Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass

Ecological Informatics - Tập 78 - Trang 102348 - 2023
Qiyu Guo1, Shouhang Du1, Jinbao Jiang1, Wei Guo2, Hengqian Zhao1, Xuzhe Yan1, Yinpeng Zhao1, Wanshan Xiao3
1College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
2College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
3China Building Materials Industry Geologic Exploration Center Liaoning Branch, Shenyang 110004, China

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