Search Results (6)

Soil erosion has always been a critical issue confronting watershed environments, impacting the progress of sustainable development. As an increasing number of countries turn their attention to this problem, numerous policies have been enacted to halt the progression of soil erosion. However, policy-driven interventions often lead to significant changes in watershed vegetation coverage, under which circumstances, the original sediment erosion models may fall short in terms of simulation accuracy. Taking the Kuye River watershed as the research subject, this study investigates soil erosion data spanning from 1981 to 2015 and utilizes the Revised Universal Soil Loss Equation (RUSLE) model to simulate soil erosion. It is found that the extensive planting of vegetation after 2000 has led to a rapid reduction in soil erosion within the Kuye River watershed. The original vegetation cover and management factor (C) proves inadequate in predicting the abrupt changes in vegetation coverage. Consequently, this study adopts two improved plant cover and management factor equations. We propose two new methods for calculating the vegetation cover and management factor, one using machine learning techniques and the other employing a segmented calculation approach. The machine learning approach utilizes the Eureqa software (version11.0, Cornell University, New York, American) to search for the relationship between Normalized Difference Vegetation Index (NDVI) and C, ultimately establishing an equation that describes this relationship. On the other hand, the piecewise method determines critical values based on data trends and provides separate formulas for C above and below these critical values. Both methods have achieved superior calculation accuracy. Specifically, the overall data calculation using the machine learning method achieved an determined coefficient (R²) of 0.5959, while the segmented calculation method achieved an R² of 0.6649. Compared to the R² calculated by the traditional RULSE method, these two new methods can more accurately predict soil erosion. The findings of this study can provide valuable theoretical reference for water and soil prediction in watersheds. Full article

The assessment of soil erosion in a region can provide an effective reference for local ecological environment management. The Yunnan–Guizhou Plateau54 is an important ecological security barrier in southwest China, owing to its unique climatic and environmental characteristics and superior natural resource endowment. The current research focus is the spatial analysis of a certain area. In this study, soil erosion in the Yunnan–Guizhou Plateau during 2000–2030 was analyzed and predicted from two aspects of structure and spatial layout by coupling several models. The report also analyzes the shift in the center of gravity of land use and analyzes the drivers of soil erosion, analyzing soil erosion by land use type. The study shows a decreasing trend in the soil erosion modulus from 2000 to 2020 from 1183.69 to 704.58 t·hm⁻²·a⁻¹, but it is expected to have an increasing trend in the future and will increase to 877.72 t·hm⁻²·a⁻¹. Analyzing the drivers of soil erosion allows for testing whether the factor affects the spatial distribution of the independent variable and to what extent it explains that dependent variable. This study showed that elevation had the highest explanatory power for soil erosion. Relatively high mountainous areas are often subject to greater soil erosion due to their steep topography, resulting in poorer vegetation cover. The north–south offset distance is greater than the east–west offset distance for forested land, water and unutilized land, and the east–west offset distance is greater than the north–south offset distance for cropland, grassland and built-up land in the Yunnan–Guizhou Plateau. The purpose of this study is to identify areas of serious soil erosion vulnerability in the Yunnan–Guizhou Plateau, and to analyze the driving factors affecting soil erosion vulnerability, so as to provide a basis for regional soil erosion management, and, at the same time, to provide a reference for the government to formulate soil and water conservation measures. Full article

Revealing the relationship between land use changes and soil erosion provides a reference for formulating future land use strategies. This study simulated historical and future soil erosion changes based on the RULSE and GeoSOS-FLUS models and used a random forest model to explain the relative importance of natural and anthropogenic factors on soil erosion. The main conclusions are as follows: (1) From 1990 to 2020, significant changes in land use occurred in Kunming, with a continuous reduction in woodland, grassland, and cropland, being converted into construction land, which grew by 195.18% compared with 1990. (2) During this period, the soil erosion modulus decreased from 133.85 t/(km²·a) in 1990 to 130.32 t/(km²·a) in 2020, with a reduction in soil loss by 74,485.46 t/a, mainly due to the conversion of cropland to construction and ecological lands (woodland, grassland). (3) The expansion of construction land will continue, and it is expected that by 2050, the soil erosion modulus will decrease by 3.77 t/(km²·a), 4.27 t/(km²·a), and 3.27 t/(km²·a) under natural development, rapid development, and ecological protection scenarios, respectively. However, under the cropland protection scenario, the soil erosion modulus increased by 0.26 t/(km²·a) compared with 2020. (4) The spatial pattern of soil erosion is influenced by both natural and anthropogenic factors, and as human activities intensify in the future, the influence of anthropogenic factors will further increase. Traditionally, the expansion of construction land is thought to increase soil loss. Our study may offer a new perspective and provide a reference for future land use planning and soil loss management in Kunming. Full article

Sign language serves as the primary mode of communication for the deaf community. With technological advancements, it is crucial to develop systems capable of enhancing communication between deaf and hearing individuals. This paper reviews recent state-of-the-art methods in sign language recognition, translation, and production. Additionally, we introduce a rule-based system, called ruLSE, for generating synthetic datasets in Spanish Sign Language. To check the usefulness of these datasets, we conduct experiments with two state-of-the-art models based on Transformers, MarianMT and Transformer-STMC. In general, we observe that the former achieves better results (+3.7 points in the BLEU-4 metric) although the latter is up to four times faster. Furthermore, the use of pre-trained word embeddings in Spanish enhances results. The rule-based system demonstrates superior performance and efficiency compared to Transformer models in Sign Language Production tasks. Lastly, we contribute to the state of the art by releasing the generated synthetic dataset in Spanish named synLSE. Full article

An evaluation of soil quality sustainability can support decision making for the sustainable use of land resources. However, certain current problems associated with these evaluations remain unaddressed, e.g., the evaluation indicators do not fully reflect soil quality risks and the evaluation scale is not sufficiently small. In this study, 25,000 spatial grids of dimensions 3 km × 3 km are used to divide the major grain-producing regions in China, namely, the Sanjiang Plain and the Songnen Plain of Heilongjiang. Then, the soil erosion modulus, nutrient balance index, soil organic carbon (SOC) storage, heavy metal soil pollution index and crop productivity are calculated for each grid using the RULSE model, nutrient balance index model, soil type method, geoaccumulation index method and mechanism method, respectively. A spatial grid cluster analysis method is used to thoroughly evaluate and analyze the sustainability of soil quality in each grid. The results show that the overall soil status of the study area is good. The soil and water conservation levels are high, the soils show low levels of contamination, the crop production potential is high and the ratio of highly sustainable to moderately sustainable soils is approximately 2:1. Only 2.74% of the land is rated extremely unsustainable and needs to be restored to a basic level of productivity before subsequent functional restoration can be carried out. This study provides a new method for the fine-scale evaluation of soil quality and contributes to the management of land resources. Full article

Hainan Island is one of China’s most ecologically diverse areas. Human activities and climate change have recently influenced Hainan Island’s ecosystem services. Therefore, scientific methods are urgently needed to investigate the characteristics of these services’ spatial and temporal variations and their driving mechanisms for maintaining Hainan Island’s biodiversity and high-quality ecological conservation. Based on multivariate remote sensing and reanalysis data, this study analysed the spatial and temporal variations in water retention, soil conservation, carbon sequestration, and oxygen release services on Hainan Island during 1980–2015 using various ecosystem service models such as INVEST, CASA and RULSE. Then, we analysed different ecosystem service drivers using a random forest model. The results indicated that (1) from 1980 to 2015, the change characteristics of different ecosystem types (arable, forest, and grassland) decreased, and the proportion of decrease was 0.98%, 0.55% and 0.36%, respectively. Built-up and water increased significantly, and the proportion of increase reached 1.46% and 0.51%, respectively. (2) Hainan Island’s functions of water retention, soil conservation, carbon sequestration, and oxygen release services decreased from 23.31 billion m3, 2.89 billion t, 9.68 million t and 56.05 million t in 1980 to 23.15 billion m3, 2.79 billion t, 9.42 million t and 55.53 million t in 2015, respectively. The high value area was mainly distributed in Hainan Island’s central mountainous area, and the low value area was mainly distributed in the lower-elevation coastal area. (3) In the past 35 years, urban expansion has been the leading factor in the reduction of Hainan Island’s ecosystem service capacity. However, its central nature reserve and other forms of ecological protection have improved its ecosystem service capacity, which has alleviated the overall declining trend of its amount of ecosystem service functions. (4) The driving forces for the spatial distribution of Hainan Island’s ecosystem services were analysed using a random forest algorithm, which indicated that its spatial distribution was mainly driven by rainfall, soil moisture, actual evapotranspiration, maximum temperature, and minimum temperature. This study is expected to help planners develop effective environmental policies to accommodate the potential ecological risks associated with urban expansion during the construction of Hainan Island’s future free trade port while filling the gaps in existing studies. Full article