An OBIA for fine-scale land cover spatial analysis over broad territories: demonstration through riparian corridor and artificial sprawl studies in France

International audienceSpatial analysis using fine-scale information over broad territories is essential to define efficient restoration strategies from local to national scale. We designed an OBIA dedicated to produce operationally reliable fine-scale information over broad territories. The originality of our OBIA lies particularly in the top-down approach for the construction of the classification tree and the use of „knowledge-based rules‟ classification technique. The implementation of this OBIA over the two study areas – (i) the Normandy region for riparian area land cover mapping (5600 km² riparian area) and (ii) fours departments over the Languedoc-Roussillon region (22644 km²) – demonstrates the operability of our approach (time-efficient, reproducible, transferable, portable). Broad scale spatial analysis conducted from resulting maps demonstrate the interest of using fine-scale information and highlight that OBIA, following our approach, will be at very short run a broadly applicable method to carry out such analysis

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas

Application of the trajectory error matrix for assessing the temporal transferability of OBIA for slum detection

High temporal and spatial-resolution imageries are a valuable data source for slum monitoring. However, the transferability of OBIA methods across space and time remains problematic, due to the complexity of the term “slum”. Hence, transparency is important when analysing the transferability of OBIA methods for slum mapping. Our research developed a framework for measuring the temporal transferability of OBIA methods employing the trajectory error matrix (TEM). We found relatively low trajectory accuracies indicating low temporal transferability of OBIA methods for slum monitoring using point-based assessment methods. However, the analysis of change needs to be combined with an analysis of the certainty of this change by considering the context of the change to deal with common problems such as variations of the viewing angles and uncertainties in producing reference data on slums

Land use/cover classification in the Brazilian Amazon using satellite images.

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation?based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi?resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical?based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data

URBAN TREE CROWN PROJECTION AREA MAPPING WITH OBJECT BASED IMAGE ANALYSIS FOR URBAN ECOSYSTEM SERVICE INDICATOR DEVELOPMENT

The continuous expansion of built-up areas in the urban environment at the expense of green spaces brings up numerous environmental problems, for which accurate and efficient solutions should be found. The assessment of ecosystem services developed within the field of landscape ecology is playing an ever more important role in environmental sciences and thus may offer suitable answers. Such assessments can be carried out by developing indicators. Accordingly, in the case of urban trees, an accurate quantitative characterization of their services (such as e.g. carbon sequestration, pollutant removal and microclimate regulation) is also needed. The aim of this study is to establish a generally applicable method based on indicator development, using widely available data. In the case of urban green spaces there are several services for which the development of proper indicators and evaluation methods requires a delineation of tree crowns, or at least the crown projection area. Accordingly, in our work, we map the crown projection area of a large and popular urban park of Szeged, Széchenyi square, using object-based image analysis on UltraCamD digital orthophotos. Following a multiresolution segmentation the classification of the resulting objects was carried out, using the eCognition image analysis software. Besides fulfilling the policy objectives related to the evaluation of urban ecosystem services, the produced crown base can also be used in several other types of urban ecological and urban climatological studies (e.g. urban climate modelling, human-comfort assessment). In this paper the first results are presented