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

Abstract

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.

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

“…Therefore, human factors have not been treated in the present study. Nevertheless, human activities such as ‘grain to green’ and ecological reconstruction might have strong effects toward the reversal of land degradation (Yue, Li, et al, ; Yue, Ye, et al, ). Because of this, we argue that human factors should be added to the indicators for a comprehensive ADSA.…”

“…Therefore, considering the huge arid area in Northern China, that index is well suited to our research needs. Some indices such as the moving standard deviation index and albedo have been proposed as powerful adjuncts to the NDVI for monitoring desertification patterns (Tanser & Palmer, ; Xu et al, ; Yue, Li, et al, ). Therefore, we argue that indicators such as albedo as an adjunct to NDVI may create a more accurate DT for classifying aeolian desertification.…”

“…These indicators cover three aspects (Table 1), that is, climate, vegetation, and soil, from which five indicators are eventually selected: mean annual precipitation, mean annual wind speed, mean annual AI, vegetation index, and soil erodibility. This selection is informed by expert knowledge from preceding studies (e.g., Du et al, 2016;FAO/UNEP, 1984;Kosmas et al, 1999;Shen et al, 2017;UNEP, 1992;Wessels, Prince, Frost, & van Zyl, 2004;Yue, Li, et al, 2016, and references therein). Among these, the precipitation, AI, and vegetation index represent environmental characteristics relevant to aeolian…”

“…Furthermore, studies have estimated that desertification is likely to expand substantially because of population growth (MEA, ; Reynolds et al, ) and climate change resulting in precipitation variability, increased drought frequency, and persistence of dry conditions (D'Odorico, Bhattachan, Davis, Ravi, & Runyan, ; Huang, Yu, Guan, Wang, & Guo, ; IPCC, ). However, the geographic extent of desertification, which is of vital importance in desertification prevention planning (Verstraete, Scholes, & Smith, ; Yue et al, ), remains poorly understood (Daily, ; D'Odorico et al, ) given the importance of the problem and its recognition as a global issue. Desertification susceptibility assessment (DSA), also known as desertification sensitivity assessment or desertification assessment, is the primary method used to map the locations, extensions, and classifications of desertification (Lavado Contador, Schnabel, Gutiérrez, & Fernández, ; Sepehr, Hassanli, Ekhtesasi, & Jamali, ).…”

“…More recently, remote sensing has become an important method for monitoring and assessment of desertification susceptibility at global and regional scales, with a large number of up‐to‐date and high‐accuracy desertification detection results (De Jong, de Bruin, Schaepman, & Dent, ; Metternicht, Zinck, Blanco, & Del Valle, ; Prince, Becker‐Reshef, & Rishmawi, ; Vorovencii, ; Yue, Li, et al, , and references therein). However, remote sensing is unable to reveal the impact mechanism of desertification impact factors (e.g., soil, vegetation, and climate) on desertification.…”