scholarly journals Combining Kriging Interpolation to Improve the Accuracy of Forest Aboveground Biomass Estimation Using Remote Sensing Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 128124-128139
Author(s):  
Yingchang Li ◽  
Mingyang Li ◽  
Zhenzhen Liu ◽  
Chao Li
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Remote Sensing Data ◽  
Kriging Interpolation ◽  
Biomass Estimation ◽  
Sensing Data ◽  
Forest Aboveground Biomass

scholarly journals Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1282
Author(s):  
Parth Naik ◽  
Michele Dalponte ◽  
Lorenzo Bruzzone
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Temporal Information ◽  
Observation Data ◽  
Multispectral Data ◽  
Sensing Data ◽  
Multispectral Remote Sensing ◽  
Forest Aboveground Biomass

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal information.

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

Predicting stem diameters and aboveground biomass of individual trees using remote sensing data

2018 ◽  
Vol 85 ◽  
pp. 367-376 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Hans Ole Ørka ◽  
Terje Gobakken ◽  
Erik Næsset ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Individual Trees

A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape

2018 ◽  
Vol 218 ◽  
pp. 32-43 ◽  
Author(s):  
Sarah J. Graves ◽  
T. Trevor Caughlin ◽  
Gregory P. Asner ◽  
Stephanie A. Bohlman
Keyword(s):  
Remote Sensing ◽  
Agricultural Landscape ◽  
Remote Sensing Data ◽  
Biomass Estimation ◽  
Sensing Data

scholarly journals Quantitative Remote Sensing of Metallic Elements for the Qishitan Gold Polymetallic Mining Area, NW China

2021 ◽  
Vol 13 (13) ◽  
pp. 2519
Author(s):  
Gong Cheng ◽  
Huikun Huang ◽  
Huan Li ◽  
Xiaoqing Deng ◽  
Rehan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Kriging Interpolation ◽  
Geochemical Data ◽  
Surface Element ◽  
Metallic Elements ◽  
Geochemical Exploration ◽  
Sensing Data ◽  
Quantitative Remote Sensing ◽  
Ore Prospecting

The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent.

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