{"id":1119,"date":"2023-09-05T07:46:21","date_gmt":"2023-09-05T07:46:21","guid":{"rendered":"https:\/\/www.satlabgeo.com\/?post_type=industries&p=1119"},"modified":"2025-03-28T02:25:27","modified_gmt":"2025-03-28T02:25:27","slug":"cygnus-slam-scanner-in-forestry-surveys","status":"publish","type":"industries","link":"https:\/\/www.satlabgeo.com\/tr\/industries\/cygnus-slam-scanner-in-forestry-surveys\/","title":{"rendered":"Ormanc\u0131l\u0131k Ara\u015ft\u0131rmalar\u0131nda Cygnus SLAM Taray\u0131c\u0131s\u0131n\u0131n Uygulanmas\u0131"},"content":{"rendered":"

BU VAKA \u00c7ALI\u015eMASINI \u0130ND\u0130RMEK \u0130\u00c7\u0130N TIKLAYIN<\/a><\/p>\n

Proje Arka Plan\u0131<\/strong><\/em><\/span><\/p>\n

Nokta bulutlar\u0131, hedef nesnelerin \u00fc\u00e7 boyutlu uzaydaki konumsal ili\u015fkilerini yans\u0131t\u0131r ve orman kaynaklar\u0131na ili\u015fkin bilgi \u00e7\u0131karmak i\u00e7in \u00f6nemli veri bi\u00e7imlerinden biridir.<\/p>\n

Orman kaynaklar\u0131 ara\u015ft\u0131rmalar\u0131n\u0131n derinle\u015fmesiyle birlikte, tek platformlu lidardataya dayal\u0131 mekansal bilgi edinme y\u00f6ntemi, hassas ormanc\u0131l\u0131\u011f\u0131n orman bilgisi \u00e7\u0131kar\u0131m\u0131nda y\u00fcksek do\u011fruluk gereksinimlerini kar\u015f\u0131layamayan d\u00fc\u015f\u00fck do\u011fruluk ve zay\u0131f veri b\u00fct\u00fcnl\u00fc\u011f\u00fc gibi eksikliklere sahiptir.<\/p>\n

\u00c7\u00f6z\u00fcme genel bak\u0131\u015f<\/strong><\/em><\/span>
\nOrman alan\u0131n\u0131n \u00f6n ara\u015ft\u0131rmas\u0131na dayanarak, hava ve yer verilerinin birle\u015ftirilmesi yoluyla daha eksiksiz a\u011fa\u00e7 bilgisi toplamak i\u00e7in bir \u00e7\u00f6z\u00fcm olarak d\u00fc\u015f\u00fck maliyetli bir \u0130HA LiDAR ve mobil SLAM sistemini se\u00e7tik.
\nMobil SLAM sistemi verilerini birle\u015ftirmek i\u00e7in Cygnus s\u0131rt \u00e7antas\u0131 sistemini RTK ile birlikte se\u00e7tik. Yerel a\u011f sinyalinin zay\u0131f olmas\u0131 nedeniyle, s\u0131rt \u00e7antas\u0131 SLAM RTK sistemimiz drone LiDAR sistemiyle ayn\u0131 baz istasyonunu payla\u015f\u0131yor ve ileti\u015fim harici radyo istasyonu modu \u00fczerinden sa\u011flan\u0131yor.<\/p>\n

\u0130ki y\u00f6ntem ayn\u0131 anda toplan\u0131r. \u0130HA, alan\u0131 yakla\u015f\u0131k 11 dakikada yakalarken, s\u0131rt \u00e7antas\u0131yla yap\u0131lan SLAM taramas\u0131 2 tarama ve tek taramay\u0131 i\u00e7erir ve 30 dakika s\u00fcrer. Mobil SLAM'in tarama y\u00f6r\u00fcngesi, a\u011fa\u00e7 etraf\u0131nda y\u0131lan \u015feklinde bir y\u00f6r\u00fcnge izler ve arazi ko\u015fullar\u0131na g\u00f6re tarama aral\u0131\u011f\u0131 10 metreyi ge\u00e7mez. Zaman zaman a\u011fa\u00e7lar\u0131n kapanmas\u0131 nedeniyle RTK'nin sabitlenememesi sorunu i\u00e7in, sabit RTK g\u00f6zlemlerinin SLAM y\u00f6r\u00fcngesi \u00fczerindeki k\u0131s\u0131tlamalar\u0131n\u0131 art\u0131rmak amac\u0131yla, sabit RTK durumunda taramay\u0131 m\u00fcmk\u00fcn oldu\u011funca tekrarlamaya \u00e7al\u0131\u015f\u0131yoruz.<\/p>\n

\u00c7ok platformlu LiDAR sistemlerinin kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/span><\/strong><\/em><\/p>\n

Karma\u015f\u0131k orman ortam\u0131 ve yo\u011fun \u00f6rt\u00fckl\u00fck nedeniyle, tek bir LiDAR platformunun \u00e7al\u0131\u015fmas\u0131 s\u0131n\u0131rl\u0131d\u0131r ve tarama aral\u0131\u011f\u0131 t\u00fcm orman alan\u0131n\u0131 kapsayamamaktad\u0131r. Bu da mekansal bilgi ediniminde bir veri a\u00e7\u0131\u011f\u0131na yol a\u00e7arak karma\u015f\u0131k orman alanlar\u0131nda uygulanmas\u0131n\u0131 zorla\u015ft\u0131rmaktad\u0131r. Daha ayr\u0131nt\u0131l\u0131 orman nokta bulutlar\u0131 elde etmek i\u00e7in genellikle birden fazla LiDAR platformu birlikte kullan\u0131l\u0131r.<\/p>\n

\"Lidar<\/p>\n

Grafik 1 3B LiDAR sistemlerinin avantaj ve dezavantajlar\u0131n\u0131n kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/i><\/p>\n

\u0130HA LiDAR verileri<\/span><\/strong><\/em><\/pre>\n
\u0130HA LiDAR sistemimiz i\u00e7in, nokta bulutu ve g\u00f6r\u00fcnt\u00fc verilerini ayn\u0131 anda toplayabilen SatLab S1 \u0130HA LiDAR'\u0131n\u0131 kullan\u0131yoruz. RINEX verilerini i\u015fledikten sonra, GNSS, IMU, lazer ve g\u00f6r\u00fcnt\u00fc verilerini tek t\u0131klamayla veri taramas\u0131 i\u00e7in SatLiDAR yaz\u0131l\u0131m\u0131na aktar\u0131yor ve nihai renkli nokta bulutunu (*.las dosyas\u0131) d\u0131\u015fa aktar\u0131yoruz.<\/p>\n
\"\u0130HA-lidar\"<\/p>\n
\u015eekil 1 SatLab S1 \u0130HA LiDAR \u0130\u015f Ak\u0131\u015f\u0131<\/i><\/p>\n
\u0130HA'n\u0131n u\u00e7u\u015f s\u00fcresi yakla\u015f\u0131k 11 dakikad\u0131r ve nokta bulutu yo\u011funlu\u011fu 508,79 pts\/m2'dir. \u0130HA LiDAR nokta bulutu ve kesiti a\u015fa\u011f\u0131daki gibidir. Eksik veriler kanopinin alt\u0131nda g\u00f6r\u00fclebilir.<\/p>\n
\"Lidar<\/p>\n
\u015eekil 2 \u0130HA LiDAR Nokta Bulutu<\/i><\/div>\n
S\u0131rt \u00e7antas\u0131 SLAM verileri<\/span><\/strong><\/em><\/p>\n
SatLab Cygnus s\u0131rt \u00e7antas\u0131 SLAM sistemi \u015eekil 2'de g\u00f6r\u00fclebilir. \u00c7\u0131kt\u0131 SLAM verileri, co\u011frafi referanslama i\u00e7in yerel koordinatlara d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir. SLAM nokta bulutu \u015eekil 10'da g\u00f6sterilmi\u015ftir. Nokta bulutu yo\u011funlu\u011fu yakla\u015f\u0131k 2539,59 pts\/m2'dir.
\n\"\"<\/p>\n
\u015eekil 3 SLAM Nokta Bulutu<\/i><\/div>\n
Veri birle\u015ftirme ve i\u015fleme<\/span><\/strong><\/em><\/p>\n
\u0130HA LiDAR nokta bulutunu ve SLAM nokta bulutunu ayn\u0131 koordinat sistemine yerle\u015ftirdik ve orman bilgilerini \u00e7\u0131karmak i\u00e7in ilgili alan\u0131 par\u00e7alara ay\u0131rd\u0131k. \u0130HA LiDAR nokta bulutu ve SLAM nokta bulutunun birle\u015ftirilmesi ve birle\u015ftirme verilerinin kesiti \u015eekil 11'de g\u00f6sterilmi\u015ftir. \u0130ki y\u00f6ntemin birle\u015fimi, a\u011fac\u0131n tam yap\u0131s\u0131n\u0131 g\u00f6stermektedir. Birle\u015ftirme nokta bulutu yo\u011funlu\u011fu 4701,48 pts\/m2'dir.<\/p>\n
\"\"
\n\"\"<\/p>\n
\u015eekil 4 F\u00fczyon Nokta Bulutu<\/i><\/div>\n
B\u00fcy\u00fck nokta bulutu i\u015flemenin b\u00fcy\u00fck miktarda bilgi i\u015flem kayna\u011f\u0131 t\u00fcketti\u011fini g\u00f6z \u00f6n\u00fcnde bulundurarak, birle\u015ftirme nokta bulutunu bloklara b\u00f6ld\u00fck ve yeniden \u00f6rnekleme ve g\u00fcr\u00fclt\u00fc giderme yoluyla nispeten temiz veriler elde ettik. Ard\u0131ndan, zemin nokta bulutunu s\u0131n\u0131fland\u0131rmak i\u00e7in zemin filtrelemesi olarak kuma\u015f sim\u00fclasyon filtresi (CSF) ve a\u015famal\u0131 TIN yo\u011funla\u015ft\u0131rma (PTD) kulland\u0131k. Zemin noktas\u0131na dayanarak DEM olu\u015fturduk. Nokta bulutu normalizasyonunu hesaplad\u0131ktan sonra, a\u011fa\u00e7 nokta bulutunu s\u0131n\u0131fland\u0131rmak i\u00e7in tek a\u011fa\u00e7 segmentasyon algoritmas\u0131 kulland\u0131k. Yanl\u0131\u015f segmentasyona u\u011fram\u0131\u015f baz\u0131 a\u011fa\u00e7lar manuel olarak da kullan\u0131labilir.
\nd\u00fczenlendi.<\/p>\n
Segmentasyon sonucuna g\u00f6re, tek bir a\u011fac\u0131n t\u00fcr\u00fc, konumu, y\u00fcksekli\u011fi, g\u00f6\u011f\u00fcs y\u00fcksekli\u011findeki \u00e7ap\u0131 (DBH) vb. gibi bilgiler \u00e7\u0131kar\u0131labilir. Ayr\u0131ca, a\u011fa\u00e7 yap\u0131s\u0131n\u0131n vekt\u00f6r\u00fc de olu\u015fturulabilir.<\/p>\n
\"\"<\/p>\n
\u015eekil 5 A\u011fa\u00e7 segmentasyon sonucu<\/i><\/div>\n
\"\"<\/p>\n
\u015eekil 6 A\u011fa\u00e7 vekt\u00f6rle\u015ftirme sonucu<\/i><\/div>\n
\"\u0130HA<\/p>\n
\u015eekil 7 A\u011fa\u00e7 bilgisi \u00e7\u0131karma sonu\u00e7lar\u0131<\/i><\/div>\n
\u00c7\u00f6z\u00fcm<\/span><\/strong><\/em><\/p>\n
Orman alan\u0131nda mobil SLAM taray\u0131c\u0131s\u0131 taraf\u0131ndan elde edilen \u00e7e\u015fitli veriler ile \u0130HA LiDAR'\u0131n\u0131n birle\u015ftirilmesiyle, y\u00fcksek hassasiyetli orman dikey yap\u0131s\u0131 h\u0131zl\u0131 bir \u015fekilde elde edilebilir ve \u00f6rnek alan\u0131n tek a\u011fa\u00e7 yap\u0131s\u0131n\u0131n parametreleri daha do\u011fru bir \u015fekilde \u00e7\u0131kar\u0131labilir.
\nverimli bir \u015fekilde.<\/p>\n
Geleneksel ara\u015ft\u0131rma y\u00f6ntemlerine k\u0131yasla, i\u015fg\u00fcc\u00fc yo\u011funlu\u011funu ve maliyetleri \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r, veri t\u00fcrlerini zenginle\u015ftirir ve izleme kalitesini art\u0131r\u0131r. Ayn\u0131 zamanda, veri toplama verimlili\u011fini ve b\u00fct\u00fcnl\u00fc\u011f\u00fcn\u00fc art\u0131rmak ve daha k\u0131sa s\u00fcrede b\u00fcy\u00fck \u00f6l\u00e7ekli orman bilgilerine ula\u015fma olas\u0131l\u0131\u011f\u0131n\u0131 art\u0131rmak i\u00e7in ULS (UAV Lazer Tarama) ve BLS (S\u0131rt \u00c7antas\u0131 Lazer Tarama) gibi platformlar kullan\u0131l\u0131rken tarama yolunun makul bir \u015fekilde planlanmas\u0131 gerekir.<\/p>\n
Yerden havaya LiDAR veri f\u00fczyonunun bu yeni uygulamas\u0131 g\u00fc\u00e7l\u00fc pratik \u00f6neme sahiptir.<\/p>\n
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    Bizimle \u0130leti\u015fime Ge\u00e7in<\/b><\/h4><\/div>\n    
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