气候变化领域集成服务门户(CCPortal): Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran

CCPortal

DOI	10.1016/j.rse.2020.111669
	Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran
	Poorazimy M.; Shataee S.; McRoberts R.E.; Mohammadi J.
发表日期	2020
ISSN	00344257
卷号	240
英文摘要	A framework for estimating aboveground forest carbon stock (AFCS) is required for measurement, reporting, and verification (MRV) systems under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation (REDD) in Developing Countries. Recently, methods for estimating the spatial distribution of AFCS using remotely sensed datasets and multiple prediction techniques have been found to be useful, particularly given the prohibitive costs of acquiring the necessary sample sizes for sufficiently precise pure field-based estimation of this variable. The objective of the study was to assess and compare the capabilities of airborne laser scanning (ALS) data, L-band radar data and UltraCam images in combination with four commonly used prediction techniques for estimating mean AFCS per unit area: multiple linear regression (MLR) and the nonparametric k-Nearest Neighbors (k-NN), support vector regression (SVR), and random forest (RF) algorithms. Our study area was a part of Hyrcanian deciduous forests in two managed and unmanaged stands at Shast Kalateh forest. We used a systematic sample consisting of 308 circular field plots of 0.1 ha located at the intersections of a 150 × 200 m grid with a random starting point and remote sensing-derived metrics as auxiliary data. We used 67% of the sample plots for training purposes and the remaining 33% for validation. Also, we used the model-assisted estimators to statistically rigorously estimate mean AFCS per unit area and its standard error (SE). Among the remotely sensed datasets, considered singly, the ALS data with R2⁎ = 0.34, rRMSE = 47.42% and relative efficiency (RE) = 1.51 produced the greatest accuracy and precision for AFCS estimation. RE can be interpreted as the factor by which the sample size for the pure field-based estimator would have to be increased to obtain the same precision as for the model-assisted estimator using auxiliary data. Among the remotely sensed datasets considered in combination, the ALS and PALSAR dataset with R2⁎ = 0.41, rRMSE = 44.80% and RE = 1.70 produced the greatest prediction accuracy and precision and increased the proportion of variability explained relative to ALS and PALSAR separately by 7% and 36%, respectively. Contrary to our expectation, the combination of PALSAR and UltraCam data decreased the precision of AFCS estimates. All the remotely sensed datasets, singly and in combination, with the most accurate prediction techniques for each combination produced estimates of AFCS that were within the 95% confidence interval for the field-based estimate. © 2020 Elsevier Inc.
英文关键词	ALS; Model-assisted estimators; Radar; REDD; Relative efficiency; UltraCam
语种	英语
scopus关键词	Aerial photography; Aluminum; Antennas; Carbon; Decision trees; Deforestation; Developing countries; Efficiency; Forecasting; Laser applications; Linear regression; Nearest neighbor search; Radar; Radar imaging; Remote sensing; Salinity measurement; Sampling; Support vector regression; Airborne Laser scanning; Digital aerial imageries; Multiple linear regressions; REDD; Reducing emissions from deforestation and forest degradation (REDD); Relative efficiency; Support vector regression (SVR); UltraCam; Random forests; aboveground biomass; aerial photography; carbon storage; deciduous forest; deforestation; developing world; digital image; environmental degradation; estimation method; laser; nearest neighbor analysis; radar; remote sensing; satellite data; spatial distribution; Iran
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/179391
作者单位	Department of Forestry, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, Iran; Northern Research Station, U.S. Forest Service, Saint Paul, MN, United States; Department of Forest Resources, University of Minnesota, Saint Paul, MN, United States
推荐引用方式 GB/T 7714	Poorazimy M.,Shataee S.,McRoberts R.E.,et al. Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran[J],2020,240.
APA	Poorazimy M.,Shataee S.,McRoberts R.E.,&Mohammadi J..(2020).Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran.Remote Sensing of Environment,240.
MLA	Poorazimy M.,et al."Integrating airborne laser scanning data, space-borne radar data and digital aerial imagery to estimate aboveground carbon stock in Hyrcanian forests, Iran".Remote Sensing of Environment 240(2020).