Assessing land degradation and quantifying its drivers in the Amudarya River delta

Jiang, Liangliang; Jiapaer, Guli; Bao, Anming; Li, Yaoming; Guo, Hao; Zheng, Guoxiong; Chen, Tao; Maeyer, Philippe De

doi:10.1016/j.ecolind.2019.105595

Cited by 58 publications

(46 citation statements)

References 55 publications

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“…Compared with the coverage in 2000, large areas of grassland have been degraded, resulting in increased grassland dispersal, landscape separation and fragmentation and ecological risks. In this study, we found that the ecology downstream of the ADD showed significant degradation, which has also been confirmed by previous studies [44,51,79].…”

Section: Temporal and Spatial Patterns Of The Erisupporting

confidence: 91%

“…The land-use/land-cover (LULC) dataset for 2000 and 2015 in ArcGIS shapefile format used in this study was developed by the Chinese Academy of Sciences through the Database of Global Change Parameters, with the data interpreted based on Landsat images (http://www.eercasia.com/# [51]). According to previous studies [11,12,15] and the environmental characteristics of the ADD, the land use types were divided into seven categories: farmland, forest land, grassland, construction The ADD has an extremely continental climate characterized by extremely dry conditions throughout the year, with hot summers and cold winters.…”

Section: Land Use/land Cover (Lulc)mentioning

confidence: 99%

“…Hence, we selected three types of data that could be obtained for free as socioeconomic drivers, population density (POP), crop yield (tons/ha) and road network data, to investigate their relationships with landscape ecological risk. Because agricultural production remains the principal socioeconomic activity of the ADD [51,57], we selected the 2000 and 2015 crop yield data provided by the WUEMoCA database (http://wuemoca.net/app/#) as one of the socioeconomic drivers in this study. In recent decades, the population of the ADD has experienced large-scale growth, which has resulted in a series of negative impacts to the ecological environment [57].…”

Section: Socioeconomic Variablesmentioning

confidence: 99%

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Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

Bao

et al. 2019

IJERPH

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Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood. In this study, the ADD was selected to construct landscape ecological risk index (ERI) values for 2000 and 2015. Based on a geographically weighted regression (GWR) model, the relationship between each of the normalized difference vegetation index (NDVI), land surface temperature (LST), digital elevation model (DEM), crop yield, population density (POP), and road density and the spatiotemporal variation in ERI were explored. The results showed that the ERI decreased from the periphery of the ADD to the centre and that high-risk areas were distributed in the ADD’s downstream region, with the total area of high-risk areas increasing by 86.55% from 2000 to 2015. The ERI was spatially correlated with Moran’s I in 2000 and 2015, with correlation of 0.67 and 0.72, respectively. The GWR model indicated that in most ADD areas, the NDVI had a negative impact on the ERI, whereas LST and DEM had positive impacts on the ERI. Crop yield, road density and POP were positively correlated with the ERI in the central region of the ADD, at road nodes and in densely populated urban areas, respectively. Based on the findings of this study, we suggest that the ecological constraints of the aforementioned factors should be considered in the process of delta development and protection.

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Section: Temporal and Spatial Patterns Of The Erisupporting

confidence: 91%

Section: Land Use/land Cover (Lulc)mentioning

confidence: 99%

Section: Socioeconomic Variablesmentioning

confidence: 99%

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Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

Bao

et al. 2019

IJERPH

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“…Land degradation triggers landscape changes affecting the ecosystems in it (Haase and Richter, 1980;Baude et al, 2019). Bank (1998) and Jiang et al (2019) reported that the expansion of irrigation channels in the upstream region triggered an increase in salinity in the downstream region of Phewa Watershed in Nepal. As a result, the diversity of ecosystems suffered considerable damaged, especially in the downstream areas around the coast (Micklin, 2004;Jiang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Bank (1998) and Jiang et al (2019) reported that the expansion of irrigation channels in the upstream region triggered an increase in salinity in the downstream region of Phewa Watershed in Nepal. As a result, the diversity of ecosystems suffered considerable damaged, especially in the downstream areas around the coast (Micklin, 2004;Jiang et al, 2019). Land degradation due to mismanagement leads to a decrease in organic matter content.…”

Section: Introductionmentioning

confidence: 99%

Relationship between soil morphology and variability of upland degradation in Bogowonto Watershed, Central Java, Indonesia

Aji

Maas

Nurudin

2020

J.Degrade.Min.Land Manage.

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Land-use change and lack of conservation in Bogowonto Watershed area have caused land degradation due to erosion and landslides. The rate of land degradation can be measured through the morphological characteristics of the soil and landscape. This study aimed to identify the relationship between the morphological characteristics of the soil and the rate of land degradation in Bogowonto Watershed area. The research framework initiated from the interpretation of thematic maps, surveys and field observations, and verification using land degradation assessment software. Sample points were determined using a stratified random sampling method. In general, land degradation is affected by slope, limited soil development, suboptimal conservation measures, and land-use change. Particularly, land degradation is affected by the type of landscape and the morphological characteristics of the soil. Volcanic hilly landscapes show a degradation rate of fine to very fine, characterized by thick soil depth (>60 cm), generous root distribution, loamy textured soil, fine soil particle size (Ø <0.002 mm), and good soil structure. The distribution of easily weathered volcanic ash causes volcanic hills to have thick and fertile soil characteristics. Quaternary-tertiary volcanic transition landscapes show a degradation rate of damaged to heavily damaged, characterized by thin soil depth (<60 cm), limited rooting, dominant coarse fraction (Ø 2-0.05 mm), and contact with fields lithological discontinuity. The data obtained provide information on the morphological characteristics of the soil and land degradation in Bogowonto Watershed area. The results of the study can also be used as a formulation for the conservation of Bogowonto Watershed area.

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Monitoring land sensitivity to desertification using the ESAI approach and evaluation of the key indicators: A spatio‐temporal study in India

Rajbanshi

Das

2021

Land Degrad Dev

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Desertification is one of the primary environmental problems that negatively affect agricultural production leading to poverty, hunger, and economic instability in a country like India. A holistic study is therefore extremely valuable rather than localized ones for addressing this issue and its' further consequences. In this study, the environmentally sensitive area index (ESAI) has been used to identify the areas vulnerable to desertification from 1992 to 2015 in India. In addition to this, the random forest (RF) model was used to identify the drivers with major influence over the phenomenon. The result revealed that the desertification process has extended from the west and spread over the southern region in the Country. Among the states of India, Rajasthan and Ladakh possess the highest mean ESAI values (1.5–1.7), where 87.61% and 83.83% of land, respectively, are critically degraded due to desertification. According to the Mann‐Kendall mutation point detection test, the years of 1996, 2009, and 2014 were identified as the abrupt change of desertification processes over the Country. The most severe form of degradation is observed in the period between 1997 and 2009, where the highly sensitive (Critical‐3) area increased up to 37.44%. Based on variable importance for the RF‐model, drought resistance has the highest importance among all the other inputs that is, 46% followed by erosion protection with 20%. Our study also points out that 33.76% of the land of the Country is still classed under a very high critical zone and without any immediate action; it may result in irreversible loss.

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Assessing land degradation and quantifying its drivers in the Amudarya River delta

Cited by 58 publications

References 55 publications

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

Relationship between soil morphology and variability of upland degradation in Bogowonto Watershed, Central Java, Indonesia

Monitoring land sensitivity to desertification using the ESAI approach and evaluation of the key indicators: A spatio‐temporal study in India

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