scholarly journals PEMETAAN PERKEBUNAN SAWIT RAKYAT DARI FOTO UDARA NON METRIK MENGGUNAKAN ANALISIS BERBASIS OBJEK

2019 ◽  
Vol 21 (1) ◽  
pp. 53 ◽  
Author(s):  
Agung Syetiawan ◽  
Muhammad Haidar
Keyword(s):  
Image Analysis ◽  
Oil Palm ◽  
Aerial Photo ◽  
Aerial Photos ◽  
Accuracy Requirement ◽  
Object Based Image Analysis ◽  
Oil Palm Plantation ◽  
East Kalimantan ◽  
Sampling Distance ◽  
Object Based

<p align="center"><strong>ABSTRAK</strong></p><p>Beberapa tahapan penerbitan Surat Tanda Daftar Usaha Perkebunan untuk Budidaya <br /> (STD-B) yang digunakan dalam kegiatan perkebunan sawit dengan luas kurang dari 25 ha yaitu pemeriksaan lapangan dan pemetaan yang dilakukan oleh tim verifikasi lintas sektoral. Penerbitan STD-B harus melampirkan peta sebagai persyaratan dalam pendaftaran STD-B, yaitu peta yang memiliki skala 1:2.000. Untuk itu diperlukan teknologi pemetaan yang mumpuni guna memenuhi kebutuhan pemetaan tersebut. Seiring dengan kemajuan teknologi, pemetaan udara menggunakan kamera non-metrik menghasilkan tampilan permukaan bumi secara detil. Tujuan penelitian adalah mengkaji kemampuan pemetaan udara menggunakan kamera non-metrik untuk pembuatan peta sawit rakyat. Proses akuisisi pemetaan sawit rakyat dilakukan di daerah Labanan Makmur Kalimantan Timur. Proses pemetaan udara menggunakan wahana tanpa awak (WTA) <em>fixed wing</em> dengan ketinggian terbang 420 meter diatas permukaan tanah menghasilkan 186 foto dengan <em>sidelap</em> dan <em>overlap</em> foto sebesar 70% dan 80%. Proses identifikasi tanaman kelapa sawit rakyat menggunakan pendekatan <em>Object Based Image Analysis</em> (OBIA). Output akhir yaitu menghasilkan foto udara dengan nilai GSD (<em>Ground Sampling Distance</em>) sebesar 13 cm/pix. Proses pengolahan foto udara dilakukan dengan memasukkan GCP dan tanpa menggunakan GCP. Hasil evaluasi geometrik nilai akurasi horisontal dengan menggunakan GCP diperoleh akurasi sebesar 0,250 meter sementara tanpa menggunakan GCP diperoleh akurasi sebesar 4,222 meter. Dari hasil evaluasi geometrik tersebut maka foto udara dengan menggunakan GCP memenuhi ketelitian geometri untuk pembuatan peta pada skala 1: 1.000, sementara foto udara tanpa menggunakan GCP memenuhi pada skala 1: 25.000. Proses pemetaan foto udara menggunakan kamera non-metrik ditambahkan dengan pengukuran GCP bisa digunakan sebagai acuan yang digunakan untuk membuat peta lampiran pendaftaran STD-B.</p><p> </p><p align="center"><strong><em>ABSTRACT</em></strong></p><p><em>Several phases in the issuance of register for plantation cultivation used for smallholder oil palm plantation with an area of less than 25 ha are field inspection and mapping that conducted by a cross-sectoral verification team. Issuance of register for plantation cultivation must attach the map as required in scale of 1:2.000. Thus, it requires robust mapping technology to meet certain standards. Along with advances in technology, aerial photo using non-metric cameras produces detailed view of the earth's surface. The aim of the study is to examine the ability of aerial photo using non-metric cameras to map smallholder oil palm plantation. The acquisition of smallholder oil palm plantation mapping is carried out in the Labanan Makmur Village, East Kalimantan. Aerial photos acquisition used a fixed-wing UAV with a flight altitude of 420 meters above ground and produced 186 photos with sidelap and overlap of 70% and 80% respectively. </em><em>The process of identifying </em><em>smallholder oil palm plantation</em><em> used the Object Based Image Analysis (OBIA) approach. </em><em>The final output is to produce aerial photos with a value of Ground Sampling Distance (GSD) of 13 cm/pixel. Aerial photo processing is performed either by using GCPs and without GCPs. </em><em>The results of geometric evaluation of horizontal accuracy value using GCP is 0.250 meters while without using GCP is 4.222 meters. </em><em>The results of the geometric evaluation showed that aerial photo using GCP meet the accuracy requirement for map in scale of 1:1,000; whilst aerial photo not using GCP could be utilized for mapping in scale of 1:25,000. Aerial photo using a non-metric camera combined with GCPs measurements can be used as a data source used to produces</em><em> the smallholder oil palm plantation map.</em></p>

scholarly journals Classification of Land Cover by Using Aerial Photo At CV. Alaska Prima Coal, Cooling Village, Sanga-Sanga Sub-district, Kutai Kartanegara District, East Kalimantan Province

AGRIFOR ◽  
2018 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Agus Sofyan
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Aerial Photo ◽  
Recording Time ◽  
Object Based Image Analysis ◽  
Optical Sensitivity ◽  
East Kalimantan ◽  
Object Based ◽  
Open Land

Remote sensing can be done visually and digitally. one of the advantages of airborne photography data generated by drone (phantom-3) compared to satellite imagery with optical sensitivity is its ability to obtain cloud-free images and freedom of recording time and the displayed area shows clearly defined objects corresponding to land cover. characteristics. To limit the object-based area of this research method applied is Object Based Image Analysis (OBIA).This study aims to classify land cover using highly resolved aerial photography with the help of Object Based Image Analysis (OBIA) technique and calculate the accuracy and accuracy, land cover classification by using Objeck Based Image (OBIA) analysis through examination of field conditions.classifying land cover, the classification includes shrubs, young shrubs, plantations (oil palms), shrubs, mines, open land, roads and water bodies with Accuracy of Overcome 0.86.

scholarly journals Peningkatan Akurasi Interpretasi Foto Udara Menggunakan Metode Pembobotan Berbasis Objek untuk Pembuatan Peta Skala 1:5000

2018 ◽  
Vol 2017 (1) ◽  
Author(s):  
Marlonroi Lumbantobing ◽  
Ketut Wikantika ◽  
Agung Budi Harto
Keyword(s):  
Image Analysis ◽  
Data Processing ◽  
Ground Truth ◽  
Accurate Information ◽  
Assessment Methodology ◽  
Aerial Photo ◽  
Aerial Photos ◽  
Research Results ◽  
Object Based Image Analysis ◽  
Object Based

ABSTRAK Kebutuhan akan adanya pengembangan metode untuk meningkatkan akurasi dari interpretasi objek memerlukan kajian metodologi yang disebut analisis citra berbasis objek. Penelitian ini ditujukan untuk menentukan dan menganalisis akurasi dari interpretasi objek secara otomatis dengan metode berbasis objek dengan memberikan bobot yang berbeda untuk setiap kanal. Data yang digunakan adalah foto hasil pemotretan udara format menengah (medium format) dengan resolusi 16 cm. Ekstrak data menggunakan teknik object based image analysis (OBIA). Data diproses berdasarkan bobot yang yang berbeda untuk setiap kanal. Nilai akurasi ditentukan berdasarkan overall accuracy. Overall accuracy merupakan hasil validasi klasifikasi objek dengan ground truth yang diperoleh dari peta garis skala 1:5000 yang diinterpretasi secara visual. Hasil penelitian menunjukkan terjadi peningkatan nilai akurasi dengan pendekatan OBIA jika setiap kanal diberikan bobot yang berbeda dibandingkan dengan bobot yang sama. Peningkatan akurasi paling tinggi dengan bobot (Red=3, Green=4, Blue=3, IR=4, dan DEM= 3) menghasilkan akurasi 85,88%. Hasil akurasi meningkat sebesar 10,27 % dibandingkan dengan interpretasi tanpa pembobotan. Kata kunci: Interpretasi, Peta 1:5000, Klasifikasi, OBIA, Pembobotan, AkurasiABSTRACT Interpretation of imagery or aerial photo is an attempt to understand or interpret imagery to obtain accurate information and in accordance with the recorded object. The need for developing methods to improve the accuracy of the object interpretation requires assessment methodology which is called as object based image analysis. This study aimed at determining and analyzing the accuracy of the interpretation of the object automatically using object based method by giving different weights to each band. The data used were medium format aerial photos with a resolution of 16 cm. The method of data processing was object based image analysis (OBIA). Data were processed by different weights for each band. Accuracy value is determined based on the overall accuracy. Overall accuracy is the result of the validated object classification with ground truth obtained from the map of 1:5000 which were interpreted visually. The research results showed that the value of the accuracy with OBIA approach increased if each band is given different weights compared with the same weight. The highest accuracy was achieved with weights (Red=3, Green=4, Blue=3 , IR=4, and DEM=3), and resulted overall accuracy 85,88%. Results accuracy increased 10,27% compared with the interpretation without weighting. Keywords: Interpretation, Map 1:5000, Classification, OBIA, Weighting, Accuracy

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.

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