scholarly journals Identifikasi Kawasan Pertambangan Timah Menggunakan Data Satelit Sentinel – 1 dengan Metode Object Based Image Analysis (OBIA)

2019 ◽  
Vol 17 (1) ◽  
pp. 140
Author(s):  
Udhi C Nugroho ◽  
Dony Kushardono ◽  
Esthi K Dewi
Keyword(s):  
Image Analysis ◽  
Nearest Neighbor ◽  
European Space Agency ◽  
Grey Level ◽  
Object Based Image Analysis ◽  
Space Agency ◽  
Object Based ◽  
Multiresolution Segmentation

Berdasarkan data Pendapatan Nasional Indonesia 2017, sektor pertambangan  dan penggalian mempunyai peran penting bagi Indonesia. Sektor ini menyumbangkan 7,57% pada produk domestik bruto Indonesia di tahun 2017 . Salah satu sektor pertambangan yang potensial di Indonesia adalah pertambangan mineral Timah di Pulau Bangka dan Belitung. Namun kegiatan pertambangan ini banyak menimbulkan dampak negatif dari sisi lingkungan. Salah satu upaya awal untuk menanggulangi dampak negatif terhadap lingkungan adalah melakukan identifikasi kawasan pertambangan timah secara spasial. Teknologi yang dapat membantu untuk hal ini salah satunya adalah teknologi penginderaan jauh radar. Penelitian ini menggunakan data satelit radar sentinel-1 yang diluncurkan oleh European Space Agency (ESA). Tujuan penelitian ini adalah pemanfaatan data radar Sentinel-1 untuk identifikasi kawasan pertambangan menggunakan metode Object-Base Image Analysis (OBIA). Data sentinel-1 disegmentasi menggunakan algorithma multiresolution segmentation kemudian di klasifikasi menggunakan algorithma nearest neighbor. Masukan data yang digunakan untuk proses klasifikasi dibuat menjadi dua variasi, yang pertama adalah data standar deviasi, mean, dan brightness pada masing – masing segmen di tiap band, kemudian variasi kedua adalah penambahan data tekstur berupa nilai grey level coocurance matrix (GLCM). Hasil klasifikasi menunjukan bahwa masukan data yang menggunakan data tekstur GLCM mempunyai akurasi lebih tinggi dibandingkan dengan yang tanpa data tekstur GLCM. Secara statisktik Hasil klasifikasi dengan type satu menunjukan bahwa total akurasi nya adalah sebesar 89,0 %, dengan nilai kappa sebesar 0,48 sedangkan untuk type dua menunjukan bahwa total akurasinya adalah 89,3%, dengan kappa sebesar 0,50. Hasil klasifikasi kawasan pertambangan dapat digunakan sebagai masukan awal dalam rangka identifikasi spasial kerusakan lingkungan akibat aktivitas pertambangan.

scholarly journals PEMETAAN ZONA GEOMORFOLOGI DAN HABITAT BENTIK DI PULAU KOTOK BESAR MENGGUNAKAN KLASIFIKASI BERBASIS OBJEK

2018 ◽  
Vol 8 (2) ◽  
pp. 209-219
Author(s):  
Ike Dori Candra ◽  
Vicentius P. Siregar ◽  
Syamsul B. Agus
Keyword(s):  
Image Analysis ◽  
Reef Flat ◽  
Reef Slope ◽  
Reef Crest ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Level 3 ◽  
Level 1 ◽  
Multiresolution Segmentation ◽  
Level 2

Penelitian ini menggunakan citra satelit resolusi tinggi worldview-2 akuisisi 5 Oktober 2013. Tujuan dari penelitian ini adalah untuk mengkaji kemampuan citra satelit resolusi tinggi worldview-2 dalam memetakan zona geomorfologi dan habitat bentik perairan dangkal di Pulau Kotok Besar. Metode yang digunakan adalah metode klasifikasi Object Based Image Analysis (OBIA). Metode ini mampu mendefinisikan kelas-kelas objek berdasarkan aspek spektral dan spasial. Segmentasi citra menggunakan algoritma multiresolution segmentation dengan parameter skala yang berbeda untuk setiap level, baik level 1, level 2 dan level 3. Shape dan compactness juga disesuaikan untuk setiap level. Penentuan kelas pada level 1 menghasilkan tiga kelas yaitu daratan, perairan dangkal dan perairan dalam. Penentuan kelas pada level 2 untuk zona geomorfologi menghasilkan tiga kelas yaitu reef flat, reef crest dan reef slope. Klasifikasi habitat bentik pada level 3 menghasilkan 7 kelas dengan akurasi keseluruhan yaitu 66.40 %.

scholarly journals UAS photogrammetry and object-based image analysis (GEOBIA): erosion monitoring at the Kazár badland, Hungary

2016 ◽  
Vol 10 (3-4) ◽  
pp. 169-178 ◽  
Author(s):  
László Bertalan ◽  
Zoltán Túri ◽  
Gergely Szabó
Keyword(s):  
Image Analysis ◽  
Manual Control ◽  
Surface Model ◽  
Digital Surface Model ◽  
Rill Erosion ◽  
Segmentation Method ◽  
Object Based Image Analysis ◽  
Erosion Processes ◽  
Object Based ◽  
Multiresolution Segmentation

A remarkable badland valley is situated near Kazár, NE-Hungary, where rhyolite tuff outcrops as greyishwhite cliffs and white barren patches. The landform is shaped by gully and rill erosion processes. Weperformed a preliminary state UAS survey and created a digital surface model and ortophotograph. Theflight was operated with manual control in order to perform a more optimal coverage of the aerial images.The overhanging forests induced overexposed photographs due to the higher contrast with the baretuff surface. The multiresolution segmentation method allowed us to classify the ortophotograph andseparate the tuff surface and the vegetation. The applied methods and final datasets in combination withthe subsequent surveys will be used for detecting the recent erosional processes of the Kazár badland

scholarly journals Aplikasi Metode Object Based Image Analysis (OBIA) Untuk Identifikasi Atap Bangunan

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alifah Noraini ◽  
I Nyoman Sudiasa ◽  
Martinus Edwin Tjahjadi
Keyword(s):  
Image Analysis ◽  
Nearest Neighbor ◽  
Object Based Image Analysis ◽  
Object Based

Salah satu permasalahan dalam proses pembuatan peta skala besar adalah belum terdapat metode ekstraksi objek secara otomatis, sehingga dijitasi secara manual masih dilakukan. Metode ekstraksi objek secara otomatis diharapkan dapat mempercepat pemetaan skala besar. Di Indonesia, pemetaan skala besar digunakan untuk penyusunan Rencana Detil Tata Ruang (RDTR) Kota/ Kabupaten. Objek detil yang terdapat dalam dokumen RDTR tersebut adalah bangunan. Tujuan dilakukan penelitian ini adalah identifikasi atap bangunan menggunakan metode klasifikasi berbasis objek. Data yang digunakan berupa citra foto udara. Dilakukan proses segmentasi menggunakan algoritma multiresolusi dengan parameter segmentasi skala, bentuk, dan kekompakan Setelah proses segmentasi, dilakukan proses klasifikasi menggunakan metode nearest neighbor. Hasil penelitian menunjukkan bahwa masih terdapat kesalahan dalam proses klasifikasi objek. Atap bangunan tidak teridentfikasi secara keseluruhan dalam kelas objek bangunan.

scholarly journals New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

2021 ◽  
Vol 193 (2) ◽  
Author(s):  
Jens Oldeland ◽  
Rasmus Revermann ◽  
Jona Luther-Mosebach ◽  
Tillmann Buttschardt ◽  
Jan R. K. Lehmann
Keyword(s):  
Image Analysis ◽  
Plant Species ◽  
Just In Time ◽  
Post Processing ◽  
Tall Grass ◽  
Field Surveys ◽  
Distribution Maps ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Before And After

AbstractPlant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

scholarly journals Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 830
Author(s):  
Adam R. Benjamin ◽  
Amr Abd-Elrahman ◽  
Lyn A. Gettys ◽  
Hartwig H. Hochmair ◽  
Kyle Thayer
Keyword(s):  
Image Analysis ◽  
Aquatic Vegetation ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Temporal Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Significant Difference ◽  
Additional Processing ◽  
Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

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