LAI-indicated vegetation dynamic in ecologically fragile region: A case study in the Three-North Shelter Forest program region of China

Hu, Yungang; Li, Huan; Dan, Wu; Chen, Wei; Zhao, Xiang; Hou, Miaole; Li, Aijia; Zhu, Yujiao

doi:10.1016/j.ecolind.2020.106932

Cited by 54 publications

(26 citation statements)

References 47 publications

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“…GPP is an indicator of plant photosynthesis [40], which is often used to quantify the quality of vegetation growth [41]. Inversion of the LAI from MODIS data products has been verified by many scholars [42][43][44]. According to the product description of the LAI and GPP in MODIS data products, when the digital number (DN) value of the LAI is between 253 and 255, it represents bare land, snow area, or water area.…”

Section: Calculation Of Indicatorsmentioning

confidence: 99%

Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang

Chang

Hou

et al. 2021

IJERPH

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Grassland ecosystems are increasingly threatened by pressures from climate change and intensified human activity, especially in the arid region of Central Asia. A comprehensive understanding of the ecological environment changes is crucial for humans to implement environmental protection measures to adapt to climate change and alleviate the contradiction between humans and land. In this study, fractional vegetation coverage (FVC), leaf area index (LAI), gross primary productivity of vegetation (GPP), land surface temperature (LST), and wetness (WET) were retrieved from Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing products in 2008 and 2018. Principal component analysis (PCA) was used to establish the MODIS data-based ecological index (MODEI) in the study area, and the spatial differentiation characteristics and driving mechanism of ecological quality in the last ten years were explored. The results showed that: (1) FVC, GPP, LAI, and WET had positive effects on the ecological environment, while LST had a negative impact on the ecological environment. FVC and GPP were more significant than other indicators. (2) The MODEI showed a spatial pattern of “excellent in the north and poor in the south” and changed from north to south in the study area. (3) From 2008 to 2018, the average MODEI of Fuyun County increased from 0.292 to 0.303, indicating that the ecological quality in Fuyun County became better overall. The improved areas were mainly located in the summer pastures at higher elevations. In comparison, the deteriorated areas were concentrated in the spring and autumn pastures and winter pastures at lower elevations. The areas where the ecological environment had obviously improved and degraded were distributed along the banks of the Irtysh River and the Ulungur River. (4) With the increase in precipitation and the decrease in grazing pressure, the MODEI of summer pasture was improved. The deterioration of ecological environment quality in spring and autumn pastures and winter pastures was related to the excessive grazing pressure. The more significant changes in the MODEI on both sides of the river were associated with implementing the herdsmen settlement project. On the one hand, the implementation of newly settled villages increased the area of construction land on both sides of the river, which led to the deterioration of ecological quality; on the other hand, due to the increase in cropland land and the planting of artificial grasses along the river, the ecological quality was improved. The study offers significant information for managers to make more targeted ecological restoration efforts in ecologically fragile areas.

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Section: Calculation Of Indicatorsmentioning

confidence: 99%

Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang

Chang

Hou

et al. 2021

IJERPH

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“…Large‐scale ecological restoration projects have been concentrated mainly in the northern Xinjiang and include the Forest Restoration Project of Tianshan Mountain, the Sand Control Project of Junggar Basin, and the Farmland Conversion Project, among others. By 2015, the Three‐North Shelter Forest Program increased the overall forest coverage from 22.80 × 10 4 to 22.96 × 10 4 (Hu et al, 2021). Under the background of the construction of the Three‐North Shelterbelt, the forest coverage rate in Xinjiang also showed an increasing trend.…”

Section: Discussionmentioning

confidence: 99%

“…More importantly, social urbanization promotes the improvement of residents' environmental awareness, the improvement of ecological restoration funds and technologies, and the restoration of the ecological system. Particularly, the implementation of the Three‐North Shelter Forest Program increasing the overall forest coverage from 22.80 × 10 4 to 22.96 × 10 4 (Hu et al, 2021). With the increase of cropland area, the function of food production and soil conservation services has also been improved to some extent.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal investigation of the interactive coercing relationship between urbanization and ecosystem services in arid northwestern China

et al. 2021

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Rapid urbanization has negative effects on ecosystem structure, function, and balance, leading to the loss of ecosystem services (ES) and posing a threat to regional sustainable development. However, the complex interactive coercing relationship between urbanization and ES is not yet fully understood, especially in arid Northwestern China. In this study, we constructed an evaluative framework for urbanization and ES using land cover/land use data and socioeconomic data from 1990 to 2018. We used global and local autocorrelation models to reveal the spatiotemporal interaction between urbanization and ES. The results revealed that the urbanization index increased from 0.06 in 1990 to 0.16 in 2018 in Xinjiang, and urbanization was significantly negatively correlated with ES. With the acceleration of urbanization, the ecosystem services value (ESV) has decreased 2,698.74 × 109 Yuan from 1990 to 2018 (6.62 Yuan = 1 .00 US$). Due to geographical advantages, resource conditions, and land use changes, the spatial relationship of urbanization and ES has obvious heterogeneity. High–low correlation areas are mainly concentrated in the Northern Tianshan Mountain Economic Zone, whereas low–high correlation areas are concentrated in the Kashgar and Hotan Oases in southern Xinjiang. Based on regional geographical conditions and the spatial heterogeneity of resources, diversified ecological, social, and economic balance models should be considered to promote future sustainable urban planning and development.

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“…The Global Land Surface Satellite (GLASS) leaf area index (LAI) has a temporal resolution of eight days and spans the period from 1981-2017. The LAI product from 1982-1999 was developed from AVHRR reflectance with a spatial resolution of 0.05 • , and that from 2000-2017 with a spatial resolution of 1 km was derived from MODIS reflectance [44]. The GLASS LAI data product is a national key project, and its dataset is superior to other similar products in terms of spatial and temporal accuracy [45].…”

Section: Datamentioning

confidence: 99%

Populus euphratica Phenology and Its Response to Climate Change in the Upper Tarim River Basin, NW China

Feng

Bai

et al. 2021

Forests

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Quantifying the phenological variations of Populus euphratica Olivier (P. euphratica) resulting from climate change is vital for desert ecosystems. There has previously been great progress in the influence of climate change on vegetation phenology, but knowledge of the variations in P. euphratica phenology is lacking in extremely arid areas. In this study, a modified method was proposed to explore P. euphratica phenology and its response to climate change using 18-year Global Land Surface Satellite (GLASS) leaf area index (LAI) time series data (2000–2017) in the upper Tarim River basin. The start of the growing season (SOS), length of the growing season (LOS), and end of the growing season (EOS) were obtained with the dynamic threshold method from the reconstructed growth time series curve by using the Savitzky–Golay filtering method. The grey relational analysis (GRA) method was utilized to analyze the influence between the phenology and the key climatic periods and factors. Importantly, we also revealed the positive and negative effects between interannual climate factors and P. euphratica phenology using the canonical correlation analysis (CCA) method, and the interaction between the SOS in spring and EOS in autumn. The results revealed that trends of P. euphratica phenology (i.e., SOS, EOS, and LOS) were not significant during the period from 2000–2017. The spring temperature and sunshine duration (SD) controlled the SOS, and the EOS was mainly affected by the temperature and SD from June–November, although the impacts of average relative humidity (RH) and precipitation (PR) on the SOS and EOS cannot be overlooked. Global warming may lead to SOS advance and EOS delay, and the increase in SD and PR may lead to earlier SOS and later EOS. Runoff was found to be a more key factor for controlling P. euphratica phenology than PR in this region.

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LAI-indicated vegetation dynamic in ecologically fragile region: A case study in the Three-North Shelter Forest program region of China

Cited by 54 publications

References 47 publications

Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang

Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang

Spatiotemporal investigation of the interactive coercing relationship between urbanization and ecosystem services in arid northwestern China

Populus euphratica Phenology and Its Response to Climate Change in the Upper Tarim River Basin, NW China

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