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Search: WFRF:(Wang Shaoqiang)

  • Result 1-9 of 9
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1.
  • Wang, Lunche, et al. (author)
  • Carbon emissions and reduction performance of photovoltaic systems in China
  • 2024
  • In: RENEWABLE & SUSTAINABLE ENERGY REVIEWS. - 1364-0321 .- 1879-0690. ; 200
  • Journal article (peer-reviewed)abstract
    • Solar energy is an inexhaustible clean energy, which can be converted into electricity through photovoltaic (PV) modules. However, the production of these modules is a process of pollution, which will generate a large amount of carbon emissions. Therefore, investigating the carbon emission performance of PV systems is of great significance in achieving carbon neutrality. Here, this study comprehensively analyze the carbon emissions and carbon emission reduction performance of PV systems in China using life cycle assessment approach. The results show that the life cycle carbon emissions of PV systems in China decreased from 1.66 kg CO2/W in 2011 to 0.75 kg CO2/W in 2018; meanwhile, the carbon intensity decreased from 74.24 to 50.91 kg CO2/kWh, and the energy payback time decreased from 2.4 to 2.2 years. Between 2008 and 2018, PV systems in China attained a cumulative net emission reduction of approximately 1889 x 108 kg CO2. In addition, for every 1 % increase in PV power generation, the total carbon emissions from the power generation sector in China from 2022 to 2035 could be reduced by approximately 2.05 %. This study analyzes the carbon emissions and carbon reduction of PV systems in China on a larger spatial-temporal scale as well as in a future perspective. The results of this study provide a better understanding of the carbon emissions and reduction performance of PV systems, and provide some effective information for the high-quality development of the PV industry in China.
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2.
  • Yi, Chuixiang, et al. (author)
  • Climate control of terrestrial carbon exchange across biomes and continents
  • 2010
  • In: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 5:3
  • Journal article (peer-reviewed)abstract
    • Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate-carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid-and high-latitudes, (2) a strong function of dryness at mid-and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45 degrees N). The sensitivity of NEE to mean annual temperature breaks down at similar to 16 degrees C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence.
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3.
  • Chen, Yuwen, et al. (author)
  • Exploring the potential of transmittance vegetation indices for leaf functional traits retrieval
  • 2023
  • In: GIScience and Remote Sensing. - : Informa UK Limited. - 1548-1603 .- 1943-7226. ; 60:1
  • Journal article (peer-reviewed)abstract
    • Leaf functional traits are key indicators of plant functions useful for inferring complex plant processes, including their responses to environmental changes. Vegetation indices (VIs) composed of a few reflectance wavelengths hold the advantages of being relatively simple and effective and have been widely used within remote sensing to estimate leaf traits. However, the difference between the reflectance from the upper and lower part of the leaf suggests that leaf reflectance mainly provides one-sided information, constraining its ability to estimate leaf functional traits. Leaf transmittance, on the other hand, gives information about the whole leaf and has more potential to be sensitive to changes in leaf biochemistry. As transmittance-based VI is rare, this study aims to propose new transmittance-based VIs for accurate estimations of leaf traits. Three forms, i.e. the normalized difference VI, the simple ratio VI, and the difference VI were employed, and wavelength selection for transmittance-based and reflectance-based VIs were conducted, respectively. The applicability of these VIs for estimating four leaf functional traits (leaf chlorophyll (Cab), leaf carotenoids (Car), equivalent water thickness (EWT), and leaf mass per area (LMA)) were evaluated. Cross-validation using three datasets of field observations and sensitivity analysis showed that the VIs constructed using transmittance were relatively less affected by interferences from other leaf parameters, improving the estimation accuracy of Car, EWT, and LMA compared to their optimal reflectance counterparts (RMSE reduced by 2% to 15%, and MAE reduced by 7% to 20% for the pooled dataset). Our study revealed that the normalized difference VI based on transmittance showed considerable sensitivity to Car, EWT, and LMA, whereas the difference VI based on reflectance was effective in indicating Cab. The proposed transmittance-based VIs will aid remote monitoring of leaf traits and thereby plant adaptations and acclimation to changes in environmental conditions.
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4.
  • Chen, Yuwen, et al. (author)
  • Optimized estimation of leaf mass per area with a 3d matrix of vegetation indices
  • 2021
  • In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:18
  • Journal article (peer-reviewed)abstract
    • Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was con-structed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R2 = 0.76 and 0.78, RMSE = 0.0016 g/cm2 and 0.0017 g/cm2, re-spectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.
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5.
  • He, Chunmei, et al. (author)
  • A new vegetation index combination for leaf carotenoid-to-chlorophyll ratio : minimizing the effect of their correlation
  • 2023
  • In: International Journal of Digital Earth. - : Informa UK Limited. - 1753-8947 .- 1753-8955. ; 16:1, s. 272-288
  • Journal article (peer-reviewed)abstract
    • The ratio of leaf carotenoid to chlorophyll (Car/Chl) is an indicator of vegetation photosynthesis, development and responses to stress. However, the correlation between Car and Chl, and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio. This study aims to investigate combinations of vegetation indices (VIs) to minimize the influence of Car-Chl correlation, thus being more sensitive to the variability in the ratio across vegetation species and sites. VIs sensitive to Car and Chl variability were combined into four candidates of combinations, using a simulated dataset from the PROSPECT model. The VI combinations were then tested using six simulated datasets with different Car-Chl correlations, and evaluated against four independent datasets. The ratio of the carotenoid triangle ratio index (CTRI) with the red-edge chlorophyll index (CIred-edge) was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability. Compared with published VIs and two machine learning algorithms, CTRI/CIred-edge also showed the optimal performance in the four field datasets. This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio, applicable for assessing vegetation physiology, phenology, and response to environmental stress. Trial registration:Clinical Trials Registry India identifier: CTRI/.
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6.
  • He, Chunmei, et al. (author)
  • PROSPECT-GPR : Exploring spectral associations among vegetation traits in wavelength selection for leaf mass per area and water contents
  • 2023
  • In: Science of Remote Sensing. - 2666-0172. ; 8
  • Journal article (peer-reviewed)abstract
    • Leaf mass per area (LMA) and equivalent water thickness (EWT) are key indicators providing information on plant growth status and agricultural management, and their retrieval is commonly done through radiative transfer models (RTMs) such as the PROSPECT model. However, the PROSPECT model is frequently hampered by the ill-posed problem as a consequence of measurement and model uncertainties. Here, we propose a wavelength selection method to improve the inversion of EWT and LMA by integrating PROSPECT with a machine learning algorithm (Gaussian process regression (GPR); PROSPECT-GPR for short). The GPR model conducted sorting of wavelengths and the PROSPECT-D was used to determine the optimal number of characteristic wavelengths. The results demonstrated that the estimation of EWT (R2 = 0.80; RMSE = 0.0021) and LMA (R2 = 0.71; RMSE = 0.0021) using the proposed wavelengths and PROSPECT inversion all exhibited superior accuracy in comparison with those from previous studies. The efficacy of PROSPECT-GPR in exploring the spectral linkage among vegetation traits was demonstrated by selecting wavelengths associated with leaf structure parameter N and EWT (1368 nm) that turn out to contribute to the estimation of LMA. The findings lay a strong foundation for understanding the spectral linkage among vegetation traits, and the proposed wavelength selection method provides valuable insights for selecting informative spectral wavelengths for RTMs inversion and designing future remote sensors.
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7.
  • Shi, Weibo, et al. (author)
  • Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery
  • 2023
  • In: Remote Sensing. - 2072-4292. ; 15:8
  • Journal article (peer-reviewed)abstract
    • Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research.
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8.
  • Sun, Jia, et al. (author)
  • Leaf pigment retrieval using the PROSAIL model : Influence of uncertainty in prior canopy-structure information
  • 2022
  • In: Crop Journal. - : Elsevier BV. - 2095-5421 .- 2214-5141. ; 10:5, s. 1251-1263
  • Journal article (peer-reviewed)abstract
    • Leaf pigments are critical indicators of plant photosynthesis, stress, and physiological conditions. Inversion of radiative transfer models (RTMs) is a promising method for robustly retrieving leaf biochemical traits from canopy observations, and adding prior information has been effective in alleviating the “ill-posed” problem, a major challenge in model inversion. Canopy structure parameters, such as leaf area index (LAI) and average leaf inclination angle (ALA), can serve as prior information for leaf pigment retrieval. Using canopy spectra simulated from the PROSAIL model, we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll (Cab) and carotenoid (Car). The retrieval accuracies of the two pigments were increased by use of the priors of LAI (RMSE of Cab from 7.67 to 6.32 μg cm−2, Car from 2.41 to 2.28 μg cm−2) and ALA (RMSE of Cab from 7.67 to 5.72 μg cm−2, Car from 2.41 to 2.23 μg cm−2). However, this improvement deteriorated with an increase of additive and multiplicative uncertainties, and when 40% and 20% noise was added to LAI and ALA respectively, these priors ceased to increase retrieval accuracy. Validation using an experimental winter wheat dataset also showed that compared with Car, the estimation accuracy of Cab increased more or deteriorated less with uncertainty in prior canopy structure. This study demonstrates possible limitations of using prior information in RTM inversions for retrieval of leaf biochemistry, when large uncertainties are present.
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9.
  • Sun, Jia, et al. (author)
  • Optimizing LUT-based inversion of leaf chlorophyll from hyperspectral lidar data : Role of cost functions and regulation strategies
  • 2021
  • In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432. ; 105
  • Journal article (peer-reviewed)abstract
    • Hyperspectral lidar (HSL) is a novel remote sensing technology that provides spectral information in addition to spatial features. This unprecedented data source leads to new possibilities for monitoring leaf biochemistry. Inversion of physically based radiative transfer models (RTMs) is a popular method for deriving leaf physiological traits due to its robustness and generalization capability. However, owing to the active nature of the HSL system, RTM inversion using the backscattered reflectance spectra may face new problems. Thus, optimization strategies for RTM inversion based on HSL measurements need to be studied. In this paper, several regulation strategies for lookup table (LUT)-based PROSPECT model inversions were explored for an HSL system. In particular, the influences of i) different cost functions, ii) multiple best solutions (1–1000), iii) different LUT sizes (100–100000), and iv) spectral domains for leaf chlorophyll (Chl) retrieval were analyzed. An evaluation against an experimental dataset of rice leaves indicated that i) least-squares estimation (LSE) provided better estimates than seven alternative cost functions when more than 200 solutions were taken; ii) accuracy in leaf Chl retrieval increased up until 200 solutions where after it stabilized; iii) the impact of LUT size became insignificant after 1000; and iv) the red edge was the spectral domain that had the largest impact on the inversion performance. The optimal performance of leaf Chl estimation reached R2 of 0.58 and RMSE of 0.69 between the z-scores from retrieved and measured leaf Chl. The practical application of combining RTM with HSL data will facilitate the detection of leaf-level biochemistry and advance research on terrestrial carbon cycle modeling.
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  • Result 1-9 of 9

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