Spatio-temporal assessment of Environmentally Sensitive Areas (ESA) in The Thar Desert India, to combat desertification under UNCCD framework

Sharma, Laxmi Kant; Sharma, Laxmi Kant; Somawat, Kritika

doi:10.1016/j.jaridenv.2021.104609

Cited by 15 publications

(4 citation statements)

References 34 publications

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“…However, the desertification inversion accuracy of the feature space model depends on the calculation accuracy of typical surface parameters, and the wrong sampling points will cause the inversion accuracy to be damaged [12]. Based on multisource data such as climate, vegetation, and soil, the establishment of comprehensive assessment models using methods such as AHP is also commonly used in desertification monitoring work [13][14][15]. However, this method has defects such as cumbersome data collation, strong subjectivity, and data coupling correlation [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Shen

et al. 2022

Sustainability

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

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Section: Introductionmentioning

confidence: 99%

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

Chen

Shen

et al. 2022

Sustainability

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“…For example, China implemented major engineering measures, such as the Three‐North Shelterbelt and Returning Farmland To Forest And Grassland (Bryan et al, 2018). According to the UN Convention to Combat Desertification (Sharma et al, 2021), the main goal of desertification control is to prevent or reduce land degradation and restore part of the degraded land, thereby achieving sustainable development (Wang et al, 2015). Grassland, an important part of the global ecosystem, has important ecological and production values (Hou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…UN Convention to Combat Desertification (Sharma et al, 2021), the main goal of desertification control is to prevent or reduce land degradation and restore part of the degraded land, thereby achieving sustainable development (Wang et al, 2015). Grassland, an important part of the global ecosystem, has important ecological and production values (Hou et al, 2021).…”

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confidence: 99%

Dynamics of blowouts indicating the process of grassland desertification

et al. 2022

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Sand desertification is a serious ecological and environmental problem in semiarid grassland areas; thus an accurate understanding of its causes and process mechanisms is of significance for grassland desertification control. With the popularization of remote sensing monitoring technology, most research has focused on analyzing the trends of grassland sand patches and their impact on regional desertification, whereas fewer studies have explored the mechanisms of grassland desertification development. This study analyzed the dynamic changes in grassland landscape in the Hulun Buir grassland of China via multiperiod high‐resolution remote sensing images from 1959 to 2020, and examined the morphological changes, erosion patterns, grain size characteristics and sediment compaction of two trough blowouts. Our study noted that under climate change and human activities, grassland desertification is becoming increasingly serious, and its main manifestation is the occurrence and expansion of blowouts. Changes in blowout morphologies indicate that the area of the deflation basin shows a state of continuous growth, whereas the area of the depositional lobe shows fluctuating changes, which could mean that the deflation basin may be a useful indicator of desertification. Based on a detailed analysis of both climate and grassland changes, we believe that human activities determine the occurrence of blowouts, and climatic conditions determine the expansion of blowouts and the desertification degree. The morphological changes of blowouts are related to the removal mode and erosion resistance of the grassland soil, and the rate of removal of the soil limits the rate of blowout expansion.

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“…According to the fifth monitoring of desertification and sandification in China, the area of desertification land was 26.12 million ha, accounting for 27.20% of the total land area [8]. The development of desertification leads to the deterioration of the ecological environment, the decline of soil quality, sandstorms and other problems, which seriously restricts the sustainable development of the ecosystem [9][10][11].…”

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confidence: 99%

Spatio-Temporal Patterns and Driving Forces of Desertification in Otindag Sandy Land, Inner Mongolia, China, in Recent 30 Years

Yang

Shi

et al. 2023

Remote Sensing

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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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Spatio-temporal assessment of Environmentally Sensitive Areas (ESA) in The Thar Desert India, to combat desertification under UNCCD framework

Cited by 15 publications

References 34 publications

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

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

Dynamics of blowouts indicating the process of grassland desertification

Spatio-Temporal Patterns and Driving Forces of Desertification in Otindag Sandy Land, Inner Mongolia, China, in Recent 30 Years

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