MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

水浇地与旱地分类的研究进展

水浇地和旱地的分类研究对于粮食安全、估算农业灌溉需水量以及完善耕地二级类型的分类有着重要的作用。本文从水浇地和旱地分类的意义、分类方法以及分类应用的现状三方面,对国内外水浇地和旱地的分类研究进展及其特点进行了归纳总结,得出了三点结论:(1)参与水浇地和旱地分类的数据源主要为中低分辨率的时间序列植被指数产品以及一些辅助信息;(2)水浇地和旱地的分类特征主要为时间序列的植被指数以及一些辅助特征;(3)水浇地和旱地的分类方法主要为数字化、非监督分类和监督分类。同时指出,丰富水浇地和旱地的分类特征、引入智能型分类方法以及探索生态环境背景在水浇地和旱地分类中的应用将成为未来水浇地和旱地分类研究的重要内容。     

村镇土地审批流程的动态构建

针对村镇土地流转业务复杂、审批处理流程标准不统一的问题,基于对象交换模型(OEM)提出一种动态数据管理方法,建立村镇土地审批流程数据结构的个性化动态定制,将审批流程数据的OEM表达映射为XML文档,开发村镇土地审批处理系统。应用结果表明,该系统实现的土地审批数字化处理和规划能辅助决策分析。     

基于遥感的长沙市城市热岛效应时空分析

利用TM/ETM+数据,以长沙市为例,反演了地表温度,并针对不同时相的遥感数据,利用城市热岛强度来反映热岛效应强弱的变化。结果表明,长沙市的热岛空间分布与城市建成区的轮廓相吻合。城市热岛的范围随着城市建设、新建开发区以及道路交通网的发展不断增大,且在东南方向为主要增长方向。根据热岛强度鉴别并提取了强热岛区与正常区,发现长沙市强热岛区的面积已经从1993年的13.18km2扩大至2008年的68.16km2。最后,对长沙市热岛未来的发展趋势进行了预测。     

基于GIS的村镇建设用地节地效果评价系统

针对目前村镇建设用地集约节约利用效果评价方法空缺及信息化低等问题,依据村镇建设用地节约集约利用效果评价模型,在地理信息系统组件技术和数据库技术支持下,采用可视化编程语言MicrosoftC#.net和GIS控件技术开发了村镇建设用地节约集约利用效果评价系统。以太仓市陆渡镇为开发案例,探讨了系统分析、设计和功能实现等主要技术问题。并利用该系统对太仓市陆渡镇村镇建设用地集约节约利用效果评价进行了评价,评价结果表明,该系统能够满足当前村镇建设用地集约节约利用效果评价工作的需要,为快速准确地实施节地效果评价提供技术支持。     

The CM-SAF operational scheme for the satellite based retrieval of solar surface irradiance — A LUT based eigenvector hybrid approach

The radiation budget at the earth surface is an essential climate variable for climate monitoring and analysis as well as for verification of climate model output and reanalysis data. Accurate solar surface irradiance data is a prerequisite for an accurate estimation of the radiation budget and for an efficient planning and operation of solar energy systems.This paper describes a new approach for the retrieval of the solar surface irradiance from satellite data. The method is based on radiative transfer modelling and enables the use of extended information about the atmospheric state. Accurate analysis of the interaction between the atmosphere, surface albedo, transmission and the top of atmosphere albedo has been the basis for the new method, characterised by a combination of parameterisations and “eigenvector” look-up tables. The method is characterised by a high computing performance combined with a high accuracy. The performed validation shows that the mean absolute deviation is of the same magnitude as the confidence level of the BSRN (Baseline Surface Radiation Measurement) ground based measurements and significant lower as the CM-SAF (Climate Monitoring Satellite Application Facility) target accuracy of 10 W/m². The mean absolute difference between monthly means of ground measurements and satellite based solar surface irradiance is 5 W/m² with a mean bias deviation of − 1 W/m² and a RMSD (Root Mean Square Deviation) of 5.4 W/m² for the investigated European sites. The results for the investigated African sites obtained by comparing instantaneous values are also encouraging. The mean absolute difference is with 2.8% even lower as for the European sites being 3.9%, but the mean bias deviation is with − 1.1% slightly higher as for the European sites, being 0.8%. Validation results over the ocean in the Mediterranean Sea using shipboard data complete the validation. The mean bias is − 3.6 W/m² and 2.3% respectively. The slightly higher mean bias deviation over ocean is at least partly resulting from inherent differences due to the movement of the ship (shadowing, allocation of satellite pixel). The validation results demonstrate that the high accuracy of the surface solar irradiance is given in different climate regions. The discussed method has also the potential to improve the treatment of radiation processes in climate and Numerical Weather Prediction (NWP) models, because of the high accuracy combined with a high computing speed.     

CBERS-02星CCD数据土地利用分类方法研究——以江苏省宜兴地区为例

以江苏省宜兴地区为试验区,首先进行了CBERS-02星CCD数据的图像预处理,然后利用ERDASI-MAGINE和Definiens7.0等软件,采用非监督分类、监督分类、混合监督分类、面向对象四种分类方法进行了土地利用分类,并将影像分类结果进行了精度评价分析。结果表明：在一级地类的提取中,面向对象的最邻近分类器的分类精度最低,混合监督分类的精度最高;在二级地类的提取中,面向对象的特征阈值法得到的分类结果较好,能够轻松提取传统分类方法难于提取的地类。     

县级尺度农村居民点景观格局时空分异研究——以徐州市睢宁县为例

以TM/ETM＋影像为基础数据源,基于3S技术及景观分析软件Fragstats对1995-2005年徐州市睢宁县的农村居民点景观格局进行动态分析,并使用马尔科夫模型对睢宁县2010年农村居民点景观格局进行预测。结果表明：1995-2010年徐州市睢宁县的农村居民点景观格局呈破碎化趋势,土地利用斑块数增加,斑块平均面积下降,面积加权平均形状指数上升,平均分维度小幅上升,散布与并列指数和香农多样性指数均在下降。随着城镇扩展、农田开发等人类干扰的不断加剧,景观生态环境保护与经济开发之间的矛盾将愈加突出。     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光G近红外多光谱波段的基础上,新增加的８个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5％。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。