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Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2 / Akiko Elders in Remote Sensing Applications: Society and Environment, RSASE, Vol 27 (August 2022)  

Public [article]  inRemote Sensing Applications: Society and Environment, RSASE > Vol 27 (August 2022) . - n° 100820 Titre : Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2 Type de document : Article/Communication Auteurs : Akiko Elders, Auteur ; Mark Carroll, Auteur ; Christopher S.R. Neigh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100820 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Burkina Faso [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Sentinel-MSI [Termes IGN] parcelle agricole [Termes IGN] régression harmonique [Termes IGN] rendement agricole [Termes IGN] variation saisonnière Résumé : (auteur) Remote Sensing affords the opportunity to monitor and evaluate data scarce regions where field collection efforts are costly. A particular challenge is monitoring and evaluation in regions with smallholder agricultural systems (∼1 ha) that are often subsistence focused, vulnerable to food insecurity and data scarce. Using multi-day moderate resolution Sentinel-2 and Random Forest models, this study shows that crop type and rice yields in Burkina Faso can be predicted with greater than ∼80% accuracy in the rainy season. Model optimization using varying spectral and vegetation index inputs can increase crop type and yield prediction accuracy in the dry season where denser cultivation is a challenge for the 10–20 m resolution of Sentinel-2. However, there is a trade-off between opting for very high-resolution imagery ( Numéro de notice : A2022-624 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rsase.2022.100820 Date de publication en ligne : 02/08/2022 En ligne : https://doi.org/10.1016/j.rsase.2022.100820 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101391 [article]

A novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 185 (March 2022) . - pp 48 - 61 Titre : A novel regression method for harmonic analysis of time series Type de document : Article/Communication Auteurs : Qiang Zhou, Auteur ; Zhe Zhu, Auteur ; George Xian, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 48 - 61 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse harmonique [Termes IGN] détection de changement [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-SWIR [Termes IGN] modèle de régression [Termes IGN] réflectance [Termes IGN] régression harmonique [Termes IGN] série temporelle [Termes IGN] variation saisonnière Résumé : (auteur) Harmonic analysis of time series is an important technique to reveal seasonal land surface dynamics using remote sensing information. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) with different scenarios using Landsat time series data across the United States. First, we evaluated methods using Landsat surface reflectance time series within a single year. HAPO showed small and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD values of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series and the result suggested that HAPO was a better predictor of relatively short time series (less than4 years) with steady small RMSD values. When a longer time series (≥4 years) was used, all four methods disclosed similar RMSD values, but HAPO outperformed other three methods when there were temporal gaps. Last, we preliminarily tested how regression methods affected change detection and classification accuracy. HAPO showed the highest change detection accuracy of all tests in terms of F1 score when using the change threshold of 0.9999. In classification, HAPO produced the highest accuracy for short time series segments (one- or two-year time series). In contrast, all methods reached similar accuracy for 5-year time series. These results suggest that for areas that have large seasonal observation gaps or for time series that have less than 4 years records, HAPO can provide more consistent and accurate analytical results than other regression methods for harmonic analysis of time series. Numéro de notice : A2022-133 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.006 Date de publication en ligne : 21/01/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99729 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2022031	SL	Revue	Centre de documentation	Revues en salle	Disponible

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data / Barry T. Wilson in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 137 (March 2018) . - pp 29 - 46 Titre : Harmonic regression of Landsat time series for modeling attributes from national forest inventory data Type de document : Article/Communication Auteurs : Barry T. Wilson, Auteur ; Joseph F. Knight, Auteur ; Ronald E. McRoberts, Auteur Année de publication : 2018 Article en page(s) : pp 29 - 46 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attribut [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Landsat [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Minnesota (Etats-Unis) [Termes IGN] plus proche voisin, algorithme du [Termes IGN] régression harmonique [Termes IGN] série temporelle Résumé : (Auteur) Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009–2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10–20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher. Numéro de notice : A2018-077 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89439 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018031	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018033	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018032	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt