Using SPOT VEGETATION for analyzing dynamic changes and influencing factors on vegetation restoration in the Three-River Headwaters Region in the last 20 years (2000–2019), China

Sun, Lijian; Zhao, Dan; Zhang, Guozhuang; Wu, Xiangjun; Yang, Yi; Wang, Zuwei

doi:10.1016/j.ecoleng.2022.106742

Cited by 17 publications

(7 citation statements)

References 43 publications

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“…Then the effects of anthropogenic factors were disaggregated based on residual analysis. However, other factors also influence vegetation growth [10,38,78,79], such as solar radiation or sunlight hours, soil moisture, etc. For example, previous studies [80,81] have reported that drought-induced soil water deficit is often a main factor affecting vegetation growth.…”

Section: Limitations and Implicationsmentioning

confidence: 99%

Vegetation Dynamics in Response to Climate Change and Human Activities in a Typical Alpine Region in the Tibetan Plateau

Zhao

Ren

2022

IJERPH

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Understanding past and future vegetation dynamics is important for assessing the effectiveness of ecological engineering, designing policies for adaptive ecological management, and improving the ecological environment. Here, inter-annual changes in vegetation dynamics during 2000–2020, contributions of climate change (CC) and human activities (HA) to vegetation dynamics, and sustainability of vegetation dynamics in the future were determined in Gannan Prefecture (a typical alpine region in the Tibetan Plateau), China. MODIS-based normalized difference vegetation index (NDVI), air temperature, precipitation, and land cover data were used, and trend analysis, multiple regression residuals analysis, and Hurst exponent analysis were employed. NDVI increased at a rate of 2.4 × 10−3∙a−1 during the growing season, and vegetation improved in most parts of the study area and some sporadically degraded areas also existed. The increasing rate was the highest in the Grain to Green Project (GTGP) areas. The vegetation in the southern and northern regions was mainly affected by CC and HA, respectively, with CC and HA contributions to vegetation change being 52.32% and 47.68%, respectively. The GTGP area (59.89%) was most evidently affected by HA. Moreover, a Hurst exponent analysis indicated that, in the future, the vegetation in Gannan Prefecture would continuously improve. The study can assist in formulating ecological protection and restoration projects and ensuring sustainable development.

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Section: Limitations and Implicationsmentioning

confidence: 99%

Vegetation Dynamics in Response to Climate Change and Human Activities in a Typical Alpine Region in the Tibetan Plateau

Zhao

Ren

2022

IJERPH

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“…This study did not deeply analyze the driving factors of NDVI evolution due to space limitations. Thus, the next step will employ geographical detectors [16,73], correlation analysis [74], multiple linear regression analysis [8], and other methods to explore the influencing factors and reasons for NDVI changes in detail.…”

Section: Limitations and Prospectsmentioning

confidence: 99%

“…Over the past several decades, the research on the dynamic change of vegetation has achieved fruitful results. From the research area and scale, there were global scales [12,13], national scales [14,15], regional scales [16,17], and basin scales [18,19]. From the research content, there were long-term NDVI temporal and spatial evolution and future sustainability analysis [20,21], the detection of driving factors [20], and the correlation analysis of NDVI, terrain, climate, and other elements [17,22], as well as mutation analysis of vegetation NDVI [23].…”

Section: Introductionmentioning

confidence: 99%

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

Zhong

Wang

et al. 2022

Remote Sensing

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Vegetation is the main body of the terrestrial ecosystem and is a significant indicator of environmental changes in the regional ecosystem. As an essential link connecting South Asia and Southeast Asia, the Lancang-Mekong River Basin(LMRB) can provide essential data support and a decision-making basis for the assessment of terrestrial ecosystem environmental changes and the research and management of hydrology and water resources in the basin by monitoring changes in its vegetation cover. This study takes the Lancang-Mekong River Basin as the study area, and employs the Sen slope estimation, Mann–Kendall test, and Hurst exponent based on the MODIS NDVI data from 2000 to 2021 to study the spatial and temporal evolution trend and future sustainability of its NDVI. Besides, the nonlinear characteristics such as mutation type and mutation year are detected and analyzed using the BFAST01 method. Results demonstrated that: (1) In the past 22 years, the NDVI of the Lancang-Mekong River Basin generally exhibited a fluctuating upward trend, and the NDVI value in 2021 was the largest, which was 0.825, showing an increase of 4.29% compared with 2000. However, the increase rate was different: China has the most considerable NDVI growth rate of 7.25%, followed by Thailand with an increase of 7.21%, Myanmar and Laos as the third, while Cambodia and Vietnam have relatively stable vegetation changes. The overall performance of NDVI is high in the south and low in the north, and is dominated by high and relatively high vegetation coverage, of which the area with vegetation coverage exceeding 0.8 accounts for 62%. (2) The Sen-MK trend showed that from 2000 to 2021, the area where the vegetation coverage in the basin showed a trend of increase and decrease accounted for 66.59% and 18.88%, respectively. The Hurst exponent indicated that the areas where NDVI will continue to increase, decrease, and remain unchanged in the future account for 60.14%, 25.29%, and 14.53%, respectively, and the future development trend of NDVI is uncertain, accounting for 0.04%. Thus, more attention should be paid to areas with a descending future development trend. (3) BFAST01 detected eight NDVI mutation types in the Lancang-Mekong River Basin over the past 22 years. The mutations mainly occurred in 2002–2018, while 2002–2004 and 2014–2018 were the most frequent periods of breakpoints. The mutation type of “interruption: increase with negative break” was changed the most during this period, which accounts for 36.54%, and the smallest was “monotonic decrease (with negative break)”, which only accounts for 0.65%. This research demonstrates that combining the conventional trend analysis method with the BFAST mutation test can more accurately analyze the spatiotemporal variation and nonlinear mutation of NDVI, thus providing a scientific reference to develop ecological environment-related work.

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“…Previous studies have determined that meteorological factors such as temperature and precipitation are the controlling factors of global vegetation change, and human activities such as agriculture and urbanization also have an impact on vegetation dynamics [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Response of Vegetation Dynamics in the Three-North Region of China to Climate and Human Activities from 1982 to 2018

Liang

Quan

et al. 2023

Sustainability

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To tackle ecological problems, many ecological restoration projects have been implemented in northern China. Identifying the drivers of vegetation change is critical for continued ecological engineering. In this study, three typical ecological reserves in the Three-North Shelter Forest Program Region (TNSFR) were selected to identify their vegetation development characteristics and driving mechanisms using the normalized difference vegetation index (NDVI), climate factors, and land use data. The results show that (1) NDVIs increased in the range of human activities of all of the three ecological reserves, indicating an obvious effect of the vegetation restoration projects. (2) In the planting period, vegetation restoration was mainly correlated with human activities. After entering the tending period, the impact of climate changes on vegetation dynamics was enhanced. (3) Temperature and precipitation provided approximate driving effects on vegetation dynamics in Region I, while vegetation dynamics in Regions II and III were more strongly correlated with precipitation. (4) The proportion of areas with ecological measures exceeded 50% in all three regions. In short, ecological projects in the three ecological reserves dominated the quantity of vegetation restoration, while climate changes influenced the quality of vegetation restoration.

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Using SPOT VEGETATION for analyzing dynamic changes and influencing factors on vegetation restoration in the Three-River Headwaters Region in the last 20 years (2000–2019), China

Cited by 17 publications

References 43 publications

Vegetation Dynamics in Response to Climate Change and Human Activities in a Typical Alpine Region in the Tibetan Plateau

Vegetation Dynamics in Response to Climate Change and Human Activities in a Typical Alpine Region in the Tibetan Plateau

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

Response of Vegetation Dynamics in the Three-North Region of China to Climate and Human Activities from 1982 to 2018

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