scholarly journals Changes in land cover and grassland area over the past 120 years in a rapidly urbanised area in Japan

One Ecosystem ◽  
2019 ◽  
Vol 4 ◽  
Author(s):  
Akira Noda ◽  
Akihiko Kondoh ◽  
Jun Nishihiro
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Agricultural Fields ◽  
Native Forest ◽  
Conifer Forests ◽  
Residential Areas ◽  
Grassland Species ◽  
Chiba Prefecture ◽  
Time Period ◽  
Changes Over Time

In wet temperate regions, human activity has played an important role in shaping the size and distribution of grasslands. We examined change in land cover type and grassland area in a 9.2 × 22.3 km area of northern Chiba Prefecture, based on historical maps and documents for four time periods (1880s, 1950s, 1980s and 2000s). In the 1880s, conifer forests occupied the largest area (43.1%) amongst land cover types and grasslands accounted for 4.2% of the total area. However, literature available from the 1880s suggests that the understorey of conifer forests may have served as additional habitat for grasses. Thus, the habitat of grassland species is suggested to have covered up to 54% of the study area during this time period. By the 1950s, much of the grassland present in the 1880s had been changed to agricultural fields and paddies and grassland area had reduced to 2.9%. Residential development prior to and during the 1980s led to the conversion of forests and agricultural fields to grassland, increasing the grassland area to 11.6% of the study area. Finally, in the 2000s, grasslands had declined to 6.0% of the study area, likely due to conversion to residential areas. Despite these changes over time, 1.5% of the study area has remained as native forest or grassland for over 120 years. The spatial data presented herein are useful for conservation planning and studying the effect of historical land use change on biodiversity.

Extending UML for Space- and Time-Dependent Applications

2002 ◽  
pp. 342-366 ◽  
Author(s):  
Rosanne Price ◽  
Nectaria Tryfona ◽  
Christian S. Jensen
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Continuous Process ◽  
Spatiotemporal Data ◽  
Temporal Dimension ◽  
Spatial Information Systems ◽  
Time Period ◽  
High Level ◽  
Changes Over Time ◽  
Measurement Location

In recent years, the need for a temporal dimension in traditional spatial information systems and for high-level models useful for the conceptual design of the resulting spatiotemporal systems has become clear. Although having in common a need to manage spatial data and their changes over time, various spatiotemporal applications may manage different types of spatiotemporal data and may be based on very different models of space, time, and change. For example, the term spatiotemporal data is used to refer both to temporal changes in spatial extents, such as redrawing the boundaries of a voting precinct or land deed, and to changes in the value of thematic (i.e., alphanumeric) data across time or space, such as variation in soil acidity measurements depending on the measurement location and date. A spatiotemporal application may be concerned with either or both types of data. This, in turn, is likely to influence the underlying model of space employed, e.g., the two types of spatiotemporal data generally correspond to an object- versus a field-based spatial model. For either type of spatiotemporal data, change may occur in discrete steps, e.g., changes in land deed boundaries, or in a continuous process, e.g., changes in the position of a moving object such as a car. Another type of spatiotemporal data is composite data whose components vary depending on time or location. An example is the minimum combination of equipment and wards required in a certain category of hospital (e.g., general, maternity, psychiatric), where the relevant regulations determining the applicable base standards vary by locality and time period.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

scholarly journals Is Call Karma Real? Resident After-Hours On-Call Imaging at the University of Saskatchewan: An Assessment of Call Volumes Including Inter-Resident Variability

2021 ◽  
pp. 084653712110263
Author(s):  
James Huynh ◽  
David Horne ◽  
Rhonda Bryce ◽  
David A Leswick
Keyword(s):  
Mean Shift ◽  
Mean Values ◽  
Body Regions ◽  
Time Period ◽  
Relative Value ◽  
Number Of Patients ◽  
After Hours ◽  
Disproportionate Number ◽  
The University ◽  
Changes Over Time

Purpose: Quantify resident caseload during call and determine if there are consistent differences in call volumes for individuals or resident subgroups. Methods: Accession codes for after-hours computed tomography (CT) cases dictated by residents between July 1, 2012 and January 9, 2017 were reviewed. Case volumes by patient visits and body regions scanned were determined and categorized according to time period, year, and individual resident. Mean shift Relative Value Units (RVUs) were calculated by year. Descriptive statistics, linear mixed modeling, and linear regression determined mean values, differences between residents, associations between independent variables and outcomes, and changes over time. Consistent differences between residents were assessed as a measure of good or bad luck / karma on call. Results: During this time there were 23,032 patients and 30,766 anatomic regions scanned during 1,652 call shifts among 32 residents. Over the whole period, there were on average 10.6 patients and 14.3 body regions scanned on weekday shifts and 22.3 patients and 29.4 body regions scanned during weekend shifts. Annually, the mean number of patients, body regions, and RVUs scanned per shift increased by an average of 0.2 (1%), 0.4 (2%), and 1.2 (5%) (all p < 0.05) respectively in regression models. There was variability in call experiences, but only 1 resident had a disproportionate number of higher volume calls and fewer lower volume shifts than expected. Conclusions: Annual increases in scan volumes were modest. Although residents’ experiences varied, little of this was attributable to consistent personal differences, including luck or call karma.

scholarly journals Constructing Damage Indices Based on Publicly Available Spatial Data: Exemplified by Earthquakes and Volcanic Eruptions in Indonesia

2021 ◽  
Author(s):  
Emmanuel Skoufias ◽  
Eric Strobl ◽  
Thomas Tveit
Keyword(s):  
Spatial Data ◽  
Volcanic Ash ◽  
Satellite Images ◽  
Fragility Curves ◽  
Volcanic Eruptions ◽  
Damage Indices ◽  
Damage Indicator ◽  
Time Period ◽  
Peak Ground Motion

AbstractThis article demonstrates the construction of earthquake and volcano damage indices using publicly available remote sensing sources and data on the physical characteristics of events. For earthquakes we use peak ground motion maps in conjunction with building type fragility curves to construct a local damage indicator. For volcanoes we employ volcanic ash data as a proxy for local damages. Both indices are then spatially aggregated by taking local economic exposure into account by assessing nightlight intensity derived from satellite images. We demonstrate the use of these indices with a case study of Indonesia, a country frequently exposed to earthquakes and volcanic eruptions. The results show that the indices capture the areas with the highest damage, and we provide overviews of the modeled aggregated damage for all provinces and districts in Indonesia for the time period 2004 to 2014. The indices were constructed using a combination of software programs—ArcGIS/Python, Matlab, and Stata. We also outline what potential freeware alternatives exist. Finally, for each index we highlight the assumptions and limitations that a potential practitioner needs to be aware of.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

2021 ◽  
Vol 14 (1) ◽  
pp. 160
Author(s):  
Najmeh Mozaffaree Pour ◽  
Tõnu Oja
Keyword(s):  
Land Cover ◽  
Driving Forces ◽  
Urban Expansion ◽  
Network Models ◽  
Influential Factors ◽  
Residential Areas ◽  
Neural Network Models ◽  
Independent Variables ◽  
The Core ◽  
Mlp Model

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

scholarly journals Effects of land cover type on carabid beetles (Coleoptera: Carabidae) of the Canterbury foothills, New Zealand

2019 ◽  
Vol 49 ◽  
Author(s):  
Lisa A. Berndt ◽  
Eckehard G. Brockerhoff
Keyword(s):  
New Zealand ◽  
Land Cover ◽  
Natural Forest ◽  
Carabid Beetle ◽  
Carabid Beetles ◽  
Native Forest ◽  
Potential Contribution ◽  
Pine Plantation ◽  
Plantation Forest ◽  
Beetle Assemblages

Background: Land cover changes during the recent history of New Zealand have had a major impact on its largely endemic and iconic biodiversity. As in many other countries, large areas of native forest have been replaced by other land cover and are now in exotic pasture grassland or plantation forest. Ground beetles (Carabidae) are often used as ecological indicators, they provide ecosystem services such as pest control, and some species are endangered. However, few studies in New Zealand have assessed the habitat value for carabid beetles of natural forest, managed regenerating natural forest, pine plantation forest and pasture. Methods: We compared the carabid beetle assemblages of natural forest of Nothofagus solandri var solandri (also known as Fuscospora solandri or black beech), regenerating N. solandri forest managed for timber production, exotic pine plantation forest and exotic pasture, using pitfall traps. The study was conducted at Woodside Forest in the foothills of the Southern Alps, North Canterbury, New Zealand, close to an area where the critically endangered carabid Holcaspis brevicula was found. Results: A total of 1192 carabid individuals from 23 species were caught during the study. All but two species were native to New Zealand, with the exotic species present only in low numbers and one of these only in the pasture habitat. Carabid relative abundance and the number of species was highest in the pine plantation, where a total of 15 species were caught; however, rarefied species richness did not differ significantly between habitats. The sampled carabid beetle assemblages were similar across the three forested habitat types but differed significantly from the pasture assemblages based on unconstrained and canonical analyses of principal coordinates. Holcaspis brevicula was not detected in this area. Conclusions: Our results show that managed or exotic habitats may provide habitat to species-rich carabid assemblages although some native species occur only in natural, undisturbed vegetation. Nevertheless, it is important to acknowledge the potential contribution of these land uses and land cover types to the conservation of native biodiversity and to consider how these can be managed to maximise conservation opportunities.

scholarly journals A multi-factor model developed on residents’ opinions for the classification of urban residential areas

Geografie ◽  
2016 ◽  
Vol 121 (1) ◽  
pp. 1-31
Author(s):  
Péter Gyenizse ◽  
András Trócsányi ◽  
Gábor Pirisi ◽  
Zita Bognár ◽  
Szabolcs Czigány
Keyword(s):  
Urban Area ◽  
Social Services ◽  
Spatial Data ◽  
Factor Model ◽  
Decision Makers ◽  
Social Differentiation ◽  
Residential Areas ◽  
The Status ◽  
Housing Needs

The process of social differentiation in post-communist states has had a clear impact on the status of neighbourhoods. Municipalities have tried to handle the problem, but planning in Hungary is still based on shallow analyses. This paper presents a method for examining and quantifying prevailing factors of residential areas, also being able of a spatial comparison. It detects problematic issues and locations and assists in the formulation of solutions. The model city for the presented study was Szeged, located in southeastern Hungary. Szeged is the economic center of the region and it was an ideal urban area for the evaluation of housing needs and for the mapping of various objects and social services. A field-collected qualitative database was processed using the Idrisi Selva GIS program, resulting in a classifying map of investigated areas. We have localized the properties of the lowest score and also determined the major issues responsible for low scores by analysing the spatial data of 27 GIS layers. The model can be used to detect the reasons causing differences in the perception of neighbourhoods, while it may serve as a tool for decision makers.

scholarly journals Experiment of Multispectral Images using Spectral Angle Mapper Algorithm for Land Cover Classification

2019 ◽  
Vol 8 (6S4) ◽  
pp. 96-99
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Water Bodies ◽  
Classification Algorithms ◽  
Multispectral Images ◽  
Rapid Urbanization ◽  
Spectral Angle Mapper ◽  
Time Period ◽  
Steep Decline ◽  
Spectral Angle

Urbanization plays a key role in the health of the water bodies in any region. In a rapidly growing country like India, especially Bangalore district, rapid urbanization has seen a steep decline in the number of water bodies the region is famous for. In this paper, Land Use and Land Cover change is analysed for the remotely sensed images of Bangalore District using Spectral Angle Mapper Algorithm. Data for the purpose of analysis was obtained from BHUVAN (NRSC, ISRO). The study area is Bangalore District and data was collected from the time period 2008-2016. The major classes used in the classification are Land(Built-up), water bodies (Lakes), Vegetation (Gardens), Soil (Barren and fertile). The satellite images and the accompanying classification algorithms indicate that the percentage of water bodies have drastically shrunk (from 2.9% in 2008to1.8% in 2016) in the area of study. The results of this study can be used by the civic authorities to implement decisions to conserve the water bodies in the area.

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