Karstic conduits and watershed identification based on the morphological characteristics of depressions in the Yunnan–Guizhou Plateau of China

Yang, Yugui; Xu, Mei; Lingmin, Zhong; Qiang, Zhang; Kang, Xiaobing; Bang-yan, Mao

doi:10.1007/s12665-020-08986-5

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Then the depression is reclassified according to the relative depth, whose value is greater than 1 will be assigned to NODATA and the segmentation threshold is set to 1, so as to identify the bottom of the depression, as shown in Figure 2(d). The bottom of an enclosed karst depression can be identified using this method [58].…”

Section: Depression Identification Using Neighborhoodmentioning

confidence: 99%

Enclosed Karst Depression Identification and Analysis for the Pumped Storage Power Station Reservoir Construction Using DEM

Bo,

Tao,

Kexun

et al. 2023

Geofluids

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An enclosed karst depression, a typical natural negative terrain, has the advantage of less engineering excavation when constructing a reservoir. In this study, the enclosed karst depression and its range identification technique have been developed. What is more, the geometric parameters and spatial distribution of enclosed karst depressions in Anlong County, Guizhou Province of China, have also been analyzed. Results show that (1) the focus statistic method and local terrain contour tree model were developed to identify enclosed karst depression and its range using regular grid DEM data with 12.5 m spatial resolution, which has been applied to enclosed karst depression identification in Anlong County. (2) 7262 independent and nested depressions with an average density of 3.7/km2 were identified by using the proposed method. The effectiveness and reliability of the proposed model have been verified through comparative analysis and visual recognition comparison. (3) High-density depression areas (5.6 depressions/km2), medium-density depression areas (2.9 depressions/km2), and low-density depression areas (1.1 depressions/km2) were well classified through kernel density analysis. (4) The geometric parameters of enclosed karst depressions (area, perimeter, circularity, depth, elevation, slope, and volume) were all analyzed in the study area. In addition, an indicator called DCK (depression is caused by karstification) was proposed to evaluate the dissolution degree and karstification stage of the enclosed karst depression. Based on the DCK, we determined that around 2.7% of depressions were identified as middle-stage and suitable for reservoir construction with enough volume and good slope stability. The idea and method in this research could provide a technological support for the engineering utilization of enclosed karst depressions.

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Section: Depression Identification Using Neighborhoodmentioning

confidence: 99%

Enclosed Karst Depression Identification and Analysis for the Pumped Storage Power Station Reservoir Construction Using DEM

Bo,

Tao,

Kexun

et al. 2023

Geofluids

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“…Therefore, DEM data with a 12.5 m spatial resolution provided by the NASA website (https://urs.earthdata.nasa.gov) were used in this work to identify and extract karst depressions (including negative terrain features such as sinkholes, waterfall holes, BAZIs, artificial negative terrain, etc.) in Guizhou (Figure 2a) with the contour tree method, which was proven to be a very effective method in practice (Miao et al, 2013;Wu et al, 2016;Wang et al, 2017;Yang et al, 2020).…”

Section: Creating An Inventory Of Karst Depressionsmentioning

confidence: 99%

Spatial patterns and controlling factors of the evolution process of karst depressions in Guizhou province, China

Zhang,

Zuo,

et al. 2023

J. Geogr. Sci.

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Karst depressions are common negative topographic landforms formed by the intense dissolution of soluble rocks and are widely developed in Guizhou province. In this work, an inventory of karst depressions in Guizhou was established, and a total of approximately 256,400 karst depressions were extracted and found to be spatially clustered based on multidistance spatial cluster analysis with Ripley's K function. The kernel density (KD) can transform the position data of the depressions into a smooth trend surface, and five different depression concentration areas were established based on the KD values. The results indicated that the karst depressions are clustered and developed in the south and west of Guizhou, while some areas in the southeast, east and north have poorly developed or no clustering. Additionally, the random forest (RF) model was used to rank the importance of factors affecting the distribution of karst depressions, and the results showed that the influence of lithology on the spatial distribution of karst depressions is absolutely dominant, followed by that of fault tectonics and hydrological conditions. The research results will contribute to the resource investigation of karst depressions and provide theoretical support for resource evaluation and sustainable utilization.

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“…South Gombong karst is a range of karst mountains located southwest of Kebumen in Central Java, Indonesia. Karst is characterized by caves, surface drainage, and closed depressions (Yanna et al 2020). As a result, karst has unique qualities and potential that can be utilized.…”

Section: Introductionmentioning

confidence: 99%

The use of non-medicinal plants by the community of Ayah Village in South Gombong Karst Area, Kebumen, Central Java, Indonesia

HANUN,

ATHAYA,

SHOLEKHA

et al. 2023

Nusantara Biosci

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Abstract. Hanun Z, Athaya DN, Sholekha AM, Damayanti CE, Nazar IA, Cahyaningsih AP, Junaedi E, Buot JR IE Setyawan AD. 2023. The use of non-medicinal plants by the community of Ayah Village in South Gombong Karst Area, Kebumen, Central Java, Indonesia. Nusantara Bioscience 15: 68-78. In the homegardens of the people of Kebumen District, Central Java Province, Indonesia, especially in the karst area of Ayah Village, usually planted various plant species with many benefits. However, the knowledge of the people of Ayah Village, Kebumen, about the various benefits of plants is only known from generation to generation, passed on orally and in daily practice habits, so a study is needed to document this information. This study aimed to determine the knowledge of local communities and various types of non-medicinal plants used to fulfill people's daily lives. Data was collected through survey techniques and open interviews with a purposive sampling method with 40 respondents. An inventory of non-medicinal plants resulted in findings of 118 plant species from 59 families. The plants used consisted of 51 species of food plants, 40 species of ornamental plants, 19 species of spices, six species of animal feed, six species of firewood, five species of building materials, and two species of hedges. Some species have more than one use. Our study showed that most of the local community uses plants as food with more diverse plant species compared to other uses.

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Karstic conduits and watershed identification based on the morphological characteristics of depressions in the Yunnan–Guizhou Plateau of China

Cited by 4 publications

References 33 publications

Enclosed Karst Depression Identification and Analysis for the Pumped Storage Power Station Reservoir Construction Using DEM

Enclosed Karst Depression Identification and Analysis for the Pumped Storage Power Station Reservoir Construction Using DEM

Spatial patterns and controlling factors of the evolution process of karst depressions in Guizhou province, China

The use of non-medicinal plants by the community of Ayah Village in South Gombong Karst Area, Kebumen, Central Java, Indonesia

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