Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets

Many investigators need and use global land cover maps for a wide variety of purposes. Ironically, after many years of very limited availability, there are now multiple global land cover maps and it is not readily apparent (1) which is most useful for particular applications or (2) how to combine the different maps to provide an improved dataset. The existing global land cover maps at 1 km spatial resolution have arisen from different initiatives and are based on different remote sensing data and employed different methodologies. Perhaps more significantly, they have different legends. As a result, comparison of the different land cover maps is difficult and information about their relative utility is limited. In an attempt to compare the datasets and assess their strengths and weaknesses we harmonized the thematic legends of four available coarse-resolution global land cover maps (IGBP DISCover, UMD, MODIS 1-km, and GLC2000) using the LCCS-based land cover legend translation protocols. Analysis of the agreement among the global land cover maps and existing validation information highlights general patterns of agreement, inconsistencies and uncertainties. The thematic classes of Evergreen broadleaf trees, Snow and Ice, and Barren show high producer and user accuracy and good agreement among the datasets, while classes of mixed tree types show high commission errors. Overall, the results show a limited ability of the four global products to discriminate mixed classes characterized by a mosaic of trees, shrubs, and herbaceous vegetation. There is a strong relationship between class accuracy, spatial agreement among the datasets, and the heterogeneity of landscapes. Suggestions for future mapping projects include careful definition of mixed unit classes, and improvement in mapping heterogeneous landscapes.     

线面拓扑和度量关系的细分描述和计算方法

由于单独的拓扑关系、度量关系无法区分复杂要素之间的空间关系,在语义描述上存在歧义,提出一种二维空间R~2下空间关系的细分表达模型.该模型将面与线求交后,线面可以表示成n-1个拓扑和度量分量的序列矩阵,在语义上进行更细致的分段描述;采用折线节点微小圆求交的算法,考虑面带洞和目标的不确定性,归纳出线面拓扑关系细分判断的7个规则,以及长度、分割度等度量项的计算方法.最后以土地利用中图斑扣除线状地物计算地类面积为实例,验证了该模型和计算方法对解决实际问题的有效性.     

An economic input-output analysis for urban stormwater quality planning

A new approach using input-output techniques is proposed for the analysis of urban stormwater pollution caused by urban land development. The input-output model provides projections of sectoral outputs within an urban region. By defining land as an input to production, these output projections may be translated into projections of commercial and industrial land development. Furthermore, the closed version of the input-output model is used to project residential land development as a function of projected wage income. The pollutant generation in urban stormwater is related to the quantity of each category of land development by a pollutant coefficient matrix. Thus, the model can be used to predict the impact of various economic growth scenarios on pollution loadings in runoff water. This will help planners in assessing the environmental costs of various scenarios, and in preparing for remedial actions. A numerical example is provided to illustrate the applications of the model.     

基于改进小波变换的农村集体土地测绘图像高分辨率自适应重建方法

为提高农村集体土地测绘图像的分辨率,改进在重建图像的过程中发生的灰度偏移的情况,研究了基于改进小波变换的农村集体土地测绘图像高分辨率自适应重建方法。采集农村集体土地测绘图像,对差值处理后的测绘图像进行Haar小波变换,高分辨率重建测绘图像,校正图像灰度化偏移的同时保证重建测绘图像的质量;采用基于贝叶斯估计的自适应小波去噪方法,去除重建测绘图像噪声,使重建的高分辨率测绘图像更清晰。实验结果表明：PSNR值接近30dm,SSIM值接近1;不同噪声方差下,PSNR值最高。提高了重建测绘图像的清晰度和分辨率,得到高分辨率的农村集体土地测绘图像。     

土地利用效益评估与在南部的实践

本文简要讨论了土地利用效益评价的内涵与原则,提出了两类评估方法,即专家估计效益综合指数法与土地直接产出法。在南部的实践说明两种方法的结果高度正相关。     

论城市土地的依法经营

从法与经济结合的角度,论述了城市土地经营的内容,对象,性质,经营模式和投资方式,分析了城市土地的依法经营问题。     

我国陆地观测卫星现状及发展战略思考

本文回顾总结了我国陆地观测卫星载荷体系、地面系统建设及数据应用等方面的发展现状和成就,分析了当前所面临的挑战和任务,以此为基础,对我国陆地观测卫星的未来发展进行了战略思考和展望,提出了加强国家顶层规划、加强地面系统统筹建设、实现系列化业务化运营、加强国际合作、推进产业化发展等建议.     

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。     

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。