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Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 124 - 135 Titre : Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods Type de document : Article/Communication Auteurs : Elisa Kamir, Auteur ; François Waldner, Auteur ; Zvi Hochman, Auteur Année de publication : 2020 Article en page(s) : pp 124 - 135 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Australie [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] carte agricole [Termes descripteurs IGN] climat [Termes descripteurs IGN] estimation de précision [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle non linéaire [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] régression [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Closing the yield gap between actual and potential wheat yields in Australia is important to meet the growing global demand for food. The identification of hotspots of the yield gap, where the potential for improvement is the greatest, is a necessary step towards this goal. While crop growth models are well suited to quantify potential yields, they lack the ability to provide accurate large-scale estimates of actual yields, owing to the sheer quantity of data they require for parameterisation. In this context, we sought to provide accurate estimates of actual wheat yields across the Australian wheat belt based on machine-learning regression methods, climate records and satellite image time series. Out of nine base learners and two ensembles, support vector regression with radial basis function emerged as the single best learner (root mean square error of 0.55 t ha−1 and R2 of 0.77 at the pixel level). At national scale, this model explained 73% of the yield variability observed across statistical units. Benchmark approaches based on peak Normalised Difference Vegetation Index (NDVI) and on a harvest index were largely outperformed by the machine-learning regression models (R2  Numéro de notice : A2020-046 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.008 date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.008 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94556 [article]

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Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya / Vijendra Kumar Pandey in Geocarto international, vol 35 n° 2 ([01/02/2020])  

Public [article]  inGeocarto international > vol 35 n° 2 [01/02/2020] . - pp 168 - 187 Titre : Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya Type de document : Article/Communication Auteurs : Vijendra Kumar Pandey, Auteur ; Hamid Reza Pourghasemi, Auteur ; Milap Chand Sharma, Auteur Année de publication : 2020 Article en page(s) : pp 168 - 187 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] autoroute [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image IRS-LISS [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] Linear Imaging Self-Scanning System [Termes descripteurs IGN] modèle statistique [Termes descripteurs IGN] mousson [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] test statistique Résumé : (Auteur) The main objective of this study to produce landslide susceptibility zones using maximum entropy (MaxEnt) and support vector machine (SVM) data-driven models along the Tipari to Ghuttu highway corridors in the Garhwal Himalaya. A landslide inventory has been prepared through field surveys and LISS-IV and Landsat 8 satellite images. The datasets of 85 landslides were categorised into training and test sets. In this study 11 landslide conditioning variables were used that are; altitude, slope angle, aspect, plan curvature, topographic wetness index, normalised difference vegetation index (NDVI), land use, soil texture, distance to rivers, distance to faults, and distance to the road. The result produced using MaxEnt and SVM model were subsequently validated using receiver operating characteristics curve (ROC) with test sets of landslide dataset. Both the models have good prediction capabilities. MaxEnt has ROC value of 0.78 while SVM has the highest prediction rate of 0.85. Numéro de notice : A2020-036 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1510038 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1510038 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94519 [article]

Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)  

Public [article]  inRemote sensing of environment > Vol 237 (February 2020) . - 13 p. Titre : Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Maylis Lopes, Auteur ; Titouan Dubo, Auteur ; Justine Rivers-Moore, Auteur ; Pierre-Louis Frison , Auteur ; Nicolas Gross, Auteur ; Annie Ouin, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] biodiversité végétale [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Haute-Garonne (31) [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de diversité [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] prairie [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] taxinomie Résumé : (auteur) The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km2). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint. Numéro de notice : A2020-004 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.111536 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.rse.2019.111536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94296 [article]

Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery / Yuanheng Sun in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 826 - 840 Titre : Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery Type de document : Article/Communication Auteurs : Yuanheng Sun, Auteur ; Qiming Qin, Auteur ; Huazhong Ren, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 826 - 840 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] bande rouge [Termes descripteurs IGN] canopée [Termes descripteurs IGN] Chine [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] teneur en chlorophylle des feuilles Résumé : (auteur) The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5–7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images. Numéro de notice : A2020-069 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2940826 date de publication en ligne : 27/09/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2940826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94615 [article]

A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems / Dong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 63 - 77 Titre : A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems Type de document : Article/Communication Auteurs : Dong Chen, Auteur ; Tatiana V. Loboda, Auteur ; Joanne V. Hall, Auteur Année de publication : 2020 Article en page(s) : pp 63 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Alaska (Etats-Unis) [Termes descripteurs IGN] Canada [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] perturbation écologique [Termes descripteurs IGN] Short Waves InfraRed [Termes descripteurs IGN] toundra Résumé : (Auteur) Satellite imagery has been widely used for the assessment of wildfire burn severity within the scientific community and fire management agencies. Multiple indices have been proposed to assess burn severity, among which the differenced Normalized Burn Ratio (dNBR) is arguably the most commonly used index that is expected to provide an objective and consistent assessment. However, although evidence of variability in the dNBR-based assessment of burn severity driven by image pair selection has been shown in many studies, the comprehensive examination of the extent of the bias resulting from the image selection has been lacking. In this study, we focus on three factors of the image selection process which are encountered by most Landsat-derived dNBR applications, including the sensor combination and the difference in timing of image acquisition (for both the year and seasonality) of pre- and post-fire image pairs. Through separate analyses, each targeting a single factor, we show that Landsat sensor combination between the pre- and post-fire images has a limited impact on the dNBR values. The difference in the year of acquisition between the images in the image pairs is shown to influence dNBR assessment with a noticeable increase in mean dNBR (>0.1) with only a single year difference between images compared to multi-year differences. However, differences in the image acquisition seasons and the resulting phenological differences is shown to impact dNBR values most considerably. Based on our results, we warn against the calculation of dNBR when the images are acquired in different seasons. We believe that despite the existence of multiple derivatives of dNBR, there remains a need for an improved version; one that is less susceptible to the phenological impacts introduced by the selected images. Numéro de notice : A2020-012 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.011 date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94400 [article]

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An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

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Residences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])  

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Automatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

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Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes / Meng Zhang in Geocarto international, vol 34 n° 10 ([15/07/2019])  

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A novel method for separating woody and herbaceous time series / Qiang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

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Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia / Enkhjargal Natsagdorj in Geocarto international, vol 34 n° 7 ([01/06/2019])  

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Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece / D.D. Alexakis in Geocarto international, vol 34 n° 7 ([01/06/2019])  

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Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols / Sen Cao in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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