卫星遥感影像在土地利用/地表覆盖数据生产中的应用

概述了卫星遥感影像及其特点 ,并对东北地区在利用卫星遥感影像进行地表分类时所出现的若干问题进行了探讨 ,这对我国的土地利用工作有一定的参考价值     

卫星遥感影像在土地利用／地表覆盖数据生产中的应用

概述了卫星遥感技术及其特点，并对东北地区在利用卫星遥感影像进行地表分类时所出现的若干问题进行了探讨，这对我国的土地利用工作有一定的参考价值。     

VIIRS高分辨率地表反射率关系库支持下的气溶胶光学厚度反演

通过遥感技术反演气溶胶光学厚度AOD对于全面、动态监测大气污染时空变化具有重要意义.可见光红外成像辐射仪VIIRS作为MODIS的后继传感器,可在全球范围内实现对气溶胶的连续时空监测.针对复杂的地表类型,通过构建基于像素的动态地表反射率关系库,能实现陆地AOD高分辨率反演.利用全球气溶胶自动观测网站AERONET地基站...     

地表反射率地形校正物理模型与效果评价方法研究进展

地形校正是准确获取地形复杂区遥感反射率的重要步骤,对提高山区地表遥感参数定量化反演精度,扩大遥感产品应用广度具有重要意义。从20世纪80年代开始,国内外学者开始对准确获取山区地表遥感反射率进行研究,建立了多种地形校正模型来减少或消除遥感图像中地形效应影响,减少同种地表类型的反射率差异,并将地形校正模型分为经验模型和物理模型。根据构建物理模型时是否考虑地表非朗伯体特性,将物理模型分为朗伯体假设模型和非朗伯体假设模型,本文分别从朗伯体假设模型和非朗伯体假设校正模型展开叙述。从两类模型构建的理论基础,模型特点,局限性等几方面进行分析和讨论,描述了两类模型的发展历程,系统阐述了朗伯体假设模型和非朗伯体假设模型的适用性和不足,剖析了目前地形校正模型存在的问题与挑战。同时,本文也比较了应用于地形校正的效果评价方法,并展望了地形校正方法和地形校正评价方法的未来主要发展方向。     

RS、GIS在海南东寨港红树林保护区地表覆盖研究中的应用

海南东寨港是红树林面积较大、分布集中、种类齐全、保护完整的地区之一,具有重要的保护、科研和旅游价值。阐述如何利用遥感、地理信息系统技术获取东寨港红树林保护区地表覆盖信息,对比分析各种地物的面积变化情况,对红树林的变化监测和保护措施的制定有着积极的指导意义。     

基于决策树分类的地表覆盖遥感信息提取

地处西南的渝北地区地表覆盖类型复杂、土地利用多元化,仅依赖于光谱特征的传统遥感信息提取方法难以获得较高的分类精度。利用决策树分类技术对渝北地区的TM遥感影像进行分类,除光谱信息外还结合地质、NDVI、PCI等多源数据进行实验。结果表明,总精度和Kappa系数分别为88.42%和0.854 7,较传统的监督分类和仅依赖于光谱特征的决策树分类方法有较大提高,这也表明基于多源数据的决策树分类技术对地表覆盖复杂地区的遥感影像分类比较适用,是遥感信息提取的一种有效手段。     

应用转移矩阵分析辽阳矿区的地表覆盖变化

为了分析辽阳地区的地表覆盖变化情况,本文选取辽阳矿区为研究区域,以2003年、2009年、2015年Landsat TM/OLI遥感影像及ASTER GDEMV2高程数据为基本数据源,提取NDVI、NDBI、MNDWI、高程值及单波段亮度遥感指数构建决策树分类模型,完成辽阳矿区地表覆盖的分类。对分类结果进行地表覆盖类型数量变化利用土地转移矩阵分析,并对驱动力因素进行研究。结果表明;决策树模型适用于辽阳矿区地表覆盖分类,分类精度较高。研究区域土地覆盖类型发生显著变化,主要表现为建设用地大幅增长,采矿用地大面积减少,植被、水体、裸地面积变化较小。人口增长、经济发展、矿区生态治理成为该矿区地表覆盖特征变化的主要驱动因素。     

GF-4增强型地表反射率库支持法的气溶胶光学厚度反演

针对GF-4等国产卫星气溶胶光学厚度反演算法存在的地表反射率估计困难、云像元污染等问题,本文发展了一种增强型地表反射率库支持的气溶胶光学厚度反演方法,改进了云筛选与地表反射率确定方案,在考虑GF-4逐像元成像角度的情况下,使用6SV模型与MOD09-CMA数据对季度尺度上的GF-4 PMS传感器数据进行大气校正,提出了百分比最小值均值法建立地表反射率库,并据此建立了NDVI与红蓝反射率关系模型,根据地表反射率的分布特点,当NDVI小于0.2的时候使用地表反射率库估计地表反射率,而当NDVI大于0.2时,则使用NDVI来估计地表反射率。使用MOD04气溶胶模式时空分布确定气溶胶参数。在京津冀地区开展气溶胶光学厚度反演实验,使用Aeronet站点数据与MOD04产品对反演结果进行了对比验证,与Aeronet相关系数R为0.964,均方根误差RMSE为0.13,满足±(0.05+0.2τ)的点多于78.9%,相关系数与均方根误差优于MODIS暗目标法产品,满足期望误差线的数量优于MODIS暗目标与深蓝算法产品。     

基于高分遥感数据的地表覆盖专题监测研究

为推动高分遥感数据在地理国情地表覆盖专题监测工作中的应用,研究了基于高分遥感数据开展地理国情地表覆盖专题监测的技术路线和数据处理与分析方法,探讨了专题监测系统的模块和功能设置,以期为地理国情地表覆盖专题监测工作提供参考。     

云下地表温度与辐射变化关系的定量分析

云下对地表温度一直是热红外遥感研究的难点,云下地表温度的变化不仅受到到达地表的辐射强度的影响,还与地表覆盖类型的热力学性质有关,本文通过开展野外观测实验,并利用地表能量平衡模型模拟地表温度在不同辐射条件的变化情况,以期获得不同地表类型的地表温度与辐射变化的定量关系,结果表明,在云覆盖情况下,到达地表面的辐射值减小,地表温度也随之减小,当云覆盖前地表温度越高,云覆盖后地表温度的变化幅度就越大,不同地表类型的云下地表温度在单位时间(min)内发生的温度值的单位变化量所需辐射值的变化量与云覆盖前温度高低呈线性关系。在长时间云覆盖情况下,地表温度的下降速度随着云覆盖时间的增加而不断放缓,当云层覆盖一定时间(10—20 min)后,地表温度会达到一个相对稳定的状态。     

国家土地覆盖数据的生产与建库

土地覆盖变化的研究和分析,能够揭示自然与人文过程交叉最密切的一些问题,为此各国均开展了本国的国家级土地覆盖数据的生产。本文在参考国外土地覆盖数据生产相关经验的基础上,结合我国的国情,基于陆地卫星影像,研制了30m土地覆盖分类系统,采用非监督分类、监督分类与人工解译相结合的方法,生产了全国范围的土地覆盖数据;并基于ORACLE数据库;建立了国家土地覆盖数据库。     

Primitives as building blocks for constructing land cover maps

Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using primitive map layers. Primitive map layers are a suite of biophysical and end member maps, with land cover primitives representing the raw information needed to make decisions in a dichotomous key for land cover classification. We present best practices to create and assemble primitives from optical satellite using computing technologies, decision tree logic and Monte Carlo simulations to integrate their uncertainties. The concept is presented in the context of a regional land cover map based on a shared regional typology with 18 land cover classes agreed on by stakeholders from Cambodia, Laos PDR, Myanmar, Thailand, and Vietnam. We created annual map and uncertainty layers for the period 2000–2017. We found an overall accuracy of 94% when taking uncertainties into account. RLCMS produces consistent time series products using free long term historical Landsat and MODIS data. The customizable architecture can include a variety of sensors and machine learning algorithms to create primitives and the best suited smoothing can be applied on a primitive level. The system is transferable to all regions around the globe because of its use of publicly available global data (Landsat and MODIS) and easily adaptable architecture that allows for the incorporation of a customizable assembly logic to map different land cover typologies based on the user's landscape monitoring objectives     

Temporal replication of the national land cover database using active machine learning

Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time.     

四川省及成都地区土地利用/覆被变化建库与对比研究

为探讨近年四川省部分地区严重旱涝灾害天气成因,利用遥感(RS)、地理信息系统(G IS)和全球定位系统(GPS)技术建立了四川省及成都地区2000~2004年土地利用/覆被变化数据库,并对四川省及成都地区2000~2004年土地利用/覆被变化信息进行对比研究,对四川省部分地区与成都地区近年气候差异成因进行探讨。     

Assessing land use/cover changes: a nationwide multidate spatial database for Mexico

A nationwide multidate GIS database was generated in order to carry out the quantification and spatial characterization of land use/cover changes (LUCC) in Mexico. Existing cartography on land use/cover at a 1:250,000 scale was revised to select compatible inputs regarding the scale, the classification scheme and the mapping method. Digital maps from three different dates (the late 1970s, 1993 and 2000) were revised, evaluated, corrected and integrated into a GIS database. In order to improve the reliability of the database, an attempt was made to assess the accuracy of the digitalisation procedure and to detect and correct unlikely changes due to thematic errors in the maps. Digital maps were overlaid in order to generate LUCC maps, transition matrices and to calculate rates of conversion. Based upon this database, rates of deforestation between 1976 and 2000 were evaluated as 0.25 and 0.76% per year for temperate and tropical forests, respectively.     

Integrating global land cover products for improved forest cover characterization: an application in North America

Six widely used coarse-resolution global land cover data-sets – Global Land Cover Characterization (GLCC), Global Land Cover 2000 (GLC2000), GlobCover land cover product (GlobCover), MODIS land cover product (MODIS LC), the University of Maryland land cover product (UMD LC), and the MODIS Vegetation Continuous Fields tree cover layer (MODIS VCF) disagree substantially in their estimates of forest cover. Employing a regression tree model trained on higher-resolution, Landsat-based data, these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America. Evaluated using a withheld test sample, the integrated percent forest cover (IPFC) data-set has a root mean square error of 11.75% – substantially better than the 17.37% of GLCC, 17.61% of GLC2000, 17.96% of GlobCover, 15.23% of MODIS LC, 19.25% of MODIS VCF, and 15.15% of UMD LC, respectively. Although demonstrated for forest, this approach based on integration of multiple products has potential for improved characterization of other land cover types as well.     

国土调查数据源优选和建库质检一体化探索

为进一步提升国土调查质量,从参考数据源优选和技术创新两个层面探索国土调查提质和提速的途径。该文根据试点区的土地利用现状、经济社会特点及具备的数据资源基础,制定了3个数据组合,分别配置到不同试点区用于开展国土调查实验。将国土调查过程中需重复性使用的功能进行批处理定制,实现关联数据的批量识别和批量自动化处理。实践表明,数据源优选评价结果是其他地区开展国土调查工作的重要先验知识,可根据各数据组优缺点结合本地区优势开展国土调查。通过新技术创新实现了批量问题自动化识别与处理,形成建库质检一体化机制,提高了国土调查建库速度和错误纠正时效,保障了国土调查数据库质量。数据源优选和建库质检一体化试点实践为全市及其他地区的第三次全国国土调查提供了重要参考。     

面向对象的分类方法在土地利用数据库建设中的应用

土地利用数据库对于土地管理、土地动态检测和土地变更调查具有重要的意义。本文主要介绍了建立土地利用数据库的技术方法和工作流程。以张家界市武陵源区为例,采用面向对象的分类方法对高分辨率遥感影像进行分割和自动分类,将分类结果导入GIS环境下,进行拓扑构建和属性编辑,实现图形数据、属性数据的关联,最后完成土地利用现状数据建库。上述方法有效提高了建库质量和建库水平,并取得了很好的效果。     

Texture classification of Mediterranean land cover

Maximum likelihood (ML) and artificial neural network (ANN) classifiers were applied to three Landsat Thematic Mapper (TM) image sub-scenes (termed urban, agricultural and semi-natural) of Cukurova, Turkey. Inputs to the classifications comprised (i) spectral data and (ii) spectral data in combination with texture measures derived on a per-pixel basis. The texture measures used were: the standard deviation and variance and statistics derived from the co-occurrence matrix and the variogram. The addition of texture measures increased classification accuracy for the urban sub-scene but decreased classification accuracy for agricultural and semi-natural sub-scenes. Classification accuracy was dependent on the nature of the spatial variation in the image sub-scene and, in particular, the relation between the frequency of spatial variation and the spatial resolution of the imagery. For Mediterranean land, texture classification applied to Landsat TM imagery may be appropriate for the classification of urban areas only.     

Comparison of large-area land cover products with national forest inventories and CORINE land cover in the European Alps

Considerable efforts have recently resulted in the development of global land cover data at large spatial scales. The main objective of this study is a comparison of different AVHRR- and MODIS-based forest and land cover products at the scale of the European Alps: a large natural ecosystem that is exposed to both natural environmental threats and human impacts and exploitation. In a first test, the accuracy of land cover products in predicting the overall amount of forest across national boundaries was assessed using national forest inventory statistics. Both variants of forest class combinations resulted in a general overestimation of the forest area. The IGBP 2.0 cover performed best with an overall mean absolute error of 13% and a bias of 0%. In a second test, large-area land cover products were tested for accuracy in predicting 13 aggregated land cover types in a spatially explicit manner using CORINE land cover as reference dataset. Due to data inconsistencies, partly insufficient spatial resolution, steep terrain and land use heterogeneity of the European Alps, only partly satisfactory results were obtained.