The dynamic monitoring of aeolian desertification land distribution and its response to climate change in northern China

Feng, Lili; Jia, Zhiqing; Li, Qingxue

doi:10.1038/srep39563

Cited by 34 publications

(21 citation statements)

References 23 publications

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“…The R was calculated using Eq. ( 1 ) 1 : where R is average annual growth rate of desertified land (%), Q 1 and Q 2 are the areas of desertified land in the last year and the initial year of the study period, and N is the number of years between start year and end year.…”

Section: Methodsmentioning

confidence: 99%

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Spatiotemporal dynamic evolution and driving factors of desertification in the Mu Us Sandy Land in 30 years

Han

Jia

Yang

et al. 2020

Sci Rep

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The Mu Us Sandy Land is located in the middle of the farming pastoral ecotone of northern China. The direction of the development of desertification has a direct impact on the economy and development of the northern region. Six remote sensing images acquired during 1990–2017 served as data sources. Using an ENVI 5.3 and ArcGIS 10.3 platform an analysis was conducted of the dynamic changes nearly 30 years in desertified land using a center of gravity moving model, annual change rate, a transfer matrix, and an aeolian desertification index; the factors driving desertification were discussed. The research shows that the time period can be divided into three stages of desertification: development (1990–2000), rapid reversal (2000–2010), and stable reversal (2010–2017). A total of 1680 km2 of desertification were managed over the three stages. Spatially, the distribution of the center of desertification from west to east includes mild, moderate, severe, and extreme desertification, which is consistent with the spatial distribution trends of desertified land in the Mu Us Sandy Land. By the end of 2017, the degree of desertification of the Mu Us Sandy Land was in the central area > northwest > southwest > east > south. Nearly 30 years, the wind speed has decreased year by year at the rate of 0.1 m s−1, which directly reduce the ability to winds to transport soil in the Mu Us Sandy Land and promoted the reversal of desertification. From 1990 to 2010, the climate tended to become warmer and drier. Environmental protection policies along with human intervention and control of desertification have played important roles in reversing desertification. From 2010 to 2020, under the general background of a warm-wet climatic tendency, rational use of sand resources and strengthening scientific control of desertification inducing factors are the keys to reversing desertification.

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Section: Methodsmentioning

confidence: 99%

“…Desertification, also known as wind erosion desertification 1 , is one of the main forms of desertification 2 , which occur mainly in arid, semi-arid, and partially semi-humid areas. Desertification is a process of land degradation caused by excessive human activities and the improper use of resources 3 – 5 .…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal dynamic evolution and driving factors of desertification in the Mu Us Sandy Land in 30 years

Han

Jia

Yang

et al. 2020

Sci Rep

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“…Duan et al (2019) identified land degradation based on the Modified Soil Adjusted Vegetation Index (MSAVI), the Fractional Vegetation Cover (FVC), Land Surface Temperature (LST), ALBEDO, and Modified Temperature Vegetation Dryness Index (MTVDI) via the QUEST (quick, unbiased, and efficient statistical tree) classification method. Feng et al (2016) analyzed the spatial-temporal change of Aeolian desertification in northern China by the Normalized Difference Desertification Index (NDDI).…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Evaluating Effects of an Ecological Water Diversion Project on Land Degradation in the Heihe River Basin, China

Xiang

Liao

Xue

et al. 2020

Front. Environ. Sci.

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To deal with the increasingly severe land degradation in the downstream of the Heihe River Basin (HRB) in northwest China, an Ecological Water Diversion Project (EWDP) was implemented since 2000. A comprehensive analysis of the effects associated with the project on land degradation is necessary. According to the concept of Land Degradation Neutrality (LDN), multi-sensor data has been used to extract information on Land Use/Land Cover (LULC), Aeolian desertification, and vegetation dynamics, which were chosen as monitoring indices to reveal the process of land degradation in the HRB. Then, these results were combined with meteorological data, socioeconomic statistics, and hydrological data to discuss the main driving factors influencing the land degradation process to evaluate the effects of the EWDP on land degradation in the HRB. The results showed that the process of land degradation in the HRB could be divided into two stages, in which the degradation trend was dominant from 1990 to 2000, and the rehabilitation trend was dominant from 2000 to 2015. Although both climate variation and human activities have been favorable to land degradation development in the HRB, climate factors have no significant influence on land degradation in the midstream and downstream in the HRB. The decrease of available water resources is the dominant driving factor of a series of ecological environment problems in the downstream of the HRB, and the land degradation process of the HRB has been stopped and reversed, mainly attributed to the EWDP. The EWDP facilitated the recovery of the deteriorated ecosystem by improving the efficiency of surface water resources reallocation in the downstream. Still, the EWDP indirectly led to the sink of the groundwater table in the midstream, resulting in local vegetation degradation.

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“…Temperate forests were changed to boreal forests in China, Northern Mongolia, and Russia near Lake Baikal. North China and Inner-Mongolia are widely known as regions facing ongoing deforestation and land degradation [17], but the Chinese and Mongolian governments strive to recover their forest ecosystems to secure ecological benefits [18][19][20], whereas, the forests near Lake Baikal and around the MLE have suffered natural or anthropogenic forest fire [21]. There is great uncertainty in measuring the effect of fire in boreal forests; one estimate revealed that 2.9% of Russian boreal forest loss was caused by forest fires [22].…”

Section: Introductionmentioning

confidence: 99%

Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN)

Song

Pietsch

Kim

et al. 2019

Forests

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The mid-latitude ecotone (MLE)—a transition zone between boreal and temperate forests, which includes the regions of Northeast Asia around 30°–60° N latitudes—delivers different ecosystem functions depending on different management activities. In this study, we assessed forest volume and net primary productivity changes in the MLE of Northeast Asia under different ecological characteristics, as well as various current management activities, using the BioGeoChemistry Management Model (BGC-MAN). We selected five pilot sites for pine (Scots pine and Korean red pine; Pinus sylvestris and P. densiflora), oak (Quercus spp.), and larch forests (Dahurian larch and Siberian larch; Larix gmelinii and L. sibirica), respectively, which covered the transition zone across the MLE from Lake Baikal, Russia to Kyushu, Japan, including Mongolia, Northeast China, and the Korean Peninsula. With site-specific information, soil characteristics, and management descriptions by forest species, we established their management characteristics as natural preserved forests, degraded forests, sandy and cold forest stands, and forests exposed to fires. We simulated forest volume (m3) and net primary productivity (Mg C ha−1) during 1960–2005 and compared the results with published literature. They were in the range of those specified in previous studies, with some site-levels under or over estimation, but unbiased estimates in their mean values for pine, oak, and larch forests. Annual rates of change in volume and net primary productivity differed by latitude, site conditions, and climatic characteristics. For larch forests, we identified a high mountain ecotype which warrants a separate model parameterization. We detected changes in forest ecosystems, explaining ecological transition in the Northeast Asian MLE. Under the transition, we need to resolve expected problems through appropriate forest management and social efforts.

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The dynamic monitoring of aeolian desertification land distribution and its response to climate change in northern China

Cited by 34 publications

References 23 publications

Spatiotemporal dynamic evolution and driving factors of desertification in the Mu Us Sandy Land in 30 years

Spatiotemporal dynamic evolution and driving factors of desertification in the Mu Us Sandy Land in 30 years

Multi-Sensor Evaluating Effects of an Ecological Water Diversion Project on Land Degradation in the Heihe River Basin, China

Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN)

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