Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data

Furberg, Dorothy; Ban, Yifang; Nascetti, Andrea

doi:10.3390/rs11202408

Cited by 23 publications

(15 citation statements)

References 80 publications

(104 reference statements)

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“…The study areas are shown in ( Figure 1) and cover 450,000 ha in Stockholm and 518,000 ha in Beijing. The classes represent the dominant land cover and land use types in the respective areas as demonstrated in [58,59], and are derived from urban planning applications and urban sprawl monitoring. The classification schemas adopted for the study areas were defined in previous projects [58,59] to monitor the urbanization process and evaluate the corresponding environmental impact; Table 1 provides an overview of the selected classes.…”

Section: Study Areas and Data Descriptionmentioning

confidence: 99%

“…The classes represent the dominant land cover and land use types in the respective areas as demonstrated in [58,59], and are derived from urban planning applications and urban sprawl monitoring. The classification schemas adopted for the study areas were defined in previous projects [58,59] to monitor the urbanization process and evaluate the corresponding environmental impact; Table 1 provides an overview of the selected classes. We used reference points that were manually collected by remote sensing experts not involved in this study [58,59] and we divided them in training and validation sets.…”

Section: Study Areas and Data Descriptionmentioning

confidence: 99%

“…The classification schemas adopted for the study areas were defined in previous projects [58,59] to monitor the urbanization process and evaluate the corresponding environmental impact; Table 1 provides an overview of the selected classes. We used reference points that were manually collected by remote sensing experts not involved in this study [58,59] and we divided them in training and validation sets. In Stockholm, these reference points are approximately uniformly distributed over most classes (≈1000 samples per class, except 500 for bare rock and wetlands).…”

Section: Study Areas and Data Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine

et al. 2019

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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.

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Section: Study Areas and Data Descriptionmentioning

confidence: 99%

Section: Study Areas and Data Descriptionmentioning

confidence: 99%

Section: Study Areas and Data Descriptionmentioning

confidence: 99%

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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

et al. 2019

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“…The extraction and analysis of earth surface features through high resolution remote sensing (HRRS) images has received extensive research, such as the buildings extraction [1][2][3], vegetation detection [4][5][6], urban expansion analysis [7][8][9] and detection of land cover changes [10]. However, there is a key issue that cannot be ignored: ensuring the security of HRRS image is the basic prerequisite for using HRRS images.…”

Section: Introductionmentioning

confidence: 99%

Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images

Ding

Su²,

et al. 2021

IEEE Access

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As a special case of perceptual hashing algorithm, subject-sensitive hashing can realize "subject-biased" integrity authentication of high resolution remote sensing (HRRS) images, which overcomes the deficiencies of existing integrity authentication technologies. However, the existing deep neural network for subject-sensitive hashing have disadvantages such as high model complexity and low computational efficiency. In this paper, we propose an efficient and lightweight deep neural network named Semi-U-net to achieve efficient subject-sensitive hashing. The proposed Semi-U-net realizes the lightweight of the network from three aspects: First, considering the general process of perceptual hashing, it adopts a semi-u-shaped structure, which simplify the model structure and prevent the model from extracting too much redundant information to enhance the robustness of the algorithm; Second, the number of model parameters and the computational cost are significantly reduced by using deep separable convolution in the entire asymmetric network; Third, the number of model parameters is further compressed by using the dropout layer several times. The experimental results show that the size of our Semi-U-Net model is only 5.38M, which is only 1/27 of MUM-net and 1/15 of MultiResUnet. The speed of the Semi-U-Net based subject-sensitive hashing algorithm is 88.6 FPS, which is 2.89 times faster than MultiResUnet based algorithm and 2.1 times faster than MUM-net Based Algorithm.

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“…For example, geostationary satellite data with high temporal resolutions provide rich temporal information to monitor environmental changes on global and regional scales [3][4][5][6][7], but their spatial resolutions are too coarse to be applied in local analyses (such data are hereafter referred to as dense time-series with coarse spatial resolution (DTCS) data). In contrast, high spatial resolution data can be used in local analyses, such as urban area monitoring [8][9][10][11][12], but their poor temporal resolutions are unsuitable for use in the detection of 2 of 21 short-term changes (such data are hereafter referred to as sparse time-series with fine spatial resolution (STFS) data).…”

Section: Introductionmentioning

confidence: 99%

A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions

2020

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Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.

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Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data

Cited by 23 publications

References 80 publications

Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine

Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine

Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images

A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions

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