Spatial-temporal vegetation succession in Yao’an County, Yunnan Province, Southwest China during 1976–2014: A case survey based on RS technology for mountains eco-engineering

Yu, Fu-ke; Huang, Xin-hui; Yuan, Hua; Ye, Jiangxia; Yao, Ping; Qi, Danhui; Yang, Guanlin; Ma, Jian-gang; Shao, Hongbo; Xiong, Hui

doi:10.1016/j.ecoleng.2014.09.026

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…Anthropogenic influences, such as ecological projects, population growth, and urbanization, were found to be more important factors that affect vegetation change [62][63][64][65]. A few previous studies highlighted the positive impacts of ecological restoration projects on vegetation dynamics [66][67][68][69].…”

Section: Drivers Of Vegetation Conditions and Dynamicsmentioning

confidence: 99%

Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China

et al. 2016

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To alleviate the severe rocky desertification and improve the ecological conditions in Southwest China, the national and local Chinese governments have implemented a series of Ecological Restoration Projects since the late 1990s. In this context, remote sensing can be a valuable tool for conservation management by monitoring vegetation dynamics, projecting the persistence of vegetation trends and identifying areas of interest for upcoming restoration measures. In this study, we use MODIS satellite time series (2001-2013) and the Hurst exponent to classify the study area (Guizhou and Guangxi Provinces) according to the persistence of future vegetation trends (positive, anti-persistent positive, negative, anti-persistent negative, stable or uncertain). The persistence of trends is interrelated with terrain conditions (elevation and slope angle) and results in an index providing information on the restoration prospects and associated uncertainty of different terrain classes found in the study area. The results show that 69% of the observed trends are persistent beyond 2013, with 57% being stable, 10% positive, 5% anti-persistent positive, 3% negative, 1% anti-persistent negative and 24% uncertain. Most negative development is found in areas of high anthropogenic influence (low elevation and slope), as compared to areas of rough terrain. We further show that the uncertainty increases with the elevation and slope angle, and areas characterized by both high elevation and slope angle need special attention to prevent degradation. Whereas areas with a low elevation and slope angle appear to be less susceptible and relevant for restoration efforts (also having a high uncertainty), we identify large areas of medium elevation and slope where positive future trends are likely to happen if adequate measures are utilized. The proposed framework of this analysis has been proven to work well for assessing restoration prospects in the study area, and due to the generic design, the method is expected to be applicable for other areas of complex landscapes in the world to explore future trends of vegetation.

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Section: Drivers Of Vegetation Conditions and Dynamicsmentioning

confidence: 99%

Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China

et al. 2016

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“…Hence, it is necessary to select more drought-resistant plant species for future afforestation activities. Furthermore, in order to avoid threats and damage to the local biodiversity, ecological risk assessment and scientific management are in urgent needs for restoration practitioners [77]. At present, vegetation restoration is still in a primary stage, and is expected to be a long-term process.…”

Section: Effects Of Ecological Restoration Activities On Vegetation Rmentioning

confidence: 99%

Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016

Pei

Wang

et al. 2019

Remote Sensing

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Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year−1 and 0.0020 year−1, respectively. Notably, the greening trends have been intensifying during the conservation period (2001–2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.

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The impacts of land cover spatial combination on nighttime light intensity in 2010 and 2020: a case study of Fuzhou, China

Yuan

Chen

2023

Comput.Urban Sci.

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As human activities highly depend on the land resources and changed the land cover (LC) condition, the relationship between LC and nighttime light (NTL) intensity has been widely analyzed to support the foundation of NTL applications and help explain the drivers of urban economic development. However, previous studies always paid attention to the effect of each LC type on NTL intensity, with limited consideration of the joint effects of any two LC types. To fill this gap, this study measured the land cover spatial combination (LCSC) by using a spatial adjacency matrix, and then analyzed its impacts on NTL intensity based on an extreme gradient boosting (XGBoost) regression model with the assistant of sharpley additive explanations (SHAP) method. Our results presented that the LCSC can better (R2 of 82.4% and 98.1% in 2010 and 2020) explain the relationship between LC and NTL intensity with the traditional LC metrics (e.g., area and patch count), since the LCSC is much more sensitive to the diverse land functions. It is noteworthy that the impacts, as well as their dynamics, of LCSC between any two LC types on NTL intensity are various. LCSC associated with artificial surface contributed more to NTL intensity. In detail, the LCSC of water/wetland and artificial surface can increasingly promote the NTL intensity while the LCSC of grassland/forest and artificial surface has a decreasing or inverse U-shaped contribution to NTL intensity. Whereas LCSC associated with non-artificial surface were not conducive to the increase in NTL intensity due to high vegetation density. We also provided three implications to help further urbanization process and discussed the applications of LCSC.

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Spatial-temporal vegetation succession in Yao’an County, Yunnan Province, Southwest China during 1976–2014: A case survey based on RS technology for mountains eco-engineering

Cited by 8 publications

References 31 publications

Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China

Assessing Future Vegetation Trends and Restoration Prospects in the Karst Regions of Southwest China

Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016

The impacts of land cover spatial combination on nighttime light intensity in 2010 and 2020: a case study of Fuzhou, China

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