Object-based image analysis for distinguishing indigenous and exotic forests in coffee production areas of Ethiopia
- 207 Downloads
Indigenous forest management and conservation has a major importance for ecosystem services in the Eastern Afromontane Biodiversity Hotspots. In the southwestern highlands of Ethiopia, indigenous forests are particularly relevant for coffee producers, given that Coffea arabica grows as understory shrub in these forests. Currently, identifying and mapping understory coffee using remote sensing is still considered a challenging task because exotic tree plantations are largely overspread among indigenous forests. In this paper, a rule set was developed for recognizing indigenous forests from high-resolution satellite imagery using object-based image analysis (OBIA). The study applies a multiscale approach, in which aerial photographs (0.5 m), SPOT-5 satellite image (2.5 m), and field observations were integrated to discriminate indigenous from exotic forests. The rule-set combined segmentation (multiresolution, spectral difference, and contrast splitting), classification algorithms, and knowledge-based threshold functions. Moreover, principal component analysis and imagery texture indexes (e.g., homogeneity) were used to feed the classification algorithms. The results show that the applied methodology could separate indigenous from exotic forests with an overall accuracy of 84.3 % based on a fourfold cross-validation. The user and producer accuracy of indigenous forest were 84.7 and 94.4 %, respectively. On the other hand, exotic forest was classified with user accuracy of 87.9 % and producer accuracy of 61.9 %. This study contributes not only to coffee and environmental researchers but also benefits local communities by allowing the identification of indigenous and exotic forest areas, and leading to better informed natural resources management and conservation.
KeywordsOBIA PCA Coffea arabica Indigenous forest Exotic forest Ethiopia
This study is a part of The Climate Change Impacts on Ecosystem Services and Food Security in Eastern Africa (CHIESA) project funded by the Ministry for Foreign Affairs of Finland and International Center of Insect Physiology and Ecology (ICIPE). The authors wish to thank the European Space Agency (ESA) for providing the SPOT5 imagery to Marion Pfeifer, PI user agreement 6972.
- CEPF (2012) Eastern Afromontagne Biodiversity Hotspot. Ecosystem Profile Summary. http://www.cepf.net. Accessed 24 January 2013
- Chavez PS Jr (1996) Image-based atmospheric corrections. Rev Impr Photogramm Eng Remote Sens 62:1025–1036Google Scholar
- FAO (2005) Land Cover Classification System: classification concepts and user manual Software version 2, revised by Di Gregorio, A and Jansen LJM. Food and Agriculture Organization of the United Nations (FAO), RomeGoogle Scholar
- Gabgkofner U, Holcomb D (2008) HP Resolution merge: ERDAS IMAGINE help. Leica geosystems geospatial imaging, LLCGoogle Scholar
- Getahun A, Feyisa A, Belay G (1990) A brief history of FRC and forestry research in Ethiopia: 1890–1990. Forestry Research Centre, Addis AbabaGoogle Scholar
- Hall-Beyer M (2000) GLCM texture: an online tutorial. http://www.fp.ucalgary.ca/ mhallbey/transposing.html. Accessed February 2013
- MosaicMill (2013) EnsoMosaic aerial digital imaging and image processing system. http://www.ensomosaic.com/