Mount Kenya森林生态系统气候变化影响建模与未来脆弱性预测

Aug 5, 2026

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Mount Kenya forest ecosystem (MKFE) is a biodiversity hotspot and a key water tower in Kenya, yet climate change increasingly undermines its ecological integrity and ecosystem services. The study links climate extremes with forest dynamics by integrating remote sensing and machine learning to quantify historical vegetation change and forecast future risk. Landsat imagery from 2000–2020 supports vegetation indices, while CHIRPS and ERA5 climate extremes are used with RF, XGBoost, and SVM under SSP245 from CMIP6 downscaled projections, informing adaptive conservation needs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Abstract

Keywords

Introduction

Study area and ecological significance

Threats from climate and human pressures

Climate extremes and ecological consequences

Environ Monit Assess (2025) 197:631 [https://doi.org/10.1007/s10661-025-14089-0](https://doi.org/10.1007/s10661-025-14089-0) Modeling climate change impacts and predicting future vulnerability in the Mount Kenya forest ecosystem using remote sensing and machine learning Terry Amolo Otieno · Loventa Anyango Otieno · Brian Rotich · Katharina Löhr · Harison Kiplagat Kipkulei Received: 25 February 2025 / Accepted: 29 April 2025 © The Author(s) 2025 Abstract The Mount Kenya forest ecosystem (MKFE), a crucial biodiversity hotspot and one of Kenya’s key water towers, is increasingly threatened by climate change, putting its ecological integrity and vital ecosystem services at risk. Understanding the interactions between climate extremes and forest dynamics is essential for conservation planning, especially in the Mount Kenya Forest Ecosystem (MKFE), where rising temperatures and erratic rainfall are altering vegetation patterns, reducing forest resilience, and threatening both biodiversity and water security. This study integrates remote sensing and machine learning to assess historical vegetation changes and predict areas at risk in the future. Landsat imagery from 2000 to 2020 was used to derive vegetation indices comprising the Normalized T. A. Otieno · L. A. Otieno · H. K. Kipkulei (*) Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box, Nairobi 62000 00200, Kenya [e-mail: harison.kipkulei@uni-a.de](e-mail: harison.kipkulei@uni-a.de) T. A. Otieno e-mail: [amoloterry1@gmail.com](amoloterry1@gmail.com) L. A. Otieno e-mail: [loventaanyango4@gmail.com](loventaanyango4@gmail.com) B. Rotich Faculty of Environmental Studies and Resources Development, Chuka University, P.O. Box 109–60400, Chuka, Kenya [e-mail: brotich@chuka.ac.ke](e-mail: brotich@chuka.ac.ke) Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), and Bare Soil Index (BSI) . Climate variables, including extreme precipitation and temperature indices, were extracted from CHIRPS and ERA5 datasets. Machine learning models, including Random Forest (RF), XGBoost, and Support Vector Machines (SVM), were trained to assess climate-vegetation relationships and predict future vegetation dynamics under the SSP245 climate scenario using Coupled Model Intercomparison Project Phase 6 (CMIP6) downscaled projections. The RF model achieved high accuracy (R2 = 0. 82, RMSE = 0. 15) in predicting the dynamics of vegetation conditions. Model projections show a 49–55% decline in EVI across forest areas by 2040, with the most pronounced losses likely in lower K. Löhr Faculty of Forest and Environment, Eberswalde University for Sustainable Development (HNEE), Alfred-Moeller-Str. 1, 16225 Eberswalde, Germany [e-mail: katharina.loehr@hnee.de](e-mail: katharina.loehr@hnee.de) H. K. Kipkulei Center for Climate Resilience, University of Augsburg, Universitätsstraße 12, 86159 Augsburg, Germany montane zones, which are more sensitive to climateinduced vegetation stress. Results emphasize the critical role of precipitation in sustaining forest health and highlight the urgent need for adaptive management strategies, including afforestation, sustainable landuse planning, and policy-driven conservation efforts. This study provides a scalable framework for modelling climate impacts on forest ecosystems globally and offers actionable insights for policymakers. Keywords Forests · Vulnerability · Climate modeling · Sustainable management Introduction Mount Kenya, standing at 5199 m, is Kenya’s highest mountain and a UNESCO World Heritage Site, recognized for its diverse ecosystems that range from montane forests to afro-alpine zones (Nature Kenya, 2019) . These forests play a critical role in carbon sequestration, water regulation, and biodiversity conservation, supporting endemic species such as the Mountain Bongo (Trage

FAQs

该研究关注Mount Kenya森林生态系统的哪些主要问题？

研究聚焦气候变化如何影响森林生态系统的植被动态与脆弱性，并强调其对生物多样性与水安全服务的风险。

研究如何获取植被变化与气候驱动信息？

植被变化使用2000至2020年的Landsat影像提取NDVI、EVI、SAVI与BSI等指数；气候极端数据来自CHIRPS与ERA5。

机器学习模型在预测中如何发挥作用？

研究训练随机森林（RF）、XGBoost和支持向量机（SVM）来刻画气候-植被关系，并在SSP245情景下结合CMIP6下尺度投影预测未来植被动态与脆弱区。

Aug 5, 2026

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