Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes

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http://hdl.handle.net/10138/305233

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Hurskainen , P , Adhikari , H , Siljander , M , Pellikka , P & Hemp , A 2019 , ' Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes ' , Remote Sensing of Environment , vol. 233 , no. 111354 , 111354 . https://doi.org/10.1016/j.rse.2019.111354

Title: Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes
Author: Hurskainen, Pekka; Adhikari, Hari; Siljander, Mika; Pellikka, Petri; Hemp, Andreas
Contributor: University of Helsinki, Department of Geosciences and Geography
University of Helsinki, Helsinki Institute of Sustainability Science (HELSUS)
University of Helsinki, Earth Change Observation Laboratory (ECHOLAB)
University of Helsinki, Helsinki Institute of Sustainability Science (HELSUS)
Date: 2019-11
Language: eng
Number of pages: 17
Belongs to series: Remote Sensing of Environment
ISSN: 0034-4257
URI: http://hdl.handle.net/10138/305233
Abstract: Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel-based classifications, and the coverage, accuracy and resolution of free and open-access auxiliary datasets have been poor until recent years. We evaluated how recent global coverage open-access geospatial datasets improve object-based LULC classification accuracy compared to using only spectral and texture features from satellite images. We applied feature sets topography, population, soil, canopy cover, distance to watercourses and spectral-temporal metrics from Landsat-8 time series on the southern foothills and savanna of Mt. Kilimanjaro, Tanzania, where the landscape is characterized by heterogeneous and fragmented mosaic of disturbed savanna vegetation, croplands, and settlements. The classification was based on image objects (groups of spectrally similar pixels) derived from segmentation of four Formosat-2 scenes with 8m spatial resolution using 1370 ground reference points for training, validation, and for defining 17 LULC classes. We built six Random Forest classification models with different sets of object features in each. The baseline model having only spectral and texture features was compared with five other models supplemented with auxiliary features. Inclusion of auxiliary features significantly improved classification overall accuracy (OA). The baseline model gave a median OA of 60.7%, but auxiliary features in other models increased median OA between 6.1 and 16.5 percentage points. The best OA was achieved with a model including all features of which elevation was the most important auxiliary feature followed by Enhanced Vegetation Index temporal range and slope degree. Applying object-based classification to geospatial information on topography, soil, settlement patterns and vegetation phenology, the discriminatory potential of challenging LULC classes can be significantly improved. We demonstrated this for the first time, and the technique shows good potential for improving LULC mapping across a multitude of fragmented landscapes worldwide.
Subject: 1171 Geosciences
image segmentation
OBIA
Land use
Land cover classification
Auxiliary data
Random Forest
Satellite Time Series
Image segmentation
OBIA
Land use/land cover mapping
Auxiliary data
Random Forest
Satellite Time Series
IMAGE-ANALYSIS
RANDOM FOREST
MT. KILIMANJARO
TIME-SERIES
SURFACE TEMPERATURE
SOUTHERN SLOPES
ANCILLARY DATA
VEGETATION
MULTISOURCE
SELECTION
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