Analysis of the heterogeneity of landscape risk evolution and driving factors based on a combined GeoDa and Geodetector model

The contributions of natural and anthropogenic factors to NDVI variations on the Loess Plateau in China during 2000–2020

In the circumstance of global change, studying the dynamic changes of vegetation and the factors that influence it holds immense practical significance to monitor the quality of the regional ecological environment and improve the structure and function of ecosystems. Therefore, based on NDVI data, combined with natural and anthropogenic activities, this study focused on the spatiotemporal evolution characteristics and stability of NDVI in the Chengdu-Chongqing region from 2001 to 2020 at multiple spatiotemporal scales, and quantitatively investigates the main influencing factors driving vegetation NDVI spatial differentiation using Theil-Sen Medium slope estimation, Mann-Kendall significance, stability analysis and geographical detector. The findings indicated that: (1) The overall NDVI exhibited favorable levels over the 2001–2020 period; (2) The upward trends observed in the NDVI values indicated an enhancement in the ecological environment of the investigation area; (3) The spatial coefficient of variation for NDVI in the investigation region was computed as 0.048, suggesting a high degree of stability in NDVI values across the region; (4) NDVI is primarily affected by a range of variances in natural phenomena and anthropogenic activities. The collective influence of environmental factors and anthropogenic activities holds a more substantial sway over NDVI. The research results aim to offer guidance for the execution of vegetation restoration initiatives and the establishment of policies for the preservation of the ecological environment in the upstream region of the Yangtze River. 

Quantitative analysis of NDVI driving factors based on the geographical detector model in the Chengdu-Chongqing region, China

Accurate and early crop-type maps are essential for agricultural policy development and food production assessment at regional and national levels. This study aims to produce a crop-type map with acceptable accuracy and spatial resolution in northern Mongolia by optimizing the combination of Sentinel-1 (S1) and Sentinel-2 (S2) images with the Google Earth Engine (GEE) environment. A total of three satellite data combination scenarios are set, including S1 alone, S2 alone, and the combination of S1 and S2. In order to avoid the impact of data gaps caused by clouds on crop classification, this study reconstructed the time series of S1 and S2 with a 10-day interval using the median composite method, linear moving interpolation, and Savitzky–Golay (SG) filter. Our results indicated that crop-type classification accuracy increased with the increase in data length to all three data combination scenarios. S2 alone has higher accuracy than S1 alone and the combination of S1 and S2. The crop-type map with the highest accuracy was generated using S2 data from 150 days of the year (DOY) (11 May) to 260 DOY (18 September). The OA and kappa were 0.93 and 0.78, respectively, and the F1-score for spring wheat and rapeseed were 0.96 and 0.80, respectively. The classification accuracy of the crop increased rapidly from 210 DOY (end of July) to 260 DOY (August to mid-September), and then it remained stable after 260 DOY. Based on our analysis, we filled the gap of the crop-type map with 10 m spatial resolution in northern Mongolia, revealing the best satellite combination and the best period for crop-type classification, which can benefit the achievement of sustainable development goals 2 (SDGs2).

/pdf/performance-and-the-optimal-integration-of-sentinel-1-2-time-2rslroc1.pdf

Performance and the Optimal Integration of Sentinel-1/2 Time-Series Features for Crop Classification in Northern Mongolia

Vegetation cover is important to the stability of regional ecosystems and is a focus of research on the relationship between natural and human environments. Although some studies have investigated the association between changes in vegetation cover and various influencing factors, these have shortcomings in quantifying direct and indirect effects. In this study, MOD13Q1 enhanced vegetation index (EVI) data for Anhui Province, China, were acquired between 2000 and 2020. The univariate linear regression, coefficient of variation and Hurst index methods were used to analyze spatial and temporal trends and fluctuations in the EVI between 2000 and 2020 and predict future trends. The impact of land-use change on EVI change was explored using 2000 and 2020 land-use data. Finally, a structural equation model (SEM) was used to quantify the effects of topography, annual average temperature, annual precipitation and human activity changes on EVI variation in Anhui Province. The results show that (1) from 2000 to 2020, the overall EVI in Anhui Province showed a fluctuating trend that increased at a rate of 0.0181·10a−1, and 67.1% of the study area showed a greening trend. The EVI was relatively stable in most regions, with regions of fluctuating EVI being mostly affected by urbanization. For a period after 2020, the overall EVI change will exhibit anti-sustainability and will likely decrease. (2) Among the regions of EVI increase, 72.2% had no change in land-use type, while 10.8% and 6.6% changed to farmland and woodland land uses, respectively. Among the regions where EVI decreased, 69.9% had no change in land-use type, while 13.7% changed from farmland to construction land. (3) Overall, human activity change was the main influence on EVI change, which was mainly reflected in the negative impacts of accelerated urbanization. Topography had direct and indirect effects on EVI variations in Central and Southern Anhui. Annual precipitation change had a stronger impact on EVI variation in Northern and Central Anhui than in Southern Anhui, while annual average temperature change had a small impact in the entire province. Compared with other study methods, SEM provides a new approach to quantifying the influences of vegetation cover dynamics. In addition, the results of this study have important implications for ecological environmental protection and sustainable development in Anhui Province.

/pdf/quantifying-the-influences-of-driving-factors-on-vegetation-218nqa2o.pdf

Quantifying the Influences of Driving Factors on Vegetation EVI Changes Using Structural Equation Model: A Case Study in Anhui Province, China

Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Taking the Mongolian Plateau as the study area, the MODIS normalized difference vegetation index (NDVI) and the land surface temperature (LST) in the growing season from 2000 to 2019 were used to construct the NDVI-LST feature space, and based on which the temperature vege-tation dryness index (TVDI) of the Mongolian Plateau was calculated. We used Theil-Sen Median trend analysis, Mann-Kendall test, and Hurst index method to analyze the spatial and temporal varia-tions and future trends of TVDI on the Mongolian Plateau. Furthermore, we examined the relationship between meteorological factors and TVDI on the Mongolian Plateau using partial correlation analysis. The results showed that the TVDI of the Mongolian Plateau during 2000-2019 showed an increasing trend with a rate of 0.0001·a-1, indicating that the Mongolian Plateau's drought condition became heavier slightly in the last 20 years. The drought condition in meadow steppe and typical steppe gradually decreased, and that in desert steppe and alpine grassland was increased. The average Hurst index of TVDI in the growing season was 0.45, and the area with TVDI less than 0.5 accounted for 71.5% of the total area, which indicated that the TVDI during 2000-2019 in most areas turned opposite to the past. In the future, the drought condition in the central desert steppe area and the eastern meadow steppe area might increase, and that in most of the typical steppe and the desert steppe in Inner Mongolia tended to decrease. The drought change in the alpine grassland area was uncertain. There was a significant positive correlation between the TVDI and temperature in 33.6% area of the Mongolian Plateau and a significant negative correlation between the TVDI and precipitation in 34.8% of the area. Moreover, the meteorological factors heavily affected the typical steppe.

Application of temperature vegetation dryness index for drought monitoring in Mongolian Plateau.

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Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Application of temperature vegetation dryness index for drought monitoring in Mongolian Plateau.