Exploring and attributing change to fractional vegetation coverage in the middle and lower reaches of Hanjiang River Basin, China

Yan, Yi; Liu, Huan; Bai, Xixuan; Zhang, Wenhao; Wang, Sen; Luo, Jiahuan; Cao, Yanmin

doi:10.1007/s10661-022-10681-w

Cited by 9 publications

(3 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Calculate indicators such as wind power density, effect wind speed occurrence, energy level frequency, stability, and resource reserves proposed by Zheng et al We used Theil-Sen Median slope estimation to analyze the Mann-Kendall trend in these indicators by using annual and seasonal average index data over a 40-year period. Theil-Sen Median slope estimation is a highly efficient method that is less sensitive to measurement errors and outliers and is often used for long-term series data trend analysis [35,36]. Mann-Kendall test is a nonparametric test method that does not require a specific distribution and is less affected by outliers, making it suitable for ordinal variables.…”

Section: Methodsmentioning

confidence: 99%

Interdecadal Variation Trend of Arctic Wind Energy

Wang,

Wu,

et al. 2023

Energies

View full text Add to dashboard Cite

The Arctic’s navigation and utilization have garnered global interest due to the economic and strategic significance of its open shipping routes. The feasibility and cost-effectiveness of Arctic wind power generation depend largely on the potential of wind energy, which in turn influences the construction of new ports. In order to effectively harness polar wind energy, we must understand and adapt to its ever-changing rules. This study leverages ERA5′s 40-year wind field data to estimate the Theil–Sen Median slope and perform Mann–Kendall trend analysis. We consider factors such as wind power density, effective wind speed occurrence, energy level frequency, stability, and resource reserves to comprehensively analyze the intergenerational variations in Arctic wind energy resources. Our findings indicate that Northeast Passage, Davis Strait, and Baffin Bay possess favorable wind power density (1~2 W/m2·yr−1), effective wind speed occurrence (0.1~0.2%·yr−1), energy level frequency (0.1~0.2%·yr−1), stability (−0.005 yr−1), and resource reserves (1 kWh/m2·yr−1). However, these indicators are inferior in the Barents Sea, Canada’s northern archipelagos, and Greenland’s vicinity, where wind energy is relatively poor and unfavorable for development. Autumn dominates the annual change trend of Arctic wind energy, while spring and summer show no significant trends.

show abstract

Section: Methodsmentioning

confidence: 99%

Interdecadal Variation Trend of Arctic Wind Energy

Wang,

Wu,

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…To overcome these limitations and improve the efficiency of utilizing 30 m FVC imagery, this study compared different reconstruction methods and proposed a deep learning based method to develop 30 m FVC products. The reconstructed FVC dataset is complete in a spatial extent and continuous with a time interval of ~16 d. The reconstructed 30 m FVC in Hubei Province can greatly improve the current understanding of spatial and temporal dynamics of vegetation in vital ecological regions, which previously was derived from coarse-resolution products [41]. By applying the proposed reconstruction method to the entire Landsat 8 FVC, a time-series, 16 d FVC dataset can be derived for the past decade.…”

Section: Implications Of the Reconstructed 30 M Fvc Datasetmentioning

confidence: 99%

Developing Spatial and Temporal Continuous Fractional Vegetation Cover Based on Landsat and Sentinel-2 Data with a Deep Learning Approach

Wang

Song

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites with fine-resolution sensors and cloud contamination has resulted in poor spatial and temporal continuity. In this study, we propose to derive a spatially and temporally continuous FVC dataset by comparing multiple methods, including the data-fusion method (STARFM), curve-fitting reconstruction (S-G filtering), and deep learning prediction (Bi-LSTM). By combining Landsat and Sentinel-2 data, the integrated FVC was used to construct the initial input of fine-resolution FVC with gaps. The results showed that the FVC of gaps were estimated and time-series FVC was reconstructed. The Bi-LSTM method was the most effective and achieved the highest accuracy (R2 = 0.857), followed by the data-fusion method (R2 = 0.709) and curve-fitting method (R2 = 0.705), and the optimal time step was 3. The inclusion of relevant variables in the Bi-LSTM model, including LAI, albedo, and FAPAR derived from coarse-resolution products, further reduced the RMSE from 5.022 to 2.797. By applying the optimized Bi-LSTM model to Hubei Province, a time series 30 m FVC dataset was generated, characterized by a spatial and temporal continuity. In terms of the major vegetation types in Hubei (e.g., evergreen and deciduous forests, grass, and cropland), the seasonal trends as well as the spatial details were captured by the reconstructed 30 m FVC. It was concluded that the proposed method was applicable to reconstruct the time-series FVC over a large spatial scale, and the produced fine-resolution dataset can support the data needed by many Earth system science studies.

show abstract

“…It has found wide application in geological, medical, agricultural, and biological protection areas 25 – 28 . Due to the fact that this model is able to fully disclose and quantify the impact of various influential factors on FVC, it has been applied to analyze FVC variation 29 – 31 .…”

Section: Introductionmentioning

confidence: 99%

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Wang,

Xing,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000–2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

show abstract

Exploring and attributing change to fractional vegetation coverage in the middle and lower reaches of Hanjiang River Basin, China

Cited by 9 publications

References 75 publications

Interdecadal Variation Trend of Arctic Wind Energy

Interdecadal Variation Trend of Arctic Wind Energy

Developing Spatial and Temporal Continuous Fractional Vegetation Cover Based on Landsat and Sentinel-2 Data with a Deep Learning Approach

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors

Contact Info

Product

Resources

About