F. Van Coillie

Semi-automated forest stand delineation using wavelet based segmentation of very high resolution optical imagery

Stand delineation is one of the cornerstones of forest inventory mapping and a key element to spatial aspects in forest management decision making. Stands are forest management units with similarity in attributes such as species composition, density, closure, height and age. Stand boundaries are traditionally estimated through subjective visual air photo interpretation. In this paper, an automatic stand delineation method is presented integrating wavelet analysis into the image segmentation process. The new method was developed using simulated forest stands and was subsequently applied to real imagery: scanned aerial photographs of a forest site in Belgium and ADS40 aerial digital data of an olive grove site in Les Beaux de Provence, France. The presented method was qualitatively and quantitatively compared with traditional spectral based segmentation, by assessing its ability to support the creation of pure forest stands and to improve classification performance. A parcel/stand purity index was developed to evaluate stand purity and the expected mapping accuracy was estimated by defining a potential mapping accuracy measure. Results showed that wavelet based image segmentation outperformed traditional segmentation. Multi-level wavelet analysis proved to be a valuable tool for characterizing local variability in image texture and therefore allowed for the discrimination between stands. In addition, the proposed evaluation measures were found appropriate as segmentation evaluation criteria.

Previously trained neural networks as ensemble members: knowledge extraction and transfer

The use of Artificial Neural Networks (ANNs) for the classification of remotely sensed imagery offers several advantages over more conventional methods. Yet their training still requires a set of pixels with known land cover. To increase ANN classification accuracy when few training data are available, an algorithm was applied that allows experience gained in previous classifications to be reused. The proposed method was evaluated by classifying a tropical savannah region in northern Togo using Landsat Thematic Mapper (TM) imagery. The presented approach reached a mean kappa coefficient that was significantly larger (at the 95% level) than that obtained after training networks with randomly initialized weights. Also, the observed variances on the obtained accuracies were significantly lower when compared to networks that were randomly initialized. Finally, Bhattacharyya (BH) distances were used to explain why some land cover classes benefit more from knowledge transfer than others.

Training neural networks on artificially generated data: a novel approach to SAR speckle removal

A neural network-based method for speckle removal in synthetic aperture radar (SAR) images is introduced. The method rests on the idea that a neural network learning machine, trained on artificially generated input–target couples, can be used to efficiently process real SAR data. The explicit plus-point of the method is that it is trained with artificially generated data, reducing the demands put on real input data such as data quality, availability and cost price. The artificial data can be generated in such a way that they fit the particular characteristics of the images to be denoised, yielding case-specific, high-performing despeckling filters. A comparative study with three classical denoising techniques (Enhanced Frost (EF), Enhanced Lee (EL) and Gamma MAP (GM)) and a wavelet filter demonstrated a superior speckle removal performance of the proposed method in terms of quantitative performance measures. Moreover, qualitative evaluation of the despeckled results was in favour of the proposed method, confirming its speckle removal efficiency.

Beyond the urban mask

Despite the great importance of cities, relatively little consistent information about their internal configuration (structure, cover and materials) is available. The World Urban Database and Access Portal Tools (WUDAPT) initiative aims at the acquisition, storage and dissemination of data on the form and function of cities indifferent levels. At the lowest level, the Local Climate Zone (LCZ) scheme provides a basic description of urban structure. This scheme is a climate-based typology of urban and natural landscapes that also provides relevant information on basic physical properties of the landscape, which can be used in modelling and observational studies. The LCZ scheme has large potential as a standard generic description of urban areas. In this paper the scheme and our standard mapping approach are presented, followed by recent improvements and research on object-based image analysis, transferability of trained LCZ classifiers, quality of crowd contributions, and the use of other data sources and methods.

Optimized feature fusion of LiDAR and hyperspectral data for tree species mapping in closed forest canopies

This study deals with data fusion of hyperspectral and LiDAR sensors for tree species mapping in complex, closed forest canopies in Belgium. In particular, seven tree species were mapped: Beech, Ash, Larch, Poplar, Copper beech, Chestnut and Oak. The added value of LiDAR height profile data on tree species mapping was assessed. Sensor data were fused in the PCA domain, while optimal feature combination was derived from the best classification performance (in terms of Kappa and producers accuracy) based on 5-fold cross-validation. Besides, varying training set sizes were tested (resp. 10%, 30% and 50% number of samples per tree species class). Feature fusion of PCA-transformed HS and LiDAR data was most effective for small sample set sizes reaching a Kappa accuracy improvement of 10.51%.

Propagation of shadow effects on typical remote sensing applications in forestry

Airborne LiDAR and hyperspectral data were acquired over a broadleaved forest area in Belgium. Shadow fractions were calculated, based on Sun angles and a digital surface model derived from the LiDAR data. Pixels in the hyper-spectral image were classified based on the shadow fractions to study the effect of shadow on canopy reflectance and how the effect propagated to typical remote sensing applications in forestry. As a first application, the photosynthetical reflectance index (PRI) was studied, which expresses the relative down-regulation of photosynthesis. A strong correlation (R2 = 0.93) was found between the shadow fraction and the PRI. The second application was a tree species classification problem. A measure for classification uncertainty (CU) was introduced, based on the Shannon entropy. It was shown that the majority of pixels with a low shadow fraction were classified with a lower uncertainty.

Directional Local Filtering for Stand Density Estimation in Closed Forest Canopies Using VHR Optical and LiDAR Data

In this letter, we present a novel object-based approach addressing individual tree crown (ITC) detection to assess stand density from remotely sensed imagery in closed forest canopies: directional local filtering (DLF). DLF is a variant of local maximum filtering (LMF). Within locally homogeneous areas, it uses a 1-D neighborhood and simultaneously searches for local directional maxima and minima. From the extracted local maxima and minima, a proxy for crown dimensions is inferred, which is in turn related to stand density. Developed on artificial imagery, the new object-based ITC method was tested on three different forest types in Belgium, which were all characterized by dense closed canopies: 1) a coniferous forest; 2) a mixed forest; and 3) a deciduous forest. Very high resolution aerial photographs, IKONOS imagery, and Light Detection and Ranging data, in conjunction with manually digitized and field survey data, were used to evaluate the new technique. The directional DLF approach yielded consistently stronger relations (in terms of R2) when compared with the conventional omnidirectional LMF technique. The qualitative evaluation clearly demonstrated that, next to stand density estimation, DLF also offered opportunities for full crown delineation.