气候变化领域集成服务门户(CCPortal): Monitoring conifer cover: Leaf-off lidar and image-based tracking of eastern redcedar encroachment in central Nebraska

CCPortal

DOI	10.1016/j.rse.2020.111961
	Monitoring conifer cover: Leaf-off lidar and image-based tracking of eastern redcedar encroachment in central Nebraska
	Filippelli S.K.; Vogeler J.C.; Falkowski M.J.; Meneguzzo D.M.
发表日期	2020
ISSN	00344257
卷号	248
英文摘要	Eastern redcedar (Juniperus virginiana L.) encroachment on the Great Plains has led to decreases in biodiversity, water availability, and grazing land while increasing the risk of catastrophic wildfires. Quantifying the current spatial distribution of eastern redcedar can aid land management efforts to combat encroachment, and extending this information over time can improve our understanding of the patterns and drivers of encroachment. We compared several remote sensing methods for mapping percent conifer cover to develop an approach that would be applicable for monitoring eastern redcedar encroachment across the Great Plains to support management goals. Leaf-off lidar was filtered through a novel approach using normalized return intensity and local canopy density to remove residual points pertaining to deciduous trees, which enabled us to calculate percent conifer cover. A sample of the conifer cover derived from leaf-off lidar was then used to test passive imagery-based methods that could be applied over a greater spatiotemporal extent. These imagery-based methods included Spatial Wavelet Analysis applied to very high-resolution imagery and random forest regression modeling with Landsat 8 and Sentinel-2 based predictive layers generated in Google Earth Engine from a single image, seasonal composites, and harmonic regression coefficients produced from an annual time series. Spatial Wavelet Analysis provided high accuracy (~5% RMSE) in areas where conifer cover was less than 10%, but accuracy rapidly decreased with increases in observed cover such that this method had a very low overall accuracy (42.5% RMSE). Landsat 8 and Sentinel-2 based predictors yielded similar performance to each other in models of conifer cover (9.7 to 13.3% RMSE), but seasonal composites from either sensor provided higher predictive power than either use of a single winter image or harmonic regression coefficients. According to recent estimates from the Forest Inventory and Analysis Program, eastern redcedar comprises more than 90% of conifer basal area in 277 counties of the central Great Plains, and thus mapping conifer cover can be assumed to reflect eastern redcedar cover. By applying the LandTrendr algorithm to Landsat seasonal composites we produced stable estimates of eastern redcedar cover from 1984 to 2018 and quantified encroachment as a 2.3% per year increase in eastern redcedar forest, defined as areas with ≥10% redcedar cover. The comparison of these methods and their application through time lays the groundwork for monitoring of redcedar encroachment across the central Great Plains and provides an approach for mapping fractional cover of conifer trees more generally. © 2020 Elsevier Inc.
英文关键词	Conifer cover; Eastern redcedar; Forest monitoring; Google earth engine; Great Plains; Harmonic regression; LandTrendr; Leaf-off lidar; Object-based image analysis; Temporal composite; Trees outside forests; Woodland; Woody encroachment
语种	英语
scopus关键词	Biodiversity; Decision trees; Landforms; Logistic regression; Mapping; Optical radar; Remote sensing; Spatial variables measurement; Time series analysis; Wavelet analysis; Annual time series; Central Great Plains; Forest inventory and analysis; Harmonic regression; Image based tracking; Overall accuracies; Very high resolution; Water availability; Forestry; algorithm; basal area; biodiversity; coniferous tree; grazing; image analysis; land management; Landsat; lidar; satellite imagery; Sentinel; wavelet analysis; Great Plains; Nebraska; United States; Coniferophyta; Juniperus virginiana
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179209
作者单位	Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, United States; USDA Forest Service, Forest Inventory and Analysis, St. Paul, MN 55108, United States
推荐引用方式 GB/T 7714	Filippelli S.K.,Vogeler J.C.,Falkowski M.J.,et al. Monitoring conifer cover: Leaf-off lidar and image-based tracking of eastern redcedar encroachment in central Nebraska[J],2020,248.
APA	Filippelli S.K.,Vogeler J.C.,Falkowski M.J.,&Meneguzzo D.M..(2020).Monitoring conifer cover: Leaf-off lidar and image-based tracking of eastern redcedar encroachment in central Nebraska.Remote Sensing of Environment,248.
MLA	Filippelli S.K.,et al."Monitoring conifer cover: Leaf-off lidar and image-based tracking of eastern redcedar encroachment in central Nebraska".Remote Sensing of Environment 248(2020).