Hasil untuk "Cartography"

R. Guimerà, Luís A. Nunes Amaral

Integrative approaches to the study of complex systems demand that one knows the manner in which the parts comprising the system are connected. The structure of the complex network defining the interactions provides insight into the function and evolution of the components of the system. Unfortunately, the large size and intricacy of these networks implies that such insight is usually difficult to extract. Here, we propose a method that allows one to systematically extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find modules in complex networks and (ii) classify nodes into universal roles according to their pattern of within- and between-module connections. The method thus yields a ‘cartographic representation’ of complex networks.

G. Cinque, L. Rizzi

William Cartwright, Georg Gartner, Liqiu Meng et al.

Arnauld Sergé, N. Bertaux, H. Rigneault et al.

Shoukai Sun, Anke Huss, Derek Karssenberg et al.

Urban greenery, as a critical urban landscape component, plays an important role in improving the living environments’ and residents’ well-being. Previous studies have predominantly adopted satellite image-based vegetation measurements. This study aims to quantify pedestrian-perspective greenery visibility using Google Street View (GSV) images and to understand how greenery types and built environment characteristics influence the correlation between pedestrian and aerial greenery assessments. We collected GSV images located on 34,601 sampling points and applied the DeepLab v3+ deep learning model to quantify green view index (GVI) from the pedestrian perspective. We distinguished green vegetation view index (GVVI) and green terrain view index (GTVI) to differentiate vertical and horizontal greenery types. Normalized difference vegetation index (NDVI) was extracted from Sentinel-2 images using circular buffers of varying radii (10–200 m) centered on GSV sampling points. Sampling points were filtered based on the buffer distance to avoid overlapping NDVI pixels in neighboring sampling points. Spearman correlation analysis was conducted across different typologies (urban, intermediate, rural) to examine GVI-NDVI relationships. Street-level greenery exhibited substantial spatial heterogeneity across the whole of the study area (Basel, Switzerland). GVI vs. NDVI in buffers with different radii had strong positive correlations, with a maximum Spearman coefficient of 0.77 for the 15 m NDVI buffer. Correlation coefficients decreased progressively from urban (0.77) to intermediate (0.72) and rural (0.66) areas. Correlation coefficients strongly decreased with increasing buffer sizes. Analysis of GSV images with high NDVI but low GVI values indicates that greenery types and building distributions significantly affect the street-level visible greenery. This study links street-level greenery with features in the built environment by using different methods for assessing green exposure. The findings provide methodological insights for greenery exposure studies and inform evidence-based urban planning strategies for optimizing green visibility.

Zhongnan Yan, Qing Ji, Bin He et al.

Snow on the Antarctic sea ice is a crucial component of the cryosphere. In response to the dynamic and highly heterogeneous Antarctic snow during the sea ice melting season, this study employed a combined multi-source data and deep learning method to accurately retrieve snow depth on Antarctic sea ice. Initially, we integrate multiple datasets, including satellite remote sensing, geospatial information, and meteorological data. Subsequently, a Convolutional Neural Network (CNN) is utilized to construct a snow depth retrieval model (PSDCNN-5_7 model). Compared to snow depth measurements from Alfred Wegener Institute (AWI) snow buoys, the PSDCNN-5_7 model outperforms existing algorithms, exhibiting a deviation of only −3.38 cm. The uncertainty of the snow depth caused by the model input is only 1.64 cm. In West Antarctica, snow depth is more affected by snowfall (SF), 2-m air temperature (T2m), and sea ice velocity (SIV). Conversely, in East Antarctica, snow depth is primarily influenced by SIV. The proposed approach accurately retrieves snow depth on Antarctic sea ice and facilitates the derivation of long-term variations and trends in snow depth, contributing to a better understanding of the relationship between sea ice, snow, and climate change.

Dominico Benedicto Kilemo

Abstract Context and background: The Sagara hills provide key ecosystem services to the communities in Kongwa and Mpwapwa districts in Dodoma region. In particular, the hills provide watershed services which is vital in a challenging semi-arid condition. However, the current situation suggests that the watershed services are at risk due to anthropogenic activities.  Goal and Objectives: This study assesses the dynamics of land use and land cover changes in Sagara catchment and its implication to watershed services for the surrounding communities. Methodology: Remote sensing and Geographical Information System (GIS) techniques were used to analyze changes in land use and land cover in the catchment between 2013 and 2021.  The study used two categories of data: Landsat 8 layers and reference data. Landsat 8 layers were used as input data for change detection and quantification of vegetation cover and other land uses at Sagara hills, while field data and higher resolution Google Earth Pro Historical images were used to create reference data for training the classifier and accuracy assessment. Results: Results show that the built area increased from 249.4 ha in 2013 to 504.2 ha in 2021 with a net gain of 254.8 ha.  Farmland increased with a net gain of 3108.1 ha whereby the farmland area was 10900.7 ha in 2013, but increased to 14008 ha in 2021. It was further observed that there were significant changes in vegetation cover from 2013 to 2021. The woodland forest which was a dominant vegetation in 2013 with an area of 24187.5 ha has been reduced to 12439 ha. This means in 9years; 11,748 ha of forest have been lost due destructive human activities. Grassland area was also observed to decrease from 995.1 ha in 2013 to 751.9 ha in 2021 with a net loss of 243.2 ha. Closed bushes and thickets which increased significantly by 2021 has become the dominant vegetation. Bare land was also observed to have increased. This is attributed to poor farming methods which resulted into soil erosion and loss of land productivity in the catchment.

Yilei Liu, Xin Pan, Jiangping Liu et al.

ABSTRACTAccurate identification and classification of forage grass are pivotal in optimizing forage resources and breeding superior forage varieties. Given the low accuracy in forage image identification and classification, and the loss of some features from preprocessing, we proposed an innovative approach that integrates preprocessing operations directly into the model instead of preceding feature analysis. We captured near-ground hyperspectral imagery of forage in the field and applied two deep learning models – Squeeze and Excitation ResNet (SEResNet) and Convolution Block Attention Module ResNet (CBAMResNet). These models not only harness the automatic learning capabilities of the ResNet deep network but also employ channel attention and a channel-plus-space dual attention mechanism to filter and label important features. This approach enhances data extraction and analysis, strengthen the correlation between the channel and space dimensions while eliminating redundancy and noise. We compared the performance of the proposed methods with the current popular methods by six evaluation parameters, including overall accuracy (OA), average accuracy (AA), Kappa coefficient, etc. Experiment results show the OA of SEResNet and CBAMResNet are 96.57% and 98.35% respectively. The experiments demonstrate the feasibility of incorporating preprocessing into the network and the effectiveness of the new idea for the classification research of forage.

Y. Ding, G. Chowdhury, S. Foo et al.

Tian Zhou, Renato César dos Santos, Jidong Liu et al.

LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies—DBSCAN-based and height/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height/density-based approaches perform well in tree detection (F1 score > 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter/threshold fine-tuning.

Theresa Foley, Ann Marie Wolf, Chloe Jackson et al.

Fear of liability from the 1980 Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA or Superfund) has prompted developers to build preferentially upon undeveloped green space rather than potentially contaminated former industrial sites, leading to urban sprawl in the suburban areas while blighted properties in the urban core remain vacant. A brownfield is defined as a property in which the presence or potential presence of a hazardous substance or contaminant poses a barrier to development. Agencies often create brownfield inventories by performing a site suitability analysis, using distinguishing features such as ecologically and culturally significant areas or neighborhoods that need revitalizing. Pima County, Arizona and the Sonora Environmental Research Institute, Inc. (SERI) developed a brownfield inventory of the large, industrial area directly to the west of Davis-Monthan Air Force Base. Because the brownfield target area has few residential neighborhoods and lacks the distinguishing features usually used in a brownfield site suitability analysis, the county and SERI used the official tax assessor database and 11 federal, state and county environmental databases to develop a brownfield inventory. The goal of the project was to prioritize properties that stood to benefit from the grant funding. The final brownfield inventory contained 531 parcels.

S. Caquard, W. Cartwright

Kurt McLaren, Karen McIntyre, Kurt Prospere

Hydroacoustic and optical remote sensing have been commonly used to map shallow nearshore benthic features. However, the number, type, scale, and accuracy of the mapping products that can be obtained from the two sensors differ; as such, there can be limited agreement between their mapping products. These differences can be further accentuated if the hydroacoustic data are interpolated to produce a map. Interpolation introduces spatial uncertainty and reduces map accuracy. Consequently, maps generated from the two sensors may provide dissimilar spatial and temporal representations of the same benthic features. We therefore compared the performance of a random forest regression (RFr) and a universal kriging (UK) interpolation method and a post-classification enhancement that can be used to increase the accuracy and complementarity of benthic habitat maps derived from hydroacoustic data. First, we used single beam echosounder (SBES) survey bathymetry data from the Bluefields Bay marine protected area (MPA) in western Jamaica (13.82 km2 in size), to create a bathymetric surface model (BSM), from which rugosity and bathymetric position index (BPI) maps were generated. Next, the RFr was used to create submerged aquatic vegetation (SAV) percentage cover maps from the SBES SAV cover data by predicting cover at un-sampled locations. Predictors included auxiliary data such as depth, BPI, survey points coordinates and radiometrically corrected, deglinted and water column corrected image reflectance index values from each of the following: WorldView-2, Geoeye-1 and Landsat 8. Additionally, a SAV map was created using the UK. The most accurate SAV cover thresholds were identified and were used to create binary maps from the RFr and UK maps. A rugosity derived coral reef map was then added to the binary maps. The resulting benthic habitat maps had comparable accuracies and class coverage to benthic maps classified from GeoEye-1 and WorldView-2 images using pixel and object-based classifiers. However, map accuracies were calculated using a suboptimal number of reference points (<50) for two of the benthic map classes (SAV absent and coral reef). This was not considered to be problematic as the addition of the coral reef class to the binary maps resulted in a significant decrease in uncertainty (standard error and confidence interval width of the overall accuracy) and a significant increase in the user’s accuracy of the SAV absent map class. Also, the difference in uncertainty and accuracy between the map classes did not change. The methods used in this study can therefore be used to increase the accuracy (and to decrease the uncertainty) and the complementarity of maps derived from hydroacoustic data.

Andrii Evdokimov, Kostiantyn Dolia, Artur Rudomakha et al.

The value of the integrated indicator of the united territorial communities land use is determined. An assessment of the integral indicator was carried out and directions for the development of methodological recommendations to improve the efficiency of land use of the united territorial communities were identified. A feature of the use of GIS for analysis and visualization of integrated indicators of land use of the united territorial communities is the development of a geoinformation analysis scheme. The developed scheme of geoinformation systems using for modelling, evaluation, and analysis of integrated indicators of the united territorial communities land use gives the opportunity to form information and analytical support of monitoring based on geospatial information and to create the basis for increasing the united territorial communities land use. The sequence obtained in the article ensures the monitoring of changes in the spatial characteristics of the lands of the united territorial communities in the region. The results of determining the integral indicators of land use of the united territorial communities obtained in the article make it possible to carry out geoinformation analysis and build a GIS map of the land use. The developed GIS map allows the formation of information and analytical monitoring support based on the values of integrated indicators of land use. Also, the data of the presented map allow to predict the directions of land use of the united territorial communities, to compare them by territorial features and features depending on changes of system spatial, urban, investment and ecological factors.

Loïc Wacquant

Katia Canova

Urban studies have always been more guided by economic modes and their techniques of production than the forms of appropriation by the citizens who live or enjoy it. It was only in the 1960s that authors such as Henri Lefebvre, Milton Santos and Jane Jacobs began to come up with a new discussion on urban space: the importance of the user's eyesight and perception, the human scale, public spaces, diversity, the value of connectivity / accessibility and the vital need for the practice of reuse and preservation of the built heritage and memory of places. In addition to these are Mark Girouard and Fraya Frehse with a more historical and social investigation of urban dynamics, Jan Gehl and Janette Sadik-Kahn with practical applications and rapid transformations of public spaces, transforming areas underutilized by the 1950s roadside practice in places to live, to contemplate, finally of enjoyment for the people who live the experience of pedestrian and cyclist in great world-wide cities. This paper intends to explore these public spaces, streets and corners, by historical cartography, photos and social studies with pedestrian as a focus. The goal is the mapping of historical scenes explored by Frehse beyond the bars and bakeries that remained through the years until today as the urban sociability resistance parts. Also brings the discussion of numerical and cartographic indicators to support urban planning decisions, as well as the diversification of discussion groups and the application of new solutions for the appropriation of urban public places.

Chen-Ling J. Hung, L. Allan James, Michael E. Hodgson

Buildings, as impervious surfaces, are an important component of total impervious surface areas that drive urban stormwater response to intense rainfall events. Most stormwater models that use percent impervious area (PIA) are spatially lumped models and do not require precise locations of building roofs, as in other applications of building maps, but do require accurate estimates of total impervious areas within the geographic units of observation (e.g. city blocks or sub-watershed units). Two-dimensional mapping of buildings from aerial imagery requires laborious efforts from image analysts or elaborate image analysis techniques using high spatial resolution imagery. Moreover, large uncertainties exist where tall, dense vegetation obscures the structures. Analyzing LiDAR point-cloud data, however, can distinguish buildings from vegetation canopy and facilitate the mapping of buildings. This paper presents a new building extraction approach that is based on and optimized for estimating building impervious areas (BIA) for hydrologic purposes and can be used with standard GIS software to identify building roofs under tall, thick canopy. Accuracy assessment methods are presented that can optimize model performance for modeling BIA within the geographic units of observation for hydrologic applications. The Building Extraction from LiDAR Last Returns (BELLR) model, a 2.5D rule-based GIS model, uses a non-spatial, local vertical difference filter (VDF) on LiDAR point-cloud data to automatically identify and map building footprints. The model includes an absolute difference in elevation (AdE) parameter in the VDF that compares the difference between mean and modal elevations of last-returns in each cell. The BELLR model is calibrated for an extensive inner-city, highly urbanized small watershed in Columbia, South Carolina, USA that is covered by tall, thick vegetation canopy that obscures many buildings. The calibration of BELLR used a set of building locations compiled by photo-analysts, and validation used independent building reference data. The model is applied to two residential neighborhoods, one of which is a residential area within the primary watershed and the other is a younger suburban neighborhood with a less-well developed tree canopy used as a validation site. Performance results indicate that the BELLR model is highly sensitive to concavity in the lasboundary tool of LAStools® and those settings are highly site specific. The model is also sensitive to cell size and the AdE threshold values. However, properly calibrated the BIA for the two residential sites could be estimated within 1% error for optimized experiments. To examine results in a hydrologic application, the BELLR estimated BIAs were tested using two different types of hydrologic models to compare BELLR results with results using the National Land Cover Database (NLCD) 2011 Percent Developed Imperviousness data. The BELLR BIA values provide more accurate results than the use of the 2011 NLCD PIA data in both models. The VDF developed in this study to map buildings could be applied to LiDAR point-cloud filtering algorithms for feature extraction in machine learning or mapping other planar surfaces in more broad-based land-cover classifications.

Levi R. Bryant

This is a study of how space and time create objects, and how these objects interact. Using real-world examples, Bryant shows how a networked concept of space and time is at the heart of our central political concerns. What sort of interaction is there between, for example, slow-moving objects like climate and comparatively fast-moving objects like governments? How can they interact with each other given their very different lifespans? How do the Amish interact with the members of the stock market, and vice versa? How do members of congress, who always exist, interact with the temporally discontinuous objects of Congressional sessions that only meet during a certain session each year - flitting in and out of existence? It proposes a new form of social and political analysis - 'onto-cartography' - that looks at how relations between objects are forged by communication and causation. It draws on the social sciences, geography, new materialist thought and object-oriented ontology.

Monica Lee, J. Martin

A. Moore