Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates
Dengsheng Lu,Qi Chen,Guangxing Wang,Emilio F. Moran,Mateus Batistella,Maozhen Zhang,Gaia Vaglio Laurin,David Saah +7 more
TLDR
Li et al. as mentioned in this paper provided a brief overview of current forest biomass estimation methods using both Landsat Thematic mapper (TM) image and LiDAR data, and demonstrated that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems.Abstract:
Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.read more
Citations
More filters
Journal ArticleDOI
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems
TL;DR: A survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables fromRemote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure.
Journal ArticleDOI
Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data
TL;DR: In this article, an area-based approach was proposed to predict wood volume, stem volume, aboveground biomass, and basal area across a wide range of canopy structures, sites and LiDAR characteristics.
Journal ArticleDOI
Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery
TL;DR: In this paper, the authors present a sampling program for the Canada National Forest Inventory (NFI) sampling program, which is designed to support reporting on forests at the national scale.
Journal ArticleDOI
Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series
Xiaolin Zhu,Desheng Liu +1 more
TL;DR: In this paper, the authors explored the use of NDVI seasonal time-series derived from Landsat images across different seasons to estimate aboveground biomass (AGB) in southeast Ohio by six empirical modeling approaches.
Journal ArticleDOI
Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years
Jingfeng Xiao,Frédéric Chevallier,Cécile Gomez,Luis Guanter,Jeffrey A. Hicke,Alfredo Huete,Kazuhito Ichii,Wenjian Ni,Yong Pang,Abdullah F. Rahman,Guoqing Sun,Wenping Yuan,Li Zhang,Xiaoyang Zhang +13 more
TL;DR: A review of the advances in remote sensing of the terrestrial carbon cycle from the early 1970s to present can be found in this paper, where the authors provide a comprehensive and insightful review.
References
More filters
Book
Geostatistics for natural resources evaluation
TL;DR: In this article, an advanced-level introduction to geostatistics and Geostatistical methodology is provided, including tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation.
Journal ArticleDOI
Geostatistics for Natural Resources Evaluation
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and estimating the effects of noise in a discrete-time model.
Book
Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
TL;DR: In this paper, the basic GWR model is extended to include local statistics and local models for spatial data, and a software for Geographically Weighting Regression is described. But this software is not suitable for the analysis of large scale data.
Journal Article
Image-Based Atmospheric Corrections - Revisited and Improved
TL;DR: In this paper, an image-based procedure that expands on the ~10s model by including a simple multiplicative correction for the effect of atmospheric transmittance was presented, and the results were compared with those generated by the models that used in-situ atmospheric field measurements and RTC software.
Journal ArticleDOI
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems
Jon C. Helton,Freddie J. Davis +1 more
TL;DR: The following techniques for uncertainty and sensitivity analysis are briefly summarized: Monte Carlo analysis, differential analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol' variance decomposition, and fast probability integration.