| 1. |
- He, Bin, et al.
(författare)
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Worldwide impacts of atmospheric vapor pressure deficit on the interannual variability of terrestrial carbon sinks
- 2022
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Ingår i: National Science Review. - : Oxford University Press (OUP). - 2095-5138 .- 2053-714X. ; 9:4
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Tidskriftsartikel (refereegranskat)abstract
- Interannual variability of the terrestrial ecosystem carbon sink is substantially regulated by various environmental variables and highly dominates the interannual variation of atmospheric carbon dioxide (CO2) concentrations. Thus, it is necessary to determine dominating factors affecting the interannual variability of the carbon sink to improve our capability of predicting future terrestrial carbon sinks. Using global datasets derived from machine-learning methods and process-based ecosystem models, this study reveals that the interannual variability of the atmospheric vapor pressure deficit (VPD) was significantly negatively correlated with net ecosystem production (NEP) and substantially impacted the interannual variability of the atmospheric CO2 growth rate (CGR). Further analyses found widespread constraints of VPD interannual variability on terrestrial gross primary production (GPP), causing VPD to impact NEP and CGR. Partial correlation analysis confirms the persistent and widespread impacts of VPD on terrestrial carbon sinks compared to other environmental variables. Current Earth system models underestimate the interannual variability in VPD and its impacts on GPP and NEP. Our results highlight the importance of VPD for terrestrial carbon sinks in assessing ecosystems' responses to future climate conditions.
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| 2. |
- Liu, Xuan, et al.
(författare)
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Increased southerly and easterly water vapor transport contributed to the dry-to-wet transition of summer precipitation over the Three-River Headwaters in the Tibetan Plateau
- 2023
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Ingår i: Advances in Climate Change Research. - 1674-9278 .- 2524-1761. ; 14:4, s. 502-510
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Tidskriftsartikel (refereegranskat)abstract
- The Three-River Headwaters (TRH) region in the Tibetan Plateau is vulnerable to climate change; changes in summer (June–August) precipitation have a significant impact on water security and sustainability in both local and downstream areas. However, the changes in summer precipitation of different intensities over the TRH region, along with their influencing factors, remain unclear. In this study, we used observational and ERA5 reanalysis data and employed a precipitation categorization and water vapor budget analysis to quantify the categorized precipitation variations and investigate their possible linkages with the water vapor budget. Our results showed an increasing trend in summer precipitation at a rate of 0.9 mm per year (p < 0.1) during 1979–2020, with a significant dry-to-wet transition in 2002. The category ‘very heavy precipitation’ (≥10 mm d−1) contributed 65.1% of the increased summer precipitation, which occurred frequently in the northern TRH region. The dry-to-wet transition was caused by the effects of varied atmospheric circulations in each subregion. Southwesterly water vapor transport through the southern boundary was responsible for the increased net water vapor flux in the western TRH region (158.2%), while southeasterly water vapor transport through the eastern boundary was responsible for the increased net water vapor flux in the central TRH (155.2%) and eastern TRH (229.2%) regions. Therefore, we inferred that the dry-to-wet transition of summer precipitation and the increased ‘very heavy precipitation’ over the TRH was caused by increased easterly and southerly water vapor transport.
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| 3. |
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| 4. |
- Wang, Wei, et al.
(författare)
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Enhanced edge detection for polarimetric SAR images using a directional span-driven adaptive window
- 2018
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Ingår i: International Journal of Remote Sensing. - : TAYLOR & FRANCIS LTD. - 0143-1161 .- 1366-5901. ; 39:19, s. 6340-6357
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Tidskriftsartikel (refereegranskat)abstract
- Automatic edge detection for polarimetric synthetic aperture radar (PolSAR) images plays a fundamental role in various PolSAR applications. The classic methods apply the fixed-shape windows to detect the edges, whereas their performance is limited in heterogeneous areas. This article presents an enhanced edge detection method for PolSAR data based on the directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and is constructed by adaptively selecting samples that follow the same statistical distribution. Therefore, it can overcome the limitation of classic fixed-shape windows. To obtain refined and reliable edge detection results in heterogeneous urban areas, we adopt the spherically invariant random vector (SIRV) product model since the complex Wishart distribution is often not met. In addition, a span ratio is combined with the SIRV distance to highlight the dissimilarity measure and to improve the robustness of the proposed method. The simulated PolSAR data and three real data sets from experimental synthetic aperture radar, electromagnetics institute synthetic aperture radar, and Radarsat-2 systems are used to validate the performance of the enhanced edge detector. Both quantitative evaluation and visual presentation of the results demonstrate the effectiveness of the proposed method and its superiority over the classic edge detectors.
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| 5. |
- Wang, Wei, et al.
(författare)
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Enhanced Edge Detection for Polarimetric SAR Images Using Directional Span-Driven Adaptive Window
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Automatic edge detection for polarimetric synthetic aperture radar (PolSAR) images plays a fundamental role in various PolSAR applications. The classic methods apply the fixed-shape windows to detect the edges, whereas their performance is limited in heterogeneous areas. This paper presents an enhanced edge detection method for PolSAR data based on the directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and is constructed by adaptively selecting samples which follow the same statistical distribution. Therefore, it can overcome the limitation of classic fixed-shape windows. To obtain refined and reliable edge detection results in heterogeneous urban areas, we adopt the spherically invariant random vector (SIRV) product model, since the complex Wishart distribution is often not met. In addition, a span ratio is combined with the SIRV distance to highlight the dissimilarity measure and to improve the robustness of the proposed method. The simulated PolSAR data and three real data sets from ESAR, EMISAR and RADARSAT-2 systems are used to validate the performance of the enhanced edge detector. Both quantitative evaluation and visual presentation of the results demonstrate the effectiveness of the proposed method and its superiority over the classic edge detectors.
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| 6. |
- Wang, Wei, et al.
(författare)
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Integrating Contextual Information With H/(alpha)over-bar Decomposition for PolSAR Data Classification
- 2016
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE. - 1545-598X .- 1558-0571. ; 13:12, s. 2034-2038
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Tidskriftsartikel (refereegranskat)abstract
- The use of contextual information is beneficial to improve both the accuracy and reliability of image classification. Based on the robust fuzzy c-means (RFCM) clustering method and an adaptive Markov random field model, this letter proposes a contextual H/(alpha) over bar classifier for polarimetric synthetic aperture radar images. At each iterative step of RFCM clustering, the prior probability extracted from the local neighborhood is combined with the fuzzy membership derived from inherent polarimetric characteristics, thus the enhanced fuzzy membership is more reliable. In addition, an adaptive smoothing factor is proposed for use during contextual information retrieval, which can prevent oversmoothing and preserve the local spatial details. The experimental results implemented using AIRSAR and ESAR L-band data validate the efficacy of the proposed method. Compared with the iterated Wishart classifier and fuzzy H/(alpha) over bar classifier, the proposed method significantly improves the classification accuracy, with less noise and increased preservation of details.
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| 7. |
- Wang, Wei, et al.
(författare)
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Superpixel-Based Segmentation of Polarimetric SAR Images through Two-Stage Merging
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a superpixel-based segmentation method for multilook polarimetric SAR (PolSAR) data. By exploring the PolSAR statistics, a two-stage merging strategy is proposed to improve the segmentation efficiency and accuracy. First, based on the initial superpixel partition, the Wishart-merging stage (WMS) simultaneously merges the regions in homogeneous areas, which can be modelled by the Wishart distribution. The Wishart energy loss together with the edge penalty is utilized so that the merged superpixels are from the same land cover and without ambiguity. In the second stage, the doubly flexible two-parameter KummerU distribution is applied to better characterize the resultant regions from the WMS, which are usually located in heterogeneous areas. This KummerU-merging stage (KUMS) iteratively merges the adjacent regions based on the KummerU energy loss. In addition, the edge penalty and the proposed homogeneity penalty are also adopted to guide the merging procedure, and prevent merging the regions from distinct land covers. Compared with the classical iterative merging methods, the two-stage merging strategy can improve the efficiency based on the WMS, and increase the segmentation accuracy through the KUMS. Two experimental PolSAR datasets acquired by ESAR and EMISAR system are used to demonstrate the effectiveness of the proposed segmentation method.
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| 8. |
- Wang, Wei, et al.
(författare)
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Superpixel Segmentation of Polarimetric SAR Data Based on Integrated Distance Measure and Entropy Rate Method
- 2017
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Ingår i: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. - : IEEE. - 1939-1404 .- 2151-1535. ; 10:9, s. 4045-4058
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes to integrate two different distances to measure the dissimilarity between neighboring pixels in PolSAR images, and introduces the entropy rate method into PolSAR image superpixel segmentation. Since the Gaussian model is commonly used for homogeneous scenes and less suitable for heterogeneous scenes, we adopt the spherically invariant random vector (SIRV) model to describe the back-scattering characteristics in heterogeneous areas. Moreover, a directional span-driven adaptive (DSDA) region is proposed such that it contains independent and identically distributed samples only, thus it can obtain accurate estimation of the distribution parameters. Using the DSDA region, the Wishart distance and SIRV distance are calculated, and then combined together through a homogeneity measurement. Therefore, the integrated distance takes advantage of the SIRV model and the Gaussian model, and suits both homogeneous and heterogeneous areas. Finally, based on the integrated distance, the superpixel segments are generated using the entropy rate framework. The experimental results on ESAR and PiSAR L-band datasets show that the proposed method can generate homogeneity-adaptive segments, resulting in smooth representation of the land covers in homogeneous areas, and better preserved details in heterogeneous areas.
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| 9. |
- Xiang, Deliang, et al.
(författare)
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Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model
- 2017
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0196-2892 .- 1558-0644. ; 55:6, s. 3115-3131
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Tidskriftsartikel (refereegranskat)abstract
- Simple linear iterative clustering (SLIC) algorithm was proposed for superpixel generation on optical images and showed promising performance. Several studies have been proposed to modify SLIC to make it applicable for polarimetric synthetic aperture radar (PolSAR) images, where the Wishart distance is adopted as the similarity measure. However, the superpixel segmentation results of these methods were not satisfactory in heterogeneous urban areas. Further, it is difficult to determine the tradeoff factor which controls the relative weight between polarimetric similarity and spatial proximity. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel generation method is proposed to overcome these limitations. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel generation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed superpixel generation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.
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| 10. |
- Xiang, Deliang, et al.
(författare)
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Edge Detector for Polarimetric SAR Images Using SIRV Model and Gauss-Shaped Filter
- 2016
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1545-598X .- 1558-0571. ; 13:11, s. 1661-1665
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Tidskriftsartikel (refereegranskat)abstract
- The classic constant false alarm rate edge detector with a rectangle-shaped filter has been proven to be effective and widely used in polarimetric synthetic aperture radar (PolSAR) images. However, in practical use, the assumption of complex Wishart distribution is often not respected, particularly in heterogeneous urban areas. In addition, as a simple smoothing filter, the rectangle-shaped window is often shown to be easy to incur false edge pixels near true edges. Therefore, its performance is limited. To overcome this restriction, we propose a new edge detector for PolSAR images, which utilizes the spherically invariant random vector product model to estimate the normalized covariance matrix for each pixel, and then replace the rectangle-shaped filter with a Gauss-shaped filter. The performance of our proposed methodology is presented and analyzed on two real PolSAR data sets, and the results show that the new edge detector attains better performance than the classic one, particularly for urban areas.
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