Quantitative assessment of the habitat quality dynamics in Yellow River Basin, China

Song, Yanan; Wang, Meng; Sun, Xuejun; Fan, Zemeng

doi:10.1007/s10661-021-09404-4

Cited by 37 publications

(18 citation statements)

References 55 publications

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“…The CS evaluation result in this paper agrees with Yang’s findings [ 78 ]. Song et al calculated HQs of 0.642 and 0.637 in 2000 and 2018 [ 79 ], respectively, which are comparable to 0.639 in 2000 and 0.637 in 2020 in this paper. The HQ deviation is primarily due to different weights being assigned to different threat sources.…”

Section: Discussionsupporting

confidence: 78%

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin

Zhang

Zhao

et al. 2022

IJERPH

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With the intensification of conflicts between different ecosystem services, how to achieve a win-win situation between socio-economic development and ecological protection is an important issue that needs to be addressed nowadays. In particular, how to better quantify and assess the intensity of ecosystem service trade-offs and their relative benefits, and to identify the influencing factors are issues that need to be studied in depth. Based on the INVEST model, this paper analyzed the evolution of spatial and temporal patterns of ecosystem services such as Carbon Storage (CS), Food Production (FP), Habitat Quality (HQ), and Water Yield (WY) in the Shandong Yellow River Basin (SYRB) in 2000, 2010 and 2020. Next, we quantitatively measured the trade-off intensity and revealed the key influencing factors of the trade-off intensity evolution using automatic linear models, root mean square deviation, and geographically weighted regressions. Subsequently, we further analyzed the impact of the correlation between environmental and socio-economic factors on the trade-off intensity of ecosystem services. The results indicated that the temporal and spatial changes of the four main ecosystem services in SYRB area were inconsistent. WY showed a fluctuating trend, with a large interannual gap. CS and FP are on the rise, while HQ is on the decline. Spatially, WY and HQ showed a decreasing distribution from the center to the periphery, while FP and CS showed a decreasing distribution from the southwest to the northeast. The location characteristics of SYRB’s four ecosystem services and their trade-offs were obvious. FP had absolute location advantage in ecosystem service trade-offs. Most of the four ecosystem services showed significant trade-offs, and the trade-off intensity had significant spatial heterogeneity, but the trade-off between FP and CS was relatively weak. At the same time, there were also differences in the trends of trade-off intensities. Counties with low trade-off intensity were mostly located in mountainous areas; these areas are less disturbed by human activities, and most of them are areas without prominent services. Counties with high trade-off intensities were mostly concentrated in areas with relatively developed agriculture; these areas are more disturbed by human activities and are mostly prominent in FP. The trade-off intensity of ecosystem services in SYRB was affected by several factors together, and there were difference characteristics in the degree and direction of influence of each factor. Moreover, these influencing factors have gradually changed over 20 years. In terms of the spatial distribution at the county scale, the influence areas of the dominant drivers of different trade-off types varied greatly, among which the areas with NDVI, CON, and PRE as the dominant factors were the largest. In the future, in effectively balancing regional economic development and ecological environmental protection, quantifiable correspondence strategies should be developed from the administrative perspective of counties and regions based on comprehensive consideration of the locational advantages of each ecosystem service and changes in trade-offs.

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Section: Discussionsupporting

confidence: 78%

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin

Zhang

Zhao

et al. 2022

IJERPH

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“…In recent years, with the development of 3S technologies, ecological models, such as the Habitat Suitability Index model (HIS) [15][16][17], the SoLVES model [18][19][20][21] and the InVEST model [22][23][24], are developed to quantitatively assess regional habitat quality. In particular, the InVEST model is the most popular among these models due to its advantages of ready data availability, accurate analysis ability and spatial visualization of assessment results [13,25,26], which enable the assessment to be conducted at large scales and within long-time span [14]. It has been widely used in habitat quality assessment, spatiotemporal changes and prediction of land consolidation projects [27,28], nature reserves [29,30] and urban agglomeration [31,32], and the study scale varies based on county [13], city [13,33], province [34,35] and watershed [36,37].…”

Section: Billion Andmentioning

confidence: 99%

“…In the Yellow River Basin, some researches have investigated the habitat quality of the Yellow River delta [2,30], wetland [47], areas affected by the South-North Water Transfer Project [38] and the whole Yellow River Basin [26,36], in terms of habitat quality assessment [26,30,38], prediction [30] and driving factors analysis [36,38]. Whereas, there are few relative studies on the loess hilly and gully area, which is located in the middle reaches of the Yellow River Basin.…”

Section: Billion Andmentioning

confidence: 99%

Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

Zhang

Lyu

Fan

et al. 2022

Land

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China has set up ecological protection and high-quality development of the Yellow River Basin as its national strategy. However, the fragile natural ecosystem and intensive human disturbances pose challenges to it. This study evaluates habitat quality change and analyzes its drivers in a representative county of this region, aiming to provide scientific basis for ecological protection and sustainable development. We took Liulin, a representative county of middle Yellow River Basin as the study area and evaluated the spatiotemporal variation of habitat quality from 2000 to 2020 with the InVEST model. Further, the influencing factors of habitat quality pattern were explored using GeoDetector, and their gradient ranges dominating the habitat quality change were determined by gradient analysis. The results showed that: (1) Areas of low and medium-low habitat quality grades were distributed interactively in the whole county; medium grade areas were scattered in the northeast and southwest parts of the county; and medium-high and high grades area were distributed sporadically along the Yellow River and its branches. (2) Habitat quality of the county almost unchanged from 2000 to 2010. However, from 2010 to 2020, with the rapid expansion of construction land (increased by 9.62 times), the area proportion of medium, medium-high, and high habitat quality grades decreased from 7.01% to 5.31%, while that of low and medium-low habitat quality grades increased from 92.99% to 94.69%. (3) The habitat quality was influenced by multiple natural-human factors. The main influencing factor was land use, followed by elevation. (4) Most changes of habitat quality occurred in areas with lower elevation, gentler slope, and higher vegetation coverage, which were affected by intensive human activities. These results suggest that in future land use policy making and, the construction land expansion in Liulin County should be restricted and differentiated ecological protection and restoration strategies should be implemented in areas with different habitat quality.

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“…Before entering the Bohai Sea, the Yellow River travels through nine provinces (autonomous regions), including Qinghai, Si-chuan, Gansu, Ningxia Hui Autonomous Region, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong. The Yellow River Basin (YRB) is the most important birthplace of Chinese civilization, spanning the following three regions: east, central, and west, located between 96° and 119° E and between 32° and 42° N, with a total area of 795,000 km 2 [ 31 , 32 ]. The YRB is comprised of 38 resource-based prefecture-level cities, according to the “Comprehensive Plan for the YRB (2012–2030)” and the “National Resource-Based City Sustainable Development Plan (2013–2020)”.…”

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Patterns of Ecosystem Services Supply-Demand and Influencing Factors: A Case Study of Resource-Based Cities in the Yellow River Basin, China

Chang

et al. 2022

IJERPH

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The aim of this study was to reveal the spatiotemporal pattern of the supply and demand of ecosystem services (ESs), as well as the significant driving factors for understanding the impact of human activities on the natural ecosystem. To provide a scientific basis for formulating regional sustainable development strategies that enhance human well-being, resource-based cities in the Yellow River Basin (YRB) were selected as the case study. The supply and demand of ecosystem services in these cities from 2000 to 2020 were measured. The spatiotemporal evolution of the supply-demand relationship was illustrated by taking its coordination degree. In addition, geographical detector and geographically weighted regression (GWR) models were applied to quantify the spatiotemporally varying effects of natural and socioeconomic factors on the ES supply--demand relationship. The results showed that resource-based cities in the YRB were experiencing expansion in supply and demand overall, but the supply-demand relationship tended to be tense. The northwest YRB had higher coordination values of supply-demand, while lower values were found in the southeast YRB. Moreover, the relationship between supply and demand was significantly affected by natural and socioeconomic factors, such as elevation, slope, precipitation, land-use type, population density, and gross domestic product (GDP) per land. Furthermore, the GWR model suggested that the effects of driving factors on the supply-demand relationship had notable spatial heterogeneity. The coordination of ES supply-demand in the resource-based cities of southeast YRB was mainly influenced by socioeconomic factors, while that of the west YRB was mainly influenced by natural factors. Our study suggested that it is necessary to enhance the awareness of environmental protection, pay attention to ecological restoration, and avoid unreasonable human disturbance to the ecosystem.

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Quantitative assessment of the habitat quality dynamics in Yellow River Basin, China

Cited by 37 publications

References 55 publications

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin

Trade-Off Analyses of Multiple Ecosystem Services and Their Drivers in the Shandong Yellow River Basin

Spatiotemporal Variation and Influence Factors of Habitat Quality in Loess Hilly and Gully Area of Yellow River Basin: A Case Study of Liulin County, China

Spatial-Temporal Patterns of Ecosystem Services Supply-Demand and Influencing Factors: A Case Study of Resource-Based Cities in the Yellow River Basin, China

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