Classification and information extraction in very high resolution satellite images for tree crops monitoring

Recent access to Very High Spatial Resolution (VHSR) Satellite Images allows vegetation monitoring at metric and sub-metric scale, with individual trees now detectable. Therefore, it dis-closes new applications in precision agriculture for orchards and other tree crops. In this paper, we present some methodological directions for classification, and extraction of specific agricultural information from these images. Aims are tree crop detection, plot mapping, species identification, and cropping-system characterization. This latter includes for instance row management (e.g. grid vs. line pattern, width of rows and inter-rows, row orientation), crown shape, and crown size estimation. In this paper, we skip the segmentation step and consider that we have got a precise delimitation of plots that have a homogeneous content. To classify these plots, we have used expert knowledge in agronomy combined with image information in a decision tree. Classification criteria were based on parameters resulting from the Fourier transform analysis or vegetation indices, derived as one single descriptor for the whole plot. As a conclusion, the proposed methodology was found capable of classification and characterization of tree crops, provided the trees are clearly seen from above, and their planting is regular enough to give a response with Fourier analysis. (Résumé d'auteur

Uganda. Coffee wilt disease and remote sensing project. Field enquiry air airborne data acquisition campaign report : 15 january - 8 february 2002

Four weeks of field work have been performed, allowing to collect a data base in support of the remote sensing mapping of the coffee wilt disease in three sites in Uganda. These sites are coffee growing areas of about 15 x 10 km in dimensions, located respectively near Mukono, Kiganda (Mubende district), and Kyenjojo. This set of data includes ground truth information records on crops and landscape, and on coffee trees architecture and sanitary status, leaf area index and crops spectral properties measurements. The work was focused on the Mukono site, selected as the training site for remote sensing tools development. For this area, a large amount of records in a large range of variability have been managed, a little less for the two others. At the same time, Borstad and Associates attempted several flybys over the sites in order to acquire CASI hyperspectral images. Due to bad weather conditions, only Kiganda and Mukono sites have been shot at the end. (Résumé d'auteur

Potential of Pléiades mono- and tri-stereoscopic images for the agricultural mapping in Mayotte

In Mayotte, the agricultural land use is only partially known, due to very little GPS plot sampling, while an exhaustive mapping would be needed for decision support. Farms are very small (<0,5ha) and based on associated crops, orchards, and agroforestry. More than 40% of their production is for local livelihood. Pléiades' VHSR images thus appear as a relevant tool for the characterization of these production systems, provided that these images are acquired at different dates (dry season/rainy season) to better discriminate crops thanks to the agricultural calendar. Besides, the complexity of Mayotte's landscapes, dominated by the presence of various trees, requires developing a methodology which takes into account both the multi-temporal radiometry and the texture, but also the vegetation height. We therefore propose to test the capabilities of Pleiades' tri-stereoscopic mode to produce a reliable DSM usable in this context. The objective of this study is to assess the suitability of this type of data to a mapping base regularly updated. No image acquisition attempt during the rainy season was successful because of the heavy cloud cover. Only two images were acquired, respectively in July 2012 and April 2013. This latest, acquired in tri- stereoscopic mode, was issued as raw data in August 2013 due to production problems. The relevance of these two dates is weak because April and July correspond to the same phenological period of the vegetation; so there is little information related to the cycle differentiation between species. Finally, one year apart does not represent sufficient development in terms of land cover nor land use dynamics. Moreover, the data late provision did not allow us to achieve all the processes and get any result. The prospects of this study are thus to implement: The assessment of the capacity of deriving the DSM from the tri- stereoscopic images and the validity of this product. The production of a DEM derived from Pléiades-DSM, followed by the analysis of the coherence of the measured heights with those of the LiDAR-DSM acquired in 2008, and the suitability of this DEM to help producing the land use map. The texture analysis characterizing Mayotte's large tree cover variety in terms of composition, structure, height or density of vegetation, and discriminating different wooded patterns on the image (e.g. natural forest, agroforestry plot, monospecific tree grove). We will thus integrate indices derived from the texture co-occurrence matrix, calculated on different spectral bands with various neighborhood sizes. We will analyze the choice of the relevant panchromatic/spectral bands for derivation, the most discriminating sizes, but also the most appropriate indices. However, anomalies due to equalization residues at Pleiades sensor strips produce vertical stripes in the texture images, which limit the use of Pleiades imagery for this type of process. These limits will be evaluated. The object-oriented analysis (in eCognition Developer) to create a map of the agricultural land use by combining all the data described above. (Résumé d'auteur

Prediction of palm-tree ganoderma affection degree by reflectance spectroscopy: Proposed methodology

The aim of this study was thus to test the relevance of statistical methods to detect the variations in spectral signature of oil-palm trees correlated to Ganoderma disease, a fungus responsible of high loss of yield and trees in palm groves. The objective is too discriminate infected palm trees and to establish a ranking in the degree of infection. Some previous studies (Lanore, 2006; et Brégand, 2007) revealed that it is feasible, but the number of individuals was too small to lead to statistically reliable models; thus, it is still to confirm and validate. More especially, the present study focuses on the possibility of infected palm-tree discrimination in accordance to four sickness degrees: Healthy, Low, Medium and High infection. It will test this potential at several scales: the leaflet, the canopy, and by remote sensing. (Résumé d'auteur

Comparison of three segmentation methods for groves recognition in very resolution satellite images

This study is dedicated to the automatic recognition and mapping of tree crops by remote sensing, using very high resolution multi-spectral satellite images (0.7 m). Our goal is to segment the images in order to perform an independent classification according to a set of pre-determined land use types: apple groves, vineyards, miscellaneous young and old groves, pastured and cropped fields, food crop, fallow lands and forests. In this article, we compare three methods of segmentation that seem to provide suitable units for the resolution of our problem: SxS, eCognition and watersheds. A set of criteria are defined to quantitatively analyze the efficiency of these segmentations. We then try to select the more relevant method in terms of subsequent classification operability. (Résumé d'auteur

Discrimination of tropical agroforestry systems in very high resolution satellite imagery using object-based hierarchical classification: A case-study in Cameroon

Tree crops and agroforestry systems are very representative of tropical agricultural landscape. Automatic recognition and mapping of this typical land cover type is thus a challenge for the use of remote sensing in driving issues in food sustainability. Therefore, this paper presents an attempt to use the potential of the object?based approach in image classification to produce a land?cover map of a complex agroforestry area. This case?study focuses on very high spatial resolution data acquired over the savannah/cocoa/forest transition region of Bokito in Cameroon, providing a large panel of various cropping systems. WorldView2 panchromatic and multispectral data are thus processed through textural indices derivation and principal component analysis, to select the more discriminant attributes for the different land?cover types, resulting in a stack of 32 image layers. The object?based approach is then run on eCognition Developer, combining several levels of multiresolution segmentation and consecutive classifications that involves different criteria at each step. At the end, a land?use map based on 13 classes was produced with 85% of global accuracy, evaluated based on ground?truth data and photointerpretation. Its typology is rather fine, especially for the agroforestry crops displaying complex structures, and that would not have been accurately delimitated nor discriminated with a pixel?based approach. (Résumé d'auteur