The dynamics in the distribution of mangrove forests in Pichavaram,South India – perception by user community and remote sensing

This study aims to monitor the forest cover of Pichavaram mangroves, South India over a period of 40 years using remote sensing, and to record the status of mangroves as perceived by the local community. Out of 1471 ha of total reserved forest area, mangroves occupy 906 ha. The remote sensing maps show that there was a loss of 471 ha from 1970 to 1991 and a gain of 531 ha in 2011. Nearby 20 hamlets depend on mangroves for their livelihood. A village survey conducted at Pichavaram shows that more than 90% of the local community is well aware of the prevailing species, their importance especially after the 2004 tsunami and the impact of management practices, increased rainfall and contribution of local community in the recent increased area of mangroves. The same can be noticed from the high-resolution IKONOS image showing the artificial canal network in the restored region and from rainfall records.     

Sensing Sprawl: Towards the Monitoring of Urban Expansion Using Dempster‐Shafer Theory

Abstract

The outward expansion of cities in the United States has been a source of concern and policy debate for well over forty years. This sprawling urban landscape has been cited as a contributing factor behind the loss of open space, environmental damage and increased congestion. To better understand urban expansion, monitoring programs are required to facilitate the systematic observation of urban expansion, and to provide critical information in order to adjust urban development policies. Monitoring the urban landscape has been a major application focus of satellite remote sensing technologies. Yet, research has shown that the complexity of the urban landscape frustrates simple characterization of cumulative land cover processes such as sprawl. In this paper an approach to the remote detection and characterization of sprawl is introduced based on the use of Dempster‐Shafer Theory of Evidence. Functioning as a soft‐classification algorithm, Demptster‐Shafer Theory offers a unique solution to the mapping problem when evidence of class structure in underscored by uncertainty. Through the use of this technique it was possible to model uncertainty based on the concept of belief. This conceptualization was instrumental in deciphering the complexities of urban land cover arrangements and offered an alternative logic which enhanced delineation of subtle changes in land cover indicative of sprawl.     

Integrity Monitoring of IGEX-98 Data – Part III: Broadcast Navigation Message Validation

At the Delft University of Technology (DUT), data of six stations participating in the first international GLONASS tracking campaign, IGEX-98, were analyzed with integrity monitoring software. The software was developed at the Department of Mathematical Geodesy and Positioning of DUT. The main function of the software is to detect slips and outliers in phase and code observations in real time. In addition, the software also allows the validation of the information contained in the broadcast navigation messages. The results of the IGEX-98 data analyses are presented in a three part series. In this third and final part of the series, GLONASS and GPS navigation message validation results will be discussed in detail. The first part was concerned with the availability of GLONASS observations (Jonkman & de Jong, 2000a), while in the second part, GLONASS slip and outlier statistics were considered (Jonkman & de Jong, 2000b). The discussion of the navigation message validation results concentrates on anomalies detected in the data of an IGEX-98 station in Switzerland. Data collected at this station from September 1998 to December 1999 was analyized. In all, 19 anomalies were detected by the integrity monitoring software, 8 in GLONASS messages and 11 in GPS messages. The cause of the anomalies was established by evaluating the original data files, studying official bulletins of the GLONASS and GPS ground segments, and in some cases tracing the anomaly through the complete IGEX-98 network. ? 2000 John Wiley & Sons, Inc.     

Ontologies to interpret remote sensing images: why do we need them?

The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts’ expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.     

Small Format Digital Cameras for Aerial Survey: Where Are We Now?

This paper introduces the subject of digital sensors for aerial survey by reviewing the use made of small format digital cameras in such an application. The major advantages and disadvantages of employing such consumer technology for aerial survey are highlighted. Finally, a specification is proposed for a minimum requirement for a digital solution based on a single area array sensor.     

Monitoring recovery after earthquakes through the integration of remote sensing,GIS, and ground observations: the case of L’Aquila (Italy)

The usefulness of remote sensing (RS), geographical information systems, and ground observations for monitoring changes in urban areas has been demonstrated through many examples over the last two decades. Research has generally focused on the relief phase following a disaster, but we have instead investigated the subsequent phases involving early recovery, recovery, and development. Our aim was to determine to what extent integration of the available tools, techniques, and methods can be used to efficiently monitor the progress of recovery following an earthquake. Changes in buildings within the Italian city of L’Aquila following the 2009 earthquake were identified from Earth observation data and are used as indicators of progress in the recovery process. These changes were identified through (1) visual analysis, (2) automated change detection using a set of decision rules formulated within an object-based image analysis framework, and (3) validation based on a combination of visual and semiautomated interpretations. An accuracy assessment of the automated analysis showed a producer accuracy of 81% (error of omission: 19%) and a user accuracy of 55% (error of commission: 45%). The use of RS made it possible for the identification of changes to be spatially exhaustive, and also to increase the number of categories used for a recovery index. In addition, using RS allowed the area requiring extensive fieldwork (to monitor the progress of the recovery process) to be reduced.     

Monitoring and analysing the Emirate of Dubai’s land use/land cover changes: an integrated,low-cost remote sensing approach

This study presents a modified low-cost approach, which integrates the spectral angle mapper and image difference algorithms in order to enhance classification maps for the purpose of monitoring and analysing land use/land cover change between 2000 and 2015 for the Emirate of Dubai. The approach was modified by collecting 320 training samples from QuickBird images with a spatial resolution of 0.6 m, as well as carrying out field observations, followed by the application of a 3?×?3 Soble filter, sieving classes, majority/minority analysis, and clump classes of the obtained classification maps. The accuracy assessment showed that the targeted 2000, 2005, 2010 and 2015 classification maps have 88.1252%, 89.0699%, 90.1225% and 96.0965% accuracy, respectively. The results showed that the built-up area increased by 233.721?km² (5.81%) between 2000 and 2005 and continues to increase even up and till the present time. The assessment of changes in the periods 2000–2005 and 2010–2015 confirmed that net vegetation area losses were more pronounced from 2000 to 2005 than from 2010 to 2015, dropping from 47,618 to 40,820?km², respectively. This study is aimed to assist urban planners and decision-makers, as well as research institutes.     

Comparing the fractality of European urban neighbourhoods: do national contexts matter?

The objective of this paper is to show that morphological similarities between built-up urban surfaces are greater across borders than within cities in Europe: living, architectural and planning trends are international. The spatial arrangement of built-up areas is analysed here by means of fractal indices using a set of 97 town sections selected from 18 European urban agglomerations. The fractal dimension is estimated by correlation techniques. Results confirm that morphological similarities are higher across countries/cities than within. Moreover, two types of fractal laws are considered: one uses the basic fractal scaling law; the other introduces a prefactor a that is often called a “form factor” in the fractal literature. Differences in the results obtained by both laws are explained empirically as well as theoretically, and suggestions are made for further measurements.     

The end of a paradigm: is Zipf’s law universal?

It is largely accepted among geographers and economists that the City Size Distribution (CSD) is well described by a power law, i.e., Zipf’s law. This opinion is shared by this community in a manner it could be treated as a paradigm. In reality, however, Zipf’s law is not always observed (even as an approximation), and we prefer to adopt a classification of the CSD into three classes. In this work, we present the characteristics of these classes and give some examples for them. We use the Israeli system of cities as an interesting case study in which the same ensemble of cities passes from one class to another. We relate this change to the urbanization process that occurred in Israel from the 1960s onwards.     

Spatial analysis in epidemiology: Nascent science or a failure of GIS?

This paper summarizes contributions of GIS in epidemiology, and identifies needs required to support spatial epidemiology as science. The objective of spatial epidemiology is to identify disease causes and correlates by relating spatial disease patterns to geographic variation in health risks. GIS supports disease mapping, location analysis, the characterization of populations, and spatial statistics and modeling. Although laudable, these accomplishments are not sufficient to fully identify disease causes and correlates. One reason is the failure of present-day GIS to provide tools appropriate for epidemiology. Two needs are most pressing. First, we must reject the static view: meaningful inference about the causes of disease is impossible without both spatial and temporal information. Second, we need models that translate space-time data on health outcomes and putative exposures into epidemiologically meaningful measures. The first need will be met by the design and implementation of space-time information systems for epidemiology; the second by process-based disease models.     

Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?

Spatial resolution of environmental data may influence the results of habitat selection models. As high-resolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25 ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25 ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m²) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC > 0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation.     

Dynamics of inequality and growth in Europe: may spatial models solve the puzzle?

Journal of Geographical Systems - The link between income inequality and economic growth remains poorly understood. The global economic crisis challenged numerous growth studies by highlighting...     

Education in cartography: what is the status of young people’s map-reading skills?

Due to recent technological progress, maps have become more popular than ever before. This is especially true for young people, who interact with these technologies on a daily basis. Therefore, it is essential that these potential map users possess sufficient knowledge and skills to process the content of cartographic products. A user study was conducted during which pupils (aged 11–18 years) and geography students (>18 years) had to solve a number of cartography questions using topographic maps. The data were analyzed statistically, taking into account a number of potentially influencing factors (user characteristics) on the participants’ results: age, gender, youth club membership, knowledge about the area, among others. The results show a rising trend in the pupils’ scores with increasing age, which can be explained by education in cartography at school. Geography students perform much better, but no influence of any other user characteristics was detected. For pupils, knowledge about the area and gender might be considered as influencing factors. However, the detected influence of gender depends on the scoring system.     

Quality of hybrid location data drawn from GPS‐enabled mobile phones: Does it matter?

Despite their increasing popularity in human mobility studies, few studies have investigated the geo‐spatial quality of GPS‐enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter “active mobile phone data”). We focus on two key issues in active mobile phone data—systematic gaps in tracking records and positioning uncertainty—and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals. We evaluate the performance of the proposed imputation strategy by comparing raw versus imputed data with participants’ online survey responses. The results indicate that imputed data are superior to raw data in identifying individuals’ frequently visited places on a weekly basis. To assess the location accuracy of active mobile phone data, we investigate the spatial and temporal patterns of the positional uncertainty of each record and examine via Monte Carlo simulation how inaccurate location information might affect human mobility pattern indicators. Results suggest that the level of uncertainty varies as a function of time of day and the type of land use at which the position was determined, both of which are closely related to the location technology used to determine the location. Our study highlights the importance of understanding and addressing limitations of mobile phone derived positioning data prior to their use in human mobility studies.     

UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy?

High quality data on plant species occurrence count among the essential data sources for ecological research and conservation purposes. Ecologically valuable small grain mosaics of heterogeneous shrub and herbaceous formations however pose a challenging environment for creating such species occurrence maps. Remote sensing can be useful for such purposes, it however faces several challenges, especially the need of ultra high spatial resolution (centimeters) data and distinguishing between plant species or genera. Unmanned aerial vehicles (UAVs) are capable of producing data with sufficient resolution; their use for identification of plant species is however still largely unexplored. A fusion of spectral data with LiDAR-derived vertical information can improve the classification accuracy, such a solution is however costly. A cheaper alternative of vertical data acquisition can be represented by the use of the structure-from-motion photogrammetry (SfM) utilizing the images taken for (multi/hyper)spectral analysis. We investigated the use of such a fusion of UAV-borne multispectral and SfM-derived vertical information acquired from a single sensor for classification of shrubland vegetation at species level and compared its accuracy with that derived from multispectral information only. Multispectral images were acquired using Tetracam Micro-MCA6 camera in the west of Czechia in a shrubland landscape protected within the NATURA 2000 network. Using (i) multispectral imagery only and (ii) multispectral-SfM fusion, we classified the vegetation into six classes representing four woody plant species and two meadow types. Our results prove that the multispectral-SfM fusion performs significantly better than multispectral only (88.2% overall accuracy, 85.2% mean producer’s accuracy and 85.7% mean user’s accuracy for fusion instead of 73.3%, 75.1% and 63.7%, respectively, for multispectral). We concluded that the fusion of multispectral and SfM information acquired from a single UAV sensor is a viable method for shrub species mapping.     

On the Importance of Time for GIS View Measures and Their Use in Hedonic Property Models: Does Being Temporally Explicit Matter?

Progress in GIScience has advanced the ability to represent and analyze view characteristics. GIS‐derived view measures requiring digital elevation surface models are used in hedonic property models to quantify the amenity value of view for parcel sales transactions. Ideally models should represent surface elevations that are temporally synchronized with parcel sale dates. Temporal synchronization for studies spanning multiple years may require significant effort. Few studies have undertaken this effort, leading us to investigate in this research the need to be temporally explicit. We evaluate two competing surface model approaches based on: (1) a single year 2000 LiDAR surface product; and (2) annual‐specific surface products for 1995–2002. Two competing view measures based on the different surface approaches are constructed for 561 parcel transactions during 1995–2002 in a coastal North Carolina county and are input into hedonic regression models. Results showed that being temporally explicit did matter in terms of finding significantly different view measures but did not matter in terms of finding significantly different effects of view on parcel sales prices. Despite mixed results for our case study, we advise that future research involving GIS‐based view measurement should consider the spatial and temporal contexts of study area development patterns when evaluating the need to be temporally explicit.     

Brazilian spatial dynamics in the long term (1872–2000): “path dependency” or “reversal of fortune”?

This paper analyzes the spatial dynamics of Brazilian regional inequalities between 1872 and 2000 using contemporary tools. The first part of the paper provides new estimates of income per capita in 1872 by municipality using census and electoral information on income by occupation. The level of analysis is the Minimum Comparable Areas 1872–2000 developed by Reis et al. (áreas mínimas comparáveis para os períodos intercensitários de 1872 a 2000, 2007). These areas are the least aggregation of adjacent municipalities required to allow consistent geographic area comparisons between census years. In the second section of the paper, Exploratory Spatial Data Analysis, Markov chains and stochastic kernel techniques (spatially conditioned) are applied to the dataset. The results suggest that, in broad terms, the spatial pattern of income distribution in Brazil during that period of time has remained stable.     

Compactness,connectivity, and walking accessibility on the neighborhood level according to sustainability certifications: improvement or downgrade? A case study of Cairo,Egypt

Urban sustainability certifications (USCs) urge developers to exceed the local norms and regulatory requirements to attain sustainability. USCs are gaining international recognition as planning and policy support tools. This study aims to assess the relevance of four USCs (LEED for Neighborhood Development, BREAAM communities, CASBEE for Urban Development, and Pearl Community Rating System) in contexts outside their country of origin using Cairo Governorate as a case study. The study focuses on compactness, street connectivity, and walking accessibility as prominent components for sustainable mobility and urban form at the neighborhood level. The study examines 202 neighborhoods in Cairo in terms of compactness and then focuses on eight urban areas in different locations and with different characteristics to assess their connectivity and walking accessibility. Different analyses were performed with ArcGIS software using data about neighborhoods’ population, residential units, street networks, established buildings, buildings’ outlines and heights, and detailed uses. Results show that USCs’ indicators and thresholds are generally lenient and insensitive to the context of formal areas in Cairo Governorate, which are significantly more compact, mixed (horizontally and vertically), and connected. This study adds to the currently limited empirical evidence refuting the use of some USCs as global tools and questioning their utilization in different contexts either as they are or even through an adaptation process.