Research graph
References from Vegetation canopy height retrieval in complex mountainous regions based on data calibration and CNN model. Local targets link to admitted publications; unresolved targets remain external evidence.
Integrating GEDI, Sentinel-2, and Sentinel-1 imagery for tree crops mapping
2025 · External reference
High-resolution canopy height mapping: integrating NASA’s global ecosystem dynamics investigation (GEDI) with multi-source remote sensing data
10.3390/rs16071281 · 2024 · External reference
Presence-only geographical priors for fine-grained image classification
2019 · External reference
Optimizing Landsat time series length for regional mapping of lidar-derived forest structure
2020 · External reference
Climate variation of different elevation zones in Qilian moutains
2010 · External reference
Unresolved reference
External reference
Xception: Deep Learning with Depthwise Separable Convolutions
2017 · External reference
Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer
10.1016/j.rse.2007.07.024 · 2008 · External reference
Unresolved reference
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The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography
2020 · External reference
Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California
2020 · External reference
Terrain slope effect on forest height and wood volume estimation from GEDI data
10.3390/rs13112136 · 2021 · External reference
A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms
2021 · External reference
ICESat-2 performance for Terrain and canopy height retrieval in complex mountainous environments
2025 · External reference
Vegetation responses to climate change in the Qilian Mountain Nature Reserve, Northwest China
2021 · External reference
Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data using Machine Learning over India
2022 · External reference
A 30 m global map of elevation with forests and buildings removed
10.1088/1748-9326/ac4d4f · 2022 · External reference
Deep Residual Learning for image Recognition
2016 · External reference
Repeat GEDI footprints measure the effects of tropical forest disturbances
2024 · External reference
Comparative study on remote sensing methods for forest height mapping in complex mountainous environments
2023 · External reference
Special issue on the moderate resolution imaging spectroradiometer (MODIS): a new generation of land surface monitoring
2002 · External reference
Unresolved reference
2017 · External reference
Combining high-resolution images and LiDAR data to model ecosystem services perception in compact urban systems
10.1016/j.ecolind.2017.05.014 · 2019 · External reference
A high-resolution canopy height model of the Earth
10.1038/s41559-023-02206-6 · 2023 · External reference
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
2022 · External reference
First validation of GEDI canopy heights in African savannas
2023 · External reference
Feature analysis of LIDAR waveforms from forest canopies
10.1007/s11430-011-4212-3 · 2011 · External reference
Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery
10.1016/j.isprsjprs.2019.03.016 · 2019 · External reference
A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data
10.1016/j.jag.2025.104474 · 2025 · External reference
Unresolved reference
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Altitude-dependent responses of dryland mountain ecosystems to drought under a warming climate in the Qilian Mountains, NW China
10.1016/j.jhydrol.2024.130763 · 2024 · External reference
Performance analysis of airborne photon-counting lidar data in preparation for the ICESat-2 Mission
10.1109/tgrs.2017.2786659 · 2018 · External reference
Mapping vegetation canopy height across the contiguous United States using ICESat-2 and ancillary datasets
2024 · External reference
Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots
10.1016/j.rse.2017.12.020 · 2018 · External reference
Comparison of three global canopy height maps and their applicability to biodiversity modeling: Accuracy issues revealed
10.1002/ecs2.70026 · 2024 · External reference
Effects of environmental conditions on ICESat-2 terrain and canopy heights retrievals in central European mountains
2022 · External reference
How to find accurate terrain and canopy height GEDI footprints in temperate forests and grasslands?
10.1029/2024ea003709 · 2024 · External reference
Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data
10.1016/j.rse.2019.01.037 · 2019 · External reference
Spatial validation reveals poor predictive performance of large-scale ecological mapping models
10.1038/s41467-020-18321-y · 2020 · External reference
Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data
10.1016/j.rse.2018.02.019 · 2018 · External reference
Mapping global forest canopy height through integration of GEDI and Landsat data
2021 · External reference
Mapping Lorey's height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images
10.1080/22797254.2017.1405717 · 2018 · External reference
Elevation affects the ecological stoichiometry of Qinghai spruce in the Qilian Mountains of northwest China
10.3389/fpls.2022.917755 · 2022 · External reference
Modeling the effect of climate Change on the potential distribution of Qinghai Spruce (Picea crassifolia Kom.) in Qilian Mountains
10.3390/f10010062 · 2019 · External reference
Towards mapping the diversity of canopy structure from space with GEDI
10.1088/1748-9326/ab9e99 · 2020 · External reference
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
2024 · External reference
Canopy height estimation at landsat resolution using convolutional neural networks
10.3390/make2010003 · 2020 · External reference
Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping
2021 · External reference
Mapping forest canopy height globally with spaceborne lidar
10.1029/2011jg001708 · 2011 · External reference
Spatially continuous mapping of forest canopy height in canada by combining GEDI and ICESat-2 with PALSAR and sentinel
2022 · External reference
Evaluation and spatiotemporal characteristics of glacier service value in the Qilian Mountains
10.1007/s11442-020-1779-7 · 2020 · External reference
Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI (vol 291, 113571, 2023)
2023 · External reference
Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data
10.1016/j.rse.2011.04.033 · 2011 · External reference
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar
2024 · External reference
Hybrid model for estimating forest canopy heights using fused multimodal spaceborne LiDAR data and optical imagery
2023 · External reference
Unresolved reference
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Unresolved reference
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Unresolved reference
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The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019
10.5194/essd-13-3907-2021 · 2021 · External reference
Spatial distribution of Qinghai spruce forests and the thresholds of influencing factors in a small catchment, Qilian Mountains, northwest China
2017 · External reference
Analysis of the Niche Space of Picea crassifolia on the Northern Slope of Qilian Mountains
2010 · External reference
Consistency analysis of forest height retrievals between GEDI and ICESat-2
2022 · External reference
Comparison of three global canopy height maps and their applicability to biodiversity modeling: Accuracy issues revealed
10.1002/ecs2.70026 · ExternalCitation · doi-reference
Feature analysis of LIDAR waveforms from forest canopies
10.1007/s11430-011-4212-3 · ExternalCitation · doi-reference
Evaluation and spatiotemporal characteristics of glacier service value in the Qilian Mountains
10.1007/s11442-020-1779-7 · ExternalCitation · doi-reference
Combining high-resolution images and LiDAR data to model ecosystem services perception in compact urban systems
10.1016/j.ecolind.2017.05.014 · ExternalCitation · doi-reference
Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery
10.1016/j.isprsjprs.2019.03.016 · ExternalCitation · doi-reference
A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data
10.1016/j.jag.2025.104474 · ExternalCitation · doi-reference
Altitude-dependent responses of dryland mountain ecosystems to drought under a warming climate in the Qilian Mountains, NW China
10.1016/j.jhydrol.2024.130763 · ExternalCitation · doi-reference
Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer
10.1016/j.rse.2007.07.024 · ExternalCitation · doi-reference
Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data
10.1016/j.rse.2011.04.033 · ExternalCitation · doi-reference
Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots
10.1016/j.rse.2017.12.020 · ExternalCitation · doi-reference
Photon counting LiDAR: an adaptive ground and canopy height retrieval algorithm for ICESat-2 data
10.1016/j.rse.2018.02.019 · ExternalCitation · doi-reference
Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data
10.1016/j.rse.2019.01.037 · ExternalCitation · doi-reference
Mapping forest canopy height globally with spaceborne lidar
10.1029/2011jg001708 · ExternalCitation · doi-reference
How to find accurate terrain and canopy height GEDI footprints in temperate forests and grasslands?
10.1029/2024ea003709 · ExternalCitation · doi-reference
Spatial validation reveals poor predictive performance of large-scale ecological mapping models
10.1038/s41467-020-18321-y · ExternalCitation · doi-reference
A high-resolution canopy height model of the Earth
10.1038/s41559-023-02206-6 · ExternalCitation · doi-reference
Mapping Lorey's height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images
10.1080/22797254.2017.1405717 · ExternalCitation · doi-reference
Towards mapping the diversity of canopy structure from space with GEDI
10.1088/1748-9326/ab9e99 · ExternalCitation · doi-reference
A 30 m global map of elevation with forests and buildings removed
10.1088/1748-9326/ac4d4f · ExternalCitation · doi-reference
Performance analysis of airborne photon-counting lidar data in preparation for the ICESat-2 Mission
10.1109/tgrs.2017.2786659 · ExternalCitation · doi-reference
Elevation affects the ecological stoichiometry of Qinghai spruce in the Qilian Mountains of northwest China
10.3389/fpls.2022.917755 · ExternalCitation · doi-reference
Modeling the effect of climate Change on the potential distribution of Qinghai Spruce (Picea crassifolia Kom.) in Qilian Mountains
10.3390/f10010062 · ExternalCitation · doi-reference
Canopy height estimation at landsat resolution using convolutional neural networks
10.3390/make2010003 · ExternalCitation · doi-reference
Terrain slope effect on forest height and wood volume estimation from GEDI data
10.3390/rs13112136 · ExternalCitation · doi-reference
High-resolution canopy height mapping: integrating NASA’s global ecosystem dynamics investigation (GEDI) with multi-source remote sensing data
10.3390/rs16071281 · ExternalCitation · doi-reference
The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019
10.5194/essd-13-3907-2021 · ExternalCitation · doi-reference
