Spatial patterns of ecosystem quality in Inner Mongolia and its driving forces analysis

Yang, 肖洋 XIAO; Zhiyun, 欧阳志云 Ouyang; Liyan, 王莉雁 WANG; Enming, 饶恩明 RAO; Ling, 江陵 JIANG; Lu, 张路 Zhang

doi:10.5846/stxb201501290245

Cited by 8 publications

(10 citation statements)

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“…The above findings suggest that human activities are essential drivers of variations in regional habitat quality [27]. Irrational human behavior can destroy the original ecological conditions and accelerate the deterioration of regional habitat quality [11].…”

Section: Drivers Of Habitat Quality Patternsmentioning

confidence: 93%

“…Conversely, habitat quality in our study is significantly negatively correlated with MAT (Figure S3). The reason may be that temperature conditions in the NSPB exceed the optimum value for plant growth [11,68]. Temperature changes directly affect the metabolic processes and growth rates of plants, influencing the ecological environment quality by altering plant habitability [69].…”

Section: Drivers Of Habitat Quality Patternsmentioning

confidence: 99%

“…Various ecological indexes have been used to rapidly monitor biodiversity and ecological quality across different scales with the advancements of remote sensing technology [6][7][8][9][10]. Recently, ecological models (e.g., Integrated Valuation of Ecosystem Services and Tradeoffs, InVEST) were primarily employed to quantify, evaluate, and simulate habitat quality at larger scales [11][12][13][14][15][16][17]. Exploring the spatiotemporal evolution in habitat quality and driving forces has garnered growing attention in research [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Spatiotemporal Patterns and Integrated Driving Forces of Habitat Quality in the Northern Sand-Prevention Belt of China

Zhang,

Zheng,

Zou

et al. 2024

Sustainability

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Understanding habitat quality patterns and their drivers in arid zones is of fundamental importance to the sustainability maintenance of terrestrial ecosystems, but remains elusive. Here, we applied the InVEST model to investigate the spatiotemporal patterns of habitat quality in the northern sand-prevention belt (NSPB) across five time periods (2000, 2005, 2010, 2015, 2018), coupled with the structural equation model (SEM) and boosted regression tree (BRT) model to identify their integrated driving forces. The results exhibited that habitat quality in high-level zones expanded gradually from 2000 to 2018, while the middle- and low-level zones shrank. Climate, soil, topography, and human activities were significantly correlated with habitat quality, with mean annual temperature (MAT) and human activities being key contributing factors in the high-level and low-level zones, respectively, whereas the contribution of factors varied considerably in the middle-level zones. The interactions among climate, soil, topography, and human activities jointly drive habitat quality changes. Climate intensified the positive effects of soil on habitat quality, while the topographic and human activities mainly affected habitat quality indirectly through climate and soil. Our findings offer a scientific guidance for the restoration and sustainable management of desertification ecosystems in northern China.

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Section: Drivers Of Habitat Quality Patternsmentioning

confidence: 93%

Section: Drivers Of Habitat Quality Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating Spatiotemporal Patterns and Integrated Driving Forces of Habitat Quality in the Northern Sand-Prevention Belt of China

Zhang,

Zheng,

Zou

et al. 2024

Sustainability

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“…The remote sensing approach, which helps identify and distinguish the stand features (species composition and structure, etc.) is also commonly applied for site quality assessment [14]. However, the results can be substantially affected by the environment and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Forest Site Quality Using the Biomass Potential Productivity Approach

Yan,

Feng,

Sharma

et al. 2023

Forests

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Biomass productivity is of great significance for the evaluation of forest quality, which is important for the improvement of forest management. We propose the computational methods of biomass potential productivity (BPP) and biomass realistic productivity (BRP), both of which provide reliable practical guides for predicting forest growth under multi-aged, multi-species, and multi-layered canopy conditions. We used 2222 national forest inventory plots that were measured in four consecutive periods in the Jilin Province for this purpose. We analyzed and verified the computational methods of BPP based on the BRP and evaluated its practical significance. The results showed that growth models of the stand height, stand basal area, and stand biomass of four forest types (pure larch forest, larch broadleaf mixed forest, Mongolian oak pure forest, and Mongolian oak broadleaf mixed forest) fit adequately, BPP was greater than BRP, and this difference decreased with an increasing stand age, suggesting that the potential productivity of the middle-aged and young forest was higher than that of the mature forest, although the difference is minimal. In addition, the realistic productivity of stands with better site quality was close to the potential productivity, which is consistent with the biological significance of the potential productivity of the biomass. The degree of difference between the potential productivity of the biomass and the realistic productivity of biomass also decreases with the decline in site quality, and it can be termed as the potentially improved stand biomass. The BPP model was able to perform well in both the pure and mixed forests. The BRP not only verifies the rationality of the BPP but can be also used to quantify the forest site quality, which is helpful for evaluating forest growth and informed decision making in forestry.

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“…However, the weighting of its indicators adopted average weighting which ignores the different importances of each indicator. Usually, multiple evaluation indicators are involved when evaluating the quality of regional ecosystems and the weights of each indicator largely influence the assessment result of the ecosystem’s quality [ 24 ]. Therefore, it is important to determine reasonable weights in assessing an ecosystem’s quality.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region

Wei,

Cheng,

Yang

et al. 2023

Sensors

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The Silk Road Economic Belt and the 21st Century Maritime Silk Road Initiative (BRI) proposed in 2013 by China has greatly accelerated the social and economic development of the countries along the Belt and Road (B&R) region. However, the international community has questioned its impact on the ecological environment and a comprehensive assessment of ecosystem quality changes is lacking. Therefore, this study proposes an objective and automatic method to assess ecosystem quality and analyzes the spatiotemporal changes in the B&R region. First, an ecosystem quality index (EQI) is established by integrating the vegetation status derived from three remote sensing ecological parameters including the leaf area index, fractional vegetation cover and gross primary productivity. Then, the EQI values are automatically categorized into five ecosystem quality levels including excellent, good, moderate, low and poor to illustrate their spatiotemporal changes from the years 2016 to 2020. The results indicate that the spatial distributions of the EQIs across the B&R region exhibited similar patterns in the years 2016 and 2020. The regions with excellent levels accounted for the lowest proportion of less than 12%, while regions with moderate, low and poor levels accounted for more than 68% of the study area. Moreover, based on the EQI pattern analysis between the years 2016 and 2020, the regions with no significant EQI change accounted for up to 99.33% and approximately 0.45% experienced a significantly decreased EQI. Therefore, this study indicates that the ecosystem quality of the B&R region was relatively poor and experienced no significant change in the five years after the implementation of the “Vision and Action to Promote the Joint Construction of the Silk Road Economic Belt and the 21st Century Maritime Silk Road”. This study can provide useful information for decision support on the future ecological environment management and sustainable development of the B&R region.

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Spatial patterns of ecosystem quality in Inner Mongolia and its driving forces analysis

Cited by 8 publications

References 0 publications

Evaluating Spatiotemporal Patterns and Integrated Driving Forces of Habitat Quality in the Northern Sand-Prevention Belt of China

Evaluating Spatiotemporal Patterns and Integrated Driving Forces of Habitat Quality in the Northern Sand-Prevention Belt of China

Evaluating Forest Site Quality Using the Biomass Potential Productivity Approach

Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region

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