Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass
Tài liệu tham khảo
Adam, 2020, Accuracy assessment of GEDI terrain elevation and canopy height estimates in European temperate forests: influence of environmental and acquisition parameters, Remote Sens., 12, 3948, 10.3390/rs12233948
Allen, 1984
Bacour, 2002, Reliability of the estimation of vegetation characteristics by inversion of three canopy reflectance models on airborne POLDER data, Agronomie, 22, 555, 10.1051/agro:2002039
Bekhor, 2009, Methodological transferability in route choice modeling, Transp. Res. B Methodol., 43, 422, 10.1016/j.trb.2008.08.003
Bulut, 2023, Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region, Türkiye, Eco. Inform., 74
Casas, 2014, Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response, Remote Sens. Environ., 148, 28, 10.1016/j.rse.2014.03.011
Castillo, 2017, Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using sentinel imagery, ISPRS J. Photogramm. Remote Sens., 134, 70, 10.1016/j.isprsjprs.2017.10.016
Chen, 2020, Research on complex classification algorithm of breast cancer chip based on SVM-RFE gene feature screening, Complexity, 2020, 1, 10.1155/2020/6632956
Chen, 2023, A quantile regression approach to model stand survival in Chinese fir plantations, Can. J. For. Res., 53, 178, 10.1139/cjfr-2022-0196
Chiarito, 2021, Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data, Eur. J. Remote Sens., 54, 209, 10.1080/22797254.2021.1901063
Dennison, 2003
Dhargay, 2022, Performance of GEDI space-borne LiDAR for quantifying structural variation in the temperate forests of south-eastern Australia, Remote Sens., 14, 3615, 10.3390/rs14153615
Duncanson, 2020, Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California, Remote Sens. Environ., 242, 10.1016/j.rse.2020.111779
Fararoda, 2021, Improving forest above ground biomass estimates over Indian forests using multi source data sets with machine learning algorithm, Eco. Inform., 65
Fernández-Guisuraga, 2022, Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus Pinaster) ecosystems, For. Ecosyst., 9, 10.1016/j.fecs.2022.100022
Ghosh, 2021, Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - the superiority of deep learning over a semi-empirical model, Comput. Geosci., 150, 10.1016/j.cageo.2021.104737
Glenn, 2016, Landsat 8 and ICESat-2: performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass, Remote Sens. Environ., 185, 233, 10.1016/j.rse.2016.02.039
Gray, 2009, Generality of models that predict the distribution of species: conservation activity and reduction of model transferability for a threatened bustard, Conserv. Biol., 23, 433, 10.1111/j.1523-1739.2008.01112.x
Hojo, 2023, Modeling forest above-ground biomass using freely available satellite and multisource datasets, Eco. Inform., 74
Huang, 2015, Sensitivity of multi-source SAR backscatter to changes in forest aboveground biomass, Remote Sens., 7, 9587, 10.3390/rs70809587
Huang, 2022, Hyperspectral monitoring driven by machine learning methods for grassland above-ground biomass, Remote Sens., 14, 2086, 10.3390/rs14092086
Huemmrich, 1995
Huete, 1988, A soil-adjusted vegetation index (SAVI), Remote Sens. Environ., 25, 295, 10.1016/0034-4257(88)90106-X
Huete, 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195, 10.1016/S0034-4257(02)00096-2
Jiang, 2021, Mapping the forest canopy height in northern China by synergizing ICESat-2 with sentinel-2 using a stacking algorithm, Remote Sens., 13, 1535, 10.3390/rs13081535
Jordan, 1969, Derivation of leaf-area index from quality of light on the forest floor, Ecology, 50, 663, 10.2307/1936256
Kaasalainen, 2015, Combining lidar and synthetic aperture radar data to estimate forest biomass: status and prospects, Forests, 6, 252, 10.3390/f6010252
Lang, 2022, Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles, Remote Sens. Environ., 268, 10.1016/j.rse.2021.112760
Lefsky, 2010, A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system: a global forest canopy height map, Geophys. Res. Lett., 37, n/a, 10.1029/2010GL043622
Liu, 2019, Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR sentinel-1B, multispectral sentinel-2A, and DEM imagery, ISPRS J. Photogramm. Remote Sens., 151, 277, 10.1016/j.isprsjprs.2019.03.016
Lu, 2020, Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and backpack LiDAR point clouds, Int. J. Appl. Earth Obs. Geoinf., 86
Meng, 2013, Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation, Int. J. Digit. Earth, 6, 203, 10.1080/17538947.2011.623189
Meng, 2022, Health assessment of plantations based on LiDAR canopy spatial structure parameters, Int. J. Digit. Earth, 15, 712, 10.1080/17538947.2022.2059114
Ouattara, 1997
Padalia, 2023, Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics, Eco. Inform., 77
Pan, 2011, A large and persistent carbon sink in the world’s forests, Science, 333, 988, 10.1126/science.1201609
Potapov, 2021, Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sens. Environ., 253, 10.1016/j.rse.2020.112165
Prakash, 2022, A new synergistic approach for Sentinel-1 and PALSAR-2 in a machine learning framework to predict aboveground biomass of a dense mangrove forest, Eco. Inform., 72
Qi, 2019, Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data, Remote Sens. Environ., 221, 621, 10.1016/j.rse.2018.11.035
Qi, 2019, Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data, Remote Sens. Environ., 232, 10.1016/j.rse.2019.111283
Roujean, 2002, Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: a pragmatic method and its validation, J. Geophys. Res., 107, 4150, 10.1029/2001JD000751
Rouse, 1974, Monitoring vegetation systems in the Great Plains with ERTS, 1, 309
Santoro, 2018, Research pathways of forest above-ground biomass estimation based on SAR backscatter and interferometric SAR observations, Remote Sens., 10, 608, 10.3390/rs10040608
Silva, 2021, Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping, Remote Sens. Environ., 253
Singh, 2015, Effects of LiDAR point density and landscape context on estimates of urban forest biomass, ISPRS J. Photogramm. Remote Sens., 101, 310, 10.1016/j.isprsjprs.2014.12.021
Singh, 2023, =Aboveground forest biomass estimation by the integration of TLS and ALOS PALSAR data using machine learning, Remote Sens., 15, 1143, 10.3390/rs15041143
Su, 2016, Spatial distribution of forest aboveground biomass in China: estimation through combination of spaceborne lidar, optical imagery, and forest inventory data, Remote Sens. Environ., 173, 187, 10.1016/j.rse.2015.12.002
Sun, 2023, Coupled retrieval of heavy metal nickel concentration in agricultural soil from spaceborne hyperspectral imagery, J. Hazard. Mater., 446, 10.1016/j.jhazmat.2023.130722
Tejada, 2020, Mapping data gaps to estimate biomass across Brazilian Amazon forests, For. Ecosyst., 7, 25, 10.1186/s40663-020-00228-1
Véga, 2015, Aboveground-biomass estimation of a complex tropical forest in India using lidar, Remote Sens., 7, 10607, 10.3390/rs70810607
Vrabel, 2000, Multispectral imagery advanced band sharpening study, Photogramm. Eng. Remote. Sens., 66, 73
Vargas-Larreta, B., 2021. Assessing above-ground biomass-functional diversity relationships in temperate forests in northern Mexico.
Wang, 2018, Estimation of forest canopy height and aboveground biomass from spaceborne LiDAR and landsat imageries in Maryland, Remote Sens., 10, 344, 10.3390/rs10020344
Wang, 2022, Remote sensing estimation of forest aboveground biomass based on lasso-SVR, Forests, 13, 1597, 10.3390/f13101597
Wang, 2023, Group feature screening based on Gini impurity for ultrahigh-dimensional multi-classification, MATH, 8, 4342, 10.3934/math.2023216
Xia
Zhang, 2021, Assessing of urban vegetation biomass in combination with LiDAR and high-resolution remote sensing images, Int. J. Remote Sens., 42, 964, 10.1080/01431161.2020.1820618
Zhang, 2015, Disturbance-induced reduction of biomass carbon sinks of China’s forests in recent years, Environ. Res. Lett., 10, 10.1088/1748-9326/10/11/114021
Zhang, 2023, Integrating sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in Northeast Guangdong, China, Int. J. Digit. Earth, 16, 158, 10.1080/17538947.2023.2165180
Zhao, 2016, Examining spectral reflectance saturation in landsat imagery and corresponding solutions to improve forest aboveground biomass estimation, Remote Sens., 8, 469, 10.3390/rs8060469