Assessing environmentally sensitive land to desertification using MEDALUS method in Mongolia

Lee, Eun Jung; Piao, Dongfan; Song, Cholho; Kim, Jiwon; Lim, Chul-Hee; Kim, Eunji; Moon, Jon Jungbien; Kafatos, Menas; Lamchin, Munkhnsan; Jeon, Seong Woo; Lee, Woo‐Kyun

doi:10.1080/21580103.2019.1667880

Cited by 35 publications

(31 citation statements)

References 22 publications

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“…Changes in the NDVI are often used as indicators of the degree of desertification development. However, many studies on this aspect only focus on the trend of vegetation change and rarely pay attention to the state of the region before the change [23,60]. To assess the degree of vegetation changes, not only do we need to obtain the trends and the amount of vegetation change, we also need to take the initial state of the vegetation into account.…”

Section: Relative Degree Of Vegetation Changesmentioning

confidence: 99%

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

et al. 2020

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As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen's slope, Mann-Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.With the continuous development of earth observation system technology and the continuous enrichment of statistical models, the research content and methods of the relationships between vegetation, climate change, and human activities have become increasingly diverse. The normalized density vegetation index (NDVI), derived from infrared channel and near-infrared channel remote sensing data, is a good indicator of vegetation growth status and spatial distribution density of vegetation, and is linearly related to vegetation distribution density [6,7]. A large number of ecological studies have been carried out related to vegetation change and its influencing factors using NDVI and statistical models. Temperature and precipitation, as important factors in the natural environment, are often used as research priorities to assess their impact on vegetation. However, the dominant factors contributing to NDVI in different regions vary considerably. The variety in vegetation and its connection to climate change have been investigated by Du et al. [5] and Zheng et al. [8] in China. These studies identified precipitation as the key climatic factor governing variation in NDVI. However, Guo et al. [9] found that temperature was the dominant factor on vegetation (NDVI) growth ...

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Section: Relative Degree Of Vegetation Changesmentioning

confidence: 99%

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

et al. 2020

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“…Diverse global and national scale of desertification researches dealing with land degradation neutrality in arid area have used the FAO Aridity Index applying precipitation (Nastos, Politi, & Kapsomenakis, 2013;Pr av alie, 2016;Spinoni, Vogt, Naumann, Carrao, & Barbosa, 2015) or MEDALUS methods combining global dataset (Ferrara et al, 2020;Ferrara, Salvati, Sateriano, & Nolè, 2012;Karamesouti, Panagos, & Kosmas, 2018). However, it is necessary to consider values suitable for regional characteristics, and also monitor and assess desertification in appropriate scale of regions in the analysis (Lamchin et al, 2016;Lee et al, 2019). This study could be useful in the view that it analyzed the potentiality of vegetation introduction in the wide area by constructing the decision tree according to the characteristics of the Aral Sea dry land by arranging a widely used vegetation and soil index.…”

Section: Discussionmentioning

confidence: 99%

Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

et al. 2020

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The Aral Sea was one of the largest lakes in the world, but almost 60,000 km 2 of the waterbody has dried up due to water withdrawal for irrigation. Afforestation on the desiccated seafloor could be important in preventing soil flation, dust storms, and negative impact on human health. In this study, we aimed to delineate potential vegetation establishment areas on the dried Aral Sea bed using remote-sensed data in support of the decision-making related to afforestation. Various indices such as normalized difference vegetation index (NDVI), topsoil grain size index (TGSI), soil salinity index (SSI), and normalized multiband drought index (NMDI) were calculated from the LANDSAT-8 OLI satellite imagery. As an indicator of vegetation existence, NDVI was classified into three groups and set as a base for classifying other indices by performing statistical analyses. Based on the decision tree method, indices were combined and the potential vegetation establishment area was detected. Higher NDVI was identified in the southeast than the west of the study area. The results of statistical analyses showed that TGSI had a positive correlation with NDVI, while SSI and NMDI had a negative correlation. Overall, the potential vegetation area comprised 7,295.21 km 2 (61.34%) of the 'unsuitable' area, 2,818.64 km 2 (23.7%) of the 'intermediate' area, 1,612.15 km 2 (13.56%) of the 'suitable' area, and 166.42 km 2 (1.4%) of the 'very suitable' area. The developed map enables to identify dried seafloor area suitable for vegetation establishment thus contributing to planning the land rehabilitation efforts and preventing further land degradation.

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“…This decline in NDVI also coincided with a drought period and an increase in temperature in the three months prior to the image capture (Figure 7(c,d)). NDVI is generally strongly related to precipitation regimes (Chamaille-Jammes et al 2006;Lee et al 2019), and the $90 days of rainfall and temperature appear to influence the NDVI pattern in Parita Bay. In addition, 1997 and 1998 were significantly impacted by the El Niño-Southern Oscillation (ENSO) (Flores-C ardenas et al 2018), which likely enhanced the drought conditions in the study region.…”

Section: Variability and Drivers Of Mangrove Qualitymentioning

confidence: 99%

Thirty-two years of mangrove forest land cover change in Parita Bay, Panama

Castillo

Kim

2021

Forest Science and Technology

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Mangrove forests have experienced a rapid decline. However, the rate of loss has decreased in recent years due to enhanced conservation and nature regeneration. The dynamics of mangrove forests in Panama have not been monitored since the year 2000, despite a significant loss during the 1980s. The objectives of our study were to quantify changes in mangrove cover and identify the dominant drivers of change in Parita Bay, Panama. Temporal changes in mangrove cover and the Normalized Difference Vegetation Index (NDVI) were determined using the supervised classification method on Landsat satellite images from 1987 to 2019. We identified a 4.7% increase in the mangrove area of Parita Bay during the 32 years; the mangrove forests were also considered healthy as reflected by high NDVI values. However, the conversion of mangroves to other land cover types resulted in a 1.26% decline in mangrove cover from 1987 to 1998. Moreover, the area of aquaculture and saltpans almost doubled during this period. During the following two decades, the conversion of other land cover classes (water, other vegetation, and bare soil) increased the mangrove area by 6%, and the annual rate of increase was greater during the second decade (0.43% year À1 ). From 2009 to 2019, mangroves declined at an annual rate of 0.11% in protected areas and increased at an annual rate of 0.50% in unprotected areas. Despite the regeneration potential of mangrove forests, our study highlights the need to continually manage and protect mangrove forests in order to facilitate their expansion in Parita Bay.

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Assessing environmentally sensitive land to desertification using MEDALUS method in Mongolia

Cited by 35 publications

References 22 publications

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

Thirty-two years of mangrove forest land cover change in Parita Bay, Panama

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