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Detecting abandoned farmland using harmonic analysis and machine learning / Heeyeun Yoon in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 201 - 212 Titre : Detecting abandoned farmland using harmonic analysis and machine learning Type de document : Article/Communication Auteurs : Heeyeun Yoon, Auteur ; Soyoun Kim, Auteur Année de publication : 2020 Article en page(s) : pp 201 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse harmonique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] Corée du sud [Termes descripteurs IGN] gestion des ressources [Termes descripteurs IGN] inventaire [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] phénologie [Termes descripteurs IGN] production agricole [Termes descripteurs IGN] Soil Adjusted Vegetation Index [Termes descripteurs IGN] surface cultivée Résumé : (auteur) It is critical to inventory abandoned farmland soon after it is generated, to better manage agricultural resources and to prevent negative consequences that would otherwise follow. This study aims to distinguish abandoned farmlands from active croplands—rice paddy and agricultural fields—by discerning the phenological trajectories over a short-term period of three years (Jan. 2016 to Dec. 2018) in Gwanyang City in South Korea. For Support Vector Machine (SVM) classification, we fully utilized parameters derived from harmonic analyses of the three vegetation indices (VIs: NDVI, NDWI, and SAVI) extracted from Sentinel-2A imagery. The harmonic analyses proved that higher-order sinusoid components produced better fitting to explain the trajectory of the VIs—the maximum adjusted was 95.23%—and the multiple VIs diversified the attributes for the classifications. Consequently, the higher-order harmonic components and the additional VIs increased the accuracy when used in SVM classification. The best performing classification was achieved with a composite of harmonic terms derived from the three VIs, yielding overall accuracy of 90.72%, Kappa index of 0.858, and user’s accuracy for abandoned farmland of 93.40%. The proposed method here would greatly improve the process of detecting abandoned farmland, despite a relatively short observation period, and enable a rapid response to the occurrence of abandonment. Numéro de notice : A2020-356 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.021 date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95243 [article]

Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 95 - 117 Titre : Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping Type de document : Article/Communication Auteurs : Alvin B. Baloloy, Auteur ; Ariel C. Blanco, Auteur ; Raymund Rhommel StaAna, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 95 - 117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] Asie du sud-est [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] espèce exotique envahissante [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] orthophotographie [Termes descripteurs IGN] Philippines [Termes descripteurs IGN] Short Waves InfraRed [Termes descripteurs IGN] surveillance du littoral Résumé : (auteur) Numéro de notice : A2020-354 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.001 date de publication en ligne : 11/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95240 [article]

Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics / Jasper A. Slingsby in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 15 - 25 Titre : Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics Type de document : Article/Communication Auteurs : Jasper A. Slingsby, Auteur ; Glenn R. Moncrieff, Auteur ; Adam M. Wilson, Auteur Année de publication : 2020 Article en page(s) : pp 15 - 25 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] incendie [Termes descripteurs IGN] internet interactif [Termes descripteurs IGN] Le Cap [Termes descripteurs IGN] milieu naturel [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] surveillance de la végétation [Termes descripteurs IGN] surveillance écologique Résumé : (auteur) Managing fire, water, biodiversity and carbon stocks can greatly benefit from early warning of changes in the state of vegetation. While near-real time tools to detect forest change based on satellite remote sensing exist, these ecosystems have relatively stable natural vegetation dynamics. Open (i.e. non-forest) ecosystems like grasslands, savannas and shrublands are more challenging as they show complex natural dynamics due to factors such as fire, postfire recovery, greater contribution of bare soil to observed vegetation indices, as well as high sensitivity to rainfall and strong seasonality. Tools to aid the management of open ecosystems are desperately required as they dominate much of the globe and harbour substantial biodiversity and carbon. We present an innovative approach that overcomes the difficulties posed by open ecosystems by using a spatio-temporal hierarchical Bayesian model that uses data on climate, topography, soils and fire history to generate ecological forecasts of the expected land surface signal under natural conditions. This allows us to monitor and detect abrupt or gradual changes in the state of an ecosystem in near-real time by identifying areas where the observed vegetation signal has deviated from the expected natural variation. We apply our approach to a case study from the hyperdiverse fire-dependent African shrubland, the fynbos of the Cape Floristic Region, a Global Biodiversity Hotspot and UNESCO World Heritage Site that faces a number of threats to vegetation health and ecosystem function. The case study demonstrates that our approach is useful for identifying a range of change agents such as fire, alien plant species invasions, drought, pathogen outbreaks and clearing of vegetation. We describe and provide our full workflow, including an interactive web application. Our approach is highly versatile, allowing us to collect data on the impacts of change agents for research in ecology and earth system science, and to predict aspects of ecosystem structure and function such as biomass, fire return interval and the influence of vegetation on hydrology Numéro de notice : A2020-349 Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.017 date de publication en ligne : 05/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95231 [article]

Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 153 - 168 Titre : Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa Type de document : Article/Communication Auteurs : Cecilia Masemola, Auteur ; Moses Azong Cho, Auteur ; Abel Ramoelo, Auteur Année de publication : 2020 Article en page(s) : pp 153 - 168 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Acacia (genre) [Termes descripteurs IGN] Afrique du sud (état) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] cartographie automatique [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] espèce exotique envahissante [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] surveillance de la végétation Résumé : (auteur) Invasive alien plants (IAPs) threaten biodiversity and critical ecosystem services worldwide. There is, therefore, an urgent need to develop intervention measures to control the spread of IAPs. Efforts to control and monitor the spread of IAPs would require their current and detailed distribution over a large geographic area. Recently launched multispectral instrument on-board Sentinel-2 provides free data with good spatiotemporal and spectral resolution, compared to Landsat datasets. The Sentinel-2 dataset, therefore, can be a useful source of the IAPs spatial information required for detection and monitoring purposes. We combined Sentinel-2 data with a radiative transfer model to discriminate IAPs (Acacia mearnsii and Acacia dealbata) from surrounding native tree species in Van Reenen, KwaZulu-Natal, South Africa. The forward mode of combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL was used to simulate reflectance corresponding to bands of Sentinel-MSI, while the PROSAIL model inversion retrieved leaf area index (LAI) and canopy chlorophyll contents (CCC) of the IAPs and native species. Both reflectance and retrieved properties were used to map the distribution of the species within the study area. Our results showed that A. mearnsii and A. dealbata could be accurately discriminated from the surrounding native trees using integrated PROSAIL Sentinel-2 based model. We found that CCC– and LAI-based (% accuracy = 92.8%, 91.4% for CCC and LAI, respectively) modelling produced a higher classification accuracy than field sampling-based modelling (Accuracy = 90.2% (IAP), 82.2% (NAT) and kappa coefficient = 0.84 (IAP), 0.78 (NAT)). Simulated bands corresponding to Sentinel-2 data, on the other hand, produced species maps comparable to field sampling-based maps. Overall, the integrated PROSAIL Sentinel-2 inversion approach proved suitable for detecting and mapping IAPs over a large area. Due to the high spatiotemporal coverage of Sentinel-2, satellite images, the model developed showed the potential to contribute to the IAPs monitoring systems. Numéro de notice : A2020-352 Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.009 date de publication en ligne : 13/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95235 [article]

Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China / Kun Tan in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 165 (July 2020) . - pp 1 - 15 Titre : Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China Type de document : Article/Communication Auteurs : Kun Tan, Auteur ; Chao Niu, Auteur ; Xiuping Jia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1 - 15 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] atténuation [Termes descripteurs IGN] correction atmosphérique [Termes descripteurs IGN] distorsion du signal [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] mine [Termes descripteurs IGN] Mongolie intérieure (Chine) [Termes descripteurs IGN] mosaïquage d'images [Termes descripteurs IGN] radiance [Termes descripteurs IGN] rayonnement infrarouge [Termes descripteurs IGN] réflectance de surface [Termes descripteurs IGN] Short Waves InfraRed [Termes descripteurs IGN] spectroradiométrie [Termes descripteurs IGN] surveillance écologique Résumé : (auteur) Airborne hyperspectral remote sensing is an important application in the ecological monitoring of the environment in mining areas, and accurate preprocessing of the original images is the key to quantitative information retrieval. The original image data need radiation correction to acquire surface reflectance data. Due to the impact of the field angle, incidental radiance, and the bidirectional reflectance distribution function (BRDF), there can be a brightness gradient between adjacent strips, which leads to radiance difference and obvious chromatic aberration of the mosaicked images. We propose a novel data correction method for seamless mosaicking of airborne hyperspectral images. Firstly, visible and near-infrared (VNIR) and shortwave infrared (SWIR) sensors are calibrated in the laboratory, and the radiation calibration model of the sensor is established by an integrating-sphere system. A correction function is then established by combining the BRDF effect and the radiation attenuation coefficients. We also normalize the exposure time, sun altitude angle, and sensor altitude angle according to the flight strip. The results showed that this method is able to eliminate the signal distortion, allowing the seamless mosaicking of 37 strip images which were taken in different date and conditions in the study area. After the atmospheric correction of the imagery was completed, the accuracy of the preprocessing results was evaluated by field-measured ASD spectroradiometer data. The coefficient of determination R2 of the results for the reflectance was greater than 0.9. The experiments show that the proposed method has a good performance in radiation accuracy, and can provide high-quality hyperspectral data for the follow-up application of the ecological monitoring of a mining area. Numéro de notice : A2020-277 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.022 date de publication en ligne : 16/05/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.022 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95092 [article]

Long time-series remote sensing analysis of the periodic cycle evolution of the inlets and ebb-tidal delta of Xincun Lagoon, Hainan Island, China / Huaguo Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

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Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery / Kasper Johansen in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

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Path length correction for improving leaf area index measurements over sloping terrains: A deep analysis through computer simulation / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

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A simple distributed water balance model for an urbanized river basin using remote sensing and GIS techniques / Olutoyin Adeola Fashae in Geocarto international, vol 35 n° 9 ([01/07/2020])  

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Using spectral indices to estimate water content and GPP in sphagnum moss and other peatland vegetation / Kirsten J. Lees in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

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Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests / Jiaxin Chen in Forest ecology and management, Vol 466 (15 June 2020)  

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An integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis / Lei Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Counting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Developing shopping and dining walking indices using POIs and remote sensing data / Yingbin Deng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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