☆ 4.6 Article

Carbon Storage Estimation of Quercus aquifolioides Based on GEDI Spaceborne LiDAR Data and Landsat 9 Images in Shangri-La

SUSTAINABILITY (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Environmental Sciences

Quantifying aboveground biomass dynamics from charcoal degradation in Mozambique using GEDI Lidar and Landsat

Mengyu Liang et al.

Summary: This study investigates the impact of forest degradation on aboveground biomass (AGB) in southern Africa. By combining 3D forest structure data from NASA's GEDI Lidar with optical time-series data from Landsat, the authors successfully estimate biomass losses associated with charcoal-related degradation over a 10-year period and create annual AGB density maps.

REMOTE SENSING OF ENVIRONMENT (2023)

添加到收藏夹

Article Chemistry, Analytical

Estimation of Aboveground Biomass in Agroforestry Systems over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data

Dan Kanmegne Tamga et al.

Summary: This study evaluates the potential of remote sensing data and methods for estimating aboveground biomass (AGB) in agroforestry systems (AFS). The combination of random forest algorithm and Sentinel-1 and -2 data performs best in AGB prediction. The AGB level varies across different climatic regions, and estimating AGB using remote sensing remains challenging.

SENSORS (2023)

添加到收藏夹

Article Forestry

Assessing the performance of NASA's GEDI L4A footprint aboveground biomass density models using National Forest Inventory and airborne laser scanning data in Mediterranean forest ecosystems

Adrian Pascual et al.

FOREST ECOLOGY AND MANAGEMENT (2023)

添加到收藏夹

Article Forestry

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

Li Xu et al.

Summary: This study used GEDI data and Kriging interpolation to establish biomass models for Quercus forests. The results showed that the random forest model had higher accuracy compared to multiple linear regression and support vector regression. This study provides a new research direction for estimating other forest structural parameters using GEDI data.

FORESTS (2023)

添加到收藏夹

Article Environmental Sciences

Comparison of GEDI LiDAR Data Capability for Forest Canopy Height Estimation over Broadleaf and Needleleaf Forests

Manizheh Rajab Pourrahmati et al.

Summary: The GEDI LiDAR system is an effective tool for estimating forest biophysical parameters, particularly canopy height, at a global scale. This study found that GEDI canopy height estimation is more accurate for needleleaf forests compared to broadleaf and mixed forests. The study also identified the impact of foliage condition and plant area index on GEDI canopy height accuracy, suggesting the importance of filtering GEDI data based on seasonal acquisition time for specific forest types.

REMOTE SENSING (2023)

添加到收藏夹

Article Environmental Sciences

Forest Emissions Reduction Assessment Using Optical Satellite Imagery and Space LiDAR Fusion for Carbon Stock Estimation

Yue Jiao et al.

Summary: In this study, a measurement method using optical satellite imagery and space LiDAR data fusion was proposed to assess forest emissions reduction. The method demonstrated efficiency and accuracy in predicting forest carbon stock at a large scale. Building a quality control framework is crucial to improve carbon stock estimation and meet carbon standards.

REMOTE SENSING (2023)

添加到收藏夹

Article Environmental Sciences

Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data

Xiaoqiang Liu et al.

Summary: This study developed a novel neural network guided interpolation (NNGI) method to map forest canopy height using integrated spaceborne lidar data and optical images. The results showed high mapping accuracy, demonstrating the feasibility of the proposed method for monitoring forest canopy height at national to global scales and advancing our understanding of forest ecosystem processes and carbon storage.

REMOTE SENSING OF ENVIRONMENT (2022)

添加到收藏夹

Article Environmental Sciences

Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods

Zhilong Xi et al.

Summary: This study proposes a new method to estimate forest canopy height by synergizing various remote sensing data, including spaceborne LiDAR, Synthetic Aperture Radar (SAR), multi-spectral images, and topographic data, while considering different forest types. The results show that models considering forest types perform better than models grouping all types of forests together. Random Forest (RF) outperforms Gradient Boosting Decision Tree (GBDT) in forest canopy height estimation. It is validated that the synergy of ICESat-2 with other remote sensing data is crucial for spatio-continuous forest canopy height mapping.

REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Initial Cross-Calibration of Landsat 8 and Landsat 9 Using the Simultaneous Underfly Event

Garrison Gross et al.

Summary: With the opportunity provided by the flyover of Landsat 9 under Landsat 8, the cross-calibration parameters for Landsat 9 were estimated using the collected imagery. The total cross-calibration uncertainty for the underfly data was estimated to be kept under 1%, after considering different types of uncertainties.

REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images

Fugen Jiang et al.

Summary: This study developed a regression kriging method to improve the estimation and mapping of forest above-ground biomass (AGB) using Landsat 9 imagery. The results showed that regression kriging significantly reduced the estimation error and achieved high accuracy, with Landsat 9 images maintaining a high degree of uniformity when compared to Landsat 8.

REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel

Camile Sothe et al.

Summary: This study compared the performance of two spaceborne LiDAR sensors, GEDI and ICESat-2, for continuous canopy height estimation in Canada. The results showed that both sensors overestimated canopy height, but GEDI performed better than ICESat-2. However, for hemi-boreal forests, ICESat-2 captured the expected tall canopy heights better than GEDI, as both sensors have limited coverage in these areas.

REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Water Quality Retrieval from Landsat-9 (OLI-2) Imagery and Comparison to Sentinel-2

Milad Niroumand-Jadidi et al.

Summary: This study evaluates water quality parameters retrieved from Landsat-9 imagery using physics-based and machine learning models, and performs a consistency analysis with near-simultaneous Sentinel-2 data. The results indicate higher accuracy and lower noise in the water quality products derived from Landsat-9, which can be attributed to its wider dynamic range.

REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Optimization of Samples for Remote Sensing Estimation of Forest Aboveground Biomass at the Regional Scale

Qingtai Shu et al.

Summary: Accurately estimating forest aboveground biomass based on remote sensing images at the regional level is challenging. This study proposes a new optimizing method for the samples and validates the accuracy of the random forest regression model with the optimal sample size.

REMOTE SENSING (2022)

添加到收藏夹

Article Environmental Sciences

Integrating spaceborne LiDAR and Sentinel-2 images to estimate forest aboveground biomass in Northern China

Fugen Jiang et al.

Summary: The study used ensemble learning algorithm to combine ICESat-2 and Sentinel-2 images, improving the accuracy of aboveground biomass (AGB) estimation in forests and providing reference for forest resource management and monitoring.

CARBON BALANCE AND MANAGEMENT (2022)

添加到收藏夹

Article Environmental Sciences

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Carlos Alberto Silva et al.

Summary: This study presents a multi-sensor data fusion approach for accurate mapping of forest aboveground biomass (AGB) using data from GEDI, ICESat-2, and NISAR missions. Testing in Sonoma County, USA, demonstrates that the fusion framework provides more accurate AGB estimates compared to individual mission data, highlighting opportunities for improved AGB mapping through data fusion.

REMOTE SENSING OF ENVIRONMENT (2021)

添加到收藏夹

Article Environmental Sciences

Mapping global forest canopy height through integration of GEDI and Landsat data

Peter Potapov et al.

Summary: The study utilized GEDI and Landsat data to create a global forest canopy height map, enabling monitoring of forest height and dynamics. The integration of GEDI data with optical imagery demonstrated in the study provides new possibilities for historical analysis and future monitoring.

REMOTE SENSING OF ENVIRONMENT (2021)

添加到收藏夹

Article Environmental Sciences

Estimating the aboveground biomass of coniferous forest in Northeast China using spectral variables, land surface temperature and soil moisture

Fugen Jiang et al.

Summary: This study demonstrates that combining the nonlinear variable selection method with optimized RF regression can improve the efficiency of AGB estimation, with nonlinear indices providing more accurate results and the importance index showing the lowest root mean square error. Additionally, the inclusion of land surface temperature and soil moisture significantly enhanced the accuracy of AGB estimation.

SCIENCE OF THE TOTAL ENVIRONMENT (2021)

添加到收藏夹

Article Environmental Sciences

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

Ivan Dorado-Roda et al.

Summary: The study evaluates the effect of combining ALS-based aboveground biomass estimates with GEDI-derived models in different forest ecosystems, finding that GEDI data shows higher uncertainty in highly multilayered forests. The proposed models could be used to monitor biomass stocks at large scale in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forests.

REMOTE SENSING (2021)

添加到收藏夹

Article Environmental Sciences

Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California

Laura Duncanson et al.

REMOTE SENSING OF ENVIRONMENT (2020)

添加到收藏夹

Article Environmental Sciences

Landsat 9: Empowering open science and applications through continuity

Jeffrey G. Masek et al.

REMOTE SENSING OF ENVIRONMENT (2020)

添加到收藏夹

Article Environmental Sciences

Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters

Markus Adam et al.

REMOTE SENSING (2020)

添加到收藏夹

Article Geography, Physical

Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery

Yanan Liu et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

添加到收藏夹

Article Environmental Sciences

Spatial Pattern of Forest Carbon Storage in the Vertical and Horizontal Directions Based on HJ-CCD Remote Sensing Imagery

Kaisheng Luo

REMOTE SENSING (2019)

添加到收藏夹

Article Environmental Sciences

Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data

Svetlana Saarela et al.

REMOTE SENSING (2018)

添加到收藏夹

Article Remote Sensing

A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests

Amir Safari et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2017)

添加到收藏夹

Article Forestry

An Assessment of Carbon Storage in China's Arboreal Forests

Weiwei Shao et al.

FORESTS (2017)

添加到收藏夹

Article Geochemistry & Geophysics

Improvement of Forest Carbon Estimation by Integration of Regression Modeling and Spectral Unmixing of Landsat Data

Enping Yan et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2015)

添加到收藏夹

Editorial Material Biodiversity Conservation

Predictability of the terrestrial carbon cycle

Yiqi Luo et al.

GLOBAL CHANGE BIOLOGY (2015)

添加到收藏夹

Article Green & Sustainable Science & Technology

Design of an EIoT system for nature reserves: a case study in Shangri-La County, Yunnan Province, China

Chunming Li et al.

INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY (2015)

添加到收藏夹

Article Environmental Sciences

Estimating spruce and pine biomass with interferometric X-band SAR

Svein Solberg et al.

REMOTE SENSING OF ENVIRONMENT (2010)

添加到收藏夹

Article Environmental Sciences

Intra and inter-class spectral variability of tropical tree species at La Selva, Costa Rica:: Implications for species identification using HYDICE imagery

Jinkai Zhang et al.

REMOTE SENSING OF ENVIRONMENT (2006)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.