基于遥感和GIS的区域生态环境分类研究

本文以石羊河流域为例,研究了基于卫星遥感数据与数字高程模型的区域生态环境分类方法。其研究 机理是:利用遥感技术获取相关生态环境专题信息,运用GIS空间分析技术自动生成流域范围的生态环境边界, 形成了一种科学的区域生态环境分类方法。     

模糊聚类的DEM确定耕地坡度方法

针对实际应用的坡度计算中没有考虑DEM高程精度影响这一状况,提出采用模糊聚类法进行坡度分级,给出了具体的精度评定方法,并采用我国南方某地区一个镇的第二次土地调查耕地成果数据进行了分析,与传统的直接计算方法进行了比较。结果表明,两种方法得到的图斑坡度级别基本相同;从总体分类精度与Kappa系数计算来看,两者基本接近,模糊聚类法略微优于直接计算法,而且由于其顾及了DEM高程精度的影响,得到的分级结果可靠性更高。     

广州市测绘基准服务系统的建立

广州市测绘基准服务系统在充分应用了现代测绘技术的基础上,优化整合了该市已有的测绘基准资源,实现了基于网络发布的测绘基准服务。本文详细阐述了该系统的建立背景、设计内容及实现过程,并对系统的框架和功能进行说明。     

基于APDM管道模型的数据入库研究

管道完整性数据库是建立在APDM管道模型基础上的,由于管道模型的复杂性,致使按常规方法进行数据入库时遇到了不符合模型要求的问题。本文在分析问题的原因后,提出3级入库的作业方法,制定主要的3类数据的入库流程,以达到为管道完整性数据库提供高质量的数据的目标。     

Leaf area index retrieval using gap fractions obtained from high resolution satellite data: Comparisons of approaches,scales and atmospheric effects

This study is aimed at demonstrating the feasibility of the large scale LAI inversion algorithms using red and near infrared reflectance obtained from high resolution satellite imagery. Radiances in digital counts were obtained in 10 m resolution acquired on cloud free day of August 23, 2007, by the SPOT 5 high resolution geometric (HRG) instrument on mostly temperate hardwood forest located in the Great Lakes – St. Lawrence forest in Southern Quebec. Normalized difference vegetation index (NDVI), scaled difference vegetation index (SDVI) and modified soil-adjusted vegetation index (MSAVI) were applied to calculate gap fractions. LAI was inverted from the gap fraction using the common Beer–Lambert's law of light extinction under forest canopy. The robustness of the algorithm was evaluated using the ground-based LAI measurements and by applying the methods for the independently simulated reflectance data using PROSPECT + SAIL coupled radiative transfer models. Furthermore, the high resolution LAI was compared with MODIS LAI product. The effects of atmospheric corrections and scales were investigated for all of the LAI retrieval methods. NDVI was found to be not suitable index for large scale LAI inversion due to the sensitivity to scale and atmospheric effects. SDVI was virtually scale and atmospheric correction invariant. MSAVI was also scale invariant. Considering all sensitivity analysis, MSAVI performed best followed by SDVI for robust LAI inversion from high resolution imagery.     

浅谈利用面向对象的方法实现GIS模型库系统

剖析了GIS中的模型分类问题,提出三模型分类的观点,并提出利用面向对象思想建立空间数据处理模型库系统,用于快速开发模型库,为各方面用户提供一个模型维护、创建、支持空间分析和模拟功能的通用工具,以缓解GIS发展中空间分析能力差、决策支持能力弱的现状。本文详细分析了GIS模型库系统中模型的表示方法、模型库管理系统的功能实现、系统与外界GIS集成的典型问题,为面向对象的G0IS模型库系统建立提供了可行性思路。     

A laser scanning-based method for fast estimation of seismic-induced building deformations

Monitoring damaged buildings in an area where an earthquake has occurred requires the use of techniques which provide rapid and safe measurements even in emergency conditions. In particular, remote sensing techniques like terrestrial laser scanning (TLS) can satisfy these requirements, since they produce very dense point clouds in little time and also allow an accurate geometric modeling of observed buildings. Nevertheless, strong constraints on TLS data acquisition geometry, such as acquisition distance and incidence angles, typically characterize an area in seismic emergency conditions. In order to correctly interpret the data, it is necessary to estimate errors affecting TLS measurements in these critical conditions. A reliable estimation can be achieved by means of experiments and numerical simulations aimed at quantifying a realistic noise level, with emphasis on reduction of artifacts due to data acquisition, registration and modeling. This paper proposes a data analysis strategy in which TLS-based morphological maps computed as point-to-primitive differences are created. The method can be easily used for accurate surveying in emergency conditions. In order to demonstrate the proposed method in very diverse situations, it was applied to rapidly detect deformation traces in the San Giacomo Roncole Campanile (Modena), the Asinelli tower (Bologna) and the Cantalovo Church (Verona), three buildings damaged by the Mw 5.9 Emilia Romagna 2012 earthquake (Italy).     

Improved topographic mapping through high-resolution SAR interferometry with atmospheric effect removal

The application of SAR interferometry (InSAR) in topographic mapping is usually limited by geometric/temporal decorrelations and atmospheric effect, particularly in repeat-pass mode. In this paper, to improve the accuracy of topographic mapping with high-resolution InSAR, a new approach to estimate and remove atmospheric effect has been developed. Under the assumptions that there was no ground deformation within a short temporal period and insignificant ionosphere interference on high-frequency radar signals, e.g. X-bands, the approach was focused on the removal of two types of atmospheric effects, namely tropospheric stratification and turbulence. Using an available digital elevation model (DEM) of moderate spatial resolution, e.g. Shuttle Radar Topography Mission (SRTM) DEM, a differential interferogram was firstly produced from the high-resolution InSAR data pair. A linear regression model between phase signal and auxiliary elevation was established to estimate the stratified atmospheric effect from the differential interferogram. Afterwards, a combination of a low-pass and an adaptive filter was employed to separate the turbulent atmospheric effect. After the removal of both types of atmospheric effects in the high-resolution interferogram, the interferometric phase information incorporating local topographic details was obtained and further processed to produce a high-resolution DEM. The feasibility and effectiveness of this approach was validated by an experiment with a tandem-mode X-band COSMO-SkyMed InSAR data pair covering a mountainous area in Northwestern China. By using a standard Chinese national DEM of scale 1:50,000 as the reference, we evaluated the vertical accuracy of InSAR DEM with and without atmospheric effects correction, which shows that after atmospheric signal correction the root-mean-squared error (RMSE) has decreased from 13.6 m to 5.7 m. Overall, from this study a significant improvement to derive topographic maps with high accuracy has been achieved by using the proposed approach.     

Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model

A recent study by Van der Schalie et al. (2015) showed good results for applying the Land Parameter Retrieval Model (LPRM) on SMOS observations over southeast Australia and optimizing and evaluating the retrieved soil moisture (θ in m³ m⁻³) against ground measurements from the OzNet sites. In this study, the LPRM parameterization is globally updated for SMOS against modelled θ from MERRA-Land (MERRA) and ERA-Interim/Land (ERA) over the period of July 2010–December 2010, mainly focusing on two parameters: the single scattering albedo (ω) and the roughness (h). The Pearson's coefficient of correlation (r) increased rapidly when increasing the ω up to 0.12 and reached a steady state from thereon, no significant spatial pattern was found in the estimation of the single scattering albedo, which could be an artifact of the used parameter estimation procedure, and a single value of 0.12 was therefore used globally. The h was defined as a function of θ and varied slightly for the different angle bins, with maximum values of 1.1–1.3 as the angle changes from 42.5° to 57.5°.This resulted in an average r of 0.51 and 0.47, with a bias (m³ m⁻³) of −0.02 and −0.01 and an unbiased root mean square error (ubrmse in m³ m⁻³) of 0.054 and 0.056 against MERRA (ascending and descending). For ERA this resulted in an r of 0.61 and 0.53, with a bias of −0.03 and an ubrmse 0.055 and 0.059. The resulting parameterization was then used to run LPRM on SMOS observations over the period of July 2010–December 2013 and evaluated against SMOS Level 3 (L3) θ and available in situ measurements from the International Soil Moisture Network (ISMN). The comparison with L3 shows that the LPRM θ retrievals are very similar, with for the ascending set very high r of over 0.9 in large parts of the globe, with an overall average of 0.85 and the descending set performing less with an average of 0.74, mainly due to the negative r over the Sahara. The mean bias is 0.03, with an ubrmse of 0.038 and 0.044. In this study there are three major areas where the LPRM retrievals do not perform well: very dry sandy areas, densely forested areas and over high latitudes, which are all known limitations of LPRM. The comparison against in situ measurement from the ISMN give very similar results, with average r for LPRM of 0.65 and 0.61 (0.64 and 0.59 for L3) for the ascending and descending sets, while having a comparable bias and ubrmse over the different networks. This shows that LPRM used on SMOS observations produce θ retrievals with a similar quality as the SMOS L3 product.     

Assessment of freely available CartoDEM V1 and V1.1R1 with respect to high resolution aerial photogrammetric DEM in high mountains

The accuracy of DEMs shows wide variations from one terrain to another and it needs to be determined. This study evaluates NRSC (National Remote Sensing Centre, Hyderabad, India) CartoDEM V1 and V1.1R1 with respect to resampled ADS80 DEM for parts of the Himalayas. Both the test DEMs were properly registered with reference to resampled ADS80 DEM and then individually subtracted to get the difference DEMs. Visual and statistical analyses were performed to assess the quality of the tested DEMs in terms of visible terrain and vertical accuracy. For calculating the accuracies in different terrain classes, slope and aspect maps were generated from the ADS80 DEM. Properly registered Landsat5 TM data were used for the development of the land cover map with four classes. The overall vertical accuracy measured for CartoDEM V1 was 269.9 m (LE90), while CartoDEM V1.1R1 showed huge improvement in the accuracy with 68.5 m (LE90).