ISPRS Journal of photogrammetry and remote sensing / International society for photogrammetry and remote sensing (1980 -) . vol 103Paru le : 01/05/2015 |
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Ajouter le résultat dans votre panierBuilding a hybrid land cover map with crowdsourcing and geographically weighted regression / Linda M. See in ISPRS Journal of photogrammetry and remote sensing, vol 103 (May 2015)
[article]
Titre : Building a hybrid land cover map with crowdsourcing and geographically weighted regression Type de document : Article/Communication Auteurs : Linda M. See, Auteur ; Dmitry Schepaschenko, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 48 - 56 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] image à moyenne résolution
[Termes IGN] image Envisat-MERIS
[Termes IGN] intégration de données
[Termes IGN] production participative
[Termes IGN] régression géographiquement pondéréeRésumé : (auteur) Land cover is of fundamental importance to many environmental applications and serves as critical baseline information for many large scale models e.g. in developing future scenarios of land use and climate change. Although there is an ongoing movement towards the development of higher resolution global land cover maps, medium resolution land cover products (e.g. GLC2000 and MODIS) are still very useful for modelling and assessment purposes. However, the current land cover products are not accurate enough for many applications so we need to develop approaches that can take existing land covers maps and produce a better overall product in a hybrid approach. This paper uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an input. Two different methods were used: (a) the GWR was used to determine the best land cover product at each location; (b) the GWR was only used to determine the best land cover at those locations where all three land cover maps disagree, using the agreement of the land cover maps to determine land cover at the other cells. The results show that the hybrid land cover map developed using the first method resulted in a lower overall disagreement than the individual global land cover maps. The hybrid map produced by the second method was also better when compared to the GLC2000 and GlobCover but worse or similar in performance to the MODIS land cover product depending upon the metrics considered. The reason for this may be due to the use of the GLC2000 in the development of GlobCover, which may have resulted in areas where both maps agree with one another but not with MODIS, and where MODIS may in fact better represent land cover in those situations. These results serve to demonstrate that spatial analysis methods can be used to improve medium resolution global land cover information with existing products. Numéro de notice : A2015-696 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.06.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.06.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78331
in ISPRS Journal of photogrammetry and remote sensing > vol 103 (May 2015) . - pp 48 - 56[article]Use of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil / Dan-Xia Song in ISPRS Journal of photogrammetry and remote sensing, vol 103 (May 2015)
[article]
Titre : Use of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil Type de document : Article/Communication Auteurs : Dan-Xia Song, Auteur ; Chengquan Huang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 81 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] Brésil
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte forestière
[Termes IGN] détection de changement
[Termes IGN] Etats-Unis
[Termes IGN] forêt tropicale
[Termes IGN] image Corona
[Termes IGN] image LandsatRésumé : (auteur) Land-cover change detection using satellite remote sensing is largely confined to the era of Landsat satellites, from 1972 to present. However, the Corona, Argon, and Lanyard intelligence satellites operated by the U.S. government between 1960 and 1972 have the potential to provide an important extension of the long-term record of Earth’s land surface. Recently declassified, the archive of images recorded by these satellites contains hundreds of thousands of photographs, many of which have very high ground resolution- 6–9 ft (1.8–2.7 m) even by today’s standards. This paper demonstrates methods for extending the span of forest-cover change analysis from the Landsat-5 and -7 era (1984 to present) to the previous era covered by the Corona archive in two study areas: one area covered predominantly by urban and sub-urban land uses in the eastern US and another area by tropical forest in central Brazil. We describe co-registration of Corona and Landsat images, extraction of texture features from Corona images, classification of Corona and Landsat images, and post-classification change detection based on the resulting thematic dataset. Second-order polynomial transformation of Corona images yielded geometric accuracy relative to Landsat-7 of 18.24 m for the urban area and 29.35 m for the tropical forest study area, generally deemed adequate for pixel-based change detection at Landsat resolution. Classification accuracies were approximately 95% and 96% for forest/non-forest discrimination for the temperate urban and tropical forest study areas, respectively. Texture within 7 × 7- to 9 × 9-pixel (∼13.0–16.5 m) neighborhoods and within 11 × 11-pixel (∼30 m) neighborhoods were the most informative metrics for forest classification in Corona images in the temperate and tropical study areas, respectively. The trajectory of change from the 1960s to 2000s differed between the two study areas: the average annual forest loss rate in the urban area doubled from 0.68% to 1.9% from the 1960s to the mid-1980s and then decreased during the following decade. In contrast, deforestation in the Brazilian study area continued at a slightly increased pace between the 1960s and 1990s at annual loss rate of 0.62–0.79% and quickly slowed down afterward. This study demonstrates the strong potential of declassified Corona images for detecting historical forest changes in these study regions and suggests increased utility for retrieving a wide range of land cover histories around the world. Numéro de notice : A2015-697 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.09.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.09.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78333
in ISPRS Journal of photogrammetry and remote sensing > vol 103 (May 2015) . - pp 81 - 92[article]