{"id":5618,"date":"2022-10-07T15:35:41","date_gmt":"2022-10-07T13:35:41","guid":{"rendered":"https:\/\/geopard.tech\/?p=5618"},"modified":"2023-05-27T19:37:02","modified_gmt":"2023-05-27T17:37:02","slug":"indice-de-difference-dhumidite-normalise-ndmi","status":"publish","type":"post","link":"https:\/\/geopard.tech\/fr\/blog\/normalized-difference-moisture-index-ndmi\/","title":{"rendered":"Indice d'humidit\u00e9 normalis\u00e9e"},"content":{"rendered":"

Le nombre de indices de v\u00e9g\u00e9tation pris en charge par GeoPard<\/a> ne cesse de cro\u00eetre. L'\u00e9quipe de GeoPard pr\u00e9sente l'indice d'humidit\u00e9 diff\u00e9rentielle normalis\u00e9e (NDMI). Cet indice d\u00e9termine la teneur en eau de la v\u00e9g\u00e9tation et l'indice de diff\u00e9rence normalis\u00e9e de l'eau (NDWI). Il est utile pour trouver les endroits o\u00f9 il y a des le stress hydrique chez les plantes<\/strong>.<\/p>\r\n\r\n\r\n\r\n

Les valeurs inf\u00e9rieures de l'IMDN marquent les endroits o\u00f9 les plantes subissent un stress d\u00fb \u00e0 une humidit\u00e9 insuffisante.
D'autre part, les valeurs plus faibles de l'indice de diff\u00e9rence d'eau normalis\u00e9 qui suivent le pic de v\u00e9g\u00e9tation mettent en \u00e9vidence les zones qui sont en train de devenir des zones \u00e0 risque. pr\u00eat pour la r\u00e9colte<\/strong> d'abord.<\/p>\r\n

\"La

La diff\u00e9rence de la teneur en eau relative de la v\u00e9g\u00e9tation entre deux images satellites (dans ce cas, la constellation Sentinel-2).<\/p><\/div>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Dans les captures d'\u00e9cran suivantes, vous trouverez les zones NDMI g\u00e9n\u00e9r\u00e9es sur la base des images satellites du 19 juin (pic de v\u00e9g\u00e9tation) et du 6 juillet, ainsi que la carte d'\u00e9quation repr\u00e9sentant la diff\u00e9rence NDMI.<\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

\"IndiceNDMI calcul\u00e9 \u00e0 partir d'une image Planet \/ Sentinel-2 \/ Landsat<\/p>\r\n

Qu'est-ce que l'indice d'humidit\u00e9 ?<\/h2>\r\n

Il s'agit d'une mesure ou d'un calcul utilis\u00e9 pour \u00e9valuer la teneur en humidit\u00e9 ou la disponibilit\u00e9 dans une zone ou une r\u00e9gion sp\u00e9cifique. Elle est g\u00e9n\u00e9ralement d\u00e9riv\u00e9e de divers facteurs environnementaux tels que les pr\u00e9cipitations, l'\u00e9vapotranspiration, les propri\u00e9t\u00e9s du sol et la couverture v\u00e9g\u00e9tale.<\/p>\r\n

Il fournit une indication relative du degr\u00e9 d'humidit\u00e9 ou de s\u00e9cheresse d'une zone, ce qui permet d'identifier un \u00e9ventuel stress hydrique ou des conditions de s\u00e9cheresse.<\/p>\r\n

Il s'agit d'un outil pr\u00e9cieux pour la surveillance et la gestion des ressources en eau, la planification agricole et la compr\u00e9hension des conditions \u00e9cologiques d'une r\u00e9gion donn\u00e9e.<\/p>\r\n

Qu'est-ce que l'indice d'humidit\u00e9 diff\u00e9rentiel normalis\u00e9 ?<\/h2>\r\n

L'indice d'humidit\u00e9 par diff\u00e9rence normalis\u00e9e (NDMI) est un indice de v\u00e9g\u00e9tation d\u00e9riv\u00e9 de donn\u00e9es de t\u00e9l\u00e9d\u00e9tection qui permet d'\u00e9valuer et de surveiller la teneur en eau de la v\u00e9g\u00e9tation. Comme d'autres indices de v\u00e9g\u00e9tation, il est calcul\u00e9 \u00e0 partir des valeurs de r\u00e9flectance spectrale des images satellitaires ou a\u00e9riennes.<\/p>\r\n

Elle est particuli\u00e8rement utile pour surveiller le stress hydrique des plantes, \u00e9valuer les conditions de s\u00e9cheresse, estimer le risque d'incendie et \u00e9tudier les effets du changement climatique sur la v\u00e9g\u00e9tation.<\/p>\r\n

Il est calcul\u00e9 en utilisant les bandes du proche infrarouge (NIR) et de l'infrarouge \u00e0 ondes courtes (SWIR), qui sont sensibles \u00e0 la teneur en eau de la v\u00e9g\u00e9tation. La formule de l'IMDN est la suivante<\/p>\r\n

NDMI<\/strong> = (NIR - SWIR) \/ (NIR + SWIR)<\/em><\/p>\r\n

Les valeurs de l'indice NDWI sont g\u00e9n\u00e9ralement comprises entre -1 et 1, les valeurs les plus \u00e9lev\u00e9es indiquant une teneur en eau plus \u00e9lev\u00e9e dans la v\u00e9g\u00e9tation et les valeurs les plus faibles une teneur en eau plus faible ou un stress hydrique dans la v\u00e9g\u00e9tation. Les valeurs n\u00e9gatives de l'indice NDMI peuvent \u00eatre associ\u00e9es \u00e0 des zones sans v\u00e9g\u00e9tation ou \u00e0 des zones \u00e0 tr\u00e8s faible teneur en eau.<\/p>\r\n

Qu'est-ce que l'NDWI ?<\/h2>\r\n

L'indice NDWI (Normalized Difference Water Index) est un indice de t\u00e9l\u00e9d\u00e9tection utilis\u00e9 pour quantifier et \u00e9valuer la teneur en eau ou les caract\u00e9ristiques li\u00e9es \u00e0 l'eau dans la v\u00e9g\u00e9tation ou les paysages.<\/p>\r\n

Elle est calcul\u00e9e en analysant la r\u00e9flectance des bandes de lumi\u00e8re proche infrarouge et verte \u00e0 partir d'images satellites ou a\u00e9riennes. Elle est particuli\u00e8rement utile pour identifier les masses d'eau, surveiller les changements dans la disponibilit\u00e9 de l'eau et \u00e9valuer la sant\u00e9 de la v\u00e9g\u00e9tation.<\/p>\r\n

En comparant l'absorption et la r\u00e9flexion de diff\u00e9rentes longueurs d'onde, elle fournit des informations pr\u00e9cieuses pour des applications telles que la surveillance de la s\u00e9cheresse, l'analyse hydrologique et la gestion des \u00e9cosyst\u00e8mes.<\/p>\r\n

Visualisation de l'IMDN pour d\u00e9terminer l'indice de diff\u00e9rence normalis\u00e9 de l'eau<\/h2>\r\n

La visualisation de l'IMDN implique le traitement d'images satellitaires ou a\u00e9riennes, le calcul des valeurs de l'IMDN et l'affichage des r\u00e9sultats sous forme de carte ou d'image cod\u00e9e en couleur. Voici les \u00e9tapes g\u00e9n\u00e9rales de la visualisation de l'IMDN :<\/p>\r\n

    \r\n
  • Acqu\u00e9rir des images satellitaires ou a\u00e9riennes :<\/strong> Obtenez des images multispectrales \u00e0 partir d'un satellite ou d'une plate-forme a\u00e9rienne, comme Landsat, Sentinel ou MODIS. Assurez-vous que l'imagerie comprend les bandes n\u00e9cessaires : le proche infrarouge (NIR) et l'infrarouge \u00e0 ondes courtes (SWIR).<\/li>\r\n
  • Pr\u00e9traiter les images :<\/strong> En fonction de la source de donn\u00e9es, il peut \u00eatre n\u00e9cessaire de pr\u00e9traiter l'image pour corriger les distorsions atmosph\u00e9riques, g\u00e9om\u00e9triques et radiom\u00e9triques. Convertir les nombres num\u00e9riques (DN) de l'image en valeurs de r\u00e9flectance spectrale.<\/li>\r\n
  • Calculer l'IMDN :<\/strong> Pour chaque pixel de l'image, utilisez les valeurs de r\u00e9flectance NIR et SWIR pour calculer le NDMI \u00e0 l'aide de la formule : NDMI = (NIR - SWIR) \/ (NIR + SWIR).<\/li>\r\n
  • Cartographie des couleurs :<\/strong> Attribuer une palette de couleurs aux valeurs NDMI. En r\u00e8gle g\u00e9n\u00e9rale, une \u00e9chelle de couleurs continue est utilis\u00e9e, allant d'une couleur (par exemple, le rouge) pour les valeurs NDMI faibles (indiquant une faible teneur en eau) \u00e0 une autre couleur (par exemple, le vert) pour les valeurs NDMI \u00e9lev\u00e9es (indiquant une forte teneur en eau). Vous pouvez utiliser des logiciels comme QGIS, ArcGIS ou des biblioth\u00e8ques de programmation comme Rasterio et Matplotlib de Python pour cr\u00e9er une carte en couleur.<\/li>\r\n
  • Visualiser la carte de la NDMI :<\/strong> Affichez la carte ou l'image de l'IMDN \u00e0 l'aide d'un logiciel SIG, d'une biblioth\u00e8que de programmation ou d'une plateforme en ligne. Cela vous permettra d'analyser la distribution spatiale de la teneur en eau de la v\u00e9g\u00e9tation et d'identifier les zones de stress hydrique ou de forte humidit\u00e9.<\/li>\r\n
  • Interpr\u00e9tation et analyse :<\/strong> Utilisez la visualisation de l'indice NDWI pour \u00e9valuer la sant\u00e9 de la v\u00e9g\u00e9tation, surveiller les conditions de s\u00e9cheresse ou \u00e9valuer le risque d'incendie. Vous pouvez \u00e9galement comparer les cartes de l'indice de diff\u00e9rence d'eau normalis\u00e9 de diff\u00e9rentes p\u00e9riodes pour analyser les changements dans la teneur en eau de la v\u00e9g\u00e9tation au fil du temps.<\/li>\r\n<\/ul>\r\n

    Rappelez-vous que les diff\u00e9rents outils logiciels ou biblioth\u00e8ques de programmation peuvent avoir des flux de travail l\u00e9g\u00e8rement diff\u00e9rents, mais que le processus global sera similaire. En outre, vous pouvez superposer d'autres couches de donn\u00e9es, telles que l'utilisation des terres, l'altitude ou les limites administratives, afin d'am\u00e9liorer votre analyse et de mieux comprendre les relations entre la teneur en eau de la v\u00e9g\u00e9tation et d'autres facteurs.\u00a0<\/p>","protected":false},"excerpt":{"rendered":"

    Le nombre d'indices de v\u00e9g\u00e9tation pris en charge par GeoPard ne cesse de cro\u00eetre. L'\u00e9quipe GeoPard pr\u00e9sente l'indice d'humidit\u00e9 par diff\u00e9rence normalis\u00e9e (NDMI). Cet indice d\u00e9termine l'humidit\u00e9 de la v\u00e9g\u00e9tation\u2026<\/p>","protected":false},"author":210157960,"featured_media":5620,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","_crdt_document":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1586,1372,1377,1376,1378,1379],"tags":[1618,1619],"class_list":["post-5618","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product-features","category-blog","category-crop-monitoring","category-management-zones","category-remote-sensing","category-soil-data","tag-crop-monitoring","tag-normalized-difference-vegetation-index"],"acf":[],"yoast_head":"\nNormalized Difference Moisture Index - GeoPard Agriculture<\/title>\n<meta name=\"description\" content=\"GeoPard team introduces the Normalized Difference Moisture Index (NDMI). The index determines vegetation water content.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/geopard.tech\/fr\/blog\/indice-de-difference-dhumidite-normalise-ndmi\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Normalized Difference Moisture Index\" \/>\n<meta property=\"og:description\" content=\"GeoPard team introduces the Normalized Difference Moisture Index (NDMI). 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