基础地理信息联动更新技术研究与应用

国民经济建设与社会发展使得人类对地理空间数据的需求结构向知识化、多元化方向发展,本文针对地理空间数据按需及时、高效、准确更新的问题,研究、应用并部分实现了基础地理信息联动更新的核心技术。采用基于数据库增量更新技术、数字外业巡查技术、无人机测绘技术、基于Arc SDE更新技术和联动更新等技术,较好地解决了传统基础测绘更新周期长、更新范围小等问题,适应了地理空间数据多元化、应需适时更新的社会需求,实现全国或区域内重要的基础地理信息数据每年更新一次,从而为整合、升级、改造和建立全国多级基础地理信息数据库提供可供借鉴的一般思路和方法。     

Land use and land cover changes over a century (1914–2007) in the Neyyar River Basin,Kerala: a remote sensing and GIS approach

Abstract

Land use and land cover change, perhaps the most significant anthropogenic disturbance to the environment, mainly due to rapid urbanization/industrialization and large scale agricultural activities. In this paper, an attempt has been made to appraise land use/land cover changes over a century (1914–2007) in the Neyyar River Basin (L=56 km; Area = 483.4 km²) in southern Kerala – a biodiversity hot spot in Peninsular India. In this study, digital remote sensing data of the Indian Remote Sensing satellite series I-D (LISS III, 2006–2007) on 1:50,000 scale, Survey of India (SOI) toposheet of 1914 (1:63,360) and 1967 (1:50,000) have been utilized to map various land use/land cover changes. Maps of different periods have been registered and resampled to similar geographic coordinates using ERDAS Imagine 9.0. The most notable changes include decreases in areas of paddy cultivation, mixed crops, scrub lands and evergreen forests, and increases in built-up areas, rubber plantations, dense mixed forests, and water bodies. Further, large scale exploitation of flood plain mud and river sand have reached menacing proportions leading to bank caving and cut offs at channel bends. Conservation of land and water resources forms an important aspect of ecosystem management in the basin.     

Mapping of wastelands of Kolar district using false colour composites of thematic mapper (TM) data

This study deals with the technique of remote sensing for identifying and deliniating wastelands in Kolar district of Karnataka. False colour composites of thematic mapper (TM) data supplemented by aerial photographs and toposheets wrere utiliesd for mapping wastelands. A map showing the geographic distribution of the wastelands in the districts was prepared on 1∶250,000 scales by compiling the individual wasteland sheets of 1∶50,000 scale. The seven different catagories of wastelands identified and mapped cover about 11.7% of the area in the district. A procedure for mapping wastelands has been worked out based on the experience gained in Kolar district which is a three phase system comprising image intrepretation of false colour composite of TM data, aerial photo interpretation and limited ground truth verification in the selected doubtful areas. This procedure was found to be adequate enough for mapping wastelands accurately in the shortest possible time with least expense and as such are recommended for mapping wastelands in other districts of the country.     

A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

ABSTRACT

Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.     

Application of Earth Remote Sensing and GIS in Mapping Land Cover Patterns in Kinangop Division, Kenya

<正>Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources.     

Multistage approach in soil mapping using remote sensing techniques

The use of Landsat images at 1∶1 Million and 1∶250,000 scales and aerial photographs at 1∶60,000 scale for preparation of soil maps has been discussed. It was possible to prepare soil maps at Suborder and its association from the Landsat images as the base and Subgroup and its association map using the aerial photographs as base. The compatability of classification of landscape units has been discussed keeping the API map as the standard.     

基于面向对象分类技术的地理国情监测研究

地物分类是地理国情变化监测的关键技术,选用福州仓山主城区两个时期的高分辨率影像作为研究对象,研究了利用面向对象分类技术进行地表覆盖分类并生成地理国情普查数据的方法和流程。同时,探讨了基于不同时期地理国情普查数据的空间分析统计结果,揭示地理国情变化规律及原因。     

Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification

LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5–1.5 m), medium (1.5–6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification.     

制作1:5万TM影像图的关键技术

为了适应面广量大且需求仍在不断增长的1:5万专题调查制图的需要,我们采用数字插值放大、优化波段组合的光机复合处理技术,探索了1:5万高质量TM影像图的制作技术。本文介绍了制作1:5万高质量TM影像图的基本工艺方案及技术关键:(1)对TM图像磁带数据进行实数倍(2.28倍)双向线性插值放大,(2)在C-4500扫描仪上用50μm光点扫描获得比例尺为1:25万的潜影图像,(3)把潜影图像经显影、定影处理,再光学放大5倍,获得1:5万TM影像图。从我们结合有关任务先后在河北省南皮县、黑龙江省穆稜县和山东省莱洲湾等地区进行的试验研究看,均取得了良好效果。     

Estimates of current status of forest types in Kolli Hill using remote sensing

The vegetation of Kolli Hill, has been classified for its forest cover types using landsat TM FCCs of two season namely summer (March) and winter (November). The FCCs of two seasons were interpreted visually based on the standard interpretation elements. Extensive field checks were done and corrections were made in both the maps wherever found necessary’. Finally the forest cover type map of Kolli Hill on 1:50,000 scale was drawn by overlaying the interpreted maps of the two seasons The different types of forest were named following Champion and Seth’s classification scheme and the areas of different forest types estimated.     

数字城市建设中的地理空间数据分类与编码

地理空间数据分类与编码是指导数字城市地理空间数据库建设的一项重要标准,本文从分析数字城市项目建设的主要地理空间数据分类与编码的基础上,依据现有国家标准、行业标准和地方标准,将地理空间数据划分为基础地理信息数据、公共管理地理信息数据、公共服务地理信息数据和专题地理信息数据四大类,并综合设计了城市级的地理空间数据编码,为数字城市建设中的多种类、多尺度和多时相的地理空间数据整合、建库、管理及共享提供参考。     

A case-based reasoning approach for task-driven spatial–temporally aware geospatial data discovery through geoportals

ABSTRACT

There is a critical need to develop a means for fast, task-driven discovery of geospatial data found in geoportals. Existing geoportals, however, only provide metadata-based means for discovery, with little support for task-driven discovery, especially when considering spatial–temporal awareness. To address this gap, this paper presents a Case-Based Reasoning-supported Geospatial Data Discovery (CBR-GDD) method and implementation that accesses geospatial data by tasks. The advantages of the CBR-GDD approach is that it builds an analogue reasoning process that provides an internal mechanism bridging tasks and geospatial data with spatial–temporal awareness, thus providing solutions based on past tasks. The CBR-GDD approach includes a set of algorithms that were successfully implemented via three components as an extension of geoportals: ontology-enhanced knowledge base, similarity assessment model, and case retrieval nets. A set of experiments and case studies validate the CBR-GDD approach and application, and demonstrate its efficiency.     

Fundamental aspects of access control for geospatial data

Abstract

In recent years, geographical information systems have been employed in a wide variety of application domains, and as a result many research efforts are being devoted to those upcoming problems. Geospatial data security, especially access control, has attracted increased research interests within the academic community. The tendency towards sharing and interoperability of geospatial data and applications makes it common to acquire and integrate geospatial data from multiple organisations to accomplish a complex task. Meanwhile, many organisations have the requirement for securing access to possessed sensitive or proprietary geospatial data. In this heterogeneous and distributed environment, consistent access control functionality is crucial to promote controlled accessibility. As an extension of general access control mechanisms in the IT domain, the mechanism for geospatial data access control has its own requirements and characteristics of granularity and geospatial logic. In this paper, we address several fundamental aspects concerning the design and implementation of an access control system for geospatial data, including the classification, requirements, authorisation models, storage structures and management approaches for authorisation rules, matching and decision-making algorithms between authorisation rules and access requests, and its policy enforcement mechanisms. This paper also presents a system framework for realising access control functionality for geospatial data, and explain access control procedures in detail.     

Development of National Database on Long-term Deforestation in Sri Lanka

Sri Lanka is one of the biodiversity hotspots of the world. This study has utilized satellite remote sensing and GIS techniques to generate a nation-wide database on forests, forest types and land use/land cover of Sri Lanka. Spatial assessment of forest cover changes was carried out for the periods 1976–1985, 1985–1994, 1994–2005 and 2005–2014. The landscape fragmentation analysis has carried out to calculate the spatial and temporal patterns of forest. Land use/land cover map was prepared representing seven classes in 2014. The plantations occupy a large area (34.2%) followed by forests (33.4%) and agriculture (26.1%) in 2014. During the period of 1976–2014, the forest has been decreased by 5.5%. From 1976 to 1985 forest recorded a loss at an annual rate of 0.49%. This annual rate decreased to 0.01% during 2005–2014 indicates declining trend of deforestation and effective conservation measures. The study found deforestation hotspots in south east and northern most parts of the Sri Lanka. Total number of patches estimated has increased from 15193 in 1976 to 16136 in 2014. The study has found that main causes of deforestation in Sri Lanka were due to expansion of agriculture and plantations. The extent of change detected in the study through geospatial techniques has significance to the forest ecology and management of natural landscapes in Sri Lanka.     

The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

Abstract

This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001–2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multi-temporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p=0.01.     

Satellite image analysis using crowdsourcing data for collaborative mapping: current and opportunities

ABSTRACT

Researchers are continually finding new applications of satellite images because of the growing number of high-resolution images with wide spatial coverage. However, the cost of these images is sometimes high, and their temporal resolution is relatively coarse. Crowdsourcing is an increasingly common source of data that takes advantage of local stakeholder knowledge and that provides a higher frequency of data. The complementarity of these two data sources suggests there is great potential for mutually beneficial integration. Unfortunately, there are still important gaps in crowdsourced satellite image analysis by means of crowdsourcing in areas such as land cover classification and emergency management. In this paper, we summarize recent efforts, and discuss the challenges and prospects of satellite image analysis for geospatial applications using crowdsourcing. Crowdsourcing can be used to improve satellite image analysis and satellite images can be used to organize crowdsourced efforts for collaborative mapping.     

Land Cover and Vegetation Change in the Yellow River Delta Nature Reserve Analyzed with Landsat Thematic Mapper Data

Abstract

Coastal wetland is a major part of wetlands in the world. Land cover and vegetation mapping in a deltaic lowland environment is complicated by the rapid and significant changes of geomorphic forms. Remote sensing provides an important tool for coastal land cover classification and landscape analysis. The study site in this paper is the Yellow River Delta Nature Reserve (YRDNR) at the Yellow River mouth in Shangdong province, China. Yellow River Delta is one of the fastest growing deltas in the world. YRDNR was listed as a national level nature reserve in 1992. The objectives of this paper are two fold: to study the land cover status of YRDNR, and to examine the land cover change since it was declared as a nature reserve. Land cover and vegetation mapping in YRDNR was developed using multi‐spectral Landsat Thematic Mapper (TM) imagery acquired in 1995. Land cover and landscape characteristics were analyzed with the help of ancillary GIS. Land use investigation data in 1991 were used for comparison with Landsat classification map. Our results show that YRDNR has experienced significant landscape change and environmental improvement after 1992.     

Earth observations during space shuttle mission STS‐28 8–13 August 1989

Abstract

The paper presents a geospatial modeling approach for the assessment of plant richness in Barsey Rhododendron Sanctuary in Sikkim, a Himalayan State of India located in the “Indo‐Burma” biodiversity hotspot. Remotely sensed data from Indian Remote Sensing Satellite IRS‐1C Linear Imaging Self‐Scanner (LISS‐III) and field‐based methods were synergistically used to model plant richness on 1:50,000 scale. It was found that the sanctuary is dominated by East Himalayan Moist Temperate Forest (55.50%), followed by Rhododendron Forest (23.77%), Degraded Forest (6.66%) and Hemlock Forest (0.78%). The vegetation map prepared through digital interpretation of satellite imagery was subjected to landscape analysis and assessment of biotic disturbance in terms of disturbance index. The disturbance index together with species richness, ecosystem uniqueness, total importance value and terrain complexity was modeled to assess the plant richness in this unique sanctuary. Out of the 120 km² of the total geographical area of the sanctuary, 28.45 per cent was found to possess very high plant richness followed by high (50.84%), medium (6.96%) and low richness (13.75%). It was noted that plant richness assessment at ecosystem level presents a more realistic picture than at landscape level. The study demonstrated that remote sensing coupled with landscape analysis, ground inventory data and geospatial modeling holds good potential for rapid and operational assessment of plant richness.     

Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model

Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model.