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PEMETAAN PERKEBUNAN SAWIT RAKYAT DARI FOTO UDARA NON METRIK MENGGUNAKAN ANALISIS BERBASIS OBJEK
Abstract
p align="center"> ABSTRAK
Beberapa tahapan penerbitan Surat Tanda Daftar Usaha Perkebunan untuk Budidaya
(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) fixed wing dengan ketinggian terbang 420 meter diatas permukaan tanah menghasilkan 186 foto dengan sidelap dan overlap foto sebesar 70% dan 80%. Proses identifikasi tanaman kelapa sawit rakyat menggunakan pendekatan Object Based Image Analysis (OBIA). Output akhir yaitu menghasilkan foto udara dengan nilai GSD ( Ground Sampling Distance ) 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.
ABSTRACT
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. The process of identifying smallholder oil palm plantation used the Object Based Image Analysis (OBIA) approach. 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. The results of geometric evaluation of horizontal accuracy value using GCP is 0.250 meters while without using GCP is 4.222 meters. 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 the smallholder oil palm plantation map. </p
... The use of drone technology with non-metric cameras is starting to be used for the provision of largescale DG (Darwin et al., 2014). In addition, drone technology is widely used for activities related to spatial planning (Arifati et al., 2017), plantations (Syetiawan & Haidar, 2019), cadastral mapping (Junarto et al., 2020), and regional administrative boundary mapping (Suciani & Rahmadi, 2019). ...
Currently, the need for large-scale mapping for the entire territory of Indonesia is urgent. Therefore, accelerating the provision of large-scale Geospatial Data (DG) is essential for better spatial planning and regional development. The use of drone technology with non-metric cameras is starting to be used for the provision of large-scale DG. To regulate the use of drones and non-metric cameras, the Geospatial Information Agency issued the Head of Geospatial Information Agency Regulation No. 1 of 2020. The purpose of this paper will review the use of drones with non-metric cameras that have been regulated in the agency's regulations. The method used in this paper uses qualitative research with a data collection strategy or source using the literature method. The results show that the use of direct georeferencing in BIG Regulation No. 1 of 2020 has fulfilled the horizontal and vertical geometry accuracy requirements stipulated in BIG Head Regulation No. 6 of 2018 on Base Map Accuracy. The GSD value requirement in BIG Regulation No 1/2020 is too high compared to the GSD value requirement specified in the ASPRS Accuracy Standards for Digital Geospatial Data. This agency regulation is a standard / reference that must be met for all mapping industry players. Therefore, the implementation of this agency regulation requires further study to truly support the issue of accelerating large-scale mapping.
... Compared to other methods such as satellite sensors, UAV-based photogrammetry provides more benefits regarding data acquisition and the resulting temporal resolution (Liu et al., 2022). With this capability, UAVs are often used for rapid mapping purposes such as disaster management (Restas, 2015), precision farming (Candiago et al., 2015;Syetiawan & Haidar, 2019), landslides monitoring (Godone et al., 2020;D. Turner et al., 2015), and coastal changes (N Long et al., 2016;Nathalie Long et al., 2016). ...
... 2. Foto udara dengan kamera nonmetrik belum dapat digunakan secara langsung sebelum penyimpangannya menjadi terkoreksi dan pengolahan data dengan metode fotogrametri yang tepat, sehingga memperoleh hasil pengukuran dengan akurasi tinggi (Purwanto, 2017). Jenis foto udara ini bisa menampilkan hasil dari permukaan bumi dengan lebih detail dan resolusi sangat bagus serta penggunaannya sangat menguntungkan dari segi ekonomis dan waktu (Syetiawan and Haidar, 2019). ...
Abstrak
Penggunaan teknologi pada geodesi serta geomatika, yaitu teknologi fotogrametri udara unmanned aerial vehicle (UAV) dapat berguna dalam mengetahui kondisi jalan raya terkini. Untuk mengetahui hal tersebut, dapat dilakukan pembuatan suatu model 3 dimensi jalan raya, sehingga mendapatkan informasi-informasi terkini dari jalan raya. Penggunaan teknologi tersebut digunakan agar memperoleh data yang digunakan dalam pembuatan model 3 dimensi. Pembuatan model tersebut dilakukan menggunakan metode level of detail (LoD)0-3 jalan raya dan dilakukan perhitungan uji validasi geometrik jalan raya, yaitu lebar jalan raya mencakup perhitungan nilai root mean square error (RMSE) serta standar deviasi. Penelitian ini memperoleh visualisasi model 3 dimensi jalan raya, kemampuan foto udara hanya mengidentifikasi objek jalan dalam pembuatan model 3 dimensi jalan raya, dan hasil uji validasi geometrik jalan raya dengan RMSE 0.0455141 meter (lapangan dengan foto), 0.155467038 meter (lapangan dengan model), serta standar deviasi 0.078829245 meter (lapangan dengan foto), 0.310609926 meter (lapangan dengan model). Visualisasi model 3 dimensi jalan raya dengan metode level of detail (LoD)0-3 telah dilaksanakan. Kemampuan foto udara hanya mengidentifikasi objek jalan pada pembuatan model 3 dimensi jalan raya. Hasil pemodelan 3 dimensi jalan raya dilakukan uji validasi untuk mengetahui kedekatannya dengan model sebenarnya.
Kata kunci: jalan raya, 3 dimensi, level of detail, validasi,
Abstract
The use of technology in geodesy and geomatics, that’s aerial photogrammetry technology using an unmanned aerial vehicle (UAV) useful to realize the current roadway condition. To find out, making a roadway 3-dimensional model is done, so that’s the current of roadway information. That technology is used to obtain data in the manufacturing 3-dimensional model. The modeling was performed using the level of detail (LoD)0-3 method of the roadway and performed the calculation roadway geometrics validation test, that’s the width of roadway including the calculation of root mean square error (RMSE) and standard deviation. This study obtained a visualization of roadway 3-dimensional model, the ability of aerial photo to only identification of roadway object in the manufacture of a 3-dimensional model, and the results of the test with RMSE 0.0455141 meters (field with photos), 0.155467038 meters (field with model), and standard deviation 0.078829245 meters (field with photos), 0.310609926 meters (field with model). The visualization of roadway 3-dimensional model with the level of detail (LoD)0-3 method already implemented. The ability of aerial photos to only identification of roadway objects in the manufacture of roadway 3-dimensional model. The 3-dimensional models of the roadway model are validated to determine its proximity to the actual model.
Keywords: roadway, 3-dimensional, level of detail, validation
Mapping using UAV has become very popular today due to the rapid development of electronic technology. UAVs are needed to support rapid mapping activities, such as natural disaster management, SAR applications, agricultural applications and forest fire monitoring. In addition to spatial resolution, it is also needed the accuracy of position accuracy. In general, the georeferenced process of aerial photo processing uses Ground Control Points (GCP) from GPS measurements. The disadvantage of using GCP is that GPS measurement takes a long time and the costs incurred are relatively more expensive. For this reason, certain techniques are needed to produce topographic maps with more efficient times. Direct Georeferencing on UAV is an aerial triangulation technique without the use of ground control points (GCP). Direct Georeferencing techniques will cut field surveys and produce high accuracy topographic maps. The hope is that by using this technique, the measurement of tie points can be as minimal as possible and do not even need to use a control point. This study is to evaluate Direct Georeferencing techniques in UAVs to produce accurate topographic map. Aerial photo using multi rotor UAV DJI Phantom 4 RTK/PPK. This multi rotor is equipped with a high accuracy GPS antenna and that is capable to storing GPS observation data. Photo coordinate processing using the Post Processing Kinematic (PPK) method. Accuracy of ortho rectification results of aerial photo geometry compared to check point coordinates (CP). The test results of horizontal accuracy indicate the value of 0.040 m using Circular Error 90% (CE90). Based on BIG standard the accuracy of the base map, these results can be entered on a scale of 1: 1,000 class 1. Direct Georeferencing techniques in UAVs produce high accuracy maps for relatively not extensive mapping areas.
Penggunaan UAS (Unmanned Aerial Systems) untuk pemetaan saat ini semakin populer dengan semakin berkembangnya teknologi drone dan kamera. UAS digunakan untuk keperluan inspeksi, monitoring atau bahkan digunakan untuk pembentukan model 3 dimensi. Salah satu model 3 dimensi yang sering digunakan untuk menghasilkan peta skala besar adalah DSM. Peta skala besar membutuhkan DSM dengan ketelitian tinggi, sementara teknologi UAS memiliki keuntungan besar dalam hal menghemat waktu pekerjaan dan biaya. Penelitian ini bertujuan untuk menguji hasil pembentukan DSM dari data foto hasil pemotretan menggunakan UAS. Proses pengumpulan data UAS menggunaan wahana Multi rotor DJI Matrice 600 dan kamera Zenmuse X3. Tinggi terbang UAS pada saat misi pemotretan udara berada pada ketinggian 100 m diatas permukaan tanah dengan sidelap dan overlap sebesar 80% sehingga menghasilkan foto sebanyak 151 foto. Proses georeferencing menggunakan 5 titik GCP yang tersebar secara merata untuk seluruh area yang dipetakan. Hasil Ground Sample Distance pada misi penerbangan ini adalah 3,2 cm/pixel dengan cakupan area yang dipetakan yaitu 20,8 hektar. Uji akurasi dilakukan dengan membandingkan hasil pembentukan DSM terhadap hasil pengukuran GPS di lapangan sesuai dengan SNI ketelitian peta dasar. Ada sekitar 13 titik yang digunakan untuk check point. Hasil pengolahan mendapat nilai akurasi horisontal sebesar 0,510 meter dan nilai akurasi vertikal sebesar 0,435 meter. Dari hasil perhitungan dapat disimpulkan bahwa data pemotretan menggunakan UAS memenuhi ketelitian geometri baik horizontal maupun vertikal pada skala 1:5000 kelas I. Pemotretan udara harus dilakukan dengan kondisi ideal (tingkat kecerahan, resolusi dan kondisi lingkungan) sehingga akan didapatkan DSM terbaik.
Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.
Research into remote sensing tools for monitoring physiological stress caused by biotic and abiotic factors is critical for maintaining healthy and highly-productive plantation forests. Significant research has focussed on assessing forest health using remotely sensed data from satellites and manned aircraft. Unmanned aerial vehicles (UAVs) may provide new tools for improved forest health monitoring by providing data with very high temporal and spatial resolutions. These platforms also pose unique challenges and methods for health assessments must be validated before use. In this research, we simulated a disease outbreak in mature Pinus radiata D. Don trees using targeted application of herbicide. The objective was to acquire a time-series simulated disease expression dataset to develop methods for monitoring physiological stress from a UAV platform. Time-series multi-spectral imagery was acquired using a UAV flown over a trial at regular intervals. Traditional field-based health assessments of crown health (density) and needle health (discolouration) were carried out simultaneously by experienced forest health experts. Our results showed that multi-spectral imagery collected from a UAV is useful for identifying physiological stress in mature plantation trees even during the early stages of tree stress. We found that physiological stress could be detected earliest in data from the red edge and near infra-red bands. In contrast to previous findings, red edge data did not offer earlier detection of physiological stress than the near infra-red data. A non-parametric approach was used to model physiological stress based on spectral indices and was found to provide good classification accuracy (weighted kappa = 0.694). This model can be used to map physiological stress based on high-resolution multi-spectral data.
Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.
Unmanned Aerial Vehicle (UAV) System has gained popularity in the field of photogrammetry, remote sensing and geospatial engineering for civilian applications. In recent times, UAVs are being deployed for many remote sensing applications such as cadastral mapping, post-flood analysis, vegetation cover assessment, crop monitoring, forest fire, traffic monitoring and etc. In Malaysia, Oil palm is one of the main economic crop and the larger percentage of the plantations are owned by private individuals categorised as small scale farmers. One of the common activities is taking stock of the number of palm trees within the plantation. This information is vital for estimating yield and productivity. Conventionally, inventorying is achieved by either manually counting oil palm tree crowns on imageries or ground surveying using GPS to gather their locations information. Obviously, it is impossible to obtain accurate inventorying with these methods in large oil palm plantation. Besides, the traditional process of counting is prone to erroneous estimation, expensive and time consuming. In this study, we present a method for oil palm counting in UAV images using template matching and object-based image analysis (OBIA). The potential applications of the proposed algorithm are rapid tree counting for effective decision making by farmers.
Photogrammetric point clouds (PPCs) provide a source of three-dimensional (3-D) remote sensing data that is well-suited to use over small areas that are within the scope of observation by unmanned aerial vehicles (UAVs). We compared PPC-based structural metrics to traditional ground surveys conducted by field personnel in order to assess the capacity of PPC data to contribute to vegetation-reclamation surveys. We found good statistical agreement between key structural vegetation parameters, such as mean and maximum vegetation height, with PPC metrics successfully predicting most height and tree-diameter metrics using multivariate linear regression. However, PPC metrics were not as useful for estimating ground-measured vegetation cover. We believe that part of the issue lies in the mismatch between PPC-and ground-based measurement approaches, including subjective judgement on behalf of ground crews: a topic that requires more investigation. Our work highlights the emerging value of UAV-based PPCs to complement, and in some cases supplement, traditional ground-based sources of measured vegetation structure.
This paper shows some practical experiences of using Unmanned Aerial Vehicle-(UAV) based platform for remote sensing in supporting precision agriculture mapping. Some of the information for land preparation, cadaster boundary, vegetation monitoring, plant healthy, and stock valuation are required periodically. So, the UAV-Based remote sensing system should have characteristics such as cost-effective, fast in producing, easy in operation by local staff, and good geometry accuracy (sub-meter). The system consists of aerial platforms from R/C plane, point and shoots digital cameras, data processing with digital photogrammetric: structure from motion algorithms, and free open source-GIS for visualizing. This system has the ability to produce imagery with spatial resolution <10 cm, measuring parcel area, assess the individual trees or plants stock, and topography. The basic products of the system are Orthophoto Image and Digital Elevation Model (DEM). The Average geometric accuracy can be obtained up to 3 pixels or equivalent to sub-meter accuracy, while the production time can be reached more than 500Ha a day. The Orthophoto image could provide visual interpretation such as the individual trees structure, plant density, and parcel boundary area, while DEM could asses tree's height information and terrain topography with accuracy 3-6 pixel or 0.5-2.5 m.
This paper looks into the latest achievement in the low-cost Unmanned Aerial Vehicle (UAV) technology in their capacity to
map the semi-development areas. The objectives of this study are to establish a new methodology or a new algorithm in image
registration during interior orientation process and to determine the accuracy of the photogrammetric products by using UAV
images. Recently, UAV technology has been used in several applications such as mapping, agriculture and surveillance. The aim
of this study is to scrutinize the usage of UAV to map the semi-development areas. The performance of the low cost UAV
mapping study was established on a study area with two image processing methods so that the results could be comparable. A
non-metric camera was attached at the bottom of UAV and it was used to capture images at both sites after it went through
several calibration steps. Calibration processes were carried out to determine focal length, principal distance, radial lens
distortion, tangential lens distortion and affinity. A new method in image registration for a non-metric camera is discussed in this
paper as a part of new methodology of this study. This method used the UAV Global Positioning System (GPS) onboard to
register the UAV image for interior orientation process. Check points were established randomly at both sites using rapid static
Global Positioning System. Ground control points are used for exterior orientation process, and check point is used for accuracy
assessment of photogrammetric product. All acquired images were processed in a photogrammetric software. Two methods of
image registration were applied in this study, namely, GPS onboard registration and ground control point registration. Both
registrations were processed by using photogrammetric software and the result is discussed. Two results were produced in this
study, which are the digital orthophoto and the digital terrain model. These results were analyzed by using the root mean square
errors and mean absolute error to determine the level of accuracy and the precision of photogrammetric products. It can be
concluded that the new method of image registration by using the GPS onboard of the UAV produces medium accuracy result
compared to the method that uses the ground control point. This new method can be used for the medium accuracy requirements.
Unmanned Aerial Vehicle can be used for several applications, which requires a medium accuracy.
Project FiRE (First Response Experiment), a disaster management technology demonstration, was performed in 2001. The experiment demonstrated the use of a thermal multispectral scanning imager, integrated on an unmanned aerial vehicle (UAV), a satellite uplink/downlink image data telemetry system, and near-real-time geo-rectification of the resultant imagery for data distribution via the Internet to disaster managers. The FiRE demonstration provided geo-corrected image data over a controlled burn to a fire management community in near-real-time by means of the melding of new technologies. The use of the UAV demonstrated remotely piloted flight (thereby reducing the potential for loss of human life during hazardous missions), and the ability to “linger and stare” over the fire for extended periods of time (beyond the capabilities of human-pilot endurance). Improvements in a high-temperature calibrated thermal imaging scanner allowed “remote” operations from a UAV and provided real-time accurate fire information collection over a controlled burn. Improved bit-rate capacity telemetry capabilities increased the amount, structure, and information content of the image data relayed to the ground. The integration of precision navigation instrumentation allowed improved accuracies in geo-rectification of the resultant imagery, easing data ingestion and overlay in a GIS framework. We present a discussion of the feasibility of utilizing new platforms, improved sensor configurations, improved telemetry, and new geo-correction software to facilitate wildfire management and mitigation strategies.
Goal: Psychophysics, e.g. Rivest and Cavanagh (1996), has shown that humans make combined use of multiple cues to detect and localize boundaries in images. We use a dataset of natural images to learn optimum cue combination of local brightness, texture and color, as well as quantify the relative power of these cues. Methods: Cue combination is formulated as supervised learning. A large dataset (∼1000) of natural images, each segmented by multiple human observers (∼10), provides the ground truth label for each pixel as having an oriented boundary element or not. The task is to model the posterior probability of a pixel being at a boundary, at a particular orientation, conditioned on local features derived from brightness, texture and color. Our features are based on computing directional gradients of outputs of V1-like mechanisms. Texture gradients are computed as differences in histograms of oriented filter outputs, and color gradients on histograms of a*, b* features in CIE L*a*b* space. Several types of classifiers ranging from logistic regression to support vector machines were trained. Performance was evaluated on a separate test set using a precision-recall curve which is a variant of the ROC curve. This curve can be summarized by its optimal F-measure, the harmonic mean of precision and recall. Results: (1)The precise form of the classifier does not matter-equally good results were obtained using logistic regression (weighted linear combination of features) as with more complicated classifiers. (2) Singly, brightness, texture and color yield F-measures of 0.62, 0.61, and 0.60 respectively. The optimal gray-scale combination of brightness and texture has an F-measure of 0.65 and addition of color boosts it to 0.67. These results indicate that the different cues are correlated but do carry independent information. By measuring inter-human consistency, the gold standard F-measure is 0.8, thus quantifying the gap left for more global and high-level processing.