Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery

Detailed land use/land cover classification at ecotope level is important for environmental evaluation. In this study, we investigate the possibility of using airborne hyperspectral imagery for the classification of ecotopes. In particular, we assess two tree-based ensemble classification algorithms: Adaboost and Random Forest, based on standard classification accuracy, training time and classification stability. Our results show that Adaboost and Random Forest attain almost the same overall accuracy (close to 70%) with less than 1% difference, and both outperform a neural network classifier (63.7%). Random Forest, however, is faster in training and more stable. Both ensemble classifiers are considered effective in dealing with hyperspectral data. Furthermore, two feature selection methods, the out-of-bag strategy and a wrapper approach feature subset selection using the best-first search method are applied. A majority of bands chosen by both methods concentrate between 1.4 and 1.8 mu m at the early shortwave infrared region. Our band subset analyses also include the 22 optimal bands between 0.4 and 2.5 mu m suggested in Thenkabail et al. [Thenkabail, RS., Enclona, E.A., Ashton, M.S., and Van Der Meer, B. (2004). Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment, 91, 354-376.] due to similarity of the target classes. All of the three band subsets considered in this study work well with both classifiers as in most cases the overall accuracy dropped only by less than 1%. A subset of 53 bands is created by combining all feature subsets and comparing to using the entire set the overall accuracy is the same with Adaboost, and with Random Forest, a 0.2% improvement. The strategy to use a basket of band selection methods works better. Ecotopes belonging to the tree classes are in general classified better than the grass classes. Small adaptations of the classification scheme are recommended to improve the applicability of remote sensing method for detailed ecotope mapping. (C) 2008 Elsevier Inc. All rights reserved

Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward

Monitoring and reporting on the state of nature gained increasing importance in the European Union with the implementation of the Habitats Directive and the Natura 2000 network. Reporting habitat conservation status requires detailed knowledge on many aspects of habitats at different spatial levels. Remote sensing is recognised as a powerful tool to acquire synoptic data on habitats, but to date, its use for Natura 2000 monitoring and reporting is still very limited. One reason for this appears to be the knowledge gap between the nature conservation agencies and the remote sensing community. We conducted a review of legal monitoring and reporting requirements on Natura 2000 habitats, looked into the current use of remote sensing in habitat reporting, and consulted monitoring experts in nature conservation administrations to find out about their attitude and expectations towards remote sensing. In this paper, we disclose and summarise the real data needs behind the legal requirements for Natura 2000 habitat monitoring and reporting, analyse opportunities and constraints for remote sensing, and highlight bottlenecks and pathways to resolve them. Monitoring experts are not unwilling to use remote sensing data, but they are unsure of whether remote sensing can suit their needs in a cost-effective way. They look upon remote sensing as a one-way process of data deliverance and fail to see the importance of their active cooperation. Based on our findings, we argue that the integration of remote sensing into Natura 2000 habitat monitoring could benefit from (1) harmonising and standardising approaches, (2) focusing on data at hand to develop readily useful products, (3) a proper validation of both traditional and remote sensing methods, and (4) an enhanced sharing and exchange of ideas and results between the different research communities involve