Spatiotemporal Variation and Climate Influence Factors of Vegetation Ecological Quality in the Sanjiangyuan National Park

Sun, Qing; Liu, Weiwei; Gao, Yiwen; Li, Junsheng; Yang, Chunyan

doi:10.3390/su12166634

Cited by 17 publications

(6 citation statements)

References 40 publications

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“…The experimental results showed that the accuracy of direct vegetation index data fusion was higher, and in-depth studies such as surface vegetation cover can be conducted on this basis. The existing studies mostly applied the fusion model to Landsat and MODIS data [34], and the study of grassland cover change and toxic weed invasion in the TRHR also focused on medium-and low-resolution remote sensing data [35]. In contrast, Sentinel-2 had a higher spatial resolution compared to Landsat data, and the accuracy of monitoring the surface vegetation cover and change was also higher.…”

Section: Discussionmentioning

confidence: 99%

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

Fan

Huang

et al. 2023

Sustainability

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The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the flexible spatiotemporal data fusion (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR.

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Section: Discussionmentioning

confidence: 99%

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

Fan

Huang

et al. 2023

Sustainability

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“…Referring to the p-value selection of existing publications [34][35][36], vegetation changes were classified into six classes in this paper: Slope < 0 & p < 0.01 (extremely significant degradation), Slope < 0 & 0.01 < p < 0.05 (significant degradation), Slope < 0 & p > 0.05 (no evident degradation), Slope > 0 & p > 0.05 (no evident improvement), Slope > 0 & 0.01 < p < 0.05 (significant improvement), Slope > 0 & p < 0.01 (extremely significant improvement). To further quantify the spatio-temporal characteristics of vegetation growth trends within the mining area, the trend analysis was utilized to perform linear fitting and sig nificance tests for each pixel, revealing the temporal variations of NDVI.…”

Section: Evaluation Of Trends In Vegetation Growthmentioning

confidence: 99%

Evaluating the Impact of Human Activities on Vegetation Restoration in Mining Areas Based on the GTWR

Guo,

Li,

Zhang

et al. 2024

IJGI

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The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and Digital Elevation Model (DEM). Furthermore, residual analysis was performed to remove the impact of natural factors and separately assess the impact of human activities on vegetation restoration. The experiment was carried out in Shangwan Mine, China, and following results were obtained: (1) During the period of 2000 to 2020, intensified huan activities corresponded to positive vegetation changes (NDVI-HA) that exhibited an upward trend over time. (2) The spatial heterogeneity of vegetation restoration was attributed to the DEM. It is negatively correlated with NDVI in natural conditions, while under the environment of mining activities, there is a positive correlation between NDVI-HA and DEM. (3) The contribution of human activities to vegetation restoration in mining areas has been steadily increasing, surpassing the influences of temperature and precipitation since 2010. The results of this study can provide important references for the assessment of vegetation restoration to some extent in mining areas.

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“…Simultaneously, human disturbances in the area have gradually decreased, especially in the core zone where human interference is strictly limited to scientific research purposes. In areas with minimal human interference, such as the core area, vegetation structure characteristics are primarily influenced by climate factors [41]. On one hand, climate change hinders or amplifies the natural succession process of different plant species, resulting in the extinction of certain plants in specific locations or the rapid expansion of climateadaptive plant species.…”

Section: Impacts Of Conservation Management On Vegetation Changes In ...mentioning

confidence: 99%

Vegetation Change and Conservation Evaluation of the Cangshan Erhai National Nature Reserve (Cangshan Mountain Part) in Southwest China

Chen

et al. 2023

Forests

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Vegetation and its spatiotemporal variations play a crucial role in regional ecological security and sustainable development. Examining vegetation dynamics in natural reserves provides valuable insights for optimizing vegetation patterns and management strategies. This study utilizes Landsat remote sensing imagery to investigate changes in vegetation pattern and coverage in the Cangshan mountain of the Cangshan Erhai National Nature Reserve, as well as assesses the effectiveness of conservation efforts. The results indicate the following: (1) The primary vegetation types in the Cangshan mountain include warm-temperate coniferous forests, deciduous broad-leaved forests, bamboo forests, and alpine meadows, exhibiting distinct vertical zonation patterns. The vegetated area expanded by 1146 hectares during the study period. (2) The average fractional of vegetation coverage (FVC) in the Cangshan mountain demonstrated an upward trend (0.82 in 1987 to 0.93 in 2017), with the proportion of highly FVC areas increasing from 59.67% in 1987 to 97.89% in 2017. (3) The vegetation landscape fragmentation in Cangshan mountain and various functional areas shows an increasing trend, while connectivity decreases, and is accompanied by a more intricate shape of the vegetation landscape. While conservation and management efforts have yielded certain results in safeguarding the vegetation in the Cangshan mountain, the degree of vegetation landscape fragmentation has intensified due to climate change and human activities. Thus, it is imperative for management authorities to promptly adjust protective measures within the Cangshan mountain. This study contributes to our understanding of vegetation changes within the Cangshan mountain and provides essential baseline information for optimizing and enhancing vegetation conservation management strategies within the reserve.

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Spatiotemporal Variation and Climate Influence Factors of Vegetation Ecological Quality in the Sanjiangyuan National Park

Cited by 17 publications

References 40 publications

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

Evaluating the Impact of Human Activities on Vegetation Restoration in Mining Areas Based on the GTWR

Vegetation Change and Conservation Evaluation of the Cangshan Erhai National Nature Reserve (Cangshan Mountain Part) in Southwest China

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