{"id":286,"date":"2021-01-18T17:57:00","date_gmt":"2021-01-18T16:57:00","guid":{"rendered":"https:\/\/geopard.tech\/?p=286"},"modified":"2024-12-01T21:40:34","modified_gmt":"2024-12-01T20:40:34","slug":"uporaba-obrezanih-rastrskih-podatkov-za-agrozivilstvo","status":"publish","type":"post","link":"https:\/\/geopard.tech\/sl\/blog\/use-of-cropped-raster-data-for-agribusiness\/","title":{"rendered":"Uporaba obrezanih rastrskih podatkov za agro\u017eivilsko dejavnost"},"content":{"rendered":"

Obrezani rastrski podatki na podlagi meje polja se sli\u0161ijo zelo preprosto. Nekateri viri podatkov so rastri s slikovnimi pikami in lo\u010dljivostjo 3 m\/10 m\/30 m, drugi pa so vektorji s poligoni ali multipoligoni.<\/p>\r\n\r\n\r\n\t\t

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\"Obrezani<\/figure><\/div>
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<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\n\r\n\r\n\r\n

Natan\u010dna meja obrezanih rastrskih podatkov je zapletena. Privzeti izhod ve\u010dine programske opreme za GIS in precizno kmetijstvo je pikseliziran raster. Natan\u010dna ocena podatkov blizu meje polja vam pomaga bolje razumeti, na primer, pogoje kro\u0161nje in vrednost naklona.\u00a0<\/p>\r\n\r\n\r\n\r\n

Primeri pikseliziranih rastrov:<\/p>\r\n\r\n\r\n\r\n

\"Bli\u017enji\r\n
Bli\u017enji infrarde\u010di pikselizirani raster<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
\"Raster\r\n
Raster z vi\u0161inskimi piksli<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

Ali ga je mogo\u010de izbolj\u0161ati in narediti natan\u010dnej\u0161ega?<\/p>\r\n\r\n\r\n\r\n

Da, GeoPard to po\u010dne in celo omogo\u010da dostop do podatkov za nadaljnjo integracijo prek API-ja. Nekaj primerov:<\/p>\r\n\r\n\r\n\r\n

    \r\n
  • Izrez surovih satelitskih posnetkov (rde\u010de, zelene, modre in bli\u017enje infrarde\u010de) na podlagi meje polja:<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n
    \"Obrezani\r\n
    Obrezani raster GeoPard Agriculture RGB<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
    \"Obrezan\r\n
    GeoPard Agriculture Obrezan raster v bli\u017enjem infrarde\u010dem obmo\u010dju<\/em><\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

     <\/p>\r\n

      \r\n
    • Izrez satelitskih posnetkov z vegetacijskim indeksom, kot je WDRVI, na podlagi meje polja:<\/li>\r\n<\/ul>\r\n

       <\/p>\r\n\r\n\r\n\r\n

      \"Obrezani\r\n
      GObrezani raster eoPard Agriculture WDRVI<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

       <\/p>\r\n

        \r\n
      • Izrez digitalnega topografskega nabora podatkov (nadmorska vi\u0161ina in hrapavost) na podlagi meje polja:<\/li>\r\n<\/ul>\r\n

         <\/p>\r\n\r\n\r\n\r\n

        \"Obrezani\r\n
        Obrezani raster GeoPard Agriculture Elevation<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
        \"Raster\r\n
        Obrezani raster hrapavosti v kmetijstvu GeoPard<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

        Kako je videti v vmesniku GeoPard in kako ga je mogo\u010de integrirati v va\u0161o kmetijsko-tehnolo\u0161ko re\u0161itev:<\/p>\r\n\r\n\r\n\r\n

        \"Obrezani\r\n
        Obrezani raster GeoPard Agriculture NIR<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
        \"Polo\u017eaj\r\n
        Polo\u017eaj za pomo\u010d kmetijstvu GeoPard<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n
        \"GeoPard\r\n
        GeoPard Agriculture WDRVI<\/em><\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

        Pri GeoPardu razumemo vrednost tak\u0161nih podrobnosti in si nenehno prizadevamo za izbolj\u0161anje re\u0161itve.<\/p>\r\n

        Kaj so rastrski podatki?<\/h2>\r\n

        Rastrski podatki so vrsta digitalnih slikovnih podatkov, ki jih predstavlja mre\u017ea slikovnih pik ali celic, kjer vsaka celica ustreza dolo\u010deni lokaciji na zemeljski povr\u0161ini. Vsaki slikovni piki v rastrski sliki je dodeljena vrednost, ki predstavlja dolo\u010den atribut ali zna\u010dilnost te lokacije, kot so nadmorska vi\u0161ina, temperatura ali pokrovnost tal.<\/p>\r\n

        Pogosto se uporablja v geografskih informacijskih sistemih (GIS) in aplikacijah daljinskega zaznavanja za predstavitev in analizo razli\u010dnih vrst prostorskih podatkov. Zbira se lahko iz razli\u010dnih virov, vklju\u010dno s satelitskimi in zra\u010dnim posnetki, digitalnimi fotoaparati in zemeljskimi senzorji.<\/p>\r\n

        Pogosto so shranjeni v razli\u010dnih formatih, kot so GeoTIFF, JPEG in PNG, ki so zasnovani za u\u010dinkovito stiskanje in shranjevanje podatkov. Programska oprema GIS in orodja za obdelavo slik se lahko uporabljajo za manipulacijo in analizo podatkov, na primer z izra\u010duni vrednosti slikovnih pik ali uporabo filtrov za izbolj\u0161anje dolo\u010denih funkcij.<\/p>\r\n

        Primeri uporabe vklju\u010dujejo kartiranje rabe zemlji\u0161\u010d in pokrovnosti tal, analizo sprememb vegetacije skozi \u010das in napovedovanje pridelka na podlagi okoljskih dejavnikov.<\/p>\r\n

        Kako se rastrski podatki uporabljajo v preciznem kmetijstvu?<\/h2>\r\n

        Je bistvena komponenta preciznega kmetijstva, saj zagotavlja podrobne informacije o zdravju pridelkov, lastnostih tal in okoljskih dejavnikih, ki jih je mogo\u010de uporabiti za sprejemanje bolj informiranih odlo\u010ditev o upravljanju pridelkov. Tukaj je nekaj primerov uporabe rastrskih podatkov v preciznem kmetijstvu:<\/p>\r\n

          \r\n
        • Analiza zdravja pridelka:<\/strong> Podatki daljinskega zaznavanja v obliki satelitskih posnetkov ali posnetkov z dronov se lahko uporabijo za ustvarjanje podatkovnih plasti, ki prikazujejo vegetacijske indekse, kot sta NDVI (normalizirani indeks razlike v vegetaciji) ali NDRE (normalizirani rde\u010di rob razlike). Ti indeksi pomagajo prepoznati obmo\u010dja polja z zdravo vegetacijo, pa tudi obmo\u010dja, kjer so pridelki lahko pod stresom zaradi bolezni, \u0161kodljivcev ali pomanjkanja hranil.<\/li>\r\n
        • Analiza tal:<\/strong> Podatke o tleh, kot sta vsebnost vlage v tleh ali tekstura tal, je mogo\u010de zbrati s senzorji, ki ustvarjajo podatkovne plasti. Te plasti lahko pomagajo prepoznati obmo\u010dja polja z razli\u010dnimi zna\u010dilnostmi tal, kar lahko prispeva k odlo\u010ditvam o gnojenju, namakanju in drugih praksah upravljanja tal.<\/li>\r\n
        • Okoljska analiza:<\/strong> Podatkovne plasti, ki prikazujejo okoljske dejavnike, kot so temperatura, padavine in hitrost vetra, se lahko uporabijo za modeliranje rasti polj\u0161\u010din in napovedovanje pridelka. Te plasti lahko pomagajo tudi pri prepoznavanju obmo\u010dij polja, ki so nagnjena k eroziji, poplavam ali drugim okoljskim te\u017eavam.<\/li>\r\n
        • Uporaba spremenljive stopnje:<\/strong> Uporablja se lahko za ustvarjanje predpisanih zemljevidov za uporabo spremenljivih koli\u010din gnojil, kot so gnojila ali pesticidi. Z uporabo razli\u010dnih koli\u010din gnojil glede na potrebe razli\u010dnih obmo\u010dij polja lahko kmetje zmanj\u0161ajo odpadke in optimizirajo rast pridelkov.<\/li>\r\n<\/ul>\r\n

          Na splo\u0161no so rastrski podatki klju\u010dno orodje v preciznem kmetijstvu, saj zagotavljajo podrobne informacije o stanju pridelkov in tal, ki jih je mogo\u010de uporabiti za sprejemanje bolj informiranih odlo\u010ditev o upravljanju pridelkov.<\/p>","protected":false},"excerpt":{"rendered":"

          Obrezani rastrski podatki na podlagi meje polja se sli\u0161ijo zelo preprosto. Nekateri viri podatkov so rastri s slikovnimi pikami in lo\u010dljivostjo 3 m\/10 m\/30 m, drugi pa ...<\/p>","protected":false},"author":210157960,"featured_media":1352,"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":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","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":[1377,1366],"tags":[1618],"class_list":["post-286","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-crop-monitoring","category-topography","tag-crop-monitoring"],"acf":[],"yoast_head":"\nUse of Cropped Raster Data for Agribusiness - GeoPard<\/title>\n<meta name=\"description\" content=\"Cropped raster data based on field boundary sounds very simple. 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