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1
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.
Анотація:
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.
2
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.
Анотація:
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.
3
Zhang, Shiwen, Yan Wang, Xuehua Wang, Yang Wu, Chengrong Li, Chao Zhang, and Yuhang Yin. "Ecological Quality Evolution and Its Driving Factors in Yunnan Karst Rocky Desertification Areas." International Journal of Environmental Research and Public Health 19, no. 24 (December 16, 2022): 16904. http://dx.doi.org/10.3390/ijerph192416904.
Анотація:
Rocky desertification is a key element affecting regional ecological quality. Rocky desertification in Southwest China directly affects the ecological security of the Yangtze River and Pearl River basins and also restricts regional economic and social development. In order to clarify the evolution laws and key influencing factors of ecological quality in Yunnan karst rocky desertification areas, a quantitative analysis based on the remote sensing-based ecological index (RSEI) model was conducted to explore the overall evolution characteristics and change laws of ecological quality in Yunnan karst rocky desertification areas in the past 30 years. The correlation between RSEI, rock outcrop rate (Fr), and driving factors was determined by redundancy analysis. The results showed the following: (1) RSEI in Yunnan karst rocky desertification areas generally showed a decreasing trend, with a fluctuation in the mid-term, followed by a tendency to recover. It fell into three stages: decline, trough, and recovery, with fitting coefficients of −0.121, −0.057, and 0.157, respectively. In contrast, Fr showed an opposite tendency, illustrating the inverse relationship between RSEI and Fr, and the rate of sequential succession was much faster than that of the reverse succession under human measures of intervention. (2) The mean value of RSEI of Yunnan karst rocky desertification areas was generally lower than that of the total Yunnan Province land areas and Yunnan non-karst rocky desertification areas, but the mean value of Fr was generally more than that of both the above-mentioned areas. In addition, the RSEI and Fr of Yunnan karst rocky desertification areas both showed lower stability values than those of both the above-mentioned areas. This generally suggested a low ecological quality and a high degree of desertification under a low stability in Yunnan karst rocky desertification areas. (3) The correlation of RSEI and Fr with driving factors followed the order of topographic factors, soil factors > water factors > anthropogenic factors. Anthropogenic factors were the driving force changing the state of rocky desertification, geological factors such as topography and soil to a larger extent determined the original macroscopic ecological relationship of rocky desertification, and water factors lay between the above two. The findings of this research will provide theoretical support and a basis for the improvement of ecological quality and comprehensive control of karst rocky desertification in Yunnan Province.
4
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.
Анотація:
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.
5
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.
Анотація:
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.
6
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.
Анотація:
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.
7
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.
Анотація:
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.
8
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.
Анотація:
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.
9
Macêdo, Theilon Henrique de Jesus, Cristiano Tagliaferre, Bismarc Lopes da Silva, Alessandro de Paula, Odair Lacerda Lemos, Felizardo Adenilson Rocha, Rosilene Gomes de Souza Pinheiro, and Ana Carolina Santos Lima. "Assessment of Land Desertification in the Brazilian East Atlantic Region Using the Medalus Model and Google Earth Engine." Land 13, no. 1 (December 26, 2023): 31. http://dx.doi.org/10.3390/land13010031.
Анотація:
Many factors drive land desertification, especially in arid and semi-arid regions. However, the sheer number of these driving factors of desertification makes analyses computer-intensive. Cloud computing offers a solution to address this problem, especially in developing countries. The objective of this work was to assess the sensitivity of the East Atlantic Basin, Brazil, to desertification using the Mediterranean Desertification and Land Use (MEDALUS) model and Google Earth Engine (GEE). The model is composed of four environmental Quality Indices (QIs) associated with soil (SQI), vegetation (VQI), climate (CQI), and management (MQI), each encompassing factors that influence the desertification process. Digital databases corresponding to these factors were pre-processed and uploaded to GEE for analysis. We report Environmentally Sensitive Areas (ESAs) and Environmentally Critical Factors (ECF) maps of the East Atlantic Basin, which show that most of the basin is in either a critical (49.4%) or fragile (35.7%) state of sensitivity. In contrast, only a smaller portion of the area is unaffected (5%) or potentially affected (10.1%). The analysis also revealed an inverse correlation between desertification sensitivity and the presence of vigorous vegetation. A joint evaluation of ESAs and ECF shed light on the importance of each factor in the sensitivity to desertification. The East Atlantic Basin shows a high degree of sensitivity to desertification, thereby demanding more attention and the establishment of measures to mitigate the negative impacts of the desertification process.
10
Liu, Wenjun, Xiumei Yin, Tong Gong, Ying Liu, and Hu Chen. "Community Structure of Epilithic Moss Mites and Their Response to Environmental Factors in Different Grades of Rocky Desertification Habitats." Sustainability 14, no. 22 (November 10, 2022): 14860. http://dx.doi.org/10.3390/su142214860.
Анотація:
This research has been undertaken to reveal the changes in the community structure of epilithic moss mites and the response of these mites to environmental factors under different grades of rocky desertification environment. In this study, epilithic moss mites were collected in a demonstration area for rocky desertification management in Bijie Salaxi, with the following rocky desertification grades as habitat gradients: without rocky desertification, potential rocky desertification, light rocky desertification, moderate rocky desertification, and severe rocky desertification. The differences in the number of individuals, taxa, diversity index, dominance index, richness index, evenness index, and the effects of environmental factors on moss mite communities were revealed by one-way ANOVA, correlation analysis, and redundancy analysis for different grades of these mites. The results show that a total of 11,563 epilithic moss mites were captured in the study area, belonging to three orders, 100 families, and 171 genera, with Nanorchestes and Trichogalumna as the dominant taxa. With the deepening of rocky desertification, the dominant number of Nanorchestes and Trichogalumna increased. Still, the percentage of very rare genera also decreased, and there were differences in the composition of the dominant genus taxa in different grades of rocky desertification. Different grades of rocky desertification habitats had significant effects on the diversity index and richness index of moss mite species but not on the number of taxa, individuals, dominance index, and evenness index. The overall epilithic moss mite communities in different habitats were moderately dissimilar. Air temperature and rock temperature had strong effects on each index of moss mite diversity, whereas light factors and air humidity had a weak impact on these indices. Amongst the communities, those of Scheloribates are more sensitive to rock temperature variation, while Blattisocius, Ledermuelleria and Camerobia correlate more with a light variation. Parholaspulus, Blattisocius, Camerobia, Haplochthonius, Gymnodamaeus, etc. were more sensitive to changes in air humidity. The research shows that there are differences in moss mite community structure under different rocky desertification grades, rocky desertification has caused some effects on moss mite community structure, and the use of mite dominant taxa genera can give preliminary indications of the rocky desertification environment; meanwhile, there is a specific correlation between mite taxa and habitat environment changes.
11
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.
Анотація:
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.
12
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.
Анотація:
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.
13
Zhao, Z., and Q. Wu. "STUDY ON DESERTIFICATION MONITORING FROM 2000 TO 2014 AND ITS DRIVING FACTORS THROUGH REMOTE SENSING IN NINGXIA，CHINA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2439–47. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2439-2018.
Анотація:
Due to the implementation of national policy, the desertification in Ningxia has been gradually reduced, but the overall situation of desertification is still serious. Rainfall Use Efficiency(RUE) can make some improvement to the problem that the precipitation has a great influence on vegetation in arid area and fully reflect the dynamic characteristics of desertification. Using the MOD13Q1 data, land use classification map, as well as non-remote sensing data such as meteorological data and social statistics data, the paper carries out the evaluation of the status of desertification based on RUE through spatial trend analysis, gravity center migration model. The driving factors of desertification are quantitatively analyzed by using grey relational analysis. Our study demonstrated that RUE in most parts of Ningxia showed a trend of improvement, mainly located in central and southern Ningxia. The area where desertification occurred from 2000 to 2014 accounted for 7.79&thinsp;%, mainly distributed in Helan Mountain, Liupan Mountain, Yinchuan Central. The proportion of desertification decreased gradually from 2005 to 2014, and the center of gravity of desertification had a tendency to migrate to northern Ningxia. By analyzing the driving factors, RUE had negative correlations with precipitation and relative humidity and there was no significant correlation between RUE and average temperature and sunshine hours. RUE was positively correlated with GDP, grain yield and number of sheep. On the basis of the results of grey relational analysis, it was found that sunshine hours, average temperature, relative humidity, population were the main influencing factors of desertification.
14
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.
Анотація:
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.
15
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.
Анотація:
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.
16
Wijitkosum, Saowanee. "Reducing Vulnerability to Desertification by Using the Spatial Measures in a Degraded Area in Thailand." Land 9, no. 2 (February 10, 2020): 49. http://dx.doi.org/10.3390/land9020049.
Анотація:
The process of desertification is complex, involving interaction between many factors, both environmental and anthropogenic. However, human activities, especially from land-use change and inappropriate land use, are the most influential factors associated with the desertification risk. This study was conducted in Huay Sai, a degraded land in Thailand. The Environmentally Sensitive Area Index (ESAI) model incorporating Geogracphic Information System (GIS) was applied to investigate and map the desertification sensitivity area. The study aimed to analyze and assess measures to reduce the desertification risk. This study emphasized three group factors with nine subcriteria influencing desertification risk: soil (texture, fertility, drainage, slope gradient, and depth), climatic (precipitation and aridity index), and vegetation factors (land use and soil erosion). In terms of the required spatial measures to reduce the desertification vulnerability, policy and defensive measures that were closely related to drought and desertification of the area were considered. Three main measures covering soil and water conservation, soil improvement, and reforestation were implemented. The area development and restoration plans have been implemented continuously. The study found that 47.29% of the Huay Sai area was at a high risk, with a further 41.16% at a moderate risk. Implementation of three measures indicated that desertification risk was significantly decreased. Addressing the causes of the highest risk areas could help reduce the overall desertification risk at Huay Sai, where most areas would then be at either a moderate (61.04%) or low (32.43%) desertification risk with no severe- or high-risk areas. The success of the area restoration is from the formulation of a restoration and development plan that understands the local conditions. Moreover, the plan integrated the restoration of the soil, forests, and water together in order to restore the ecosystem so that the implementation was able to solve problems directly.
17
Lan, Mingyu, Chunquan Xue, Jiazhi Yang, Ning Wang, Chuanxi Sun, Guozhang Wu, Hongyu Chen, and Zhiyao Su. "Changes in Plant Diversity and Soil Factors under Different Rocky Desertification Degrees in Northern Guangdong, China." Forests 14, no. 4 (March 28, 2023): 694. http://dx.doi.org/10.3390/f14040694.
Анотація:
Revegetation is an important restoration strategy for the control of rocky desertification. However, few studies have focused on the effects of different rocky desertification degrees (RDDs) on plant diversity and soil fertility in northern Guangdong over long periods of time. In this study, variance analysis, correlation analysis, and canonical correlation analysis (CCA) were used to examine plant diversity, soil physicochemical properties, and their correlations in various rocky desertification areas in northern Guangdong. The results showed that the Pinaceae, Lauraceae, and Fagaceae species were relatively abundant in the rocky desertification areas of northern Guangdong. Additionally, Cinnamomum camphora, Schima superba, Pinus massoniana, Quercus stewardiana, and Acer camphora could be used as indicators for rocky desertification. There were significant differences in plant community compositions and diversity characteristics between the five RDDs, and the vegetation exhibited the trend of initial destruction and then gradual improvement and stabilization. There were significant differences in soil bulk density, mechanical composition, organic matter, total nitrogen, alkaline hydrolysis nitrogen, and available potassium between the different RDDs. Except for pH, the soil chemical characteristics all had clear aggregation effects. Soil organic matter, total nitrogen, total potassium, and alkaline hydrolysis nitrogen all exhibited degradation–improvement cycles. The correlation analysis revealed that there was a significant correlation between soil physicochemical properties and species diversity. The CCA analysis showed that the most important soil factors affecting plant community structures were total phosphorus and available phosphorus. In conclusion, some achievements have been made in the restoration of rocky desertification in northern Guangdong; while the plant community structure improved, some soil nutrients also improved. Vegetation and soil have a strong coupling relationship. In the later stages of recovery, suitable species for rocky desertification could be considered in varying degrees and P and K could be supplemented appropriately. Our study will have implications for the revegetation of rocky desertification.
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JianHu Wang, Zengjin Liu, Kaixu Wang, Shijun Lu, Guoqing Yu, Lixiao Wang,. "Spatial-temporal Evolution and Driving Factors of Desertification Based on Computer Interpretation in Tarim River Basin." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 1834–45. http://dx.doi.org/10.52783/jes.1631.
Анотація:
The Tarim River Basin is an important life hinge in Xinjiang, which plays a vital role in maintaining ecological stability. In recent years, it is faced with challenges such as the expansion of sandy land, the decrease of precipitation and sand-dust storm, which lead to the aggravation of desertification and the contradiction between man and land. This study used remote sensing images from 2000 to 2020 to analyze desertification trends through automatic interpretation by computer, combined with NDVI(Normalized Difference Vegetation Index) and FVC(Fractional Vegetation Cover) indicators. The results showed that the total area of desertification land decreased, but the area of serious desertification land increased. Precipitation, Sunshine, wind speed, air temperature, cultivated land area change, urbanization and other factors affect desertification, of which cultivated land area change, temperature change and precipitation change are the most important factors. Policies focused on preventing wind erosion have been inadequate as the economy has developed and conflicts between people and land have intensified.
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Li, Qun, Puxia Wu, Huaye Fan, Yandong Ma, Rong Li, and Guoping Zhao. "Spatial Distribution Pattern and Natural Causes Analysis of Sandy Desertification Land in Ali Area." Sustainability 14, no. 14 (July 17, 2022): 8734. http://dx.doi.org/10.3390/su14148734.
Анотація:
In order to explore the spatial distribution pattern and natural causes of the sandy desertification land in the Ali area, on the basis of clarifying the dynamic change laws of sandy desertification land areas, sandy desertification degree and spatial distribution pattern, the main controlling factors of sandy desertification land distribution are analyzed from three aspects of landform, climate and vegetation. During the 22 years from 1992 to 2014, the sandy desertification land area in the Ali area shows a law of first increase and then decrease, reaching a peak of 61,054.14 km2 in 2004, accounting for 20.57%, and decreasing to 60,892.65 km2 in 2014, accounting for 20.51%, which do not return to the level of 1992. Sandy land desertification is mainly slight and moderate, accounting for 53.29% and 45.73%, respectively, in 2014. Sandy desertification land in the Ali area is mainly distributed among intermountain basins, river valleys, lake basins, piedmont plains and other landform units. The landform and wind speed are the main natural factors that determine the spatial distribution pattern of sandy desertification land in the Ali area, that is, the spatial distribution pattern of sandy desertification land in the Ali area is the coupled result of sand source and wind speed.
20
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.
Анотація:
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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Mazhar, Nausheen, and Safdar Ali Shirazi. "The Preliminary Study of Anthropogenic and Natural Drivers of Desertification in Drylands of South Punjab, Pakistan." International Journal of Economic and Environmental Geology 11, no. 1 (July 7, 2020): 102–7. http://dx.doi.org/10.46660/ijeeg.vol11.iss1.2020.420.
Анотація:
This study aims to investigate the perceptions of farmers residing in the drylands of south Punjab regarding the drivers of desertification mainly associated with meteorological and anthropogenic factors. Dataset of 399 respondents was collected using disproportionate stratified sampling technique from Bahawalpur, Rahim Yar Khan and Rajanpur districts. Pearson correlation and cross tabulation were performed to explore relation between variables. Simple Linear Regression (SLR) helped in investigating the association between natural and anthropogenic causes of desertification. The findings of this study indicate the significant variability in natural causes of desertification such as increasing temperature extremes, soil salinization and variation in rainfall patterns, while extensive land degradation, caused by anthropogenic factor, as leading to desertification in the study area. For Rajanpur, mean rainfall variation, supports the perception regarding major natural driver of desertification. Small-scale farmers were found to be most vulnerable to climatic extremes. SLR concluded that anthropogenic factors trigger or intensify the natural drivers of desertification in the study area. Useful insights are provided regarding the perceptions of the local farming community regarding causes of desertification as appropriate perception of a risk leads to fruitful adaptation measures
22
Mazhar, Nausheen, and Safdar Ali Shirazi. "The Preliminary Study of Anthropogenic and Natural Drivers of Desertification in Drylands of South Punjab, Pakistan." International Journal of Economic and Environmental Geology 11, no. 1 (July 7, 2020): 102–7. http://dx.doi.org/10.46660/ojs.v11i1.420.
Анотація:
This study aims to investigate the perceptions of farmers residing in the drylands of south Punjab regarding the drivers of desertification mainly associated with meteorological and anthropogenic factors. Dataset of 399 respondents was collected using disproportionate stratified sampling technique from Bahawalpur, Rahim Yar Khan and Rajanpur districts. Pearson correlation and cross tabulation were performed to explore relation between variables. Simple Linear Regression (SLR) helped in investigating the association between natural and anthropogenic causes of desertification. The findings of this study indicate the significant variability in natural causes of desertification such as increasing temperature extremes, soil salinization and variation in rainfall patterns, while extensive land degradation, caused by anthropogenic factor, as leading to desertification in the study area. For Rajanpur, mean rainfall variation, supports the perception regarding major natural driver of desertification. Small-scale farmers were found to be most vulnerable to climatic extremes. SLR concluded that anthropogenic factors trigger or intensify the natural drivers of desertification in the study area. Useful insights are provided regarding the perceptions of the local farming community regarding causes of desertification as appropriate perception of a risk leads to fruitful adaptation measures
23
Anvarova, Zebo Musaevna, Istat Dilmurodovna Mirzayeva, and Nigora Sharipovna Shodiyeva. "The factors that influence begetting desertification process." South Asian Journal of Marketing & Management Research 10, no. 11 (2020): 129–32. http://dx.doi.org/10.5958/2249-877x.2020.00085.5.
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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.
Анотація:
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.
25
Issanova, G., A. Saduakhas, J. Abuduwaili, K. Tynybayeva, and S. Tanirbergenov. "DESERTIFICATION AND LAND DEGRADATION IN KAZAKHSTAN." BULLETIN 5, no. 387 (October 15, 2020): 95–102. http://dx.doi.org/10.32014/2020.2518-1467.148.
Анотація:
Desertification and land degradation are common processes in arid and semi-arid regions of Kazakhstan, especially southern parts, where areas are covered by a great variety of desert types. In deserts, soil-forming processes take place in conditions of severe water shortage, and high level of soil degradation and desertification. The main natural factors for these processes are a flat terrain, a high degree of arid climate, soil salinity, carbonate content, a lack of structure and low natural soil fertility. However, the anthropogenic factors of desertification and soil degradation became dominant last decades. The study considers the actual problems of natural and anthropogenic factors of desertification and land degradation within Kazakhstan. The desertification of huge territories is accompanied by soil contamination, waterlogging by surface water and groundwater, soil salinization, erosion (water, wind), degradation of vegetation cover, dehumidification and a decrease in general regional biological capacity. Analysis of the current status of the soil cover has shown intensive land degradation 43 % of the territory of Kazakhstan is subjected to degradation in significant degree; over 14 % of pastures have reached an extreme degree of degradation or are completely degradated. The Aral Sea region, Northern Caspian Sea and Southern Balkhash deserts can be observed as areas of intensive soil desertification, salinization and deflation processes. As well as the desertification process are progressing in the irrigated soils of the deltas of Syrdarya, Shu, Ile and Karatal rivers.
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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.
Анотація:
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.
27
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.
Анотація:
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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Plaiklang, S., I. Sutthivanich, T. Sritarapipat, K. Panurak, S. Ogawa, S. Charungthanakij, U. Maneewan, and N. Thongrueang. "DESERTIFICATION ASSESSMENT USING MEDALUS MODEL IN UPPER LAMCHIENGKRAI WATERSHED, THAILAND." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1257–62. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1257-2020.
Анотація:
Abstract. Desertification is a global environmental problem. It affects harmful on economic, social and environmental that ultimately effects on quality of human life. Thailand is the 174th member of the UNCCD, according to the Thailand report of desertification by LDD (2004). It was found that the area of degraded land or desertification land in Thailand was 33.57 million hectares which were agricultural soil problem. Soil erosion and soil salinity are major problems for agricultural soil in Thailand. Thus, to prevent and fix such problems, assessment and evaluation of soil properties are essential. Lamchiengkrai watershed in Nakhon Ratchasima province presents soil salinity exposure area which is a major problem in the Northeast region of Thailand. This study aims to access a new approach for assessing the extent and the risk of desertification land by MEDALUS model based on geoinformatics technology in upper Lamchiengkrai watershed, Nakhon Ratchasima province. MEDALUS model is the factors of desertification assessment. Four groups of factors were examined, including vegetation (fire risk, erosion protection, and drought resistance), climatic (rainfall and rainfall erosivity), soil (soil texture, electrical conductivity, organic matter, soil depth, drainage, and slope), and human activity factor (land use and soil erosion). The results of the study indicated that 67.25% of the area was classified as high risk, 30.54% was classified as moderate risk and 2.22% was classified as low risk to desertification land. In addition, the factors affected on the high-risk area were climate and vegetation factors. Moderate risk area was influenced by the human activity factor and soil factors.
29
Sidiropoulos, Pantelis, Nicolas R. Dalezios, Athanasios Loukas, Nikitas Mylopoulos, Marios Spiliotopoulos, Ioannis N. Faraslis, Nikos Alpanakis, and Stavros Sakellariou. "Quantitative Classification of Desertification Severity for Degraded Aquifer Based on Remotely Sensed Drought Assessment." Hydrology 8, no. 1 (March 17, 2021): 47. http://dx.doi.org/10.3390/hydrology8010047.
Анотація:
Natural and anthropogenic causes jointly lead to land degradation and eventually to desertification, which occurs in arid, semiarid, and dry subhumid areas. Furthermore, extended drought periods may cause soil exposure and erosion, land degradation and, finally, desertification. Several climatic, geological, hydrological, physiographic, biological, as well as human factors contribute to desertification. This paper presents a methodological procedure for the quantitative classification of desertification severity over a watershed with degraded groundwater resources. It starts with drought assessment using Standardized Precipitation Index (SPI), based on gridded satellite-based precipitation data (taken from the CHIRPS database), then erosion potential is assessed through modeling. The groundwater levels are estimated with the use of a simulation model and the groundwater quality components of desertification, based on scattered data, are interpolated with the use of geostatistical tools. Finally, the combination of the desertification severity components leads to the final mapping of desertification severity classification.
30
Hien, Le Thi Thu, Anne Gobin, and Pham Thi Thanh Huong. "Spatial indicators for desertification in southeast Vietnam." Natural Hazards and Earth System Sciences 19, no. 10 (October 29, 2019): 2325–37. http://dx.doi.org/10.5194/nhess-19-2325-2019.
Анотація:
Abstract. Desertification is influenced by different factors that relate to climate, soil, topography, geology, vegetation, human pressure, and land and water management. The quantification of these factors into spatially explicit indicators and subsequent evaluation provides for a framework that allows us to identify areas currently at risk of desertification and to evaluate important contributing biophysical and socio-economic factors. Based on local knowledge of environmental contributing factors to the risk of desertification in the Binh Thuan Province of southeast Vietnam, a baseline 2010 map showed that 14.4 % of the area, mainly along the coast and in the northeast, is desertified with another 35.4 % at severe risk of desertification. The Vietnamese Ministry of Natural Resource and Environment has defined the area with a ratio of rainfall to evapotranspiration smaller or equal to 0.65, which equals 1233 km2 or 15 % of the province, as desertified area, which corresponds well with the baseline 2010 map. The developed framework incorporates the important contributing factors and therefore allows for decision support in a “what if” structure and for the projection of potentially vulnerable areas under future scenarios. With projected climate change and population growth, the desertified area is expected to increase by 122 % (or 137 850 ha) towards 2050. The developed methodology can be extended to neighbouring provinces that experience similar sensitivities to desertification.
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Wu, Qian, Wei Zheng, Chengjiao Rao, Enwen Wang, and Wende Yan. "Soil Quality Assessment and Management in Karst Rocky Desertification Ecosystem of Southwest China." Forests 13, no. 9 (September 18, 2022): 1513. http://dx.doi.org/10.3390/f13091513.
Анотація:
Karst rocky desertification is a common phenomenon in terrestrial ecosystems, and the deterioration of soil quality has a serious side effect on the aboveground vegetation and underground environmental factors. To clarify the variety of soil quality in different rocky desertification grades in typical karst areas of southwest China, the soil quality of four rocky desertification grades was calculated by a single model (SQI: soil quality index), two screening processes (TDS: total dataset and MDS: minimum dataset) and three scoring methods (SSF: standard scoring function, SL: linear scoring function and SNL: nonlinear scoring function). The key results are as follows: Significant differences were found in the soil environment factors in non-rocky desertification (NRD), light rocky desertification (LRD) and moderate rocky desertification (MRD) as compared to intense rocky desertification (IRD) (p < 0.01). Except for total potassium (TK), manganese (Mn) and amylase, the other soil environmental factors showed U-shaped changes. In contrast, TK, Mn and amylase increased first and then decreased. Additionally, the SQI based on MDS in SSF, SL and SNL was IRD (0.58) > NRD (0.48) > LRD (0.45) > MRD (0.43), IRD (0.53) > NRD (0.42) > LRD (0.39) > MRD (0.36) and IRD (0.57) > NRD (0.47) > MRD (0.42) > LRD (0.40), respectively. However, the SQI was always in the trend of IRD > NRD > MRD > LRD based on the TDS. Overall, although the soil area is scarce, the edaphic properties, enzyme activities and soil quality are not poor in the IRD. Furthermore, we found that SNL was more suitable for the evaluation of soil quality in the karst rocky desertification area (R2 = 0.63, p < 0.001 and the coefficient of variation = 30.69%). This research helps to clarify the variation in soil properties and quality during the succession of rocky desertification and provides guidelines for the sustainable management of soil quality in areas of southwest China.
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Gadzhiev, A. Kh. "Dynamics of meteorological factors affecting desertification of the Kura-Aras Lowland during 1991–2020." Hydrometeorological research and forecasting 1 (March 30, 2023): 148–60. http://dx.doi.org/10.37162/2618-9631-2023-1-148-160.
Анотація:
The influence of meteorological factors on the changes in landscapes and the climatic regime of the Kura-Aras Lowland over a 30-year period (1991–2020) is investigated. An interest in the region is caused by the major environmental problem: an increase in the area of saline lands of the Kura-Aras Lowland and the expansion of the area of its desertification. The dynamics of temperature, precipitation, wind, as well as the influence of the Caspian Sea level during 1991–2020 is compared with the parameters for 1961–1990. It is shown that over the recent 30 years, the average annual temperature in the lowland has increased by 0.8 °C as compared to the period of 1961–1990. Keywords: Kura-Aras Lowland, climate change, desertification, precipitation, temperature anomaly, correlation, interpolation, trend
33
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.
Анотація:
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.
34
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.
Анотація:
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, 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.
Анотація:
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.
36
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.
Анотація:
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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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.
Анотація:
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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Zhao, Yazhou, Shengyu Li, Dazhi Yang, Jiaqiang Lei, and Jinglong Fan. "Spatiotemporal Changes and Driving Force Analysis of Land Sensitivity to Desertification in Xinjiang Based on GEE." Land 12, no. 4 (April 8, 2023): 849. http://dx.doi.org/10.3390/land12040849.
Анотація:
Land desertification profoundly affects economic and social development, thus necessitating a collective response. Regional land control planning needs to assess the land sensitivity to desertification across different regions. In this study, we selected 12 factors from soil, vegetation, climate, and terrain aspects to calculate and evaluate Xinjiang’s land sensitivity to desertification, from 2001 to 2020, and analyzed its trends and drivers. The results indicated that the region is highly (22.93%) to extremely sensitive (34.63%) to desertification. Of these, deserts, Gobi lands, oasis–desert transitional zones, and the downstream of rivers are highly and extremely sensitive areas. Mountainous areas, oases, and along rivers are non- and mildly sensitive areas. Over the past two decades, most areas have experienced stability (45.07%) and a slight improvement of desertification (26.18%), while the Junggar Basin and Central Taklamakan Desert have seen slight and severe intensification trends, respectively. Climate-related indicators, such as surface temperature and potential evapotranspiration (PET), were identified as the most important drivers of changes in land sensitivity to desertification. Having an integrated water resource allocation and establishing the long-term monitoring of land sensitivity to desertification would have positive implications for desertification control.
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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.
Анотація:
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.
40
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.
Анотація:
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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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.
Анотація:
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.
42
Martínez-Valderrama, Jaime, Javier Ibáñez, Francisco J. Alcalá, and Silvio Martínez. "SAT: A Software for Assessing the Risk of Desertification in Spain." Scientific Programming 2020 (June 29, 2020): 1–12. http://dx.doi.org/10.1155/2020/7563928.
Анотація:
Desertification is a major global environmental issue exacerbated by climate change. Strategies to combat desertification include prevention which seeks to reverse the process before the system reaches the stable desertified state. One of these initiatives is to implement early warning tools. This paper presents SAT (the Spanish acronym for Early Warning System), a decision support system (DSS), for assessing the risk of desertification in Spain, where 20% of the land has already been desertified and 1% is in active degradation. SAT relies on three versions of a Generic Desertification Model (GDM) that integrates economics and ecology under the predator-prey paradigm. The models have been programmed using Vensim, a type of software used to build and simulate System Dynamics (SD) models. Through Visual Basic programming, these models are operated from the Excel environment. In addition to the basic simulation exercises, specially designed tools have been coupled to assess the risk of desertification and determine the ranking of the most influential factors of the process. The users targeted by SAT are government land-use planners as well as desertification experts. SAT tool is implemented for five case studies, each one of them representing a desertification syndrome identified in Spain. Given the general nature of the tool and the fact that all United Nations Convention to Combat Desertification (UNCCD) signatory countries are committed to developing their National Plans to Combat Desertification (NPCD), SAT could be exported to regions threatened by desertification and expanded to cover more case studies.
43
Sepehr, Adel, Claudio Zucca, and Mohammad Nowjavan. "Desertification Inherent Status Using Factors Representing Ecological Resilience." British Journal of Environment and Climate Change 4, no. 3 (January 10, 2014): 279–91. http://dx.doi.org/10.9734/bjecc/2014/12353.
44
Fan, Wen Yi, Ming Ze Li, and Ying Yu. "Quantitative Retrieving of Vegetation Factors for Desertification Area." Advanced Materials Research 183-185 (January 2011): 376–80. http://dx.doi.org/10.4028/www.scientific.net/amr.183-185.376.
Анотація:
Quantitative RSIM (Remote Sensing Information Model) was studied to retrieve the vegetation factors (vegetation cover and biomass) for desertification assessment by using the data of state-produced hyperspectral resolution imagining spectrometer (OMIS-I), and the corresponding vegetation factors recoding maps based on pixel were obtained. The result shows that it is reliable to retrieve quantitatively the vegetation cover and the biomass by the data of hyperspectral resolution imagining spectrometer. When there are both the shrub and the grassland in the retrieved region, the precision of the polynomial model is obvious higher than that of the linear model, contrastingly when the type of the vegetations is simplification, the linear model has the higher precision. The quantitative retrieving of the vegetation factors are related to the vegetation type.
45
Hu, Yunfeng, Yueqi Han, and Yunzhi Zhang. "Land desertification and its influencing factors in Kazakhstan." Journal of Arid Environments 180 (September 2020): 104203. http://dx.doi.org/10.1016/j.jaridenv.2020.104203.
46
Tereshchenko, Iryna, Alexander N. Zolotokrylin, Tatiana B. Titkova, Luis Brito-Castillo, and Cesar Octavio Monzon. "Seasonal Variation of Surface Temperature–Modulating Factors in the Sonoran Desert in Northwestern Mexico." Journal of Applied Meteorology and Climatology 51, no. 8 (August 2012): 1519–30. http://dx.doi.org/10.1175/jamc-d-11-0160.1.
Анотація:
AbstractThe authors explore a new approach to monitoring of desertification that is based on use of results on the relation between albedo and surface temperature for the Sonoran Desert in northwestern Mexico. The criteria of predominance of radiation by using the threshold value of Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were determined. The radiation mechanism for regulating the temperature of the surface and the definition of threshold values for AVHRR and MODIS NDVI have an objective justification for the energy budget, which is based on the dominance of radiation surface temperature regulation in relation to evapotranspiration. Changes in the extent of arid regions with AVHRR NDVI of <0.08 and MODIS NDVI of <0.10 can be considered to be a characteristic in the evolution of desertification in the Sonoran Desert region. This is true because, in a certain year, the time span of the period when radiation factor predominates is important for the desertification process.
47
Ying, Zhang, Lijun Wang, AiJun Yi, Fei Li, and Ernst-August Nuppenau. "Study on desertification reversal factors in Maowusu sandy land in China." E3S Web of Conferences 199 (2020): 00015. http://dx.doi.org/10.1051/e3sconf/202019900015.
Анотація:
Based on the data of China’s economic and social big data platform from 2000 to 2019, this paper studies and analyzes the development status and influencing factors of desertification in Maowusu sandy land in China. Based on the exploratory analysis (EFA) and the dummy variable regression model (DVRM), the research result shows that the annual precipitation is the main climate factor affecting the vegetation coverage in this area. And for every 100 mm increase in annual precipitation, vegetation coverage will increase by 10%. In addition, the annual average temperature also has a significant impact on the vegetation coverage. For every 1 °C increase in the annual average temperature, the vegetation coverage will increase by 2.5%. Analysis of policy factors shows that the policy effects of the 2005 National Desert Control Plan (2005-2010) and the 2011 National Desert Control Plan (2011-2020) etc. can increase vegetation coverage by 3.4% and 4.7%respectively compared with the base period level in 2000. The study reveals the important role of climate and policy factors in the reversion of desertification in Maowusu sandy land. The study is of great significance and value to desertification management and related policy-making in China.
48
Qian, Chunhua, Hequn Qiang, Feng Wang, and Mingyang Li. "Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic." Remote Sensing 13, no. 15 (July 26, 2021): 2935. http://dx.doi.org/10.3390/rs13152935.
Анотація:
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.
49
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.
Анотація:
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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Lyu, Yanli, Peijun Shi, Guoyi Han, Lianyou Liu, Lanlan Guo, Xia Hu, and Guoming Zhang. "Desertification Control Practices in China." Sustainability 12, no. 8 (April 17, 2020): 3258. http://dx.doi.org/10.3390/su12083258.
Анотація:
Desertification is a form of land degradation principally in semi-arid and arid areas influenced by climatic and human factors. As a country plagued by extensive sandy desertification and frequent sandstorms and dust storms, China has been trying to find ways to achieve the sustainable management of desertified lands. This paper reviewed the impact of climate change and anthropogenic activities on desertified areas, and the effort, outcome, and lessons learned from desertification control in China. Although drying and warming trends and growing population pressures exist in those areas, the expanding trend of desertified land achieved an overall reversal. In the past six decades, many efforts, including government policies, forestry, and desertification control programs, combined with eco-industrialization development, have been integrated to control the desertification in northern China. Positive human intervention including afforestation, and the rehabilitation of mobile sandy land, and water conservation have facilitated the return of arid and semi-arid ecosystems to a more balanced state. China’s practices in desertification control could provide valuable knowledge for sustainable desertified land management on a global scale.