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A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Siheng Wang, Dong Yang, Zhen Li, Liangyun Liu, Changping Huang, Lifu Zhang
2019 Remote Sensing  
The sensitivities of VIs to different parameters are analyzed on the basis of PROSPECT-SAIL (PROSAIL) radiative transfer model simulations, which apply for homogeneous canopies, and random forest (RF)  ...  In this study, a global sensitivity analysis (GSA) is made for several commonly used satellite-derived VIs in order to better understand the application of these VIs at large scales.  ...  J.Verrelst for the contribution of the ARTMO GUI tool. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs11212547 fatcat:e4q77uy5xvdmhjbwoodccqf2eq

Improving the Retrieval of Forest Canopy Chlorophyll Content from MERIS Dataset by Introducing the Vegetation Clumping Index

Qi Sun, Quanjun Jiao, Liangyun Liu, Xinjie Liu, Xiaojin Qian, Xiao Zhang, Bing Zhang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Overall, it is concluded that include the CI, particularly the approach using forest random regression, have the potential for satellite-based forest CCC mapping at regional and global scales.  ...  Firstly, the PROSPECT-D and 4-Scale models were used to simulate forest canopy spectra, and the forest CCCe including CI was found more feasible to be remotely sensed than the CCC.  ...  Holly Croft from University of Toronto for her kindly help during the published field data collection.  ... 
doi:10.1109/jstars.2021.3082621 fatcat:z5644kpn7vbu5m7jorzato2k2e

Remote Sensing-Based Biomass Estimation [chapter]

José Mauricio Galeana Pizaña, Juan Manuel Núñez Hernández, Nirani Corona Romero
2016 Environmental Applications of Remote Sensing  
We present a case study that explores the capability of canopy fraction cover and digital canopy height model (DCHM) for modeling the spatial distribution of the aboveground biomass of two forests, dominated  ...  In this chapter, we present a review of different models of spatial distribution of biomass and resources based on remote sensing that are widely used.  ...  Regression tree and random forest are a family of tree-based models; in the first one, data are stratified into homogeneous subsets by decreasing the within-class entropy, whereas in the second one, a  ... 
doi:10.5772/61813 fatcat:olp4nwuqu5dy7dxlmtpzyt4xde

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Jin Xu, Lindi J. Quackenbush, Timothy A. Volk, Jungho Im
2020 Remote Sensing  
Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and  ...  The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation.  ...  Empirical Models The empirical models most commonly used to estimate LAI use spectral reflectance-based parameters [103] , vegetation indices [104] , derived metrics, and machine learning methods.  ... 
doi:10.3390/rs12182934 fatcat:wh3p2pgq35cizailwwv4wlaqje

Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method

Chuanwen Wei, Jingfeng Huang, Lamin Mansaray, Zhenhai Li, Weiwei Liu, Jiahui Han
2017 Remote Sensing  
Based on PROSAIL (coupling of PROSPECT and SAIL) simulation datasets, nine vegetation indices (VIs) were analyzed to identify the optimal independent variables for estimating LAI values.  ...  Our study indicates that based on the estimation results derived from different datasets, RFR is the optimal modeling algorithm amidst curve fitting and kNN with R 2 > 0.954 and RMSE <0.218.  ...  Mansaray assisted in the preparation and revision of the manuscript; Zhenhai Li offered valuable comments on the methods and manuscript; Weiwei Liu and Jiahui Han were involved in the field experiments  ... 
doi:10.3390/rs9050488 fatcat:s5bh6jiad5gw5np74ispdy5kwi

Linking Land Use and Plant Functional Diversity Patterns in Sabah, Borneo, through Large-Scale Spatially Continuous Sentinel-2 Inference

Leon T. Hauser, Joris Timmermans, Nadejda A. Soudzilovskaia, Peter M. van Bodegom
2022 Land  
Functional traits (leaf water content, chlorophyll-a and -b, and leaf area index) were estimated from Sentinel-2 spectral reflectance using a pre-trained neural network on radiative transfer modeling simulations  ...  By linking large-scale patterns of functional diversity as derived from satellite remote sensing to land-use information, this study indicated initial responsiveness to broad human disturbance gradients  ...  The analysis was based on repeated PROSAIL simulations with random variations of the trait values while mapping the spectral responses and the correlation between Sentinel-2's spectral bands and trait  ... 
doi:10.3390/land11040572 fatcat:fp4fih3iujg6dg6al6u5u6d45e

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Dengsheng Lu, Qi Chen, Guangxing Wang, Lijuan Liu, Guiying Li, Emilio Moran
2014 International Journal of Digital Earth  
This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issuescollection of field-based biomass reference data, extraction and selection  ...  This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates  ...  Cycling in Forest Ecosystems and Carbon Sequestration at Zhejiang A&F University, and the Center for Global Change and Earth Observations at Michigan State University.  ... 
doi:10.1080/17538947.2014.990526 fatcat:bkc3syq3pfgzxkawig2pog7i3y

Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest

Aline Bernarda Debastiani, Carlos Roberto Sanquetta, Ana Paula Dalla Corte, Naiara Sardinha Pinto, Franciel Eduardo Rex
2019 Annals of Forest Research  
The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (  ...  The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm.  ...  Data acquired with financial support from the US Agency for International Development (USAID) and the US Department of State (USDS) and with technical support from the US Forest Service.  ... 
doi:10.15287/afr.2018.1267 fatcat:wyuyagd3b5a3vizn5spsg2akk4

Towards scalable estimation of plant functional diversity from Sentinel-2: In-situ validation in a heterogeneous (semi-)natural landscape

Leon T. Hauser, Jean-Baptiste Féret, Nguyen An Binh, Niels van der Windt, Ângelo F. Sil, Joris Timmermans, Nadejda A. Soudzilovskaia, Peter M. van Bodegom
2021 Remote Sensing of Environment  
We thank Altino Geraldes, Joao Carlos Azevedo, and the local farmers and foresters in the Montesinho-Nogueira Natura 2000 site for their help and collaboration.  ...  Henebry, and the anonymous reviewers for their valuable comments and suggestions that greatly improved the manuscript. This work was supported financially by the Ecology Fund of the Royal L.T.  ...  CAB was derived using a protocol based on Lichtenthaler (1987) .  ... 
doi:10.1016/j.rse.2021.112505 fatcat:fj3vfmetvzf7lg2mayh6iabi6e

Biomass Estimation Using Satellite-Based Data [chapter]

Patrícia Lourenço
2021 Forest Biomass - From Trees to Energy  
regression algorithms used (parametric and non-parametric) that contribute to a more robust, practical, and cost-effective approach for forest AGB estimation at different levels.  ...  This chapter aims to present different types of predicted variables derived from multi-sources sensors, such as original spectral bands, transformed images, vegetation indices, textural features, and different  ...  Acknowledgements This work is funded by the National Funds through FCT -Foundation for Science and Technology under the Project UIDB/05183/2020 and by Programa Operativo de Cooperação Transfronteiriço  ... 
doi:10.5772/intechopen.93603 fatcat:xdij2dyqwjcyzdfzywikogb3fm

Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content

Xiangzhong Luo, Holly Croft, Jing M. Chen, Liming He, Trevor F. Keenan
2019 Global Change Biology  
The rate of photosynthesis is determined jointly by environmental variables and the intrinsic photosynthetic capacity of plants (i.e. maximum carboxylation  ...  The terrestrial biosphere plays a critical role in mitigating climate change by absorbing anthropogenic CO 2 emissions through photosynthesis.  ...  Jane Liu from University of Toronto for the helpful comments on the paper in its thesis format.  ... 
doi:10.1111/gcb.14624 fatcat:5tchx2c5bnhq5exz75jfmdciqe

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

Iftikhar Ali, Felix Greifeneder, Jelena Stamenkovic, Maxim Neumann, Claudia Notarnicola
2015 Remote Sensing  
) from remote sensing data using machine learning methods.  ...  The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture  ...  parameters (i.e., vegetation indices, textural features, backscatter) for the development of regression-based retrieval models, • Machine learning algorithms andSimulation or biophysical models (data  ... 
doi:10.3390/rs71215841 fatcat:jiktp2klrzfffbzdf2ozbbk2ke

Lidar Boosts 3D Ecological Observations and Modelings: A review and perspective

Qinghua Guo, Yanjun Su, Tianyu Hu, Hongcan Guan, Shichao Jin, Jing Zhang, Xiaoxia Zhao, Kexin Xu, Dengjie Wei, Maggi Kelly, Nicholas Coops
2020 IEEE Geoscience and Remote Sensing Magazine  
lidar-derived surfaces, hierarchical semantic segmentation based on lidar point clouds, and deep learning-based methods.  ...  [271] used a function-structure-plant model to provide in silico experiments for exploring the feasibility of lidar-based phenotype estimation (e.g., a green area index) with machine learning methods  ... 
doi:10.1109/mgrs.2020.3032713 fatcat:vot6c4ceabectd6xcpgev5duyu

Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess Pinus Pinaster Aiton. Forest Defoliation in South-Eastern Spain

Ariza Salamanca, Navarro-Cerrillo, Bonet-García, Palazón, Polo
2019 Remote Sensing  
between the mortality processes of Pinus pinaster plantations and the hydrological regime, using different spectral indices of vegetation and machine learning algorithms.  ...  Random Forest was the most accurate model (R2 = 0.79; RMSE = 0.059), showing a high model performance and prediction.  ...  Acknowledgments: The authors thank the Andalusia Department of Agriculture and Environment, which  ... 
doi:10.3390/rs11192291 fatcat:7ii3wdkpx5e4xi2uizopsqi5vu

Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data

Hong Chi, Guoqing Sun, Jinliang Huang, Rendong Li, Xianyou Ren, Wenjian Ni, Anmin Fu
2017 Remote Sensing  
Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest  ...  The relationships between the GLAS-derived maximum canopy height and Landsat-derived LAI were modeled using a linear model, which was based on the assumption that the power law between LAI and the maximum  ...  Random Forest is an algorithm based on ensemble techniques for classification and regression analysis [81] .  ... 
doi:10.3390/rs9070707 fatcat:vxlegf26kvbznbu6iegr53azo4
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