CLC code<\/th>	Simplified description<\/th>	Degree of suitability for bees (\u03bc)<\/th>	Justification<\/th><\/tr><\/thead>
111\u2013142<\/td>	Urban\/artificial areas<\/td>	0.1<\/td>	Very low, few natural floral resources<\/td><\/tr>
211\u2013244<\/td>	Agricultural areas (crops, agricultural mosaics)<\/td>	0.6<\/td>	Presence of meadows and possible honey crops<\/td><\/tr>
311\u2013313<\/td>	Forests (deciduous, coniferous, mixed<\/td>	0.8<\/td>	Rich in honey species depending on the season<\/td><\/tr>
321\u2013324<\/td>	Natural vegetation (scrub, maquis, etc.)<\/td>	0.9<\/td>	Very favorable, widespread flowering, few disturbances<\/td><\/tr>
331\u2013335<\/td>	Open spaces, beaches, rocky areas<\/td>	0.3<\/td>	Few flowers, difficult conditions<\/td><\/tr>
411\u2013423<\/td>	Wetlands<\/td>	0.4<\/td>	Unstable environments, sometimes rich but fragile<\/td><\/tr>
511\u2013523<\/td>	Water bodies\/sea<\/td>	0.0<\/td>	Obviously not favorable<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n \n\n\n\n <\/span>Step 3: Fuzzy transformation<\/span><\/h3>\n\n\n\n Using the FuzzyAttributes plugin \u2192 Classes module \u2192 Fuzzy:<\/p>\n\n\n\n <\/span>1-Load the CLC raster (reclassified or original).<\/span><\/h4>\n\n\n\n $\"L\u2019attribut$ <\/a><\/figure>\n\n\n\n <\/span>2-Define the correspondence table above (codes and \u03bc).<\/span><\/h4>\n\n\n\n $\"L\u2019attribut$ <\/a><\/figure>\n\n\n\n <\/span>3-Generate the fuzzy raster fzy_corine.tif.<\/span><\/h4>\n\n\n\n <\/span>4-View: favorable areas (\u03bc close to 1) will appear lighter.<\/span><\/h4>\n\n\n\n $\"L\u2019attribut$ <\/a><\/figure>\n\n\n\n \n\n\n\n <\/span>Step 4: Interpretation<\/span><\/h3>\n\n\n\n \n This produces a continuous map of suitability for beekeeping, rather than a patchwork of rigid classes.<\/li>\n\n\n\n It can then be combined with other fuzzy criteria:\n \n proximity to water (need for watering)<\/li>\n\n\n\n altitude\/slope<\/li>\n\n\n\n distance to roads (accessibility)<\/li>\n\n\n\n absence of treated crops<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n A fuzzy aggregation of these criteria will produce a multi-criteria map of optimal hive locations.<\/p>\n\n\n\n \n\n\n\n <\/span>In summary<\/span><\/h3>\n\n\n\n Transforming a raster of classes into a fuzzy raster means moving from a \u201ccategorical\u201d logic to a \u2018gradual\u2019 logic\u2014we move away from \u201call or nothing\u201d to represent the degree of relevance of each class to an objective.<\/p>\n\n\n\n \n","protected":false},"excerpt":{"rendered":" In some cases, the data is not continuous (altitude, slope, distance, etc.), but discrete, i.e., consisting of classes. Each pixel then corresponds to a category: a soil type, land use, risk level, etc. These \u201ccategorical\u201d rasters…<\/p>\n","protected":false},"author":1,"featured_media":15762,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"give_campaign_id":0,"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"sfsi_plus_gutenberg_text_before_share":"","sfsi_plus_gutenberg_show_text_before_share":"","sfsi_plus_gutenberg_icon_type":"","sfsi_plus_gutenberg_icon_alignemt":"","sfsi_plus_gutenburg_max_per_row":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"jetpack_post_was_ever_published":false,"_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":""},"categories":[3394,1377],"tags":[3779,3630,3727],"class_list":["post-15828","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pluigin-en","category-qgis-en","tag-classes-en","tag-fuzzyattributes-en","tag-v2"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.sigterritoires.fr\/wp-content\/uploads\/2025\/10\/fzy_exemple_raster_out.jpg?fit=1982%2C1649&ssl=1","jetpack_shortlink":"https:\/\/wp.me\/p6XU0A-47i","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts\/15828","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/comments?post=15828"}],"version-history":[{"count":0,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts\/15828\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/media\/15762"}],"wp:attachment":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/media?parent=15828"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/categories?post=15828"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/tags?post=15828"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

Contenu <\/p>\nToggle<\/span>

The principle<\/a>
- Steps in the FuzzyAttributes plugin<\/a><\/li><\/ul><\/li>
- Best practices<\/a><\/li>
- A concrete example<\/a>
  - Step 1: Data source<\/a><\/li>
  - Step 2: Class correspondence<\/a><\/li>
  - Step 3: Fuzzy transformation<\/a>
    - 1-Load the CLC raster (reclassified or original).<\/a><\/li>
    - 2-Define the correspondence table above (codes and \u03bc).<\/a><\/li>
    - 3-Generate the fuzzy raster fzy_corine.tif.<\/a><\/li>
    - 4-View: favorable areas (\u03bc close to 1) will appear lighter.<\/a><\/li><\/ul><\/li>
    - Step 4: Interpretation<\/a><\/li>
    - In summary<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<\/p>\n\n\n\n
      \n\n\n\n
      In some cases, the data is not continuous (altitude, slope, distance, etc.), but discrete, i.e., consisting of classes.<\/p>\n\n\n\n
      Each pixel then corresponds to a category: a soil type, land use, risk level, etc.<\/p>\n\n\n\n
      These \u201ccategorical\u201d rasters are very common in spatial analysis, but they pose a problem:<\/p>\n\n\n\n
      how can they be integrated into a fuzzy multi-criteria analysis when they do not contain continuous numerical values?<\/p>\n\n\n\n
      This is where the Classes \u2192 Fuzzy module of the FuzzyAttributes plugin (version 2) comes in.<\/p>\n\n\n\n
      \n\n\n\n
      <\/span>The principle<\/span><\/h2>\n\n\n\n
      Rather than working directly with classes (1, 2, 3… or \u201cforest,\u201d \u201curban,\u201d \u201cwater\u201d), we seek to express for each of them the extent to which it satisfies a given criterion.<\/p>\n\n\n\n
      Examples:<\/p>\n\n\n\n
      \n
      For a criterion of \u201careas favorable to infiltration,\u201d\n
      \n
      sandy soils \u2192 satisfaction 0.9<\/li>\n\n\n\n
      clay soils \u2192 satisfaction 0.3<\/li>\n<\/ul>\n<\/li>\n\n\n\n
      urban areas \u2192 satisfaction 0.1<\/li>\n\n\n\n
      For a criterion such as \u201cerosion risk,\u201d\n
      \n
      steep slopes \u2192 0.8<\/li>\n\n\n\n
      forests \u2192 0.2<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
      The result is a fuzzy raster, in which each pixel takes a value between 0 and 1 representing the degree of satisfaction of the criterion.<\/p>\n\n\n\n
      \n\n\n\n
      <\/span>Steps in the FuzzyAttributes plugin<\/span><\/h3>\n\n\n\n
      \n
      Menu: Classes \u2192 Fuzzy<\/li>\n\n\n\n
      Select a categorical raster (single band, integer type).<\/li>\n\n\n\n
      The plugin reads the unique values present in the layer.<\/li>\n\n\n\n
      For each class, enter the fuzzy membership degree (between 0 and 1).
      You can also load a correspondence table from an existing CSV<\/a> file.<\/li>\n\n\n\n
      Choose a name for the output fuzzy raster (e.g., fzy_classes.tif).<\/li>\n\n\n\n
      Click Create fuzzy raster.<\/li>\n<\/ol>\n\n\n\n
      \n\n\n\n
      <\/span>Best practices<\/span><\/h2>\n\n\n\n
      \n
      Consistency depends on the criterion studied: the same class may be favorable in one analysis and unfavorable in another.<\/li>\n\n\n\n
      Avoid absolute values of 0 and 1 unless you are completely certain: fuzzy logic allows you to express nuances.<\/li>\n\n\n\n
      Document your correspondences: they can be saved in a CSV file or a GeoPackage<\/a> table for reuse.<\/li>\n<\/ul>\n\n\n\n
      \n\n\n\n
      <\/span>A concrete example<\/span><\/h2>\n\n\n\n
      Objective:<\/strong><\/p>\n\n\n\n
      Create a suitability map for installing beehives based on the CORINE Land Cover raster.<\/p>\n\n\n\n
      Principle:<\/strong><\/p>\n\n\n\n
      Each type of land use (forest, grassland, agricultural area, urban area, etc.) is assigned a satisfaction rating between 0 and 1 according to its suitability for beekeeping.<\/p>\n\n\n\n
      \n\n\n\n<\/span>Step 1: Data source<\/span><\/h3>\n\n\n\n
      You can download the CLC raster directly here:<\/p>\n\n\n\n
      https:\/\/land.copernicus.eu\/pan-european\/corine-land-cover\/clc2018<\/a><\/p>\n\n\n\n
      Available formats: GeoTIFF, 100 m resolution.<\/p>\n\n\n\n
      You can then crop it to your study area (e.g., Corsica).<\/p>\n\n\n\n
      \n\n\n\n
      <\/span>Step 2: Class correspondence<\/span><\/h3>\n\n\n\n
      Here is an example correspondence table for bee assessment:<\/p>\n\n\n\n
      CLC code<\/th> Simplified description<\/th> Degree of suitability for bees (\u03bc)<\/th> Justification<\/th><\/tr><\/thead>
      111\u2013142<\/td> Urban\/artificial areas<\/td> 0.1<\/td> Very low, few natural floral resources<\/td><\/tr>
      211\u2013244<\/td> Agricultural areas (crops, agricultural mosaics)<\/td> 0.6<\/td> Presence of meadows and possible honey crops<\/td><\/tr>
      311\u2013313<\/td> Forests (deciduous, coniferous, mixed<\/td> 0.8<\/td> Rich in honey species depending on the season<\/td><\/tr>
      321\u2013324<\/td> Natural vegetation (scrub, maquis, etc.)<\/td> 0.9<\/td> Very favorable, widespread flowering, few disturbances<\/td><\/tr>
      331\u2013335<\/td> Open spaces, beaches, rocky areas<\/td> 0.3<\/td> Few flowers, difficult conditions<\/td><\/tr>
      411\u2013423<\/td> Wetlands<\/td> 0.4<\/td> Unstable environments, sometimes rich but fragile<\/td><\/tr>
      511\u2013523<\/td> Water bodies\/sea<\/td> 0.0<\/td> Obviously not favorable<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \n\n\n\n
      <\/span>Step 3: Fuzzy transformation<\/span><\/h3>\n\n\n\n
      Using the FuzzyAttributes plugin \u2192 Classes module \u2192 Fuzzy:<\/p>\n\n\n\n
      <\/span>1-Load the CLC raster (reclassified or original).<\/span><\/h4>\n\n\n\n
      $\"L\u2019attribut$ <\/a><\/figure>\n\n\n\n
      <\/span>2-Define the correspondence table above (codes and \u03bc).<\/span><\/h4>\n\n\n\n
      $\"L\u2019attribut$ <\/a><\/figure>\n\n\n\n
      <\/span>3-Generate the fuzzy raster fzy_corine.tif.<\/span><\/h4>\n\n\n\n
      <\/span>4-View: favorable areas (\u03bc close to 1) will appear lighter.<\/span><\/h4>\n\n\n\n
      $\"L\u2019attribut$ <\/a><\/figure>\n\n\n\n
      \n\n\n\n
      <\/span>Step 4: Interpretation<\/span><\/h3>\n\n\n\n
      \n
      This produces a continuous map of suitability for beekeeping, rather than a patchwork of rigid classes.<\/li>\n\n\n\n
      It can then be combined with other fuzzy criteria:\n
      \n
      proximity to water (need for watering)<\/li>\n\n\n\n
      altitude\/slope<\/li>\n\n\n\n
      distance to roads (accessibility)<\/li>\n\n\n\n
      absence of treated crops<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
      A fuzzy aggregation of these criteria will produce a multi-criteria map of optimal hive locations.<\/p>\n\n\n\n
      \n\n\n\n
      <\/span>In summary<\/span><\/h3>\n\n\n\n
      Transforming a raster of classes into a fuzzy raster means moving from a \u201ccategorical\u201d logic to a \u2018gradual\u2019 logic\u2014we move away from \u201call or nothing\u201d to represent the degree of relevance of each class to an objective.<\/p>\n\n\n\n
      \n","protected":false},"excerpt":{"rendered":"
      In some cases, the data is not continuous (altitude, slope, distance, etc.), but discrete, i.e., consisting of classes. Each pixel then corresponds to a category: a soil type, land use, risk level, etc. These \u201ccategorical\u201d rasters…<\/p>\n","protected":false},"author":1,"featured_media":15762,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"give_campaign_id":0,"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"sfsi_plus_gutenberg_text_before_share":"","sfsi_plus_gutenberg_show_text_before_share":"","sfsi_plus_gutenberg_icon_type":"","sfsi_plus_gutenberg_icon_alignemt":"","sfsi_plus_gutenburg_max_per_row":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"jetpack_post_was_ever_published":false,"_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":""},"categories":[3394,1377],"tags":[3779,3630,3727],"class_list":["post-15828","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pluigin-en","category-qgis-en","tag-classes-en","tag-fuzzyattributes-en","tag-v2"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.sigterritoires.fr\/wp-content\/uploads\/2025\/10\/fzy_exemple_raster_out.jpg?fit=1982%2C1649&ssl=1","jetpack_shortlink":"https:\/\/wp.me\/p6XU0A-47i","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts\/15828","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/comments?post=15828"}],"version-history":[{"count":0,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts\/15828\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/media\/15762"}],"wp:attachment":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/media?parent=15828"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/categories?post=15828"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/tags?post=15828"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}