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Following consecutive years of governance efforts, there has been a substantial reduction in sediment transport in the Yellow River, resulting in significant changes in its water–sediment dynamics. This necessitates precise monitoring of sediment-bearing tributary inflows, a crucial requirement for effective governance strategies on the Loess Plateau's current developmental stage. While satellite remote sensing technology has been widely used to estimate suspended particulate matter concentration (CSPM) in open water bodies like oceans and lakes, its application in narrow rivers presents challenges related to hybrid pixel and proximity effects. As a result, the effectiveness and competence of satellite remote sensing in monitoring CSPM in such confined river environments are reduced. This study attempted to use unmanned aerial vehicle (UAV) remote sensing with multispectral technology to invert CSPM in the Wuding River, a sediment-bearing Yellow River tributary. A novel CSPM concentration inversion model was introduced for highly turbid river settings. The results showed that the accuracy of the new band ratio model in this study is significantly improved compared with the existing models. The validation dataset had a coefficient of determination (R2) of 0.83, a root mean square error (RMSE) of 3.73 g/L, and a mean absolute percentage error (MAPE) of 44.95% (MAPE is 40.68% at 1–20 g/L, and 12.37% at >20 g/L). On this basis, the UAV also monitored the impacts of heavy rainfall on the CSPM, resulting in a rapid rise and fall in CSPM over a period of ten hours. This study demonstrated the potential of UAV remote sensing for CSPM monitoring in extremely turbid narrow rivers (tens to tens of meters), especially before and after rainfall sediment production events, which can provide technical support for accurate sediment management and source identification in the main tributaries of the Yellow River and help realize the goal of high-quality development of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle surveys were conducted in the summer season of 2016 and the winter season of 2017 to investigate the large wild herbivore population, including kiangs, Tibetan gazelles and bharals, in Madoi County; the source region of the Yellow River. The study generated forage grass production data in 30 m spatial resolution in Madoi County in 2016 using a downscaling algorithm; estimated a forage-livestock balance including wild animals and domestic animals; and analyzed the effect of the large wild herbivore population on the balance between forage grass and herbivory in Madoi County. The large wild herbivore population was estimated based on the density of the animals in the survey sample strip and compared and verified with available statistical data and the two survey results from the summer season of 2016 and winter season of 2017. The results showed that: (1) in the winter season of 2017, the populations of kiang, Tibetan gazelle and bharal were 17,100, 16,000 and 9300, respectively, while the populations of domestic yak, Tibetan sheep and horse were 70,800, 102,200 and 1200, respectively. The total population of large wild herbivores and domestic animals was 475,000 (sheep units). The ratio (in sheep units) between large wild herbivores and domestic animals was 1:4.5; (2) When only considering domestic animals, the grazing pressure index was 1.13, indicating slight overloading of the grassland. When considering domestic animals and large wild herbivores (kiang, Tibetan gazelle and bharal), the grazing pressure index was 1.38, suggesting moderate overloading of the grassland; (3) If large wild herbivores are not taken into consideration when the forage-livestock balance is calculated, the grazing pressure will be under-estimated by 22%. Overgrazing is the major cause of grassland degradation in Madoi County. An additional 79,000 tons of hay or a 30% reduction in domestic animals is required to maintain a forage-livestock balance in Madoi County. [ABSTRACT FROM AUTHOR]

Frequent snowfall and low temperatures led to a considerable snow disaster in some areas of China's Three-River Headwaters Region (TRHR) in Qinghai province in the spring of 2019, exerting a considerably negative influence on animal husbandry production in local grasslands. Based on a model of snow disaster classification and quantitative estimations of disaster-stricken animal husbandry, we propose a comprehensive disaster resistance capability index (CDRCI) using remote sensing, ground monitoring, and statistical investigations. With a comprehensive assessment of the space distribution and the magnitude of snow disasters, combined with a quantitative determination of disaster-stricken animal husbandry, the proposed CDRCI calculates how grassland animal husbandry is affected by snow disasters in different counties of the TRHR. The results indicate that approximately 2.31 million sheep and yaks were affected by moderate to severe snow disasters in the TRHR, accounting for 78.3% of the total livestock in the affected region. Of these affected livestock, approximately 1.54 million sheep and yaks were specifically affected by severe snow disasters, accounting for 52.1% of the total number of livestock. The CDRCIs for grassland animal husbandry in both Yushu and were moderate, being higher for the former than for the latter. We confirmed that the proposed CDRCI can accurately evaluate the magnitude of snow disasters in terms of how they affect grassland animal husbandry. The CDRCI is a way of relating the number of animal deaths to spatial disaster prevention and resistance. We expect that this research will provide important theoretical support for formulating snow disaster resistance policy, for example for increasing the construction of grassland animal husbandry infrastructure as well as providing greater stored forage material. [ABSTRACT FROM AUTHOR]

The Loess Plateau is one of the most fragile areas in the world, where the problem of soil erosion is particularly prominent. The spatial and temporal variation characteristics and mechanisms of soil erosion in this region have always been hot topics for researchers. In this study, Revised Universal Soil Loss Equation (RUSLE) is used to estimate the soil erosion modulus of the Loess Plateau from 2000 to 2015, the dynamic characteristics of its temporal and spatial variations and driving mechanisms are determined, and meteorological data are combined with remote sensing data to quantitatively calculate the contribution rate of human activities. The results show that from 2000 to 2015, the soil erosion modulus of the Loess Plateau had a downward trend as a whole, with a rate of −0.6408 t/ha/a, but the downward trend gradually slowed down. Precipitation mainly resulted in changes in the soil erosion modulus in the northwestern part of the Loess Plateau, where a significant positive correlation was seen. Meanwhile, the Vegetation Fractional Coverage (VFC) mainly affected the southeastern part, where a significant negative correlation was measured. The human-activity contribution rate was −1.0774 on the Loess Plateau, which means human activities effectively reduced the soil erosion modulus while climate change promoted soil erosion combined with the result of the analysis of variance (ANOVA). "Hilly and gully regions" and "Gully region of Loess Plateau" as the main implementation areas of ecological projects, human activities had contribution rate of 0.5513 and 0.7805 toward the declining of soil erosion, respectively. Interestingly, the spatial differentiation characteristic of the soil erosion driving mechanisms and human contribution rates on the Loess Plateau showed the same boundary line from northeast to southwest, which was well explained by the 400-mm isohyetal line and Hu's Line. This boundary can guide the geographical layout of the ecological management projects and urban development spaces on the Loess Plateau. [ABSTRACT FROM AUTHOR]

We used unmanned aerial vehicles (UAVs) to carry out a relatively complete population census of large wild herbivores in Maduo County on the Tibetan Plateau in the spring of 2017. The effective area covered by aerial surveys was 326.6 km2, and 23,784 images were acquired. Interpretation tag libraries for UAV images were created for wild animals, including Kiang (Equus kiang), Tibetan gazelle (Procapra picticaudata), and blue sheep (Pseudois nayaur), as well as livestock, including yaks and Tibetan sheep. Large wild herbivores in the survey transect were identified through manual imagery interpretation. Densities ranged from 1.15/km2 for Kiang, 0.61/km2 for Tibetan gazelle, 0.62/km2 for blue sheep, 4.12/km2 for domestic yak, and 7.34/km2 for domestic sheep. A method based on meadows in the cold and warm seasons was used for estimating the densities and numbers of large wild herbivores and livestock, and was verified against records of livestock numbers. Population estimates for Kiang, Tibetan gazelle, blue sheep, domestic yak, and domestic sheep were 17,109, 15,961, 9324, 70,846, and 102,194, respectively. Based on published consumption estimates, the results suggest that domestic stock consume 4.5 times the amount of vegetation of large wild herbivores. Compared with traditional ground survey methods, performance of UAV remote sensing surveys of large wild herbivore populations was fast, economical and reliable, providing an effective future method for surveying wild animals. [ABSTRACT FROM AUTHOR]