The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation

Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally.     

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.     

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.     

Resee矿山遥感监测系统变化向导模块在矿山变化信息提取中的应用

依靠自主研发的Resee矿山遥感自动监测系统中的变化向导模块可对矿区进行方法评价及变化图斑提取,辅助相关部门监视工作区存在的部分违规采矿行为,很好地实现对矿区采矿行为及影像的动态监测,对矿山环境的变化进行有效的监督管理,大大提高了矿山监测的效率和准确性.     

传感光纤-砂土界面力学性质的试验研究

近年来分布式光纤感测技术在岩土与地质工程领域的应用研究已成为一个研究热点,但工程实践表明,埋入式传感光纤与被测岩土体之间的变形协调性会对监测结果产生显著的影响,该问题一直制约着光纤感测技术在工程监测中的推广。本文通过一系列室内拉拔试验,对比了3种预埋于砂土中的单模光纤在不同上覆压力的作用下的拉拔力-拉拔位移曲线,并建立了一个用于描述光纤-砂土界面力学性质的三段式的拉拔模型,分析峰值拉拔力、残余拉拔力、有效拉拔位移和残余拉拔位移4个参数在不同上覆压力作用下的变化情况。分析结果揭示传感光纤-砂土界面的力学特性和应力传递规律,为分布式光纤传感技术在岩土体变形监测中的应用,尤其在变形监测光纤的选用标准方面,提供了参考依据。     

Remote sensing-based analysis of the planform changes in the Upper Amazon River over the period 1986–2006

An analysis of the planform changes of the Colombian reach of the Amazon River was carried out over a period of 19.9 years. Remote sensing image processing techniques were applied to Landsat images acquired in 1986, 1994, 2001 and, 2006. These images were selected based on minimal daily water level variations, while providing the widest temporal span. Plan view river changes and geomorphologic characteristics were examined to identify which channel pattern classification best represents this large tropical river system. Discharge was also analyzed to determine whether changes in the river's plan view are a direct response to variations in discharge. The system had a depositional tendency between 1986 and 2006, with a period where erosion was more intense than deposition between 1994 and 2001. Percent change in the plan view area of the system (1.4% yr⁻¹) and the maximum migration rates (125 m yr⁻¹) suggest that this reach of the Amazon is less active than reaches upstream and the downstream reach between the confluences of the Jutaí and Japurá Rivers. Variations in discharge appear to be responsible for deposition and erosion dynamics observed after this remote sensing analysis in the Colombian reach of the Amazon River. Characteristics including multiple channels with vegetated islands developed from within-channel deposition, meandering planform, lateral activity of channel margins, and the absence of islands with saucer-like morphology suggest a multichannel, meandering pattern for this reach of the Amazon, that corresponds to a laterally active anabranching river.     

基于ArcGIS镶嵌数据集的正射影像数据库构建方法初探

数字正射影像数据具有直观、信息量大、覆盖面广和经济、方便、获取快速等优点,是我国基础地理信息数据库的重要内容。本文讨论了基于ArcGIS镶嵌数据集栅格模型构建数字正射影像数据库的一些基本问题。首先对近年来的影像空间数据库技术进行了梳理,然后基于ArcGIS镶嵌数据集模型提出了数字正射影像数据库构建方法,其中对影像入库涉及的栅格数据存储模型、建库步骤、相关参数等问题进行了分析与探讨。最后,开展了海南省某测区小型正射影像数据库构建试验,通过试验证明了文中方法的可用性与可靠性。     

一种大文件多版本遥感影像数据组织管理方法

近年来,遥感技术的飞速发展使得影像数据的使用也越来越广泛和深入,而影像分辨率的增加,使得数据量急剧增长,对数据的存储和管理提出了很高的要求。针对传统遥感影像数据组织存储的不足,本文提出了一种大文件金字塔数据存储结构,用来高效存储管理影像数据;同时针对多时相遥感影像数据,提出了基于版本机制的影像数据管理模式。实验表明这种方法具有更高的存储检索效率。     

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.