Vulnerability assessment and its driving forces in terms of NDVI and GPP over the Loess Plateau, China

Zhang, Xiaoyuan; Liu, Kai; Li, Xueke; Wang, Shudong; Wang, Jinnian

doi:10.1016/j.pce.2022.103106

Cited by 25 publications

(11 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, the ecological environment quality was developing towards a good trend. The degree of ecological vulnerability is related to vegetation index, wetness, bare land surface, urban land and land surface temperature, which is consistent with the results of other studies (Tang et al, 2022;Zhang et al, 2022). In addition to investigating the change characteristics of ecological vulnerability indicators, another aspect of this study is to analyze the spatial and temporal evolution pattern of ecological vulnerability.…”

Section: Discussionsupporting

confidence: 87%

Ecological vulnerability assessment based on remote sensing ecological index (RSEI): A case of Zhongxian County, Chongqing

Jiang

Guo

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

The ecological vulnerability evaluation index was established through Normalized Difference Vegetation Index (NDVI), Wetness (WET), Normalized Difference Build-up and Soil Index (NDBSI) and Land Surface Temperature (LST) indicators, comprehensively evaluate the ecological vulnerability of Zhongxian County of Chongqing in 2002, 2009, and 2016 by Principal Components Analysis (PCA), and analyze its spatio-temporal evolution. The vulnerability areas of five levels were calculated respectively, and the overall index of ecological vulnerability was also calculated. The index of remote sensing ecological index (RSEI) and Normalized Difference Vegetation Index decreased first and then increased; the Wetness index showed an upward trend; the Normalized Difference Build-up and Soil Index index increased first and then decreased; and the Land Surface Temperature index decreased. The ecological vulnerability body index (EVBI) shows a downward trend, and the incremental changes are mainly concentrated in the negligible vulnerability areas and light vulnerability area, while the medium vulnerability, strong vulnerability and extreme vulnerability area generally show a downward trend. Furthermore, the new increment of ecological vulnerability grade area concentrates on negligible vulnerability area and light vulnerability area from 2002 to 2016. In general, the ecological vulnerability gradually shifts to low vulnerability, and the ecological environment tends to develop healthily.

show abstract

Section: Discussionsupporting

confidence: 87%

Ecological vulnerability assessment based on remote sensing ecological index (RSEI): A case of Zhongxian County, Chongqing

Jiang

Guo

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…As we know, water resources were relatively scarce in semi-arid area, the increased GPP was helpful for regional-vegetation recovery and more water consumption may have led to runoff reduction under global climate change in this region, subsequently resulting in a water shortage for social-economic usage and river environmental requirements. Besides, the increased ET mainly caused by increased transpiration will lead to more soil water-consumption of the soil layer and intensify the drought, resulting in high ecological sensitivity and serious vulnerability in water-limited zones [6]. The attributions of ET and GPP changes reveal that the multitude of factors dominating water and carbon fluxes are different in the region over different time scales.…”

Section: Discussionmentioning

confidence: 99%

“…GPP, the total amount of carbon dioxide fixed by vegetation through photosynthesis, is of great importance to regulate the terrestrial carbon sink in terrestrial ecosystems [5]. In semi-arid regions, ET is the major flux occurring in the water cycle and accounting for most of the precipitation; furthermore, GPP also plays an important role in the interannual variability of the global carbon sink [6]. Researches show that increased ET and GPP lead to decreased runoff, subsequently resulting in water shortages and drought aggravation in semi-arid region [7].…”

Section: Introductionmentioning

confidence: 99%

Attributions of Evapotranspiration and Gross Primary Production Changes in Semi-Arid Region: A Case Study in the Water Source Area of the Xiong’an New Area in North China

Zeng

Xia

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Investigating the attributions of evapotranspiration (ET) and gross primary production (GPP) changes is of great importance for regional, sustainable water resources and ecological management in semi-arid regions. Based on the simulation conducted during 2000–2019 by improving water-carbon coupling Distributed Time Variant Gain Model, the trends of ET and GPP were estimated and the driving factors were identified via 10 experimental scenarios in the water source area of the Xiong’an New Area in North China. The results show significant increases both in ET and GPP by 2.4 mm/a and 6.0 gC/m2/a in the region, respectively. At the annual scale, increasing precipitation dominates the ET uptrend. Air temperature, humidity and the interactive effects also contribute to the ET uptrend, and the contributions are 12.8%, 2.0% and 2.3%, respectively, while elevated atmospheric CO2 concentration (eCO2) and solar dimming lead to ET changes of about −7.2% and −12.4%, respectively. For the GPP changes, the increase in GPP is mainly caused by eCO2, increasing precipitation and rising temperature with the contributions of 56.7%, 34.8% and 27.8%, respectively. Solar dimming, humidity and windspeed contribute −6.8%, −4.8% and −3.5% of the GPP changes. Compared to climate change, land use and cover change has smaller effects on both ET and GPP for the few changes in land coverage. At the seasonal scale, ET and GPP increase to a greater extent during the growing season in spring and summer than in autumn and winter. Precipitation, temperature and eCO2 are generally the main causes for ET and GPP changes. Meanwhile, the decreasing humidity and rising temperature are dominant factors for ET and GPP increases, respectively, in winter. Furthermore, solar dimming has strong effects on ET reduction in autumn. The contribution of the interactive effects is much higher on a seasonal scale than annual scale, contributing to considerable decreases in ET and GPP in spring, increases in ET in autumn and winter, and an increase in GPP in winter. This study highlights the importance of considering water-carbon coupling on the attributions of ET and GPP changes and the differentiation of the effects by the abovementioned influential factors at annual and seasonal scales.

show abstract

“…It has been shown by related studies that from rural areas to urban centers, with the increase of distance, the increase of population density is not a single linear growth, but a nearly reverse change based on light intensity and vegetation index [66]. Therefore, considering the change of population density, this study proposes a composite index model CEANI to modify NTL index, whose formula is:…”

Section: 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝐼𝐼 = (1 − 𝑉𝑉𝑉𝑉𝑁𝑁𝐼𝐼) * 𝑉𝑉𝑁𝑁𝑁𝑁(1)mentioning

confidence: 99%

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Chen

Deng

2022

IEEE Access

View full text Add to dashboard Cite

The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.

show abstract

Vulnerability assessment and its driving forces in terms of NDVI and GPP over the Loess Plateau, China

Cited by 25 publications

References 64 publications

Ecological vulnerability assessment based on remote sensing ecological index (RSEI): A case of Zhongxian County, Chongqing

Ecological vulnerability assessment based on remote sensing ecological index (RSEI): A case of Zhongxian County, Chongqing

Attributions of Evapotranspiration and Gross Primary Production Changes in Semi-Arid Region: A Case Study in the Water Source Area of the Xiong’an New Area in North China

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Contact Info

Product

Resources

About