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1
Middleton, N. J. "Desertification." Journal of Arid Environments 23, no. 4 (November 1992): 456–57. http://dx.doi.org/10.1016/s0140-1963(18)30626-8.
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Habib, Benbader, Mohamed Saadaoui, Abdallah Boumakhleb, Malika Brahimi, Abdelghafour Doghbage, Adel Djoughlafi, Hafidh Zemour, and Fathi Abdellatif Belhouadjeb. "Aspects of the ecosystem services threatened by desertification in Algerian steppe rangelands: concepts, status and stakes." Journal of Agriculture and Applied Biology 5, no. 1 (January 12, 2024): 1–17. http://dx.doi.org/10.11594/jaab.05.01.01.
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This study focuses on the imminent threat of desertification within Djelfa's steppe grazing lands, emphasizing its implications for land management and the sustainability of livestock practices. The methodology adopted employs a stringent approach that commences with a precise definition of desertification as a critical risk. It then proceeds to thoroughly assess the susceptibility of the local ecosystem to this phenomenon and delineates its consequential impact on both the human inhabitants and the surrounding environment. This comprehensive analysis effectively contextualizes human activities within the sphere of desertification's influence. Methodologically, the study employs a multidimensional framework to categorize the array of environmental goods and services rendered by these grazing lands. By identifying the beneficiaries associated with each service, the research aims to elucidate the complex threat posed at various levels. Crucially, the findings highlight the severe jeopardy that desertification imposes, not only endangering essential resources vital for extensive livestock production but also triggering a decline in invaluable environmental goods pivotal for the sustainability of the ecosystem and activities supporting human welfare. Statistically substantiated through an integration of diverse methodologies such as field surveys, satellite imagery analysis, and stakeholder consultations, this study validates the correlations between desertification and the degradation of ecosystem services. It provides empirical evidence showcasing the gradual decline of grazing lands, thereby compelling an urgent call for intervention strategies. In summary, this research underscores the urgent need for holistic strategies to mitigate the adverse effects of desertification. Its findings provide critical insights into the complex dynamics between human activities, ecosystem vulnerabilities, and the looming threat of desertification. It emphasizes the necessity for immediate collective action and sustainable resource management practices to safeguard ecosystems, ensure long-term sustainability, and protect the well-being of communities in Djelfa and beyond.
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Liu, Q. G. "Spatial and Temporal Changes and Driving Factors of Desertification Around Qinghai Lake, China." Nature Environment and Pollution Technology 22, no. 1 (March 2, 2023): 119–27. http://dx.doi.org/10.46488/nept.2023.v22i01.010.
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The area around Qinghai Lake is one of the most serious desertification areas on the Qinghai-Tibet Plateau. In this paper, combined with field investigation and indoor analysis, the classification and grading system of desertification around Qinghai Lake was established. On this basis, through remote sensing data processing and parameter inversion, the desertification monitoring index model was established. Based on the analysis of Landsat-5/TM remote sensing data from 1990 to 2020, the dynamic change characteristics of desertification land around Qinghai Lake in recent 30 years were obtained. The results show that the desertification area around Qinghai Lake was 1,359.62 km2, of which the light desertification land was the main one. The desertification spread in a belt around Qinghai Lake, concentrated in Ketu sandy area in the east, Ganzi River sandy area in the northeast, Bird Island sandy area in the northwest, and Langmashe sandy area in the southeast. From 1990 to 2000, the annual expansion rate of desertification around Qinghai Lake was 2.68%, the desertification spread rapidly, and light desertification land was the main part of desertification expansion. From 2000 to 2010, the annual expansion rate of desertification was only 0.83%, but severe desertification land and moderate desertification land developed more rapidly than in the previous period. From 2010 to 2020, the annual expansion rate of desertification was 2.66%, and the desertification was spreading rapidly, mainly with moderate desertification land and light desertification land. In the process of desertification land transfer around Qinghai Lake, the transfer of desertification land and non-desertification land was the main, accompanied by the mutual transformation of different levels of desertification land. The process of desertification around Qinghai Lake was essentially the result of natural and human factors. The special geographical location, climate changes, rodent damage, and human factors around Qinghai Lake were the main causes of desertification.
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Feng, Yuanyuan, Shihang Wang, Mingsong Zhao, and Lingmei Zhou. "Monitoring of Land Desertification Changes in Urat Front Banner from 2010 to 2020 Based on Remote Sensing Data." Water 14, no. 11 (June 1, 2022): 1777. http://dx.doi.org/10.3390/w14111777.
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Monitoring the spatio-temporal dynamics of desertification is critical for desertification control. Using the Urat front flag as the study area, Landsat remote sensing images between 2010 and 2020 were selected as data sources, along with MOD17A3H as auxiliary data. Additionally, RS and GIS theories and methods were used to establish an Albedo–NDVI feature space based on the normalized difference vegetation index (NDVI) and land surface albedo. The desertification difference index (DDI) was developed to investigate the dynamic change and factors contributing to desertification in the Urat front banner. The results show that: ① the Albedo–NDVI feature space method is effective and precise at extracting and classifying desertification information, which is beneficial for quantitative analysis and monitoring of desertification; ② from 2010 to 2020, the spatial distribution of desertification degree in the Urat front banner gradually decreased from south to north; ③ throughout the study period, the area of moderate desertification land increased the most, at an annual rate of 8.2%, while the area of extremely serious desertification land decreased significantly, at an annual rate of 9.2%, indicating that desertification degree improved during the study period; ④ the transformation of desertification types in Urat former banner is mainly from very severe to moderate, from severe to undeserted, and from mild to undeserted, with respective areas of 22.5045 km2, 44.0478 km2, and 319.2160 km2. Over a 10-year period, the desertification restoration areas in the study area ranged from extremely serious desertification to moderate desertification, from serious desertification to non-desertification, and from weak desertification to non-desertification, while the desertification aggravation areas ranged mainly from serious desertification to moderate desertification; ⑤ NPP dynamic changes in vegetation demonstrated a zonal increase in distribution from west to east, and significant progress was made in desertification control. The change in desertification has accelerated significantly over the last decade. Climate change and irresponsible human activities have exacerbated desertification in the eastern part of the study area.
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Yang, Wei, and Shu Wen Zhang. "Monitoring Desertification Process in Songnen Sandy Land during the past 10 Years." Advanced Materials Research 518-523 (May 2012): 4740–44. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.4740.
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In this paper, we used MODIS remote sensing image data of Songnen Sandy Land in July 2000 and 2010, extracted the value of MSAVI and vegetation cover index. Based on their values, degrees of desertification were classified including: un-desertification, micro-desertification, mild desertification, moderate desertification and severe desertification. The result show that the area of the desertification decreased in the past 10 years. The desertification is under a decreasing trend.
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Ying, Bin, Shi Zhen Xiao, Kang Ning Xiong, Qi Wei Chen, and Jing Sheng Luo. "The Distribution Characteristics of Rocky Desertification and Land Use/Land Cover in Karst Gorge Area." Advanced Materials Research 518-523 (May 2012): 4661–69. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.4661.
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The Huajiang Demonstration Area, which is a representative karst area with typical rocky desertification landscape in Guizhou was chosen as the study area. Based on the 5m resolution Spot 5 remote sensing images, the topographic maps (1:10 000) and the land use map etc, the rocky desertification of the area was interpreted. We took quantitative analysis through introducing two concepts: the rocky desertification occurrence among land use/land cover and the land use/ cover structure value of rocky desertification. The data showed that(1) the proportion of highly steep slopes in the study area is one of the reasons leading to large area of rocky desertification; (2)The rocky desertification occurrence among different land use/cover is different, and the land use/ cover type structure among rocky desertification is also different among all grades of desertification; (3)Rocky desertification intensity in different land use/cover can’t be measured in means of rocky desertification occurrence, high rocky desertification occurrence may be in a low-intensity state, and vice versa. (4)It is suggested that land use conditions, rocky desertification grades, and terrain of the land should be fully considered in the process of designing and matching rocky desertification control measures.
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Liu, Zhihui, Long Ma, Tingxi Liu, Zixu Qiao, and Yang Chen. "Influence of Key Climate Factors on Desertification in Inner Mongolia." Atmosphere 14, no. 9 (September 6, 2023): 1404. http://dx.doi.org/10.3390/atmos14091404.
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Desertification is a major environmental problem facing the world today, and climate change is an important factor influencing desertification. This study investigates the impact of changes in key climate factors on desertification based on normalized difference vegetation index data, precipitation data and evaporation data from Inner Mongolia between 1982 and 2020 using correlation analysis, regression modelling, and residual analysis. The results show that precipitation and evaporation are significantly correlated with mild desertification and severe desertification, respectively, with correlation coefficients reaching 0.98 and −0.96, respectively. In severely desertified areas in central-eastern Inner Mongolia, there is a high correlation between desertification and temperature, the characteristics of the correlation of average maximum and minimum temperatures with desertification are similar to those of the correlation of average temperature with desertification, and the average maximum and minimum temperatures are well correlated with mild desertification, with correlation coefficients as high as 0.98 and 0.978, respectively. Climate contribution accounts for 97% of desertification in severely desertified areas, indicating that climate change has increased desertification in these areas. In regions with improved desertification, approximately 75% are primarily influenced by climate change (with a relative contribution greater than 50%), with climate factors exhibiting a relative contribution greater than 75% to desertification in 30% of these regions.
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Xie, Jiali, Zhixiang Lu, and Kun Feng. "Effects of Climate Change and Human Activities on Aeolian Desertification Reversal in Mu Us Sandy Land, China." Sustainability 14, no. 3 (January 31, 2022): 1669. http://dx.doi.org/10.3390/su14031669.
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The aeolian desertification in Mu Us Sandy Land (MUSL) in northern China have been paid much attention, but the relative contributions of climate change and human activities to desertification dynamics are still not clear. Based on the Landsat MSS, TM, ETM+ and OLI images in 1975, 1990, 1995, 2000, 2005, 2010 and 2015, we developed a database of aeolian desertification land distribution, discussed the spatial and temporal variation of aeolian desertification, and discovered the relative contributions of climate change and human activities to desertification reversal, using the trends of the potential net primary productivity (NPP) and the human-influenced NPP with meteorological data and MODIS NPP products. The results indicated that aeolian desertification developed firstly from 1975 to 2000, with serious and severe aeolian desertification land continually increasing, and then changed into a reversal state from 2000 to 2015, as the serious aeolian desertification land decreased, although the severe, moderate and light aeolian desertification land lightly increased. Human activities were the dominant factor in desertification dynamics in MUSL and had different contributions to aeolian desertification reversal in different periods. This study will improve our understanding of the processes of aeolian desertification.
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Bartuska, Ann. "Beyond desertification." Frontiers in Ecology and the Environment 13, no. 1 (February 2015): 3. http://dx.doi.org/10.1890/1540-9295-13.1.3.
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Weissmann, Haim, and Nadav M. Shnerb. "Stochastic desertification." EPL (Europhysics Letters) 106, no. 2 (April 1, 2014): 28004. http://dx.doi.org/10.1209/0295-5075/106/28004.
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Verón, S. R., J. M. Paruelo, and M. Oesterheld. "Assessing desertification." Journal of Arid Environments 66, no. 4 (September 2006): 751–63. http://dx.doi.org/10.1016/j.jaridenv.2006.01.021.
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Mabbutt, J. A. "Desertification indicators." Climatic Change 9, no. 1-2 (1986): 113–22. http://dx.doi.org/10.1007/bf00140530.
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Jia, Hong, Rui Wang, Hang Li, Baijian Diao, Hao Zheng, Lanlan Guo, Lianyou Liu, and Jifu Liu. "The Changes of Desertification and Its Driving Factors in the Gonghe Basin of North China over the Past 10 Years." Land 12, no. 5 (May 1, 2023): 998. http://dx.doi.org/10.3390/land12050998.
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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.
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Cao, Jiaju, Xingping Wen, Meimei Zhang, Dayou Luo, and Yinlong Tan. "Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data." Sustainability 14, no. 20 (October 17, 2022): 13385. http://dx.doi.org/10.3390/su142013385.
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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.
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Aslanov, Ilhomjon, Nozimjon Teshaev, Kholmurod Khayitov, Uzbekkhon Mukhtorov, Jamila Khaitbaeva, and Dilrabo Murodova. "Analysis of desertification trends in Central Asia based on MODIS Data using Google Earth Engine." E3S Web of Conferences 443 (2023): 06015. http://dx.doi.org/10.1051/e3sconf/202344306015.
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Desertification is a significant environmental issue affecting arid and semi-arid regions globally, including Central Asia. Monitoring and analyzing desertification trends is crucial for understanding the extent of land degradation and implementing effective management strategies. This literature review aims to provide an overview of existing research on analyzing desertification trends in Central Asia using MODIS data and the application of Google Earth Engine for analysis. Remote Sensing and Desertification Monitoring: Remote sensing techniques, particularly those utilizing satellite data, have been widely employed for monitoring desertification processes. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard various NASA satellites provides valuable data for assessing vegetation dynamics and land cover changes associated with desertification. Central Asia and Desertification: Central Asia, encompassing countries such as Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan, and Tajikistan, faces significant desertification challenges. Studies have highlighted the impacts of climate change, unsustainable land management practices, and population growth on desertification in the region. Monitoring and analyzing desertification trends in Central Asia are essential for developing targeted mitigation and adaptation strategies.
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Ren, Yu, Xiangjun Liu, Bo Zhang, and Xidong Chen. "Sensitivity Assessment of Land Desertification in China Based on Multi-Source Remote Sensing." Remote Sensing 15, no. 10 (May 21, 2023): 2674. http://dx.doi.org/10.3390/rs15102674.
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Desertification, a current serious global environmental problem, has caused ecosystems and the environment to degrade. The total area of desertified land is about 1.72 million km2 in China, which is extensively affected by desertification. Estimating land desertification risks is the top priority for the sustainable development of arid and semi-arid lands in China. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model was used to assess the sensitivity of land desertification in China. Based on multi-source remote sensing data, this study integrated natural and human factors, calculated the land desertification sensitivity index by overlaying four indicators (soil quality, vegetation quality, climate quality, and management quality), and explored the driving forces of desertification using a principal component and correlation analysis. It was found that the spatial distribution of desertification sensitivity areas in China shows a distribution pattern of gradually decreasing from northwest to southeast, and the areas with very high and high desertification sensitivities were about 620,629 km2 and 2,384,410 km2, respectively, which accounts for about 31.84% of the total area of the country. The very high and high desertification sensitivity areas were mainly concentrated in the desert region of northwest China. The principal component and correlation analysis of the sub-indicators in the MEDALUS model indicated that erosion protection, drought resistance, and land use were the main drivers of desertification in China. Furthermore, the aridity index, soil pH, plant coverage, soil texture, precipitation, soil depth, and evapotranspiration were the secondary drivers of desertification in China. Moreover, the desertification sensitivity caused by drought resistance, erosion protection, and land use was higher in the North China Plain region and Guanzhong Basin. The results of the quantitative analysis of the driving forces of desertification based on mathematical statistical methods in this study provide a reference for a comprehensive strategy to combat desertification in China and offer new ideas for the assessment of desertification sensitivity at macroscopic scales.
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Yi, Ta Na, Li Xin Wang, Hua Min Liu, and Yi Zhuo. "The Land Desertification Change of Wuliangsu Lake." Advanced Materials Research 955-959 (June 2014): 3724–29. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.3724.
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This paper analyzes the long-terms (from 1987 to 2010) land desertification in Wuliangsu Lake by interpreting TM images in 1987, 2000 and 2010, and then the land desertification information divided into five different levels by Albedo-NDVI feature space, these five levels represent the different desertification land covering types. Also we explored the change trend among different types of land desertification though the adoption of Markov model, the results suggests that there is a significant increasing of extremely serious desertification land from 1987 to 2000, which indicates the deterioration of environmental conditions during this period. While, there is a significant decreasing of extremely serious desertification land, moderate desertification land and weak desertification land, which indicates the amelioration of environmental conditions during this period from 2000 to 2010.
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Liang, Xiya, Pengfei Li, Juanle Wang, Faith Ka Shun Chan, Chuluun Togtokh, Altansukh Ochir, and Davaadorj Davaasuren. "Research Progress of Desertification and Its Prevention in Mongolia." Sustainability 13, no. 12 (June 17, 2021): 6861. http://dx.doi.org/10.3390/su13126861.
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Mongolia is a globally crucial region that has been suffering from land desertification. However, current understanding on Mongolia’s desertification is limited, constraining the desertification control and sustainable development in Mongolia and even other parts of the world. This paper studied spatiotemporal patterns, driving factors, mitigation strategies, and research methods of desertification in Mongolia through an extensive review of literature. Results showed that: (i) remote sensing monitoring of desertification in Mongolia has been subject to a relatively low spatial resolution and considerable time delay, and thus high-resolution and timely data are needed to perform a more precise and timely study; (ii) the contribution of desertification impacting factors has not been quantitatively assessed, and a decoupling analysis is desirable to quantify the contribution of factors in different regions of Mongolia; (iii) existing desertification prevention measures should be strengthened in the future. In particular, the relationship between grassland changes and husbandry development needs to be considered during the development of desertification prevention measures; (iv) the multi-method study (particularly interdisciplinary approaches) and desertification model development should be enhanced to facilitate an in-depth desertification research in Mongolia. This study provides a useful reference for desertification research and control in Mongolia and other regions of the world.
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Sterk, Geert, and Jetse J. Stoorvogel. "Desertification–Scientific Versus Political Realities." Land 9, no. 5 (May 18, 2020): 156. http://dx.doi.org/10.3390/land9050156.
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Desertification is defined as land degradation occurring in the global drylands. It is one of the global problems targeted under the Sustainable Development Goals (SDG 15). The aim of this article is to review the history of desertification and to evaluate the scientific evidence for desertification spread and severity. First quantitative estimates of the global extent and severity of desertification were dramatic and resulted in the establishment of the UN Convention to Combat Desertification (UNCCD) in 1994. UNCCD’s task is to mitigate the negative impacts of desertification in drylands. Since the late 1990s, science has become increasingly critical towards the role of desertification in sustainable land use and food production. Many of the dramatic global assessments of desertification in the 1970s and 1980s were heavily criticized by scientists working in drylands. The used methodologies and the lack of ground-based evidence gave rise to critical reflections on desertification. Some even called desertification a myth. Later desertification assessments relied on remote sensing imagery and mapped vegetation changes in drylands. No examples of large areas completely degraded were found in the scientific literature. In science, desertification is now perceived as a local feature that certainly exists but is not as devastating as was earlier believed. However, the policy arena continues to stress the severity of the problem. Claims that millions of hectares of once productive land are annually lost due to desertification are regularly made. This highlights the disconnection between science and policy, and there is an urgent need for better dialogue in order to achieve SDG 15.
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Jin, Ao, Kangning Xiong, Juan Hu, Anjun Lan, and Shirong Zhang. "Remote Sensing Ecological Quality and Its Response to the Rocky Desertification in the World Heritage Karst Sites." Land 13, no. 4 (March 23, 2024): 410. http://dx.doi.org/10.3390/land13040410.
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Clarifying the spatial and temporal evolution characteristics of the ecological environment quality of World Heritage Karst Sites (WHKSs) and its response to different rocky desertification grades at spatial scales is crucial for the monitoring and protection of WHKSs as well as the implementation of ecological and environmental policies in karst regions. The ecological evaluation model of Remote Sensing Ecological Index (RSEI) was used to evaluate the ecological environment of Libo–Huanjiang World Heritage Karst site and Shibing World Heritage Karst site, and then the spatial autocorrelation and geo-detection model was used to further analyze the ecological environment, and final spatial overlay of RSEI and rocky desertification by year to analyze the linkage relationship between RSEI and rocky desertification. The results showed that (1) in the three-phase ecological environmental quality evaluation of the two heritage sites, the RSEI in 2010, 2016, and 2022 reached 0.60, 0.67, and 0.64 for the Libo–Huanjiang heritage site, and RSEI in 2010, 2016, and 2022 for the Shibing heritage site reached 0.60, 0.74, and 0.70, respectively; (2) the RSEI of both heritage sites show a gradually increasing positive spatial correlation, and has significant spatial aggregation characteristics, with both heritage sites dominated by the high-high and low-low spatial aggregation categories; (3) both heritage sites have the highest degree of explanation of changes in ecological quality by the NDBSI factor, indicating that this factor plays a key role in changes in ecological quality at heritage sites; (4) the response of the RSEI mean value of Libo–Huanjiang in each grade of rocky desertification area is, from high to low, no rocky desertification, non-karst, potential rocky desertification, mild rocky desertification, moderate rocky desertification, intensive rocky desertification, and extreme intensity rocky desertification, and the response of the RSEI mean value of Shibing is, from high to low, non-karst, no rocky desertification, potential rocky desertification, mild rocky desertification, and moderate rocky desertification. The spatial superposition analysis of the RSEI index and rocky desertification index can quantitatively study the changing status of the ecological environment in different rocky desertification areas, and the results of the study can provide theoretical references for the environmental monitoring and the prevention and control of rocky desertification in the karst areas and WHKSs.
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Ding, Dengshan, Haosheng Bao, and Yongli Ma. "Progress in the study of desertification in China." Progress in Physical Geography: Earth and Environment 22, no. 4 (December 1998): 551–57. http://dx.doi.org/10.1177/030913339802200404.
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The study of desertification in China began in 1977 with the UN Conference on Desertification. Since then, much progress has been made. This article reviews progress over the last five years in China. A brief description of desertification research in China is given, and developments and changes in the definition of desertification described. Finally, the causes and patterns of desertification are discussed.
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Qian, Chunhua, Hequn Qiang, Changyou Qin, Zi Wang, and Mingyang Li. "Spatiotemporal Evolution Analysis and Future Scenario Prediction of Rocky Desertification in a Subtropical Karst Region." Remote Sensing 14, no. 2 (January 9, 2022): 292. http://dx.doi.org/10.3390/rs14020292.
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Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui.
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Lu, Chun, Hui Hui Zhang, and Chi Ma. "The Analysis on Dynamic Changes of Land Desertification and Salinization Based on RS and GIS." Advanced Materials Research 726-731 (August 2013): 4668–73. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4668.
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Abstract:For studying the characters of dynamic changes of land Desertification in Songnen Plain, this paper makes use of remote sensing data and geography information system technology and introduces some characteristic parameters, such as the degree of desertification change, to get the numerical value of these characters. The research result shows that: in 1975-1988, the land area of desertification increases rapidly; the deterioration in the border region of land desertification is stronger than that in the center; and in 1988-2001, the area of desertification decreases slowly, the reversion in the border of desertification is stronger than that in the center, then the tendency of expansion of land desertification been initially contained.
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Xie, Jiali, Zhixiang Lu, Shengchun Xiao, and Changzhen Yan. "The Latest Desertification Process and Its Driving Force in Alxa League from 2000 to 2020." Remote Sensing 15, no. 19 (October 8, 2023): 4867. http://dx.doi.org/10.3390/rs15194867.
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Alxa League of Inner Mongolia Autonomous Region is a concentrated desert distribution area in China, and the latest desertification process and its driving mechanism under the comprehensive influence of the extreme dry climate and intense human activities has attracted much attention. Landsat data, including ETM+ images obtained in 2000, TM images obtained in 2010, and OLI images obtained in 2020, were used to extract three periods of desertification land information using the classification and regression tree (CART) decision tree classification method in Alxa League. The spatio-temporal variation characteristics of desertification land were analyzed by combining the transfer matrix and barycenter migration model; the effects of climate change and human activities on regional desertification evolution were separated and recombined using the multiple regression residual analysis method and by considering the influence of non-zonal factors. The results showed that from 2000 to 2020, the overall area of desertification land in Alxa League was reduced, the desertification degree was alleviated, the desertification trend was reversed, and the desertification degree in the northern part of the region was more serious than in the southern part. The barycenter of the slight, moderate, and severe desertification land migrated to the southeast, whereas the serious desertification land’s barycenter migrated to the northwest in the period of 2000–2010; however, all of them hardly moved from 2010 to 2020. The degree of desertification reversal in the south was more significant than in the north. Regional desertification reversal was mainly influenced by the combination of human activities and climate change, and the area accounted for 61.5%; meanwhile, the localized desertification development was mainly affected by human activities and accounted for 76.8%.
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Guo, Bing, Rui Zhang, Miao Lu, Mei Xu, Panpan Liu, and Longhao Wang. "A New Large-Scale Monitoring Index of Desertification Based on Kernel Normalized Difference Vegetation Index and Feature Space Model." Remote Sensing 16, no. 10 (May 16, 2024): 1771. http://dx.doi.org/10.3390/rs16101771.
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As a new vegetation monitoring index, the KNDVI has certain advantages in characterizing the evolutionary process of regional desertification. However, there are few reports on desertification monitoring based on KNDVI and feature space models. In this study, seven feature parameters, including the kernel normalized difference vegetation index (KNDVI) and Albedo, were introduced to construct different models for desertification remote-sensing monitoring. The optimal desertification remote-sensing monitoring index model was determined with the measured data; then, the spatiotemporal evolution pattern of desertification in Gulang County from 2013 to 2023 was analyzed and revealed. The main conclusions were as follows: (1) Compared with the NDVI and MSAVI, the KNDVI showed more advantages in the characterization of the desertification evolution process. (2) The point–line pattern KNDVI-Albedo remote-sensing index model had the highest monitoring accuracy, reaching 94.93%, while the point–line pattern NDVI-TGSI remote-sensing monitoring index had the lowest accuracy of 54.38%. (3) From 2013 to 2023, the overall desertification situation in Gulang County showed a trend of improvement with a pattern of “firstly aggravation and then alleviation.” Additionally, the gravity center of desertification in Gulang County first shifted to the southeast and then to the northeast, indicating that the northeast’s aggravating rate of desertification was higher than in the southwest during the period. (4) From 2013 to 2023, the area of stable desertification in Gulang County was the largest, followed by the slightly weakened zone, and the most significant transition area was that of extreme desertification to severe desertification. The research results provide important decision support for the precise monitoring and governance of regional desertification.
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Ji, Xinyang, Jinzhong Yang, Jianyu Liu, Xiaomin Du, Wenkai Zhang, Jiafeng Liu, Guangwei Li, and Jingkai Guo. "Analysis of Spatial-Temporal Changes and Driving Forces of Desertification in the Mu Us Sandy Land from 1991 to 2021." Sustainability 15, no. 13 (July 1, 2023): 10399. http://dx.doi.org/10.3390/su151310399.
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Desertification is one of the most critical environmental and socioeconomic issues in the world today. Located in the transitional region between the desert and the Loess Plateau, the Mu Us Sandy Land is one of the nine most environmentally sensitive areas in the world. Remote sensing provides an effective technical method for desertification monitoring. In order to analyze the spatiotemporal distribution of desertification in the Mu Us Sandy Land from 1991 to 2021, the “MSAVI-Albedo” model was employed to extract desertification data in 1991, 2002, 2009 and 2021. The clustering characteristics of desertification were analyzed based on Moran’s I statistic. Subsequently, the driving forces in desertification changes were investigated using a geographical detector to analyze the influence of soil, meteorology, and topography on desertification. Additionally, the impact of meteorological and human factors on desertification change in the Mu Us Sandy Land was assessed. From 1991 to 2021, the degree of desertification of the Mu Us Sandy Land showed an overall decreasing trend, and the percentage of land classified as undergoing extremely severe, severe, moderate and mild desertification was improved by 86.11%, 81.82%, 52.5% and 37.42%, respectively. The proportion of land classified as undergoing extremely severe desertification decreased from 29.22% to 5.62%, and the proportion of land undergoing no desertification increased from 4.16% to 18.33%. At the same time, the desertification center shifted westward, and the desertification distribution showed a clustering trend. It is known that different factors affect the formation and distribution of desertification in the Mu Us Sandy Land in the following order: soil, meteorology, and topography. Over the past 30 years, the mean annual temperature and annual precipitation increased at rates of 0.01871 °C/a and 1.0374 mm/a, respectively, while the mean annual wind speed decreased at a rate of 0.00945 m/s·a. These changes provided more favorable natural conditions for vegetation growth and sand fixation. Human factors, such as economic development, agriculture and animal husbandry practices, and the policy of returning farmland to forest (grassland) also had a significant impact on the desertification process, leading to a year-by-year improvement in the ecological environment of the Mu Us Sandy Land.
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Xie, Zhen Hong. "Research Advance in Remote Sensing to Land Desertification Monitoring." Advanced Materials Research 864-867 (December 2013): 2817–20. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.2817.
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This Land desertification has been a worldwide ecological and environmental problem. It is the significance for effective remedy of land desertification to monitor the desertification ,know well the present situation ,intensity as well as dynamic variation rules of the desertification. In recent years, remote sensing has become an important technology to monitor land desertification.Firstly, we summarize the research progress in monitoring land desertification using remote sensing data acquisition.Then, we discuss about themethods to extract information of land desertification from remote sensing image, which includes artificial visual interpretation, supervised classification, unsupervised classification, hierarchical decision tree classification, neural network classification and spectral mixture analysis£¬and also we comprehensively compare the strength and weaknesses of each method. Finally, We point out the problems in the remote sensing technology application to land desertification monitoring and put forward the development prospects in the application of remote sensing to monitoring land desertification.
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Gao, Weijie, Siyi Zhou, and Xiaojie Yin. "Spatio-Temporal Evolution Characteristics and Driving Factors of Typical Karst Rocky Desertification Area in the Upper Yangtze River." Sustainability 16, no. 7 (March 25, 2024): 2669. http://dx.doi.org/10.3390/su16072669.
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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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Xu, Xue, Luyao Liu, Peng Han, Xiaoqian Gong, and Qing Zhang. "Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV." International Journal of Environmental Research and Public Health 19, no. 24 (December 14, 2022): 16793. http://dx.doi.org/10.3390/ijerph192416793.
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Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study, 19 vegetation indices were calculated for 30 unmanned aerial vehicle (UAV) visible light images at five grades of grassland desertification in the Mu Us Sandy. Fractional Vegetation Coverage (FVC) with high accuracy was obtained through Support Vector Machine (SVM) classification, and the results were used as the reference values. Based on the FVC, the grassland desertification grades were divided into five grades: severe (FVC < 5%), high (FVC: 5–20%), moderate (FVC: 21–50%), slight (FVC: 51–70%), and non-desertification (FVC: 71–100%). The accuracy of the vegetation indices was assessed by the overall accuracy (OA), the kappa coefficient (k), and the relative error (RE). Our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. Excess Green Red Blue Difference Index (EGRBDI), Visible Band Modified Soil Adjusted Vegetation Index (V-MSAVI), Green Leaf Index (GLI), Color Index of Vegetation Vegetative (CIVE), Red Green Blue Vegetation Index (RGBVI), and Excess Green (EXG) accurately assessed grassland desertification at severe, high, moderate, and slight grades. In addition, the Red Green Ratio Index (RGRI) and Combined 2 (COM2) were accurate in assessing severe desertification. The assessment of the 19 indices of the non-desertification grade had low accuracy. Moreover, our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. This study emphasizes that the applicability of the vegetation indices varies with the degree of grassland desertification and hopes to provide scientific guidance for a more accurate grassland desertification assessment.
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Wang, Yongfang, Enliang Guo, Yao Kang, and Haowen Ma. "Assessment of Land Desertification and Its Drivers on the Mongolian Plateau Using Intensity Analysis and the Geographical Detector Technique." Remote Sensing 14, no. 24 (December 16, 2022): 6365. http://dx.doi.org/10.3390/rs14246365.
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Desertification is one of the most harmful ecological disasters on the Mongolian Plateau, placing the grassland ecological environment under great pressure. Remote-sensing monitoring of desertification and exploration of the drivers behind it are important for effectively combating this issue. In this study, four banners/counties on the border of China and Mongolia on the Mongolian Plateau were selected as the target areas. We explored desertification dynamics and their drivers by using remote sensing imagery and a product dataset for the East Ujimqin Banner and three counties in Mongolia during the period 2000–2015. First, remote sensing information on desertification in the fourth phase of the study area was extracted using the visual interpretation method. Second, the dynamic change characteristics of desertification were analyzed using the intensity analysis method. Finally, the drivers of desertification and their explanatory powers were identified using the geographical detector method. The results show that the desertification of the East Ujimqin Banner has undergone a process of reversion, development, and mild development, with the main transition occurring between slight (SL) and non-desertified land (N), very serious desertified land (VS), and water areas. The dynamics of desertification in this region are influenced by a combination of natural and anthropogenic factors. Desertification in the three counties of Mongolia has undergone processes of development, mild development and mild development with SL and vs. as the main types. Desertification in Mongolia is mainly concentrated in Matad County, which is greatly affected by natural conditions and has little impact from anthropogenic activities. In addition, the change intensity of desertification dynamics in the study area showed a decreasing trend, and the interaction between natural and anthropogenic drivers could enhance the explanatory power of desertification dynamics. The research results provide a scientific basis for desertification control, ecological protection, and ecological restoration on the Mongolian Plateau.
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Naifeisai, Abudukeremu·, and Xinglei Chen. "Investigation and Evaluation of the Effectiveness of Land Desertification Control in Yili Area, Xinjiang." Frontiers in Humanities and Social Sciences 3, no. 4 (April 20, 2023): 95–98. http://dx.doi.org/10.54691/fhss.v3i4.4773.
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At present, land desertification is still a serious ecological environment problem in China. Xinjiang is the administrative region with the largest desertified soil area in China and the most severe wind and sand damage. This paper chooses Yili area of Xinjiang as an example to study the situation of soil desertification prevention and control, and summarizes the control mode of soil desertification, and uses comparative analysis method to analyze its soil desertification prevention and ecological benefits. Propose the effectiveness of improving and controlling land desertification, and improve the level of land desertification control in Yili from the perspective of management and technology.
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Yi, Yang, Mingchang Shi, Jie Wu, Na Yang, Chen Zhang, and Xiaoding Yi. "Spatio-Temporal Patterns and Driving Forces of Desertification in Otindag Sandy Land, Inner Mongolia, China, in Recent 30 Years." Remote Sensing 15, no. 1 (January 3, 2023): 279. http://dx.doi.org/10.3390/rs15010279.
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Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification.
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Khosravi, H., G. R. Zehtabian, H. Eskandari Damaneh, and A. Abolhasani. "ASSESSMENT AND MAPPING OF IRAN DESERTIFICATION INTENSITY USING ARCGIS ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 639–44. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-639-2019.
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Abstract. Desertification phenomenon is described as one of the most obvious forms of natural resources degradation in the world. This phenomenon, which occurs because of natural factors or anthropogenic factors, is accounted as the third most important global challenge after crisis of water shortage and drought in the 21st century. Awareness of desertification criteria and indicators, investigation of a regional model and determining the most important factors affecting desertification are essential for combating desertification. So in this study, IMDPA model (Iranian Model of Desertification Potential Assessment) was used in order to prepare Atlas of Iran desertification. 8 criteria and 130 indicators affecting desertification have determined for this model. Regarding to these criteria and indicators and quantifying them in arid, semi-arid and dry sub-humid region of Iran, the map of desertification intensity was prepared. The results of this study showed that 88.73% of the country surface was affected by desertification that is equal to 143365238.6 hectare. The surface more than 49425703.3 hectare equal to 30.59% of total surface of country was in low desertification class, the surface more than 935677913.6 hectare equal to 57.91% was in class II or medium and the surface about 371621.7 hectare equal to 0.23% was in class III or intense. Class IV of desertification or very intense was omitted regarding to IMDPA model and 8 criteria, and natural desert areas which their surface was equal to 15624274.3 hectare or 9.67% is beyond this class.
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Yang, Jingyi, Qinjun Wang, Dingkun Chang, Wentao Xu, and Boqi Yuan. "A High-Precision Remote Sensing Identification Method for Land Desertification Based on ENVINet5." Sensors 23, no. 22 (November 14, 2023): 9173. http://dx.doi.org/10.3390/s23229173.
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Land desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of great theoretical value and practical significance to carry out accurate identification and monitoring of land desertification and its influencing factors in ecologically fragile areas of China. This is conducive to curbing land desertification and ensuring regional ecological security. Minqin County, Gansu Province, located in northwestern China, is one of the most serious areas of land desertification, which is also one of the four sandstorm sources in China. Based on ENVINet5, this paper constructs a high-precision land desertification identification method with an accuracy of 93.71%, which analyzes the trend and reasons of land desertification in this area, provides suggestions for disaster prevention in Minqin County. and provides a reference for other similar areas to make corresponding desertification control policies.
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Aldabbagh, Yasir Abdulameer Nayyef, Helmi Zulhaidi Mohd Shafri, Shattri Mansor, and Mohd Hasmadi Ismail. "Desertification Susceptibility Assessment Using Expert-Based Approach in Al-Khidhir District, Southern Iraq." International Journal of Environmental Science and Development 14, no. 6 (2023): 364–72. http://dx.doi.org/10.18178/ijesd.2023.14.6.1456.
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Desertification is a serious threat to environment, agriculture, and public health all over the world. Iraq is concerned about desertification, particularly in the southern regions. As a result, this project will use geospatial methodologies to measure desertification susceptibility in Al-Khidhir, Al-Muthanna. The assessment was based on Landsat 5 TM data from 1998, Landsat 7 ETM+ data from 2008, and Landsat 8 OLI data from 2018. A Multicriteria Decision-Making (MCDM) technique based on the Analytic Hierarchy Process (AHP) was developed to assess the spatial distribution of desertification in the research area and the likelihood that it will occur soon. An examination of the geographical distribution of desertification in Al-Khidhir in 1998 indicated that desertification was most prevalent in the southern region, accounting for 25.9% of the total. Desertification spread to neighboring areas and increased (50.8%) in the research region’s north in 2008. The findings suggested that using satellite photos, such as Landsat, can be extremely effective for assessing desertification.
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Cao, Xiaoming, Mengchun Cui, Lei Xi, and Yiming Feng. "Spatial-Temporal Process of Land Use/Land Cover and Desertification in the Circum-Tarim Basin during 1990–2020." Land 13, no. 6 (May 23, 2024): 735. http://dx.doi.org/10.3390/land13060735.
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The problem of desertification in the Tarim Basin, an area with a unique geography and climatic conditions, has received extensive research attention not only in China but also around the world. Between natural factors and human activities, the latter are considered the main cause of desertification, with the excessive use of land resources accelerating its risk. This study classified the degree of desertification into five types, no, light, moderate, severe, and extremely severe desertification, and focused on the spatio-temporal changes in LULC, desertification development, and their relationship in the Circum-Tarim Basin during the period of 1990–2020, and the results indicated the following. (1) Over the 30-year study period, farmland development was frequent in the basin. The total farmland area increased significantly by 1.40 × 104 km2, which resulted from the occupation of grassland (mainly low-covered and medium-covered grassland) and unused land (mainly saline–alkali land). (2) There was a general alleviation of the effects of desertification, but also local deterioration. The area of no-desertification land has significantly increased (an increase of 2.10 × 104 km2), and the degree of desertification has shifted significantly to adjacent lighter degrees, but the area of extremely severe desertification in certain regions has increased (an increase of 7.89 × 104 km2). (3) There was an inseparable relationship between LULC and desertification. Oasisization and desertification were two processes that interacted and were interrelated. There was an approximately 54.42% increase in no-desertification land area mainly occurring in the region where LULC types changed (Region II), although this area only accounted for 9.71% of the total area of the basin. There was an approximately 98.28% increase in the area of extremely severe desertification occurring where there were no changes in LULC types (Region I). Region II demonstrated the best effects of desertification prevention and control in the 30-year study period in the Circum-Tarim Basin. Land development and oasis expansion have led to concentrated water use, resulting in water scarcity in certain areas, which cannot support the needs of vegetation growth, thus aggravating the degradation. Hence, “adapting measures to local conditions, rational planning, zoning policies, precise prevention and control” will be the way forward for desertification control in the future in the Circum-Tarim Basin.
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Guo, Bing, Fei Yang, Junfu Fan, and Yuefeng Lu. "The Changes of Spatiotemporal Pattern of Rocky Desertification and Its Dominant Driving Factors in Typical Karst Mountainous Areas under the Background of Global Change." Remote Sensing 14, no. 10 (May 12, 2022): 2351. http://dx.doi.org/10.3390/rs14102351.
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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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Martínez-Valderrama, Jaime, Gabriel del Barrio, María E. Sanjuán, Emilio Guirado, and Fernando T. Maestre. "Desertification in Spain: A Sound Diagnosis without Solutions and New Scenarios." Land 11, no. 2 (February 10, 2022): 272. http://dx.doi.org/10.3390/land11020272.
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The latest world atlas of desertification represents a turning point in the diagnosis of desertification. While it forgoes desertification mapping due to the intrinsic complexity of the phenomenon and the impossibility of measuring it using a single indicator, it introduces the convergence of evidence paradigm, which identifies socioeconomic and biophysical variables whose behaviour allows pointing out those areas prone to desertification. The Spanish National Action Program Against Desertification (PAND), back in 2008, already implemented a similar approach to identify five “desertification landscapes” within Spain using both socio-economic and climatic information. The PAND was not only pioneering but also, unfortunately, accurate. Desertification in Spain has continued to worsen and the first two decades of the 21st century have consolidated an agri-food model whose dynamics have exacerbated the desertification processes identified in the PAND. Despite its scientific value, the PAND lacked a proper action plan and was completely detached from the diagnosis. As a result, the diagnosis it provided was not followed by effective actions to halt desertification in Spain. The Spanish government’s recent declaration of climate and environmental emergency requires a new strategy to combat desertification. This commitment is an excellent opportunity to update the diagnosis of the situation and, more crucially, to unify the different desertification sectoral policies and actions under a single front. We provide here elements (e.g., analysis of agri-food trends and integration of plans and policies at different geographical and sectoral levels) for a roadmap to be designed around the pressures, impacts, and drivers that define today’s Spanish desertification landscapes to effectively manage and avoid their further degradation.
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Smith, Rachel. "Disasters and Archaeology: A Remote Sensing Approach for Determination of Archaeology At-Risk to Desertification in Sistan." Remote Sensing 16, no. 13 (June 28, 2024): 2382. http://dx.doi.org/10.3390/rs16132382.
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Desertification in semi-arid environments poses a significant risk to the archaeology within arid and semi-arid regions. Due to multiple political and physical barriers, accessing desertification-prone areas is complex, complicating pathways towards generating a hands-on understanding of the time–depth and distribution of archaeology throughout these regions. This research developed a remote sensing methodology to determine the areas of Sistan experiencing the highest levels of desertification and the threat of that desertification to known and potential archaeology. As desertification processes are occurring rapidly, this work’s methodology is straightforward and efficient. In a region of vast archaeological value, desertification threatens to prevent archaeologists from potential insight and discovery. This work showcases the opportunity for remote sensing to work as a tool for accessing archaeology in physically inaccessible desertification-prone regions.
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Chen, Xiaowen, Naiang Wang, Simin Peng, Nan Meng, and Haoyun Lv. "Analysis of Spatiotemporal Dynamics of Land Desertification in Qilian Mountain National Park Based on Google Earth Engine." ISPRS International Journal of Geo-Information 13, no. 4 (April 1, 2024): 117. http://dx.doi.org/10.3390/ijgi13040117.
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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.
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Zhang, J. Y., M. H. Dai, L. C. Wang, C. F. Zeng, and W. C. Su. "The challenge and future of rocky desertification control in Karst areas in Southwest China." Solid Earth Discussions 7, no. 4 (November 20, 2015): 3271–92. http://dx.doi.org/10.5194/sed-7-3271-2015.
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Abstract. Karst rocky desertification occurs after vegetation deteriorates as a result of intensive land use, which leads to severe water loss and soil erosion and exposes basement rocks, creating a rocky landscape. The karst rocky desertification is found in humid areas in Southwest China, the region most seriously affected by rocky desertification in the world. In order to promote ecological restoration and help peasants out of poverty, the Chinese government carried out the first phase of a rocky desertification control project from 2006 to 2015, which initially contained the expansion of rocky desertification. Currently, the Chinese government is prepared to implement the second phase of the rocky desertification control project, and therefore it is essential to summarize the lessons learned over the last ten years of the first phase. In this paper, we analyze the driving social and economic factors behind rocky desertification, summarize the scientific research on rocky desertification in the region, and finally identify the main problems facing rocky desertification control. In addition, we put forward several policy suggestions that take into account the perspective of local peasants, the scientific research, and China's economic development and urbanization process. These suggestions include: promoting the non-agriculturalization of household livelihoods, improving ecological compensation, strengthening the evaluation of rocky desertification control and dynamic monitoring, and strengthening research on key ecological function recovery technologies and supporting technologies.
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Zhang, Nannnan, Rongbao Wang, and Feng Zhang. "RESEARCH AND APPLICATION OF REMOTE SENSING MONITORING METHOD FOR DESERTIFICATION LAND UNDER TIME AND SPACE CONSTRAINTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3 (April 23, 2018): 259–65. http://dx.doi.org/10.5194/isprs-annals-iv-3-259-2018.
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Serious land desertification and sandified threaten the urban ecological security and the sustainable economic and social development. In recent years, a large number of mobile sand dunes in Horqin sandy land flow into the northwest of Liaoning Province under the monsoon, make local agriculture suffer serious harm. According to the characteristics of desertification land in northwestern Liaoning, based on the First National Geographical Survey data, the Second National Land Survey data and the 1984&ndash;2014 Landsat satellite long time sequence data and other multi-source data, we constructed a remote sensing monitoring index system of desertification land in Northwest Liaoning. Through the analysis of space-time-spectral characteristics of desertification land, a method for multi-spectral remote sensing image recognition of desertification land under time-space constraints is proposed. This method was used to identify and extract the distribution and classification of desertification land of Chaoyang City (a typical citie of desertification in northwestern Liaoning) in 2008 and 2014, and monitored the changes and transfers of desertification land from 2008 to 2014. Sandification information was added to the analysis of traditional landscape changes, improved the analysis model of desertification land landscape index, and the characteristics and laws of landscape dynamics and landscape pattern change of desertification land from 2008 to 2014 were analyzed and revealed.
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Zhang, J. Y., M. H. Dai, L. C. Wang, C. F. Zeng, and W. C. Su. "The challenge and future of rocky desertification control in karst areas in southwest China." Solid Earth 7, no. 1 (January 15, 2016): 83–91. http://dx.doi.org/10.5194/se-7-83-2016.
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Abstract. Karst rocky desertification occurs after vegetation deteriorates as a result of intensive land use, which leads to severe water loss and soil erosion and exposes basement rocks, creating a rocky landscape. Karst rocky desertification is found in humid areas in southwest China, the region most seriously affected by rocky desertification in the world. In order to promote ecological restoration and help peasants out of poverty, the Chinese government carried out the first phase of a rocky desertification control project from 2006 to 2015, which initially contained the expansion of rocky desertification. Currently, the Chinese government is prepared to implement the second phase of the rocky desertification control project, and therefore it is essential to summarise the lessons learned over the last 10 years of the first phase. In this paper, we analyse the driving social and economic factors behind rocky desertification, summarise the scientific research on rocky desertification in the region, and finally identify the main problems facing rocky desertification control. In addition, we put forward several policy suggestions that take into account the perspective of local peasants, scientific research, and China's economic development and urbanisation process. These suggestions include promoting the non-agriculturalization of household livelihoods, improving ecological compensation, strengthening the evaluation of rocky desertification control and dynamic monitoring, and strengthening research on key ecological function recovery technologies and supporting technologies.
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Jiang, Zhaolin, Xiliang Ni, and Minfeng Xing. "A Study on Spatial and Temporal Dynamic Changes of Desertification in Northern China from 2000 to 2020." Remote Sensing 15, no. 5 (February 28, 2023): 1368. http://dx.doi.org/10.3390/rs15051368.
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Desertification is of significant concern as one of the world’s most serious ecological and environmental problems. China has made great achievements in afforestation and desertification control in recent years. The climate varies greatly across northern China. Using a long-time series of remote sensing data to study the effects of desertification will further the understanding of China’s desertification control engineering and climate change mechanisms. The moist index was employed in this research to determine the climate type and delineate the potential occurrence range of desertification in China. Then, based on the Google Earth Engine platform, MODIS data were used to construct various desertification monitoring indicators and applied to four machine learning models. By comparing different combinations of indicators and machine learning models, it was concluded that the random forest model with four indicator combinations had the highest accuracy of 86.94% and a Kappa coefficient of 0.84. Therefore, the random forest model with four indicator combinations was used to monitor desertification in the study area from 2000 to 2020. According to our studies, the area of desertification decreased by more than 237,844 km2 between 2000 and 2020 due to the impact of human activities and in addition to climatic factors such as the important role of precipitation. This research gives a database for the cause and control of desertification as well as a reference for national-scale desertification monitoring.
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Li, Jingbo, Chunxiang Cao, Min Xu, Xinwei Yang, Xiaotong Gao, Kaimin Wang, Heyi Guo, and Yujie Yang. "A 20-Year Analysis of the Dynamics and Driving Factors of Grassland Desertification in Xilingol, China." Remote Sensing 15, no. 24 (December 13, 2023): 5716. http://dx.doi.org/10.3390/rs15245716.
Full textAbstract:
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.
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Hasan, Samia S., Omar A. Alharbi, Abdullah F. Alqurashi, and Amr S. Fahil. "Assessment of Desertification Dynamics in Arid Coastal Areas by Integrating Remote Sensing Data and Statistical Techniques." Sustainability 16, no. 11 (May 27, 2024): 4527. http://dx.doi.org/10.3390/su16114527.
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Arid coastal regions are threatened by land desertification, which poses a serious threat to desert ecosystems, urban areas, and sustainability on a local as well as global scale. The present study aims to map desertification and the degree of its severity over the Jazan province on the western coast of Saudi Arabia. This investigation was conducted through the integration of remote sensing data (2001 and 2020) and statistical techniques. A scatter diagram, Karl Pearson correlation coefficient, and significance p-value test were performed on various spectral indices and tasseled cap transformation (TCT) derivative matrices to determine the strong significant relation of the spectral indices combination. Based on these analyses, the desertification degree index (DDI) was developed using a NDVI–TCG combination. The desertification grades were mapped and categorized into five classes, namely, non-desertification, low, moderate, severe, and extreme desertification. The results indicated that the spatial distribution of desertification grades declined from west to east during the period from 2001 to 2020. The degree of desertification improved during the study period since there was a significant reduction in extremely serious desertification land by 15.5% and an increase in weak desertification land by 7.8%. The dynamic changes in the DDI classes in the Jazan province mainly involve transformation from extremely serious to serious, serious to moderate, and moderate to weak, with areas of 2268.1 km2, 1518.5 km2, and 1062.5 km2, respectively. Generally, over the 19-year period, the restoration of vegetated areas accounted for 41.99% of the total study area, while desertification degradation land represented 15.57% of the total area of the Jazan province.
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Wang, Xunming, Ting Hua, and Wenyong Ma. "Responses of aeolian desertification to a range of climate scenarios in China." Solid Earth 7, no. 3 (June 16, 2016): 959–64. http://dx.doi.org/10.5194/se-7-959-2016.
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Abstract. Aeolian desertification plays an important role in earth-system processes and ecosystems, and has the potential to greatly impact global food production. The occurrence of aeolian desertification has traditionally been attributed to increases in wind speed and temperature and decreases in rainfall. In this study, by integrating the aeolian desertification monitoring data and climate and vegetation indices, we found that although aeolian desertification is influenced by complex climate patterns and human activities, increases in rainfall and temperature and decreases in wind speed may not be the key factors of aeolian desertification controls in some regions of China. Our results show that, even when modern technical approaches are used, different approaches to desertification need to be applied to account for regional differences. These results have important implications for future policy decisions on how best to combat desertification.
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Li, Peixian, Peng Chen, Jiaqi Shen, Weinan Deng, Xinliang Kang, Guorui Wang, and Shoubao Zhou. "Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning." Sustainability 14, no. 12 (June 18, 2022): 7470. http://dx.doi.org/10.3390/su14127470.
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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.
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Lee, Jinmeng, Xiaojun Yin, Honghui Zhu, and Xin Zheng. "Geographical Detector-Based Research of Spatiotemporal Evolution and Driving Factors of Oasification and Desertification in Manas River Basin, China." Land 12, no. 8 (July 27, 2023): 1487. http://dx.doi.org/10.3390/land12081487.
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Oasification and desertification are two essential processes of land use and cover (LULC) change in arid regions. Compared to desertification, which is widely regarded as the most severe global ecological issue, the importance of oasification has not received universal recognition. However, neglecting oasification can lead to detrimental outcomes to the effectiveness of ecological governance by affecting the comprehensiveness of environmental policies proposed only based on desertification. Therefore, this study incorporates oasification into the examination of desertification by analyzing land use data for five representative periods spanning from 1980 to 2020, as well as socioeconomic and environmental data from 2000 to 2010. The aim is to evaluate the spatial and temporal dynamics of oasification and desertification in the Manas River Basin and identify the underlying factors driving these processes. The findings indicated that (1) the general trend of oasification and desertification exhibited the expansion of oases and the retreat of deserts. Specifically, the oasification area showed a “decrease-increase-decrease” pattern over time, while the desertification area consistently decreased. (2) In terms of spatial distribution, oasification and desertification displayed a transition from scattered and disordered patterns to an overall more organized pattern, with the hotspot area of desertification shifting from Shawan County to Manas County over time. (3) Population density, average land GDP, soil type and annual precipitation significantly influenced the degree of oasification, with driving force q-values above 0.4, which were the key factors driving oasification. Population density and average land GDP significantly affected the degree of desertification, with driving force q-values above 0.35, which were the key factors driving desertification. The driving force of all factors increased significantly after the interaction, and socioeconomic factors influenced oasification and desertification more than other factors. The study’s findings aim to provide a scientific basis for land resource use, ecological governance and sustainable development in the Manas River basin.
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Qiu, Kaiyang, Zhigang Li, Yingzhong Xie, Dongmei Xu, Chen He, and Richard Pott. "Desertification Reversal Promotes the Complexity of Plant Community by Increasing Plant Species Diversity of Each Plant Functional Type." Agronomy 14, no. 1 (December 30, 2023): 96. http://dx.doi.org/10.3390/agronomy14010096.
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Desertification reversal is globally significant for the sustainable development of land resources. However, the mechanisms of desertification reversal at the level of plant community are still unclear. We hypothesized that desertification reversal has clear effects on plant community composition, plant functional types (PFTs), and other vegetation characteristics, including plant diversity and biomass, and their changes in the early stages of reversal are more dramatic than in later stages. We investigated the vegetation of four to five different stages of desertification reversal at each of seven large study sites in southwestern Mu Us Sandy Land, China. The results show that the dominant species in very severe desertification areas were replaced by perennial grasses in potential desertification areas. The importance values of annual forbs and perennial sub-shrubs decreased dramatically (from 42.59 and 32.98 to 22.13 and 5.54, respectively), whereas those of perennial grasses and perennial forbs increased prominently (from 13.26 and 2.71 to 53.94 and 11.79, respectively) with the reversal of desertification. Desertification reversal increased the complexity of plant community composition by increasing plant species in each PFT, and C3 plants replaced C4 plants to become the dominant PFT with reversal. Plant species richness and species diversity rose overall, and aboveground plant biomass significantly (p < 0.05) increased with the reversal of desertification. Most vegetation characteristics changed more strikingly in the early stages of desertification reversal than in later stages. Our results indicate that the type and composition of the plant community were dramatically affected by desertification reversal. Anthropogenic measures are more applicable to being employed in early stages than in later stages, and Amaranthaceae C4 plants are suggested to be planted in mobile dunes for the acceleration of desertification reversal. This study is useful for designing strategies of land management and ecological restoration in arid and semiarid regions.