利用地理信息系统和遥感技术对肯尼亚内罗毕国家公园土地覆盖类型进行分类并预测草原鸟类数量和分布

Q1 Social Sciences
Frank Juma Ong'ondo , Shrinidhi Ambinakudige , Philista Adhiambo Malaki , Peter Njoroge , Hafez Ahmad
{"title":"利用地理信息系统和遥感技术对肯尼亚内罗毕国家公园土地覆盖类型进行分类并预测草原鸟类数量和分布","authors":"Frank Juma Ong'ondo ,&nbsp;Shrinidhi Ambinakudige ,&nbsp;Philista Adhiambo Malaki ,&nbsp;Peter Njoroge ,&nbsp;Hafez Ahmad","doi":"10.1016/j.ijgeop.2025.02.003","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate and high-resolution mapping of land cover is essential for modeling species response, guiding habitat management practices, and prioritizing conservation efforts, especially in restricted and remote areas. Geographic information systems (GIS) and remote sensing (RS) techniques offer potential solutions. This study assessed the utility of GIS and RS techniques to map and predict grassland bird species in Nairobi National Park (NNP), Kenya. We utilized Sentinel-2B median imagery, which was accessible through Google Earth Engine (GEE), for January 2022 to classify six land cover classes: forest, shrubland, woodland, grassland, water, and bare soil. Grassland bird data were extracted from Kenya Bird Map (KBM) website for the period between 2015 and 2022, using full protocol card records. We hypothesized that grassland and shrubland would cover a larger portion of NNP and that grassland birds would respond positively to grassland, shrubland and woodland. We tested the second hypothesis using KBM data. Training samples for various land cover types were collected and used to train a Random Forest (RF) classifier on Sentinel-2B imagery. Model accuracy was evaluated with a confusion matrix, showing an overall accuracy of 99.93% and a Kappa statistic of 0.9989. Land cover composition indicated that grassland had the highest composition (44.9%), while water had the least (0.003%). Woodland, shrubland, forest and bare soil comprised 33.7%, 15.4%, 5.9%, and 0.2%, respectively. Logistic regression results showed that grassland birds responded positively to grassland and shrubland but tended to avoid woodland and bare soil. These findings demonstrate that land cover maps derived from GIS and RS techniques are fundamental tools for studying the abundance and distribution of grassland bird species, especially in remote areas. These tools are also essential for conservation and habitat management.</div></div>","PeriodicalId":36117,"journal":{"name":"International Journal of Geoheritage and Parks","volume":"13 1","pages":"Pages 92-101"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using geographic information systems and remote sensing technique to classify land cover types and predict grassland bird abundance and distribution in Nairobi National Park, Kenya\",\"authors\":\"Frank Juma Ong'ondo ,&nbsp;Shrinidhi Ambinakudige ,&nbsp;Philista Adhiambo Malaki ,&nbsp;Peter Njoroge ,&nbsp;Hafez Ahmad\",\"doi\":\"10.1016/j.ijgeop.2025.02.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate and high-resolution mapping of land cover is essential for modeling species response, guiding habitat management practices, and prioritizing conservation efforts, especially in restricted and remote areas. Geographic information systems (GIS) and remote sensing (RS) techniques offer potential solutions. This study assessed the utility of GIS and RS techniques to map and predict grassland bird species in Nairobi National Park (NNP), Kenya. We utilized Sentinel-2B median imagery, which was accessible through Google Earth Engine (GEE), for January 2022 to classify six land cover classes: forest, shrubland, woodland, grassland, water, and bare soil. Grassland bird data were extracted from Kenya Bird Map (KBM) website for the period between 2015 and 2022, using full protocol card records. We hypothesized that grassland and shrubland would cover a larger portion of NNP and that grassland birds would respond positively to grassland, shrubland and woodland. We tested the second hypothesis using KBM data. Training samples for various land cover types were collected and used to train a Random Forest (RF) classifier on Sentinel-2B imagery. Model accuracy was evaluated with a confusion matrix, showing an overall accuracy of 99.93% and a Kappa statistic of 0.9989. Land cover composition indicated that grassland had the highest composition (44.9%), while water had the least (0.003%). Woodland, shrubland, forest and bare soil comprised 33.7%, 15.4%, 5.9%, and 0.2%, respectively. Logistic regression results showed that grassland birds responded positively to grassland and shrubland but tended to avoid woodland and bare soil. These findings demonstrate that land cover maps derived from GIS and RS techniques are fundamental tools for studying the abundance and distribution of grassland bird species, especially in remote areas. These tools are also essential for conservation and habitat management.</div></div>\",\"PeriodicalId\":36117,\"journal\":{\"name\":\"International Journal of Geoheritage and Parks\",\"volume\":\"13 1\",\"pages\":\"Pages 92-101\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Geoheritage and Parks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2577444125000085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Geoheritage and Parks","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2577444125000085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0

摘要

准确和高分辨率的土地覆盖测绘对于模拟物种响应、指导栖息地管理实践和确定保护工作的优先顺序至关重要,特别是在受限和偏远地区。地理信息系统(GIS)和遥感(RS)技术提供了潜在的解决方案。本研究评估了GIS和RS技术在肯尼亚内罗毕国家公园(NNP)草地鸟类种类地图和预测中的应用。我们利用2022年1月通过谷歌地球引擎(GEE)访问的Sentinel-2B中值图像对森林、灌木、林地、草地、水和裸土6个土地覆盖类别进行了分类。利用完整的协议卡记录,从肯尼亚鸟类地图(KBM)网站提取2015年至2022年期间的草原鸟类数据。我们假设草地和灌丛覆盖了NNP的大部分,草地鸟类对草地、灌丛和林地有积极的反应。我们使用KBM数据检验了第二个假设。收集了不同土地覆盖类型的训练样本,并将其用于在Sentinel-2B图像上训练随机森林(RF)分类器。用混淆矩阵评价模型的准确率,总体准确率为99.93%,Kappa统计量为0.9989。土地覆被组成表明,草地构成最高(44.9%),水体构成最低(0.003%)。林地、灌丛、森林和裸地分别占33.7%、15.4%、5.9%和0.2%。Logistic回归结果表明,草地鸟类对草地和灌丛有积极响应,对林地和裸地有回避倾向。这些发现表明,基于GIS和RS技术的土地覆盖图是研究草原鸟类物种丰富度和分布的基本工具,特别是在偏远地区。这些工具对于保护和生境管理也是必不可少的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using geographic information systems and remote sensing technique to classify land cover types and predict grassland bird abundance and distribution in Nairobi National Park, Kenya
Accurate and high-resolution mapping of land cover is essential for modeling species response, guiding habitat management practices, and prioritizing conservation efforts, especially in restricted and remote areas. Geographic information systems (GIS) and remote sensing (RS) techniques offer potential solutions. This study assessed the utility of GIS and RS techniques to map and predict grassland bird species in Nairobi National Park (NNP), Kenya. We utilized Sentinel-2B median imagery, which was accessible through Google Earth Engine (GEE), for January 2022 to classify six land cover classes: forest, shrubland, woodland, grassland, water, and bare soil. Grassland bird data were extracted from Kenya Bird Map (KBM) website for the period between 2015 and 2022, using full protocol card records. We hypothesized that grassland and shrubland would cover a larger portion of NNP and that grassland birds would respond positively to grassland, shrubland and woodland. We tested the second hypothesis using KBM data. Training samples for various land cover types were collected and used to train a Random Forest (RF) classifier on Sentinel-2B imagery. Model accuracy was evaluated with a confusion matrix, showing an overall accuracy of 99.93% and a Kappa statistic of 0.9989. Land cover composition indicated that grassland had the highest composition (44.9%), while water had the least (0.003%). Woodland, shrubland, forest and bare soil comprised 33.7%, 15.4%, 5.9%, and 0.2%, respectively. Logistic regression results showed that grassland birds responded positively to grassland and shrubland but tended to avoid woodland and bare soil. These findings demonstrate that land cover maps derived from GIS and RS techniques are fundamental tools for studying the abundance and distribution of grassland bird species, especially in remote areas. These tools are also essential for conservation and habitat management.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Geoheritage and Parks
International Journal of Geoheritage and Parks Social Sciences-Urban Studies
CiteScore
6.70
自引率
0.00%
发文量
43
审稿时长
72 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信