Relationships between lithology, topography, soil, and vegetation, and their implications for karst vegetation restoration

Zhong, Fangchuan; Xu, Xianli; Li, Zhenwei; Zeng, Xiangming; Yi, Ruzhou; Luo, Wei; Zhang, Yaohua; Xu, Chaohao

doi:10.1016/j.catena.2021.105831

Cited by 54 publications

(20 citation statements)

References 33 publications

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“…Consistent with the conclusions underlying changes in vegetation in the karst regions of Southwest China [12,33,34], this study determined that the overall forest coverage in the study area improved steadily. Specifically, the proportion of improved forest status in non-karst regions (96.40%) was slightly larger than in karst areas (94.28%), which does not suggest an obvious difference.…”

Section: Driving Forces Based On Socio-environmental Archetypessupporting

confidence: 82%

Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China

Zhou

Chen

2022

Remote Sensing

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To address ecological threats such as land degradation in the karst regions, several ecological restoration projects have been implemented for improved vegetation coverage. Forests are the most important types of vegetation. However, the evaluation of forest restoration is often uncertain, primarily owing to the complexity of the underlying factors and lack of information related to changes in forest coverage in the future. To address this issue, a systematic case study based on the Guizhou Province, China, was carried out. First, three archetypes of driving factors were recognized through the self-organizing maps (SOM) algorithm: the high-strength ecological archetype, marginal archetype, and high-strength archetype dominated by human influence. Then, the probability of forest restoration in the context of ecological restoration was predicted using Bayesian belief networks in an effort to decrease the uncertainty of evaluation. Results show that the overall probability of forest restoration in the study area ranged from 22.27 to 99.29%, which is quite high. The findings from regions with different landforms suggest that the forest restoration probabilities of karst regions in the grid and the regional scales were lower than in non-karst regions. However, this difference was insignificant mainly because the ecological restoration in the karst regions accelerated local forest restoration and decreased the ecological impact. The proposed method of driving-factor clustering based on restoration as well as the method of predicting restoration probability have a certain reference value for forest management and the layout of ecological restoration projects in the mid-latitude ecotone.

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Section: Driving Forces Based On Socio-environmental Archetypessupporting

confidence: 82%

Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China

Zhou

Chen

2022

Remote Sensing

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“…8 ). Previous studies have also shown that topography and soils are important drivers of vegetation restoration ( He, Shi & Fu, 2021 ; Zhong et al, 2022 ). The northern slope (292.5∼67.5°) was the most suitable for vegetation restoration ( Fig.…”

Section: Discussionmentioning

confidence: 99%

Quantitative analysis of vegetation restoration and potential driving factors in a typical subalpine region of the Eastern Tibet Plateau

Feng

Wang

Zhou

et al. 2022

PeerJ

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Vegetation restoration is an essential approach to re-establish the ecological balance in subalpine areas. Changes in vegetation cover represent, to some extent, vegetation growth trends and are the consequence of a complex of different natural factors and human activities. Microtopography influences vegetation growth by affecting the amount of heat and moisture reaching the ground, a role that is more pronounced in subalpine areas. However, little research is concerned with the characteristics and dynamics of vegetation restoration in different microtopography types. The respective importance of the factors driving vegetation changes in subalpine areas is also not clear yet. We used linear regression and the Hurst exponent to analyze the trends in vegetation restoration and sustainability in different microtopography types since 2000, based on Fractional Vegetation Cover (FVC) and identified potential driving factors of vegetation change and their importance by using Geographical Detector. The results show that: (1) The FVC in the region under study has shown an up-trend since 2000, and the rate of increase is 0.26/year (P = 0.028). It would be going from improvement to degradation, continuous decrease or continuous significant decrease in 47.48% of the region, in the future. (2) The mean FVC is in the following order: lower slope (cool), lower slope, lower slope (warm), valley, upper slope (warm), upper slope, valley (narrow), upper slope (cool), cliff, mountain/divide, peak/ridge (warm), peak/ridge, peak/ridge (cool). The lower slope is the microtopographic type with the best vegetation cover, and ridge peak is the most difficult to be afforested. (3) The main factors affecting vegetation restoration in subalpine areas are aspect, microtopographic type, and soil taxonomy great groups. The interaction between multiple factors has a much stronger effect on vegetation cover than single factors, with the effect of temperatures and aspects having the most significant impact on the vegetation cover changes. Natural factors have a greater impact on vegetation restoration than human factors in the study area. The results of this research can contribute a better understanding of the influence of different drivers on the change of vegetation cover, and provide appropriate references and recommendations for vegetation restoration and sustainable development in typical logging areas in subalpine areas.

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“…The unique geological conditions generate a low rate of soil formation, resulting in a shallow soil layer and a lack of arable land resources, which is not conducive to agricultural farming [13,35]. The hot and humid climate promotes karst landform's development, forming a runoff system of overground and underground dual patterns, resulting in a shortage of surface water [21,34,35]. Significant surface fluctuation provides potential conditions for soil erosion and other geological disasters.…”

Section: Ecological Restorationmentioning

confidence: 99%

“…Researchers proposed different agricultural development models to coordinate the human-earth areal system in the ecologically vulnerable karst areas and realize sustainable development up to a point. They argued that the primary task in karst areas is to restore regional surface vegetation while addressing ecological poverty caused by the fragile ecological environment [21][22][23]. Focusing on the farmers' livelihood, policymakers have combined ecological engineering projects to form five development models with rocky desertification management as the core in Guizhou and Guangxi provinces.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Agricultural Development Models of the Ecologically Vulnerable Karst Areas in Southeast Yunnan from the Perspective of Human–Earth Areal System

Xiao-qing

Wang

et al. 2022

Land

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Rocky desertification in ecologically-fragile karst areas limit regional socio-economic development in the face of significant human–earth conflict. Coordination of ecological restoration and agricultural development is critical for sustainable development in karst areas. From the perspective of the human–earth areal system, the framework of sustainable agricultural development was proposed in typically karst areas. We integrated principles of ecological vulnerability, resource and environmental carrying capacity, agricultural foundation, suitability of agricultural land, and the farmers’ willingness. In this study, we found the ecological vulnerability of Guangnan County was slight, but the proportion of moderate and severe vulnerability areas was high, with significant differences between the two sides of the line “Zhe (Zhetu)-Lian (Liancheng)-Yang (Yang Liu-jing)-Ban (Banbang)”. Then, we divided Guangnan County into three ecologically vulnerable zones. Following that, we proposed sustainable agricultural models for various zones. In slightly to mildly vulnerable zones, we propose constructing economic–ecological agricultural models, including woody oil, plateau characteristic fruiting forest, ecological tea plantations, suburban agriculture, and cultural–ecological tourism. In moderately to severely vulnerable zones, we recommend developing a stereoscopic agriculture model that combines planting and breeding, vegetation restoration, and herbivorous animal husbandry. In extremely vulnerable zones, we suggest constructing an ecologically natural restoration model and an agricultural ecological–tourism model. Our research provides references for ecological restoration, agricultural development, poverty alleviation consolidation, and rural revitalization in ecologically vulnerable karst areas of southeast Yunnan and similar regions.

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Relationships between lithology, topography, soil, and vegetation, and their implications for karst vegetation restoration

Cited by 54 publications

References 33 publications

Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China

Mapping Forest Restoration Probability and Driving Archetypes Using a Bayesian Belief Network and SOM: Towards Karst Ecological Restoration in Guizhou, China

Quantitative analysis of vegetation restoration and potential driving factors in a typical subalpine region of the Eastern Tibet Plateau

Sustainable Agricultural Development Models of the Ecologically Vulnerable Karst Areas in Southeast Yunnan from the Perspective of Human–Earth Areal System

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