Exploring the spatial and temporal dynamics of land use in Xizhuang watershed of Yunnan, southwest China

Over the last two decades, China has introduced a series of agricultural and forestland use reforms, aiming to feed the largest population in the world and maintain ecological services locally and nationally. This paper studies the impacts of local government-driven reforestation on land use and land cover change, as well as its further impacts on livelihoods of upland farmers in Xizhuang watershed. An analysis of aerial photographs and ASTER satellite imagery from 1987 to 2002, respectively, showed that the forest has significantly increased at the expense of decreasing farmland. However, the monoculture reforestation of pine has caused both biophysical and socio-economic consequences. This case study also shows forestry decentralization in China remains incomplete. Land use and land cover change is also a political economic issue. Some of the reforms designed to protect forest resources have had a negative impact on rural livelihoods.     

关于国土资源数据共享分类的思考

数据共享分类是制定数据共享政策和数据资源开发利用规划的一项基础性工作。本文 提出国土资源数据共享分类的意义、原则和方法,探讨建立以数据共享分类为核心的 国土资源数据分类体系,以期为全面开展国土资源数据共享分类提供参考。     

存储区域网技术及其在国土资源行业中的应用

本文简要介绍了存储区域网(SAN)的概念、结构及特点,对SAN与DAS、NAS存 储模式在性能、可用性等方面做了比较,并结合具体建设案例,指出了国土资源行业 存储系统建设采用SAN结构的优势。     

国土资源行业“3S”技术集成研究及实用技术体系构建

本文面向国土资源信息化建设的实际需求,通过关键技术研究和技术集成,建立基于“3S”的国土资源数据获取、更新、管理、交换与应用的技术体系,通过应用示范研究,形成实用、可推广的技术流程和应用软件。研究结果表明,基于“3S”的技术体系,以其精确的空间定位、快速准确的数据获取、强大的数据管理能力,能够满足国土资源信息化建设的实际需要。     

基于GPRS的土地变更调查一体化系统应用探讨

介绍了基于GPRS公网的实时差分技术的土地变更调查的技术方法和工作流程,以徐州市数字国土土地变更调查为例,实现了基于GPRS公网的GPS-PDA采集、处理、分析土地变更信息的内外业一体化系统。     

土地利用更新调查需要注意的几个问题

以土地利用更新调查生产中的体会．从工作前期准备、技术路线、正射影像图制作、权属界线调查几种特殊情况和表示方法的处理，提出了土地利用更新调查需要注意的几个问题和建议。     

利用主成分分析改善土地利用变化的遥感监测精度——以珠江三角洲城市用地扩张为例

近年来,珠江三角洲由于经济的快速发展,城市用地急剧增加,利用多时相的遥感图,可以定量地监测这种城市化的现象。但理,由一般的遥感动态监测方法所得的结果往往夸大变化的程度,以及获得一些不合理的结论．该文提出主成分分析的方法来改善遥感动态监测的精度。将该方法应用应用于珠江三角洲发展最快的东莞市,获得了较满意的结果。     

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.     

Accuracy assessment of NLCD 2011 percent impervious cover for selected USA metropolitan areas

The emergence of high-resolution land cover data has created the opportunity to assess the accuracy of impervious cover (IC) provided by the National Land Cover Database (NLCD). We assessed the accuracy of the 900 m² NLCD2011 %IC for 18 metropolitan areas throughout the conterminous United States using reference data from 1 m² land cover data developed as part of the United States Environmental Protection Agency’s EnviroAtlas project. Agreement was assessed from two perspectives: 1) sensitivity to the size of the assessment unit used for the comparison, and 2) utility of NLCD %IC to serve as a proxy for high-resolution IC. The former perspective was considered because statistical relationships can be sensitive to assessment unit size and shape, and the latter perspective was considered because high resolution (reference) %IC data are not available nationwide. The utility of NLCD %IC as a proxy for the high resolution data was assessed for seven lattice (square) cell sizes ranging from 1 ha to 200 ha using four EnviroAtlas IC indicators: 1) %IC per 100 ha (1 km²); 2) %IC by Census block group; 3) %IC within a 15 m (radius) of the riparian zone, and; 4) %IC within a 50 m (radius) of the riparian zone. Agreement was quantified as per assessment unit deviation (NLCD %IC – reference %IC) and summarized as Mean Absolute Deviation (MAD) and Mean Deviation (MD) both within and across the 18 metropolitan areas. Ordinary least squares (OLS) regression (y = reference %IC and x = NLCD %IC) was also used to evaluate the quality of the NLCD %IC data. MAD was ≤ 5% for six of the seven lattice cell sizes. MAD was also ≤ 5% for Census block groups > 100 ha and for both riparian units. These results suggest that uncertainty attributable to the measurement of %IC was no greater than the uncertainty related to the effect of IC on aquatic resources that have been derived from studies of aquatic condition (e.g., benthic fauna) over a range of %IC. Overall, agreement was variable from one metropolitan area to the next. Agreement improved as assessment unit size increased and declined as the level of urbanization (NLCD %IC) increased. NLCD %IC tended to underestimate reference %IC overall, but NLCD %IC was sometimes greater than reference %IC in urbanized settings.     

An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.