Desertification Information Research Articles

Analysis of Spatiotemporal Dynamics of Land Desertification in Qilian Mountain National Park Based on Google Earth Engine

Notwithstanding the overall improvement in the ecological condition of the Qilian Mountains, there are localized occurrences of grassland degradation, desertification, and salinization. Moreover, timely and accurate acquisition of desertification information is a fundamental prerequisite for effective monitoring and prevention of desertification. Leveraging the Google Earth Engine (GEE) platform in conjunction with machine learning techniques, this study aims to identify and extract the spatiotemporal dynamics of desertification in the Qilian Mountain National Park (QMNP) and its surroundings (QMNPs) spanning from 1988 to 2023. Results show that based on the random forest algorithm, the multi-index inversion methodology achieves a commendable overall accuracy of 91.9% in desertification extraction. From 1988 to 2023, the gravity center of light desertification shifts southeastward, while centers characterized by moderate, severe, and extremely severe desertification display a westward retreat with fluctuations. The area of sandy land shows an expansion trend in the medium term, but after 2018, desertification in QMNPs reversed. As of 2023, the sandy land area measured 16,897.35 km2, accounting for 18.29% of the total area of QMNPs. The insights garnered from this study provide a valuable reference for regional desertification prevention and control in the future.

A 20-Year Analysis of the Dynamics and Driving Factors of Grassland Desertification in Xilingol, China

Grassland desertification stands as an ecological concern globally. It is crucial for desertification prevention and control to comprehend the variation in area and severity of desertified grassland (DGL), clarify the intensities of conversion among DGLs of different desertification levels, and explore the spatial and temporal driving factors of desertification. In this study, a Desertification Difference Index (DDI) model was constructed based on albedo-EVI to extract desertification information. Subsequently, intensity analysis, the Geo-detector model, and correlation analysis were applied to analyze the dynamics and driving factors of desertification. The results showed the following: (1) Spatially, the DGL in Xilingol exhibited a zonal distribution. Temporally, the degree of DGL decreased, with the proportion of severely and moderately desertified areas decreasing from 51.77% in 2000 to 37.23% in 2020, while the proportion of nondesertified and healthy areas increased from 17.85% in 2000 to 37.40% in 2020; (2) Transition intensities among different desertification levels were more intense during 2000–2012, stabilizing during 2012–2020; (3) Meteorological factors and soil conditions primarily drive the spatial distribution of DDI, with evapotranspiration exhibiting the most significant influence (q-value of 0.83), while human activities dominate interannual DDI variations. This study provides insights into the conversion patterns among different desertification levels and the divergent driving forces shaping desertification in both spatial and temporal dimensions in Xilingol.

Long‐term desertification process monitoring and driving factors analysis in rare earth mining area

With the exploitation of rare earth elements (REE) in southern China, desertification has become a serious ecological problem in this region. To accurately understand the desertification process in mining areas and formulate targeted ecological restoration programs, taking the Lingbei mining area in Ganzhou City, Jiangxi Province as the study area, we explored desertification information extraction methods, desertification dynamics monitoring, and desertification drivers in REE mining areas using multisource data from 1986 to 2021. The results show that from the method of extracting desertification information, Random Forest is the prominent (83.33%), followed by Albedo–NDVI (Normalized Difference Vegetation Index) space (74.44%), and thirdly Linear Spectral Mixture Model (65.56%). From 1986 to 2021, the desertification land area in Lingbei mining areas first increased and then decreased, showing a reverse trend, but it has not recovered to the pre‐mining level. The government's ecological reclamation measures are the key factors for the restoration of desertification land. There are still 17.81 km2 areas that have not been restored, of which 2.16 km2 are at the level of severe desertification. Moderate desertification and light desertification have not been completely curbed, which needs continuous attention. Vegetation coverage has the highest explanatory power to the distribution of land desertification in mining areas, followed by land use classification, indicating that human activities have a great influence on the spatial differentiation of land desertification in mining areas, and it is necessary to pay attention to regional balanced and sustainable development in the future.

Extraction of Rocky Desertification Information in the Karst Area Based on the Red-NIR-SWIR Spectral Feature Space

The complex topography, severe surface fragmentation and landscape heterogeneity of the karst region of southwest China make it extremely difficult to extract information on rocky desertification in the region. In order to overcome the disadvantages of the surface parameter-based feature space approach, which is difficult to construct and apply, this study uses the reflectance of Landsat 8 Operational Land Imager (OLI) in the red (Red), near-infrared (NIR) and shortwave infrared (SWIR) bands as the feature variables, and establishes a two-dimensional SWIR-NIR, Red-NIR and SWIR-Red reflectance spectral feature space. The three models of perpendicular rocky desertification index 1 (PRDI1), perpendicular rocky desertification index 2 (PRDI2) and perpendicular rocky desertification index 3 (PRDI3) were also constructed based on the variation of the degree of rocky desertification in each spectral feature space. The accuracy of the rocky desertification extracted by these three index models was verified and compared with the karst rocky desertification index (KRDI) and rocky desertification difference index (RSDDI), which are constructed based on the surface parameter feature space. The results show that: (1) The waveband reflectance-based feature space model provides a new method for large-scale rocky desertification information extraction, characterized by easy data acquisition, simple index calculation and good stability, and is conducive to the monitoring and quantitative analysis of rocky desertification in karst areas. (2) The overall accuracy and Kappa coefficient of PRDI1 are 0.829 and 0.784, respectively, both higher than other index models, showing the best applicability, accuracy and effectiveness in rocky desertification information extraction. (3) According to the results extracted from PRDI1, the total area of rocky desertification in Huaxi District of Guizhou province is 320.44 km2, with the more serious grades of rocky desertification, such as severe and moderate, mainly distributed in the southwestern, western and southeastern areas of Huaxi District. This study provides important information on the total area and spatial distribution of different degrees of rocky desertification in the study area, and these results can be used to support the local government’s ecological and environmental management decisions. The method proposed in this study is a scientific and necessary complement to the characteristic spatial methods based on different surface parameters, and can provide important methodological support for the rapid and efficient monitoring of karstic rocky desertification over large areas.

The Changes of Desertification and Its Driving Factors in the Gonghe Basin of North China over the Past 10 Years

Desertification is one of the most severe environmental and socioeconomic issues facing the world today. Gonghe Basin is located in the monsoon marginal zone of China, is a sensitive area of climate change in the northeastern of the Qinghai-Tibet Plateau in China, desertification issue has become very severe. Remote sensing monitoring provides an effective technical means for desertification control. In this study, we used Landsat images in 2010 and 2020 to extract desertification information to constructed the Albedo-NDVI feature space in the Gonghe Basin. And then analyzed temporal and spatial evolution of desertification and its driving factors using Geodetector in the Gonghe Basin from 2010 to 2020. The main conclusions are as follows: (1) Albedo-NDVI feature space method can accurately classify desertification information with accuracy of more than 90%, which was benefit to quantitative analysis of desertification. (2) The desertification situation in the Gonghe Basin had improved from 2010 to 2020, especially in the west of the basin, desertification land area decreased by 827.46 km2, and desertification intensity had been obviously reversed. (3) The changes of the desertification in the Gonghe Basin from 2010 to 2020 was affected by both natural and human factors, and the influence of human activities on desertification reversal had increased gradually. The results indicate that the desertification status in the Gonghe Basin had been effectively controlled, and can provide useful basis for the desertification combat in the Gonghe Basin.

A dataset of desertification distribution in Mongolia in 2015

Desertification is one of the most serious eco-environmental and socio-economic problems in the world. Mongolia is a hot area of global desertification because of its fragile ecological environment and serious land degradation. In this study, based on Google Earth Engine platform, we selected Landsat7 remote sensing images, normalized vegetation index (NDVI), surface albedo (Albedo), improved soil adjusted vegetation index (MSAVI) and topsoil grain size index (TGSI) as desertification discrimination indexes, and combined the geographical division with desertification inversion characteristic space models to complete the fine inversion of Mongolian desertification information. In this way, we obtained a dataset of desertification distribution in Mongolia in 2015. The quality and accuracy of this dataset are verified by referring to field survey data and high-resolution Google Earth images. The overall evaluation accuracy is 87.00% and the Kappa coefficient is 83.19%. This dataset directly reflects the spatial distribution of different degrees of desertification in Mongolia, and can provide detailed and reliable data support for the delineation of key areas for desertification control and the formulation of restoration strategies in Mongolia. It is of great significance for the ecological environment and green and sustainable development of the China-Mongolia-Russia Economic Corridor.

Optimal Scale Selection and an Object-Oriented Method Used for Measuring and Monitoring the Extent of Land Desertification

Desertification has become a major problem in the field, affecting both the global ecological environment and economy. The effective monitoring of desertified land is an important prerequisite for land desertification protection and governance. With the aim of addressing the problems of spectral confusion as well as the salt and pepper phenomenon concerning the successful extraction of desertification information by utilizing the pixel-based method in the studies, Landsat remote sensing images obtained from the year 2001 to 2021 were selected in this study as the data source, and then, the object-oriented random forest classification method was improved by using different optimal segmentation scale selection techniques and combining multi-thematic index characteristics for measuring the extent of land desertification. Finally, the improved method was applied to study the dynamic changes in desertification in the Mu Us Sandy Land Ecological Function Reserve. The results show that the optimal scale determined by different optimal segmentation scale selection methods is not entirely consistent, and a minor scale should be selected as the optimal scale. Compared with the pixel-based classification method, the overall accuracy of object-oriented classification based on the optimal segmentation scale was improved by 8.06%, the Kappa coefficient increased by 0.1114, and the salt and pepper phenomenon was significantly reduced. From 2001 to 2021, the area of desertified land decreased by 587.12 km2 and the area of severely desertified land decreased by 4115.92 km2, indicating that the control effect was remarkable. This study can provide effective decision-making evidence and support for the successful governance of desertification.

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

AbstractMixed 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.

Extraction of rocky desertification information in karst area by using different multispectral sensor data and multiple endmember spectral mixture analysis method

As a typical form of land degradation, karst rocky desertification seriously restricts the development of the regional social economy and seriously threatens the living environment of residents. Fractional vegetation cover (fVC) and bare rock fraction (fBR) are important indicators to identify and evaluate rocky desertification. However, it is a great challenge to obtain fVC and fBR due to the complex terrain and fragmentation of karst rocky desertification areas. In this study, comparisons between Sentinel-2A Multispectral Instrument (Sentinel-2), Landsat-8 Operational Land Imager (Landsat-8), and GF-6 Wide Field View (GF-6) sensors for retrieving fVC and fBR are presented. The multiple endmember spectral mixture analysis (MESMA) and measured spectral dates were used to overcome the limitations of Spectral mixture analysis (SMA). Subsequently, fVC and fBR were validated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results showed that: 1) Sentinel-2 performed best in estimating fVC and obtained the highest accuracy (R2 = 0.6259, root mean square error = 0.1568, mean absolute error = 0.1215), followed by GF-6 and Landsat 8; in the extraction of the fBR, the performance was relatively the same, and the implementation of Sentinel-2 was also the best (R2 = 0.4911, root mean square error = 0.0714, mean absolute error = 0.0539), followed by GF-6 and Landsat 8. 2) Sentinel-2 images have higher resolution, the narrowest band range, and the most significant number of bands, which can better extract information about fVC and fBR in rocky desertification areas. 3) For the three optical sensors, the spatial resolution of the images is more important to extract the information of fVC and fBR in the rocky desertification areas. 4) In general, the extraction accuracy of fBR is not as good as that of fVC. The complicated ecological and geological environment of decertified areas has more influence on the effect of extraction of the fBR. 5) The Sentinel-2 achieves high accuracy for both fVC and fBR under different-level application scenarios. It thus has great potential for application in rocky desertification information extraction.

Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data

Rock desertification has become the third most serious ecological problem in western China after desertification and soil erosion. It is also the primary environmental problem to be solved in the karst region of southwest China. Karst landscapes in China are mainly distributed in southwest China, and the area centered on the Guizhou plateau is the center of karst landscape development in southern China. It has a fragile ecological environment, and natural factors and human activities have influenced the development of stone desertification in the karst areas to different degrees. In this paper, Dafang County, Guizhou Province, was selected as the study area to analyze the effect of the decision tree and multiple linear regression model on stone desertification and to analyze the evolution characteristics of stone desertification in Dafang County from 2005 to 2020. The FLUS model was applied to predict and validate the stone desertification information. The results show that the overall accuracy of multiple linear regression extraction of stone desertification is 70%, and the Kappa coefficient is 0.69; the overall accuracy of decision tree extraction of stone desertification is 60%, and the Kappa coefficient is 0.521. The multiple linear regression stone desertification extraction model is more accurate than the traditional decision tree classification. The overlay analysis of stone desertification and slope, elevation, slope direction and vegetation cover showed that stone desertification was more distributed between 1300–1900 m in elevation; stone desertification decreased gradually with the increase in slope; each grade of stone desertification was mainly distributed in the range of 5 to 25° in slope, which might be related to human activities. The FLUS model was used to predict the accuracy of 2015 data in the region and project the changes in stone desertification area in 2035 under a conventional scenario and an ecological protection scenario in the region to provide a new reference for predicting stone desertification.

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

The ecological stability of mining areas in Northwest China has been threatened by desertification for a long time. Remote sensing information combined with machine learning algorithms can effectively monitor and evaluate desertification. However, due to the fact that the geological environment of a mining area is easily affected by factors such as resource exploitation, it is challenging to accurately grasp the development process of desertification in a mining area. In order to better play the role of remote sensing technology and machine learning algorithms in the monitoring of desertification in mining areas, based on Landsat images, we used a variety of machine learning algorithms and feature combinations to monitor desertification in Ningdong coal base. The performance of each monitoring model was evaluated by various performance indexes. Then, the optimal monitoring model was selected to extract the long-time desertification information of the base, and the spatial-temporal characteristics of desertification were discussed in many aspects. Finally, the factors driving desertification change were quantitatively studied. The results showed that random forest with the best feature combination had better recognition performance than other monitoring models. Its accuracy was 87.2%, kappa was 0.825, Macro-F1 was 0.851, and AUC was 0.961. In 2003–2017, desertification land in Ningdong increased first and then slowly improved. In 2021, the desertification situation deteriorated. The driving force analysis showed that human economic activities such as coal mining have become the dominant factor in controlling the change of desert in Ningdong coal base, and the change of rainfall plays an auxiliary role. The study comprehensively analyzed the spatial-temporal characteristics and driving factors of desertification in Ningdong coal base. It can provide a scientific basis for combating desertification and for the construction of green mines.

Characteristics of Desertification Change in Lake Basin Area in Gangcha County

Qinghai Lake Basin area in Gangcha county is selected as the study area in terms of desertification change features in this paper. Based on the remote sensing (RS) and global positioning system (GPS) technologies, the desertification information range from 1989 to 2014 in the study area is extracted. Using the method of the decision tree, the desertification in the research area is been divided into four grades including mild desertification, moderate desertification, severe desertification and serious desertification. The change characteristics of desertification in the study area were analyzed in detail, which showed that the desertification in the study area experienced a process of first development and then a reversal. The rapid development of desertification appears in the 1990s, where about 1101.22 kilometers of desertification area was increased in this stage. Since the twenty-first Century, the desertification is gradually significant recovered and local area exist intensified desertification. There are tendencies of interactive transform in different types of desertification. The tendencies of different degrees of desertification land are rising, and there’re some differences in rising rates, where the expansion rate of moderate desertification is the biggest, increasing by 7.27 kilometers per year.

Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic

Building a high-precision, stable, and universal automatic extraction model of the rocky desertification information is the premise for exploring the spatiotemporal evolution of rocky desertification. Taking Guizhou province as the research area and based on MODIS and continuous forest inventory data in China, we used a machine learning algorithm to build a rocky desertification model with bedrock exposure rate, temperature difference, humidity, and other characteristic factors and considered improving the model accuracy from the spatial and temporal dimensions. The results showed the following: (1) The supervised classification method was used to build a rocky desertification model, and the logical model, RF model, and SVM model were constructed separately. The accuracies of the models were 73.8%, 78.2%, and 80.6%, respectively, and the kappa coefficients were 0.61, 0.672, and 0.707, respectively. SVM performed the best. (2) Vegetation types and vegetation seasonal phases are closely related to rocky desertification. After combining them, the model accuracy and kappa coefficient improved to 91.1% and 0.861. (3) The spatial distribution characteristics of rocky desertification in Guizhou are obvious, showing a pattern of being heavy in the west, light in the east, heavy in the south, and light in the north. Rocky desertification has continuously increased from 2001 to 2019. In conclusion, combining the vertical spatial structure of vegetation and the differences in seasonal phase is an effective method to improve the modeling accuracy of rocky desertification, and the SVM model has the highest rocky desertification classification accuracy. The research results provide data support for exploring the spatiotemporal evolution pattern of rocky desertification in Guizhou.

Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6’s lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.

Spatiotemporal changes in desertified land in rare earth mining areas under different disturbance conditions.

Special mining methods and red soil lead to large-scale land degradation and desertification in ion-type rare earth (RE) mining areas. Therefore, it is crucial for ecological management and restoration of mining areas to accurately understand the evolution process of desertification. In this study, remote sensing Landsat images from 1986 to 2019 were used to extract desertified land information from the Lingbei mining areas, Dingnan County, Ganzhou, China. To improve the reliability of the experiment, samples selected from Google images were used for verification to compare the accuracy of the desertification difference index (DDI) model and random forest (RF) algorithm for extracting land desertification information. The results showed that compared with the DDI model, the overall accuracy and kappa coefficient of the RF model based on multiple features were improved by 7% and 9.37%, respectively, indicating its higher applicability. Spatiotemporal change analysis of desertification in the mining area showed that the total area of desertification in the mining area increased most rapidly during 1986-1994 and reached 60.75 km2. The area of desertified land increased continuously from 1994 to 2004 and reached a maximum of 143.08 km2 in 2004. The area of desertified land decreased by 50.27 km2, but the severe desertified land (SDL) area increased by 1.69 km2 during 2004-2011. The area of desertified land gradually declined and stabilized from 2011 to 2019. Analysis of the desertification process in mining areas under different disturbance conditions showed that the desertified land disturbed by RE mining was most severely damaged. There is still an area of 16.77 km2 in the process of restoration, of which 2.24 km2 belongs to the SDL level. Moderate desertified land (MDL) and light desertified land (LDL) have not been completely contained and require the attention of the relevant departments to ensure their timely reclamation.

Desertification Information Extraction Along the China–Mongolia Railway Supported by Multisource Feature Space and Geographical Zoning Modeling

The China–Mongolia railway is the core foundation for the construction of traffic connection in the China–Mongolia–Russia economic corridor. Long-term desertification has brought significant ecological risks to the railway area. Owing to the large variety of vegetation cover in this region, desertification information is easily confused with other weak vegetation cover information. This article proposes a refined desertification information extraction method based on multisource feature spaces and geographical zoning modeling. First, based on the geographical zoning, land cover, and vegetation coverage data for Mongolia, the railway area is divided into the Central provinces and their northern region, the Eastern Mongolian Plateau, and the Southern Gobi region. According to the vegetation coverage characteristics and the applicability of various feature space models to different geographical regions, Albedo–normalized difference vegetation index, Albedo–modified soil adjusted vegetation index, and Albedo–topsoil grain size index feature space models were constructed for three geographical regions. Faced with new challenges presented by global warming and the impact of monsoons on the classification and extraction of desertification information, we established a desertification classification system with six levels (severe desertification, high desertification, medium desertification, low desertification, withered grassland, and nondesertification) and complete desertification information extraction. The results show that the overall classification accuracy of the method selected in this article is 85.21%. We further analyzed the mechanism of this method, compared it with previous studies, and thereby proved that this method is feasible to extract the fine information of desertification in large areas and complex geographical environments.

An Optimal Monitoring Model of Desertification in Naiman Banner Based on Feature Space Utilizing Landsat8 Oli Image

Current feature space models of desertification were almost linear, which ignored the complicated and non-linear relationships among variables for monitoring desertification. Fully considering the influencing factors of the desertification process in Naiman Banner, four sensitive indices including MSAVI, NDVI, TGSI, and Albedo have been selected to construct five feature spaces. Then, the precisions of different feature space models for monitoring desertification information (including non-linear and linear models) have been compared and analyzed. The non-linear Albedo-MSAVI feature space model for Naiman Banner has higher efficiency with the overall precision of 90.1%, while that of Albedo-TGSI had the worst precision with 0.69. Overall, the feature space model (non-linear) of Albedo-MSAVI has the highest applicability for monitoring the desertification information in Naiman Banner.

Desertification detection model in Naiman Banner based on the albedo-modified soil adjusted vegetation index feature space using the Landsat8 OLI images

The current desertification feature space models are almost linear and ignore the complicated and nonlinear relations that exist among variables when monitoring desertification. Herein, point-to-point and point-to-line models have been proposed by completely considering the nonlinear relations between the Albedo-Modified Soil Adjusted Vegetation Index (MSAVI) and the effects of soil background. Further, the applicability of these models for monitoring different levels of desertification information was compared and analyzed. The point-to-line feature space model exhibited a larger inversion accuracy (93.8%) for Naiman Banner with respect to albedo-MSAVI when compared with that exhibited by the point-to-point model (88.9%). In addition, the monitoring accuracy is observed to slightly differ for different levels of desertification, and slight and mild desertification exhibit the best inversion accuracy in case of both point-to-point and point-to-line models. Furthermore, the point-to-line model exhibits better applicability in case of intensive (92.7%) and severe (93.3%) desertification when compared with those exhibited by the point-to-point model (87.5% and 88.9%, respectively). The results obtained in this study can provide improved data and decision support for preventing and managing land degradation.

贵州省后寨河小流域地形因子、人为干扰与石漠化定量研究

PDF HTML阅读 XML下载 导出引用 引用提醒 贵州省后寨河小流域地形因子、人为干扰与石漠化定量研究 DOI: 10.5846/stxb201809111955 作者: 作者单位: 贵州大学贵州省森林资源与环境研究中心/林学院,贵州大学贵州省森林资源与环境研究中心/林学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重大科学研究计划项目（2013CB956702）；贵州省一流学科建设项目（GNYL[2017]007）；贵州省“百层次”培养计划（QKHRC-2015-4022） Quantitative relationships among topography anthropogenic disturbance, and rocky desertification in the Houzhai River Basin, Guizhou Province, China Author: Affiliation: Guizhou University, Institute for Forest Resources ＆ Environment of Guizhou/ College of Forestry,Guizhou University, Institute for Forest Resources ＆ Environment of Guizhou/ College of Forestry Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为定量研究喀斯特区域地形因子、人为干扰和石漠化之间的关系。基于网格法和实地观测，建立后寨河流域石漠化信息数据库并分析。结果表明：后寨河流域石漠化面积为14.43 km2，不同利用类型石漠化面积呈以下规律：荒地 > 坡耕地 > 乔木林地 > 耕地 > 灌木林地 > 灌草地 > 草地 > 弃耕地 > 园地 > 乔灌木林地 > 水田 > 经果林地；流域内岩石裸露率和土壤厚度主要受到坡度、坡位和人为干扰的制约，而人为干扰具有主观性，主要在岩石裸露率低、土壤较厚和坡度较缓的区域进行农业生产活动。荒地和弃耕地占据了石漠化面积的32.64％，且存在较大潜在石漠化的风险。回归模型发现人为干扰程度对岩石裸露率（负效应-0.286）和土壤厚度（正效应0.264）的直接效应最大，坡向对岩石裸露率（正效应0.067）和土壤厚度（负效应-0.054）的直接效应最小。认清地形因子、人为干扰和石漠化之间的耦合关系有利于喀斯特小流域石漠化治理。 Abstract:The relationship among topographic factors, human disturbance, and rocky desertification in karst areas was quantitatively assessed. A rocky desertification information database was established for the Houzhai River Basin and analyzed using grid-sampling and field observation. Rocky desertification affected a total of 14.43 km2, and affected multiple land use types, including uncultivated lands, sloping croplands, arbor forestlands, arable land, shrub lands, shrub grass-lands, grasslands, abandoned lands, garden lands, arbor-shrub mixed forestlands, paddy lands, and artificial fruit forestlands. The rock exposure rate and soil thickness were mainly restricted by slope gradient, slope position, and human disturbance. Otherwise, human disturbance is subjective, mainly in areas with low rock exposure, thick soil, and gentle slope gradient. Uncultivated lands and abandoned lands accounted for 32.64% of the rocky desertification area, and there was a greater risk rocky desertification under these types of land use. A regression model revealed that the degree of human disturbance directly affects both rock exposure rate (negative effect:-0.286) and soil thickness (positive effect:0.264). However, only insignificant effects were observed for slope direction on either rock bareness (positive effect:0.067) or soil thickness (negative effect:-0.054). According to these findings, rocky desertification in karst mountainous basin can be easily remedied by, understanding its relationships with topography and human disturbance. 参考文献 相似文献 引证文献

Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau.