Spatiotemporal Variation of NDVI in the Vegetation Growing Season in the Source Region of the Yellow River, China

Could ecological restoration reduce income inequality? An analysis of 290 Chinese prefecture-level cities

Ecosystem degradation and the serious wealth gap caused by rapid economic development have become problems that cannot be neglected during the progress of pursuing sustainable development and reducing income inequality in China. To determine whether ecological restoration such as vegetation cover could affect the income gap, we used data for 290 prefecture-level cities in China from 2007 to 2018 and analyzed the effect of ecological restoration on income inequality in China. In addition, we chose the year 2012 as a boundary and performed heterogeneity analysis to permit a detailed comparison of the variation in the effect over time. We found that ecological restoration can reduce income inequality in general, but this effect was not statistically significant until 2012. However, due to some practical obstacles (e.g., employment opportunities, educational attainment, social discrimination), reducing income inequality through ecological restoration will be a time consuming process and requires constant effort from the Chinese government and local managers such as funding green industries, providing more targeted technical training for the poor and social services for the rural migrant workers.

Spatiotemporal variation and driving forces of NDVI from 1982 to 2015 in the Qinba Mountains, China

The spatiotemporal variation and driving force of the Normalized Difference Vegetation Index (NDVI) are helpful to ecological environment protection and natural resource management. Using the Sen and Mann-Kendall methods, Hurt index, and the Geodetector, this study investigated the temporal and spatial changes and driving forces of NDVI during 1982-2015. The results showed that (1) From 1982 to 2015, the high vegetation coverage was mainly distributed in the Qinling Mountains and the Daba Mountains, while the low vegetation coverage was in high altitude areas in the west, low altitude in the east, and the Hanjiang River valley. (2) NDVI in the Qinba Mountains increased continuously accounting for 81.1%, with 68% showing slow growth. In the future, only 37.8% of the vegetation will have significant change. The area of vegetation increase will be greater than the area of decrease. (3) NDVI increased firstly and then decreased with the increase of altitude, reaching the maximum value at 1100 m. NDVI showed a trend of fluctuating growth. It reached the maximum value of 0.86 in 2015. (4) Through the Geodetector, the main factors affecting NDVI were natural factors mainly including rainfall, soil type, and digital elevation model (DEM), while human activities, including population density, had little influence on NDVI. Natural environment factors and human activities together had a greater impact on the spatial distribution of NDVI. This study could provide help for the sustainable development of the natural environment in the Qinba Mountains.

Editorial on Special Issue “Geo-Information Technology and Its Applications”

Geo-information technology plays a critical role in urban planning and management, land resource quantification, natural disaster risk and damage assessment, smart city development, land cover change modeling and touristic flow management. In particular, the development of big data mining and machine learning techniques (including deep learning) in recent years has expanded the potential applications of geo-information technology and promoted innovation in approaches to mining in different fields. In this context, the International Conference on Geo-Information Technology and its Applications (ICGITA 2019) was held in Nanchang, Jiangxi, China, 11–13 October 2019, co-organized by the Key Laboratory of Digital Land and Resources, East China University of Technology, the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), which was renamed in 2017 the Aerospace Information Research Institute (AIR), CAS, and the Institute of Space and Earth Information Science of the Chinese University of Hong Kong. The outstanding papers presented at this event and some other original articles were collected and published in this Special Issue “Geo-Information Technology and Its Applications” in the International Journal of Geo-Information. This Special Issue consists of 14 high-quality and innovative articles that explore and discuss the typical applications of geo-information technology in the above-mentioned domains.

Analysis of Future Meteorological Drought Changes in the Yellow River Basin under Climate Change

The Yellow River Basin is an important economic belt and key ecological reservation area in China. In the context of global warming, it is of great significance to project the drought disaster risk for ensuring water security and improving water resources management measures in practice. Based on the five Global Climate Models (GCMs) projections under three scenarios of the Shared Socioeconomic Pathways (SSP) (SSP126, SSP245, SSP585) released in the Sixth Coupled Model Intercomparison Project (CMIP6), this study analyzed the characteristics of meteorological drought in the Yellow River Basin in combination with SPEI indicators over 2015–2100. The result indicated that: (1) The GCMs from CMIP6 after bias correction performed better in reproducing the spatial and temporal variation of precipitation. The precipitation in the Yellow River Basin may exhibit increase trends from 2015 to 2100, especially under the SSP585 scenario. (2) The characteristics of meteorological drought in the Yellow River Basin varied from different combination scenarios. Under the SSP126 scenario, the meteorological drought will gradually intensify from 2040 to 2099, while the drought intensity under SSP245 and SSP585 scenarios will likely be higher than SSP126. (3) The spatial variation of meteorological drought in the Yellow River Basin is heterogeneous and uncertain in different combination scenarios and periods. The drought tendency in the Loess Plateau will increase significantly in the future, and the drought frequency and duration in the main water conservation areas of the Yellow River Basin was projected to increase.

Quantitative Analysis of Land Subsidence and Its Effect on Vegetation in Xishan Coalfield of Shanxi Province

It is of great significance for the monitoring and protection of the original ecological environment in coal mining areas to identify the ground subsidence and quantify its influence on the surface vegetation. The surface deformation and vegetation information were obtained by using spaceborne SAR and Landsat OLI images in the Xishan Coalfield. The relative change rate, coefficient of variation, and trend analysis methods were used to compare the vegetation growth trends in the subsidence center, subsidence edge, and non-subsidence zones; and the vegetation coverage was predicted by the pixel dichotomy and grey model from 2021 to 2025. The results indicated that the proportions of vegetation with high fluctuation and serious degradation were 6.60% and 5.64% in the subsidence center, and its NDVI values were about 10% lower than that in the subsidence edge and non-subsidence zones. In addition, vegetation coverage showed a wedge ascending trend from 2013 to 2020, and the prediction values of vegetation coverage obtained by GM (1,1) model also revealed this trend. The residuals of the predicted values were 0.047, 0.047, and 0.019 compared with the vegetation coverage in 2021, and the vegetation coverage was the lowest in the subsidence center, which was consistent with the law obtained by using NDVI. Research suggested that ground subsidence caused by mining activities had a certain impact on the surface vegetation in the mining areas; the closer to the subsidence center, the greater the fluctuation of NDVI, and the stronger the vegetation degradation trend; conversely, the smaller the fluctuation, and the more stable the vegetation growth.

On the suitability of using vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought

Drought remains one of the world's most devastating phenomena, exhibiting impacts in both magnitude and frequency. African vegetation remains highly vulnerable to drought impacts and this is heightened by a changing climate. In this study, we evaluated the suitability of vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought. Here, we used the SPEI, a global drought index to investigate the spatiotemporal characteristics of drought on vegetation. In addition, TVDI, TCI, VCI, NVSWI, VSWI and DSI, which are remotely sensed derived drought indices were also used to characterize drought. For the vegetation indices, we used the optical satellite calculated NDVI; VOD, a passive microwave remote sensing product; and derived Nvod as proxies for vegetation. The climatology of climate and vegetation data was calculated, and the trend of the variables was examined. Additionally, comparisons were performed between the SPEI and the other drought indices. Subsequently, we computed the correlations between the SPEI and vegetation indices spatially, temporally and seasonally. Our results show that VOD and the NDVI have similar spatial distribution, with higher values of the indices recorded over the Democratic Republic of Congo (DRC) and Central African Republic (C.A.R) compared to the rest of the region. Furthermore, we also found that the indices have similar seasonal patterns as precipitation and an inverse relationship with temperature. The study also reveals that there is a declining long-term trend of precipitation over evergreen needleleaf forest, evergreen broadleaf forest, and woody savanna; and an increasing trend of VOD and NDVI over Africa's ecoregions. Furthermore, the results show a high SPEI - VOD correlations (r² = 0.8) in southern Africa and the Horn of Africa, and a weak response in the Sahelian region. While the response of NDVI is similar to a spatial distribution as VOD, the magnitudes of response are generally weaker in the NDVI, and the magnitudes and distribution of response by Nvod are similar to VOD. Also, the response of Nvod is the weakest across all the timescales although its magnitudes vary significantly from year - year, with the timescale of occurrence mostly shorter for JJA but largely longer for MAM. However, the magnitudes of the response of vegetation indices are different for remotely sensed derived drought indices. In addition, the mean and trend of the response of VOD are consistently stronger in evergreen needleleaf forest and open shrublands but weaker over the evergreen broadleaf forest. Our study has presented insights on methods by which the impacts of droughts on plant activities and functions may be monitored.

Comprehensive Insights into Spatial-Temporal Evolution Patterns, Dominant Factors of NDVI from Pixel Scale, as a Case of Shaanxi Province, China

Based on long term NDVI (1982–2015), climate, topographic factors, and land use type data information in Shaanxi Province, multiple methods (linear regression, partial and multiple correlation analysis, redundancy analysis and boosted regression trees method) were conducted to evaluate the spatial-temporal change footprints and driving mechanisms in the pixel scale. The results demonstrated that (1) the overall annual average and seasonal NDVI in this region showed a fluctuating upward trend, especially in spring. The difference between the end of season (eos) and start of season (sos) gradually increased, indicating the occurrence of temporal “greening” across most Shaanxi Province. (2) The overall spatial distribution of annual mean NDVI in Shaanxi Province was prominent in the south and low in the north, and 98.83% of the areas had a stable and increasing trend. Pixel scale analysis reflected the spatial continuity and heterogeneity of NDVI evolution. (3) Trend and breakpoint evaluation results showed that evolutionary trends were not homogeneous. There were obvious breakpoints in the latitude direction of NDVI evolution in Shaanxi Province, especially between 32–33 °N and in the north of 37 °N. (4) Compared with precipitation, the annual average temperature was significantly correlated with the vegetation indices (annual NDVI, max NDVI, time integrated NDVI) and phenology metrics (sos, eos). (5) Considering the interaction between environmental variables, the NDVI evolution was dominated by the combined influence of climate and geographic location factors in most areas.

Response of Landscape Evolution to Human Disturbances in the Coastal Wetlands in Northern Jiangsu Province, China

Human disturbance is one of the essential driving forces of landscape evolution. The quantitative evaluation of the spatial and temporal characteristics of landscape evolution and its relationship with human disturbance are of great significance to regional ecological protection and management and are crucial for achieving coordinated socioeconomic development and ecological–environmental protection. In this study, we took the coastal wetlands in northern Jiangsu province, China, as the research area, and proposed a quantitative evaluation method for directional landscape evolution. On this basis, the spatiotemporal characteristics of the landscape evolution from 1980 to 2020 and the relationship with human disturbance were quantitatively evaluated by combining a human disturbance index and statistical methods. The results showed that: (1) The area of the natural wetlands decreased significantly over the past 40 years, while the areas of artificial wetlands and non-wetlands increased significantly. (2) The landscape evolution process was dominated by the degradation process. The main types of degradation were natural wetland conversion to artificial wetland and non-wetland areas and Spartina alterniflora invasion. The restoration type was mainly restoration among artificial and natural wetlands. (3) The degradation of wetland landscapes demonstrated a southward shift trend and the spatial consistency with the change of the human disturbance index was high (the correlation coefficient was 0.89). (4) The human disturbance index was significantly and positively correlated with the rate of degradation, with a correlation coefficient of 0.43, and was not significantly and positively correlated with the restoration rate, with a correlation coefficient of 0.14. The findings in this paper provide additional information and theoretical guidance for the control of coastal wetland development and utilization, as well as for achieving coordinated wetland resource development together with utilization and ecological protection in the coastal wetlands of Jiangsu province, China.

Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.