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Spatial-temporal variation of satellite-based gross primary production estimation in wheat-maize rotation area during 2000–2015 / Wenquan Xie in Geocarto international, vol 37 n° 9 ([15/05/2022])
[article]
Titre : Spatial-temporal variation of satellite-based gross primary production estimation in wheat-maize rotation area during 2000–2015 Type de document : Article/Communication Auteurs : Wenquan Xie, Auteur ; Huini Wang, Auteur ; Hong Chi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2506 - 2523 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] blé (céréale)
[Termes IGN] Chine
[Termes IGN] image Terra-MODIS
[Termes IGN] maïs (céréale)
[Termes IGN] photosynthèse
[Termes IGN] production primaire brute
[Termes IGN] rotation de culture
[Termes IGN] série temporelle
[Termes IGN] variation temporelleRésumé : (auteur) North China Plain is the largest agricultural production center in China and wheat-maize rotation is a widespread cultivation practice in this area. As gross primary production (GPP) is a proxy of land productivity, research on its spatial-temporal dynamics helps understand the variation of grain production in wheat-maize rotation. Here, Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground observation data were combined to drive Vegetation Photosynthesis Model (VPM) in GPP estimation over wheat-maize rotation area during 2000–2015. Annual GPP has increased by 540.95 g C m−2 year−1 from 2000 to 2015, while total annual GPP has grown ∼150% than that of 2000. Moreover, annual GPP showed an increasing trend in the consecutively wheat-maize rotation area between 2000 and 2015. A strong linear relationship between GPP estimates and grain production demonstrated the potential of using VPM model to evaluate grain production in wheat-maize rotation area of Henan province, China. Numéro de notice : A2022-566 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1822928 Date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1080/10106049.2020.1822928 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101249
in Geocarto international > vol 37 n° 9 [15/05/2022] . - pp 2506 - 2523[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022091 RAB Revue Centre de documentation En réserve L003 Disponible Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation / Toshihiro Sakamoto in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)
[article]
Titre : Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation Type de document : Article/Communication Auteurs : Toshihiro Sakamoto, Auteur ; Daisuke Ogawa, Auteur ; Satoko Hiura, Auteur ; Nobusuke Iwasaki, Auteur Année de publication : 2022 Article en page(s) : pp 323 - 332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bande spectrale
[Termes IGN] blé (céréale)
[Termes IGN] chlorophylle
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] indice de végétation
[Termes IGN] orthophotoplan numérique
[Termes IGN] point d'appui
[Termes IGN] précision du positionnement
[Termes IGN] rizière
[Termes IGN] structure-from-motionRésumé : (Auteur) Vegetation indices (VIs), such as the green chlorophyll index and normalized difference vegetation index, are calculated from visible and near-infrared band images for plant diagnosis in crop breeding and field management. The DJI P4 Multispectral drone combined with the Agisoft Metashape Structure from Motion/Multi View Stereo software is some of the most cost-effective equipment for creating high-resolution orthomosaic VI images. However, the manufacturer's procedure results in remarkable location estimation inaccuracy (average error: 3.27–3.45 cm) and alignment errors between spectral bands (average error: 2.80–2.84 cm). We developed alternative processing procedures to overcome these issues, and we achieved a higher positioning accuracy (average error: 1.32–1.38 cm) and better alignment accuracy between spectral bands (average error: 0.26–0.32 cm). The proposed procedure enables precise VI analysis, especially when using the green chlorophyll index for corn, and may help accelerate the application of remote sensing techniques to agriculture. Numéro de notice : A2022-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00064R2 Date de publication en ligne : 01/05/2022 En ligne : https://doi.org/10.14358/PERS.21-00064R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101379
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 5 (May 2022) . - pp 323 - 332[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022052 SL Revue Centre de documentation Revues en salle Disponible 105-2022051 SL Revue Centre de documentation Revues en salle Disponible A continuous change tracker model for remote sensing time series reconstruction / Yangjian Zhang in Remote sensing, vol 14 n° 9 (May-1 2022)
[article]
Titre : A continuous change tracker model for remote sensing time series reconstruction Type de document : Article/Communication Auteurs : Yangjian Zhang, Auteur ; Li Wang, Auteur ; Yuanhuizi He, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de filtrage
[Termes IGN] analyse harmonique
[Termes IGN] compression d'image
[Termes IGN] détection de changement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Leaf Area Index
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] phénologie
[Termes IGN] production primaire brute
[Termes IGN] reconstruction d'image
[Termes IGN] réflectance de surface
[Termes IGN] série temporelleRésumé : (auteur) It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions. Numéro de notice : A2022-384 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14092280 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.3390/rs14092280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100662
in Remote sensing > vol 14 n° 9 (May-1 2022) . - n° 2280[article]Effects of climate and drought on stem diameter growth of urban tree species / Vjosa Dervishi in Forests, vol 13 n° 5 (May 2022)
[article]
Titre : Effects of climate and drought on stem diameter growth of urban tree species Type de document : Article/Communication Auteurs : Vjosa Dervishi, Auteur ; Werner Poschenrieder, Auteur ; Thomas Rötzer, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 641 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Aesculus hippocastanum
[Termes IGN] Allemagne
[Termes IGN] arbre urbain
[Termes IGN] changement climatique
[Termes IGN] croissance des arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] Paris (75)
[Termes IGN] Platanus ×hispanica
[Termes IGN] Quercus nigra
[Termes IGN] Quercus pedunculata
[Termes IGN] Robinia pseudoacacia
[Termes IGN] sécheresse
[Termes IGN] Tilia cordata
[Termes IGN] zone urbaine
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Urbanization and climate change are two inevitable megatrends of this century. Knowledge about the growth responses of urban trees to climate is of utmost importance towards future management of green infrastructure with the aim of a sustainable provision of the environmental ecosystem services. Using tree-ring records, this study analyzed growth response to climate by stem diameter at breast height (DBH) of 1178 trees in seven large cities worldwide, including Aesculus hippocastanum L. in Munich; Platanus × hispanica Münchh. in Paris; Quercus nigra L. in Houston; Quercus robur L. in Cape Town; Robinia pseudoacacia L. in Santiago de Chile, Munich, and Würzburg; and Tilia cordata Mill. in Berlin, Munich, and Würzburg. Climate was characterized following the de Martonne aridity index (DMI). Overall, trees showed an 8.3% lower DBH under arid than humid climate at the age of 100. Drought-tolerant tree species were overall not affected by climate. However, R. pseudoacacia showed a lower diameter when growing in semi-dry than humid climate. In contrast, drought-sensitive tree species were negatively affected by arid climate. Moreover, the effect of drought years on annual diameter increment was assessed. P. × hispanica and R. pseudoacacia appeared as the most drought-resistant species. The highest sensitivity to drought was detected in T. cordata and Q. robur. A. hippocastanum and Q. nigra showed a lower diameter growth during drought events, followed by a fast recovery. This study’s findings may contribute to a better understanding of urban tree growth reactions to climate, aiming for sustainable planning and management of urban trees. Numéro de notice : A2022-348 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f13050641 En ligne : https://doi.org/10.3390/f13050641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100534
in Forests > vol 13 n° 5 (May 2022) . - n° 641[article]A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance / Shuo Shi in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)
[article]
Titre : A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance Type de document : Article/Communication Auteurs : Shuo Shi, Auteur ; Lu Xu, Auteur ; Wei Gong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102719 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chlorophylle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] écosystème forestier
[Termes IGN] feuille (végétation)
[Termes IGN] modèle de transfert radiatif
[Termes IGN] processus gaussien
[Termes IGN] réflectance spectrale
[Termes IGN] régressionRésumé : (auteur) Forest leaf chlorophyll (Cab) and carotenoid (Cxc) are key functional indicators for the state of the forest ecosystem. Current machine learning models based on hyperspectral reflectance are widely applied to estimate leaf Cab and Cxc contents at leaf scale. However, these models have certain accuracy for non-independent datasets but have poor generalization for independent datasets when they are used to estimate leaf Cab and Cxc contents. This fact limits that hyperspectral remote sensing completely replaces destructive measurements for leaf Cab and Cxc contents. Thus, the development of an estimation model with high accuracy and satisfactory generalization is necessary. Convolutional neural networks (CNNs) have certain accuracy and generalization in many domains, and have the potential to solve above-mentioned problem. Therefore, this study developed a CNN using one-dimensional hyperspectral reflectance, which aimed to improve the model's accuracy and generalization in leaf Cab and Cxc content estimation at leaf scale. The proposed CNN was developed by three steps. First, in consideration of the correlation between leaf Cab and Cxc contents in natural leaves, 2500 physical data with leaf reflectance and corresponding Cab and Cxc contents were generated by leaf radiative transfer model and multivariable gaussian distribution function. Then, the proposed CNN was built by five strategies based on the architecture of the AlexNet. Finally, five-fold cross validation was performed with 70% of the physical data to determine the best strategy to develop the proposed CNN. These were executed to ensure the proposed CNN with the maximum accuracy and generalization. In addition, the accuracy and generalization of the proposed CNN were tested using a non-independent dataset and an independent dataset, respectively. The proposed CNN was also compared with back propagation neural network (BPNN), support vector regression (SVR) and gaussian process regression (GPR). Results showed that the best CNN could be developed with one input, five convolutional, three max-pooling and three fully-connected layers. Comprehensively considering the model's accuracy and generalization, the proposed CNN was the best model for leaf Cab and Cxc content estimation compared with BPNN, SVR and GPR. This study provides a development strategy of CNN estimation model using one-dimensional hyperspectral reflectance at leaf scale. The proposed CNN could further promote the practical application of hyperspectral remote sensing in leaf Cab and Cxc content estimation. Numéro de notice : A2022-231 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102719 Date de publication en ligne : 16/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100119
in International journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102719[article]Detecting and mapping drought severity using multi-temporal Landsat data in the uMsinga region of KwaZulu-Natal, South Africa / Shenelle Lottering in Geocarto international, vol 37 n° 6 ([01/04/2022])PermalinkEffect of climate change on the growth of tree species: Dendroclimatological analysis / Archana Gauli in Forests, vol 13 n° 4 (April 2022)PermalinkFertilization modifies forest stand growth but not stand density: consequences for modelling stand dynamics in a changing climate / Hans Pretzsch in Forestry, an international journal of forest research, vol 95 n° 2 (April 2022)PermalinkParcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data / Yanyan Wang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)PermalinkRecent changes in the climate-growth response of European larch (Larix decidua Mill.) in the Polish Sudetes / Malgorzata Danek in Trees, vol 36 n° 2 (April 2022)PermalinkProjections of climate change impacts on flowering-veraison water deficits for Riesling and Müller-Thurgau in Germany / Chenyao Yang in Remote sensing, vol 14 n° 6 (March-2 2022)PermalinkAssessing the dependencies of scots pine (Pinus sylvestris L.) structural characteristics and internal wood property variation / Ville Kankare in Forests, vol 13 n° 3 (March 2022)PermalinkChanges of tree stem biomass in European forests since 1950 / Aleksandr Lebedev in Journal of forest science, vol 68 n° 3 (March 2022)PermalinkDeep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping / Jiangsan Zhao in Remote sensing, vol 14 n° 5 (March-1 2022)PermalinkLand surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)Permalink