An improvement of the Ts-NDVI space drought monitoring method and its applications in the Mongolian plateau with MODIS, 2000–2012

Cao, Xiaoming; Yin, Feng; Wang, Juanle

doi:10.1007/s12517-016-2451-5

Cited by 20 publications

(18 citation statements)

References 38 publications

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“…The images were segmented into homogeneous polygons (namely, objects) of varying sizes, according to the spectral heterogeneity between pixels (Yue, 2011;Ma et al, 2015). Then, the objects are classified according to specified rules, taking full advantage of the textural, spatial, and spectral characteristics of ground objects (Cao et al, 2016;Ren et al, 2017;.…”

Section: Remote-sensing Interpretation Of Land Cover Datamentioning

confidence: 99%

Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia

Wang

Wei

Cheng

et al. 2020

Environmental Development

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Highlights  A map of land degradation and restoration with 30 m resolution in Mongolia was obtained for the first time.  For the past 25 years, the trend of land change in Mongolia was dominated by land degradation.  Land degradation is accompanied by ongoing restoration of some land areas, and the capacity for land restoration is gradually improving.  The central regions of Mongolia are the area with the most significant land degradation, and the combined effects from natural and socioeconomic factors are the main driving forces.  The northwestern and northeastern parts of Mongolia is the most significant land restoration area, and owns relatively sufficient water resources for improving land restoration.

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Section: Remote-sensing Interpretation Of Land Cover Datamentioning

confidence: 99%

Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia

Wang

Wei

Cheng

et al. 2020

Environmental Development

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“…As a popular and dangerous natural disaster, it can result in many hazards for farming, which makes the environment worse and potentially causes other natural disasters to happen. As such, it is very important to assess the spatial-temporal variations of droughts [3].…”

Section: Introductionmentioning

confidence: 99%

The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016)

Liu

Yue

2018

Remote Sensing

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Traditional NDVI-T s space is triangular or trapezoidal, but Liu et al. (2015) discovered that the NDVI-T s space was bi-parabolic when the study area was covered with low biomass vegetation. Moreover, the numerical value of the indicator was considered in most of the study when the drought conditions in the space domain were evaluated. In addition, quantitatively assessing the spatial-temporal changes of the drought was not enough. In this study, first, we used MODIS NDVI and T s data to reexamine if the NDVI-T s space with "time" and a single pixel domain is bi-parabolic in the Shaanxi province of China, which is vegetated with low biomass to high biomass. This is compared with the triangular NDVI-T s space and one of the well-known drought indexes called the temperature-vegetation index (TVX). The results demonstrated that dry and wet edges exhibited a parabolic shape again in scatter plots of T s and NDVI in the Shaanxi province, which was linear in the triangular NDVI-T s space. The Temperature Vegetation Dryness Index (TVDI c) was obtained from bi-parabolic NDVI-T s andTVDI t was obtained from the triangular NDVI-T s space and TVX were compared with 10-cm depth relative soil moisture. By estimating the 10-cm depth soil moisture, TVDI c was better than TVDI t , which were all apparently better than TVX. Second, combined with MODIS data, the drought conditions of the study area were assessed by TVDI c between 2000 to 2016. Spatially, the drought in the Shaanxi Province between 2000 to 2016 were mainly distributed in the northwest, North Shaanxi, and the North and East Guanzhong plain. The drought area of the Shaanxi province accounted for 31.95% in 2000 and 27.65% in 2016, respectively. Third, we quantitatively evaluated the variation of the drought status by using Gradient-based Structural Similarity (GSSIM) methods. The area of the drought conditions significantly changed and moderately changed at 5.34% and 40.22%, respectively, between 2000 and 2016. Finally, the possible reasons for drought change were discussed. The change of precipitation, temperature, irrigation, destruction or betterment of vegetation, and the enlargement of opening mining, etc., can lead to the variations of drought.

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“…Based on the above operations, the images were segmented into homogeneous polygons (namely objects) of varying sizes according to the spectral heterogeneity between pixels [25,26]. The objects were then classified according to specified rules [27][28][29], taking full advantage of the textural, spatial, and spectral characteristics of ground objects.…”

Section: Remote-sensing Interpretation Of Land Cover Datamentioning

confidence: 99%

Spatio-Temporal Pattern of Land Degradation along the China-Mongolia Railway (Mongolia)

et al. 2019

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The increasingly serious problem of land degradation has a direct impact on the ecosystem and sustainable development in Mongolia. The influence of land degradation on the main China–Mongolia–Russia traffic arteries is currently unclear and poses a risk to the construction of transportation infrastructure. In this study, for the first time, we obtained land cover data from 1990, 2010, and 2015, at a 30 m, resolution based on the Landsat Thematic Mapper (TM) and Landsat Operational Land Imager (OLI) images along the China–Mongolia railway (Mongolia section). We then analyzed the change in the obtained land cover data using the geographic information system (GIS) spatial analysis technology to obtain the land pattern and development trend of this region. Results showed that the newly-increased degraded land was distributed mainly in the center of the region and tended to expand northward. The joint effect of natural and socioeconomic factors resulted in land degradation. We speculated that the significant temperature fluctuation and the decrease in rainfall were the inducing factors. Population migration, overgrazing, infrastructure construction, unreasonable mineral exploitation, and rapid urbanization aggravate the degree of land degradation. It is thus necessary to use a broader view to observe patterns of desertification and variations in regions along the Railway facing its sustainable development.

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An improvement of the Ts-NDVI space drought monitoring method and its applications in the Mongolian plateau with MODIS, 2000–2012

Cited by 20 publications

References 38 publications

Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia

Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia

The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016)

Spatio-Temporal Pattern of Land Degradation along the China-Mongolia Railway (Mongolia)

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