Evolution characteristics of karst rocky desertification in typical small watershed and the key characterization factor and driving factor

Mingming, 王明明 Wang; Shijie, 王世杰 Wang; Xiaoyong, 白晓永 Bai; Shijie, 李世杰 Li; Huiwen, 李汇文 Li; Yue, 操玥 Cao; Huipeng, 习慧鹏 Xi

doi:10.5846/stxb201902170278

Cited by 7 publications

(7 citation statements)

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“…As was evident, GLMM showed the highest performance, with an overall accuracy close to 90% and the kappa coefficient reaching 0.845, indicating a clear advantage over other methods. According to the classification criteria of rock desertification grade (Su et al, 2020; Wang et al, 2019), the bedrock bareness was divided into five grades to explore the prediction performance of each method at different grades of bedrock bareness. As shown in Figure 8, producer accuracy and user accuracy were higher in the grades of <20% and >70%.…”

Section: Resultsmentioning

confidence: 99%

“…The proportion of rocks in each grid was calculated as a ground reference for regression modeling and accuracy verification; the steps are shown in Figure 4. According to the existing research literature on the division of rocky desertification degree (Su et al, 2020; Wang et al, 2019), the bedrock exposure rate is divided into five grades: < 20%, 20%–35%, 36%–50%, 51%–70%, and 71%–100%. A total of 2650 sample points were obtained (Figure 5).…”

Section: Methodsmentioning

confidence: 99%

“…In the above expressions Red, Green, NIR, SWIR1, and SWIR2 represent the corresponding surface reflectance at band 4, band 3, band 5, band 6, and band 7 in LANDSAT-8 OLI images, respectively. erature on the division of rocky desertification degree (Su et al, 2020;Wang et al, 2019), the bedrock exposure rate is divided into five grades: < 20%, 20%-35%, 36%-50%, 51%-70%, and 71%-100%. A total of 2650 sample points were obtained (Figure 5).…”

Section: Comparison Indicesmentioning

confidence: 99%

“…In general, karst landscapes are ecologically fragile and have a harsh ecological environment and poor resistance to disturbance owing to their geological environment (Shu & Xiong, 2020; Yue et al, 2012). Karst areas feature a characteristic land degradation process known as rocky desertification, where extensive exposure of the bedrock to the surface occurs due to soil erosion by water, resulting in a significant decrease in land productivity, weak support for dense vegetation, and the formation of a desert‐like landscape (Wang et al, 2019; Zhang, Liu, et al, 2021; Zuazo & Pleguezuelo, 2008). Karst rocky desertification not only disrupts the ecological environment, but also affects agricultural production and seriously hinders the sustainability of local socio‐economic development, which is the most important ecological and environmental problem in southwest China (Jiang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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LANDSAT multispectral image analysis of bedrock exposure rate in highly heterogeneous karst areas through mixed pixel decomposition considering spectral variability

et al. 2023

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Mixed pixels and spectral variations commonly occur in images of karst areas because these areas have rugged topography and high surface heterogeneity. Consequently, the bedrock exposure rate cannot be accurately estimated using the existing rock index method based on remote sensing and the fixed endmember mixed pixel decomposition model. In order to solve this problem, using LANDSAT operational land imager/thematic mapper (OLI/TM) images as the data source, this study estimated the bedrock exposure rate based on a generalized linear mixed model (GLMM) considering spectral variability. In addition, this approach was compared with existing commonly used methods for estimating bedrock exposure rate. The results show that: (1) GLMM showed the highest performance in estimating bedrock exposure rate, with a total accuracy of 88.05% and a kappa coefficient of 0.845, and the accuracy exceeded 70% for different levels of bedrock exposure rate. However, the total accuracy of other commonly used bedrock exposure methods is was below 66%, being higher only in areas with bedrock exposure rates less than 20% and more than 70%. (2) By applying each method to different terrain scenes, GLMM was found to be more stable than other methods, and the estimated bedrock exposure rate is the closest to the ground reference value with a root mean square error less than 0.093.Other methods, including fully constrained least squares unmixing (FCLSU) and karst bare-rock index (KBRI), have a certain gap in different scenes, and their accuracy is not high. (3) Without any ground reference data, the accuracy of the direct unmixing result of GLMM is very close to that calculated by the regression model, and the accuracy difference among different grades of bedrock exposure rate is less than 5%, thus outperforming the other methods. GLMM can effectively estimate the bedrock exposure rate at different times using different data. Therefore, GLMM has great potential in extracting rocky desertification information in karst areas. It can also be a reference method for the rapid, accurate, and long-term evaluation of rocky desertification in karst areas.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Comparison Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LANDSAT multispectral image analysis of bedrock exposure rate in highly heterogeneous karst areas through mixed pixel decomposition considering spectral variability

et al. 2023

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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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Monitoring the evolution process of karst desertification and quantifying its drivers in the karst area of Southwest China

Liu,

Jiang,

Liu

et al. 2023

Environ Sci Pollut Res

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Evolution characteristics of karst rocky desertification in typical small watershed and the key characterization factor and driving factor

Cited by 7 publications

References 0 publications

LANDSAT multispectral image analysis of bedrock exposure rate in highly heterogeneous karst areas through mixed pixel decomposition considering spectral variability

LANDSAT multispectral image analysis of bedrock exposure rate in highly heterogeneous karst areas through mixed pixel decomposition considering spectral variability

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

Monitoring the evolution process of karst desertification and quantifying its drivers in the karst area of Southwest China

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