Search Results (10)

With the acceleration of urbanization and the increased frequency of extreme rainfall events, flooding has emerged as one of the most serious natural disaster problems, particularly affecting riparian cities. This study conducted a flooding risk assessment and an analysis of the driving factors behind flood disasters in the Songhua River Basin utilizing an improved Soil Conservation Service Curve Number (SCS-CN) model. First, the model was improved by slope adjustments and effective precipitation coefficient correction, with its performance evaluated using the Nash–Sutcliffe efficiency coefficient (NSE) and the Root Mean Square Error (RMSE). Second, flood risk mapping was performed based on the improved model, and the distribution characteristics of the flooding risk were analyzed. Additionally, the Geographical Detector (GD), a spatial statistical method for detecting factor interactions, was employed to explore the influence of natural, economic, and social factors on flooding risk using factor detection and interaction detection methods. The results demonstrated that the improvements to the SCS-CN model encompassed two key aspects: (1) the optimization of the CN value through slope correction, resulting in an optimized CN value of 50.13, and (2) the introduction of a new parameter, the effective precipitation coefficient, calculated based on rainfall intensity and the static infiltration rate, with a value of 0.67. Compared to the original model (NSE = 0.71, rRMSE = 19.96), the improved model exhibited a higher prediction accuracy (NSE = 0.82, rRMSE = 15.88). The flood risk was categorized into five levels based on submersion depth: waterlogged areas, low-risk areas, medium-risk areas, high-risk areas, and extreme-risk areas. In terms of land use, the proportions of high-risk and extreme-risk areas were ranked as follows: water > wetland > cropland > grassland > shrub > forests, with man-made surfaces exacerbating flood risks. Yilan (39.41%) and Fangzheng (31.12%) faced higher flood risks, whereas the A-cheng district (6.4%) and Shuangcheng city (9.4%) had lower flood risks. Factor detection results from the GD revealed that river networks (0.404) were the most significant driver of flooding, followed by the Digital Elevation Model (DEM) (0.35) and the Normalized Difference Vegetation Index (NDVI) (0.327). The explanatory power of natural factors was found to be greater than that of economic and social factors. Interaction detection indicated that interactions between factors had a more significant impact on flooding than individual factors alone, with the highest explanatory power for flood risk observed in the interaction between annual precipitation and DEM (q = 0.762). These findings provide critical insights for understanding the spatial drivers of flood disasters and offer valuable references for disaster prevention and mitigation strategies. Full article

The Chinese government has made the realization of sustainable development in grassland animal husbandry an important policy objective, and achieving a reasonable input of production factors is the key to realizing that goal. Based on the assumption of “rational economic man”, this study measures the economically optimal inputs and actual input bias of production factors, and constructs an econometric model focusing on analyzing the impact of operation scale on the factor input bias. The results indicate that herdsmen deviate from the economically optimal production input levels in forage, labor, and machinery, with the degree of bias decreasing as the livestock size or pasture size expands. Furthermore, it is established that market risk plays a role in mediating the impact of operation scale on the bias of variable production factors. Overall, large-scale herding households have a smaller bias in factor inputs, and should be promoted to operate on an appropriate scale, while paying attention to the prevention of market risk and the enhancement of information symmetry between herders and factor markets. Full article

The current paper provides a detailed review of the historical outbreaks of each of the four plague locust species found in South Africa, namely the brown locust, the African migratory locust, the red locust, and the southern African desert locust. The history and dynamics of the plague infestations and the major local outbreaks are summarized. The typical patterns of the outbreaks of the different species are described, and the threat of these locusts to agriculture in South Africa is defined. The brown locust produces regular outbreaks in the semi-arid Karoo, with large-scale eruptions of plague proportions occurring about once per decade. Patterns of outbreaks often repeat themselves, but the sheer size of the plague outbreaks is almost impossible to stop, and the brown locust has the potential to threaten food security throughout southern Africa. The African migratory locust produces outbreaks in some of the main maize and wheat cropping areas where it is difficult to control. This locust has taken advantage of the man-made crop environment to produce an extra generation per year that was not previously possible in the original grasslands. The coastal area of KwaZulu Natal Province in South Africa was a prime reception and breeding area for plague invasions of the red locust in the past, and the country, therefore, relies on the successful control of outbreaks in east and central Africa to prevent the recurrence of the plague invasions. The southern African desert locust occurs in the Kalahari Desert area, and outbreaks requiring chemical control are rare. Full article

Gross primary productivity (GPP) is an important indicator in research on carbon cycling in terrestrial ecosystems. High-accuracy GPP prediction is crucial for ecosystem health and climate change assessments. We developed a site-level GPP prediction method based on the GeoMAN model, which was able to extract spatiotemporal features and fuse external environmental factors to predict GPP on the Tibetan Plateau. We evaluated four models’ behavior—Random Forest (RF), Support Vector Machine (SVM), Deep Belief Network (DBN), and GeoMAN—in predicting GPP at nine flux observation sites on the Tibetan Plateau. The GeoMAN model achieved the best results (R² = 0.870, RMSE = 0.788 g Cm⁻² d⁻¹, MAE = 0.440 g Cm⁻² d⁻¹). Distance and vegetation type of the flux sites influenced GPP prediction, with the latter being more significant. The different grassland vegetation types exhibited different sensitivity to environmental factors (Ta, PAR, EVI, NDVI, and LSWI) for GPP prediction. Among them, the site located in the alpine swamp meadow was insensitive to changes in environmental factors; the GPP prediction accuracy of the site located in the alpine meadow steppe decreased significantly with the changes in environmental factors; and the GPP prediction accuracy of the site located in the alpine Kobresia meadow also varied with environmental factor changes, but to a lesser extent than the former. This study provides a good reference that deep learning model is able to achieve good accuracy in GPP simulation when considers spatial, temporal, and environmental factors, and the judgement made by deep learning model conforms to basic knowledge in the relevant field. Full article

Land use is a process that turns a piece of land’s natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth’s surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth’s surface. In China’s Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China’s northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km² of construction land is converted to cropland, and 424.11 km² of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km² and 148.42 km², respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively. Full article

The Selenga River basin is an important section of the Sino-Mongolian Economic Corridor. It is an important connecting piece of the Eurasian Continental Bridge and an important part of Northeast Asia. Against the background of the evolution of the geopolitical pattern since the disintegration of the Soviet Union and global warming, based on the land cover data in the Selenga River basin from 1992, 2000, 2009, and 2015, this paper describes the dynamic changes in land use in the basin. Through a logistic model, the driving factors of land cover change were revealed, and the CA-Markov model was used to predict the land cover pattern of 2027. The results showed that (1) from 1992 to 2015, the agricultural population in the Selenga River basin continued to decrease, which led to a reduction in agricultural sown area. The intensification of climate warming and drying had a significant impact on the spatial distribution of crops. Grassland expansion mostly occurred in areas with relatively abundant rainfall, low temperature, and low human activity. (2) The simulation results showed that, according to the current development trend, the construction land area of the Selenga River basin will be slightly expanded in 2027, the area of arable land and grassland will be slightly reduced, and the areas of forest, water/wetland, and bare land will remain stable. In the future, human activities in the basin will increase in the process of the construction of the China-Mongolia-Russia economic corridor. Coupled with global warming, the land/cover of the basin will be affected by both man-made and natural disturbances, and attention should be paid to the possible risk of vegetation degradation. Full article

The Qinghai Lake Basin (QLB), located in the northeastern part of the Qinghai–Tibet Plateau, has a fragile ecological environment and is sensitive to global climate change. With the progress of societal and economic development, the tourism industry in the QLB has also developed rapidly, which is bound to result in great changes in landscape patterns. In this study, we first analyzed the change characteristics of landscape patterns in the QLB from 1990 to 2018, and we then used the Markov model and the future land use simulation (FLUS) model, combined with natural, social, and ecological factors, to predict the changes in the number and spatial distribution of landscape patterns in the period between 2026 and 2034. The results of the study show that desert areas have been greatly reduced and transformed into grasslands. The grassland area expanded from 49.22% in 1990 to 59.45% in 2018, corresponding to an increase of 10.23%. The direct cause of this result is the combined effects of natural and man-made factors, with the latter playing a leading role. As such, government decision-making is crucial. Lastly, we simulated the landscape patterns in the period from 2018 to 2034. The results show that in the next 16 years, the proportion of various landscapes will change little, and the spatial distribution will be stable. This research provides a reference for the formulation of ecological environment management and protection policies in the QLB. Full article

Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity. Full article

Iberian man-made oak savannahs (so called dehesas) are traditional silvopastoral systems with a high natural value. Scattered trees provide shelter and additional food to livestock (cattle in our study sites), which also makes possible for animals depending on trees in a grass-dominated landscape to be present. We compared dehesas with nearby treeless grasslands to assess the effects of oaks on ant communities. Formica subrufa, a species associated with decayed wood, was by far the most abundant species, especially in savannahs. Taxa specialized in warm habitats were the most common both in dehesas and grasslands, as expected in areas with a Mediterranean climate. Within dehesas, the number of species was higher below oak canopies than outside tree cover. Compared to treeless grasslands, the presence of oaks resulted in a higher species richness of aphid-herding and predator ants, probably because trees offer shelter and resources to predators. The presence of oaks changed also the species composition, which differed between grasslands and dehesas. In self-standing scattered oaks, ant communities did not differ between the trunks and soil below canopies. These results stress the conservation value of trees in dehesas; within grasslands, they offer an additional microhabitat for species that would otherwise be scarce or absent. Full article

Soil organic carbon (SOC) and total nitrogen (total N) are important soil components for agricultural production. Soil quality is related to the total amount of SOC and total N sequestered in the soil. Land use plays a major role in the distribution and amount of SOC and total N. This study analyses the amount of SOC and total N under various land cover types in 1987, 2005 and 2010, and evaluated their storage in land use conversions in a comprehensively managed watershed on the Loess Plateau, China. Results show that concentrations of SOC and total N in shrub land and natural grassland areas were significantly higher than for other land uses (farmland, orchard, abandoned farmland, manmade grassland) while cropland had the lowest concentration. Storage of SOC and total N increased along the revegetation chronosequence. As the storage of SOC in 2005 and 2010, they were 3461.86 × 10⁸ and 4504.04 × 10⁸ g respectively. Soil organic carbon storage were enhanced one third just during 5 years. The effects of land use on SOC and total N were the most significant in the upper soil layers. The correlation between SOC, total N, and the C/N ratio indicated that the best combination of land uses were natural grassland and shrub land. They efficiently influenced the distribution and storage of SOC and total N, and benefited vegetation restoration. Full article