Capability of models to predict leaf N and P across four seasons for six sub-tropical forest evergreen trees

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 101, Issue , 2015, Pages 209-220

  Van Deventer, H ^a   Cho, M A ^a,b   Mutanga, O ^b   Ramoelo, A ^a,c  

^a DSI CSIR Nanotechnology Innovation Centre   (South Africa)

^b UNIVERSITY OF KWAZULU NATAL   (South Africa)

^c UNIVERSITY OF LIMPOPO   (South Africa)

Author keywords

Foliar N and P; Leaf seasonal variation; Nutrient phenology; Predictive models; Remote sensing; Subtropical evergreen forests

Indexed keywords

ANALYSIS OF VARIANCE (ANOVA); BIOLOGY; FORECASTING; MEAN SQUARE ERROR; NUTRIENTS; PHOTOSYNTHESIS; REMOTE SENSING; TROPICS;

COEFFICIENT OF VARIATION; EVERGREEN FORESTS; FOLIAR N; NUTRIENT CONCENTRATIONS; PREDICTIVE MODELS; REMOTE SENSING TECHNIQUES; ROOT MEAN SQUARE ERRORS; SEASONAL VARIATION;

FORESTRY;

ACCURACY ASSESSMENT; ECOLOGICAL MODELING; ERROR ANALYSIS; EVERGREEN TREE; GLOBAL CHANGE; NITROGEN; PHENOLOGY; PHOSPHORUS; REMOTE SENSING; SEASONAL VARIATION; SPECTRAL ANALYSIS; TROPICAL FOREST;

FORESTS; MODELS; PHENOLOGY; PHOTOSYNTHESIS; REMOTE SENSING;

EID: 84921344858     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2014.12.017     Document Type: Article

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