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Your search keyword '"Prishchepov, Alexander V."' showing total 338 results
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338 results on '"Prishchepov, Alexander V."'

10. Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Author

Chen, Tzu-Hsin Karen, Prishchepov, Alexander V., Fensholt, Rasmus, and Sabel, Clive E.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Monitoring long-term landslide activity is important for risk assessment and land management. Despite the widespread use of open-access 30m Landsat imagery, their utility for landslide detection is often limited when separating landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), with multi-seasonal daytime optical Landsat time-series, and digital elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We employed a non-parametric machine-learning classifier, random forest, to classify the satellite imagery. The classifier was trained with data from three years (2005, 2010, and 2015), and was validated with an independent reference sample from twelve years. Our results demonstrated that combining nighttime light data and multi-seasonal imagery significantly improved the classification (p<0.001), compared to conventional methods based on single-season optical imagery. The results confirmed that the developed classification model enabled mapping of landslides across Taiwan over a long period with annual overall accuracy varying between 96% and 97%, user's and producer's accuracies between 73% and 86%. Spatiotemporal analysis of the landslide inventories from 1998 to 2017 revealed different temporal patterns of landslide activities, showing those areas where landslides were persistent and other areas where landslides tended to reoccur after vegetation regrowth. In sum, we provide a robust method to detect long-term landslide activities based on freely available satellite imagery, which can be applied elsewhere.

Published
2020
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11. Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution

Author

Chen, Tzu-Hsin Karen, Qiu, Chunping, Schmitt, Michael, Zhu, Xiao Xiang, Sabel, Clive E., and Prishchepov, Alexander V.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, I.4.6, I.4.9, J.2, J.4
Abstract

Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat data, particularly its 30m spatial resolution, for monitoring three-dimensional urban densification. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both the dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images., Comment: Accepted manuscript including appendix (supplementary file)

Published
2020
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26. An Improved Approach to Estimate Stocking Rate and Carrying Capacity Based on Remotely Sensed Phenology Timings.

Author

Shi, Yan, Brierley, Gary, Perry, George L. W., Gao, Jay, Li, Xilai, Prishchepov, Alexander V., Li, Jiexia, and Han, Meiqin

Subjects
GRASSLANDS, MOUNTAIN ecology, BIOMASS estimation, RANGE management, MOUNTAIN meadows, OVERGRAZING, BIOMASS
Abstract

Accurate estimation of livestock carrying capacity (LCC) and implementation of an appropriate actual stocking rate (ASR) are key to the sustainable management of grazing adapted alpine grassland ecosystems. The reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here, we demonstrate the limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in an effort to support more accurate and reliable estimation of LCC and ASR. The results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. The findings from a smaller-scale study that incorporate phenology timings into the estimation of annual aboveground biomass reveal that summer pastures in Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000 to 2005. The methods proposed can be extended to map grassland grazing pressure by predicting the LCC and tracking the ASR, thereby improving sustainable resource use in alpine grasslands. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Higher depression risks in medium- than in high-density urban form across Denmark

Author

Chen, Tzu-Hsin Karen, Horsdal, Henriette Thisted, Samuelsson, Karl, Closter, Ane Marie, Davies, Megan, Barthel, Stephan, Pedersen, Carsten Bøcker, Prishchepov, Alexander V., Sabel, Clive E., Chen, Tzu-Hsin Karen, Horsdal, Henriette Thisted, Samuelsson, Karl, Closter, Ane Marie, Davies, Megan, Barthel, Stephan, Pedersen, Carsten Bøcker, Prishchepov, Alexander V., and Sabel, Clive E.

Abstract

Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensional (3D) urban form (i.e., building density and height) over time. Combining satellite-derived urban form data and individual-level residential addresses, health, and socioeconomic registers, we conduct a case-control study (n = 75,650 cases and 756,500 controls) to examine the association between 3D urban form and depression in the Danish population. We find that living in dense inner-city areas did not carry the highest depression risks. Rather, after adjusting for socioeconomic factors, the highest risk was among sprawling suburbs, and the lowest was among multistory buildings with open space in the vicinity. The finding suggests that spatial land-use planning should prioritize securing access to open space in densely built areas to mitigate depression risks.

Published
2023
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