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Pramaditya Wicaksono
Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Indonesia

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Journal article
Published: 26 June 2021 in Remote Sensing Applications: Society and Environment
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The availability of frequently updated maps has always been the ultimate goal of remote sensing in benthic habitat data acquisition. Although the techniques offer cost and time effectiveness, the lack of consistency of inputted images can cause issues in successfully performing accurate multi-temporal mapping with minimum effort. This research aimed to assess the consistency of Sentinel-2 images for benthic habitat mapping in the waters of Labuan Bajo, East Nusa Tenggara, Indonesia. We employed two approaches: 1) assessing the consistency of Sentinel-2 image used to map four classes of benthic habitat from 2017 to 2019 and 2) an in-depth analysis of three images in May 2019 to assess the consistency of four-class benthic habitat mapping (coral reef, seagrass, macroalgae and bare substratum). This research incorporated atmospheric, sunglint and water column corrections and four classification algorithms: Isodata, Random Forest, Support Vector Machine, and Maximum Likelihood. The consistency of Sentinel-2 images was assessed using overall accuracy (OA), agreement percentage (AP) and total classification performance (TCP). Our results indicate that Sentinel-2 images are reliable enough for accurate benthic habitat mapping with OA >80% and consistent with agreement >80% in both approaches given the right image conditions, which are minimum cloud cover, haze, and sunglint. These factors strongly contribute to the consistency of the resulting benthic habitat classification from the Sentinel-2 images. Also, to maximize the rich image availability in the Sentinel-2 archive database, we suggest selecting images with minimal atmospheric and sunglint disturbances instead of performing image corrections that may introduce noise, leading to lower accuracy and consistency. Finally, we encourage the use of Sentinel-2 images in benthic habitat monitoring since the long-term plan of the Sentinel-2 mission guarantees the availability of these datasets.

ACS Style

Pramaditya Wicaksono; Shafa Arum Wulandari; Wahyu Lazuardi; Miftakhul Munir. Sentinel-2 images deliver possibilities for accurate and consistent multi-temporal benthic habitat maps in optically shallow water. Remote Sensing Applications: Society and Environment 2021, 23, 100572 .

AMA Style

Pramaditya Wicaksono, Shafa Arum Wulandari, Wahyu Lazuardi, Miftakhul Munir. Sentinel-2 images deliver possibilities for accurate and consistent multi-temporal benthic habitat maps in optically shallow water. Remote Sensing Applications: Society and Environment. 2021; 23 ():100572.

Chicago/Turabian Style

Pramaditya Wicaksono; Shafa Arum Wulandari; Wahyu Lazuardi; Miftakhul Munir. 2021. "Sentinel-2 images deliver possibilities for accurate and consistent multi-temporal benthic habitat maps in optically shallow water." Remote Sensing Applications: Society and Environment 23, no. : 100572.

Research article
Published: 21 June 2021 in International Journal of Remote Sensing
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The rapid development of remote sensing technology has increased the possibility to produce a spatially explicit estimation of Total Suspended Solids (TSS). However, factual information regarding the effective depth of waters where TSS can be effectively mapped using remote sensing data is rare. This information is essential to determine the effective depth of TSS mapping and the effect of the water column on the representativeness of the estimation results. This study aimed to (1) determine the effective water depths of TSS mapping using PlanetScope imagery, (2) map the spatial distribution of TSS at every effective depth using PlanetScope imagery, and (3) analyse the spatial distribution of the mapped TSS based on the best empirical modelling for every effective depth. PlanetScope image offered new opportunities in high spatial resolution remote sensing with daily revisit time. Four single bands of PlanetScope images (blue, green, red, and near-infrared) – corrected to the Bottom of Atmosphere (BOA) reflectance, 12 band ratios, and four Principal Component bands (PC-band) were inputted to the determination process of the effective water depths. Empirical modelling between the in-situ TSS at each effective water depth andures what was the issue, wasnt able to o PlanetScope pixels at the corresponding locations was conducted. However, only image bands that exceeded the significance limit (r) were used for modelling using linear, exponential, logarithmic, second-order polynomial, or power regressions. The results showed that the band ratios of PlanetScope images could record TSS up to the effective depth of 1.8 m. The best empirical modelling in each effective depth of waters was the band ratio which is dominated by the contribution of the blue bands (B1), red (B3), and NIR (B4). B4/B3 bands combination produced TSS information at the effective depths of 0–0.2 and 0–0.4 m, B3/B4 bands at 0–0.6 and 0–0.8 m, B1/B4 bands at 0–1 and 0–1.2 m, and B1/B3 bands at 0–1.4, 0–1.61, and 0–1.8 m. For all effective depths, the spatial distribution pattern of TSS in the study site (Menjer Lake, Central Java, Indonesia) showed that high concentrations evenly spread along the edge and became increasingly lower to the centre of the lake. Meanwhile, the vertical distribution showed that the deeper the cumulative water depth, the higher the TSS.

ACS Style

Putu Wirabumi; Muhammad Kamal; Pramaditya Wicaksono. Determining effective water depth for total suspended solids (TSS) mapping using PlanetScope imagery. International Journal of Remote Sensing 2021, 42, 5774 -5800.

AMA Style

Putu Wirabumi, Muhammad Kamal, Pramaditya Wicaksono. Determining effective water depth for total suspended solids (TSS) mapping using PlanetScope imagery. International Journal of Remote Sensing. 2021; 42 (15):5774-5800.

Chicago/Turabian Style

Putu Wirabumi; Muhammad Kamal; Pramaditya Wicaksono. 2021. "Determining effective water depth for total suspended solids (TSS) mapping using PlanetScope imagery." International Journal of Remote Sensing 42, no. 15: 5774-5800.

Journal article
Published: 10 March 2021 in Regional Studies in Marine Science
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Mangroves play a pivotal role in providing ecological benefits and services to reduce and adapt to climate change impact on coastal ecosystem. They are capable of absorbing carbon, which is crucial in controlling CO2 levels in the atmosphere. This research aims to assess the accuracy of selected vegetation indices for estimating above-ground carbon (AGC) stocks of mangroves using PlanetScope images in Bedul, Banyuwangi, East Java Province, Indonesia. A semi-empirical approach was used to assess and map mangrove AGC, starting with applying the allometric equation to calculate field-measured species-specific AGC stocks. Regression analyses were applied to develop a relationship between field AGC and vegetation indices derived from PlanetScope Image, including Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), and Enhanced Vegetation Index (EVI). The Standard Errors of Estimates (SE) were 31.41, 32.93, and 31.63 tons/ha for DVI, EVI, and NDVI, respectively. Thus, carbon stocks estimation, including DVI as an independent variable, is considered more accurate than other vegetation indices tested in this research

ACS Style

Eva Purnamasari; Muhammad Kamal; Pramaditya Wicaksono. Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image. Regional Studies in Marine Science 2021, 44, 101730 .

AMA Style

Eva Purnamasari, Muhammad Kamal, Pramaditya Wicaksono. Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image. Regional Studies in Marine Science. 2021; 44 ():101730.

Chicago/Turabian Style

Eva Purnamasari; Muhammad Kamal; Pramaditya Wicaksono. 2021. "Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image." Regional Studies in Marine Science 44, no. : 101730.

Journal article
Published: 15 February 2021 in JURNAL GEOGRAFI
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Indonesia has many types of unique and rare landforms, one of which is sand dunes, which is located in Parangtritis. Sand dune has the main function as a conservation area, natural wall for the tsunami disaster, water catchment area, and habitat for sand dune flora and fauna. However, the existence of sand dunes is currently threatened with extinction due to the decrease in their area, which is caused by changes in land use. Every year, the land use in the Parangtritis sand dune changes. Therefore, it is important to map land use changes to determine the changes that occur in the sand dune core zone. This study aims to map land use change in the core zone of sand dunes using small format aerial images and the OBIA (Object-Based Image Analysis) method. Land use in the study area is classified into nine classes, namely sand dunes, dry land forest, shrubs, coastal shoals, open field, built-up area and settlements, dry land agricultural fields, roads, and fishponds. The results showed that there were changes in all land use classes. Based on the accuracy assessment, the overall accuracy for 2020 was 68.95%, while the classification results for 2015 were 61.81%.Keywords: land use changes, OBIA, Small Format Aerial PhotographyIndonesia memiliki banyak jenis bentuklahan yang unik dan langka, salah satunya adalah gumuk pasir yang terletak di wilayah Parangtritis, Daerah Istimewa Yogyakarta. Gumuk pasir memiliki fungsi utama sebagai kawasan konservasi, tembok alami bencana tsunami, kawasan resapan air, serta habitat untuk flora fauna gumuk pasir. Namun, keberadaan gumuk pasir saat ini terancam punah oleh adanya penurunan luasannya, yang disebabkan oleh perubahan penggunaan lahan. Setiap tahun, penggunaan lahan di gumuk pasir Parangtritis mengalami perubahan, yang akhirnya menyebabkan luasan gumuk pasir selalu berkurang setiap tahunnya. Oleh karena itu, pemetaan perubahan penggunaan lahan penting untuk dilakukan untuk mengetahui perubahan yang terjadi di zona inti gumuk pasir. Penelitian ini bertujuan untuk memetakan perubahan penggunaan lahan di zona inti gumuk pasir menggunakan foto udara format kecil dan metode OBIA (Object-Based Image Analysis). Penggunaan lahan di wilayah kajian diklasifikasikan menjadi sembilan kelas yaitu gumuk pasir, hutan lahan kering, semak belukar, beting pantai, lahan terbuka, lahan terbangun dan permukiman, ladang, jalan dan tambak. Hasil penelitian menunjukkan adanya perubahan pada semua kelas penggunaan lahan. Berdasarkan uji akurasi, akurasi keseluruhan (overall accuracy) hasil klasifikasi penggunaan lahan tahun 2020 sebesar 68,95%, sedangkan hasil klasifikasi penggunaan lahan tahun 2015 sebesar 61,81%.Kata kunci: Perubahan Penggunaan Lahan, OBIA, Foto Udara Format Kecil

ACS Style

Latifa Melani Putri; Pramaditya Wicaksono. MAPPING OF LAND USE CHANGES IN THE CORE ZONE OF PARANGTRITIS SAND DUNES USING OBIA METHOD 2015-2020. JURNAL GEOGRAFI 2021, 13, 109 -120.

AMA Style

Latifa Melani Putri, Pramaditya Wicaksono. MAPPING OF LAND USE CHANGES IN THE CORE ZONE OF PARANGTRITIS SAND DUNES USING OBIA METHOD 2015-2020. JURNAL GEOGRAFI. 2021; 13 (1):109-120.

Chicago/Turabian Style

Latifa Melani Putri; Pramaditya Wicaksono. 2021. "MAPPING OF LAND USE CHANGES IN THE CORE ZONE OF PARANGTRITIS SAND DUNES USING OBIA METHOD 2015-2020." JURNAL GEOGRAFI 13, no. 1: 109-120.

Journal article
Published: 22 January 2021 in Land
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Coastal regions are one of the most vulnerable areas to the effects of global warming, which is accompanied by an increase in mean sea level and changing shoreline configurations. In Indonesia, the socioeconomic importance of coastal regions where the most populated cities are located is high. However, shoreline changes in Indonesia are relatively understudied. In particular, detailed monitoring with remote sensing data is lacking despite the abundance of datasets and the availability of easily accessible cloud computing platforms such as the Google Earth Engine that are able to perform multi-temporal and multi-sensor mapping. Our study aimed to assess shoreline changes in East Java Province Indonesia from 2000 to 2019 using variables derived from a multi-sensor combination of optical remote sensing data (Landsat-7 ETM and Landsat-8 OLI) and radar data (ALOS Palsar and Sentinel-1 data). Random forest and GMO maximum entropy (GMO-Maxent) accuracy was assessed for the classification of land and water, and the land polygons from the best algorithm were used for deriving shorelines. In addition, shoreline changes were quantified using Digital Shoreline Analysis System (DSAS). Our results showed that coastal accretion is more profound than coastal erosion in East Java Province with average rates of change of +4.12 (end point rate, EPR) and +4.26 m/year (weighted linear rate, WLR) from 2000 to 2019. In addition, some parts of the shorelines in the study area experienced massive changes, especially in the deltas of the Bengawan Solo and Brantas/Porong river with rates of change (EPR) between −87.44 to +89.65 and −18.98 to +111.75 m/year, respectively. In the study areas, coastal erosion happened mostly in the mangrove and aquaculture areas, while the accreted areas were used mostly as aquaculture and mangrove areas. The massive shoreline changes in this area require better monitoring to mitigate the potential risks of coastal erosion and to better manage coastal sedimentation.

ACS Style

Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Siti Saringatin; Pramaditya Wicaksono; Bachtiar Wahyu Mutaqin; Raihan Rafif. Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data. Land 2021, 10, 100 .

AMA Style

Sanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Siti Saringatin, Pramaditya Wicaksono, Bachtiar Wahyu Mutaqin, Raihan Rafif. Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data. Land. 2021; 10 (2):100.

Chicago/Turabian Style

Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Siti Saringatin; Pramaditya Wicaksono; Bachtiar Wahyu Mutaqin; Raihan Rafif. 2021. "Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data." Land 10, no. 2: 100.

Journal article
Published: 04 November 2020 in ISPRS International Journal of Geo-Information
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The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

ACS Style

Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Raihan Rafif; Siti Saringatin; Pramaditya Wicaksono. Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia. ISPRS International Journal of Geo-Information 2020, 9, 663 .

AMA Style

Sanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Raihan Rafif, Siti Saringatin, Pramaditya Wicaksono. Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia. ISPRS International Journal of Geo-Information. 2020; 9 (11):663.

Chicago/Turabian Style

Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Raihan Rafif; Siti Saringatin; Pramaditya Wicaksono. 2020. "Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia." ISPRS International Journal of Geo-Information 9, no. 11: 663.

Articles
Published: 13 July 2020 in Geocarto International
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Biodiversity of benthic habitats is among the highest of all ecological communities. This study was conducted to model benthic habitat biodiversity indices using a remote sensing approach in optically shallow waters in Karimunjawa Islands-Indonesia. These islands have a wide variety of benthic environments. Two multispectral imagers, namely Sentinel-2A and Landsat 8 OLI, were used. A series of statistical tests were applied in the empirical modeling using the pixel values of both images with in situ Shannon index (H), Simpson index (D), and Shannon’s Equitability (EH) calculations. The modeling inputs were sunglint-corrected bands, water column-corrected bands, PCA-transformed bands, MNF bands, and occurrence texture bands. The results indicate that multispectral remote sensing images can be used to map benthic habitat biodiversity indices. However, the difference between the concepts of H, D, and EH calculations and the reflectance value recorded by the sensor remove the possibility of obtaining higher accuracy. H, D, and EH maps derived from Sentinel-2A had varying levels of accuracy, namely 46.8%, 59.1%, and 54.5%, respectively, while Landsat 8 OLI produced these three maps with 45.81%, 57.34%, and 53.81% accuracy.

ACS Style

Pramaditya Wicaksono; Ignatius Salivian Wisnu Kumara; Muhammad Afif Fauzan; Rifka Noviaris Yogyantoro; Wahyu Lazuardi. Sentinel-2A and Landsat 8 OLI to model benthic habitat biodiversity index. Geocarto International 2020, 1 -17.

AMA Style

Pramaditya Wicaksono, Ignatius Salivian Wisnu Kumara, Muhammad Afif Fauzan, Rifka Noviaris Yogyantoro, Wahyu Lazuardi. Sentinel-2A and Landsat 8 OLI to model benthic habitat biodiversity index. Geocarto International. 2020; ():1-17.

Chicago/Turabian Style

Pramaditya Wicaksono; Ignatius Salivian Wisnu Kumara; Muhammad Afif Fauzan; Rifka Noviaris Yogyantoro; Wahyu Lazuardi. 2020. "Sentinel-2A and Landsat 8 OLI to model benthic habitat biodiversity index." Geocarto International , no. : 1-17.

Journal article
Published: 30 May 2020 in Remote Sensing Applications: Society and Environment
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Theoretically, spectral separability will greatly affect the accuracy of multispectral classification. This study aims to understand the relationship between the inter-class spectral separability and the accuracy of benthic habitat classification using a WorldView-2 multispectral image. The study area for this research is Kemujan Island, Jepara Regency, Central Java Province, Indonesia. The datasets used are sunglint-corrected bands, Principle Component Analysis (PCA)-derived bands, vegetation indices, and filter occurrence bands. Benthic habitat field data were obtained through a photo-transect survey technique and were used to construct nine levels of benthic habitat hierarchical classification schemes. We used maximum likelihood (ML) and random forest (RF) as the classification algorithms. Spectral separability was calculated using the Jeffries-Matusita separability analysis algorithm. The results from both RF and ML showed that the increased number of class pairs with spectral separability less than 1.0 (S1.0-1.9 increased the OA. Especially for scheme Level 1 with the greatest number of classes, an increased number of class pairs with S>1.9 to is required to improve the OA. This has proven that the spectral separability between classes does affect the accuracy of benthic habitat classification and there is a significant relationship between spectral separability and the accuracy of benthic habitat classification.

ACS Style

Pramaditya Wicaksono; Prama Ardha Aryaguna. Analyses of inter-class spectral separability and classification accuracy of benthic habitat mapping using multispectral image. Remote Sensing Applications: Society and Environment 2020, 19, 100335 .

AMA Style

Pramaditya Wicaksono, Prama Ardha Aryaguna. Analyses of inter-class spectral separability and classification accuracy of benthic habitat mapping using multispectral image. Remote Sensing Applications: Society and Environment. 2020; 19 ():100335.

Chicago/Turabian Style

Pramaditya Wicaksono; Prama Ardha Aryaguna. 2020. "Analyses of inter-class spectral separability and classification accuracy of benthic habitat mapping using multispectral image." Remote Sensing Applications: Society and Environment 19, no. : 100335.

Research article
Published: 01 February 2020 in IET Image Processing
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Sentinel-2A accuracy for benthic habitat composition mapping was tested and compared to ALOS AVNIR-2. Aerial image acquired using custom-made unmanned aerial vehicle was used to train and validate the model. The mapping was conducted regardless of the benthic class and at individual benthic class. Benthic habitat class spatial distribution was obtained using the combination of image segmentation and classification tree analysis. The aerial image was interpreted based on the percentage of the constructed and non-constructed classes. The constructed class includes coral reefs, dead coral, seagrass, and macroalgae, while non-constructed class covers carbonate sand, rock, and rubble. Sentinel-2A produced higher accuracy (92%) than ALOS AVNIR-2 (78%) for benthic habitat spatial distribution mapping. However, in the empirical modelling of benthic habitat composition, ALOS AVNIR-2 (SE 23–24%) produced slightly better accuracy than Sentinel-2A (SE 23–27%). Several factors affected the low accuracy, which include the sub-pixel mixing of benthic habitat and constructed class, the delay between dates of acquisition, and radiometric quality of the images. Since the fundamental relationship between reflectance value and the percentage of the constructed class has been justified and consistent, given more experiments it has the potential to predict benthic habitat composition with higher accuracy in the future.

ACS Style

Pramaditya Wicaksono; Muhammad Afif Fauzan; Septian Galih Widhi Asta. Assessment of Sentinel‐2A multispectral image for benthic habitat composition mapping. IET Image Processing 2020, 14, 279 -288.

AMA Style

Pramaditya Wicaksono, Muhammad Afif Fauzan, Septian Galih Widhi Asta. Assessment of Sentinel‐2A multispectral image for benthic habitat composition mapping. IET Image Processing. 2020; 14 (2):279-288.

Chicago/Turabian Style

Pramaditya Wicaksono; Muhammad Afif Fauzan; Septian Galih Widhi Asta. 2020. "Assessment of Sentinel‐2A multispectral image for benthic habitat composition mapping." IET Image Processing 14, no. 2: 279-288.

Journal article
Published: 23 December 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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There are not many discussion or previous works that specifically address the issue of integrating small field plot size (1 m2) and image at different spatial resolutions in the seagrass percent cover (PC) mapping using remote sensing. This is important to determine the spatial resolution of image that can still be effectively integrated with 1 × 1 m plot size field data. This research aimed at assessing the accuracy and spatial distribution of seagrass PC map modelled from image at different spatial resolutions, using seagrass field data measure at 1 m2 plot size. Two multispectral satellite images namely WorldView-2 (2 m) and PlanetScope (3 m) were used for this research and simulated to 5 m, 10 m, 15 m, and 30 m. Kemujan and Lombok Island were selected as the study area, and seagrass beds in each island have different characteristics. Machine learning random forest regression was used to perform empirical modelling and the mapping accuracy was assessed using independent seagrass PC samples. The results indicated that 1 m2 plot size is still effective to be integrated with image up to 30 m spatial resolution, where the RMSE and overall seagrass PC pattern is relatively similar but the level of information precision is reduced at lower spatial resolution. Furthermore, we found out that the main factor that strongly determines the success use of 1 m2 plot size and the mapping accuracy is the configuration of the seagrass bed in the study area. Seagrass PC in the more continuous seagrass bed can be mapped with higher accuracy than in patchy seagrass bed.

ACS Style

P. Wicaksono; W. Lazuardi; M. Munir. INTEGRATING IMAGE AT DIFFERENT SPATIAL RESOLUTIONS AND FIELD DATA FOR SEAGRASS PERCENT COVER MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W19, 487 -492.

AMA Style

P. Wicaksono, W. Lazuardi, M. Munir. INTEGRATING IMAGE AT DIFFERENT SPATIAL RESOLUTIONS AND FIELD DATA FOR SEAGRASS PERCENT COVER MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W19 ():487-492.

Chicago/Turabian Style

P. Wicaksono; W. Lazuardi; M. Munir. 2019. "INTEGRATING IMAGE AT DIFFERENT SPATIAL RESOLUTIONS AND FIELD DATA FOR SEAGRASS PERCENT COVER MAPPING." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19, no. : 487-492.

Journal article
Published: 30 August 2019 in Indonesian Journal of Geography
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Experiences with natural disasters have intensified recent efforts to enhance cooperation mechanisms among official disaster management institutions to community participation. These experiences reveal a need to enhance rapid mapping technical assistance to be developed and shared among young scientists through a summer school. However, the question arose of how effective this summer school to be used as a tool to increase scientists’ understanding and capacity. This study sought to evaluate the extent to which human resource capacity building can be effectively implemented. The methods used for this evaluation is through observations, questionnaires and a weighted scoring based on knowledge, skills and attitudes’ criteria. The results indicate a significant improvement in knowledge (94.56%), skills (82%) and attitudes (85.20%) among the participants. Even though there are still gaps in participants’ skills, the summer school was found to be an effective way to train the young scientists for rapid mapping.

ACS Style

Dewayany Sutrisno; Peter Tian-Yuan Shih; Mazlan Bin Hashim; Rongjun Qin; Pramaditya Wicaksono; Rahman Syaifoel. Improving Community Capacity in Rapid Disaster Mapping: An Evaluation of Summer School. Indonesian Journal of Geography 2019, 51, 155 -164.

AMA Style

Dewayany Sutrisno, Peter Tian-Yuan Shih, Mazlan Bin Hashim, Rongjun Qin, Pramaditya Wicaksono, Rahman Syaifoel. Improving Community Capacity in Rapid Disaster Mapping: An Evaluation of Summer School. Indonesian Journal of Geography. 2019; 51 (2):155-164.

Chicago/Turabian Style

Dewayany Sutrisno; Peter Tian-Yuan Shih; Mazlan Bin Hashim; Rongjun Qin; Pramaditya Wicaksono; Rahman Syaifoel. 2019. "Improving Community Capacity in Rapid Disaster Mapping: An Evaluation of Summer School." Indonesian Journal of Geography 51, no. 2: 155-164.

Articles
Published: 31 May 2019 in International Journal of Remote Sensing
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Mapping of the distribution of individual seagrass species is essential for any attempts to manage seagrass ecosystems. It is therefore important to understand how the spectra of different seagrass species vary, in order to establish their unique absorption features and how these can be utilised for mapping by making use of remote-sensing images. This paper presents measurements of the reflectance spectra between 400 and 900 nm for nine tropical species of seagrass. Continuum removal and multispectral resampling procedures were applied to the spectra. Dendrogram analysis was carried out to identify species clustering as the basis for a mapping scheme. Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) approaches were employed for the classification of seagrass species using WorldView-2 images and measured spectra as the input endmember. Classification Tree Analysis (CTA) and an image segmentation approach using CTA (Object-Based Image Analysis – OBIA) were performed as a means of comparison. The results indicate that the absorption features and overall shape of the spectra for all seagrass species are relatively similar, and implied that the major differences are attributable to the absolute reflectance values. Consequently, SAM and SID produced results of low accuracy (<30%), whereas, CTA and OBIA delivered results exhibiting higher accuracy (60–92%). The use of a spectral-based classification algorithm was ineffective for the classification and mapping of seagrass species using multispectral images. The utilisation of absolute reflectance values was beneficial for the classification of seagrass species having similar spectral shape.

ACS Style

Pramaditya Wicaksono; M. Afif Fauzan; Ignatius Salivian Wisnu Kumara; Rifka Noviaris Yogyantoro; Wahyu Lazuardi; Zhafirah Zhafarina. Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images. International Journal of Remote Sensing 2019, 1 -24.

AMA Style

Pramaditya Wicaksono, M. Afif Fauzan, Ignatius Salivian Wisnu Kumara, Rifka Noviaris Yogyantoro, Wahyu Lazuardi, Zhafirah Zhafarina. Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images. International Journal of Remote Sensing. 2019; ():1-24.

Chicago/Turabian Style

Pramaditya Wicaksono; M. Afif Fauzan; Ignatius Salivian Wisnu Kumara; Rifka Noviaris Yogyantoro; Wahyu Lazuardi; Zhafirah Zhafarina. 2019. "Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images." International Journal of Remote Sensing , no. : 1-24.

Journal article
Published: 29 May 2019 in Remote Sensing
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This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.

ACS Style

Pramaditya Wicaksono; Prama Ardha Aryaguna; Wahyu Lazuardi. Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms. Remote Sensing 2019, 11, 1279 .

AMA Style

Pramaditya Wicaksono, Prama Ardha Aryaguna, Wahyu Lazuardi. Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms. Remote Sensing. 2019; 11 (11):1279.

Chicago/Turabian Style

Pramaditya Wicaksono; Prama Ardha Aryaguna; Wahyu Lazuardi. 2019. "Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms." Remote Sensing 11, no. 11: 1279.

Journal article
Published: 25 October 2018 in Geoplanning: Journal of Geomatics and Planning
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Spatial distribution and concentration of Total Suspended Solid (TSS) is one of the coastal parameters which are required to be examined in order to understand the quality of the water. Rapid development of remote sensing technology has resulted in the emergence of various methods to estimate TSS concentration. SPOT-6 data has spatial, spectral, and temporal characteristics that can be used to estimate TSS concentration. The purposes of this research are (1) to determine the best method for estimating TSS concentration, (2) to map TSS distribution, and (3) to determine the correlation between TSS concentration and chlorophyll-a concentration using SPOT-6 data in Segara Anakan. The estimation of TSS concentration in this research was performed using empirical model built from SPOT-6 and TSS field data. Bands used in this research are single band data (blue, green, red, and near infrared) and transformed bands such as band ratio (12 combinations), Normalized Difference Suspended Solid Index (NDSSI), and Suspended Solid Concentration Index (SSC). The result shows that blue, green, red, and near infrared bands and SSC index significantly correlated to TSS. Afterwards, regression analysis was performed to determine the function that can be used to predict TSS concentration using SPOT-6 data. Regression function used are linear and non-linear (exponential, logarithmic, 2nd order polynomial, and power). The best model was chosen based on the accuracy assessment using Standard Error of Estimate (SE). The selected model was used to calculate total TSS concentration and was correlated with chlorophyll-a field data. The result of accuracy test shows that the model from blue band has an accuracy of 70.68 %, green band 70.68 %, red band 75.73 %, near infrared band 65.58 %, and SSC 73.67 %. The accuracy test shows that red band produced the best prediction model for mapping TSS concentration distribution. The total TSS concentration, which was calculated using red band empirical model, is estimated to be 6.13 t. According to the correlation test, TSS concentration in Segara Anakan has no significant correlation with chlorophyll-a concentration, with a coefficient correlation value of -0.265.

ACS Style

Aisya Jaya Dhannahisvara; Hartono Harjo; Pramaditya Wicaksono; Ferman Setia Nugroho. TOTAL SUSPENDED SOLID DISTRIBUTION ANALYSIS USING SPOT-6 DATA IN SEGARA ANAKAN, CILACAP. Geoplanning: Journal of Geomatics and Planning 2018, 5, 177 -188.

AMA Style

Aisya Jaya Dhannahisvara, Hartono Harjo, Pramaditya Wicaksono, Ferman Setia Nugroho. TOTAL SUSPENDED SOLID DISTRIBUTION ANALYSIS USING SPOT-6 DATA IN SEGARA ANAKAN, CILACAP. Geoplanning: Journal of Geomatics and Planning. 2018; 5 (2):177-188.

Chicago/Turabian Style

Aisya Jaya Dhannahisvara; Hartono Harjo; Pramaditya Wicaksono; Ferman Setia Nugroho. 2018. "TOTAL SUSPENDED SOLID DISTRIBUTION ANALYSIS USING SPOT-6 DATA IN SEGARA ANAKAN, CILACAP." Geoplanning: Journal of Geomatics and Planning 5, no. 2: 177-188.

Proceedings article
Published: 24 October 2018 in Remote Sensing of the Open and Coastal Ocean and Inland Waters
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Coral reef live percent cover (LPC) mapping has always been a challenging application for remote-sensing. The adoption of machine-learning algorithm in remote-sensing has opened-up the possibility of mapping coral reef at higher accuracy. This paper presents the application of machine-learning regression in the empirical modeling of coral reef LPC mapping. Stepwise regression, Support Vector Machine (SVM) regression, and Random Forest (RF) regression were used model the percentage of live coral cover in optically shallow water of Parang Island, Central Java, Indonesia using field photo-transect data to train the PlanetScope image. PlanetScope multispectral bands were transformed into water column corrected bands, Principle Component bands, and Cooccurrence texture analysis bands to be used as predictors in the regression process. The results indicate that the accuracy of machine learning algorithm to map coral reef LPC is relatively low due to the radiometric quality issue in the PlanetScope image (RMSE = 15.43%). We could not yet fairly justify the performance of machine learning algorithm until we applied the algorithms in other images.

ACS Style

Pramaditya Wicaksono; Wahyu Lazuardi; Muhammad Kamal; Afif Al Hadi. Machine-learning regression for coral reef percentage cover mapping. Remote Sensing of the Open and Coastal Ocean and Inland Waters 2018, 10778, 107780F .

AMA Style

Pramaditya Wicaksono, Wahyu Lazuardi, Muhammad Kamal, Afif Al Hadi. Machine-learning regression for coral reef percentage cover mapping. Remote Sensing of the Open and Coastal Ocean and Inland Waters. 2018; 10778 ():107780F.

Chicago/Turabian Style

Pramaditya Wicaksono; Wahyu Lazuardi; Muhammad Kamal; Afif Al Hadi. 2018. "Machine-learning regression for coral reef percentage cover mapping." Remote Sensing of the Open and Coastal Ocean and Inland Waters 10778, no. : 107780F.

Proceedings article
Published: 09 October 2018 in Earth Resources and Environmental Remote Sensing/GIS Applications IX
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Mangrove species inventory and mapping is very important as an effort to preserve the ecosystem and biodiversity of mangrove forests. One way of efficient mangrove species inventory and mapping is to use remote sensing imagery, especially through the analysis of its spectral reflectance pattern. This study aims to map the fourteen mangrove species on Karimunjawa Island, Central Java, Indonesia by: (1) measuring the mangrove species spectral reflectance pattern in the field, (2) characteristic analysis of the mangrove species reflectance pattern, and (3) mapping the dominant mangrove species distribution. The spectral reflectance measurement of mangrove species objects in the field was done by using JAZ EL-350 VIS-NIR (ranges from 300 to 1100 nm). The JAZ field spectrometer was pointed at a distance of 2 cm from the target objects with 10 reading repetitions for each species. Field measurements results were then taken to the laboratory for analysis of spectral reflectance and absorbance patterns, which served as key object recognition in this study. To combine the field and image spectral reflectance patterns, the field reflectance patterns were resampled to the spectral resolution of WorldView-2 image (8 bands, 2 m pixel size). The spectral angle mapper (SAM) method was the used to locate and map the distribution of each targeted mangrove species. As expected, the results showed that the largest difference of spectral curves between species was at the NIR wavelength spectrum (700-900nm). Hence, it is potential to be used as the basis for identification of species mangrove from remote sensing imagery. However, the result of this mapping approach only showed a low accuracy of 62%. The low value of map accuracy was attributed to the inaccuracy in defining threshold in SAM for each class. This study provides a basic understanding of the use of spectral reflectance for mangrove species mapping from remote sensing imagery.

ACS Style

Muhammad Kamal; Muhammad U. L. Ningam; Finni Alqorina; Pramaditya Wicaksono; Sigit Heru Murti. Combining field and image spectral reflectance for mangrove species identification and mapping using WorldView-2 image. Earth Resources and Environmental Remote Sensing/GIS Applications IX 2018, 10790, 107901P .

AMA Style

Muhammad Kamal, Muhammad U. L. Ningam, Finni Alqorina, Pramaditya Wicaksono, Sigit Heru Murti. Combining field and image spectral reflectance for mangrove species identification and mapping using WorldView-2 image. Earth Resources and Environmental Remote Sensing/GIS Applications IX. 2018; 10790 ():107901P.

Chicago/Turabian Style

Muhammad Kamal; Muhammad U. L. Ningam; Finni Alqorina; Pramaditya Wicaksono; Sigit Heru Murti. 2018. "Combining field and image spectral reflectance for mangrove species identification and mapping using WorldView-2 image." Earth Resources and Environmental Remote Sensing/GIS Applications IX 10790, no. : 107901P.

Journal article
Published: 22 September 2018 in Estuarine, Coastal and Shelf Science
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Mangrove ecosystems store large amounts of carbon in biomass and sediments. This so called ‘blue carbon’ that is captured by oceanic and coastal ecosystems plays an important role in climate change mitigation strategies. However, most biomass and carbon measurements have been conducted in coastal and delta mangroves, while oceanic mangroves are still insufficiently researched. In this paper we present results from our research on the Karimunjawa archipelago in the Java Sea north of Central Java, Indonesia, where we measured soil carbon stocks (soil total organic carbon – TOC) of an oceanic mangrove ecosystem. In previous research, we had already analyzed above-ground carbon (AGC) and below-ground biomass carbon (BGBC), so that we are now able to present the total ecosystem carbon stock. We took 35 soil samples along seven transects to analyze the relationship between (a) soil TOC and distance from shoreline, (b) total ecosystem carbon stock (AGC + BGBC + soil TOC) and distance from shoreline, (c) total C of living biomass (AGC + BGBC) and distance from shoreline, as well as (d) soil TOC and living biomass. We took another nine soil samples to analyze the distribution of soil TOC in the soil profile at a greater resolution. Our results show that there is a wide range of soil carbon stocks that varies from 3.3 t C ha−1 to 366.7 t C ha−1. On average of the 35 samples soils contribute to 45.5% of the total ecosystem carbon stock. Overall there is no correlation between the analyzed variables. However, there is a correlation between distance from the shoreline and soil carbon stock for the longest transect and a strong relationship between soil depth and soil carbon stock for all samples. Carbon stock per increment decreases with a conspicuous drop at 15 cm.

ACS Style

Udo Nehren; Pramaditya Wicaksono. Mapping soil carbon stocks in an oceanic mangrove ecosystem in Karimunjawa Islands, Indonesia. Estuarine, Coastal and Shelf Science 2018, 214, 185 -193.

AMA Style

Udo Nehren, Pramaditya Wicaksono. Mapping soil carbon stocks in an oceanic mangrove ecosystem in Karimunjawa Islands, Indonesia. Estuarine, Coastal and Shelf Science. 2018; 214 ():185-193.

Chicago/Turabian Style

Udo Nehren; Pramaditya Wicaksono. 2018. "Mapping soil carbon stocks in an oceanic mangrove ecosystem in Karimunjawa Islands, Indonesia." Estuarine, Coastal and Shelf Science 214, no. : 185-193.

Journal article
Published: 31 August 2018 in International Journal of Remote Sensing
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ACS Style

Pramaditya Wicaksono; Wahyu Lazuardi. Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment. International Journal of Remote Sensing 2018, 39, 5739 -5765.

AMA Style

Pramaditya Wicaksono, Wahyu Lazuardi. Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment. International Journal of Remote Sensing. 2018; 39 (17):5739-5765.

Chicago/Turabian Style

Pramaditya Wicaksono; Wahyu Lazuardi. 2018. "Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment." International Journal of Remote Sensing 39, no. 17: 5739-5765.

Conference paper
Published: 31 July 2018 in IOP Conference Series: Earth and Environmental Science
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The atmospheric disturbance in remote sensing imagery greatly influences the object's spectral response in the imagery. This, in turn, will impact the object characterization. The atmospheric effects on remote sensing imagery can be reduced through atmospheric correction. There are various types of atmospheric correction methods and each of them has its own working principles. Daerah Istimewa Yogyakarta (DIY) Province, Indonesia, was chosen to be study area for this research. The research objectives are to evaluate the atmospheric correction method, which consist of Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS), Second Simulation of the Satellite Signal in the Solar Spectrum (6S), Atmospheric Correction (ATCOR2), and Landsat 8 Surface Reflectance Code (LaSRC) by NASA. The compared objects consist of water, vegetation, and soil objects. The evaluation was based on Standard Error of Estimate (SEE), accuracy, and curve pattern. The result shows that the best atmospheric correction varies on each object. The spectral response curve pattern shows similarity but each object has its own accurate atmospheric method based on SEE result. The FLAASH, 6s, and ATCOR2 method show the lowest SEE result for mature vegetation leaves, beach sand, sand suns, and, lagoon, while QUAC method shows the lowest SEE result for young vegetation leaves, paddy plants, grass, and reservoir.

ACS Style

Febrina Ramadhani Yusuf; Kurniawan Budi Santoso; Muhammad Ulul Lizamun Ningam; Muhammad Kamal; Pramaditya Wicaksono. Evaluation of atmospheric correction models and Landsat surface reflectance product in Daerah Istimewa Yogyakarta, Indonesia. IOP Conference Series: Earth and Environmental Science 2018, 169, 012004 .

AMA Style

Febrina Ramadhani Yusuf, Kurniawan Budi Santoso, Muhammad Ulul Lizamun Ningam, Muhammad Kamal, Pramaditya Wicaksono. Evaluation of atmospheric correction models and Landsat surface reflectance product in Daerah Istimewa Yogyakarta, Indonesia. IOP Conference Series: Earth and Environmental Science. 2018; 169 (1):012004.

Chicago/Turabian Style

Febrina Ramadhani Yusuf; Kurniawan Budi Santoso; Muhammad Ulul Lizamun Ningam; Muhammad Kamal; Pramaditya Wicaksono. 2018. "Evaluation of atmospheric correction models and Landsat surface reflectance product in Daerah Istimewa Yogyakarta, Indonesia." IOP Conference Series: Earth and Environmental Science 169, no. 1: 012004.

Journal article
Published: 04 April 2018 in Jurnal Penelitian Karet
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Nitrogen merupakan salah satu unsur hara yang dibutuhkan dalam jumlah banyak oleh tanaman. Tanaman yang mengalami kekurangan unsur hara nitrogen akan menyebabkan terhambatnya pertumbuhan dan penurunan produktivitas tanaman. Penerapan sistem pertanian presisi pada kegiatan pemupukan di perkebunan karet dilakukan dengan cara dosis pemupukan dibuat berdasarkan kandungan hara tanah dan kandungan hara pada tanaman. Pada areal yang luas membutuhkan biaya analisa hara tanaman yang cukup mahal. Oleh karena itu sangat dibutuhkan suatu teknologi yang dapat mengestimasi kondisi hara tanaman dengan cepat dan biaya yang murah. Teknologi penginderaan jauh merupakan alternatif yang dapat digunakan untuk areal yang luas dan dengan waktu yang cepat serta biaya yang relatif murah. Penelitian ini bertujuan untuk mengetahui pengaruh resolusi spasial citra terhadap peta hasil estimasi kandungan nitrogen perkebunan karet. Citra multi resolusi yang digunakan antara lain GeoEye-1 (2 m) Sentinel-2A (10 dan 20 m) dan Landsat 8 OLI (30 m). Metode yang digunakan adalah membangun hubungan semi-empiris antara band tunggal dan indeks vegetasi citra dengan kandungan hara nitrogen perkebunan karet. Hasil penelitian menunjukkan bahwa peta hasil estimasi kandungan hara nitrogen perkebunan karet menggunakan citra Sentinel-2A (SE 0,369) memiliki akurasi yang lebih tinggi dibandingkan dengan menggunakan citra GeoEye-1 (SE 0,519) dan Landsat 8 OLI (SE 0,462).

ACS Style

Jamin Saputra; Muhammad Kamal; Pramaditya Wicaksono. PENGARUH RESOLUSI SPASIAL CITRA TERHADAP HASIL PEMETAAN KANDUNGAN HARA NITROGEN PERKEBUNAN KARET. Jurnal Penelitian Karet 2018, 13 -24.

AMA Style

Jamin Saputra, Muhammad Kamal, Pramaditya Wicaksono. PENGARUH RESOLUSI SPASIAL CITRA TERHADAP HASIL PEMETAAN KANDUNGAN HARA NITROGEN PERKEBUNAN KARET. Jurnal Penelitian Karet. 2018; ():13-24.

Chicago/Turabian Style

Jamin Saputra; Muhammad Kamal; Pramaditya Wicaksono. 2018. "PENGARUH RESOLUSI SPASIAL CITRA TERHADAP HASIL PEMETAAN KANDUNGAN HARA NITROGEN PERKEBUNAN KARET." Jurnal Penelitian Karet , no. : 13-24.