Evaluating the influence of the Red Edge band from RapidEye sensor in quantifying leaf area index for hydrological applications specifically focussing on plant canopy interception

Physics and Chemistry of the Earth

Volumn 100, Issue , 2017, Pages 73-80

  Dube, Timothy ^a   Mutanga, Onisimo ^b   Sibanda, Mbulisi ^b   Shoko, Cletah ^b   Chemura, Abel ^b,c  

^a UNIVERSITY OF LIMPOPO   (South Africa)

^b UNIVERSITY OF KWAZULU NATAL   (South Africa)

^c Chinhoyi University of Technology   (Zimbabwe)

Author keywords

Canopy interception; Maximum storage capacity; NDVI RE; New generation RapidEye sensor; Plantation forests; Strageically position bands

Indexed keywords

ADAPTIVE BOOSTING; DECISION TREES; PLANTS (BOTANY); RANDOM FORESTS; STOCHASTIC SYSTEMS;

CANOPY INTERCEPTION; PLANTATION FORESTS; RAPIDEYE; STORAGE CAPACITY; STRAGEICALLY-POSITION BANDS;

MACHINE LEARNING;

ALGORITHM; FOREST CANOPY; INTERCEPTION; LEAF AREA INDEX; NDVI; PLANT; PLANTATION FORESTRY; RAPIDEYE; SENSOR;

PINUS TAEDA;

EID: 85013469551     PISSN: 14747065     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pce.2017.02.016     Document Type: Article

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