Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR
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Lênio Soares Galvão | Ricardo Dalagnol | Fabien Hubert Wagner | Oliver L. Phillips | Luiz E. O. C. Aragão | Emanuel Gloor | Charton J. Locks | O. Phillips | L. Aragão | E. Gloor | F. Wagner | Ricardo Dalagnol | Charton J. Locks | L. Galvão
[1] B. R. Ramesh,et al. Pan‐tropical prediction of forest structure from the largest trees , 2018, Global Ecology and Biogeography.
[2] David B. Clark,et al. Application of merged 1-m and 4-m resolution satellite data to research and management in tropical forests , 2003 .
[3] A. Contreras-Hermosilla,et al. The economics of illegal logging and associated trade. , 2008 .
[4] Virgilio Gómez-Rubio,et al. Spatial Point Patterns: Methodology and Applications with R , 2016 .
[5] P. Jones,et al. Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .
[6] Sam Lawson and Larry MacFaul. Illegal Logging and Related Trade: Indicators of the Global Response , 2010 .
[7] B. Poulter,et al. Environmental change and the carbon balance of Amazonian forests , 2014, Biological reviews of the Cambridge Philosophical Society.
[8] M. Keller,et al. Structural Dynamics of Tropical Moist Forest Gaps , 2015, PloS one.
[9] A. K. Skidmore,et al. Free satellite data key to conservation , 2018, Science.
[10] N. Brokaw,et al. The definition of treefall gap and its effect on measures of forest dynamics. , 1982 .
[11] T. E. Martin,et al. Logging impacts on avian species richness and composition differ across latitudes and foraging and breeding habitat preferences , 2017, Biological reviews of the Cambridge Philosophical Society.
[12] Robert J. McGaughey,et al. Monitoring selective logging in western Amazonia with repeat lidar flights , 2014 .
[13] Joanne C. White,et al. Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring , 2008 .
[14] Cibele Hummel do Amaral,et al. Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations , 2017 .
[15] M. d'Oliveira,et al. Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon , 2012 .
[16] J. Dalling,et al. Spatial scale and sampling resolution affect measures of gap disturbance in a lowland tropical forest: implications for understanding forest regeneration and carbon storage , 2014, Proceedings of the Royal Society B: Biological Sciences.
[17] Cardona Alzate,et al. Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .
[18] A. Huete,et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .
[19] Julien Michel,et al. Orfeo ToolBox: open source processing of remote sensing images , 2017, Open Geospatial Data, Software and Standards.
[20] T. Stone,et al. Using multi-temporal satellite data to evaluate selective logging in Para, Brazil , 1998 .
[21] W. Buermann,et al. Mapping tropical disturbed forests using multi-decadal 30 m optical satellite imagery , 2019, Remote Sensing of Environment.
[22] M. Cochrane. Fire science for rainforests , 2003, Nature.
[23] S. Hubbell,et al. Adult mortality in a low-density tree population using high-resolution remote sensing. , 2017, Ecology.
[24] J. A. Schell,et al. Monitoring vegetation systems in the great plains with ERTS , 1973 .
[25] Hurtado Abril,et al. Generación de un índice espectro-temporal para la identificación de zonas afectadas por deforestación usando imágenes Landsat. , 2020 .
[26] A. Zanne,et al. Selective logging: do rates of forest turnover in stems, species composition and functional traits decrease with time since disturbance? - A 45 year perspective. , 2015, Forest ecology and management.
[27] David B. Clark,et al. APPLICATION OF 1-M AND 4-M RESOLUTION SATELLITE DATA TO ECOLOGICAL STUDIES OF TROPICAL RAIN FORESTS , 2004 .
[28] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[29] D. Clark,et al. Quantifying mortality of tropical rain forest trees using high-spatial-resolution satellite data , 2004 .
[30] P. Diggle. A Kernel Method for Smoothing Point Process Data , 1985 .
[31] Nicolas Barbier,et al. Remote sensing detection of droughts in Amazonian forest canopies. , 2010, The New phytologist.
[32] David B. Clark,et al. GETTING TO THE CANOPY: TREE HEIGHT GROWTH IN A NEOTROPICAL RAIN FOREST , 2001 .
[33] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[34] Julien Radoux,et al. Bayesian Data Fusion for Adaptable Image Pansharpening , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[35] B. Hérault,et al. Environmental control of natural gap size distribution in tropical forests , 2016 .
[36] Maggi Kelly,et al. An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery , 2007 .
[37] G. Powell,et al. High-resolution forest carbon stocks and emissions in the Amazon , 2010, Proceedings of the National Academy of Sciences.
[38] Didier Tanré,et al. Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..
[39] C. Justice,et al. High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.
[40] Marcos Longo,et al. El Niño drought increased canopy turnover in Amazon forests. , 2018, The New phytologist.
[41] M. Keller,et al. Selective Logging in the Brazilian Amazon , 2005, Science.