TUP.P19: Forest and Vegetation Mapping Through Machine Learning Methods II
Tue, 18 Jul, 14:15 - 15:45 Pacific Time (UTC -7)
Location: Poster Area 19
Session Type: Poster
Session Co-Chairs: Fabien Wagner, CTrees and Yan Cheng, University of Copenhagen
Session Managers: Ge Jiang and Lanying Wang and Ayoti Banerjee and Shubham Awasthi
Track: Land Applications

TUP.P19.1: COMPARISON OF SINGLE TREE SPECIES CLASSIFICATION USING VERY DENSE ALS DATA OR DUAL-WAVE ALS DATA
Henrik Persson, Christoffer Axelsson, Ritwika Mukhopadhyay, Langning Huo, Johan Holmgren, Swedish University of Agricultural Sciences, Sweden

TUP.P19.2: EXPLORING THE EFFECTS OF DIFFERENT DROUGHT TYPES ON FORESTS WITH EXPLAINABLE AI
Stenka Vulova, Alby Duarte Rocha, Technische Universität Berlin, Germany; Akpona Okujeni, Humboldt-Universität zu Berlin, Germany; Johannes Vogel, Freie Universität Berlin, Germany; Michael Förster, Birgit Kleinschmit, Technische Universität Berlin, Germany

TUP.P19.3: ESTIMATION OF LIVE FUEL MOISTURE CONTENT BASED ON A MACHINE LEARNING APPROACH
Wenli Wang, Rui Chen, Chunquan Fan, Mingzhao Li, Miao Jiao, University of Electronic Science and Technology of China, China; ,

TUP.P19.4: USING MACHINE LEARNING AND GOOGLE EARTH ENGINE TO DEVELOP A HIGH-RESOLUTION FOREST CANOPY COVER DATASET: A CASE STUDY IN ARKANSAS, USA
Hamdi Zurqani, University of Arkansas, United States

TUP.P19.5: DEEP LEARNING FOR MONITORING THE ECUADORIAN PARAMO
MARCO JAVIER CASTELO CABAY, ROSA MARIA AYALA PALENZUELA, JOSE ANTONIO PIEDRA FERNANDEZ, Applied Computing Group (ACG), University of Almeria, Almeria (Spain), Spain; ,

TUP.P19.7: MAPPING FOREST DISTURBANCE TYPES IN CHINA WITH LANDSAT TIME SERIES
Lian-Zhi Huo, Ping Tang, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), China

TUP.P19.8: SPATIOTEMPORAL ANALYSIS OF VEGETATION DYNAMICS BASED ON MULTI-RESOLUTION VEGETATION PRODUCTS IN THE THREE PARALLEL RIVERS BASIN FROM 2000 TO 2018
Dantong Zhong, Dan-Xia Song, Zihao Wang, Central China Normal University, China

TUP.P19.9: MODELING CROWN-BULK DENSITY FROM AIRBORNE AND TERRESTRIAL LASER SCANNING DATA IN A LONGLEAF PINE FOREST ECOSYSTEM
Carlos Alberto Silva, Kleydson Diego Rocha, Diogo N. Cosenza, Midhun Mohan, Carine Klauberg, Monique Bohora Schlickmann, Jinyi Xia, University of Florida, United States; Danilo Almeida, Department of Forest Sciences, University of São Paulo, “Luiz de Queiroz” College of Agriculture (USP/ESALQ), Piracicaba, SP, Brazil;, Brazil; Jeff W. Atkins, Southern Research Station, USDA Forest Service, Savannah River Site, United States; Adrian Cardil, Tecnosylva. Parque Tecnológico de León. 24009, León, Spain, Spain; Eric Rowell, Division of Atmospheric Sciences, Desert Research Institute, United States; Russ Parsons, Rocky Mountain Research Station, USDA Forest Service, Fire Sciences Laboratory,, United States; Nuria Sánchez-López, College of Natural Resources, University of Idaho, United States; Susan J. Prichard, University of Washington School of Environmental and Forest Sciences, Seattle, United States; Andrew T. Hudak, Rocky Mountain Research Station, USDA Forest Service, Forestry Sciences Laboratory, United States

TUP.P19.10: MAPPING GLOBAL LEAF INCLINATION ANGLE (LIA) BASED ON FIELD MEASUREMENT DATA
Sijia Li, Hongliang Fang, LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China

TUP.P19.11: VEGETATION FUEL TYPE CLASSIFICATION USING OPTIMISED SYNERGY OF SENTINEL DATA AND TEXTURE FEATURE
Pegah Mohammadpour, University of Coimbra & University of Alcala, Portugal; Domingos Xavier Viegas, University of Coimbra, Portugal; Emilio Chuvieco, University of Alcala, Spain; Alcides Pereira, Vasco Mantas, University of Coimbra, Portugal