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Land cover harmonization using Latent Dirichlet Allocation / Zhan Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 348 - 374 Titre : Land cover harmonization using Latent Dirichlet Allocation Type de document : Article/Communication Auteurs : Zhan Li, Auteur ; Joanne C. White, Auteur ; Michael A. Wulder, Auteur Année de publication : 2021 Article en page(s) : pp 348 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] allocation de Dirichlet latente [Termes IGN] Canada [Termes IGN] carte d'occupation du sol [Termes IGN] chevauchement [Termes IGN] erreur de classification [Termes IGN] harmonisation des données [Termes IGN] matrice d'erreur [Termes IGN] matrice de co-occurrence [Termes IGN] segmentation sémantique [Termes IGN] utilisation du sol Résumé : (auteur) Large-area land cover maps are produced to satisfy different information needs. Land cover maps having partial or complete spatial and/or temporal overlap, different legends, and varying accuracies for similar classes, are increasingly common. To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences. We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores). The results were compared with the benchmark maps generated using simple legend crosswalks and showed that using LDA outputs with error matrices performed better and increased harmonized map overall accuracy by 6–19% for areas of disagreement between the source maps. Our results revealed the importance of error matrices to harmonization, since excluding error matrices reduced overall accuracy by 4–20%. The LDA-based harmonization approach demonstrated in this paper is quantitative, transparent, portable, and efficient at leveraging the strengths of multiple land cover maps over large areas. Numéro de notice : A2021-027 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1796131 Date de publication en ligne : 27/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1796131 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96701 [article]

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use / Alexis Comber in Transactions in GIS, Vol 23 n° 5 (October 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 5 (October 2019) . - pp 879 - 891 Titre : Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use Type de document : Article/Communication Auteurs : Alexis Comber, Auteur ; Michael A. Wulder, Auteur Année de publication : 2019 Article en page(s) : pp 879 - 891 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] détection de changement [Termes IGN] données spatiotemporelles [Termes IGN] inventaire de la végétation [Termes IGN] métadonnées [Termes IGN] occupation du sol [Termes IGN] série temporelle [Termes IGN] télédétection [Termes IGN] utilisation du sol Résumé : (auteur) Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time. The role of proximity in spatial process is well understood, but its value is much more uncertain for many temporal processes. Using the domain of land cover/land use (LCLU), this article asserts that analyses of big data should be grounded in understandings of underlying process. Processes exhibit behaviors over both space and time. Observations and measurements may or may not coincide with the process of interest. Identifying the presence or absence of a given process, for instance disentangling vegetation phenology from stress, requires data analysis to be informed by knowledge of the process characteristics and, critically, how these manifest themselves over the spatio‐temporal unit of analysis. Drawing from LCLU, we emphasize the need to identify process and consider process phase to quantify important signals associated with that process. The aim should be to link the seriality of the spatio‐temporal data to the phase of the process being considered. We elucidate on these points and opportunities for insights and leadership from the geographic community. Numéro de notice : A2019-549 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12559 Date de publication en ligne : 08/07/2019 En ligne : https://doi.org/10.1111/tgis.12559 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94199 [article]

Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)  

Public [article]  inRemote sensing of environment > vol 227 (15 June 2019) . - pp 110 - 124 Titre : Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data Type de document : Article/Communication Auteurs : Piotr Tompalski, Auteur ; Joanne C. White, Auteur ; Nicholas C. Coops, Auteur ; Michael A. Wulder, Auteur Année de publication : 2019 Article en page(s) : pp 110 - 124 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] méthode des moindres carrés [Termes IGN] méthode robuste [Termes IGN] modèle mathématique [Termes IGN] régression [Termes IGN] semis de points [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Airborne laser scanning (ALS) is a reliable source of accurate information for forest stand inventory attributes including height, cover, basal area, and volume. The commonly applied area-based approach (ABA) allows the derivation of wall-to-wall geospatial coverages representing each of the modeled attributes at a grid-cell level, with spatial resolutions typically between 20 and 30 m. The ABA predictive models are developed using stratified inventory data from field plots, the requirement for which can increase the overall cost of the ALS-based inventory. Parsimonious use of ground plots is a key means to control variable costs in the operational implementation of the ABA. In this paper, we demonstrate how the prediction accuracy of Lorey's height (HL, m), quadratic mean diameter (QMD, cm), and gross volume (V, m3) vary when existing ABA models are transferred to different areas or are applied to point cloud data with different characteristics than those on which the original model was developed. Specifically, we consider three scenarios of model transferability: (i) same point cloud characteristics, different areas; (ii) different point cloud characteristics, same areas; and (iii) different point cloud characteristics, different areas. We generated area-based models using three modeling approaches: linear regression (OLS), random forests (RF), and k-nearest neighbour (kNN) imputation. Results indicated that the prediction accuracy of area-based models varied by attribute and by modeling approach. We found that when the models were transferred their prediction accuracy decreased, with an average increase in relative bias up to 22.04%, and increase in relative RMSE up to 29.31%. Prediction accuracies for HL were higher than those of QMD or V when models were transferred, and had the lowest average increase in relative bias and relative RMSE of Numéro de notice : A2019-227 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.04.006 Date de publication en ligne : 13/04/2019 En ligne : https://doi.org/10.1016/j.rse.2019.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92741 [article]

Optical remotely sensed time series data for land cover classification: A review / Cristina Gómez in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 55 – 72 Titre : Optical remotely sensed time series data for land cover classification: A review Type de document : Article/Communication Auteurs : Cristina Gómez, Auteur ; Joanne C. White, Auteur ; Michael A. Wulder, Auteur Année de publication : 2016 Article en page(s) : pp 55 – 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification automatique [Termes IGN] détection de changement [Termes IGN] image Landsat [Termes IGN] image Sentinel-MSI [Termes IGN] occupation du sol [Termes IGN] série temporelle [Termes IGN] surveillance agricole [Termes IGN] traitement d'image Résumé : (auteur) Accurate land cover information is required for science, monitoring, and reporting. Land cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring and mapping of land cover and land cover change in a consistent and robust manner over large areas is made possible with Earth Observation (EO) data. Land cover products satisfying a range of science and policy information needs are currently produced periodically at different spatial and temporal scales. The increased availability of EO data—particularly from the Landsat archive (and soon to be augmented with Sentinel-2 data)—coupled with improved computing and storage capacity with novel image compositing approaches, have resulted in the availability of annual, large-area, gap-free, surface reflectance data products. In turn, these data products support the development of annual land cover products that can be both informed and constrained by change detection outputs. The inclusion of time series change in the land cover mapping process provides information on class stability and informs on logical class transitions (both temporally and categorically). In this review, we present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization. Numéro de notice : A2016-578 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.008 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.03.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81716 [article]

Remote sensing technologies for enhancing forest inventories: A review / Joanne C. White in Canadian journal of remote sensing, vol 42 n° 5 ([01/05/2016])  

Public [article]  inCanadian journal of remote sensing > vol 42 n° 5 [01/05/2016] . - pp 619 - 641 Titre : Remote sensing technologies for enhancing forest inventories: A review Type de document : Article/Communication Auteurs : Joanne C. White, Auteur ; Nicholas C. Coops, Auteur ; Michael A. Wulder, Auteur ; Mikko Vastaranta, Auteur ; Thomas Hilker, Auteur ; Piotr Tompalski, Auteur Année de publication : 2016 Article en page(s) : pp 619 - 641 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] image optique [Termes IGN] image satellite [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] photogrammétrie numérique [Termes IGN] télémétrie laser aéroporté [Termes IGN] télémétrie laser terrestre [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time. Numéro de notice : A2016--127 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/07038992.2016.1207484 En ligne : http://dx.doi.org/10.1080/07038992.2016.1207484 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85113 [article]

Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with the area-based approach / Joanne C. White in Canadian Journal of Forest Research, vol 45 n° 11 (November 2015)  

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Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm / Oumer S. Ahmed in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)  

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Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review / Margaret E. Andrew in GIScience and remote sensing, vol 52 n° 3 (2015)  

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Integration of Lidar and Landsat to estimate forest canopy cover in coastal British Columbia / Oumer S. Ahmed in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)  

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Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest / O. Tsui in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)  

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SilviLaser Vancouver 2012 full proceedings / Michael A. Wulder (2012) 

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Efficient multiresolution spatial predictions for large data arrays / Magnussen, Steen in Remote sensing of environment, vol 109 n° 4 (30 August 2007)  

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Assessment of Quickbird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation / Nicholas C. Coops in Remote sensing of environment, vol 103 n° 1 (15 July 2006)  

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Predicting forest age classes from high spatial resolution remotely sensed imagery using Voronoi polygon aggregation / T. Nelson in Geoinformatica, vol 8 n° 2 (June - August 2004)  

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vol 29 n° 5 - 01/10/2003 - La télédétection LIDAR de la structure de la forêt et du Terrain (Bulletin de Canadian journal of remote sensing) / Michael A. Wulder

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