Journal of Resources and Ecology最新文献

{"title":"Geographic Distribution and Ecological Adaptability of Fagopyrum Species in Yunnan Province","authors":"Wang Ziyuan, Chu Xiuying, Li Wei","doi":"10.5814/j.issn.1674-764x.2023.06.013","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.06.013","url":null,"abstract":"The genus Fagopyrum is in the flowering plant family Polygonaceae, and includes some important food plants, such as F. esculentum (common buckwheat) and F. tataricum (tartary buckwheat). Except for these two cultivated species, the other buckwheat plants are all wild species. They are mainly distributed in southern China in general, and in Yunnan Province in particular. However, our understanding of their richness and geographic distributions in Yunnan remains very limited. The aim of the present study is to establish a list of buckwheat species found in Yunnan, examine their geographic distributions and patterns, and analyze their conservation and utilization status. The results showed a high richness of buckwheat plants in Yunnan, which accounts for nearly 70% of the global buckwheat richness. Species such as F. capillatum and F. gracilipedoides are endemic to Yunnan, and they exist nowhere else in the world. Also, the northwestern Yunnan and central Yunnan regions represent two important distribution centers of buckwheat species in Yunnan, and the highest buckwheat richness was found at the altitude range of 1500–3000 m. Many buckwheat species are rich in amino acids, fiber, vitamins, minerals and bioactive substances. They are also adapted to the high-altitude regions in Yunnan with harsh climatic and soil conditions. As climate change has direct impacts on agricultural biodiversity and food security, the conservation of diversity in buckwheat species, which have both high dietary beneficial components and great ecological adaptability, merits more attention. We believe that it is important to find a balance between the protection and utilization of buckwheat resources in order to achieve the sustainable utilization of this precious natural resource.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"25 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135935118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Spatial Distribution Pattern and Influencing Factors of Rural Governance Demonstration Villages and Towns in China","authors":"Wang Jishu, Guolei Chen, Zhang Jisha, Lianlian Li","doi":"10.5814/j.issn.1674-764x.2023.05.017","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.05.017","url":null,"abstract":"Abstract: Rural governance is the basic requirement for promoting the modernization of the national governance system and governance capacity, so it is closely related to the implementation of the national rural revitalization strategy and the realization of the modernization goal of national governance. Taking 2189 rural governance demonstration villages and towns in China as the research object, the spatial distribution structure and influencing factors of rural governance demonstration villages and towns were explored in this study by using the nearest neighbor index method, the kernel density estimation method, the grid dimension analysis method and the spatial autocorrelation analysis method. The results show that the spatial distribution of rural governance demonstration villages and towns in China tends to be clustered, and the spatial differentiation is obvious. The analysis of kernel density in the rural governance demonstration villages and towns presents a number of kernel centers in space, and the distribution pattern of secondary centers is in the form of a belt distribution, which is formed by decreasing and spreading around the surrounding kernel centers. The rural governance demonstration village and town system features obvious scale-free areas and fractal characteristics. The spatial distribution of the rural governance demonstration villages and towns is mainly influenced by natural and cultural factors, among which, the topography and lake water systems are the main influencing factors. Among the humanistic factors, the social economy, transportation and national culture are the main influencing factors, while the influence of population distribution is not significant.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"1061 - 1074"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49095710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Change of Agriculture Area Over the Last 20 Years: A Case Study of Nainital District, Uttarakhand, India","authors":"Saurabh Pargaien, R. Prakash, V. P. Dubey","doi":"10.5814/j.issn.1674-764x.2023.05.009","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.05.009","url":null,"abstract":"Abstract: This study performs the time series analysis of agriculture land in the Nainital District of Uttarakhand, India. The study utilizes Landsat satellite images for the classification of agriculture and non-agriculture land over a time duration of 21 years (2000–2021). Landsat 5, 7 and 8 satellites data have been used to classify the study area with Random Forest classifier. The Landsat satellite images are processed using the Google Earth Engine (GEE) platform. The selection of Random Forest classier has been based on a comparative analysis among Random Forest (RF), Support Vector Machines (SVM) and Classification and Regression Trees (CART). Overall accuracy, user accuracy and producer accuracy and Kappa coefficient has been evaluated to determine the best classifier for the study area. The overall accuracy for RF, SVM and CART for the year 2021 is 96.38%, 94.44% and 91.94% respectively. Similarly, the Kappa coefficient for RF, SVM and CART was 0.96, 0.89, 0.81 respectively. The classified images of Landsat in agriculture and non-agriculture area over a period of 21 years (2000–2021) shows a decrement of 4.71% in agriculture land which is quite significant. This study has also shown that the maximum decrease in agriculture area in last four years, i.e., from 2018 to 2021. This kind of study is very important for a developing country to access the change and take proper measure so that flora and fauna of the region can be maintained.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"983 - 990"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48989078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking the Drivers of the Tourism Ecological Footprint in Mount Wutai, China, Based on the STIRPAT Model","authors":"S. Luo, Jianshu Yin, Hailong Bai, Cai Fuyan","doi":"10.5814/j.issn.1674-764x.2023.05.016","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.05.016","url":null,"abstract":"Abstract: Tourism can cause serious environmental pollution due to high consumption levels. With the development of tourism in Mount Wutai, the environmental pressure has been increasing. This study explored the influences of tourist arrivals in Mount Wutai, ticket revenue from domestic tourists in Mount Wutai, national passenger turnover, energy intensity, GDP per capita in Wutai County and GDP per capita in China on the tourism ecological footprint in Mount Wutai from 2005 to 2019. The extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model was constructed by using principal component regression. The results were as follows: (1) The tourism ecological footprint in Mount Wutai increased during the study period, from 27798.07 gha in 2005 to 67467.36 gha in 2019. (2) From 2005 to 2019, tourist arrivals in Mount Wutai, ticket revenue from domestic tourists in Mount Wutai, national passenger turnover, GDP per capita in Wutai County and GDP per capita in China grew, while energy intensity declined. (3) The extended STIRPAT model showed that the elasticity coefficients of tourist arrivals in Mount Wutai, ticket revenue from domestic tourists in Mount Wutai and national passenger turnover were 0.086%, 0.075% and 0.164%, respectively, which indicated that the tourism ecological footprint in Mount Wutai would increase by 0.086%, 0.075% and 0.164%, respectively, when those parameters increased by 1%; the elasticity coefficients of GDP per capita in Wutai County and GDP per capita in China increased at an escalating pace, but the environmental Kuznets curve did not exist, indicating that economic growth did not alleviate the environmental pressure during the study period; the elasticity coefficient of energy intensity was –0.108%, which indicated that the tourism ecological footprint would decrease by 0.108% when energy intensity increased by 1%. Therefore, the implementation of effective policies and technological innovation would significantly reduce the tourism ecological footprint in Mount Wutai.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"1053 - 1060"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43671771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Spatial Distribution and Driving Factors of Carbon Storage in the Grassland Ecosystems of the Northern Tibetan Plateau","authors":"Huangqing Dongzhi, Xueying Chen, Mingming Shi, Liusheng Yang, Ou Baoxi, Du Yan, Baolong Wang, Xiaodong Guo, Liang Zeyu, Shi Peili","doi":"10.5814/j.issn.1674-764x.2023.05.001","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.05.001","url":null,"abstract":"Abstract: The investigation of carbon storage in ecosystems and its driving factors is crucial for understanding carbon cycling and achieving the goal of carbon neutrality. The grassland in the Northern Tibetan Plateau is an important grassland ecosystem in China, although the accurate estimation of its carbon stock and our knowledge of its spatial patterns and driving factors in the Northern Tibetan Plateau remain unclear due to insufficient field investigations. In this study, a dataset of 150 measured sample points on the Northern Tibetan Plateau, kriging interpolation and statistical methods were used to estimate the densities of aboveground biomass carbon, belowground root carbon and soil organic carbon at a soil depth of 30 cm, as well as to explore the spatial distribution and the main influencing factors of each carbon pool. The average carbon densities were 0.038 kg C m–2 in aboveground biomass, 0.284 kg C m–2 in belowground biomass, and 7.445 kg C m–2 in the soil. The soil organic carbon accounted for 95.85% of the grassland carbon density. The total carbon storage of the grassland ecosystem in the Northern Tibetan Plateau was about 4.08 Pg C, with a decreasing trend from southeast to northwest. Of the total, the organic carbon stocks of vegetation and soil were 0.58 Pg C (including the aboveground and belowground biomass) and 2.58 Pg C, accounting for 28.29% of the total vegetation carbon and 26.60% of the total soil carbon, respectively, on the Tibetan Plateau, with the remainder stored in the bare land. While the precipitation, temperature and soil texture all affected the ecosystem carbon storage, precipitation played the most significant role and the combination of these three factors explained up to 86.47% of the aboveground carbon density. The aboveground carbon pools in grassland ecosystems of the Northern Tibetan Plateau were most sensitive to climatic factors, while the spatial patterns of belowground and soil carbon storage were more complex. This study provides a spatially accurate assessment of the carbon storage in the grasslands on the Northern Tibetan Plateau.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"893 - 902"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41506992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Vulnerability to Environmental Changes of Freshwater Crab, Thaipotamon chulabhorn in the Dun Lumpun Non-Hunting Area, Thailand","authors":"A. Bridhikitti, Bidur Khadka, Suraj Sharma","doi":"10.5814/j.issn.1674-764x.2023.05.020","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.05.020","url":null,"abstract":"Abstract: Thaipotamon chulabhorn (Mealy Crab) is a freshwater crab, listed as ‘Least Concerned’ in the IUCN Red List of Threatened Species. Nonetheless, this crab is exclusively found in one small area of the Dun Lumpun Forest, Thailand, and currently faces high exposure to environmental changes. This study aims to investigate key dynamic factors that influence vulnerability to environmental changes of the Mealy Crab. The study was conducted between 15 March and 23 October 2016, covering the local summer until the end of the rainy season and being influenced by strong El Niño, thus suggesting drought episodes. This vulnerability assessment was carried out through crab population records from 1997 to 2016, field measurements/surveys, and interviews with experienced park rangers. The results revealed that drought is the major vulnerability factor that is threatening the population of the crabs and their livelihoods (7.96 out of 10), followed by an increased number of their natural enemies (7.41), variations in groundwater level (6.11), changes in groundwater quality (4.63), changes in forest soil (4.63) and human intervention (4.26). Since 1996, human intervention has been found to have a little direct impact on the crab population due to the restricted access to forest resources. The 2016 drought was accompanied by anomalously low rainfall and the early onset of the rainy season, contributing to an earlier and shorter mating period of the crabs. Nonetheless, a consistently increased number of the Mealy Crab population indicated that they are not significantly vulnerable to a larger number of their natural enemies, and even to the changes in groundwater and forest soil. In conclusion, the crab population was increasing along with changes in the habitat and climate and its growth cycle was unusual during the drought.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"1092 - 1103"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45136755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Evaluation of Rural Characteristic Industry Development in Metropolitan Areas Based on the Topsis Method—Taking the Xi'an Metropolitan Area as an Example","authors":"Xiaomeng Fu, Z. Pei, Meng-meng Zhang, Zhijun Li","doi":"10.5814/j.issn.1674-764x.2023.05.015","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.05.015","url":null,"abstract":"Abstract: The coordinated development of urban and rural areas is an important measure for releasing the potential of domestic demand and promoting industrial upgrading. The development of rural industries in metropolitan areas, especially those with rural characteristics, is an important pathway for achieving comprehensive rural revitalization and promoting urban and rural integration in the metropolitan area. Based on the development goal of promoting rural industries in metropolitan areas, this study constructed a performance evaluation index system, including industrial development, industrial integration, rural construction, and farmers' life, and applied the TOPSIS method to evaluate the development of rural characteristic industries in the metropolitan area of Xi'an. The results indicated that the overall development performance of rural characteristic industries in the metropolitan area was at a medium level. The developmental performance at the level of “primary industry-secondary industry-tertiary industry” integration was relatively good, with a certain extension of the industrial chain and expansion of industrial functions, and certain economic benefits were created in this process. However, the development of rural characteristic industries has not effectively driven the development of rural society. In terms of space, the development of rural characteristic industries in the metropolitan area presents a circular distribution feature that decreases from the core circle to the outer circle. In terms of types, there are significant differences in the development levels between the different types, and the weaknesses of different industries vary. Based on these considerations, the key pathway for the collaborative and typified development of regional characteristic industries is proposed.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"1044 - 1052"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43598737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ecological Function Service Value and Quantity of Fruit-Tree Economic Forests in the Semi-Arid Loess Hilly and Gully Region of Central Gansu—A Case Study of the Hulu River Basin","authors":"Wang Fu, He Qian, Han Fen, Zhang He, Zhao Qiang, Xiaoyan Sha","doi":"10.5814/j.issn.1674-764x.2023.05.002","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.05.002","url":null,"abstract":"Abstract: The compensatory effect and deep impact of the fruit tree economic forest on water and soil conservation ecology in a semi-arid region are investigated by exploring the ecological service value and ecological functions of the fruit tree economic forest, and further by analyzing its functional effects on reducing water and soil loss, conserving water and soil, conserving the water source, improving environmental quality and maintaining biodiversity. This analysis provides a theoretical basis and support for coordinating the relationship between economic and social development, ecological protection, agriculture and forestry in the semi-arid area of the Loess Plateau; promoting the systematic management of mountain, water, forest, farmland, lake grass and sand; and promoting the ecological protection and high-quality development of the whole Hulu River Basin. According to the Standards for Evaluation of Forest Ecosystem Service Function (GB/T38582-2020), the forestry industry standard of the People's Republic of China, the current market method, shadow price method, opportunity cost method, Swedish Carbon Tax law and other methods were adopted. The main functions of the fruit-tree economic forest ecosystem and its eco-economic value in the Hulu River Basin in Pingliang City were quantitatively analyzed, and the existing measured data from domestic ecological stations were combined with quantitative analysis and qualitative evaluation. The calculations included the ecological service values of the fruit tree economic forest ecosystem in water conservation, soil conservation, carbon sequestration, oxygen production, nutrient accumulation, environment purification and biodiversity protection, and the dynamic change characteristics of the ecological function quantity corresponding to its value were systematically analyzed. (1) In the four developmental stages of the fruiting economic forest in the Hulu River Basin in Pingliang City, the ecological function service value showed an increasing trend. Among the stages, the total value contribution of the first stage (2005–2009) is 1.299×1010 yuan; the second stage (2010–2013) is 2.497×1010 yuan; the third stage (2014–2017) is 2.662×1010 yuan; and total value contribution of the fourth stage (2018–2020) is 2.774×1010 yuan. (2) In the composition of the ecological functional service value of the fruit tree economic forest, the value of water conservation is the highest, accounting for the largest proportion at 32.97% of the total value of ecosystem services. Therefore, it plays an important role in regulating the hydrological balance of the basin in the arid and semi-arid region of the Loess Plateau. The function value of the purifying environment is relatively small, accounting for only 0.19% of the total value, followed by the function value of species conservation, accounting for 5.42%. In order of service value, the ecological function values of water conservation, oxygen release, carbon seques","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"903 - 913"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42738246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and Temporal Evolutionary Characteristics of Landscape Pattern of a Typical Karst Watershed Based on GEE Platform","authors":"Qingqing Wei, H. Wen, Wang Jinye, Xinran Zhou, Yuefeng Yao","doi":"10.5814/j.issn.1674-764x.2023.005.004","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.005.004","url":null,"abstract":"Abstract: Given the high degree of fragmentation and poor resistance to disturbance in karst landscapes, it is important to clarify the spatial and temporal dynamics of landscape patterns in karst areas when designing karst ecological protection strategies. Using the Li River Basin as the study area, the spatial distribution and dynamic evolution of landscape patterns in the basin were analyzed at the levels of landscape utilization, landscape type dynamics and landscape pattern indices based on the Landsat series images for 2000 to 2020 obtained from the GEE platform as the data source. The results show three important aspects of this typical karst watershed. (1) There are large differences in landscape structure and landscape type trends between the karst and non-karst areas in the Li River Basin. (2) The comprehensive landscape type dynamic attitude of the Li River Basin is 0.22%, and the composite index of landscape type use varies from 239.49 to 244.88. The degree of landscape use is higher in karst areas than in non-karst areas, and the rate of landscape change in karst areas is more intense. The integrated index of landscape use in karst areas ranges from 262.32 to 270.50, and in non-karst areas it spans 225.28 to 227.01. The integrated landscape type motility in the karst areas is 0.31%, which is about twice as high as that in non-karst areas. (3) The overall landscape evolution of the Li River Basin shows trends of increasing fragmentation, decreasing connectivity, decreasing dominance and increasing heterogeneity, and these trends are particularly prominent in the karst areas. The results of this study can provide a scientific basis for realizing the construction goals of the National Sustainable Development Innovation Demonstration Zone in Guilin, and a technical reference for the ecological environmental management of the karst watershed.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"928 - 939"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46974502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can the Soil Erosion in Coastal Mountainous Areas Disturbed by Electric-Transmission-Line Construction be Estimated with a Deep Learning Model?","authors":"Li Xi, Shixiong Jiang, Shanshan Zhao, Xiaomei Li, Chen Yao, Chongqing Wang, Sunxian Weng","doi":"10.5814/j.issn.1674-764x.2023.05.013","DOIUrl":"https://doi.org/10.5814/j.issn.1674-764x.2023.05.013","url":null,"abstract":"Abstract: Soil erosion monitoring in coastal mountainous areas is very important during the construction of Electric-Transmission-Line (ETL) because of the impact this disturbance has on the sensitive environment. In this study, high-resolution remote sensing data and deep learning models including Dense and Long Short-Term Memory (LSTM) were used to fit the popular soil erosion equation, which is called the Revised Universal Soil Loss Equation (RUSLE), for the Min-Yue ETL (in Fujian). The accuracy of soil erosion regression was then evaluated in the transmission line buffer area and sampling spots at two spatial scales in order to obtain the optimized parameters and a suitable model. The results show that the Dense and LSTM models can meet the accuracy requirements by using 10 characteristic values, including soil erodibility, annual rainfall, mountain vegetation index (NDMVI), DEM, slope, four bands gray values of high-spectral image, construction attributes. The optimized parameters for the priority machine-learning model LSTM are as follows: the layer depth is 3, the layer capacity is 512, the dropout ratio is 0.1, and the epoch of the LSTM model is 7060. The regression accuracy of the LSTM model decreases with an increase in soil erosion levels, and the average regression accuracy is greater than 0.98 for the slight level of soil erosion. Therefore, the machine-learning model of LSTM can be applied for quickly monitoring the soil erosion using high resolution remote sensing data.","PeriodicalId":53414,"journal":{"name":"Journal of Resources and Ecology","volume":"14 1","pages":"1026 - 1033"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45631888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}