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Studi Komparasi Teknik Klasifikasi berbasis Objek terhadap Citra Resolusi Spasial Menengah dan Tinggi untuk Pemetaan Tutupan Lahan di Sebagian Kabupaten Kulonprogo Putri, Erisa Ayu Waspadi; Danoedoro, Projo; Farda, Nur Mohammad
Majalah Geografi Indonesia Vol 38, No 1 (2024): Majalah Geografi Indonesia
Publisher : Fakultas Geografi, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/mgi.81374

Abstrak Tingginya kebutuhan informasi tutupan lahan dalam berbagai sektor perencanaan dapat disediakan secara time-and-cost effective melalui analisa citra penginderaan jauh, diantaranya menggunakan OBIA (object based image analysis). Teknik tersebut banyak diterapkan pada berbagai macam citra, baik resolusi spasial tinggi maupun menengah. Namun studi komparasi pada citra resolusi spasial yang berbeda masih belum banyak dilakukan secara comparable, dimana umumnya terdapat banyak perbedaan variable komparasinya. Penelitian ini bertujuan untuk membandingkan secara langsung penerapan OBIA terhadap resolusi spasial citra yang berbeda dengan membatasi variable berpengaruh terhadap akurasi, diantaranya: klasifikasi jenis tutupan lahan, saluran masukan citra, dan kriteria serta teknik klasifikasi OBIA. Berdasarkan studi komparasi, diketahui bahwa penggunaan Pleaides memberikan akurasi yang lebih tinggi dibanding Landsat-8 OLI namun memerlukan strategi klasifikasi yang lebih rumit. Sedangkan ditinjau dari overall accuracy dan indeks kappa, disimpulkan bahwa OBIA mampu memberikan akurasi hasil yang termasuk dalam acceptable thresholds untuk derivasi tutupan lahan menggunakan citra resolusi spasial tinggi ataupun menengah.Abstract The high demand for land cover information for vast planning sectors could be provided in time-and-cost-effective techniques using remote sensing image analysis, including employing OBIA (object based image analysis). The technique is widely applied to various kinds of imageries, for high and medium spatial resolution as well. However, comparative studies on the usage of different spatial resolution imageries have not been carried out in a comparable condition, where several variables could be in different terms. The study aims is to straightly compare OBIA’s application in diverse spatial resolution of imagery by limiting the affecting variables to its accuracy, including classification of land cover schemes, imagery channels input, and OBIA’s criteria and techniques. The comparative study reveals the usage of Pleaides provides higher accuracy than Landsat-8 OLI but requires a more complicated classification strategy. Meanwhile, the overall accuracy and kappa index of both maps exposes that OBIA could provide scientifically acceptable accurate land cover maps derived from both high and medium spatial resolution imagery.

Comparing canopy density measurement from UAV and hemispherical photography: an evaluation for medium resolution of remote sensing-based mapping Umarhadi, Deha Agus; Danoedoro, Projo
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp356-364

UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.

Land-Cover Change Detection in Batur Catchment Area Using Remote Sensing Ni Kadek Oki Febrianti; Projo Danoedoro; Prima Widayani
JURNAL GEOGRAFI Vol 15, No 1 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i1.32670

Land cover information is an essential aspect in the planning and management of earth modeling and understanding. Land cover changes impact the physical and social environment, such as hydrological conditions and ecological systems. This study aimed to identify spatial differences in the land cover of the Batur catchment area from 2015-2021 by using a remote sensing approach to describe the existing land-cover site and to detect its changes. The methods used in this study are a combination of the vegetation index and a supervised classification maximum likelihood algorithm with Landsat 8 OLI/TIRS in 2015 and 2021. Furthermore, the Change Detection Feature, identified from two image periods in 2015-2021 and processed, is used to detect changes in land cover. The accuracy assessment utilized QuickBird imagery recorded in 2015; field survey data were taken in 2021. The results showed that between 2015 to 2021, built-up area, bare land, shrubs, and lake have increased by 102,66% (306,01 ha), 27,95% (452,25 ha), 15,20% (215,72 ha) and 4,05 % (62,73 ha) while dryland forest and dry-dry-field have decreased by -25,84% (-606,29 ha) and -14.59% (-430,42 ha), respectively. The overall accuracy of the multispectral classification results in 2015 and 2021 was 82,63% and 89,57%.Keywords: Land-Cover Change; Batur; Catchment Area; Remote Sensing

Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado Fela Pritian Cera; Projo Danoedoro; Pramaditya Wicaksono; Moh Yasir
JURNAL GEOGRAFI Vol 15, No 2 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i2.30795

The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 – 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests

PEMETAAN PENGGUNAAN LAHAN SAWAH BERDASARKAN PENDEKATAN EKOLOGI BENTANG LAHANMENGGUNAKAN CITRA PEREKAMAN TUNGGAL Hasibuan, Algi Variski; Danoedoro, Projo; Murti, Sigit Heru
Jurnal Tanah dan Sumberdaya Lahan Vol. 12 No. 1 (2025)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2025.012.1.2

A rice field land-use map is essential in the sustainable land management of rice fields for physical monitoring and planning. Such maps are usually created using multitemporal image data with a spectral approach, but this method can only be applied to certain areas and cannot be easily applied to other areas with different land characteristics. While multitemporal data has been widely used by researchers and proven effective, using single-date imagery can be more efficient. This study aimed to map rice field land-use based on a single-date Sentinel-2 imagery and landform maps. These landform maps were derived through visual interpretation of false colour composite bands, DEMNAS, and land system map. The interpretation resulted in eleven landform classes. The landscape ecology approach assumed the influence of landforms on land-use. The use of ten optical bands in multispectral classification using the maximum likelihood algorithm and convolutional neural network algorithm resulted in twelve land cover classes. The land cover map and the landform map were implemented through a two-dimensional ecological spatial relationship matrix that produced nine land-use classes. The convolutional neural network algorithm obtained an overall accuracy of 90,28% with a Kappa of 0,87. This result was better than the maximum likelihood algorithm, which obtained an overall accuracy of 86,81% with Kappa 0,83. The land-use map for the rice field class produced by the convolutional neural network algorithm had a total area of 33.686,69 ha and a mean absolute error (MAE) value of 0,0241, while the maximum likelihood algorithm produced a total area of 29.590,21 ha with a larger MAE value of 0,0343.

PEMETAAN PENGGUNAAN LAHAN SAWAH BERDASARKAN PENDEKATAN EKOLOGI BENTANG LAHANMENGGUNAKAN CITRA PEREKAMAN TUNGGAL Hasibuan, Algi Variski; Danoedoro, Projo; Murti, Sigit Heru
Jurnal Tanah dan Sumberdaya Lahan Vol. 12 No. 1 (2025)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2025.012.1.2

A rice field land-use map is essential in the sustainable land management of rice fields for physical monitoring and planning. Such maps are usually created using multitemporal image data with a spectral approach, but this method can only be applied to certain areas and cannot be easily applied to other areas with different land characteristics. While multitemporal data has been widely used by researchers and proven effective, using single-date imagery can be more efficient. This study aimed to map rice field land-use based on a single-date Sentinel-2 imagery and landform maps. These landform maps were derived through visual interpretation of false colour composite bands, DEMNAS, and land system map. The interpretation resulted in eleven landform classes. The landscape ecology approach assumed the influence of landforms on land-use. The use of ten optical bands in multispectral classification using the maximum likelihood algorithm and convolutional neural network algorithm resulted in twelve land cover classes. The land cover map and the landform map were implemented through a two-dimensional ecological spatial relationship matrix that produced nine land-use classes. The convolutional neural network algorithm obtained an overall accuracy of 90,28% with a Kappa of 0,87. This result was better than the maximum likelihood algorithm, which obtained an overall accuracy of 86,81% with Kappa 0,83. The land-use map for the rice field class produced by the convolutional neural network algorithm had a total area of 33.686,69 ha and a mean absolute error (MAE) value of 0,0241, while the maximum likelihood algorithm produced a total area of 29.590,21 ha with a larger MAE value of 0,0343.

Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado Nama Penulis; Fela Pritian Cera; Projo Danoedoro; Pramaditya Wicaksono; Moh Yasir
JURNAL GEOGRAFI Vol. 15 No. 2 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i2.30795

The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 “ 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests

Land-Cover Change Detection in Batur Catchment Area Using Remote Sensing Nama Penulis; Ni Kadek Oki Febrianti; Projo Danoedoro; Prima Widayani
JURNAL GEOGRAFI Vol. 15 No. 1 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i1.32670

Land cover information is an essential aspect in the planning and management of earth modeling and understanding. Land cover changes impact the physical and social environment, such as hydrological conditions and ecological systems. This study aimed to identify spatial differences in the land cover of the Batur catchment area from 2015-2021 by using a remote sensing approach to describe the existing land-cover site and to detect its changes. The methods used in this study are a combination of the vegetation index and a supervised classification maximum likelihood algorithm with Landsat 8 OLI/TIRS in 2015 and 2021. Furthermore, the Change Detection Feature, identified from two image periods in 2015-2021 and processed, is used to detect changes in land cover. The accuracy assessment utilized QuickBird imagery recorded in 2015; field survey data were taken in 2021. The results showed that between 2015 to 2021, built-up area, bare land, shrubs, and lake have increased by 102,66% (306,01 ha), 27,95% (452,25 ha), 15,20% (215,72 ha) and 4,05 % (62,73 ha) while dryland forest and dry-dry-field have decreased by -25,84% (-606,29 ha) and -14.59% (-430,42 ha), respectively. The overall accuracy of the multispectral classification results in 2015 and 2021 was 82,63% and 89,57%.Keywords: Land-Cover Change; Batur; Catchment Area; Remote Sensing

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