THEMATIC SESSION 2 / Land cover and change, water bodies, snow and ice and disturbances
Preference : Oral
CONTRIBUTION OF AN OBJECT-ORIENTED IMAGE SEGMENTATION TO FOREST COVER
MAPPING
Cano, E.1, Bisquert, M.2, Denux, JP.1, Chéret, V.1
Email address of the presenting author: emmanuelle.cano@purpan.fr
In this study we provide forest cover maps of the Pyrenees Mountains (Spain, Andorra, France) using
supervised classification. The final prospect of this work is to carry out a change analysis and will be
achieved obtaining a sufficient accuracy level for our classification results. The study presented aims
at evaluating the contribution of a stratification in improving the quality of the classification results.
The study area covers about 50 000 km2 and a wide topographic, climatic and altitudinal variability is
observed for this territory. These abiotic conditions contribute to the diversity of the forest cover. This
context implies for our study to consider thirty different types of forest or natural vegetation. The
classification process is based on a MODIS Normalized Difference Vegetation Index (NDVI) time
series composed of MOD13Q1 images from 2000 to 2012. The distinction of vegetation types is
performed using the annual NDVI profile as a temporal signature. Maximum likelihood has been
selected for our study, this algorithm being considered as a robust methodological reference. To
perform classification with this method, a good match is needed between each forest type and its
temporal signature. The training data set must be accurately representative of the variability of each
forest type and ensure the Gaussian distribution of its pixels. The stratification tested is a partitioning
of the study area by image segmentation. A previous study showed a possible application for the
stratification of the French territory in radiometrically homogeneous regions. This methodology used a
temporal series of vegetation and texture indices and an Object Based Image Analysis (OBIA).
Additionally, an unsupervised analysis of several segmentations allowed selecting the best
combination of input variables (seasonal/monthly vegetation and texture indices) and the best
segmentation parameters. By applying this methodology to the Pyrenees Mountains, the use of the
Enhanced Vegetation Index (EVI) and the dissimilarity index in the months of January, April and
September was identified as the best combination of the input variables. This stratification is therefore
used to improve the classification method. The study area was consequently divided into 33 strata,
considered as 33 distinct landscape units. We consider that this partitioning allows building up a more
representative training data set. The forest cover maps produced for each stratum are supposed to
reproduce with a better accuracy local appearances or small areas of specific forest categories.
Stratified classification should also avoid, for a given stratum, temporal signature confusions coming
from outer strata. A supervised classification was performed for each stratum, based on fitted training
data sets. The accuracy of the results obtained was evaluated calculating for each stratum the Kappa
Index, the overall accuracy and the thematic accuracy of each class (proportion of area with a correct
classification). To highlight the contribution of the stratification, these results were compared to a
classification of the whole study area without stratification, using a similar training data set and a
similar nomenclature. Our study is still being proceeded, but we already identified locations for which
the classification accuracy was improved. An improved local distinction between Scots Pine and Holm
Hoak was for example detected in several parts of the study area.
Keywords: Forest cover mapping, Pyrenees Mountains, MODIS NDVI time series, object-based
image segmentation, change analysis
1 - Université de Toulouse, Institut National Polytechnique de Toulouse, Ecole d’ingénieurs de Purpan, UMR
DYNAFOR, France
2 - UMR TETIS, France