Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat

J. Vogeler, P. Fekety, Lisa H. Elliott, Neal C. Swayze, S. Filippelli, Brent Barry, Joseph D. Holbrook, K. Vierling
{"title":"Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat","authors":"J. Vogeler, P. Fekety, Lisa H. Elliott, Neal C. Swayze, S. Filippelli, Brent Barry, Joseph D. Holbrook, K. Vierling","doi":"10.3389/frsen.2023.1196554","DOIUrl":null,"url":null,"abstract":"Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R 2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.","PeriodicalId":198378,"journal":{"name":"Frontiers in Remote Sensing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frsen.2023.1196554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R 2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.
评估GEDI数据融合对森林野生动物栖息地连续特征的影响
森林结构的连续特征对于模拟野生动物栖息地以及评估与额外生态系统服务的权衡至关重要。为了克服机载激光雷达数据在研究大范围动物和监测野生动物栖息地方面的时空限制,可以在数据融合框架中纳入新的采样数据源,包括星载全球生态系统动力学调查(GEDI)激光雷达仪器,以扩大基于卫星的森林结构估算在连续空间范围内的规模。本研究的目的是:1)研究卫星数据源在生成gedi融合模型和美国西部6个州(科罗拉多州、怀俄明州、爱达荷州、俄勒冈州、华盛顿州和蒙大拿州)8种森林结构测量的30米分辨率预测图方面的价值和局限性;2)评估GEDI作为参考数据源的适用性,并利用机载激光雷达数据样本评估GEDI融合地图的时空偏差;3)研究了不同森林结构需求的三种关键啄木鸟物种的gedi融合产物在野生动物栖息地模型中的差异。我们重点研究了两种融合模型,一种是结合了Landsat、Sentinel-1合成孔径雷达、干扰、地形和生物气候预测信息的融合模型(组合模型),另一种是仅限于Landsat、地形和生物气候预测信息的融合模型(Landsat/topo/生物模型)。模型性能在八个GEDI结构测量中有所不同,尽管它们都代表中等到高的预测性能(模型检验r2值从0.36到0.76不等)。融合模型之间以及模型创建年份(2019-2020年)和后推年份(2016-2018年)的地图验证结果相似。在我们的野生动物案例研究中,使用gedi融合输入对三种啄木鸟物种的相遇率进行建模,得到的AUC值范围为0.76-0.87,并根据我们对物种的生态学理解观察到关系。虽然我们的研究结果显示了遥感数据融合在生境建模和其他广泛连续应用中扩大GEDI结构价值指标的前景,但还需要进一步的评估来测试它们在生境建模中的表现,以评估其他具有保护价值的物种以及生物多样性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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