Improving k-nearest neighbor predictions in forest inventories by combining high and low density airborne laser scanning data

Remote Sensing of Environment

Volumn 117, Issue , 2012, Pages 358-365

  Breidenbach, Johannes ^a,b   Næsset, Erik ^b   Gobakken, Terje ^b  

^a NORWEGIAN INSTITUTE OF BIOECONOMY RESEARCH   (Norway)

^b NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

Author keywords

Area based approach; Biomass; Forest inventory; Individual tree crown detection; KNN; Lidar

Indexed keywords

ABOVE GROUND BIOMASS; AIRBORNE LASER SCANNING; AREA-BASED; DATA RANGES; EXPLANATORY VARIABLES; FLIGHT DIRECTION; FLIGHT STRIPS; FOREST INVENTORY; HEIGHT MEASUREMENT; HIGH DENSITY; INDIVIDUAL TREE CROWN; K-NEAREST NEIGHBORS; KNN; LOW DENSITY; PARALLEL STRIPS; ROOT MEAN SQUARED; TREE CROWNS;

BIOMASS; FORECASTING; LASER APPLICATIONS; OPTICAL RADAR;

FORESTRY;

ABOVEGROUND BIOMASS; ACCURACY ASSESSMENT; AIRBORNE SENSING; CANOPY ARCHITECTURE; DATA SET; FOREST INVENTORY; LASER; MEASUREMENT METHOD; NUMERICAL MODEL; POTASSIUM; PREDICTION;

AREA; BIOMASS; CROWNS; DATA PROCESSING; FORECASTS; FORESTRY; FORESTS; INVENTORY CONTROL; LASERS; OPTICAL ACTIVITY; RADAR;

EID: 84855465036     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2011.10.010     Document Type: Article

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