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International audience; We introduce a multi-branch Deep Learning architecture that allows for the extraction of multi-scale features. Exploiting the data multi-modality structure through the combined use of various feature extractors provides high performance on data fusion tasks. Furthermore, the representation of the multitemporality of the data using sensor-specific 3D convolutions with custom kernel size extracts temporal features at an early computation stage. Our methodology allows reaching performance up to 0.8876 F1 Score on the development phase dataset and around 0.8798 on the test phase dataset. Finally, we demonstrate the contribution of each sensor to the prediction task with the design of data-focused experiments.

The authors developed a land cover and land use mapping framework specifically designed for agricultural systems of the Sudan-Sahel region. The mapping approach extracts information from inter and intra annual vegetation dynamics from dense stacks of Landsat 8 images. They applied this framework to create a 30-m spatial resolution land use map with a focus on agricultural landscapes of northern Nigeria for 2015. This map provides up-to-date information with a higher level of spatial and thematic detail resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5 percent of the area, higher than the reported national average for Nigeria (38.4 percent). Irrigated agriculture covers nearly 2.2 percent of the total area, reaching nearly 20 percent of the cultivated land when traditional floodplain agriculture systems are included, above the reported national average (0.63 percent). There is significant variability in land use within the region. Cultivated land in the northern section can reach values higher than 75 percent, most land suitable for agriculture is already under cultivation and there is limited land for future agricultural expansion. Marginal lands, not suitable for permanent agriculture, can reach 30 of the land at lower altitudes in the Northeast and Northwest. In contrast, the southern section presents lower land use intensity that results in a complex landscape that intertwines areas farms and larger patches of natural vegetation. This map improves the spatial detail of existing sources of LCLU information for the region and provides updated information of the current status of its agricultural landscapes. This study demonstrates the feasibility of multi temporal medium resolution remote sensing data to provide detailed and up-to-date information about agricultural systems in arid and sub arid landscapes of the Sahel region.

The analysis of the landscape evolution before and after a landslide is of paramount importance for investigating the landslide itself and the effects on the involved area. Such analysis can be particularly sharp, when high resolution digital elevation models, e.g. LiDAR, are available at multiple time steps, before and after the event. This work presents some preliminary results of a quantitative geomorphic analysis based on 2D discrete wavelet transform of two LiDARs, ante and post event. In particular, the large landslide of Montescaglioso, south Italy, is investigated. This landslide is a complex large movement, which involved a relatively poor steeping clay slope. Numerical results of this analysis will emphasize the magnitude of variations between the ante and post landslide scenarios

Kemampuan ekosistem hutan di wilayah gunungapi pasca erupsi dapat kembali berfungsi seperti sebelumnya sangat dipengaruhi oleh resiliensi atau daya lentur/lembam ekosistem tersebut. Resiliensi suatu ekosistem dalam studi ini didefinisikan sebagai kemampuan ekosistem untuk bangkit kembali setelah guncangan/gangguan. Tujuan penelitian ini adalah untuk memodelkan sebaran resiliensi spasial ekosistem hutan di kawasan Taman Nasional Gunung Merapi (TNGM) lima tahun pasca erupsi dan intervensi restorasi. Analisis dilakukan dengan pendekatan penginderaan jauh multi temporal dan analisis spasial menggunakan sistem informasi geografis untuk menggambarkan perubahan kondisi ekosistem gunungapi sebelum dan pasca erupsi dan kegiatan restorasi. Hasil pemodelan menunjukkan bahwa setelah lima tahun pasca erupsi, telah terjadi transisi resiliensi/perbaikan ekosistem dari lahan terbuka ke lahan dengan tutupan vegetasi berupa rumput, semak belukar, dan hutan sekunder. Transisi resiliensi ini terjadi baik secara suksesi alami maupun campur tangan manusia dalam bentuk tindakan restorasi. Salah satu catatan hasil dari penelitian ini antara lain adalah keberhasilan kegiatan restorasi untuk mengembalikan kondisi ekosistem seperti sebelum erupsi tidak selalu dapat dideteksi dalam jangka lima tahun setelah erupsi. Kata kunci: Gunungapi Merapi; multi temporal; penginderaan jauh; resiliensi spasial; sistem informasi geografis Spatial Resilience Modelling of Merapi Volcano Ecosystem Post-Eruption The ability of volcano ecosystem to recover post an eruption to the pre eruption status is affected by its ecological resilience. Resilience of an ecosystem can be defined as the ability of an ecosystem to bounch back after (a) disturbance(s). This study aimed to model spatial resilience of Merapi volcano ecosystem within the area of National Park of Merapi Volcano (TNGM) five years post 2010 eruption and restoration intervention. Analysis was conducted using multi temporal remote sensing and spatial analysis using geographic information system to draw the changes of the ecosystem over time, particularly post eruption and restoration actions. The modelling resulted that five years post eruption, there was resilience transisition/ recovery in volcano ecosystem in TNGM post 2010 eruption. The resilience was shown by the changes from open area to vegetation covers as grass, shrubs, and secondary forests. The transitions occured in term of natural succession as well as human intervention in restoration programs. However, the success of restoration actions to recover the ecosystem to the pre eruption status was not always able to be detected within the period of five years post eruption.

International audience; Over lands, the cloud detection on remote sensing images is not an easy task, because of the frequent difficulty to distinguish clouds from the underlying landscape, even at a high resolution. Up to now, most high resolution images have been distributed without an associated cloud mask. This situation should change in the near future, thanks to two new satellite missions that will provide optical images combining 3 features: high spatial resolution, high revisit frequency and constant viewing angles. The VENµS (French and Israeli cooperation) mission should be launched in 2012 and the European SENTINEL-2 mission in 2013. Fortunately, two existing satellite missions, FORMOSAT-2 and LANDSAT, enable to simulate the future data of these sensors. Multi-temporal imagery at constant viewing angles provides a new way to discriminate clouded and unclouded pixels, using the relative stability of the earth surface reflectances compared to the quick variations of the reflectance of pixels affected by clouds. In this study, we have used time series of images from FORMOSAT-2 and LANDSAT to develop and test a Multi-Temporal Cloud Detection (MTCD) method. This algorithm combines a detection of a sudden increase of reflectance in the blue wavelength on a pixel by pixel basis, and a test of the linear correlation of pixel neighborhoods taken from couples of images acquired successively. MTCD cloud masks are compared with cloud cover assessments obtained from FORMOSAT-2 and LANDSAT data catalogs. The results show that the MTCD method provides a better discrimination of clouded and unclouded pixels than the usual methods based on thresholds applied to reflectances or reflectance ratios. This method will be used within VENµS level 2 processing and will be proposed for SENTINEL-2 level 2 processing.

Remote sensing classification procedures have progressed in recent decades to enhance the interpretation of high resolution data. Contemporary research has focused on improving techniques that can analyze the spectral, spatial, temporal, and polarization properties that influence how platforms interpret substance reflectance. However, such methods are focused primarily on examining these properties for single multispectral images and do not consider their importance for evaluating multiimage datasets. To address this gap, the purpose of this research was to develop a systematic classification procedure that analyzes the temporal, spectral, spatial, and anisotropic properties of remote sensing data of a multiimage dataset. This was accomplished by first employing an orthophotography approach to correctly register multiple images of the same study sites that were collected at different dates. The resulting multitemporal image was evaluated using image filtering techniques, factor analysis, principal component analysis, and stepwise discriminant function analysis to analyze the contribution of all bands from each original dataset. The datasets consisted of high spatial resolution images acquired at several dates of a forest environment with mountain pine beetle forest health concerns. The systematic classification procedures developed in this study were compared to results from a traditional maximum likelihood supervised classification on the same dataset. The results show that there is a significant benefit for integrating multitemporal datasets rather than using a single image for classification. This finding is a step forward in augmenting human image interpretation by computerized image analysis., File is printable.