1. 2024-10-23 西北农林科技大学 韦应欣 用途：Paper Title: "Predicting the Growth and Distribution of Biological Soil Crusts in China under Current and Future Climate Scenarios: A Machine Learning Approach" Paper Abstract: This study employs machine learning techniques to forecast the growth and distribution of biological soil crusts (biocrusts) across China under current and future climate conditions. Utilizing a comprehensive dataset encompassing 2752 records of biocrust occurrence points, coverage, thickness, and chlorophyll a content, we developed MaxEnt models to delineate the suitable growth probability of biocrusts. We identified 23 environmental variables significantly influencing biocrust distribution, excluding highly correlated ones from an initial set of 33 variables. The models were trained and validated using the Maximum training sensitivity plus specificity Cloglog threshold (Mtspsct) to determine the biocrust's suitable range under current and four future climate scenarios (SSP123, SSP245, SSP370, SSP585). Additionally, we constructed Random Forest models to predict biocrust coverage, thickness, and photosynthetic biomass, calculating the volume distribution and assessing the total coverage and proportion of China's total land area. The models were evaluated through ROC curves, omission curves, and variance explained to ensure robustness. Environmental variable importance was ranked, and partial dependence plots were generated to understand the relationship between key drivers and biocrust growth indicators. Our findings provide insights into the vulnerability of biocrusts to climate change and offer a scientific basis for conservation strategies in China. Paper Type: Original Research Article This abstract and title provide a concise overview of your study's objectives, methods, and significance. The paper type indicates that this is an original research article, which is suitable for presenting new scientific findings and data analysis.

2. 2024-10-23 西北农林科技大学 韦应欣 用途：Paper Title: "Predicting the Growth and Distribution of Biological Soil Crusts in China under Current and Future Climate Scenarios: A Machine Learning Approach" Paper Abstract: This study employs machine learning techniques to forecast the growth and distribution of biological soil crusts (biocrusts) across China under current and future climate conditions. Utilizing a comprehensive dataset encompassing 2752 records of biocrust occurrence points, coverage, thickness, and chlorophyll a content, we developed MaxEnt models to delineate the suitable growth probability of biocrusts. We identified 23 environmental variables significantly influencing biocrust distribution, excluding highly correlated ones from an initial set of 33 variables. The models were trained and validated using the Maximum training sensitivity plus specificity Cloglog threshold (Mtspsct) to determine the biocrust's suitable range under current and four future climate scenarios (SSP123, SSP245, SSP370, SSP585). Additionally, we constructed Random Forest models to predict biocrust coverage, thickness, and photosynthetic biomass, calculating the volume distribution and assessing the total coverage and proportion of China's total land area. The models were evaluated through ROC curves, omission curves, and variance explained to ensure robustness. Environmental variable importance was ranked, and partial dependence plots were generated to understand the relationship between key drivers and biocrust growth indicators. Our findings provide insights into the vulnerability of biocrusts to climate change and offer a scientific basis for conservation strategies in China. Paper Type: Original Research Article This abstract and title provide a concise overview of your study's objectives, methods, and significance. The paper type indicates that this is an original research article, which is suitable for presenting new scientific findings and data analysis.

3. 2024-10-23 西北农林科技大学 韦应欣 用途：Paper Title: "Predicting the Growth and Distribution of Biological Soil Crusts in China under Current and Future Climate Scenarios: A Machine Learning Approach" Paper Abstract: This study employs machine learning techniques to forecast the growth and distribution of biological soil crusts (biocrusts) across China under current and future climate conditions. Utilizing a comprehensive dataset encompassing 2752 records of biocrust occurrence points, coverage, thickness, and chlorophyll a content, we developed MaxEnt models to delineate the suitable growth probability of biocrusts. We identified 23 environmental variables significantly influencing biocrust distribution, excluding highly correlated ones from an initial set of 33 variables. The models were trained and validated using the Maximum training sensitivity plus specificity Cloglog threshold (Mtspsct) to determine the biocrust's suitable range under current and four future climate scenarios (SSP123, SSP245, SSP370, SSP585). Additionally, we constructed Random Forest models to predict biocrust coverage, thickness, and photosynthetic biomass, calculating the volume distribution and assessing the total coverage and proportion of China's total land area. The models were evaluated through ROC curves, omission curves, and variance explained to ensure robustness. Environmental variable importance was ranked, and partial dependence plots were generated to understand the relationship between key drivers and biocrust growth indicators. Our findings provide insights into the vulnerability of biocrusts to climate change and offer a scientific basis for conservation strategies in China. Paper Type: Original Research Article This abstract and title provide a concise overview of your study's objectives, methods, and significance. The paper type indicates that this is an original research article, which is suitable for presenting new scientific findings and data analysis. Tutor: chongfengbu

4. 2024-10-17 青海大学 王哲诚 用途：用于实验测试

5. 2024-09-24 中科院寒旱所 任彦润 用途：进行中巴经济走廊冰雪灾害的相关研究

6. 2024-06-14 空天院 王兴斌 用途：学习

7. 2024-05-11 西安科技大学 洪扬 用途：科研论文

8. 2024-04-11 北京师范大学 李佳欣 用途：用于数据预处理的学习

9. 2024-04-07 江苏师范大学 姚撼 用途：实验使用，想学习一下如何根据该数据提取DEM

10. 2023-12-18 南京航空航天大学航天学院 王义辉 用途：用于做试验的遥感数据集

11. 2023-12-18 南京航空航天大学航天学院 王义辉 用途：用作试验数据集

12. 2023-07-03 westdc weber 用途：论文研究

13. 2023-07-03 湖南科技大学 王振峰 用途：生成DEM

14. 2022-12-29 None 李文逵 用途：数据测试

15. 2022-12-08 海军工程大学 朱博 用途：用于撰写本科毕业论文

16. 2022-11-19 长安大学 史悦祥 用途：您好，数据用于基于卫星遥感的地理信息识别研究，感谢

17. 2022-11-19 长安大学 史悦祥 用途：您好，本人申请此数据希望研究基于卫星遥感的地理信息识别方面的研究，感谢

18. 2022-11-03 华为 高歌 用途：科研

19. 2022-11-02 同济大学测绘与地理信息学院 Yu Chengxuan 用途：论文

20. 2022-11-02 同济大学测绘与地理信息学院 Yu Chengxuan 用途：毕业论文

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