Works matching AU Du, Huaqiang
Results: 63
Spatiotemporal Evolution and Influencing Factors of Forest Carbon Storage Based on BIOME-BGC Model and Geographical Detector in Eight Basins of Zhejiang Province in China.
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- Forests (19994907), 2025, v. 16, n. 2, p. 316, doi. 10.3390/f16020316
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Mapping spatiotemporal decisions for sustainable productivity of bamboo forest land.
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- Land Degradation & Development, 2020, v. 31, n. 8, p. 939, doi. 10.1002/ldr.3509
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Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event.
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- PeerJ, 2018, p. 1, doi. 10.7717/peerj.5747
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Analysis of Spatiotemporal Evolution and Driving Forces of Vegetation from 2001 to 2020: A Case Study of Shandong Province, China.
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- Forests (19994907), 2024, v. 15, n. 7, p. 1245, doi. 10.3390/f15071245
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Coupling the PROSAIL Model and Machine Learning Approach for Canopy Parameter Estimation of Moso Bamboo Forests from UAV Hyperspectral Data.
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- Forests (19994907), 2024, v. 15, n. 6, p. 946, doi. 10.3390/f15060946
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Assessing Carbon Sequestration Potential in State-Owned Plantation Forests in China and Exploring Feasibility for Carbon Offset Projects.
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- Forests (19994907), 2024, v. 15, n. 5, p. 886, doi. 10.3390/f15050886
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Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model.
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- Forests (19994907), 2024, v. 15, n. 3, p. 552, doi. 10.3390/f15030552
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Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model.
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- Forests (19994907), 2019, v. 10, n. 11, p. 1004, doi. 10.3390/f10111004
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Spatiotemporal Simulation of Net Ecosystem Productivity and Its Response to Climate Change in Subtropical Forests.
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- Forests (19994907), 2019, v. 10, n. 8, p. 708, doi. 10.3390/f10080708
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- Article
Estimating Crown Structure Parameters of Moso Bamboo: Leaf Area and Leaf Angle Distribution.
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- Forests (19994907), 2019, v. 10, n. 8, p. 686, doi. 10.3390/f10080686
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Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County.
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- ISPRS International Journal of Geo-Information, 2020, v. 9, n. 12, p. 718, doi. 10.3390/ijgi9120718
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Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests.
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- ISPRS International Journal of Geo-Information, 2020, v. 9, n. 2, p. 64, doi. 10.3390/ijgi9020064
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Partitioning of respired CO<sub>2</sub> in newly sprouted Moso bamboo culms.
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- Frontiers in Plant Science, 2023, p. 1, doi. 10.3389/fpls.2023.1154232
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Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China.
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- Frontiers in Plant Science, 2023, v. 13, p. 1, doi. 10.3389/fpls.2023.1067552
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Pixel-based Minnaert Correction Method for Reducing Topographic Effects on a Landsat 7 ETM+ Image.
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- Photogrammetric Engineering & Remote Sensing, 2008, v. 74, n. 11, p. 1343
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Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance.
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- International Journal of Remote Sensing, 2016, v. 37, n. 22, p. 5270, doi. 10.1080/01431161.2016.1239287
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Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models.
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- International Journal of Remote Sensing, 2015, v. 36, n. 21, p. 5384, doi. 10.1080/01431161.2015.1095371
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Exploring the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based approach.
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- International Journal of Remote Sensing, 2015, v. 36, n. 13, p. 3544, doi. 10.1080/01431161.2015.1065357
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Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping.
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- International Journal of Remote Sensing, 2014, v. 35, n. 3, p. 1126, doi. 10.1080/01431161.2013.875634
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Moso bamboo forest extraction and aboveground carbon storage estimation based on multi-source remotely sensed images.
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- International Journal of Remote Sensing, 2013, v. 34, n. 15, p. 5351, doi. 10.1080/01431161.2013.788260
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Spatiotemporal heterogeneity of Moso bamboo aboveground carbon storage with Landsat Thematic Mapper images: a case study from Anji County, China.
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- International Journal of Remote Sensing, 2013, v. 34, n. 14, p. 4917, doi. 10.1080/01431161.2013.782115
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Satellite-based carbon stock estimation for bamboo forest with a non-linear partial least square regression technique.
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- International Journal of Remote Sensing, 2012, v. 33, n. 6, p. 1917, doi. 10.1080/01431161.2011.603379
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Estimation of aboveground carbon stock of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest with a Landsat Thematic Mapper image.
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- International Journal of Remote Sensing, 2011, v. 32, n. 5, p. 1431, doi. 10.1080/01431160903551389
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The carbon storage in moso bamboo plantation and its spatial variation in Anji County of southeastern China.
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- Journal of Soils & Sediments: Protection, Risk Assessment, & Remediation, 2014, v. 14, n. 2, p. 320, doi. 10.1007/s11368-013-0665-7
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Spatial heterogeneity and carbon contribution of aboveground biomass of moso bamboo by using geostatistical theory.
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- Plant Ecology, 2010, v. 207, n. 1, p. 131, doi. 10.1007/s11258-009-9659-3
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Estimation of Leaf Area Index of Moso Bamboo Canopies.
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- Journal of Sustainable Forestry, 2023, v. 42, n. 2, p. 189, doi. 10.1080/10549811.2021.1979413
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Estimating Aboveground Carbon of Moso Bamboo Forests Using the k Nearest Neighbors Technique and Satellite Imagery.
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- Photogrammetric Engineering & Remote Sensing, 2011, v. 77, n. 11, p. 1123, doi. 10.14358/PERS.77.11.1123
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Biotic and abiotic influences on monthly variation in carbon fluxes in on-year and off-year Moso bamboo forest.
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- Trees: Structure & Function, 2019, v. 33, n. 1, p. 153, doi. 10.1007/s00468-018-1765-1
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Eddy covariance analysis of the implications of drought on the carbon fluxes of Moso bamboo forest in southeastern China.
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- Trees: Structure & Function, 2016, v. 30, n. 5, p. 1807, doi. 10.1007/s00468-016-1414-5
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Interactive effects of plant litter chemistry and organic/inorganic forms of nitrogen addition on Moso bamboo (Phyllostachys edulis) soil respiration.
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- Biology & Fertility of Soils, 2025, v. 61, n. 1, p. 109, doi. 10.1007/s00374-024-01875-0
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Urban Forest Above-Ground Biomass Estimation Based on UAV 3D Real Scene.
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- Drones (2504-446X), 2023, v. 7, n. 7, p. 455, doi. 10.3390/drones7070455
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Multi-scenario carbon storage analysis based on PLUS model and InVEST model: a case study of Zhejiang province, China.
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- Earth Science Informatics, 2025, v. 18, n. 2, p. 1, doi. 10.1007/s12145-024-01683-y
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UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests.
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- Remote Sensing, 2024, v. 16, n. 4, p. 705, doi. 10.3390/rs16040705
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Detection of the Infection Stage of Pine Wilt Disease and Spread Distance Using Monthly UAV-Based Imagery and a Deep Learning Approach.
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- Remote Sensing, 2024, v. 16, n. 2, p. 364, doi. 10.3390/rs16020364
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A Deep Learning Network for Individual Tree Segmentation in UAV Images with a Coupled CSPNet and Attention Mechanism.
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- Remote Sensing, 2023, v. 15, n. 18, p. 4420, doi. 10.3390/rs15184420
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Optimizing the Spatial Structure of Metasequoia Plantation Forest Based on UAV-LiDAR and Backpack-LiDAR.
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- Remote Sensing, 2023, v. 15, n. 16, p. 4090, doi. 10.3390/rs15164090
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Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery.
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- Remote Sensing, 2023, v. 15, n. 10, p. 2566, doi. 10.3390/rs15102566
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Wavelet Vegetation Index to Improve the Inversion Accuracy of Leaf V 25 cmax of Bamboo Forests.
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- Remote Sensing, 2023, v. 15, n. 9, p. 2362, doi. 10.3390/rs15092362
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Prediction of Urban Forest Aboveground Carbon Using Machine Learning Based on Landsat 8 and Sentinel-2: A Case Study of Shanghai, China.
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- Remote Sensing, 2023, v. 15, n. 1, p. 284, doi. 10.3390/rs15010284
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Tree Species Classifications of Urban Forests Using UAV-LiDAR Intensity Frequency Data.
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- Remote Sensing, 2023, v. 15, n. 1, p. 110, doi. 10.3390/rs15010110
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Intelligent Estimating the Tree Height in Urban Forests Based on Deep Learning Combined with a Smartphone and a Comparison with UAV-LiDAR.
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- Remote Sensing, 2023, v. 15, n. 1, p. 97, doi. 10.3390/rs15010097
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Estimation of Urban Forest Characteristic Parameters Using UAV-Lidar Coupled with Canopy Volume.
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- Remote Sensing, 2022, v. 14, n. 24, p. 6375, doi. 10.3390/rs14246375
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Estimating 3D Green Volume and Aboveground Biomass of Urban Forest Trees by UAV-Lidar.
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- Remote Sensing, 2022, v. 14, n. 20, p. 5211, doi. 10.3390/rs14205211
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Simulating Future LUCC by Coupling Climate Change and Human Effects Based on Multi-Phase Remote Sensing Data.
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- Remote Sensing, 2022, v. 14, n. 7, p. 1698, doi. 10.3390/rs14071698
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Spatiotemporal Patterns and Driving Force of Urbanization and Its Impact on Urban Ecology.
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- Remote Sensing, 2022, v. 14, n. 5, p. N.PAG, doi. 10.3390/rs14051160
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Spatiotemporal Evolution of the Carbon Fluxes from Bamboo Forests and their Response to Climate Change Based on a BEPS Model in China.
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- Remote Sensing, 2022, v. 14, n. 2, p. 366, doi. 10.3390/rs14020366
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Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression.
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- Remote Sensing, 2021, v. 13, n. 15, p. 2962, doi. 10.3390/rs13152962
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Preliminary Application of Ground-Penetrating Radar for Reconstruction of Root System Architecture in Moso Bamboo.
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- Remote Sensing, 2021, v. 13, n. 14, p. 2816, doi. 10.3390/rs13142816
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A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification.
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- Remote Sensing, 2021, v. 13, n. 11, p. 2234, doi. 10.3390/rs13112234
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Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China.
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- Remote Sensing, 2021, v. 13, n. 5, p. 913, doi. 10.3390/rs13050913
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