基于时间序列分析和灰色理论的建筑物沉降预测模型研究

基于时间序列分析方法建立建筑物沉降预测模型,其中通过二次移动平均法提取出沉降监测序列中的趋势项,并在此基础上采用灰色系统理论动态GM（1,1）模型进行趋势项预测。实际算例结果表明,该模型能够较好地预测沉降变化的发展趋势,并具有较高的预测精度,证明了该预测模型具有一定的可行性和有效性。     

基于坐标差分的山体变形监测方法探讨

在本文中提出了全站仪自由设站坐标差分的观测方法,论述了该方法的原理、误差分析,以及其在山体变形监测中的应用。对在特殊环境下的变形观测有一定的指导意义。     

浅谈基于粗纠正影像源进行1∶10000 DLG生产“内外业一体化”工艺流程

为了减少外业工作量,利用航空摄影测量进行基础地理信息采集是最有效手段之一,国内外推出了一系列数字摄影测量的软件和系统.本文结合GEOWAY DPS/IS及VirtuoZo AAT(PATB)两种软件的生产实践,探讨了基于粗纠正影像源进行1∶10000数字线划图(DLG)生产“内外业一体化”的工艺流程.本文对以后采用此种方法成图具有一定的指导作用.     

MATLAB中实现联合测站平差自动化

水平角观测中,分组观测方向值后要进行联合测站平差。当观测组数或联测共同方向多于两个时,只能采用条件平差法,建立条件方程式后,解法方程得出各个方向值的改正数。采用MATLAB语言编制的测站平差程序,实现了读取数据、搜索共同方向、建立条件方程、解算改正数、首方向归0并同一和标准格式输出整个流程的自动化。     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery

We tested the effects of three fast pansharpening methods – Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) – on sugarcane classification in a Landsat 8 image (bands 1–7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified “sugarcane” and “other” land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the band-stacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments.     

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity–hue–saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.     

A simple two-band semi-analytical model for retrieval of specific absorption coefficients in coastal waters

A non-linear iterative method is used to replace the traditional spectral slope technique in initializing the total absorption decomposition model. Based on comparison of absorption coefficient by QAA and two-band semi-analytical model (TSAA) models with field measurements collected from the West Florida Shelf waters and Bohai Sea, it is shown that both models are effective in estimating absorption coefficients from the West Florida Shelf waters, but the TSAA model is superior to the QAA model. Use of the TSAA model in estimating absorption coefficient in the West Florida Shelf and Bohai Sea decreases the uncertainty of estimation by 1.3–74.7% from the QAA model. The TSAA model’s sensitivity to the input parameters was evaluated by varying one parameter and keeping the others fixed at their default values. Our results indicate that the TSAA model has quite a strong noise tolerance to addressing the field data of the total absorption coefficient.     

Optimising three-band spectral indices to assess aerial N concentration,N uptake and aboveground biomass of winter wheat remotely in China and Germany

Remotely and accurately quantifying the canopy nitrogen status in crops is essential for regional studies of N budgets and N balances. In this study, we optimised three-band spectral algorithms to estimate the N status of winter wheat. This study extends previous work to optimise the band combinations further and identifies the optimised central bands and suitable bandwidths of the three-band nitrogen planar domain index (NPDI) for estimating the aerial N uptake, N concentration and aboveground biomass. Analysis of the influence of bandwidth change on the accuracy of estimating the canopy N status and aboveground biomass indicated that the suitable bandwidths for optimised central bands were 37 nm at 846 nm, 13 nm at 738 nm and 57 nm at 560 nm for assessing the aerial N uptake and were 37 nm at 958 nm, 21 nm at 696 nm and 73 nm at 578 nm for the assessment of the aerial N concentration and were 49 nm at 806 nm, 17 nm at 738 nm and 57 nm at 560 nm for the estimation of aboveground biomass. The optimised three-band NPDI could consistently and stably estimate the aerial N uptake and aboveground biomass of winter wheat in the vegetative stage and the aerial N concentration in the reproductive stage compared to the fixed band combinations. With suitable bandwidths, the broadband NPDI demonstrated excellent performance in estimating the aerial N concentration, N uptake and biomass. We conclude that the band-optimised algorithm represents a promising tool to measure the improved performance of the NPDI in estimating the aerial N uptake and biomass in the vegetative stage and the aerial N concentration in the reproductive stage, which will be useful for designing improved nitrogen diagnosis systems and for enhancing the applications of ground- and satellite-based sensors.     

Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan,Canada

Soil respiration (R_s) is of great importance to the global carbon balance. Remote sensing of R_s is challenging because of (1) the lack of long-term R_s data for model development and (2) limited knowledge of using satellite-based products to estimate R_s. Using 8-years (2002–2009) of continuous R_s measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that R_s was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between R_s and NDVI × LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled R_s closely tracked the seasonal patterns of measured R_s and explained 74–92% of the variance in R_s with a root mean square error (RMSE) less than 1.0 g C/m²/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of R_s with an overall R² of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped R_s of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of R_s were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in R_s. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of R_s.