Feasibility of Vegetation Temperature Condition Index for monitoring desertification in Bulgan, Mongolia

Yu, Hangnan; Lee, Jong Yeol; Lee, Woo‐Kyun; Lamchin, Munkhnasan; Tserendorj, Dejee; Choi, Sol-E; Song, Yena; Kang, Ho Duck

doi:10.7780/kjrs.2013.29.6.5

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Various index-based approaches are widely used to investigate the degradation of various land features (Zhao et al 2013). For instance, Normalized Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI) are commonly used to monitor the conditions of vegetation while assessing desertification (Linli et al 2006;Xu et al 2009;Yu et al 2013;Ren et al 2014;Wu et al 2019). Indices such as albedo and the Topsoil Grain Size Index (TGSI) can detect spatial heterogeneity of soil texture (Xiao et al 2006;Liu et al 2018a).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Desertification in the Middle Moulouya Basin (North-East Morocco) Using Sentinel-2 Images and Spectral Index Techniques

et al. 2022

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This article focuses on the quantitative assessment of desertification in the Middle Moulouya basin located in the North-East of Morocco. Indeed, this study aimed to map the degree of desertification at the level of the basin in 2018 using the Sentinel-2 image. To do this, we have adopted a methodology based on several stages. First, we extracted the spectral indices, in particular, the NDVI, the albedo, the TGSI and the MSAVI. Then, different combinations of these indices were the subject of a linear regression analysis (NDVI-albedo, MSAVI-albedo, albedo-TGSI and TGSI-MSAVI) to use the best correlated combinations to construct the feature space. The results obtained showed that the NDVI-albedo and MSAVI-albedo combinations are the best correlated with respective correlation coefficients of r = − 0.73 and r = − 0.76, respectively. As a result, they were used to propose the desertification degree index (DDI) by exploiting the NDVI-albedo and MSAVI-albedo feature spaces. Finally, a desertification map was generated for the entire basin. It has five degrees of desertification (extreme, severe, moderate, low, non-desertification). According to our results, the situation of desertification in the basin is alarming. Indeed, 86.86% of the study area is located in the moderate to extreme desertification class. While only 12.25% and 0.89% fall in the low and no desertification categories, respectively. The MSAVI-albedo model gave a high overall accuracy of 93.75%, so it is perfectly effective for the quantitative analysis and monitoring of desertification at the level of the basin studied.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Desertification in the Middle Moulouya Basin (North-East Morocco) Using Sentinel-2 Images and Spectral Index Techniques

et al. 2022

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“…As the second largest plateau in Asia, the Mongolian Plateau (MP) consists primarily of the Inner Mongolia Autonomous Region of China (IMAR, a total area of 1.18×10 6 km 2 ) and the entire territory of Mongolia (a total area of 1.57×10 6 km 2 ; Fang et al, 2015). Approximately 90% of Mongolia's territory is occupied by hyper-arid, arid, semi-arid, and dry sub-humid areas, and roughly 72% of the country is vulnerable to desertification (Yu et al, 2013;Eckert et al, 2015). In the IMAR, 60% of the land area is occupied by arid, semi-arid, and dry sub-humid areas and suffers from desertification (Ci and Wu, 1997).…”

Section: Introductionmentioning

confidence: 99%

“…Generally, changes in vegetation are the most direct indicator of desertification (land degradation), and can reflect the dynamic processes of desertification (Yang et al, 2005;Xu et al, 2009). Image-based vegetation indices such as the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), modified SAVI (MSAVI), enhanced vegetation index (EVI), vegetation temperature condition index (VTCI), and optimized vegetation index (OPVI), were most commonly used for monitoring the vegetation conditions in desertification assessment (Cui et al, 2006;Xu et al, 2009;Yu et al, 2013). In addition, some indices that can reflect spatial heterogeneity, such as the land surface temperature (LST), land surface albedo, land surface water index (LSWI), moving standard deviation index (MSDI), and rain use efficiency (RUE), are also used to assess desertification.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with NDVI and EVI, MSAVI2 derived from multi-temporal SPOT-4 VEGETATION data from 1998 to 2001 is found to estimate vegetation biomass and monitor vegetation degradation well on the Mongolian desert steppes (Javzandulam et al, 2005). The VTCI from multi-temporal Landsat-5 images has been used to predict the distribution of vegetation and efficiently monitor desertification in Bulgan, Mongolia (Yu et al, 2013). The NDVI, MSDI, and land surface albedo from Landsat images have been used in conjunction to quantitatively assess the desertification of the Ordos Plateau, China in 1980, 1990, and 2000(Xu et al, 2009.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring desertification processes in Mongolian Plateau using MODIS tasseled cap transformation and TGSI time series

Liu

Huang

2018

J. Arid Land

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Most remote sensing studies assess the desertification using vegetation monitoring method. But it has the insufficient precision of vegetation monitoring for the limited vegetation cover of the desertification region. Therefore, it offers an alternative approach for the desertification research to assess sand dune and sandy land change using remote sensing in the desertification region. In this study, the indices derived from the well-known tasseled cap transformation (TCT), tasseled cap angle (TCA), disturbance index (DI), process indicator (PI), and topsoil grain size index (TGSI) were integrated to monitor and assess the desertification at the thirteen study sites including sand dunes and sandy lands distributed in the Mongolian Plateau (MP) from 2000 to 2015. A decision tree was used to classify the desertification on a regional scale. The average overall accuracy of 2000, 2005, 2010 and 2015 desertification classification was higher than 90%. Results from this study indicated that integration of the advantages of TCA, DI and TGSI could better assess the desertification. During the last 16 years, Badain Jaran Desert, Tengger Desert, and Ulan Buh Desert showed a relative stabilization. Otindag Sandy Land and the deserts of Khar Nuur, Ereen Nuur, Tsagan Nuur, Khongoryn Els, Hobq, and Mu Us showed a slow increasing of desertification, whereas Bayan Gobi, Horqin and Hulun Buir sandy lands showed a slow decreasing of desertification. Compared with the other 11 sites, the fine sand dunes occupied the majority of the Tengger Desert, and the coarse sandy land occupied the majority of the Horqin Sandy Land. Our findings on a three or four years' periodical fluctuated changes in the desertification may possibly reflect changing precipitation and soil moisture in the MP. Further work to link the TCA, DI, TGSI, and PI values with the desertification characteristics is recommended to set the thresholds and improve the assessment accuracy with field investigation.

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Agricultural drought survey using MODIS-based image indices at the regional scale: case study of the Urmia Lake Basin, Iran

Shamloo

Sattari

Apaydín

2022

Theor Appl Climatol

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Feasibility of Vegetation Temperature Condition Index for monitoring desertification in Bulgan, Mongolia

Cited by 5 publications

References 15 publications

Evaluation of Desertification in the Middle Moulouya Basin (North-East Morocco) Using Sentinel-2 Images and Spectral Index Techniques

Evaluation of Desertification in the Middle Moulouya Basin (North-East Morocco) Using Sentinel-2 Images and Spectral Index Techniques

Monitoring desertification processes in Mongolian Plateau using MODIS tasseled cap transformation and TGSI time series

Agricultural drought survey using MODIS-based image indices at the regional scale: case study of the Urmia Lake Basin, Iran

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