scholarly journals Mapping and Monitoring Urban Environment through Sentinel-1 SAR Data: A Case Study in the Veneto Region (Italy)

2020 ◽  
Vol 9 (6) ◽  
pp. 375
Author(s):  
Andrea Semenzato ◽  
Salvatore Eugenio Pappalardo ◽  
Daniele Codato ◽  
Umberto Trivelloni ◽  
Silvano De Zorzi ◽  
...  
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Local Governments ◽  
Veneto Region ◽  
The Sustainable Development ◽  
Radar Images ◽  
Territorial Planning ◽  
2030 Agenda ◽  
Land Cover Map ◽  
Urban Footprint

Focusing on a sustainable and strategic urban development, local governments and public administrations, such as the Veneto Region in Italy, are increasingly addressing their urban and territorial planning to meet national and European policies, along with the principles and goals of the 2030 Agenda for the Sustainable Development. In this regard, we aim at testing a methodology based on a semi-automatic approach able to extract the spatial extent of urban areas, referred to as “urban footprint”, from satellite data. In particular, we exploited Sentinel-1 radar imagery through multitemporal analysis of interferometric coherence as well as supervised and non-supervised classification algorithms. Lastly, we compared the results with the land cover map of the Veneto Region for accuracy assessments. Once properly processed and classified, the radar images resulted in high accuracy values, with an overall accuracy ranging between 85% and 90% and percentages of urban footprint differing by less than 1%–2% with respect to the values extracted from the reference land cover map. These results provide not only a reliable and useful support for strategic urban planning and monitoring, but also potentially identify a solid organizational dataflow process to prepare geographic indicators that will help answering the needs of the 2030 Agenda (in particular the goal 11 “Sustainable Cities and Communities”).

scholarly journals Using machine learning to produce a very high-resolution land-cover map for Ireland and beyond

2021 ◽  
Author(s):  
Geoffrey Bessardon ◽  
Emily Gleeson ◽  
Eoin Walsh
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Weather Forecasting ◽  
Land Cover Classification ◽  
Surface Processes ◽  
Land Cover Map ◽  
Very High

<p>An accurate representation of surface processes is essential for weather forecasting as it is where most of the thermal, turbulent and humidity exchanges occur. The Numerical Weather Prediction (NWP) system, to represent these exchanges, requires a land-cover classification map to calculate the surface parameters used in the turbulent, radiative, heat, and moisture fluxes estimations.</p><p>The land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM NWP system for operational weather forecasting is ECOCLIMAP. ECOCLIMAP-SG (ECO-SG), the latest version of ECOCLIMAP, was evaluated over Ireland to prepare ECO-SG implementation in HARMONIE-AROME. This evaluation suggested that sparse urban areas are underestimated and instead appear as vegetation areas in ECO-SG [1], with an over-classification of grassland in place of sparse urban areas and other vegetation covers (Met Éireann internal communication). Some limitations in the performance of the current HARMONIE-AROME configuration attributed to surface processes and physiography issues are well-known [2]. This motivated work at Met Éireann to evaluate solutions to improve the land-cover map in HARMONIE-AROME.</p><p>In terms of accuracy, resolution, and the future production of time-varying land-cover map, the use of a convolutional neural network (CNN) to create a land-cover map using Sentinel-2 satellite imagery [3] over Estonia [4] presented better potential outcomes than the use of local datasets [5]. Consequently, this method was tested over Ireland and proven to be more accurate than ECO-SG for representing CORINE Primary and Secondary labels and at a higher resolution [5]. This work is a continuity of [5] focusing on 1. increasing the number of labels, 2. optimising the training procedure, 3. expanding the method for application to other HIRLAM countries and 4. implementation of the new land-cover map in HARMONIE-AROME.</p><p> </p><p>[1] Bessardon, G., Gleeson, E., (2019) Using the best available physiography to improve weather forecasts for Ireland. In EMS Annual Meeting.Retrieved fromhttps://presentations.copernicus.org/EMS2019-702_presentation.pdf</p><p>[2] Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W.,. . . Køltzow, M. Ø. (2017). The HARMONIE–AROME Model Configurationin the ALADIN–HIRLAM NWP System. Monthly Weather Review, 145(5),1919–1935.https://doi.org/10.1175/mwr-d-16-0417.1</p><p>[3] Bertini, F., Brand, O., Carlier, S., Del Bello, U., Drusch, M., Duca, R., Fernandez, V., Ferrario, C., Ferreira, M., Isola, C., Kirschner, V.,Laberinti, P., Lambert, M., Mandorlo, G., Marcos, P., Martimort, P., Moon, S., Oldeman,P., Palomba, M., and Pineiro, J.: Sentinel-2ESA’s Optical High-ResolutionMission for GMES Operational Services, ESA bulletin. Bulletin ASE. Euro-pean Space Agency, SP-1322,2012</p><p>[4] Ulmas, P. and Liiv, I. (2020). Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification, pp. 1–11,http://arxiv.org/abs/2003.02899, 2020</p><p>[5] Walsh, E., Bessardon, G., Gleeson, E., and Ulmas, P. (2021). Using machine learning to produce a very high-resolution land-cover map for Ireland. Advances in Science and Research, (accepted for publication)</p>

scholarly journals Mobility-as-a-Service – global trends and implementation potential in urban areas in Poland

2018 ◽  
Vol 79 ◽  
pp. 39-51
Author(s):  
Jana Pieriegud ◽  
Jakub Zawieska
Keyword(s):  
Sustainable Development ◽  
Urban Areas ◽  
Financial Incentives ◽  
Local Governments ◽  
City Development ◽  
The Sustainable Development ◽  
Global Trends ◽  
Implementation Processes ◽  
Sustainability Strategies

The concept of sustainable development increasingly affects cities and the challenges they face. At the present stage of sustainability awareness it is desired that the discussion about the city development combines the financial aspects and harmonious social relationships with the natural environment. The role of local authorities in implementation processes is undeniable. The objective of the paper is to verify theoretical assumptions regarding sustainable development governance in cities. A special attention is paid to transport and logistics solutions as supported measures but also as barriers of implementation. The detailed questionnaire in the form of the survey was chosen to examine how local governments practice the concept of the green urban economy to strengthen the sustainable development in different cities. Results prove that Scandinavian cities, in comparison to other European and some North American cities, are indisputable leaders in the development and implementation of sustainability strategies. They extensively involve stakeholders and facilitate open dialogue approach, create public-private partnerships and stimulate more sustainable behaviour through variety of financial incentives.

scholarly journals Trend analysis of the decadal variations of water bodies and land cover/land Use through MODIS imagery: an in-depth study from Gilgit Baltistan, Pakistan

2020 ◽  
Author(s):  
Zeeshan Zafar ◽  
Muhammad Sajid Mehmood ◽  
Muhammad Irfan Ahamad ◽  
Amna Chudhary ◽  
Rana Muhammad Zulqarnain ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Human Life ◽  
Water Bodies ◽  
Environment Change ◽  
The Sustainable Development ◽  
Modis Imagery ◽  
Depth Study ◽  
Decadal Variations

Abstract Water is primary element for human life on Earth. Fresh surface water including rivers, lakes, streams, and pounds contribute less than one thousandth of a percent of the water on the Earth, but they serve many critical functions for the environment and for human life. Change in Land use and land cover (LULC) is a foremost concern in global environment change. Rapid changes in LULC lead to the degradation of its ecosystems and have adverse effects on the environment. There is an urgent need to monitor changes in LULC and to evaluate the effects of these changes in order to inform decision makers to support the sustainable development. The study used MODIS images to detect LULC patterns in GB from 2008 to 2017, and to investigate changes in LULC between 2008 and 2017. Six types of LULC has been discussed in study to explain major changes of LULC in study area. The results showed that shrinking in barren lands and expansion in urban areas. Study also showed the abrupt behavior of water bodies in study duration. Snow area also showed an expansion which needs attention as well.

Using Combination Technique for Land Cover Classification of Optical Multispectral Images

2021 ◽  
Vol 12 (4) ◽  
pp. 22-39
Author(s):  
Keerti Kulkarni ◽  
Vijaya P. A.
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Geographical Area ◽  
Land Cover Classification ◽  
Combination Method ◽  
Support Vector ◽  
Combination Technique ◽  
Land Cover Map

The need for efficient planning of the land is exponentially increasing because of the unplanned human activities, especially in the urban areas. A land cover map gives a detailed report on temporal dynamics of a given geographical area. The land cover map can be obtained by using machine learning classifiers on the raw satellite images. In this work, the authors propose a combination method for the land cover classification. This method combines the outputs of two classifiers, namely, random forests (RF) and support vector machines (SVM), using Dempster-Shafer combination theory (DSCT), also called the theory of evidence. This combination is possible because of the inherent uncertainties associated with the output of each classifier. The experimental results indicate an improved accuracy (89.6%, kappa = 0.86 as versus accuracy of RF [87.31%, kappa = 0.83] and SVM [82.144%, kappa = 0.76]). The results are validated using the normalized difference vegetation index (NDVI), and the overall accuracy (OA) has been used as a comparison basis.

scholarly journals First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Duong Cao Phan ◽  
Ta Hoang Trung ◽  
Van Thinh Truong ◽  
Taiga Sasagawa ◽  
Thuy Phuong Thi Vu ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Environmental Changes ◽  
Wetland Loss ◽  
Interdisciplinary Studies ◽  
Data Sets ◽  
Automatic Production ◽  
The Sustainable Development ◽  
Spatio Temporal

AbstractExtensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km2) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km2). The spatial–temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. The explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals.

scholarly journals Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

2018 ◽  
Vol 18 (7) ◽  
pp. 1905-1918 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Heavy Rain ◽  
Geospatial Information ◽  
Torrential Rain ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Intensity Images ◽  
Elevation Model

Abstract. Torrential rain triggered by two typhoons hit the Kanto and Tohoku regions of Japan from 9 to 11 September 2015. Due to the record-breaking amount of rainfall, several riverbanks were overflowed and destroyed, causing floods over wide areas. The PALSAR-2 sensor on board the ALOS-2 satellite engaged in emergency observations of the affected areas during and after the heavy rain. Two pre-event and three co-event PALSAR-2 images were employed in this study to extract flooded areas in the city of Joso, Ibaraki Prefecture. The backscattering coefficient of the river water was investigated first using the PALSAR-2 intensity images and a land-cover map with a 10 m resolution. The inundation areas were then extracted by setting threshold values for backscattering from water surfaces in the three temporal synthetic aperture radar (SAR) images. The extracted results were modified by considering the land cover and a digital elevation model (DEM). Next, the inundated built-up urban areas were extracted from the changes in SAR backscattering. The results were finally compared with those from visual inspections of airborne imagery by the Geospatial Information Authority of Japan (GSI), and more than 85 % of the maximum inundation areas were extracted successfully.

scholarly journals Can Differing Opinions Hinder Partnerships for the Localization of the Sustainable Development Goals? Evidence from Marginalized Urban Areas in Andalusia

2020 ◽  
Vol 12 (14) ◽  
pp. 5797
Author(s):  
Antonio Sianes ◽  
Rocío Vela-Jiménez
Keyword(s):  
Sustainable Development ◽  
Urban Areas ◽  
Sustainable Development Goals ◽  
Civil Society Organizations ◽  
Worth Living ◽  
Collaborative Partnerships ◽  
The Sustainable Development ◽  
2030 Agenda ◽  
Starting Point ◽  
Development Goals

The 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDG) were set up under the idea that no one—and no place—be left behind. Today, the tendency for population growth concentrates in cities, causing social segregation and the proliferation of marginalized urban areas. In this global context, SDG 11, which addresses the urban dimension of the 2030 Agenda, is becoming crucial. To achieve inclusive and sustainable development, especially in disadvantaged urban areas, collaborative partnerships have been suggested as essential to building habitable spaces where life is worth living. However, the literature reveals how the commitment to multistakeholder partnerships depends on many factors, such as the perceptions the participants have about their reality and the problems they face. In this study, we rely on the information collected from 118 surveys conducted among the leaders of private, public, and civil society organizations already collaborating in six disadvantaged neighborhoods in Andalusia. The results show how and where their perceptions about their own neighborhoods differ and the intersectional reasons behind these differing opinions. This is a critical starting point to elucidate how to enable and sustain local collective actions to start the process of fighting for human dignity.

scholarly journals Land cover change modelling in Hyrcanian forests, Northern Iran: a landscape pattern and transformation analysis perspective

2018 ◽  
Vol 44 (2) ◽  
pp. 743 ◽  
Author(s):  
S.J. Shooshtari ◽  
K. Shayesteh ◽  
M. Gholamalifard ◽  
M. Azari ◽  
J.I. López-Moreno
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Timber Harvest ◽  
Water Yield ◽  
Hyrcanian Forest ◽  
Northern Iran ◽  
Territorial Planning ◽  
Spatio Temporal ◽  
Shape Complexity ◽  
Land Cover Maps

The main objective of this study is to analyze the spatio-temporal changes in land cover and land use, (1984–2010), as well to simulate future land cover for 2030 in the Neka River Basin, including the Hyrcanian forest, in northern Iran. For this purpose, we used detailed land cover maps for the years 1984, 2001 and 2010. The results showed that the highest deforestation occurred in the boundaries between forest and agriculture areas between 1984 and 2010. Comparing the observed and predicted land cover in 2010 yielded agreement of 96.41%. From 1984 to 2010, landscape metrics showed that the forest area evolved to more fragmentation, with less shape complexity and less connectivity. Projections for the future are consistent with observed changes for the Neka landscape, with a tendency to continue disaggregating and increasing diversity in a number of different patch types. Between 2010 and 2030, we observed the arrival of new crops, rangelands, and urban areas within the remaining areas of homogeneous forest. Changes in the Hyrcanian forest will cause alteration in ecosystem services, such as erosion control, water yield, timber harvest, and ground water reservation. Results of this work may represent a useful tool to provide strategies and territorial planning for sustainable management of the fragile Hyrcanian forest ecosystems in the Neka Basin. 

scholarly journals Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery

2018 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Heavy Rain ◽  
Geospatial Information ◽  
Torrential Rain ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Intensity Images ◽  
Elevation Model

Abstract. Torrential rain triggered by two typhoons hit the Kanto and Tohoku regions of Japan from September 9 to 11, 2015. Due to the record-breaking amount of rainfall, several river banks were overflowed and destroyed, causing floods over wide areas. The PALSAR-2 sensor onboard the ALOS-2 satellite engaged in emergency observations of the affected areas during and after the heavy rain. Two pre-event and three co-event PALSAR-2 images were employed in this study to extract flooded areas in Joso city, Ibaraki prefecture. The backscattering coefficient of the river water was investigated first using the PALSAR-2 intensity images and a land-cover map with a 10-m resolution. The inundation areas were then extracted by setting threshold values for backscattering from water surfaces in the three temporal Synthetic Aperture Radar (SAR) images. The extracted results were modified by considering the land-cover and a digital elevation model (DEM). Next, the inundated built-up urban areas were extracted from the changes in SAR backscattering. The results were finally compared with those from visual inspections of airborne imagery by the Geospatial Information Authority of Japan (GSI), and they showed a good level of agreement.

scholarly journals VERY HIGH RESOLUTION LAND COVER MAPPING OF URBAN AREAS AT GLOBAL SCALE WITH CONVOLUTIONAL NEURAL NETWORKS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 201-208
Author(s):  
T. Tilak ◽  
A. Braun ◽  
D. Chandler ◽  
N. David ◽  
S. Galopin ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Ground Truth ◽  
Bare Soil ◽  
Aerial Images ◽  
Training Dataset ◽  
Large Area ◽  
Land Cover Map ◽  
Very High

Abstract. This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model.We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class.A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions.The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization.

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