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  • 1
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    Multi-Temporal Sentinel-1 SAR and Sentinel-2 MSI Data for Flood Mapping and Damage Assessment in Mozambique
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 2 ( 2023-02-07), p. 53-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 2 ( 2023-02-07), p. 53-
    Abstract: Floods are one of the most frequent natural disasters worldwide. Although the vulnerability varies from region to region, all countries are susceptible to flooding. Mozambique was hit by several cyclones in the last few decades, and in 2019, after cyclones Idai and Kenneth, the country became the first one in southern Africa to be hit by two cyclones in the same raining season. Aiming to provide the local authorities with tools to yield better responses before and after any disaster event, and to mitigate the impact and support in decision making for sustainable development, it is fundamental to continue investigating reliable methods for disaster management. In this paper, we propose a fully automated method for flood mapping in near real-time utilizing multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data acquired in the Beira municipality and Macomia district. The procedure exploits the processing capability of the Google Earth Engine (GEE) platform. We map flooded areas by finding the differences of images acquired before and after the flooding and then use Otsu’s thresholding method to automatically extract the flooded area from the difference image. To validate and compute the accuracy of the proposed technique, we compare our results with the Copernicus Emergency Management Service (Copernicus EMS) data available in the study areas. Furthermore, we investigated the use of a Sentinel-2 multi-spectral instrument (MSI) to produce a land cover (LC) map of the study area and estimate the percentage of flooded areas in each LC class. The results show that the combination of Sentinel-1 SAR and Sentinel-2 MSI data is reliable for near real-time flood mapping and damage assessment. We automatically mapped flooded areas with an overall accuracy of about 87–88% and kappa of 0.73–0.75 by directly comparing our prediction and Copernicus EMS maps. The LC classification is validated by randomly collecting over 600 points for each LC, and the overall accuracy is 90–95% with a kappa of 0.80–0.94.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12020053
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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  • 2
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    Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data
    MDPI AG ; 2013
    In:  ISPRS International Journal of Geo-Information Vol. 2, No. 2 ( 2013-05-10), p. 371-384
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 2, No. 2 ( 2013-05-10), p. 371-384
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi2020371
    Language: English
    Publisher: MDPI AG
    Publication Date: 2013
    detail.hit.zdb_id: 2655790-3
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  • 3
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    GOES-R Time Series for Early Detection of Wildfires with Deep GRU-Network
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 17 ( 2022-09-01), p. 4347-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 17 ( 2022-09-01), p. 4347-
    Abstract: Early detection of wildfires has been limited using the sun-synchronous orbit satellites due to their low temporal resolution and wildfires’ fast spread in the early stage. NOAA’s geostationary weather satellites GOES-R Advanced Baseline Imager (ABI) can acquire images every 15 min at 2 km spatial resolution, and have been used for early fire detection. However, advanced processing algorithms are needed to provide timely and reliable detection of wildfires. In this research, a deep learning framework, based on Gated Recurrent Units (GRU), is proposed to detect wildfires at early stage using GOES-R dense time series data. GRU model maintains good performance on temporal modelling while keep a simple architecture, makes it suitable to efficiently process time-series data. 36 different wildfires in North and South America under the coverage of GOES-R satellites are selected to assess the effectiveness of the GRU method. The detection times based on GOES-R are compared with VIIRS active fire products at 375 m resolution in NASA’s Fire Information for Resource Management System (FIRMS). The results show that GRU-based GOES-R detections of the wildfires are earlier than that of the VIIRS active fire products in most of the study areas. Also, results from proposed method offer more precise location on the active fire at early stage than GOES-R Active Fire Product in mid-latitude and low-latitude regions.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14174347
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 4
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    Continuous Monitoring of Urban Land Cover Change Trajectories with Landsat Time Series and LandTrendr-Google Earth Engine Cloud Computing
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 18 ( 2020-09-05), p. 2883-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 18 ( 2020-09-05), p. 2883-
    Abstract: Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed on the two Landsat scenes, respectively, acquired in 1987 and 2019 over Kigali, Rwanda. The resulting land cover maps were then imported in the GEE platform and used to label the interannual LandTrendr-derived changes. The changes in duration, year, and magnitude of land cover disturbance were derived from six different indices/bands using the LandTrendr algorithm. The interannual change LandTrendr results were then combined using a robust estimation procedure based on principal component analysis (PCA) for reconstructing the annual land cover change maps. The produced yearly land cover maps were assessed using validation data and the GEE-based Area Estimation and Accuracy Assessment (Area2) application. The results were used to study the Kigali’s urbanization in the last three decades since 1987. The results illustrated that from 1987 to 1998, the urbanization was characterized by slow development, with less than a 2% annual growth rate. The post-conflict period was characterized by accelerated urbanization, with a 4.5% annual growth rate, particularly from 2004 onwards due to migration flows and investment promotion in the construction industry. The five-year interval analysis from 1990 to 2019 revealed that impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7% average annual growth rate. The proposed scheme was found to be cost-effective and useful for continuously monitoring the complex urban land cover dynamics, especially in environments with EO data affordability issues, and in data-sparse regions.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12182883
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 5
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    Dynamic Online 3D Visualization Framework for Real-Time Energy Simulation Based on 3D Tiles
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 3 ( 2020-03-11), p. 166-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 3 ( 2020-03-11), p. 166-
    Abstract: Energy co-simulation can be used to analyze the dynamic energy consumption of a building or a region, which is essential for decision making in the planning and management of smart cities. To increase the accessibility of energy simulation results, a dynamic online 3D city model visualization framework based on 3D Tiles is proposed in this paper. Two types of styling methods are studied, attribute-based and ID map-based. We first perform the energy co-simulation and save the results in CityGML format with EnergyADE. Then the 3D geometry data of these city objects are combined with its simulation results as attributes or just with object ID information to generate Batched 3D Models (B3DM) in 3D Tiles. Next, styling strategies are pre-defined and can be selected by end-users to show different scenarios. Finally, during the visualization process, dynamic interactions and data sources are integrated into the styling generation to support real-time visualization. This framework is implemented with Cesium. Compared with existing dynamic online 3D visualization framework such as directly styling or Cesium Language (CZML), a JSON format for describing a time-dynamic graphical scene, primarily for display in a web browser running Cesium, the proposed framework is more flexible and has higher performance in both data transmission and rendering which is essential for real-time GIS applications.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9030166
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 6
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    Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning
    MDPI AG ; 2021
    In:  Remote Sensing Vol. 13, No. 8 ( 2021-04-14), p. 1509-
    Details
    In: Remote Sensing, MDPI AG, Vol. 13, No. 8 ( 2021-04-14), p. 1509-
    Abstract: Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs13081509
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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  • 7
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    Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data
    MDPI AG ; 2019
    In:  Remote Sensing Vol. 11, No. 20 ( 2019-10-17), p. 2408-
    Details
    In: Remote Sensing, MDPI AG, Vol. 11, No. 20 ( 2019-10-17), p. 2408-
    Abstract: There has been substantial urban growth in Stockholm, Sweden, the fastest-growing capital in Europe. The intensifying urbanization poses challenges for environmental management and sustainable development. Using Sentinel-2 and SPOT-5 imagery, this research investigates the evolution of land-cover change in Stockholm County between 2005 and 2015, and evaluates urban growth impact on protected green areas, green infrastructure and urban ecosystem service provision. One scene of 2015 Sentinel-2A multispectral instrument (MSI) and 10 scenes of 2005 SPOT-5 high-resolution instruments (HRI) imagery over Stockholm County are classified into 10 land-cover categories using object-based image analysis and a support vector machine algorithm with spectral, textural and geometric features. Reaching accuracies of approximately 90%, the classifications are then analyzed to determine impact of urban growth in Stockholm between 2005 and 2015, including land-cover change statistics, landscape-level urban ecosystem service provision bundle changes and evaluation of regional and local impact on legislatively protected areas as well as ecologically significant green infrastructure networks. The results indicate that urban areas increased by 15%, while non-urban land cover decreased by 4%. In terms of ecosystem services, changes in proximity of forest and low-density built-up areas were the main cause of lowered provision of temperature regulation, air purification and noise reduction. There was a decadal ecosystem service loss of 4.6 million USD (2015 exchange rate). Urban areas within a 200 m buffer zone around the Swedish environmental protection agency’s nature reserves increased 16%, with examples of urban areas constructed along nature reserve boundaries. Urban expansion overlapped the deciduous ecological corridor network and green wedge/core areas to a small but increasing degree, often in close proximity to weak but important green links in the landscape. Given these findings, increased conservation/restoration focus on the region’s green weak links is recommended.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs11202408
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
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  • 8
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    Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine
    MDPI AG ; 2019
    In:  Remote Sensing Vol. 12, No. 1 ( 2019-12-24), p. 76-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 1 ( 2019-12-24), p. 76-
    Abstract: Mapping Earth’s surface and its rapid changes with remotely sensed data is a crucial task to understand the impact of an increasingly urban world population on the environment. However, the impressive amount of available Earth observation data is only marginally exploited in common classifications. In this study, we use the computational power of Google Earth Engine and Google Cloud Platform to generate an oversized feature set in which we explore feature importance and analyze the influence of dimensionality reduction methods to object-based land cover classification with Support Vector Machines. We propose a methodology to extract the most relevant features and optimize an SVM classifier hyperparameters to achieve higher classification accuracy. The proposed approach is evaluated in two different urban study areas of Stockholm and Beijing. Despite different training set sizes in the two study sites, the averaged feature importance ranking showed similar results for the top-ranking features. In particular, Sentinel-2 NDVI, NDWI, and Sentinel-1 VV temporal means are the highest ranked features and the experiment results strongly indicated that the fusion of these features improved the separability between urban land cover and land use classes. Overall classification accuracies of 94% and 93% were achieved in Stockholm and Beijing study sites, respectively. The test demonstrated the viability of the methodology in a cloud-computing environment to incorporate dimensionality reduction as a key step in the land cover classification process, which we consider essential for the exploitation of the growing Earth observation big data. To encourage further research and development of reliable workflows, we share our datasets and publish the developed Google Earth Engine and Python scripts as free and open-source software.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12010076
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
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  • 9
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    Comparative Analysis on Topological Structures of Urban Street Networks
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 10 ( 2017-09-24), p. 295-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 10 ( 2017-09-24), p. 295-
    Abstract: Street systems are the backbone of cities. With global urbanization and economic development, street systems have undergone significant development along with the growth of cities. In this paper, the authors select three cities with varying sizes, histories, locations, and growth dynamics: Stockholm, Toronto, and Nanjing. We analyze topological structures of their public street systems based on GIS and complex network theory. Considering the planarity of street systems, we first calculate various topological measures, including α, β, and γ indices, and density. This is followed by comparing three centrality measures, i.e., degree, betweenness, and closeness in complex network theory. In this part, we investigate these characteristics of nodes and edges in a primal representation, and discuss their relations with urban growth mechanisms.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6100295
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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  • 10
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    Disturbance Observer-Enhanced Adaptive Fault-Tolerant Control of a Quadrotor UAV against Actuator Faults and Disturbances
    MDPI AG ; 2023
    In:  Drones Vol. 7, No. 8 ( 2023-08-21), p. 541-
    Details
    In: Drones, MDPI AG, Vol. 7, No. 8 ( 2023-08-21), p. 541-
    Abstract: For a quadrotor unmanned aerial vehicle (UAV), this paper proposes an adaptive sliding mode control (SMC) strategy enhanced with a disturbance observer to attain precise trajectory and attitude tracking performance while compensating for the detrimental impacts of actuator faults and disturbances. First, an adaptive SMC strategy that utilizes an integral sliding surface is presented to enhance the fault-tolerance capabilities of the studied quadrotor UAV against actuator faults. In addition, a disturbance observer is further created to compensate for the disturbances. By integrating the proposed adaptive SMC strategy with the designed disturbance observer, both actuator faults and disturbances can be effectively accommodated. It was theoretically demonstrated that the system is stable while using the proposed adaptive fault-tolerant control strategy. The effectiveness and benefits of the proposed strategy is verified with comparative simulation results under different faulty scenarios.
    Type of Medium: Online Resource
    ISSN: 2504-446X
    URL: Article
    DOI: 10.3390/drones7080541
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2934569-8
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