Existing GIS Data Research Articles

Historical Atlas of the Low Countries: A gis Dataset of Locality-Level Boundaries (1350–1800)

Abstract This historical gis provides reconstructions of premodern village-level boundaries in the Low Countries, covering the present-day Netherlands, Belgium, Luxembourg and adjacent parts of France and Germany. While some countries have historical municipal gis maps for the nineteenth and twentieth centuries, this resource provides a detailed and contiguous historical gis for the premodern period. It also covers an area spanning several modern states, a rare undertaking. It combines modern and historical sources, including existing gis data, historical maps, and written records, focusing on the ‘smallest mappable units’ referred to as administrative areas in contemporary documents, such as parishes, lordships, or towns. This article accompanies the first tranche of the dataset for the 1500 cross-section, which can be used to link statistics for any period between 1350 and 1800. When completed, the gis dataset will cover four cross-sections: 1350, 1500, 1650, and 1800. Each cross-section will comprise an estimated 18.000 geographical units – typically around 5 to 10 km2 each. The dataset facilitates cross-temporal analysis of historical statistics while allowing users to consider the wider geographical and territorial micro and macro context of these statistics. The result will be an invaluable resource for historians, archaeologists, geographers, and social scientists alike.

Plant Community Assessment and Management Recommendations for Minneapolis Park Natural Areas

In 2017 the Minneapolis Park and Recreation Board (MPRB) began a two-phase study to collect quantitative and qualitative data for urban park natural areas in Minneapolis, MN parks to inform management activities. The first phase took existing GIS data and quality ranking systems and tailored them to the Minneapolis park system. The second phase, which is still in process, involves field checking the data, applying the quality ranking system, and writing a management plan.

SEMANTIC SEGMENTATION AND UNREGISTERED BUILDING DETECTION FROM UAV IMAGES USING A DECONVOLUTIONAL NETWORK

Abstract. Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.

GIS based optimal impervious surface map generation using various spatial data for urban nonpoint source management

Impervious surfaces are mainly artificial structures such as rooftops, roads, and parking lots that are covered by impenetrable materials. These surfaces are becoming the major causes of nonpoint source (NPS) pollution in urban areas. The rapid progress of urban development is increasing the total amount of impervious surfaces and NPS pollution. Therefore, many cities worldwide have adopted a stormwater utility fee (SUF) that generates funds needed to manage NPS pollution. The amount of SUF is estimated based on the impervious ratio, which is calculated by dividing the total impervious surface area by the net area of an individual land parcel. Hence, in order to identify the exact impervious ratio, large-scale impervious surface maps (ISMs) are necessary. This study proposes and assesses various methods for generating large-scale ISMs for urban areas by using existing GIS data. Bupyeong-gu, a district in the city of Incheon, South Korea, was selected as the study area. Spatial data that were freely offered by national/local governments in S. Korea were collected. First, three types of ISMs were generated by using the land-cover map, digital topographic map, and orthophotographs, to validate three methods that had been proposed conceptually by Korea Environment Corporation. Then, to generate an ISM of higher accuracy, an integration method using all data was proposed. Error matrices were made and Kappa statistics were calculated to evaluate the accuracy. Overlay analyses were performed to examine the distribution of misclassified areas. From the results, the integration method delivered the highest accuracy (Kappa statistic of 0.99) compared to the three methods that use a single type of spatial data. However, a longer production time and higher cost were limiting factors. Among the three methods using a single type of data, the land-cover map showed the highest accuracy with a Kappa statistic of 0.91. Thus, it was judged that the mapping method using the land-cover map is more appropriate than the others. In conclusion, it is desirable to apply the integration method when generating the ISM with the highest accuracy. However, if time and cost are constrained, it would be effective to primarily use the land-cover map.

Classifying natural-language spatial relation terms with random forest algorithm

ABSTRACTThe exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.

Web GIS Based Spatial Data Infrastructure (SDI) System for Forestry Management in Sri Lanka

Web mapping is the process of using geo-spatial information through the cyberspace. It consists both a service activities and end-user activities. Web GIS emphasises geo-data distribution aspects more involved with design aspects such as data accruing, storage, processing with server software architecture such as data storage and algorithms. Web based system is more advanced than stand-alone system because its provide pathway to overcome geographical boundaries. As a result of this one, any user who has internet facility will be able to access the system. This paper aims to address the role of Spatial Data Infrastructure (SDI) as a framework for the development of a web-based system as a tool for facilitating forestry management by resolving current problems with spatial data. Centralise web GIS tool facilitates to visualisation and update geo-spatial data for forestry management sector in Sri Lanka. This research is a one of node to open the gate to access the cyberspace which consists of SDI. Collected forest data were cross checked with existing GIS data sources and Google Earth before incorporate to the system. Web system was created using HTML5 and PHP language. Google Map class library and Java script library were used to render the Google Map to the web GIS system and Arc GIS cloud database is used as a geo-database for storing forestry data. in addition to that, several software packages have been used such as Adobe Dreamweaver, Adobe Photoshop, PHP editor, MySQL database, Java, Ajax, Arc GIS, Arc server etc. Web administrator will be able to maintain and update the system using the given authentication facility and to maintain and change query function of the geo-database. End user will be able to read the map and gather information using attribute data. In addition to that, the system facilities to get the print out of the selected area with all map components such as legend, map title, scale etc. Keywords: Web GIS, Cloud GIS, Forest management, SDI, GIS modelling

Urban Expansion and Its Influences on The Suburban Land Use Change in Jakarta Metropolitan Region (JABODETABEK)

The recent urbanization process in emerging countries is entirely different from the process seen in developed countries. In Asia’s emerging countries, urbanization occurs along with fast population growth that follows rapid economic development, and industries and population are centralized into large cities. One typical example of this centralization is seen in the Jakarta Metropolitan Region (JABODETABEK). In this study, the satellite remote sensing images, as well as the existing GIS data, were integrated to generate temporal (2001 and 2009) land use/cover maps which could be used to understand the recent expansion of urban areas and the spatial changes in land use of the suburbs (suburban farmlands). Those are also used to analyze land use with a landscape ecological patch analysis, and to explain land use change from traditional agricultural farmlands to urban areas due to the sprawling phenomenon, which indicates chaotic urban expansion. The result shows that urban land use was rapidly expanded to suburban areas. Agriculture and forest lands were mainly converted into urban land use, and urban and traditional agriculture landscapes were, consequently, extremely mixed. An analysis using landscape metrics also quantitatively revealed that the mixture of land uses and the fragmentation of agricultural landscapes widely existed in the suburban areas at a patch level.

Development of Building Inventory Data and Earthquake Damage Estimation in Lima, Peru for Future Earthquakes

Even though detailed building inventory data are necessary for estimating earthquake damage reliably, most developing countries do not have sufficient data for such estimations. This necessitates a way for finding building distribution and feature easily. In this study for estimating the number of households in all building categories of different structures or floor numbers in Lima, Peru, where a great earthquake is expected, we propose an estimation method based on existing GIS data from a census, satellite imagery, and building data from field surveys, and apply it to estimate the entire area of Lima for create building inventory data. Building fragility functions were used to calculate a severe damage ratio of buildings due to the expected earthquake. The rate was multiplied by created building inventory data to estimate the number of households in damaged buildings. Furthermore we clarified damage reduction by retrofitting for low earthquake-resistant buildings.

The Application of GIS 3D Modeling and Analysis Technology in Real Estate Mass Appraisal – Taking landscape and sunlight factors as the example

Abstract. Based on procedural modeling approach and buildings 2D GIS data of Shenzhen, 3D external models of buildings are generated by CityEngine in a quick and batch mode. And 3D internal model is generated by vectorization of houses distribution within the target building. Following that, the landscape analysis and the sunlight analysis based on GIS visibility analysis method are applied on 3D model of the target building to get the concrete quantization indexes, such as landscape visual range and sunshine duration which could significantly influence real estate value. Finally, the drawing with 3D visualization effect for landscape information and sunshine information is produced. Compared with traditional manual modeling method, the results showed that rule-based 3D modeling method in CityEngine platform could take full advantage of existing GIS data. It could improve the efficiency of 3D modeling by rapidly and automatically generate refined building 3D models in batch mode. Meanwhile, compared with man-made subjective judgment, the building landscape and sunlight analysis model built by visibility analysis could quantify landscape and sunshine indexes more accurately. Furthermore, the application in real estate mass appraisal model for calculation and analysis will reduce the index errors caused by man-made subjective judgment. In addition, precise 3D visualization effect can provide appraisers with more intuitive and efficient view for real estate expression. It greatly improves the efficiency and accuracy in real estate appraisal.

Using Pavement Management to Support Maintenance and Engineering Policy Decisions for Small and Mid-Sized Municipalities

This article presents the development of a pavement management system (PMS) for the town of Christiansburg, VA, and the effectiveness of pavement management applications as a decision support tool. Researchers worked with personnel from the town of Christiansburg to gather inventory data and past work history on all of the pavements within the town’s network. First, the network was defined by importing existing GIS data into commercially available PMS software. The expected pavement performance was then modeled using age data along with surface condition information gathered during a survey of the network. The surface condition, age, and performance data were all combined to develop several maintenance and rehabilitation scenarios for the pavement network. The results indicated that implementing the PMS provided a framework that significantly enhanced the ability of the municipality’s engineers to determine the optimal work types and work scenarios for long-term budgeting and planning.

THE USE OF SATELLITE IMAGERY TO GUIDE FIELD PLOT SAMPLING SCHEME FOR BIOMASS ESTIMATION IN GHANAIAN FOREST

Abstract. Accurate and reliable estimation of biomass in tropical forest has been a challenging task because a large proportion of forests are difficult to access or inaccessible. So, for effective implementation of REDD+ and fair benefit sharing, the proper designing of field plot sampling schemes plays a significant role in achieving robust biomass estimation. The existing forest inventory protocols using various field plot sampling schemes, including FAO's regular grid concept of sampling for land cover inventory at national level, are time and human resource intensive. Wall to wall LiDAR scanning is, however, a better approach to assess biomass with high precision and spatial resolution even though this approach suffers from high costs. Considering the above, in this study a sampling design based on a LiDAR strips sampling scheme has been devised for Ghanaian forests to support field plot sampling. Using Top-of-Atmosphere (TOA) reflectance value of satellite data, Land Use classification was carried out in accordance with IPCC definitions and the resulting classes were further stratified, incorporating existing GIS data of ecological zones in the study area. Employing this result, LiDAR sampling strips were allocated using systematic sampling techniques. The resulting LiDAR strips represented all forest categories, as well as other Land Use classes, with their distribution adequately representing the areal share of each category. In this way, out of at total area of 15,153km2 of the study area, LiDAR scanning was required for only 770 km2 (sampling intensity being 5.1%). We conclude that this systematic LiDAR sampling design is likely to adequately cover variation in above-ground biomass densities and serve as sufficient a-priori data, together with the Land Use classification produced, for designing efficient field plot sampling over the seven ecological zones.

Small-scale and extensive hydrogeomorphic modification and water redistribution in a desert city and implications for regional nitrogen removal

There are numerous examples of small-scale hydrogeomorphic manipulations within urban ecosystems. These modifications are motivated both by a need to handle storm drainage and by a human desire for aquatic ecosystems as places for recreation and aesthetics. In the Phoenix Arizona metropolitan area, two examples of these local modifications are artificial lakes and stormwater retention basins. Although lakes are not a natural feature of Sonoran Desert ecosystems, numerous artificial lakes are evident in the region. Retention basins are a common landscaping practice for preventing damage from rare but potentially large storm events. Here we attempt to quantify the heretofore unknown number and extent of these designed aquatic ecosystems and consider their potential impact on hydrologic landscape connectivity and regional nitrogen (N) removal. For lakes, we found that official GIS layers from local and state agencies had significant misclassifications and omissions. We used two published GIS datasets and state impoundment-permit information to determine the number, areal extent, and water source for artificial lakes. We discovered that there are 908–1,390 lakes in the Phoenix area, with the number varying according to level of aggregation. There are no existing GIS data on retention basins, so we employed drywell-permit data to estimate that there may be 10,000 retention basins in the region. Basic data on N stocks in these ecosystems are discussed within the context of the regional N budget. Accurate data on the extent and distribution of these designed ecosystems will be vital for water-resources planning and stormwater management.

Status and distribution of mangrove forests of the world using earth observation satellite data

ABSTRACTAim Our scientific understanding of the extent and distribution of mangrove forests of the world is inadequate. The available global mangrove databases, compiled using disparate geospatial data sources and national statistics, need to be improved. Here, we mapped the status and distributions of global mangroves using recently available Global Land Survey (GLS) data and the Landsat archive.Methods We interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Each image was normalized for variation in solar angle and earth–sun distance by converting the digital number values to the top‐of‐the‐atmosphere reflectance. Ground truth data and existing maps and databases were used to select training samples and also for iterative labelling. Results were validated using existing GIS data and the published literature to map ‘true mangroves’.Results The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I‐IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude.Main conclusions We report that the remaining area of mangrove forest in the world is less than previously thought. Our estimate is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. We present the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created. We developed and used better mapping techniques and data sources and mapped mangroves with better spatial and thematic details than previous studies.

Extraction of Impervious Surface Areas from High Spatial Resolution Imagery by Multiple Agent Segmentation and Classification

In recent years impervious surface areas (ISA) have emerged as a key paradigm to explain and predict ecosystem health in relationship to watershed development. The ISA data are essential for environmental monitoring and management in coastal State of Rhode Island. However, there is lack of information on high spatial resolution ISA. In this study, we developed an algorithm of multiple agent segmentation and classification (MASC) that includes submodels of segmentation, shadow-effect, MANOVA-based classification, and post-classification. The segmentation sub-model replaced the spectral difference with heterogeneity change for regions merging. Shape information was introduced to enhance the performance of ISA extraction. The shadow-effect sub-model used a split-and-merge process to separate shadows and the objects that cause the shadows. The MANOVA-based classification sub-model took into account the relationship between spectral bands and the variability in the training objects and the objects to be classified. Existing GIS data were used in the classification and post-classification process. The MASC successfully extracted ISA from high spatial resolution airborne true-color digital orthophoto and space-borne QuickBird-2 imagery in the testing areas, and then was extended for extraction of high spatial resolution ISA in the State of Rhode Island.

Synthesizing Old and New: Joining Existing Remote-Sensing and GIS Data to Assess Fire Issues in Sagebrush Steppe Ecosystems

ABSTRACT Creeping environmental changes are impacting some of the largest remaining intact parcels of shrub-steppe ecosystems in the western United States and are creating major problems for land managers. The Idaho National Laboratory (INL) is part of the largest sagebrush steppe ecosystem on the continent. Scientists at the INL and the University of Idaho have integrated existing field and remotely-sensed data with geographic information systems technology to analyze how recent fires on the INL have influenced the current distribution of terrestrial vegetation. Three different vegetation mapping and classification systems were used to evaluate the changes in vegetation due to fires between 1994 and 2003. Twenty-four percent of the sagebrush steppe community on the INL was altered by fire, mostly over a seven year period. There were notable differences between methods, especially for juniper and grasslands. One method stood out in representing the landscape because it includes playa/bare ground/disturbed area and lava as vegetation categories. This study found that assessing existing data sets is useful for quantifying fire impacts and should be helpful in future fire and land use planning. It also demonstrated how different mapping and classification methods can influence the outcome from fire impact assessments.

A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data

Urban sprawl has been identified as one of the most negative effects of global population growth on the environment and biodiversity. Frequent monitoring of urban sprawl is needed to limit the impact of this ongoing phenomenon. This paper proposes precise monitoring of building construction using an object-based classification methodology applied to Spot 5 images with a 2.5 m resolution. An application at a regional scale on Reunion Island in the Indian Ocean shows that this building extraction methodology has limitations in the production of reference urban maps because of difficulties in defining the shape and the number of buildings compared to classical photo-interpretation of aerial photography. However, these results are of great value for planning in urban sprawl areas where up-to-date information is lacking because of the rapid pace of house construction and residential development.

Visual Analysis and Semantic Exploration of Urban LIDAR Change Detection

AbstractMany previous approaches to detecting urban change from LIDAR point clouds interpolate the points into rasters, perform pixel‐based image processing to detect changes, and produce 2D images as output. We present a method of LIDAR change detection that maintains accuracy by only using the raw, irregularly spaced LIDAR points, and extracts relevant changes as individual 3D models. We then utilize these models, alongside existing GIS data, within an interactive application that allows the chronological exploration of the changes to an urban environment. A three‐tiered level‐of‐detail system maintains a scale‐appropriate, legible visual representation across the entire range of view scales, from individual changes such as buildings and trees, to groups of changes such as new residential developments, deforestation, and construction sites, and finally to larger regions such as neighborhoods and districts of a city that are emerging or undergoing revitalization. Tools are provided to assist the visual analysis by urban planners and historians through semantic categorization and filtering of the changes presented.

Pocono mesic till barrens in retreat: topography, fire and forest contagion effects

The Pocono mesic till barrens (PMTB) are a unique assemblage of fire-maintained shrub communities that support numerous rare species. Historically these barrens covered a large area in the vicinity of Long Pond, Pennsylvania, USA. However, due largely to regional fire suppression instituted in the early 1960s, over 70% of the area covered by barrens succeeded to fire-intolerant forest that does not support the rare species. We investigated the influence of forest proximity on barrens succession across three geomorphic types during periods of high fire frequency and fire suppression, testing the hypothesis that forest processes such as seed rain, shading, and detrital enrichment of soils enhances barrens succession through a contagion effect. Evidence of a forest contagion effect should be shown by increased rates of barrens succession with increasing proximity to the nearest forest edge. In order to detect a forest contagion effect, barrens persistence and barrens succession were modeled in proximity zones of 0-50 m, 50-100 m, 100-200 m, and greater than 200 m from the nearest forest edge. We used existing GIS data layers for fire, geomorphology, and vegetation distribution in 1938, 1963, and 1992. The layers were modified and overlain using ArcView software to determine persistence and succession rates for each unique combination of layers in each proximity zone from 1938 to 1963 (pre-fire suppression) and 1963 to 1992 (post-fire suppression). ANCOVA results indicate that proximity to the nearest forest edge significantly affected barrens persistence rates in both time periods, but succession rates were significantly affected in 1938 to 1963 only. Twenty-eight percent of the 1938 barrens succeeded to forest by 1963; 56% of the 1963 barrens became forest by 1992. Results support previous findings that barrens persistence is enhanced by increased fire frequency, and that barrens persist longer where they overlie flat glacial till than on other geomorphology types.