Improved landsat-based forest mapping in steep mountainous terrain

By Luuk Dorren, B. Maier, Arie C. Seijmonsbergen. 2003

282 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/4aeb8571b4f44c85f202baae8288878e957a413f

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In Environmental Engineering

Citations in the corpus

Object based image analysis for remote sensing (2010, methodology): “here can the specific advantages of the OBIA approach be deployed, although regionalisation approaches have also been applied to other situations, for example to Landsat images, and recent studies have also utilised OBIA methods for medium or coarse resolution data (Dorren et al., 2003; Geneletti an”
Spectral analysis and classification accuracy of coffee crops using Landsat and a topographic‐environmental model (2007, methodology): “2.4 Topographic data preparation The effective incident angle was calculated using equation (1), described by Holben and Justice (1980), as cited by Colby and Keating (1998) and Dorren et al. (2003).”
Developments in Landsat Land Cover Classification Methods: A Review (2017, methodology): “Similar results were reported in Dorren, Maier and Seijmonsbergen [78] in which the classification accuracy was low (62%) at a segmentation scale value of 2, increased to its highest value (73%) at a scale of 20, before decreasing to 40% at a large scale factor”
Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues (2009, background): “0099-1112/09/7507–0819/$3.00/0 © 2009 American Society for Photogrammetry and Remote Sensing”
Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets (2015, methodology): “…the results of this study seem to be promising, the terrain in Zululand is relatively flat and therefore this method should be tested in rugged terrain, where both the land cover and topography would affect the spectral values of remotely sensed imagery (Dorren, Maier, and Seijmonsbergen 2003).”
Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery (2017, methodology): “While OBIA is often more accurate than pixel-based methods for mapping forest cover at high spatial resolutions (Agarwal et al., 2013; Dorren et al., 2003; Oruc et al., 2004), comparative studies indicate that coupling pixel-based and OBIA techniques can improve the accuracy of forest type classific”
Unsupervised image segmentation evaluation and refinement using a multi-scale approach (2011, unknown): “For remote sensing studies, this ability to choose segmentation scale(s) is useful but challenging (Dorren et al., 2003; Kim et al., 2009).”