How can UAV contribute in satellite-based Phragmites australis aboveground biomass estimating?
International Journal of Applied Earth Observation and Geoinformation - Tập 114 - Trang 103024 - 2022
Tài liệu tham khảo
Acorsi, M.G., das Dores Abati Miranda, F., Martello, M., Smaniotto, D.A. and Sartor, L.R., 2019. Estimating Biomass of Black Oat Using UAV-Based RGB Imaging. Agronomy. 9. 10.3390/agronomy9070344.
Alvarez-Vanhard, 2020, Can UAVs fill the gap between in situ surveys and satellites for habitat mapping?, Remote Sens. Environ., 243, 10.1016/j.rse.2020.111780
Bater, 2011, Stability of Sample-Based Scanning-LiDAR-Derived Vegetation Metrics for Forest Monitoring, IEEE Trans. Geosci. Remote Sens., 49, 2385, 10.1109/TGRS.2010.2099232
Bendig, 2014, Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging, Remote Sensing., 6, 10395, 10.3390/rs61110395
Bendig, 2015, Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley, Int. J. Appl. Earth Obs. Geoinf., 39, 79
Berra, 2019, Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations, Remote Sens. Environ., 223, 229, 10.1016/j.rse.2019.01.010
Chen, 2022, Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network, Remote Sens. Environ., 270, 10.1016/j.rse.2021.112885
Chi, 2018, Spatial heterogeneity of estuarine wetland ecosystem health influenced by complex natural and anthropogenic factors, Sci Total Environ., 634, 1445, 10.1016/j.scitotenv.2018.04.085
Doughty, 2019, Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Remote Sensing., 11, 10.3390/rs11050540
Fassnacht, 2021, Using Sentinel-2 and canopy height models to derive a landscape-level biomass map covering multiple vegetation types, Int. J. Appl. Earth Obs. Geoinf., 94
Fracz, 2012, Impacts of declining water levels on the quantity of fish habitat in coastal wetlands of eastern Georgian Bay, Lake Huron, Hydrobiologia, 702, 151, 10.1007/s10750-012-1318-3
Fu, 2021, Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis, Remote Sensing., 13, 10.3390/rs13040581
Geipel, 2014, Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System, Remote Sensing., 6, 10335, 10.3390/rs61110335
Han, 2019, Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data, Plant Methods., 15, 10, 10.1186/s13007-019-0394-z
Higgisson, 2021, Estimating the cover of Phragmites australis using unmanned aerial vehicles and neural networks in a semi-arid wetland, River Res. Appl., 37, 1312, 10.1002/rra.3832
Higgisson, 2022, The Role of Environmental Water and Reedbed Condition on the Response of Phragmites australis Reedbeds to Flooding, Remote Sensing., 14, 10.3390/rs14081868
Jensen, 2019, Integrating Imaging Spectrometer and Synthetic Aperture Radar Data for Estimating Wetland Vegetation Aboveground Biomass in Coastal Louisiana, Remote Sensing., 11, 10.3390/rs11212533
Jin, 2016, Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model, Agric. For. Meteorol., 218–219, 250, 10.1016/j.agrformet.2015.12.062
Jin, 2020, Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model, Agric. Water Manag., 227, 10.1016/j.agwat.2019.105846
Jing, 2017, Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China, ISPRS J. Photogramm. Remote Sens., 134, 122, 10.1016/j.isprsjprs.2017.11.002
Kameyama, 2020, Estimating Tree Height and Volume Using Unmanned Aerial Vehicle Photography and SfM Technology, with Verification of Result Accuracy, Drones., 4, 10.3390/drones4020019
Kaplan, 2018, Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data, ISPRS Int. J. Geo-Inf., 7, 10.3390/ijgi7100411
Klemas, 2013, Remote Sensing of Coastal Wetland Biomass: An Overview, J. Coastal Res., 290, 1016, 10.2112/JCOASTRES-D-12-00237.1
Koma, 2021, Quantifying 3D vegetation structure in wetlands using differently measured airborne laser scanning data, Ecol. Ind., 127, 10.1016/j.ecolind.2021.107752
Li, 2021, Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data, ISPRS J. Photogramm. Remote Sens., 173, 262, 10.1016/j.isprsjprs.2021.01.018
Li, 2016, Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system, Ecol. Ind., 67, 637, 10.1016/j.ecolind.2016.03.036
Li, 2018, Estimation of above-ground biomass of reed (Phragmites communis) based on in situ hyperspectral data in Beijing Hanshiqiao Wetland, China. Wetlands Ecology and Management., 27, 87, 10.1007/s11273-018-9644-5
Li, 2020, High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data, Int. J. Appl. Earth Obs. Geoinf., 92
Li, 2020, Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging, ISPRS J. Photogramm. Remote Sens., 162, 161, 10.1016/j.isprsjprs.2020.02.013
Li, 2021, Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China, Remote Sens., 13
Liu, 2021, Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals, Remote Sens. Environ., 264, 10.1016/j.rse.2021.112571
Lucy, 2020, A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems, Remote Sens., 12
Luo, 2017, Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data, Int. J. Appl. Earth Obs. Geoinf., 58, 107
Miller, 2019, Estimating Aboveground Biomass and Its Spatial Distribution in Coastal Wetlands Utilizing Planet Multispectral Imagery, Remote Sensing., 11, 10.3390/rs11172020
Mulverhill, 2022, Evaluating ICESat-2 for monitoring, modeling, and update of large area forest canopy height products, Remote Sens. Environ., 271, 10.1016/j.rse.2022.112919
Mutanga, 2012, High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm, Int. J. Appl. Earth Obs. Geoinf., 18, 399
O’Donnell, 2016, Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast, Remote Sensing., 8, 10.3390/rs8060477
Pandit, 2018, Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data, Remote Sensing., 10, 10.3390/rs10040601
Petrou, 2015, Discrimination of Vegetation Height Categories With Passive Satellite Sensor Imagery Using Texture Analysis, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 1442, 10.1109/JSTARS.2015.2409131
Proisy, 2007, Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images, Remote Sens. Environ., 109, 379, 10.1016/j.rse.2007.01.009
Ronchi-Virgolini, 2013, Temporal Variation of Bird Assemblages in a Wetland: Influence of Spatial Heterogeneity, Avian Biol. Res., 6, 198, 10.3184/175815513X13739097841679
Tilly, 2015, Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass, Remote Sensing., 7, 11449, 10.3390/rs70911449
Vahtmäe, 2021, Mapping spatial distribution, percent cover and biomass of benthic vegetation in optically complex coastal waters using hyperspectral CASI and multispectral Sentinel-2 sensors, Int. J. Appl. Earth Obs. Geoinf., 102
Walter, 2019, Estimating Biomass and Canopy Height With LiDAR for Field Crop Breeding, Front. Plant Sci., 10, 10.3389/fpls.2019.01145
Wang, 2020, Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery, Int. J. Appl. Earth Obs. Geoinf., 85
Wang, 2021, Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data, Ecol. Ind., 126, 10.1016/j.ecolind.2021.107645
Xie, 2021, Crop height estimation based on UAV images: Methods, errors, and strategies, Comput. Electron. Agric., 185, 10.1016/j.compag.2021.106155
Xu, 2021, A comprehensive yield evaluation indicator based on an improved fuzzy comprehensive evaluation method and hyperspectral data, Field Crops Research., 270, 10.1016/j.fcr.2021.108204
Yadav, 2017, A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa, Remote Sensing., 9, 10.3390/rs9090966
Yang, 2017, The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager, Remote Sensing., 9, 10.3390/rs9070642
Yue, 2019, Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices, ISPRS J. Photogramm. Remote Sens., 150, 226, 10.1016/j.isprsjprs.2019.02.022
Zeng, 2019, Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm, Ecol. Ind., 102, 479, 10.1016/j.ecolind.2019.02.023
Zhang, 2021, Estimating the maize biomass by crop height and narrowband vegetation indices derived from UAV-based hyperspectral images, Ecol. Ind., 129, 10.1016/j.ecolind.2021.107985
Zhao, 2022, Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images, Remote Sensing., 14
Zhu, 2015, Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series, ISPRS J. Photogramm. Remote Sens., 102, 222, 10.1016/j.isprsjprs.2014.08.014
Zhuo, 2022, UAV Mapping of the Chlorophyll Content in a Tidal Flat Wetland Using a Combination of Spectral and Frequency Indices, Remote Sensing., 14, 10.3390/rs14040827
Zoffoli, 2020, Sentinel-2 remote sensing of Zostera noltei-dominated intertidal seagrass meadows, Remote Sens. Environ., 251, 10.1016/j.rse.2020.112020