从卫星遥感全色图像中自动提取城市目标

提出了一种从卫星遥感全色图像中自动提取城市目标的方法。在总结城市目标的卫星遥感全色图像特性的基础上,设计了一种层次提取的方法。首先在低分辨率图像中提取城市目标的候选区域;接着在候选区域内利用城市目标一些更复杂的特性在高分辨率图像中证实城市目标。为了获得城市目标的边界,提出了一种基于边缘点密度的算法,并将算法应用去提取SPOT图像中的城市目标,取得了较好的试验结果。     

汶川地震灾后农田和森林植被恢复遥感监测

2008年汶川8.0级特大地震对当地的生态系统造成了极大的破坏,为了评估5年来灾区农田和森林植被的恢复情况,利用逐年机载高分辨率遥感影像,结合星载遥感数据和地面调查数据,开展了灾区农林植被恢复状况监测。在农田恢复监测方面,结合2008年地震发生后以及2013年5月中旬的机载高分辨率遥感数据,采用目视解译的方式对汶川地震中受损农田的恢复状况进行监测与评估,同时利用GVG(GPS、Video和GIS)农情采样系统的作物种植成数调查结果,分析了灾后作物种植结构的变化。结果表明,灾区1592 ha受损农田,5年后仅有约17.5%得到了恢复和耕种使用。就耕地利用强度而言,重灾区耕地利用率较高,作物种植结构没有发生重大变化。在森林恢复状况监测方面,对典型区(岷江干旱河谷区和盆周山地区的3个重点区域)采用目视解译方式识别出森林变化,并结合大区域尺度规一化植被指数(NDVI)时间序列变化分析,对整个灾区的森林损毁和恢复情况做出评价。监测结果显示,汶川县、什邡市和绵竹市的森林植被恢复情况总体较好,但是一些坡度较大的损毁区、次生灾害频发区的森林尚未恢复,大区域尺度的统计结果显示,地震重灾区的46381 ha重度损毁森林植被和177025 ha中度损毁森林植被区域,完全恢复的区域占13.52%和25.84%,部分恢复的区域都占到50%。在自然恢复较为困难的区域,如汶川县中部和东北部、都江堰市北部、彭州市北部、什邡市北部、绵竹市北部、安县北部及北川县南部等,需要加强人工干预。遥感监测方法既适用于震后的农田和森林恢复状况动态监测,也适用于其他自然灾害发生时对灾区农田和森林植被破坏状况进行应急监测,具有实际应用价值和良好的发展前景。     

Automatic change detection for road networks from images based on GIS

Up to now, detailed strategies and algorithms of automatic change detection for road networks based on GIS have not been discussed. This paper discusses two different strategies of automatic change detection for images with low resolution and high resolution using old GIS data, and presents a buffer detection and tracing algorithm for detecting road from low-resolution images and a new profile tracing algorithm for detecting road from high-resolution images. For feature-level change detection (FL-CD), a so-called buffer detection algorithm is proposed to detect changes of features. Some ideas and algorithms of using GIS prior information and some context information such as substructures of road in high-resolution images to assist road detection and extraction are described in detail.     

Evaluation of the spatial changes in seagrass cover in the lagoons of Lakshadweep islands,India, using IRS LISS III satellite images

A study was conducted in Lakshadweep islands to determine the feasibility of using Indian Remote Sensing (IRS) satellites for detecting changes in the seagrass from other coastal features. IRS ID and IRS P6 LISS III having spatial resolution of 23.5 m with lower cost compared to all other contemporary satellites with the same spatial resolution have not been widely used for monitoring the changes in seagrass cover. In this context, the present study attempted to explore the effectiveness of LISS III data for mapping seagrasses and to inform the international community about the usefulness of these low-cost imageries for coastal resource monitoring. Supervised classification and change detection studies found a significant decrease in seagrass cover of 73.03 ha in the Lakshadweep group of islands. An overall accuracy of 67.5% was obtained for the change maps, and seagrass cover and its changes vary at different islands.     

Characterizing wetland dynamics: a post-classification change detection analysis of the East Kolkata Wetlands using open source satellite data

The East Kolkata Wetlands is a unique resource recovery system. The Ramsar Convention recognized it as a ‘Wetland of International Importance’ in August 2002. However, the long-term resource exploitation and land use changes in the dynamic ecosystem have resulted in non-linear environmental responses. This is an attempt using open source remote sensing datasets to capture the spatio-temporal transformation of the wetland resulting from various anthropogenic activities. Landsat MSS and TM imageries of 1973, 1980, 1989, 2001 and 2010 were classified using Maximum Likelihood Classifier to monitor the wetland change; however, to study wetland dynamics, the post-classification wetland change detection maps have been generated for two temporal phases, i.e. 1973–1989 and 1989–2010. This study finds that the area under wetlands has reduced comprehensively in the past 40 years due to the conversion of wetlands into various other uses such as urban expansion of the Kolkata metropolitan city.     

Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal Landsat TM imagery

Being able to quantify land cover changes due to mining and reclamation at a watershed scale is of critical importance in managing and assessing their potential impacts to the Earth system. In this study, a remote sensing-based methodology is proposed for quantifying the impact of surface mining activity and reclamation from a watershed to local scale. The method is based on a Support Vector Machines (SVMs) classifier combined with multi-temporal change detection of Landsat TM imagery. The performance of the technique was evaluated at selected open mining sites located in the island of Milos in Greece. Assessment of the mining impact in the studied areas was based on the confusion matrix statistics, supported by co-orbital QuickBird-2 very high spatial resolution imagery. Overall classification accuracy of the thematic land cover maps produced was reported over 90%. Our analysis showed expansion of mining activity throughout the whole 23-year study period, while the transition of mining areas to soil and vegetation was evident in varying rates. Our results evidenced the ability of the method under investigation in deriving highly and accurate land cover change maps, able to identify the mining areas as well as those in which excavation was replaced by natural vegetation. All in all, the proposed technique showed considerable promise towards the support of a sustainable environmental development and prudent resource management.     

利用天宫一号和Landsat7对地观测数据的森林变化检测

由于自然演替和一些干扰因素的影响,森林覆盖处在不断的变化中.结合云南省西双版纳地区的天宫一号高光谱数据以及Landsat影像,研究了热带森林覆盖制图与变化检测的自动化识别方法.首先分析了每景影像中红光波段的光谱属性,依据直方图提取出纯净森林像元,然后计算影像中各像元与纯净森林像元之间的光谱相似性,从而得到森林指数并以此为依据提取出每景影像对应的森林覆盖图,将多期的森林覆盖专题图进行叠加分析即可得到森林变化专题图.结果表明:(1)使用天宫一号高光谱影像可以进行森林覆盖自动化提取,生成的森林覆盖图合理地反映了森林分布状况;(2)与多期遥感影像结合进行森林变化信息提取,提取结果很好地体现了森林减少和森林恢复情况,对新恢复的未郁闭森林也可以进行有效检测.     

中国地区气溶胶类型特性分析

利用全球气溶胶观测网（AERONET）2001年—2012年中国地区12个站点的地基遥感气溶胶物理光学特性产品研究了中国地区气溶胶类型特性及其时空变化规律。研究发现中国地区不同类别的气溶胶光学特性（单次散射反照率、不对称因子、光学厚度等）差异较大,由沙尘型、混合型、强散射型细粒子、弱吸收型细粒子、中度吸收型细粒子和强吸收型细粒子等6类组成。中国地区气溶胶类型具有极强的空间变化特性和明显的季节、年际变化特征,沙尘气溶胶主要出现在春季,强散射型细粒子气溶胶主要出现在夏季,而强吸收型细粒子气溶胶主要出现在秋冬季节;近10年间沙尘型、混合型和强散射型细粒子气溶胶呈现上升趋势,而强吸收型细粒子气溶胶则处于下降趋势。在此基础上对比了中国和美国、法国典型站点气溶胶类型,研究发现3个站点的气溶胶差异较大,中国强吸收细粒子类所占的比重高于后两者,同时强散射细粒子类所占的比重低于后两者。     

Development of time series stacks of Landsat images for reconstructing forest disturbance history

Abstract

Forest dynamics is highly relevant to a broad range of earth science studies, many of which have geographic coverage ranging from regional to global scales. While the temporally dense Landsat acquisitions available in many regions provide a unique opportunity for understanding forest disturbance history dating back to 1972, large quantities of Landsat images will need to be analysed for studies at regional to global scales. This will not only require effective change detection algorithms, but also highly automated, high level preprocessing capabilities to produce images with subpixel geolocation accuracies and best achievable radiometric consistency, a status called imagery-ready-to-use (IRU). This paper describes a streamlined approach for producing IRU quality Landsat time series stacks (LTSS). This approach consists of an image selection protocol, high level preprocessing algorithms and IRU quality verification procedures. The high level preprocessing algorithms include updated radiometric calibration and atmospheric correction for calculating surface reflectance and precision registration and orthorectification routines for improving geolocation accuracy. These automated routines have been implemented in the Landsat Ecosystem Disturbance Adaptive System (LEDAPS) designed for processing large quantities of Landsat images. Some characteristics of the LTSS developed using this approach are discussed.     

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and photosynthetically active radiation (PAR). The first three products cover the years 1982–2012 (LAI) and 1981–2010 (albedo and emissivity) at 1–5 km and 8-day resolutions, and the last two radiation products span the period 2008–2010 at 5 km and 3-h resolutions. These products have been evaluated and validated, and the preliminary results indicate that they are of higher quality and accuracy than the existing products. In particular, the first three products have much longer time series, and are therefore highly suitable for various environmental studies. This paper outlines the algorithms, product characteristics, preliminary validation results, potential applications and some examples of initial analysis of these products.