Developing a method to map coconut agrosystems from high-resolution satellite images

https://icaci.org/files/documents/ICC_proceedings/ICC2015/papers/38/fullpaper/T38-504_1427765394.pdfInternational audienceOur study aims at developing a generalizable method to exploit high resolution satellite images(VHR) for mapping coconut-based agrosystems, differentiating them from oil palm agrosystems.We compared two methods of land use classification. The first one is similar to that described byTeina (2009), based on spectral analysis and watershed segmentation, which we simplified byusing the NDVI vegetation index. The second one is the semi-automatic classification based ontexture analysis (PAPRI method of Borne, 1990). These methods were tested in two differentenvironments: Vanua Lava (Vanuatu; heterogeneous landscape, very ancient plantations) andIvory Coast (Marc Delorme Research Station, monoculture, regular spacing, oil palm plantations);and their results were evaluated against manually digitized photo-interpretation maps.In both situations, the PAPRI method produced better results than that of Teina (global kappa of0.60 vs. 0.40). Spectral signatures do not allow a sufficiently accurate mapping of coconut and donot differentiate it from oil palm, despite their different NDVI signatures. The PAPRI methoddifferentiates productive coconut from mixed plantations and other vegetation, either high or low(70% accuracy). In both situations, Teina’s method allows counting 65% of the coconut treeswhen they are well spaced. To increase the method accuracy, we suggest (1) field surveys (forsmall scale studies) and/or finer image resolution, allowing a high precision in manual mappingwith a better discrimination between coconut and oil palm, thus limiting the proportion of mixedpixels. (2) A phenological monitoring could improve the distinction between coconut and oil palmagrosystems. (3) Hyper-spectral images should allow extracting more precisely the respectivesignatures of both species. Another possibility would be (4) an object-oriented analysis asproposed by the eCognition software. Finally, (5) coupling the Lidar system with watershedanalysis would allow a better characterization of coconut varietal types

Mapping local density of young Eucalyptus plantations by individual tree detection in high spatial resolution satellite images

International audienceLocal tree density may vary in young Eucalyptus plantations under the effects of environmental conditions or inadequate management, and these variations need to be mapped over large areas as they have a significant impact on the final biomass harvested. High spatial resolution optical satellite images have the potential to provide crucial information on tree density at an affordable cost for forest management. Here, we test the capacity of this promising technique to map the local density of young and small Eucalyptus trees in a large plantation in Brazil. We use three Worldview panchromatic images acquired at a 50 cm resolution on different dates corresponding to trees aged 6, 9 and 13 months and define an overall accuracy index to evaluate the quality of the detection results. The best agreement between the local densities obtained by visual detection and by marked point process modeling was found at 9 months, with only small omission and commission errors and a stable 4% underestimation of the number of trees across the density gradient. We validated the capability of the MPP approach to detect trees aged 9 months by making a comparison with local densities recorded on 112 plots of ~590 m² and ranging between 1360 and 1700 trees per hectare. We obtained a good correlation (r²=0.88) with a root mean square error of 31 trees/ha. We generalized detection by computing a consistent map over the whole plantation. Our results showed that local tree density was not uniformly distributed even in a well-controlled intensively managed Eucalyptus plantation and therefore needed to be monitored and mapped. Use of the marked point process approach is then discussed with respect to stand characteristics (canopy closure), acquisition dates and recommendations for algorithm parameterization

Texture-based classification for characterizing regions on remote sensing images

Remote sensing classification methods mostly use only the physical properties of pixels or complex texture indexes but do not lead to recommendation for practical applications. Our objective was to design a texture-based method, called the Paysages A PRIori method (PAPRI), which works both at pixel and neighborhood level and which can handle different spatial scales of analysis. The aim was to stay close to the logic of a human expert and to deal with co-occurrences in a more efficient way than other methods. The PAPRI method is pixelwise and based on a comparison of statistical and spatial reference properties provided by the expert with local properties computed in varying size windows centered on the pixel. A specific distance is computed for different windows around the pixel and a local minimum leads to choosing the class in which the pixel is to be placed. The PAPRI method brings a significant improvement in classification quality for different kinds of images, including aerial, lidar, high-resolution satellite images as well as texture images from the Brodatz and Vistex databases. This work shows the importance of texture analysis in understanding remote sensing images and for future developments