Quantitative Evaluation of Ecosystem Health in a Karst Area of South China

Chen, Shengzi; Zhou, Zhongfa; Lihui, Yan; Li, Bo

doi:10.3390/su8100975

Cited by 16 publications

(14 citation statements)

References 25 publications

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“…The previous study resulted in a SOC stock in Guizhou Province of 1.97 Pg r [37]. That value is approximately 0.39 Pg larger than the result of our calculation.…”

Section: Discussioncontrasting

confidence: 70%

Discrepancies in Karst Soil Organic Carbon in Southwest China for Different Land Use Patterns: A Case Study of Guizhou Province

Zhang

Huang

Zhou

et al. 2019

IJERPH

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The assessment of soil organic carbon (SOC) in mountainous karst areas is very challenging, due to the high spatial heterogeneity in SOC content and soil type. To study and assess the SOC storage in mountainous karst areas, a total of 22,786 soil samples were collected from 2,854 soil profiles in Guizhou Province in Southwest China. The SOC content in the soil samples was determined by the oxidation of potassium dichromate (K2Cr2O7), followed by titration with iron (II) sulfate (FeSO4). The SOC storage was assessed based on different land uses. The results suggested that the average SOC density in the top 1.00 m of soil associated with different land uses decreased in the following order: Croplands (9.58 kg m−2) > garden lands (9.07 kg m−2) > grasslands (8.07 kg m−2) > forestlands (7.35 kg m−2) > uncultivated lands (6.94 kg m−2). The SOC storage values in the 0.00–0.10 m, 0.00–0.20 m, 0.00–0.30 m and 0.00–1.00 m soil layers of Guizhou Province were 0.50, 0.87, 1.11 and 1.58 Pg, respectively. The SOC in the top 0.30 m of soil accounted for 70.25% of the total within the 0.00–1.00 m layer in Guizhou Province. It was concluded that assessing SOC storage in mountainous karst areas was more accurate when using land use rather than soil type. This result can supply a scientific reference for the accurate assessment of the SOC storage in the karst areas of southwestern China, the islands of Java, northern and central Vietnam, Indonesia, Kampot Province in Cambodia and in the general area of what used to be Yugoslavia, along with other karst areas with similar ecological backgrounds.

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“…The previous study resulted in a SOC stock in Guizhou Province of 1.97 Pg r [37]. That value is approximately 0.39 Pg larger than the result of our calculation.…”

Section: Discussioncontrasting

confidence: 70%

Discrepancies in Karst Soil Organic Carbon in Southwest China for Different Land Use Patterns: A Case Study of Guizhou Province

Zhang

Huang

Zhou

et al. 2019

IJERPH

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“…With the increase in the study scale, the correlation between rock contents and soil properties is more significant. Chen Shengzi et al believe that the scale variance at large scales increases with fluctuations as the scale increases, and at this moment, stochastic effects have no obvious influences on soil properties [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Spatial Distribution of Stony Desertification and Key Influencing Factors on Different Sampling Scales in Small Karst Watersheds

Zhang

Wang²,

Huang

2018

IJERPH

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Karst areas are typical ecologically fragile areas, and stony desertification has become the most serious ecological and economic problems in these areas worldwide as well as a source of disasters and poverty. A reasonable sampling scale is of great importance for research on soil science in karst areas. In this paper, the spatial distribution of stony desertification characteristics and its influencing factors in karst areas are studied at different sampling scales using a grid sampling method based on geographic information system (GIS) technology and geo-statistics. The rock exposure obtained through sampling over a 150 m × 150 m grid in the Houzhai River Basin was utilized as the original data, and five grid scales (300 m × 300 m, 450 m × 450 m, 600 m × 600 m, 750 m × 750 m, and 900 m × 900 m) were used as the subsample sets. The results show that the rock exposure does not vary substantially from one sampling scale to another, while the average values of the five subsamples all fluctuate around the average value of the entire set. As the sampling scale increases, the maximum value and the average value of the rock exposure gradually decrease, and there is a gradual increase in the coefficient of variability. At the scale of 150 m × 150 m, the areas of minor stony desertification, medium stony desertification, and major stony desertification in the Houzhai River Basin are 7.81 km2, 4.50 km2, and 1.87 km2, respectively. The spatial variability of stony desertification at small scales is influenced by many factors, and the variability at medium scales is jointly influenced by gradient, rock content, and rock exposure. At large scales, the spatial variability of stony desertification is mainly influenced by soil thickness and rock content.

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“…With the increase of the study scale, the correlation between rock contents and soil properties is more significant. It is believed by Chen Shengzi et al that scale variance on large scales increases with fluctuations as the scale is rising, and at this moment, stochastic effects have no obvious influen ces on soil properties [25].…”

Section: Influencing Factors Of Stony Desertification In Different Samentioning

confidence: 99%

Spatial Distribution of Stony Desertification and Key Influencing Factors in Different Sampling Scales in Small Karst Watersheds

Zhang¹,

Zhou²,

Wang³

et al. 2018

Preprint

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Abstract:In this paper, the spatial distribution of stony desertification characteristics and its influencing factors in Karst areas in different sampling scales are studied using a grid sampling method based on geographic information system (GIS) technology and geo-statistics, with the rock bareness rate obtained through sampling with 150m × 150m grids in the Houzhai River Basin being taken as the original data and five grid scales (300m × 300m, 450m × 450m, 600m × 600m, 750m × 750m, and 900m × 900m) as the subsample sets. The results show that the rock bareness rate does not vary much from one sampling scale to another while average values of the five sub-samples all fluctuate around the average value of the entire set. As the sampling scale is expanding, the maximum value and the average value of rock bareness rate are decreasing gradually, with a gradual increase in the coefficient of variability. In the scale of 150m × 150m, the areas of minor stony desertification, medium stony desertification, and major stony desertification in the Houzhai River Basin are 7.81 km 2 , 4.50 km 2 , and 1.87 km 2 , respectively. The spatial variability of stony desertification on small scales is influenced by many factors, and that on medium scales is jointly influenced by gradient, rock contents, and rock bareness rate. On large scales, the spatial variability of stony desertification is mainly influenced by soil thickness and rock bareness rate.

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Quantitative Evaluation of Ecosystem Health in a Karst Area of South China

Cited by 16 publications

References 25 publications

Discrepancies in Karst Soil Organic Carbon in Southwest China for Different Land Use Patterns: A Case Study of Guizhou Province

Discrepancies in Karst Soil Organic Carbon in Southwest China for Different Land Use Patterns: A Case Study of Guizhou Province

Spatial Distribution of Stony Desertification and Key Influencing Factors on Different Sampling Scales in Small Karst Watersheds

Spatial Distribution of Stony Desertification and Key Influencing Factors in Different Sampling Scales in Small Karst Watersheds

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