Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China

Yi-ming, Wang; Zhang, Zengxin; Chen, Xi

doi:10.3390/rs13245081

Cited by 45 publications

(23 citation statements)

References 60 publications

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“…Best time lag changes are not influenced by a single factor but by the overall interaction effect between the vegetation type and soil texture factors. Previous studies indicated that the interaction effect of two factors was always greater than that of a single factor [53]. In this study, the interaction effect between the vegetation type and soil texture factors indicated enhancement.…”

Section: Time-lag Effects Of Climate Conditions On Npp With Vegetatio...supporting

confidence: 45%

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Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

Tian

Jiang

et al. 2022

Forests

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Climate conditions can significantly alter the vegetation net primary productivity (NPP) in many of Earth’s ecosystems, although specifics of NPP–climate condition interactions, especially time-lag responses on seasonal scales, remain unclear in ecologically sensitive forest–grassland ecotones. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, we analyzed the relationship between NPP and precipitation, temperature, and drought during the growing season (April–August), considering the time-lag effect (0–5 months) at the seasonal scale in Hulunbuir, Inner Mongolia, China from 2000 to 2018. The results revealed a delayed NPP response to precipitation and drought throughout the growing season. In April, the precipitation in the 4 months before (i.e., the winter of the previous year) explained the variation in NPP. In August, the NPP in some areas was influenced by the preceding 1~2 months of drought. The time-lag effect varied with vegetation type and soil texture at different spatial patterns. Compared to grass and crop, broadleaf forest and meadow exhibited a longer legacy of precipitation during the growing season. The length of the time-lag effects of drought on NPP increased with increasing soil clay content during the growing season. The interaction of vegetation types and soil textures can explain 37% of the change in the time-lag effect of the NPP response to PPT on spatial pattern. Our findings suggested that preceding precipitation influences vegetation growth at the early stages of growth, while preceding drought influences vegetation growth in the later stages of growth. The spatial pattern of the time lag was significantly influenced by interaction between vegetation type and soil texture factors. This study highlights the importance of considering the time-lag effects of climate conditions and underlying drivers in further improving the prediction accuracy of NPP and carbon sinks in temperate semiarid forest–grassland ecotones.

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Section: Time-lag Effects Of Climate Conditions On Npp With Vegetatio...supporting

confidence: 45%

“…The Geodetector model is a new spatial statistical tool for exploring spatial heterogeneity and quantitatively evaluating the contribution of driving factors [53]. This tool comprises four modules, namely, factor detector, ecological detector, risk detector, and interaction detector.…”

Section: The Geodetector Modelmentioning

confidence: 99%

Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

Tian

Jiang

et al. 2022

Forests

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“…Geodetector (Microsoft, Redmond, Washington, DC, USA) is a set of statistical methods that detect spatial differentiation and reveal its driving forces, which can effectively detect the causes and mechanisms of spatial patterns of geographical elements [ 42 , 43 , 44 ]. We used the factor detection method of Geodetector to study the influencing factors of the spatial distribution equilibrium of two-tier medical and health resources in Taiyuan.…”

Section: Data Source and Methodologymentioning

confidence: 99%

Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province

Mao

Guo

2022

IJERPH

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COVID-19 has caused more than 500 million infections and 6 million deaths. Due to a continuous shortage of medical resources, COVID-19 has raised alarm about medical and health resource allocation in China. A balanced spatial distribution of medical and health resources is a key livelihood issue in promoting the equalization of health services. This paper explores the spatial allocation equilibrium of two-tier medical and health resources and its influencing factors in Taiyuan. Using extracted POIs of medical and health resources of AMAP, we evaluated the spatial quantitative characteristics through the Health Resources Density Index, researched the spatial distribution pattern by kernel density analysis, hot spot analysis, and service area analysis, and identified the influencing factors of the spatial distribution equilibrium by the Geodetector model. The findings are as follows. The overall allocation level of medical and health resources in Taiyuan is low. There are tiered and regional differences; the response degree of primary care facilities to external factors is greater than that of hospitals; and the comprehensive influence of economic and topographic systems is crucial compared with other factors. Therefore, in order to promote the rational spatial distribution of medical and health resources in Taiyuan and to improve the construction of basic medical services within a 15 min radius, it is important to continuously improve the tiered healthcare system, uniformly deploy municipal medical and health resources, and increase the resource allocation to surrounding counties and remote mountainous areas. Future research should focus on collecting complete data, refining the research scale, analyzing qualitative differences, and proposing more accurate resource allocation strategies.

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“…The average strength of the explanatory power of natural factors on vegetation cover over the 21-year study period varied with temperature (0.216) > elevation (0.205) > soil clay content (0.172) > precipitation (0.163) > soil sand content (0.138) > slope (0.059) > slope orientation (0.014) ( Figure 6 ). Changes in vegetation cover are complex processes and are significantly influenced by human activities (land use type, GDP density, population density and distance from roads) [ 23 , 54 , 55 ]. Previous studies in Xinjiang have shown that human activities and active policies are the main influences on the regional vegetation cover [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Evolution and Driving Forces of Vegetation Cover in the Urumqi River Basin

Mamattursun

Han

Ablikim

et al. 2022

IJERPH

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It is important to determine long-term changes in vegetation cover, and the associated driving forces, to better understand the natural and human-induced factors affecting vegetation growth. We calculated the fractional vegetation coverage (FVC) of the Urumqi River basin and selected seven natural factors (the clay and sand contents of surface soils, elevation, aspect, slope, precipitation and temperature) and one human factor (land use type). We then used the Sen–Man–Kendall method to calculate the changing trend of the FVC from 2000 to 2020. We used the optimal parameters-based geographical detector (OPGD) model to quantitatively analyze the influence of each factor on the change in vegetation coverage in the basin. The FVC of the Urumqi River basin fluctuated from 2000 to 2020, with average values between 0.22 and 0.33. The areas with no and low vegetation coverage accounted for two-thirds of the total area, whereas the areas with a medium, medium–high and high FVC accounted for one-third of the total area. The upper reaches of the river basin are glacial and forest areas with no vegetation coverage and a high FVC. The middle reaches are concentrated in areas of urban construction with a medium FVC. The lower reaches are in unstable farmland with a medium and high FVC and deserts with a low FVC and no vegetation. From the perspective of the change trend, the areas with an improved FVC accounted for 62.54% of the basin, stable areas accounted for 5.66% and degraded areas accounted for 31.8%. The FVC showed an increasing trend in the study area. The improvement was mainly in the areas of urban construction and desert. Degradation occurred in the high-elevation areas, whereas the transitional zone was unchanged. The analysis of driving forces showed that the human factor explained more of the changes in the FVC than the natural factors in the order: land use type (0.244) > temperature (0.216) > elevation (0.205) > soil clay content (0.172) > precipitation (0.163) > soil sand content (0.138) > slope (0.059) > aspect (0.014). Apart from aspect, the explanatory power (Q value) of the interaction of each factor was higher than that of the single factor. Risk detection showed that each factor had an interval in which the change in the FVC was inhibited or promoted. The optimum elevation interval of the study area was 1300–2700 m and the greatest inhibition of the FVC was seen above 3540 m. Too much or too little precipitation inhibited vegetation coverage.

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Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China

Cited by 45 publications

References 60 publications

Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

Equilibrium of Tiered Healthcare Resources during the COVID-19 Pandemic in China: A Case Study of Taiyuan, Shanxi Province

Spatiotemporal Evolution and Driving Forces of Vegetation Cover in the Urumqi River Basin

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