Veraldo Liesenberg
Veraldo Liesenberg
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Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data
GV Laurin, N Puletti, W Hawthorne, V Liesenberg, P Corona, D Papale, ...
Remote Sensing of Environment 176, 163-176, 2016
Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa
GV Laurin, V Liesenberg, Q Chen, L Guerriero, F Del Frate, A Bartolini, ...
International Journal of Applied Earth Observation and Geoinformation 21, 7-16, 2013
Evaluating Sentinel-2 and Landsat-8 data to map sucessional forest stages in a subtropical forest in Southern Brazil
C Sothe, CM Almeida, V Liesenberg, MB Schimalski
Remote Sensing 9 (8), 838, 2017
Variations in reflectance with seasonality and viewing geometry: implications for classification of Brazilian savanna physiognomies with MISR/Terra data
V Liesenberg, LS Galvão, FJ Ponzoni
Remote Sensing of Environment 107 (1-2), 276-286, 2007
Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs
AA Santos, J Marcato Junior, MS Araújo, DR Di Martini, EC Tetila, ...
Sensors 19 (16), 3595, 2019
Evaluating SAR polarization modes at L-band for forest classification purposes in Eastern Amazon, Brazil
V Liesenberg, R Gloaguen
International Journal of Applied Earth Observation and Geoinformation 21 …, 2013
Tree species classification in a highly diverse subtropical forest integrating UAV-based photogrammetric point cloud and hyperspectral data
C Sothe, M Dalponte, CM Almeida, MB Schimalski, CL Lima, ...
Remote Sensing 11 (11), 1338, 2019
Possibilities of discriminating tropical secondary succession in Amazônia using hyperspectral and multiangular CHRIS/PROBA data
LS Galvão, FJ Ponzoni, V Liesenberg, JR dos Santos
International Journal of Applied Earth Observation and Geoinformation 11 (1 …, 2009
A principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne LiDAR data
CA Silva, C Klauberg, AT Hudak, LA Vierling, V Liesenberg, ...
Forestry: An International Journal of Forest Research 89 (4), 422-433, 2016
Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery
D Lobo Torres, R Queiroz Feitosa, P Nigri Happ, L Elena Cue La Rosa, ...
Sensors 20 (2), 563, 2020
Análise da dinâmica sazonal e separabilidade espectral de algumas fitofisionomias do Cerrado com índices de vegetação dos sensores MODIS/TERRA e AQUA
V Liesenberg, FJ Ponzoni, LS Galvão
Revista Árvore 31, 295-305, 2007
Object-oriented and pixel-based classification approaches to classify tropical successional stages using airborne high–spatial resolution images
GA Piazza, AC Vibrans, V Liesenberg, JC Refosco
GIScience & Remote Sensing 53 (2), 206-226, 2016
Predicting canopy nitrogen content in citrus-trees using random forest algorithm associated to spectral vegetation indices from UAV-imagery
L Prado Osco, AP Marques Ramos, D Roberto Pereira, ...
Remote Sensing 11 (24), 2925, 2019
Retrieval of forest attributes in complex successional forests of Central Indonesia: Modeling and estimation of bitemporal data
A Wijaya, V Liesenberg, R Gloaguen
Forest Ecology and Management 259 (12), 2315-2326, 2010
Multi-temporal airborne LiDAR-survey and field measurements of tropical peat swamp forest to monitor changes
HDV Boehm, V Liesenberg, SH Limin
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2013
Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and …
C Sothe, CM De Almeida, MB Schimalski, LEC La Rosa, JDB Castro, ...
GIScience & Remote Sensing 57 (3), 369-394, 2020
Delineation of potential sites for rice cultivation through multi-criteria evaluation (MCE) using remote sensing and GIS
SMH Raza, SA Mahmood, AA Khan, V Liesenberg
International Journal of Plant Production 12 (1), 1-11, 2018
A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements
LP Osco, APM Ramos, MM Faita Pinheiro, ÉAS Moriya, NN Imai, ...
Remote Sensing 12 (6), 906, 2020
Optical and SAR remote sensing synergism for mapping vegetation types in the endangered Cerrado/Amazon ecotone of Nova Mutum—Mato Grosso
F de Souza Mendes, D Baron, G Gerold, V Liesenberg, S Erasmi
Remote Sensing 11 (10), 1161, 2019
Análise comparativa de classificadores digitais em imagens do Landsat-8 aplicados ao mapeamento temático
DFT Garofalo, CG Messias, V Liesenberg, ÉL Bolfe, MC Ferreira
Pesquisa Agropecuária Brasileira 50, 593-604, 2015
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