Change detection from high-resolution airborne laser scans using penalized composite likelihood screening

Anselin, 1995, Local indicators of spatial association - LISA, Geogr. Anal., 27, 93, 10.1111/j.1538-4632.1995.tb00338.x Arbia, 2014, Pairwise likelihood inference for spatial regressions estimated on very large datasets, Spatial Stat., 7, 21, 10.1016/j.spasta.2013.10.001 Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B Stat. Methodol., 57, 289 Besag, 1975, Statistical analysis of non-lattice data, J. R. Statist. Soc. Ser. D, 24, 179 Bevilacqua, 2015, Comparing composite likelihood methods based on pairs for spatial Gaussian random fields, Stat. Comput., 25, 877, 10.1007/s11222-014-9460-6 Bevilacqua, 2012, Estimating space and space-time covariance functions for large data sets: a weighted composite likelihood approach, J. Amer. Statist. Assoc., 107, 268, 10.1080/01621459.2011.646928 Bollerslev, 1986, Generalized autoregressive conditional heteroskedasticity, J. Econometrics, 31, 307, 10.1016/0304-4076(86)90063-1 Caragea, 2006 Cavalli, 2017, Assessment of erosion and deposition in steep mountain basins by differencing sequential digital terrain models, Geomorphology, 291, 4, 10.1016/j.geomorph.2016.04.009 Chen, 1992, Object modelling by registration of multiple range images, Image Vis. Comput., 10, 145, 10.1016/0262-8856(92)90066-C Cohen, 1960, A coefficient of agreement for nominal scales, Educ. Psychol. Meas., 20, 37, 10.1177/001316446002000104 Croke, 2013, The use of multi temporal LiDAR to assess basin-scale erosion and deposition following the catastrophic January 2011 Lockyer flood, SE queensland, Australia, Geomorphology, 184, 111, 10.1016/j.geomorph.2012.11.023 Cucchiaro, 2019, Geomorphic effectiveness of check dams in a debris-flow catchment using multitemporal topographic surveys, Catena, 174, 73, 10.1016/j.catena.2018.11.004 Curriero, 1999, A composite likelihood approach to semivariogram estimation, J. Agric. Biol. Environ. Stat., 4, 9, 10.2307/1400419 Dong, 2017 Eidsvik, 2014, Estimation and prediction in spatial models with block composite likelihoods, J. Comput. Graph. Statist., 23, 295, 10.1080/10618600.2012.760460 Engle, 1982, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987, 10.2307/1912773 Fan, 2001, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Amer. Statist. Assoc., 96, 1348, 10.1198/016214501753382273 Fronterrè, 2018, Geostatistical inference in the presence of geomasking: a composite-likelihood approach, Spatial Stat., 28, 319, 10.1016/j.spasta.2018.06.004 Giraud, 2021 Huang, 2010, Spatial Lasso with applications to GIS model selection, J. Comput. Graph. Statist., 19, 963, 10.1198/jcgs.2010.07102 James, 2012, Geomorphic change detection using historic maps and DEM differencing: The temporal dimension of geospatial analysis, Geomorphology, 137, 181, 10.1016/j.geomorph.2010.10.039 Joerg, 2012, Uncertainty assessment of multi-temporal airborne laser scanning data: A case study on an Alpine glacier, Remote Sens. Environ., 127, 118, 10.1016/j.rse.2012.08.012 Li, 2020, Variable selection of partially linear varying coefficient spatial autoregressive model, J. Stat. Comput. Simul., 90, 2681, 10.1080/00949655.2020.1788560 Lindsay, 1988, Composite likelihood methods, 80, 221 Liu, 2018, A penalized quasi-maximum likelihood method for variable selection in the spatial autoregressive model, Spatial Stat., 25, 86, 10.1016/j.spasta.2018.05.001 Merk, 2022, Estimation of the spatial weighting matrix for regular lattice data - an adaptive lasso approach with cross-sectional resampling, Environmetrics, 33, 10.1002/env.2705 Milan, 2011, Filtering spatial error from DEMs: Implications for morphological change estimation, Geomorphology, 125, 160, 10.1016/j.geomorph.2010.09.012 Mora, 2018, Landslide change detection based on multi-temporal airborne LiDAR-derived DEMs, Geosciences, 8, 23, 10.3390/geosciences8010023 Okyay, 2019, Airborne LiDAR change detection: An overview of earth sciences applications, Earth-Sci. Rev., 198, 10.1016/j.earscirev.2019.102929 O’Neal, 2011, The rates and spatial patterns of annual riverbank erosion revealed through terrestrial laser-scanner surveys of the South River, Virginia, Earth Surf. Process. Landforms, 36, 695, 10.1002/esp.2098 Otto, 2019, spGARCH: An R-package for spatial and spatiotemporal ARCH and GARCH models, R J., 11, 401, 10.32614/RJ-2019-053 Otto, 2020 Otto, 2018, Generalised spatial and spatiotemporal autoregressive conditional heteroscedasticity, Spatial Stat., 26, 125, 10.1016/j.spasta.2018.07.005 Otto, 2021, Stochastic properties of spatial and spatiotemporal arch models, Statist. Papers, 62, 623, 10.1007/s00362-019-01106-x Picco, 2013, Evaluating short-term morphological changes in a gravel-bed braided river using terrestrial laser scanner, Geomorphology, 201, 323, 10.1016/j.geomorph.2013.07.007 Rice, 2008, Methods for handling multiple testing, Adv. Genet., 60, 293, 10.1016/S0065-2660(07)00412-9 Romano, 2007, Control of generalized error rates in multiple testing, Ann. Statist., 35, 1378, 10.1214/009053606000001622 Salach, 2018, Accuracy assessment of point clouds from LiDAR and dense image matching acquired using the UAV platform for DTM creation, ISPRS Int. J. Geo-Inf., 7, 342, 10.3390/ijgi7090342 Sangalli, 2013, Spatial spline regression models, J. R. Stat. Soc. Ser. B Stat. Methodol., 75, 681, 10.1111/rssb.12009 Schaffrath, 2015, Landscape-scale geomorphic change detection: Quantifying spatially variable uncertainty and circumventing legacy data issues, Geomorphology, 250, 334, 10.1016/j.geomorph.2015.09.020 Simpson, 2017, Assessment of errors caused by forest vegetation structure in airborne LiDAR-derived DTMs, Remote Sens., 9, 1101, 10.3390/rs9111101 Stein, 2004, Approximating likelihoods for large spatial data sets, J. R. Stat. Soc. Ser. B Stat. Methodol., 66, 275, 10.1046/j.1369-7412.2003.05512.x Tibshirani, 1996, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B Stat. Methodol., 58, 267 Varin, 2011, An overview of composite likelihood methods, Statist. Sinica, 21, 5 Vecchia, 1988, Estimation and model identification for continuous spatial processes, J. R. Stat. Soc. Ser. B Stat. Methodol., 50, 297 Vericat, 2014, Patterns of topographic change in sub-humid badlands determined by high resolution multi-temporal topographic surveys, Catena, 120, 164, 10.1016/j.catena.2014.04.012 Vericat, 2017, 121 Wheaton, 2010, Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets, Earth Surf. Process. Landf. J. Br. Geomorphol. Res. Group, 35, 136, 10.1002/esp.1886 Williams, 2012, DEMs of difference, Geomorphol. Tech., 2 Xiang, 2006, Interval estimation in a finite mixture model: Modeling P-values in multiple testing applications, Comput. Statist. Data Anal., 51, 570, 10.1016/j.csda.2005.11.011 Zhang, 2010, Nearly unbiased variable selection under minimax concave penalty, Ann. Statist., 38, 894, 10.1214/09-AOS729 Zhu, 2010, On selection of spatial linear models for lattice data, J. R. Stat. Soc. Ser. B Stat. Methodol., 72, 389, 10.1111/j.1467-9868.2010.00739.x Zou, 2006, The adaptive lasso and its oracle properties, J. Amer. Statist. Assoc., 101, 1418, 10.1198/016214506000000735 Zou, 2005, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B Stat. Methodol., 67, 301, 10.1111/j.1467-9868.2005.00503.x