David Sheeren

Vegetation cover degradation assessment in Madagascar savanna based on trend analysis of MODIS NDVI time series

Abstract Like other African countries, Madagascar is concerned by vegetation cover degradation especially in savanna ecosystems. In this article, we describe an approach to quantify and localise savanna vegetation cover degradation. To this end, we analyse using STL decomposition method the trends measured between 2000 and 2007 of two phenological indicators which are derived from NDVI MODIS time series and characterizing vegetation activity during the growing season. Three types of trend were observed – null, positive or negative – over the study period with which we can associate a state of vegetation cover degradation. Future work will provide validation of this result. Next a comparison between the spatial variations of vegetation cover degradation and fire pressure for the same period should improve knowledge on the effect of fire on savanna vegetation activity. This information will be useful for local managers in order to implement savanna management strategies.

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

Natural reforestation of European mountain landscapes raises major environmental and societal issues. With local stakeholders in the Pyrenees National Park area (France), we studied agricultural landscape colonisation by ash (Fraxinus excelsior) to enlighten its impacts on biodiversity and other landscape functions of importance for the valley socio-economics. The study comprised an integrated assessment of land-use and land-cover change (LUCC) since the 1950s, and a scenario analysis of alternative future policy. We combined knowledge and methods from landscape ecology, land change and agricultural sciences, and a set of coordinated field studies to capture interactions and feedback in the local landscape/land-use system. Our results elicited the hierarchically-nested relationships between social and ecological processes. Agricultural change played a preeminent role in the spatial and temporal patterns of LUCC. Landscape colonisation by ash at the parcel level of organisation was merely controlled by grassland management, and in fact depended on the farmer’s land management at the whole-farm level. LUCC patterns at the landscape level depended to a great extent on interactions between farm household behaviours and the spatial arrangement of landholdings within the landscape mosaic. Our results stressed the need to represent the local SES function at a fine scale to adequately capture scenarios of change in landscape functions. These findings orientated our modelling choices in the building an agent-based model for LUCC simulation (SMASH–Spatialized Multi-Agent System of landscape colonization by ASH). We discuss our method and results with reference to topical issues in interdisciplinary research into the sustainability of multifunctional landscapes.

Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach

While small, fragmented wooded elements do not represent a large surface area in agricultural landscape, their role in the sustainability of ecological processes is recognized widely. Unfortunately, landscape ecology studies suffer from the lack of methods for automatic detection of these elements. We propose a hybrid approach using both aerial photographs and ancillary data of coarser resolution to automatically discriminate small wooded elements. First, a spectral and textural analysis is performed to identify all the planted-tree areas in the digital photograph. Secondly, an object-orientated spatial analysis using the two data sources and including a multi-resolution segmentation is applied to distinguish between large and small woods, copses, hedgerows and scattered trees. The results show the usefulness of the hybrid approach and the prospects for future ecological applications.

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ = 0 . 93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.

Discovering Rules with Genetic Algorithms to Classify Urban Remotely Sensed Data

The classification methods applied in the object- oriented image analysis approach are often based on the use of domain knowledge. A key issue in this approach is the acquisition of this knowledge which is generally implicit and not formalized. In this paper, we examine the possibilities of using genetic programming for the automatic extraction of classification rules from urban remotely sensed data. The method proposed is composed of several steps: segmentation, feature extraction, selection of training sets, acquisition of rules, classification. Features related to the spectral, spatial and contextual properties of the objects are used in the classification procedure. Experiments are made on a Quickbird MS image. The quality of the results shows the effectiveness of the proposed genetic classifier in the object-oriented, knowledge-based approach.

Detection of Hedges in a Rural Landscape Using a Local Orientation Feature: From Linear Opening to Path Opening

The detection of hedges is addressed in this paper. A hierarchical detection scheme in two steps is proposed. It is based on the use of both spatial information and spectral information in the detection process. First, woody elements are detected using the spectral information. From the membership map, the local orientation of each pixel is computed using directional filters. The morphological directional profile is defined as the composition of the outputs of directional filters with a varying orientation parameter. The local orientation feature is defined as the difference between the maximum and the minimum values of the morphological directional profile. A second detection step is done using the local orientation feature and the membership value to the woody elements class to extract the hedges. Experiments conducted on several real satellite images show that the method provides very good results in terms of detection accuracies. For one experiment, the overall accuracy is increased 80% to 91% with the proposed method. Furthermore, the methods is robust even if the size of the training samples is limited.

Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space

In this chapter, we describe an automatic procedure to capture features on old maps. Early maps contain specific informations which allow us to reconstruct trajectories over time and space for land use/cover studies or urban area development. The most commonly used approach to extract these elements requires a user intervention for digitizing which widely limits its utilization. Therefore, it is essential to propose automatic methods in order to establish reproducible procedures. Capturing features automatically on scanned paper maps is a major challenge in GIS for many reasons: (1) many planimetric elements can be overlapped, (2) scanning procedure may conduct to a poor image quality, (3) lack of colors complicates the distinction of the elements. Based on a state of art, we propose a method based on color image segmentation and unsupervised classification (K-means algorithm) to extract forest features on the historical ‘Map of France’. The first part of the procedure conducts to clean maps and eliminate elevation contour lines with filtering techniques. Then, we perform a color space conversion from RGB to L*a*b color space to improve uniformity of the image. To finish, a post processing step based on morphological operators and contextual rules is applied to clean-up features. Results show a high global accuracy of the proposed scheme for different excerpt of this historical map.

Hedges detection using local directional features and support vector data description

The detection of hedges in very high spatial resolution remote sensing image is discussed in the paper. A spatial-spectral detector is proposed. The spatial information is modeled per pixel as the local orientation of the structure to which the pixel belongs. The local orientation is computed from the morphological directional profile built with a series of linear openings in several directions. These features are used as inputs to a support vector data description, a detection algorithm. Experimental results on a real satellite image show that the local orientation helps in discriminating hedges from other woody elements, which is not possible using the spectral information only.

La morphologie mathématique binaire pour l'extraction automatique des bâtiments dans les images THRS

This paper presents a new method for building extraction in Very High Resolution remotely sensed images in urban areas. The approach proposed is based on the use binary mathematical morphology operators. The method is composed of several steps: (1) conversion of grey level images to binary images, (2) smoothing by means of morphological filtering, (3) building detection with an adaptive hit-or-miss transform, (4) shape restoration. Two strategies of binarization are proposed. The first one consists in performing an interactive or automatic thresholding. The second one is based on an unsupervised classification. The method has been applied on a Quickbird panchromatic image. Results show the interest of the approach. MOTS-CLES : morphologie mathematique, segmentation, transformee en « tout ou rien ».

Object-based classification of grasslands from high resolution satellite image time series using Gaussian mean map kernels

This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object scale by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the a-Gaussian mean kernel. It allows to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in Support Vector Machine for the supervised classification of grasslands from south-west France. A dense intra-annual multispectral time series of Formosat-2 satellite is used for the classification of grasslands management practices, while an inter-annual NDVI time series of Formosat-2 is used for permanent and temporary grasslands discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method shows to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.