask. Finally, these three results are integrated at a TM scale with the aid of 25 m resolution land use data. We applied the proposed solution and obtained a good result in the main agricultural area of the Yiluo River Basin. The identified winter wheat planting acreage is 161,050.00 hm². The result is validated based on the five-hundred random validation points. Overall accuracy is 94.80% and Kappa coefficient is 0.85. This demonstrates that the temporal information reflecting crop growth is also an important indicator, and the KL divergence makes it more convenient in identifying winter wheat. This research provided a new perspective for the combination of low and medium spatial resolution remote sensing images. The proposed solution can also be effectively applied in other places and countries for the crop which has a clear temporal change characteristic that is different from others.     

The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian Savanna

Multi-sensor image fusion using the wavelet approach provides a conceptual framework for the improvement of the spatial resolution with minimal distortion of the spectral content of the source image. This paper assesses whether images with a large ratio of spatial resolution can be fused, and evaluates the potential of using such fused images for mapping the Brazilian Savanna. Three types of wavelet transforms were used to perform the fusion between MODIS and Landsat TM images. Five quality measures were defined to assess the quality of the fused images. The results showed that it was possible to perform the fusion of MODIS and TM images and the pyramidal in Fourier space wavelet transform provided the best quality measures for the fused images. Classification results showed that fused images could be used for mapping the Brazilian Savanna with an accuracy level comparable to the Landsat TM image.     

ASTER和TM/ETM+遥感数据融合监测土地覆盖变化

在人们纷纷选择IKONOS、QUICKBIRD、SPOT-5等高分辨率影像监测土地利用/覆盖变化之际,以北京海淀区为例,尝试采用Brovey变换和主成分分析(PCA)法融合ASTER、TM/ETM+中等分辨率影像,充分利用ASTER、TM/ETM+数据的多光谱和较高空间分辨率特性,挖掘其在土地覆盖变化监测中的潜力,为大规模监测土地利用/覆盖变化提供科学参考。研究将2003年ASTER多光谱3N、2、1波段与1999年ETM+PAN波段进行Brovey变换;1992年TM543与1999年ETM+PAN波段进行PCA融合,快速发现土地覆盖变化信息。经验证,变化发现精度达92.50%,符合项目精度要求。试验表明:在缺乏高分辨率影像的地区,选择价格相对便宜的AS-TER和TM/ETM+数据,采用Brovey变换和主成分分析(PCA)法进行融合,可有效监测土地覆盖变化,节约动态监测成本,二者具有很大的应用价值,值得推广。     

分层随机抽样下全球30 m火烧迹地产品验证

火烧迹地是全球及区域碳循环和气候变化等研究所需的重要参数之一,卫星遥感技术为快速获取大区域火烧迹地空间分布信息提供了有效手段。中国科学院基于Landsat系列卫星数据研发了首个30 m空间分辨率全球火烧迹地产品GABAM (Global Annual Burned Area Map)。遥感数据产品的精度验证对产品使用具有重要意义,迄今尚未有研究机构对GABAM产品精度进行独立评价和分析。为系统评价GABAM产品精度,利用2010年全球30 m空间分辨率火烧迹地产品(GABAM2010)开展精度验证研究工作,在全球和几个陆地生物群落中估算了产品精度,并探索了全球遥感专题产品精度验证的技术框架。基于分层随机抽样选择80个非重叠的泰森多边形区域TSA (Thiessen Scene Areas),采用误差矩阵和6个精度指标对GABAM2010产品做全面精度评价和分析,以满足火烧迹地产品用户的使用要求。结果表明:在全球范围内,GABAM2010产品的错分率和漏分率分别为24.32%和31.60%,总体精度为97.85%;由于数据质量(如条带、云)等的影响,火烧迹地的范围会被低估,对于较容易发生火灾的生物群落,如热带亚热带草原区域,GABAM2010产品精度较高;在生物群落内部,高密度火烧迹地区域的精度高于低密度火烧迹地区域。     

TM影像几何校正算法的精度比较

同大多数的线阵推扫式影像相比,TM影像作为双向扫描型的影像其几何校正方法有其特殊性和复杂性。针对这个问题,本文简要介绍了一般多项式、空间斜墨卡托投影、有理函数模型等几种几何近似校正算法,并利用广州地区的TM影像进行了各种试验分析和精度比较。结果表明:同其他方法相比,空间斜墨卡托投影是一种较好的算法,校正精度最高,能够达到一个像素左右,而且该方法不受地形起伏的影响,无需地面高程信息。     

Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban–rural landscape in the Brazilian Amazon

This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban–rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method.     

Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery

A global systematic sampling scheme has been developed by the UN FAO and the EC TREES project to estimate rates of deforestation at global or continental levels at intervals of 5 to 10 years. This global scheme can be intensified to produce results at the national level. In this paper, using surrogate observations, we compare the deforestation estimates derived from these two levels of sampling intensities (one, the global, for the Brazilian Amazon the other, national, for French Guiana) to estimates derived from the official inventories. We also report the precisions that are achieved due to sampling errors and, in the case of French Guiana, compare such precision with the official inventory precision.We extract nine sample data sets from the official wall-to-wall deforestation map derived from satellite interpretations produced for the Brazilian Amazon for the year 2002 to 2003. This global sampling scheme estimate gives 2.81 million ha of deforestation (mean from nine simulated replicates) with a standard error of 0.10 million ha. This compares with the full population estimate from the wall-to-wall interpretations of 2.73 million ha deforested, which is within one standard error of our sampling test estimate. The relative difference between the mean estimate from sampling approach and the full population estimate is 3.1%, and the standard error represents 4.0% of the full population estimate.This global sampling is then intensified to a territorial level with a case study over French Guiana to estimate deforestation between the years 1990 and 2006. For the historical reference period, 1990, Landsat-5 Thematic Mapper data were used. A coverage of SPOT-HRV imagery at 20 m × 20 m resolution acquired at the Cayenne receiving station in French Guiana was used for year 2006.Our estimates from the intensified global sampling scheme over French Guiana are compared with those produced by the national authority to report on deforestation rates under the Kyoto protocol rules for its overseas department. The latter estimates come from a sample of nearly 17,000 plots analyzed from same spatial imagery acquired between year 1990 and year 2006. This sampling scheme is derived from the traditional forest inventory methods carried out by IFN (Inventaire Forestier National). Our intensified global sampling scheme leads to an estimate of 96,650 ha deforested between 1990 and 2006, which is within the 95% confidence interval of the IFN sampling scheme, which gives an estimate of 91,722 ha, representing a relative difference from the IFN of 5.4%.These results demonstrate that the intensification of the global sampling scheme can provide forest area change estimates close to those achieved by official forest inventories (<6%), with precisions of between 4% and 7%, although we only estimate errors from sampling, not from the use of surrogate data.Such methods could be used by developing countries to demonstrate that they are fulfilling requirements for reducing emissions from deforestation in the framework of an REDD (Reducing Emissions from Deforestation in Developing Countries) mechanism under discussion within the United Nations Framework Convention on Climate Change (UNFCCC). Monitoring systems at national levels in tropical countries can also benefit from pan-tropical and regional observations, to ensure consistency between different national monitoring systems.