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[Objective] This study is aimed to understand the spatiotemporal changes in vegetation in the Qinghai Hehuang Valley and clarify the effects of climate change, land development and utilization and human activities on vegetation change. [Methods] The MODIS NDVI dataset from 2000 to 2020 was used to characterize vegetation changes. Based on Theil-Sen Median trend test, partial correlation analysis, geographical detectors and other methods, this paper explored the spatiotemporal changes of NDVI in Qinghai Hehuang Valley and its relationship with temperature, precipitation, slope, soil type, human activities and other influencing factors. [Results] (1) In the past 20 years, the vegetation NDVI in the Hehuang Valley region had shown a fluctuating growth trend, with a significant increase in the area of 2.21×104 km2 (p

Rapid changes in land use have rendered existing data for land-use classification insufficient to meet the current data requirements for rural revitalization and improvements in the living environment. Therefore, we used unmanned aerial vehicle (UAV) remote sensing imagery and an object-based human-assisted approach to obtain ultra-high-resolution land-use data for 55 villages and accurately analyzed village land-use composition and distribution patterns. The highest proportion of land use in the villages is built-up land (33.01% ± 8.89%), and the proportion of road land is 17.76% ± 6.92%. The proportions for forest land and grassland are 16.41% ± 7.80% and 6.51% ± 4.93%, respectively. The average size of the villages is 25.85± 17.93 hm2, which is below the national average. The villages have a relatively scattered distribution, mostly concentrated on both sides of the main roads. The correlation analysis indicates that mean annual temperature (MAT) and annual precipitation (AP) are the primary factors influencing the land-use composition of villages, with contribution rates of 50.56% and 12.51%, respectively. The use of UAV remote sensing imagery to acquire ultra-high-resolution land-use data will provide a scientific basis for the planning of the living environment in the villages of the Hehuang Valley. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing concern that vegetation changes on the Tibetan Plateau are associated with climate change (temperature and precipitation) and human activities. This study used six types of remote sensing vegetation data, including GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI, MODIS EVI (Enhanced Vegetation Index), SPOT Vegetation (Spot-VGT) NDVI, LAI (Leaf Area Index) and NPP (Net Primary Productivity), and applied the maximum synthesis method, trend analysis, correlation analysis, and multivariate statistical analysis to investigate vegetation change processes since the 1980s. The study showed that the amount of vegetation on the TP had increased significantly since 2000 (p <.01), especially in the northeastern part of the TP. There was no significant change prior to 2000. The different vegetation data sources varied greatly. Four remote sensing indices, MODIS EVI, Spot-VGT NDVI, LAI, and NPP, showed a significant increase in vegetation from 2000, accounting for 16.18%, 44.55%, 30.44% and 8.94% of the total area, respectively (p <.05). Multiple data sources provided a more comprehensive understanding, whereas a single data source had substantial uncertainty. Human activities, such as the implementation of large-scale ecological projects, played a dominant role in increasing vegetation, while climate change played a subsidiary role. The MODIS EVI, Spot-VGT NDVI, LAI, and NPP data showed that the area of increased vegetation caused by human activities accounted for 53.51%, 45.68%, 37.52%, and 31.79% of the total area of the TP, respectively. The relative increase from climate change was 10.28%, 17.49%, 13.15%, and 8.82%, respectively. The current study applied multi-source remotely sensed vegetation data, which effectively reduced the uncertainty caused by individual data sources and provided more rigorous and scientific research conclusions. [ABSTRACT FROM AUTHOR]

In order to explore the impact of climate change on the ecosystem at high altitudes, dendroclimatology was used to study the response of radial growth of Juniperus squamata Buch.-Ham. ex D.Don to the rapid warming in Nangqian County over the past 60 years, and a tree-ring width chronology for 115 years was established. (1) Meteorological data showed that the temperature in Nangqian County of the Tibetan Plateau has increased continuously during the past 60 years, and the minimum temperature has had the most significant change (0.63 °C/10a), especially between 2000–2019 (0.12 °C/a). Over the same time period precipitation has not changed significantly (0.94 mm/a, p > 0.10). The standard chronology was used to reconstruct the mean temperature series from July to September in Nangqian meteorological station during the past 115 years (1905–2019). The explained variance of the reconstructed equation was 42.8% (40.8%, after adjusting for degrees of freedom). The reconstructed temperature series can be roughly divided into two stages: from 1905 to 1999, the temperature fluctuated around the average value, 12.10 °C, and from 2000 to 2019, the temperature showed a significant upward trend. (2) The analysis of the climate-tree growth relationship indicated that the response of radial growth of Juniperus squamata to temperature was significantly stronger than the response to precipitation; especially in the last 20 years, when the radial growth of Juniperus squamata was positively correlated with temperature (p < 0.01). Compared to the maximum temperature and mean temperature, the correlation between radial growth of Juniperus squamata and minimum temperature was more significant. (3) Under the background of climate warming, the radial growth trend of Juniperus squamata in Nangqian county was consistent with temperature changes. Particularly in the past 20 years, the radial growth of Juniperus squamata showed a significantly increased trend and entered a rapid growth period. [ABSTRACT FROM AUTHOR]

Forest-sediment relationship is a hot and important issue in Ecohydrology studies. China has implemented many large-scale reforestation programmes in the last decades to address the growing soil erosion and desertification. In this study, we made statistical and graphic analyses on the long-term hydrological data of the 39 watersheds in the rocky mountain area of the North China, and then we were able to analyze the effect of forest on sediment yield. Our results show that the effect is weak in the lees-precipitation regions (when MAP < 500 mm). And in the more-precipitation regions(when MAP >500 mm), the impact of forest on reducing sediment yield is different with the varied forest coverage (f), the relationship between the sediment yield and forest coverage show a quadratic polynomial.

Aim of study: The aim is to find a way increasing gain yield and lessen area of farmland, and then increasing vegetation cover, improving environment and alleviating soil erosion.Area of study: The Hilly-Gully region at the loess plateau of China.Material and methods: In this study, an adjusted and optimized land use pattern was developed in Luoyugou watershed in the Yellow River valley based on the gradient distribution of land use types, and its effect on water and sediment transport was simulated using the SWAT model and GIS, with remote sensing images, land use maps and hydrologic data.Main results: The results indicate: average simulated runoff and sediment for the period 1986-2000 under conditions of the three land use pattern (2011, 2008 and optimized land use) reduced by 0.002-0.013 m3/s (2.7-17.6%) and 0.66 million tons, respectively. The runoff and sediment data obtained were compared with observed data from 2008, which showed that runoff and sediment production would be reduced by 467625 m3 and 22754 tons, respectively.Research highlights: The adjustment of the land use pattern in comprehensive consideration of vegetation and geography have a positive effect on water and sediment transport which will be important for decision making and water resources management, and provides a reference for future environmental management and ecological construction in the loess plateau Hilly-Gully region.

Soil organic carbon (SOC) is a critical indicator for the global carbon cycle and the overall carbon pool balance. Obtaining soil maps of surface SOC is fundamental to evaluating soil quality, regulating climate change, and global carbon cycle modeling. However, efficient approaches for obtaining accurate SOC information remain challenging, especially in remote or inaccessible regions of the Qinghai–Tibet Plateau (QTP), which is influenced by complex terrains, climate change, and human activities. This study employed field measurements, SoilGrids250m (SOC_250m, a spatial resolution of 250 m × 250 m), and Sentinel-2 images with different machine learning methods to map SOC content in the QTP. Four machine learning methods including partial least squares regression (PLSR), support vector machines (SVM), random forest (RF), and artificial neural network (ANN) were used to construct spatial prediction models based on 396 field-collected sampling points and various covariates from remote sensing images. Our results revealed that the RF model outperformed the PLSR, SVM, and ANN models, with a higher determination coefficient (R2 of 0.82 is from the training datasets) and the ratio of performance to deviation (RPD = 2.54). The selected covariates according to the variable importance in projection (VIP) were: SOC_250m, B2, B11, Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), B5, and Soil-Adjusted Total Vegetation Index (SATVI). The predicted SOC map showed an overall decrease in SOC content ranging from 69.30 g·kg−1 in the southeast to 1.47 g·kg−1 in the northwest. Our prediction showed spatial heterogeneity of SOC content, indicating that Sentinel-2 images were acceptable for characterizing the variability of SOC. The findings provide a scientific basis for carbon neutrality in the QTP and a reference for the digital mapping of SOC in the alpine region. [ABSTRACT FROM AUTHOR]

Lager scale ecological restoration practices are increasing soil organic carbon (SOC) stocks. Meta‐analysis is by far the most widely applied technique for describing SOC at large spatial scales, but there is a lack of field‐based investigations on a series of individual watersheds. In this study, a watershed transect of six watersheds distributed in a hilly and gully area of the Loess Plateau was investigated in order to achieve sustainable practices. The results indicate that ecological restoration practices significantly increased SOC stocks to varying degrees (p < 0.05). Grasslands were associated with the largest SOC stocks (39.42 ± 22.21 t hm−2), which were larger than the SOC stocks of artificial immature forest (<20 years) (26.58 ± 14.32 t hm−2) in the 50 cm soil profile. However, in the long term, afforestation seems to increase SOC stocks to the level of secondary forest, 81.15 ± 4.69 t hm−2, in a suitable environment. Appropriate land use management is also an effective approach to improving soil C sequestration capacity. SOC stocks were measured as 34.11 ± 7.66 t hm−2 in abandoned terraced farmland, which was similar to the grassland SOC stocks. Soil and plant factors contributed approximately 60% to the variation in the spatial distribution of SOC stocks at 0–50 cm; their contributions increased and diminished with increasing soil depth, respectively. This study indicates naturally restored grassland and engineering practices are suitable on the Loess Plateau by investigating a watershed transect and enhances our understanding of the comprehensive contributions of environmental factors to the spatial distribution of SOC stocks on a large spatial scale. [ABSTRACT FROM AUTHOR]