Spatial-temporal changes and driving forces of aeolian desertification of grassland in the Sanjiangyuan region from 1975 to 2015 based on the analysis of Landsat images

2020 ◽  
Vol 193 (1) ◽  
Author(s):  
Xiaohui Zhai ◽  
Changzhen Yan ◽  
Xuegang Xing ◽  
Haowei Jia ◽  
Xiaoxu Wei ◽  
...  
Keyword(s):  
Driving Forces ◽  
Temporal Changes ◽  
Landsat Images ◽  
Aeolian Desertification

scholarly journals Monitoring the Spatiotemporal Dynamics of Aeolian Desertification Using Google Earth Engine

2021 ◽  
Vol 13 (9) ◽  
pp. 1730
Author(s):  
Ang Chen ◽  
Xiuchun Yang ◽  
Bin Xu ◽  
Yunxiang Jin ◽  
Jian Guo ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Driving Forces ◽  
Google Earth ◽  
Chinese Government ◽  
Landsat Images ◽  
Aeolian Desertification ◽  
The Past ◽  
Sand Control ◽  
Google Earth Engine ◽  
Desertification Monitoring

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region.

scholarly journals Analysis of land cover change and its driving forces in a desert oasis landscape of Xinjiang, northwest China

Solid Earth ◽  
2014 ◽  
Vol 5 (2) ◽  
pp. 1071-1085 ◽  
Author(s):  
T. Amuti ◽  
G. Luo
Keyword(s):  
Land Cover ◽  
Land Degradation ◽  
Driving Forces ◽  
Northwest China ◽  
Landsat Images ◽  
Desert Oasis ◽  
Standard Classification ◽  
Effects Of Drought ◽  
Arid Desert ◽  
Southern Xinjiang

Abstract. The combined effects of drought, warming and the changes in land cover have caused severe land degradation for several decades in the extremely arid desert oases of southern Xinjiang, northwest China. Land cover classifications of Landsat images in 1990, 2000 and 2008 were performed based on the multistage supervised classification scheme using the maximum likelihood classifier integrated with conventional vegetation and soil indexes, which improved overall accuracies by 4–5% compared to the standard classification method. Based on the detection of changes in land cover during 1990–2008 using remote sensing (RS) and a geographic information system (GIS), it can be found that the oasis significantly (+35%) increased, while the area of ecotone decreased (−43%). The major trends of the land cover changes were the notable growth of the oasis and the reduction of the desert–oasis ecotone. These changes were mainly a result of the intensified human activities such as land and water exploitation as well as overgrazing. The results of this study indicate that the oasis environment will be deteriorated by increase in potential areas of land degradation if the trend of desert moving further inward and the shrinking of the ecotone continues over the next decades.

scholarly journals MONITORING CHANGES OF ECOSYSTEM SERVICES SUPPLY AND DEMAND PATTERN IN CENTRAL AND SOUTHERN LIAONING URBAN AGGLOMERATIONS, CHINA USING LANDSAT IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 765-770
Author(s):  
B. Li ◽  
F. Huang ◽  
S. Chang ◽  
H. Qi ◽  
H. Zhai
Keyword(s):  
Ecosystem Services ◽  
Population Density ◽  
Foreign Investment ◽  
Driving Forces ◽  
Supply And Demand ◽  
Reference Value ◽  
Landsat Images ◽  
Urban Agglomerations ◽  
Spatio Temporal

Indentifying the spatio-temporal patterns of ecosystem services supply and demand and the driving forces is of great significance to the regional ecological security and sustainable socio-economic development. Due to long term and high-intensity development, the ecological environment in central and southern Liaoning urban agglomerations has been greatly destroyed thereafter has restricted sustainable development in this region. Based on Landsat ETM and OLI images, land use of this urban agglomeration in 2005, 2010 and 2015 was extracted. The integrative index of multiple-ecosystem services (IMES) was used to quantify the supply (IMESs), demand (IMESd) and balance (IMESb) of multiple-ecosystem services, The spatial patterns of ecosystem services and its dynamics for the period of 2005–2015 were revealed. The multiple regression and stepwise regression analysis were used to explore relationships between ecosystem services and socioeconomic factors. The results showed that the IMESs of the region increased by 2.93 %, whereas IMESd dropped 38 %. The undersupplied area was reduced to 2. The IMESs and IMESb were mainly negatively correlated with gross domestic product (GDP), population density, foreign investment and industrial output, while GDP per capita and the number of teachers had significant positive impacts on ecosystem services supply. The positive correlation between IMESd and GDP, population density and foreign investment were found. The ecosystem services models were established. Supply and balance of multiple-ecosystem services were positively correlated with population density, but the demand was the opposite. The results can provide some reference value for the coordinately economic and ecological development in the study area.

scholarly journals Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

2020 ◽  
Vol 12 (22) ◽  
pp. 3826 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
Xulin Guo
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Driving Forces ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Arid Grasslands ◽  
Spectral Mixture ◽  
The Impact ◽  
Semi Arid

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

scholarly journals Impacts of land-use change and elevated CO<sub>2</sub> on the interannual variations and seasonal cycles of gross primary productivity in China

2019 ◽  
Author(s):  
Binghao Jia ◽  
Xin Luo ◽  
Ximing Cai ◽  
Atul Jain ◽  
Deborah N. Huntzinger ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Primary Productivity ◽  
Driving Forces ◽  
Southern China ◽  
Co2 Concentration ◽  
Mean Value ◽  
Temporal Changes ◽  
Gross Primary Productivity ◽  
Control Factor

Abstract. Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are confounded. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP, driven by common climate forcing, LULCC, and CO2 data, was found to be 7.4 ± 1.8 Pg C yr−1, which was in close agreement with the independent upscaling GPP estimate (7.1 Pg C yr−1). In general, climate was the dominant control factor of the annual trends, IAV, and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China where vegetation is dense. LULCC decreased the IAV of China's total GPP by ~ 7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.

scholarly journals Continuous Change Mapping to Understand Wetland Quantity and Quality Evolution and Driving Forces: A Case Study in the Liao River Estuary from 1986 to 2018

2021 ◽  
Vol 13 (23) ◽  
pp. 4900
Author(s):  
Jianwei Peng ◽  
Shuguang Liu ◽  
Weizhi Lu ◽  
Maochou Liu ◽  
Shuailong Feng ◽  
...  
Keyword(s):  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Driving Forces ◽  
Land Reclamation ◽  
River Estuary ◽  
Tidal Flats ◽  
Liaoning Province ◽  
Coastal Zones ◽  
Continuous Change ◽  
Landsat Images

Coastal wetland ecosystems, one of the most important ecosystems in the world, play an important role in regulating climate, sequestering blue carbon, and maintaining sustainable development of coastal zones. Wetland landscapes are notoriously difficult to map with satellite data, particularly in highly complex, dynamic coastal regions. The Liao River Estuary (LRE) wetland in Liaoning Province, China, has attracted major attention due to its status as Asia’s largest coastal wetland, with extensive Phragmites australis (reeds), Suaeda heteroptera (seepweed, red beach), and other natural resources that have been continuously encroached upon by anthropogenic land-use activities. Using the Continuous Change Detection and Classification (CCDC) algorithm and all available Landsat images, we mapped the spatial–temporal changes of LRE coastal wetlands (e.g., seepweed, reed, tidal flats, and shallow marine water) annually from 1986 to 2018 and analyzed the changes and driving forces. Results showed that the total area of coastal wetlands in the LRE shrank by 14.8% during the study period. The tidal flats were the most seriously affected type, with 45.7% of its total area lost. One of the main characteristics of wetland change was the concurrent disappearance and emergence of wetlands in different parts of the LRE, creating drastically different mixtures of wetland quality (e.g., wetland age composition) in addition to area change. The reduction and replacement/translocation of coastal wetlands were mainly caused by human activities related to urbanization, tourism, land reclamation, and expansion of aquaculture ponds. Our efforts in mapping annual changes of wetlands provide direct, specific, and spatially explicit information on rates, patterns, and causes of coastal wetland change, both in coverage and quality, so as to contribute to the effective plans and policies for coastal management, preservation, and restoration of coastal ecosystem services.

scholarly journals Long-Term Lake Area Change and Its Relationship with Climate in the Endorheic Basins of the Tibetan Plateau

2021 ◽  
Vol 13 (24) ◽  
pp. 5125
Author(s):  
Junxiao Wang ◽  
Mengyao Li ◽  
Liuming Wang ◽  
Jiangfeng She ◽  
Liping Zhu ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Large Scale ◽  
Driving Forces ◽  
Atlantic Multidecadal Oscillation ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Climate Variables ◽  
Landsat Images ◽  
Area Change ◽  
Local Climate

Lakes are sensitive indicators of climate change in the Tibetan Plateau (TP), which have shown high temporal and spatial variability in recent decades. The driving forces for the change are still not entirely clear. This study examined the area change of the lakes greater than 1 km2 in the endorheic basins of the Tibetan Plateau (EBTP) using Landsat images from 1990 to 2019, and analysed the relationships between lake area and local and large-scale climate variables at different geographic scales. The results show that lake area in the EBTP has increased significantly from 1990 to 2019 at a rate of 432.52 km2·year−1. In the past 30 years, lake area changes in the EBTP have mainly been affected by local climate variables such as precipitation and temperature. At a large scale, Indian Summer Monsoon (ISM) has correlations with lake area in western sub-regions in the Inner Basin (IB). While Atlantic Multidecadal Oscillation (AMO) has a significant connection with lake area, the North Atlantic Oscillation (NAO) does not. We also found that abnormal drought (rainfall) brought by the El Niño/La Niña events are significantly correlated with the lake area change in most sub-regions in the IB.

scholarly journals Impacts of land use change and elevated CO<sub>2</sub> on the interannual variations and seasonal cycles of gross primary productivity in China

2020 ◽  
Vol 11 (1) ◽  
pp. 235-249 ◽  
Author(s):  
Binghao Jia ◽  
Xin Luo ◽  
Ximing Cai ◽  
Atul Jain ◽  
Deborah N. Huntzinger ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Primary Productivity ◽  
Driving Forces ◽  
Southern China ◽  
Co2 Concentration ◽  
Mean Value ◽  
Temporal Changes ◽  
Gross Primary Productivity ◽  
Control Factor

Abstract. Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are uncertain. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP between 1981 and 2010, driven by common climate forcing, LULCC and CO2 data, was found to be 7.4±1.8 Pg C yr−1. In general, climate was the dominant control factor of the annual trends, IAV and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China, where vegetation is dense. LULCC decreased the IAV of China's total GPP by ∼7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation, and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.

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