The Construction and Application of an Aledo-NDVI Based Desertification Monitoring Model

Ma, Zhuguo; Xie, Yaowen; Jiao, Jizong; li, Linlin; Wang, Xiangqian

doi:10.1016/j.proenv.2011.09.318

Cited by 61 publications

(61 citation statements)

References 6 publications

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“…Field verification reveals that with an increase in the degree of desertification, the surface morphology changes, the surface roughness decreases, and the surface albedo increases continuously [26,27]. As shown in Figure 2 [28], there is a significant negative correlation between the NDVI and albedo in different desertification areas. In the figure, AC represents the high albedo line and reflects the drought situation, which is the limit of high albedo corresponding to the complete arid land under certain vegetation coverage [13].…”

Section: Principles Of Feature Spacementioning

confidence: 90%

“…where "DDI" was the desertification divided index and K was determined by the slope of the straight line fitted in the feature space. According to the research conclusions made by Ma et al in 2011, DDI values can be divided into five different levels by the natural break (Jenks) classification [28]. These five desertification levels are severe desertification, high desertification, medium desertification, low desertification, and non-desertification.…”

Section: Classification Of Desertificationmentioning

confidence: 99%

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Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

et al. 2018

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The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau.

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Section: Principles Of Feature Spacementioning

confidence: 90%

Section: Classification Of Desertificationmentioning

confidence: 99%

Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

et al. 2018

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“…Land quality degradation can be observed with increasing land-surface albedo. Vegetation cover is a key parameter that can be used to evaluate the vegetation restoration conditions in degraded sandy grasslands (Ma et al 2011). However, the vegetation condition cannot be appropriately expressed in regions with sparse vegetation because of the influence of the soil background on the NDVI .…”

Section: Sensitive Feature Space Indicesmentioning

confidence: 99%

“…Zeng et al (2006) analyzed the relations between desertification and the land-surface parameters and proposed the albedonormalized difference vegetation index (NDVI) feature space model. Further, Ma et al (2011) determined the quantitative relation between albedo and NDVI in case of different desertification levels using statistical regression analysis. Wei et al (2018) analyzed the effects and applicability of different feature space models with respect to the extraction of the desertification information of the Mongolian plateau based on four sensitive parameters, i.e., NDVI, topsoil grain size index, and land-surface albedo.…”

Section: Introductionmentioning

confidence: 99%

Desertification detection model in Naiman Banner based on the albedo-modified soil adjusted vegetation index feature space using the Landsat8 OLI images

Wen

Guo

Zang

et al. 2020

Geomatics, Natural Hazards and Risk

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“…Some of them use spectral biophysical indicators [7,8,9] and other integrating anthropogenic indicators [10]. To represent the desertification process in a comprehensive and easy way, a conjugal methodology can be used by taking into consideration quantitative and qualitative methods and by using specific indicators [11,12].…”

Section: Introductionmentioning

confidence: 99%

Desertification Sensitivity Analysis Using MEDALUS Model and GIS: A Case Study of the Oases of Middle Draa Valley, Morocco

Lamqadem

Pradhan

Saber

et al. 2018

Sensors

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Oases can play a significant role in the sustainable economic development of arid and Saharan regions. The aim of this study was to map the desertification-sensitive areas in the Middle Draa Valley (MDV), which is in the southeast of Morocco. A total of 13 indices that affect desertification processes were identified and analyzed using a geographic information system. The Mediterranean desertification and land use approach; which has been widely used in the Mediterranean regions due to its simplicity; flexibility and rapid implementation strategy; was applied. All the indices were grouped into four main quality indices; i.e., soil quality; climate quality; vegetation quality and management quality indices. Each quality index was constructed by the combination of several sub-indicators. In turn; the geometric mean of the four quality index maps was used to construct a map of desertification-sensitive areas; which were classified into four classes (i.e., low; moderate; high and very high sensitivity). Results indicated that only 16.63% of the sites in the study were classified as least sensitive to desertification; and 50.34% were classified as highly and very highly sensitive areas. Findings also showed that climate and human pressure factors are the most important indicators affecting desertification sensitivity in the MDV. The framework used in this research provides suitable results and can be easily implemented in similar oasis arid areas.

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The Construction and Application of an Aledo-NDVI Based Desertification Monitoring Model

Cited by 61 publications

References 6 publications

Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

Desertification detection model in Naiman Banner based on the albedo-modified soil adjusted vegetation index feature space using the Landsat8 OLI images

Desertification Sensitivity Analysis Using MEDALUS Model and GIS: A Case Study of the Oases of Middle Draa Valley, Morocco

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