Research on land use optimization for reducing wind erosion in sandy desertified area: a case study of Yuyang County in Mu Us Desert, China

Yue, Yaojie; Ye, Xinyue; Zou, Xueyong; Wang, Jing ́ai; Gao, Lu

doi:10.1007/s00477-016-1223-9

Cited by 11 publications

(15 citation statements)

References 37 publications

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“…Since the quality of the extracted knowledge depends heavily on the quality of the expert(s) [54,55], an expert should be, we emphasize, someone who has an extensive theoretical understanding, as well as field experience in land degradation studies. In this study, the experts are the co-authors of this paper, who have worked on land degradation in northern and western China for a long time [42,49,50,[56][57][58][59][60][61][62]. Land degradation detecting knowledge from local domain experts can be constructed following two steps.…”

Section: Research Frameworkmentioning

confidence: 99%

“…This might be because the Ordos Plateau lies between a semi-arid and a semi-humid zone; the land capacity is much higher than the arid zone. Thus, many of the lands are cultivated and disturbed greatly because of a dense population [50][51][52][53], which may lead to some local non-degraded land being seriously destroyed in a very short term, where it is detected as lightly degraded land. On the other hand, some of the low degradation lands might have very high vegetation coverage, especially in summer when rainfall is rich, which makes it very difficult to distinguish them from none degraded lands.…”

Section: Modeling and Validation Of Land Degradation Detectionmentioning

confidence: 99%

“…There are debates on the causes and driving forces of land degradation in the region [49,50,76]. For example, it was indicated that the land degradation expansion was dominated by human activities [23,40], whereas its reversion was dominated by climate change [40].…”

Section: Land Degradation and Its Control In The Ordos Plateaumentioning

confidence: 99%

“…For example, it was indicated that the land degradation expansion was dominated by human activities [23,40], whereas its reversion was dominated by climate change [40]. Although it is universally held that apart from such natural factors as climate change, human elements, like deforestation, irrational cultivation and overgrazing, are the chief culprits [16,49,50,71,[77][78][79]. However, at different spatial and temporal scales, the relative role of climate factors and human activities on land degradation is very complicated; this requires a specific analysis of specific areas.…”

Section: Land Degradation and Its Control In The Ordos Plateaumentioning

confidence: 99%

“…Land degradation, which essentially describes the circumstances of the reduced biological productivity of land [1,2], ranks among the greatest global environmental challenges and affects The Ordos Plateau has been extensively studied in the research of land degradation, especially wind-induced desertification [16,21,27,44,[46][47][48][49][50]. Research by Cui et al showed that the total desertified area of the Kubuqi Desert on the Ordos Plateau decreased by 13.29% from 1989-2002, but the land desertification of the southeast area, which has a dense population, worsened by 2007 [20].…”

Section: Introductionmentioning

confidence: 99%

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Land Degradation Monitoring in the Ordos Plateau of China Using an Expert Knowledge and BP-ANN-Based Approach

Yue

Zhu

et al. 2016

Sustainability

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Abstract:Land degradation monitoring is of vital importance to provide scientific information for promoting sustainable land utilization. This paper presents an expert knowledge and BP-ANN-based approach to detect and monitor land degradation in an effort to overcome the deficiencies of image classification and vegetation index-based approaches. The proposed approach consists of three generic steps: (1) extraction of knowledge on the relationship between land degradation degree and predisposing factors, which are NDVI and albedo, from domain experts; (2) establishment of a land degradation detecting model based on the BP-ANN algorithm; and (3) land degradation dynamic analysis. A comprehensive analysis was conducted on the development of land degradation in the Ordos Plateau of China in 1990China in , 2000China in and 2010 The results indicate that the proposed approach is reliable for monitoring land degradation, with an overall accuracy of 91.2%. From 1990-2010, a reverse trend of land degradation is observed in Ordos Plateau. Regions with relatively high land degradation dynamic were mostly located in the northeast of Ordos Plateau. Additionally, most of the regions have transferred from a hot spot of land degradation to a less changed area. It is suggested that land utilization optimization plays a key role for effective land degradation control. However, it should be highlighted that the goals of such strategies should aim at the main negative factors causing land degradation, and the land use type and its quantity must meet the demand of population and be reconciled with natural conditions. Results from this case study suggest that the expert knowledge and BP-ANN-based approach is effective in mapping land degradation.

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Section: Research Frameworkmentioning

confidence: 99%

Section: Modeling and Validation Of Land Degradation Detectionmentioning

confidence: 99%

Section: Land Degradation and Its Control In The Ordos Plateaumentioning

confidence: 99%

Section: Land Degradation and Its Control In The Ordos Plateaumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Land Degradation Monitoring in the Ordos Plateau of China Using an Expert Knowledge and BP-ANN-Based Approach

Yue

Zhu

et al. 2016

Sustainability

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A data‐mining‐based approach for aeolian desertification susceptibility assessment: A case‐study from Northern China

Yue

Wang

et al. 2019

Land Degrad Dev

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Desertification is a grave threat to the environment and livelihoods. Desertification susceptibility assessment (DSA) plays a critical role in reasonable desertification prevention planning by mapping the extent, intensity, and classification of desertification. Numerous desertification maps have been produced using various DSA methods. However, the method of rapid desertification mapping by objectively discovering valuable DSA knowledge from experienced experts stored in such maps has rarely been explored. We propose a data‐mining‐based approach to mapping aeolian desertification that applies the decision tree (DT) C5.0 (C5) algorithm as a knowledge discovery tool to the reference map and corresponding environmental variables. The results of our case‐study in Northern China show that the overall accuracy of aeolian desertification classification based on C5 is 86.69%, and the predicted map is highly consistent with the reference map. The DT algorithm outperforms the artificial neural network and naive Bayes approaches. Our results highlight the importance of selecting more representative training samples across where interleaved distributions of multiple aeolian desertification land exist when applying the DT algorithm. The findings of the present study are valuable for highlighting the significance of the data mining approach in DSA, with the growth of desertification maps. Given that aeolian desertification is a complex process coupling natural and human factors, and there are significant regional and scale differences in Northern China, further studies at a fine‐scale regarding human factors deserve more attention.

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Desert classification based on a multi-scale residual network with an attention mechanism

et al. 2020

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Research on land use optimization for reducing wind erosion in sandy desertified area: a case study of Yuyang County in Mu Us Desert, China

Cited by 11 publications

References 37 publications

Land Degradation Monitoring in the Ordos Plateau of China Using an Expert Knowledge and BP-ANN-Based Approach

Land Degradation Monitoring in the Ordos Plateau of China Using an Expert Knowledge and BP-ANN-Based Approach

A data‐mining‐based approach for aeolian desertification susceptibility assessment: A case‐study from Northern China

Desert classification based on a multi-scale residual network with an attention mechanism

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