DOI: 10.57760/sciencedb.31591
title: Identifying Key Degradation Factors in Robinia pseudoacacia Shelterbelts using a Comprehensive Degradation Index and Explainable Machine Learning
title: 基于综合退化指数与可解释机器学习的黄河故道刺槐防护林退化关键因子识别

description: This dataset supports the research presented in the manuscript entitled "Identifying Key Degradation Factors in Robinia pseudoacacia Shelterbelts of the Historic Abandoned Channels of the Yellow River using a Comprehensive Degradation Index and Explainable Machine Learning".Data Content: The dataset comprises field measurements and calculated indices from 40 sample plots within Robinia pseudoacacia shelterbelts located in the historic abandoned channels of the Yellow River, China. Data were collected during the 2024 growing season (May to July). It includes 27 variables across four key dimensions for assessing forest degradation: Productivity (e.g., diameter at breast height, tree height, stand volume, biomass), Structural Function (e.g., canopy closure, uniform angle index, competition indices), Disturbance Pressure (e.g., dead branch incidence, insect/disease incidence, disturbance level), and Soil Environment (e.g., soil bulk density, soil organic matter, total nitrogen).Usage and Application: This primary data was used to construct a Comprehensive Degradation Index (CDI) and to identify key drivers of shelterbelt degradation using a machine learning workflow integrating LASSO regression, Random Forest with spatial cross-validation, and SHAP (SHapley Additive exPlanations) analysis. The dataset is essential for reproducing the study's findings, including the identification of ecological thresholds for critical degradation factors.File Description: The dataset is contained within a single Microsoft Excel file (Dataset_S1.xlsx), which includes one sheet with 40 rows (plots) and 27 columns (variables). A detailed README.docx file is provided, containing a complete description of all variables, their units, measurement methods, and the indicator system framework.
description: This dataset supports the research presented in the manuscript entitled "Identifying Key Degradation Factors in Robinia pseudoacacia Shelterbelts of the Historic Abandoned Channels of the Yellow River using a Comprehensive Degradation Index and Explainable Machine Learning".Data Content: The dataset comprises field measurements and calculated indices from 40 sample plots within Robinia pseudoacacia shelterbelts located in the historic abandoned channels of the Yellow River, China. Data were collected during the 2024 growing season (May to July). It includes 27 variables across four key dimensions for assessing forest degradation: Productivity (e.g., diameter at breast height, tree height, stand volume, biomass), Structural Function (e.g., canopy closure, uniform angle index, competition indices), Disturbance Pressure (e.g., dead branch incidence, insect/disease incidence, disturbance level), and Soil Environment (e.g., soil bulk density, soil organic matter, total nitrogen).Usage and Application: This primary data was used to construct a Comprehensive Degradation Index (CDI) and to identify key drivers of shelterbelt degradation using a machine learning workflow integrating LASSO regression, Random Forest with spatial cross-validation, and SHAP (SHapley Additive exPlanations) analysis. The dataset is essential for reproducing the study's findings, including the identification of ecological thresholds for critical degradation factors.File Description: The dataset is contained within a single Microsoft Excel file (Dataset_S1.xlsx), which includes one sheet with 40 rows (plots) and 27 columns (variables). A detailed README.docx file is provided, containing a complete description of all variables, their units, measurement methods, and the indicator system framework.