Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Environmental Monitoring and Assessment

Volumn 188, Issue 3, 2016, Pages 1-19

  Michez, Adrien ^a   Piégay, Hervé ^b   Lisein, Jonathan ^a   Claessens, Hugues ^a   Lejeune, Philippe ^a  

^a UNIVERSITY OF LIÈGE   (Belgium)

^b UNIVERSITÉ DE LYON   (France)

Author keywords

Forest health condition; Hyperspatial imagery; Multi temporal remote sensing; Random forests; Riparian forest; UAS; UAV; Unmanned aerial system; Unmanned Aerial Vehicle

Indexed keywords

COST EFFECTIVENESS; DECISION TREES; ECOLOGY; FORESTRY; HEALTH; REMOTE SENSING; UNMANNED AERIAL VEHICLES (UAV);

FOREST HEALTH; HYPERSPATIAL IMAGERY; MULTI-TEMPORAL REMOTE SENSING; RANDOM FORESTS; RIPARIAN FORESTS; UNMANNED AERIAL SYSTEMS;

IMAGE ANALYSIS;

AIRBORNE SENSING; FOREST HEALTH; IMAGE ANALYSIS; IMAGE CLASSIFICATION; REMOTE SENSING; RIPARIAN FOREST; RIPARIAN ZONE; UNMANNED VEHICLE;

CLASSIFICATION; IMAGE ANALYSIS; IMAGERY; MODEL; MONITORING; RAMET; RIPARIAN ECOSYSTEM; SPECIES; SPECIES COMPOSITION; TIME SERIES ANALYSIS; ENVIRONMENTAL MONITORING; FOREST; PROCEDURES; REMOTE SENSING; RIVER; THEORETICAL MODEL; TREE;

ALNUS; ALNUS GLUTINOSA;

ENVIRONMENTAL MONITORING; FORESTS; MODELS, THEORETICAL; REMOTE SENSING TECHNOLOGY; RIVERS; TREES;

EID: 84957558105     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-015-4996-2     Document Type: Article

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