Spatio-temporal characteristics of rocky desertification in typical Karst areas of Southwest China: A case study of Puding County, Guizhou Province

Huipeng, 习慧鹏 Xi; Shijie, 王世杰 Wang; Xiaoyong, 白晓永 Bai; Hong, 唐红 Tang; Luhua, 吴路华 Wu; Fei, 陈飞 Chen; Jianyong, 肖建勇 Xiao; Mingming, 王明明 Wang; Huiwen, 李汇文 Li; Yue, 操玥 Cao; Huan, 陈欢 Chen; Chen, 冉晨 Ran; Xuling, 罗旭玲 Luo

doi:10.5846/stxb201806011216

Cited by 7 publications

(4 citation statements)

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“…With reference to the relevant studies on the driving factors of rocky desertification, 11 factors were identified from the natural environment and socio-economic aspects for analyzing the driving factors of rocky desertification in the study area, which were selected in the year 2000, and the spatial resolution was uniformly re-sampled to 1 km × 1 km [21][22][23][24].…”

Section: Environmental Factor Datamentioning

confidence: 99%

Spatio-Temporal Evolution Characteristics and Driving Factors of Typical Karst Rocky Desertification Area in the Upper Yangtze River

Gao,

Zhou,

Yin

2024

Sustainability

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Karst rocky desertification (KRD) has become the most serious ecological disaster in the southwest of China and is a major obstacle to the sustainable development of the karst region in the southwest. Remarkably, scientific understanding of the spatial-temporal evolution of rocky desertification and the corresponding driving mechanism is the primary prerequisite crucial to controlling rocky desertification. Hence, the typical rocky desertification area of Qujing City, located in the upper reaches of the Yangtze River, was selected as the research object. On the basis of the Google Earth Engine (GEE) cloud platform and decision tree classification, the spatial-temporal evolution process of rocky desertification in Qujing City from 1990 to 2020 was investigated, and the driving factors of rocky desertification were explored in terms of the natural environment and socio-economic aspects. Consequently, over this period, the area of rocky desertification had decreased by 1728.38 km2, while the no rocky desertification area had increased by 1936.61 km2. Notably, the major driving factors of rocky desertification were fractional vegetation cover (FVC) (q = 0.41), land use type (q = 0.26), slope (q = 0.21), and land reclamation rate (q = 0.21). Typically, rocky desertification is likely to occur in areas with moderate or low FVC (<0.7), a low slope (0–8°) or high slope (35°–80°), a land type of cultivated-land or grassland, and a land reclamation rate of 10–70%. In addition, all two-factor interactions acted as drivers that exacerbate rocky desertification. Furthermore, FVC ∩ slope (q = 0.79) and slope ∩ land use type (q = 0.56) were two interacting drivers that promote rocky desertification strongly.

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Section: Environmental Factor Datamentioning

confidence: 99%

Spatio-Temporal Evolution Characteristics and Driving Factors of Typical Karst Rocky Desertification Area in the Upper Yangtze River

Gao,

Zhou,

Yin

2024

Sustainability

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“…Combined with the pixel binary model method, they proposed a model of the 1‐vegetation coverage‐soil exposure rate for BRR calculation, thereby improving the speed and accuracy of rocky desertification interpretation in karst areas (Zhang et al, 2010). Zhang, based on LANDSAT data and the normalized differential rocky index (NDRI) constructed by Huang from the mid‐infrared and red bands (Huang & Cai, 2009), found that the NDRI constructed from the shortwave infrared and near‐infrared bands attained a higher accuracy (Zhang & Gan, 2014), and this method has been widely used for BRR calculation (Gong et al, 2022; Wang et al, 2019; Wen & Li, 2020; Xi et al, 2018; Xu et al, 2022). In conclusion, the common features of these studies were (1) a small study area and (2) use of the NDRI.…”

Section: Introductionmentioning

confidence: 99%

Extraction of the bare rock rate in counties of typical karst areas based on unmanned aerial vehicle images and satellite data, China

Wang,

Sui

2023

Land Degrad Dev

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Rocky desertification is a land degradation process. The bare rock rate (BRR) is an important index. Satellite‐based methods for BRR estimation can generate BRR datasets, but the spatial resolution and accuracy are low. Unmanned aerial vehicle (UAV) data can be used for BRR extraction with much higher resolution and higher accuracy, but the data are laborious and costly to collect. This study proposed a stepwise upscaling method employing the UAV‐extracted BRR as the ground truth to establish a multiband regression model and calibrated the BRR derived from 30‐m resolution LANDSAT 8 data, which was then used to calibrate 500‐m resolution moderate resolution imaging spectroradiometer (MODIS) data. We compared the results with the satellite‐based BRR and the results of UAV‐MODIS upscaling methods. The results showed that (1) the UAV provides high‐resolution data, and exposed rocks can be efficiently extracted from UAV images. This method could replace ground surveys. (2) The accuracy of the stepwise upscaling method was higher than that of the nonstepwise upscaling method in large areas. R2 increased by 64.9%, and the root mean square error (RMSE) decreased by 82.9%. (3) Compared with that of the satellite‐based BRR, the accuracy of the multiband regression model‐extracted BRR was higher. R2 increased by 21.5%, and the RMSE decreased by 62.2%. This study demonstrated that the stepwise upscaling method and multiband regression can be combined to extract high‐precision and large‐scale BRR data, which can be used to serve ecological engineering monitoring and ecological governance.

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“…However, the comprehensive index method would amplify one certain factor and ignore the interactions among different evaluating factors. In order to analyze the spatial and temporal evolution characteristics of rocky desertification, GeoDetector was introduced to analyze the dominant factor of rocky desertification [22]. Using field-observed data and remote sensing images, the rocky desertification index was constructed based on the multiterminal spectral mixture analysis method, and then the temporal and spatial change characteristics of rocky desertification was explored [23].…”

Section: Introductionmentioning

confidence: 99%

The Changes of Spatiotemporal Pattern of Rocky Desertification and Its Dominant Driving Factors in Typical Karst Mountainous Areas under the Background of Global Change

et al. 2022

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There are significant differences in the dominant driving factors of rocky desertification evolution in different historical periods in southwest karst mountainous areas. However, previous studies were mostly conducted in specific periods. In this study, taking Bijie City as an example, the spatial and temporal evolution pattern of rocky desertification in Bijie City in the recent 35 years was analyzed by introducing the feature space model and the gravity center model, and then the dominant driving factors of rocky desertification in the study area in different historical periods were clarified based on GeoDetector. The results were as follows: (1) The point-to-point B (bare land index)-DI (dryness index) feature space model has high applicability for rocky desertification monitoring, and its inversion accuracy was 91.3%. (2) During the past 35 years, the rocky desertification in Bijie belonged to the moderate rocky desertification on the whole, and zones of intensive and severe rocky desertification were mainly distributed in the Weining Yi, Hui, and Miao Autonomous Region. (3) During 1985–2020, the rocky desertification in Bijie City showed an overall weakening trend (‘weakening–aggravating–weakening’). (4) From 1985 to 2020, the gravity center of rocky desertification in Bijie City moved westward, indicating that the aggravating degree of rocky desertification in the western region of the study area was higher than that in the eastern region. (5) The dominant factors affecting the evolution of rocky desertification in the past 35 years shifted from natural factor (vegetation coverage) to human activity factor (population density). The research results could provide decision supports for the prevention and control of rocky desertification in Bijie City and even the southwest karst mountainous area.

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Spatio-temporal characteristics of rocky desertification in typical Karst areas of Southwest China: A case study of Puding County, Guizhou Province

Cited by 7 publications

References 0 publications

Spatio-Temporal Evolution Characteristics and Driving Factors of Typical Karst Rocky Desertification Area in the Upper Yangtze River

Spatio-Temporal Evolution Characteristics and Driving Factors of Typical Karst Rocky Desertification Area in the Upper Yangtze River

Extraction of the bare rock rate in counties of typical karst areas based on unmanned aerial vehicle images and satellite data, China

The Changes of Spatiotemporal Pattern of Rocky Desertification and Its Dominant Driving Factors in Typical Karst Mountainous Areas under the Background of Global Change

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