Image-Based Change Detection of Geographic Information Using Spatial Constraints

The Japan Digital Road Map Association (JDRMA) was founded to establish the standards for digital road map (DRM) and to produce a DRM database in Japan. The JDRMA is now in the process of refining the database using 1/25,000 scale topographical maps. The JDRMA supplies users with an updated version of the database every year. Car navigation is currently the largest application of DRM. Police departments, fire departments, ambulance services, and taxi companies use location systems that use DRM. Significant application of DRM is expected to come in the future in the planning and research fields. Road sign checking system, traffic accident analysis system, road census reference system and logistics system are now being studied. As the applications expand, more advanced DRMs must be made available, To meet this requirement, development of more detailed and precise DRMs and linkage to dynamic information systems is being pursued.

In recent years, there has been a rapid advancement in the use of unmanned aerial vehicles (UAVs) in various aspects of life, especially in the area of automated data collection. These advancements have brought about numerous possibilities. The article describes the use of an unmanned aerial vehicle, specifically the PHANTOM 4 pro, to gather remote sensing data and create digital topographic plans at a scale of 1:2000 in Phu Tho province's Thanh Son district. A method was suggested for improving the current systems for obtaining remote sensing data for cartography using UAVs. The ease of controlling the UAVs and the quality and timeliness of the data they transmit to control points confirm the value of using them to create topographic maps. In addition to UAVs, fieldwork also involves the use of the GNSS brand CHCNAV I50. The high precision GNSS system enables the camera's 3D position to be detected within a few centimeters at the time of each capture. A digital topographic map was compiled of Thanh Son district, covering 165 hectares, and processed using software such as Agisoft and Global Mapper. The digital topographic map that was produced satisfies the documentation requirements of government organizations. The maximum error in height is 4.7 cm, the error of coordinates north and east are 1.8 cm and 1.4 cm respectively. This was achieved by using 590 raster images, which had a resolution of 2.3 cm and a size of 5472x3648 pixels. Based on the findings, the map's accuracy is within an acceptable range of less than 2 cm, which is suitable for a map scale of 1:2000.

Japan Digital Road Map Association (JDRMA) started to construct the digital road map database for the whole Japan in 1988. The first database was completed in 1989, and consequently the updating started in 1990. There are two kinds of data sources for updating the JDRMA database. One is the newly issued topographic maps, which include both roads and other features but are not updated every year. The other is the information from the road administrators; it is used to update the road networks. Every year the information of the newly opened or improved roads is collected and used to update the road network in the database. Other information is also used to supplement the two main information sources. Documents of transportation facilities other than roads and information from the users are utilized. The investigation to improve the updating procedure is continued. >

Abstract. This research examines the problem of automation of quality control of digital topographic maps at the scale 1:50 000 of the Main State Topographic Map in Ukraine for the creation and maintaining the seamless topographic database for national needs, which is located on the Geoportal to ensure the development of the National Spatial Data Infrastructure (NSDI) in Ukraine. The authors determined stages of quality control of digital topographic maps at the scale of 1:50 000 for the automation one according to ISO 19157:2013 Geographic Information – Data quality. The realized automated quality control of geodatabases of updated digital topographic maps at a scale of 1:50 000 provides high speed and quality of validation: validation of quality data elements; validation of quality metadata; validation of positional accuracy. The software package “Validate” was developed for automation quality control geodatabases of digital topographic maps at the scale of 1:50 000 using the Python programming language to verify logical consistency, compliance with the rules of topological relationships between features on the map, availability and content of metadata. The “Validate” can be used for automation quality control geodatabase for any map scale, performing setting according to the requirements of the geospatial models.

Facing-slope measurement from topographic maps was the traditional method used to divide sub-compartment for forest management division. However, the sub-compartment, which usually covers a large area and contains multiple forest types, can not be divided accurately and intuitively. Therefore it is not easy to operate in forestry management practice. An improved forest management planning method based on 3S technology with considering the reality of forest management was proposed in this paper. Firstly, forest function regions were distinguished based on digital topographic map. Secondly, the stands were divided in every sub-compartment based on high spatial resolution images by visual interpretation method in ArcGIS software. Thirdly, many neighboring sub-compartments with same geographical characteristics were combined into one operating area which was convenient for operating management forest. Then stands with the same management mode in one operating area were merged. Finally, compiling computer program to implement the aim of inputting some attribute information to generate other attribute information automatically. The approach put forward in this paper not only allows the forest management sophisticated, targeted but also easy to practice and implement.

Large Scale Topographic Mapping Using Stereo Pair Imagery from CAS500-1 Satellite

This paper intends to classify, review, and compare the existing languages for the specification of spatial integrity constraints at the conceptual level. We classify these languages into natural, visual, first-order logic, and hybrid and review their syntax and semantics. We compare these language categories based on expressiveness and pragmatics qualities. The results of this study show that controlled natural languages and natural hybrid languages with pictograms are good candidates for expressing spatial integrity constraints at the conceptual level of spatial databases. At the end, we identify new research challenges that should be addressed in this domain in order to facilitate spatial integrity constraint specification and representation.

An overview of the results of the FIFE-87 and FIFE-89 campaigns

In topographic maps, It is difficult to separate the linear elements, including contour lines, roads, latitude and longitude lines from complicated background due to the pixels with aliasing and false colors, and there exists some background in the result images extracted by the existing methods, especially when the color and energy of linear elements and background are similar in some particular maps, or the maps have low contrast contour lines. To solve these problems, this paper introduces the idea of seed spreading, and puts forward a novel method for separating linear elements. In this method, all the seeds carry the color information of the pixels and the energy information in the negative grayscale images, and they can search other pixels as their brothers to be combined into seed groups according to the color and energy similarity. The seeds have good perception of the environment around them, and the shapes of the seed groups are variable. Furthermore, the seeds are determined as linear elements by analyzing the color and energy differences between the seed groups and the areas around them. The experimental results show that our method can distinguish linear elements from the background more accurately than the previous methods.

With the development of modern small satellite technology, it is potential and significant to use the new technology for the earth observation. The stereo mapping micro-satellite—“Experiment Satellite 1”, designed and built by Harbin Institute of Technology, is taken as an example for discussion of the relevant technologies and corresponding principle of the satellite payload and its ground application system. The gr ound application system recei ves the stereo photographs data from the “Experiment Satellite 1” and then produces surveying products such as digital elevation map, digital orthoimage and digital topographic map, etc. “Experiment Satellite 1” is the first satellite where three-line-array CCD camera is used triumphantly in world for stereo mapping. It will have profound influence on the earth observation technique. Keywords: Stereo mapping micro-satellite, Payload, Photogrammet ry, Ground application system, Ground station, Data processing center, Accessorial photogrammetry data, Digital orth oimage, Digital topographic map.

High spatial resolution airborne and satellite sensors have been used with varying degrees of success to measure deciduousness, canopy structure, and light interception, and to identify tree species in the Panama Canal Watershed. Results reported to date indicate that remotely sensed data have a high degree of accuracy in measuring deciduousness and leaf density in the upper canopy, but less accuracy than in temperate systems in measuring canopy light interception for semi-deciduous lowland tropical forests in the canal watershed. Of particular relevance to evergreen forests like the upper Rio Chagres basin, this work examines whether high spectral resolution data, like that collected by the HYDICE system, can separate canopy species based on hyperspectral signatures in the 0.4 to 2.5-µm wavelength region. If a few well-selected spectral bands could accurately differentiate species, tropical canopies in remote and rugged terrain could be mapped using relatively simple, but optimized sensor systems.

CwRS (Control with Remote Sensing) is a control method foreseen by the CAP (Common Agricultural Policy) of the European Union (EU) which is used to check if agriculture area-based subsidies are correctly granted to EU farmers. A series of Very High Resolution (VHR) and High Resolution (HR) satellite sensors participate in the acquisition program. Imagery is collected in specific multi-temporal, short time-windows and used for parcel area determination, for crop identification and for control of Good Agricultural and Environmental Conditions (GAECs). In the 2003 Campaign 37 VHR zones with an overall area of 12.500 km2 were checked with the CwRS technique; in the 2011 Campaign 426 VHR control zones were acquired covering an overall area of 242.000 km2, with a total expenditure of 7.1 M euro. This is an enormous increase due to the success of the methodology which needs pointing out. Of interest is also the increasing requirements put on the imagery quality (higher elevation angle, better resolution and better radiometry.). One of the crucial features requested by EU Member States (MS) is window length, for VHR this is usually quite short (6-8 weeks). A feasibility analysis for all zones is therefore done before each VHR Campaign starts to ensure a maximal statistical success rate. This paper describes the complexity of the technical and competitive feasibility assessment taking into account parameters such as satellite characteristics (revisit capacity, number of passes), zone size, shape and latitude; elevation angle, acquisition window length, programming priority level, weather forecast and competitive conflicting tasking. To increase the efficiency of the image acquisition a real local tasking with the use of a Direct Access Facility (DAF) can be compared to a tasking performed through an Imaging and Processing Facility (IPF). Both approaches allow the integration of last minute information into the collection plan and yield for instance better chances of avoiding cloud cover. Illustrative examples are presented.

A method for the radiometric correction of satellite imagery over mountainous terrain has been developed to remove atmospheric and topographic effects. The algorithm accounts for horizontally varying atmospheric conditions and also includes the height dependence of the atmospheric radiance and transmittance functions to simulate the simplified properties of a three-dimensional atmosphere. A database has been compiled that contains the results of radiative transfer calculations (atmospheric transmittance, path radiance, direct and diffuse solar flux) for a wide range of weather conditions. A digital elevation model is used to obtain information about surface elevation, slope, and orientation. Based on the Lambertian assumption the surface reflectance in rugged terrain is calculated for the specified atmospheric conditions. Regions with extreme illumination geometries sensitive to BRDF effects can be optionally processed separately. The method is restricted to high spatial resolution satellite sensors with a small swath angle such as the Landsat thematic mapper and Systeme pour l'Observation de la Terre high resolution visible, since some simplifying assumptions were made to reduce the required image processing time.

The work aimed at developing a methodology which integrates high and low spatial resolution satellite sensor data to assess inter-annual crop area variation on a regional scale. The methodology is based on the use of high spatial resolution images of the main crops for a training year joint to a long-term series of low spatial resolution NDVI images. These data allow the identification of crop specific NDVI profiles for the training year, which can be compared to low spatial resolution images of other study years to detect NDVI differences convertible into crop area variation. The method was preliminarily tested in Tuscany (central Italy), for which five Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) frames and a 13-year series of 10-day National Oceanic Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites were available together with reference agricultural statistics. This test demonstrated the suitability of the method for inter-annual winter crop area change estimation and revealed its potential for further improvement.

Regional estimates of crop yield are critical for a wide range of applications, including agricultural land management and carbon cycle modelling. Remotely sensed images offer great potential in estimating crop extent and yield over large areas owing to their synoptic and repetitive coverage. Over the last few decades, the most commonly used yield–vegetation index relationship has been criticized because of its strong empirical character. Therefore, the present study was mainly focused on estimating regional wheat yield by remote sensing from the parametric Monteith's model, in an intensive agricultural region (Haryana state) in India. Discrimination and area estimates of wheat crop were achieved by spectral classification of image from AWiFS (Advanced Wide Field Sensor) on‐board the IRS‐P6 satellite. Remotely sensed estimates of the fraction of absorbed photosynthetically active radiation (fAPAR) and daily temperature were used as input to a simple model based on light‐use efficiency to estimate wheat yields at the pixel level. Major winter crops (wheat, mustard and sugarcane) were discriminated from single‐date AWiFS image with an accuracy of more than 80%. The estimates of wheat acreage from AWiFS had less than 5% relative deviation from official reports, which shows the potential of single‐date AWiFS image for estimating wheat acreage in Haryana. The physical range of yield estimates from satellites using Monteith's model was within reported yields of wheat for both methods of fAPAR, in an intensive irrigated wheat‐growing region. Comparison of satellite‐based and official estimates indicates errors in regional yields within 10% for 78% and 68% of cases with fAPAR_M1 and fAPAR_M2, respectively. However, wheat yields in general are over‐ and underestimated by the fAPAR_M1 and fAPAR_M2 methods, respectively. The validation with district level wheat yields revealed a root mean square error of 0.25 and 0.35 t ha−1 from fAPAR_M1 and fAPAR_M2, respectively, which shows the better performance of the fAPAR_M1 method for estimating regional wheat yields. Future work should address improvement in crop identification and field‐scale yield estimation by integration of high and coarse resolution satellite sensor data.