Scholarlink

Background <p>An Ecological Site Group (ESG) is a classifi cation framework used in rangeland management to group Ecological Sites(ESs) that exhibit similar responses to land use, disturbance, conservation practices, and environmental change.</p> Methods <p>In this study, we applied supervised machine learning techniques to predict expert-assigned ESGs for rangelandpoints from the USDA Natural Resources Conservation Service (NRCS) National Resources Inventory (NRI) dataset.The analysis focused on Major Land Resource Areas (MLRAs) 65 and 69, located in Nebraska and Colorado, USA. Wedeveloped a predictive ESG classifi cation model using the XGBoost algorithm and compared it to the performancesof benchmark models: Random Forest and Decision Tree. Predictor variables included soil properties derived fromgSSURGO, climate variables from PRISM, and vegetation classes from the GAP land cover dataset. Model evaluationwas conducted using an 80/20 stratifi ed train–test split.</p> Results <p>The XGBoost model achieved overall test accuracies of 0.84 and 0.95 for MLRAs 65 and 69, respectively,outperforming the benchmark models. Point-based predictions were then scaled to the landscape level usingSSURGO soil map units and associated environmental attributes, generating ESG classifi cation maps covering 53,467km² in MLRA 65 and 30,710 km² in MLRA 69. Landscape-scale validation against expert-assigned NRI points showedstrong spatial agreement between predicted and observed ESGs in the map units, with 78% and 92% agreement inMLRAs 65 and 69, respectively.</p> Conclusions <p>These results demonstrate that ensemble boosting methods such as XGBoost can eff ectively translate point-basedESG observations into reproducible, landscape-scale ecological classifi cations. The resulting maps provide a scalable,data-driven framework to support conservation planning, targeted management, and assessment of rangelandresponses across heterogeneous environmental gradients.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From expert knowledge to data-driven landscape classification: mapping ecological site groups across climatic and edaphic gradients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

查看全文
  • Menberu B. Meles,
  • D. Phillip Guertin,
  • I. Shea Burns,
  • David C. Goodrich,
  • Mariano Hernandez,
  • Loretta J. Metz,
  • Mahmoud Saeedimoghaddam,
  • Guillermo Ponce-Campos,
  • Grey Nearing,
  • Jason Williams,
  • Steve Barker,
  • Carrie-Ann Houdeshell,
  • Steven R. Archer

摘要

Background

An Ecological Site Group (ESG) is a classifi cation framework used in rangeland management to group Ecological Sites(ESs) that exhibit similar responses to land use, disturbance, conservation practices, and environmental change.

Methods

In this study, we applied supervised machine learning techniques to predict expert-assigned ESGs for rangelandpoints from the USDA Natural Resources Conservation Service (NRCS) National Resources Inventory (NRI) dataset.The analysis focused on Major Land Resource Areas (MLRAs) 65 and 69, located in Nebraska and Colorado, USA. Wedeveloped a predictive ESG classifi cation model using the XGBoost algorithm and compared it to the performancesof benchmark models: Random Forest and Decision Tree. Predictor variables included soil properties derived fromgSSURGO, climate variables from PRISM, and vegetation classes from the GAP land cover dataset. Model evaluationwas conducted using an 80/20 stratifi ed train–test split.

Results

The XGBoost model achieved overall test accuracies of 0.84 and 0.95 for MLRAs 65 and 69, respectively,outperforming the benchmark models. Point-based predictions were then scaled to the landscape level usingSSURGO soil map units and associated environmental attributes, generating ESG classifi cation maps covering 53,467km² in MLRA 65 and 30,710 km² in MLRA 69. Landscape-scale validation against expert-assigned NRI points showedstrong spatial agreement between predicted and observed ESGs in the map units, with 78% and 92% agreement inMLRAs 65 and 69, respectively.

Conclusions

These results demonstrate that ensemble boosting methods such as XGBoost can eff ectively translate point-basedESG observations into reproducible, landscape-scale ecological classifi cations. The resulting maps provide a scalable,data-driven framework to support conservation planning, targeted management, and assessment of rangelandresponses across heterogeneous environmental gradients.