Empirical prediction of leaf area index (LAI) of endangered tree species in intact and fragmented indigenous forests ecosystems using WorldView-2 data and two robust machine learning algorithms

Remote Sensing

Volumn 8, Issue 4, 2016, Pages

  Omer, Galal ^a,b   Mutanga, Onisimo ^a   Abdel Rahman, Elfatih M ^a,b   Adam, Elhadi ^c  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

^b UNIVERSITY OF KHARTOUM   (Sudan)

^c UNIVERSITY OF THE WITWATERSRAND   (South Africa)

Author keywords

Artificial neural networks; Indigenous forest; WorldView 2; Leaf area index; Support vector machines; Tree species

Indexed keywords

CONSERVATION; ECOSYSTEMS; FORECASTING; FORESTRY; NEURAL NETWORKS; PLANTS (BOTANY); REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

ENDANGERED TREE SPECIES; LEAF AREA INDEX; REGRESSION ALGORITHMS; SPECTRAL VEGETATION INDICES; STRUCTURAL CHARACTERISTICS; TREE SPECIES; VERY HIGH SPATIAL RESOLUTIONS; WORLDVIEW-2;

LEARNING ALGORITHMS;

EID: 84971618277     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8040324     Document Type: Article

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