Dony Kushardono

@brin.go.id

Remote Sensing Research Center
Research Organization of Aeronautics and Space, National Research and Innovation Agency of Indonesia (BRIN)

Dony Kushardono


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https://researchid.co/donykushardono
He is a researcher at the Indonesian National Institute of Aeronautics and Space (LAPAN) since 1986, from 2021 until now he is a researcher at the National Research and Innovation Agency. Dony is a research professor in the field of remote sensing technology, from 2001 to 2006 he led the utilization of remote sensing for the economic development of the community and the region, was the head of the LAPAN-Jakarta remote sensing ground station, had been the head of the LAPAN remote sensing technology development from 2007 to 2012, and also as a supervisor for postgraduate students at IPB University and the University of Indonesia.

EDUCATION

Ph.D. in image processing of remote sensing data, Tokai University 1996

RESEARCH INTERESTS

Remote Sensing
39

Scopus Publications

Scopus Publications

  • Probability to Reality: Neural Network-Enhanced Bayesian Belief Network to Mapping Wildfires Vulnerability in Bromo, Indonesia
    Bayu Satya Adhitama, Ika Siwi Supriyani, Karunika Diwyacitta, Yenni Vetrita, Andy Indradjad, et al.
    Journal of the Indian Society of Remote Sensing, 2026
  • Assessing the recurrent neural network approach for identifying rice fields using C-band synthetic aperture radar data in Indramayu Regency, Indonesia
    Anugrah Indah Lestari, Dony Kushardono, Budhi Gustiandi, Mukhoriyah Mukhoriyah, Krisna Malik Sukarno, et al.
    Paddy and Water Environment, 2026
  • CNN-Random Forest Hybrid Method for Phenology-Based Paddy Rice Mapping Using Sentinel-2 and Landsat-8 Satellite Images
    Dodi Sudiana, Sayyidah Hanifah Putri, Dony Kushardono, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo, et al.
    Computers, 2025
    The agricultural sector plays a vital role in achieving the second Sustainable Development Goal: “Zero Hunger”. To ensure food security, agriculture must remain resilient and productive. In Indonesia, a major rice-producing country, the conversion of agricultural land for non-agricultural uses poses a serious threat to food availability. Accurate and timely mapping of paddy rice is therefore crucial. This study proposes a phenology-based mapping approach using a Convolutional Neural Network-Random Forest (CNN-RF) Hybrid model with multi-temporal Sentinel-2 and Landsat-8 imagery. Image processing and analysis were conducted using the Google Earth Engine platform. Raw spectral bands and four vegetation indices—NDVI, EVI, LSWI, and RGVI—were extracted as input features for classification. The CNN-RF Hybrid classifier demonstrated strong performance, achieving an overall accuracy of 0.950 and a Cohen’s Kappa coefficient of 0.893. These results confirm the effectiveness of the proposed method for mapping paddy rice in Indramayu Regency, West Java, using medium-resolution optical remote sensing data. The integration of phenological characteristics and deep learning significantly enhances classification accuracy. This research supports efforts to monitor and preserve paddy rice cultivation areas amid increasing land use pressures, contributing to national food security and sustainable agricultural practices.
  • Analysis of urban environmental comfort using Landsat-8 multitemporal data and Artificial Neural Network
    Nurwita Mustika Sari, Dony Kushardono, Mukhoriyah Mukhoriyah, Kustiyo Kustiyo, Masita Dwi Mandini Manessa
    Journal of Degraded and Mining Lands Management, 2025
    The presence of greenery in urban residential and office areas can improve the comfort of residents who live in these environments. In an urban setting, vegetation serves an ecological purpose by absorbing carbon dioxide, supplying oxygen, lowering the temperature to produce a tolerable microclimate, acting as a water catchment area, and reducing noise. Urbanization and anthropogenic activity-driven growth of urban and sub-urban regions put stress on the local vegetation and have the potential to lower environmental comfort. To promote the creation of a sustainable urban environment, a thorough analysis of the urban environment is required. Applications for remote sensing in all spectral, geographic, and temporal dimensions have increasingly adopted the usage of deep learning methods with artificial neural networks. This study attempted to predict the application of remote sensing data for analyzing environmental comfort in metropolitan areas based on multitemporal Landsat-8 data. The study area is Greater Jakarta. The approach was based on supervised classification with neural network techniques and land parameters like surface temperature, brightness index, greenness index, and wetness index. According to the study's findings, the proposed method could accurately predict that very uncomfortable classes predominated in Jakarta, Bogor, Depok, Tangerang, Bekasi, and surrounding areas by more than 92%. In addition to being densely populated with communities, urban environments are uncomfortable due to a lack of vegetation cover, which increases surface temperatures. In the future, this research can provide input for similar research, especially in the use of deep learning Artificial Neural Network methods for environmental analysis.
  • Remote Sensing-Based Urban Environmental Quality Indicators: A Review
    Nurwita Mustika Sari, Dwi Nowo Martono, Raldi Hendrotoro Seputro Koestoer, Dony Kushardono
    Journal of Multidisciplinary Applied Natural Science, 2025
    Most of the global population lives in urban areas, which also serve as hubs of economy, industry, and government activities. Various factors that affect the quality of cities have been studied in different locations. This article reviews various papers that examine environmental quality indicators in urban areas that can be extracted from remote sensing data. The first aspect is vegetation cover, which is known from the vegetation index normalized difference vegetation index (NVDI), and the second aspect is surface temperature, which is known from land surface temperature (LST). In this work, urban environmental conditions in various countries are compared with urban conditions in Indonesia based on these indicators. It is found that NDVI and LST are indicators from remote sensing that are widely used to analyze urban environmental conditions. The vegetation index has a negative correlation with surface temperature. High surface temperature creates discomfort in urban quality of life and brings mental stress to residents living in those areas. Based on bibliometric analysis and network map, it is known that there are 30 most relevant words or terms to the keywords “urban remote sensing” and “remote sensing environment indicator” with the highest frequency of occurrence and relevance. This study can serve as input for the government as policymakers and urban planners to formulate spatial planning policies oriented towards sustainability and to research current topics related and relevant to remote sensing-based urban environmental quality indicators.
  • A CNN-RF Hybrid Approach for Rice Paddy Fields Mapping in Indramayu Using Sentinel-1 and Sentinel-2 Data
    Dodi Sudiana, Mia Rizkinia, Rahmat Arief, Tiara De Arifani, Anugrah Indah Lestari, et al.
    IEEE Access, 2025
    Rice, cultivated in paddy fields, is one of the staple foods in the world, especially in Asia. In Indonesia, a substantial number of rice paddy fields have been converted into residential or industrial areas, threatening food security. Therefore, it is necessary to monitor the adequacy of rice paddy field areas. Recently, remote sensing has become the most widely used method for mapping rice paddy fields. This research focuses on developing a classification model for rice paddy field mapping using remote sensing with radar and optical data fusion, including input variations in polarization, texture, and optical derivative indices. This study proposes the CNN-RF method, which combines a convolutional neural network (CNN) as a feature extractor and a random forest (RF) as a classifier. The experiment used combinations of input data, including variations of single and multisource data, to achieve optimal results. Research findings in some districts of Indramayu show that the scheme combining Sentinel-1 features with GLCM (gray-level co-occurrence matrix) and Sentinel-2 features with selected bands provides the best results, with an overall accuracy of 98.23% and a Kappa coefficient of 0.96, using the CNN-RF method. CNN-RF outperforms other classifiers owing to the hybrid learning combination, which improves the accuracy through feature extraction by CNN and handles complex relationships between features while reducing overfitting by RF. This study contributes to the development of accurate and efficient rice paddy field mapping techniques using remote sensing.
  • SMART DETECTION OF ILLICIT CANNABIS PLANTATIONS USING REMOTE SENSING TECHNOLOGY AND MACHINE LEARNING
    Dedi Irawadi, Tuga Mauritsius, Dony Kushardono, Syarif Budhiman, Karunika Diwyacitta, et al.
    Geography Environment Sustainability, 2025
    Remote sensing technology and machine learning classifiers can be utilized to develop smart detection systems for illicit crops such as Cannabis sativa L. Machine learning algorithms for classifying medium-resolution optical satellite data can be compared to identify the best model for enhancing law enforcement’s detection of illicit crops efficiently and accurately. Remote sensing-based smart detection systems have been developed in South America and Central Asia; however, these methods cannot be used effectively for Indonesia due to high cloud coverage, geographical differences, and the smaller area of Cannabis sativa L. plantations. This research developed an agile methodology that employs backpropagation neural networks to analyze the statistical growth phenology of cannabis derived from multitemporal medium-resolution remote sensing data. Using datasets derived from Indonesian law enforcement eradication records, the method achieved 94% accuracy and a kappa coefficient of 0.9. Further, plant growth phenology based on vegetation index values from multitemporal data was used to assess the condition of identified cannabis plantations.
  • HOW DRONES AND LIDAR HELP IN COUNTING MANGROVE TREES: A PRACTICAL APPROACH
    Muhammad Rizki Nandika, Jeverson Renyaan, Bayu Prayudha, La Ode Alifatri, Herlambang Aulia Rachman, et al.
    Geography Environment Sustainability, 2025
    Mangrove forests provide critical ecosystem services, including coastal protection, habitat for biodiversity, and carbon sequestration. Monitoring these ecosystems is essential for their conservation and sustainable management. This study was conducted on Pramuka Island, Indonesia, focusing on high-density Rhizophora stylosa vegetation. Data was collected using the DJI M300 RTK UAV equipped with the Zenmuse L1 LiDAR sensor, which generated a Canopy Height Model (CHM) and identified treetops. Various kernel sizes (3×3, 5×5, 9×9, 11×11, 21×21) and Local Maximum Filter (LMF) window sizes (0.5, 1, 3 meters) were applied to analyze mangrove tree density. The study found that the combination of a 3×3 kernel with a 0.5 meter window size yielded the best results, achieving the highest F-score and balancing precision and recall. However, despite the optimized settings, LiDAR still struggled to detect individual trees in dense mangrove stands, resulting in the underestimation of tree counts compared to field data. This highlights the challenges LiDAR faces in dense vegetation environments. The study emphasizes the need for optimized kernel and window size configurations for more accurate tree detection and calls for further development of LiDAR-based algorithms to improve detection in mangrove forests. Improved methodologies will enhance the effectiveness of mangrove forest conservation and management efforts.
  • SuperDove Sensor to Monitor Illegal Cannabis Plantation in Indonesia
    Karunika Diwyacitta, Olivia Maftukhaturrizqoh, Fauzan Al Ayyubi, Bayu Satya Adhitama, Ika Siwi Supriyani, et al.
    Springer Proceedings in Physics, 2025
  • Sentinel-2A multispectral image analysis for seagrass mapping in Bintan's shallow water ecosystem: A case study of Teluk Bakau, Malang Rapat, and Berakit villages
    Pragunanti Turissa, Bisman Nababan, Vincentius P. Siregar, Dony Kushardono, Hawis H. Madduppa, et al.
    Kuwait Journal of Science, 2024
  • Rice fields classification through spectral-temporal data fusion during the rainy and dry seasons using Sentinel-2 optical images in Subang Regency, West Java, Indonesia
    Kustiyo Kustiyo, Rokhmatuloh Rokhmatuloh, Adhi Harmoko Saputro, Dony Kushardono
    Paddy and Water Environment, 2024
  • Paddy Fields Classification Using A 2-Dimensional Scatterplot of Growth Phenological Features from Sentinel-1 Data
    Kustiyo Kustiyo, Rokhmatuloh Rokhmatuloh, Adhi Harmoko Saputro, Dony Kushardono, Ratih Dewanti Dimyati, et al.
    Jurnal Pengelolaan Sumberdaya Alam Dan Lingkungan, 2024
  • Spatial distribution models for the four commercial tuna in the sea of maritime continent using multi-sensor remote sensing and maximum entropy
    Emi Yati, Lilis Sadiyah, Fayakun Satria, Irene D. Alabia, Sayidah Sulma, et al.
    Marine Environmental Research, 2024
  • Assessing land subsidence and analyzing tidal flooding in Tangerang, Banten
    Risti Endriani Arhatin, Jonson Lumban Gaol, I Wayan Nurjaya, Setyo Budi Susilo, Dony Kushardono, et al.
    Bio Web of Conferences, 2024
  • BURNED AREA DETECTION USING CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL INFORMATION OF SYNTHETIC APERTURE RADAR DATA IN INDONESIA
    Anugrah I. Lestari, Dony Kushardono, Athar A. Bayanuddin
    Geography Environment Sustainability, 2024
  • Monitoring Land Cover Change and Its Estimated Carbon Loss in the Nusantara Capital City Indonesia on High-Resolution Planet Satellite Imagery
    Galdita A. Chulafak, Gatot Nugroho, Parwati Sofan, Anjar I. Pambudi, Dony Kushardono
    Proceedings of SPIE the International Society for Optical Engineering, 2024
  • Bibliometric Analysis of Synthetic Aperture Radar (SAR) Remote Sensing Satellite Data Utilization for Rice Paddy Mapping
    Budhi Gustiandi, Anugrah Indah Lestari, Dony Kushardono, Mukhoriyah, Krisna Malik Sukarno, et al.
    IEEE Asia Pacific Conference on Geoscience Electronics and Remote Sensing Technology Agers, 2024
  • Semantic Deep Learning for Open-Pit Mining Detection Using High Resolution SAR Data
    Udhi C Nugroho, Tri Muji Susantoro, Dony Kushardono, Gatot Nugroho, Herru L. Setiawan, et al.
    2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology Icares 2024 Proceedings, 2024
  • Rapid Detection of Devegetation Using Multitemporal Sentinel-1 C-Band SAR Data
    Anugrah Indah Lestari, Dony Kushardono, Yenni Vetrita, Imam Santoso, Tatik Kartika, et al.
    Aip Conference Proceedings, 2023
  • A Bibliometric Analysis of Urban Environment Quality Studies Based on Satellite Remote Sensing Data
    Nurwita Mustika Sari, Dony Kushardono, Masita Dwi Mandini Manessa, Kustiyo, Mukhoriyah, et al.
    Aip Conference Proceedings, 2023
  • Analysis of land use and spatial planning in the Upstream Citarum watershed of West Java based on remote sensing data
    Mukhoriyah Mukhoriyah, Samsul Arifin, Dony Kushardono, Mohammad Ardha, Fajar Yulianto
    Journal of Degraded and Mining Lands Management, 2023
  • AN OPTIMIZED ARTIFICIAL NEURAL NETWORK FOR THE CLASSIFICATION OF URBAN ENVIRONMENT COMFORT USING LANDSAT-8 REMOTE SENSING DATA IN GREATER JAKARTA AREA, INDONESIA
    Nurwita Mustika Sari, Dony Kushardono, Mukhoriyah Mukhoriyah, Kustiyo Kustiyo, Masita Dwi Mandini Manessa
    Journal of Applied Engineering and Technological Science, 2023
  • Fishing Ground and Primary Production Analysis Based on DNB SNPP-VIIRS and Aqua-MODIS Imagery in Mentawai Island and Nias Waters
    Teguh Prayogo, Hastuadi Harsa, Sayidah Sulma, Emi Yati, Sartono Marpaung, et al.
    Iop Conference Series Earth and Environmental Science, 2023
  • Urban Vegetation Quality Assessment Using Vegetation Index and Leaf Area Index from Spot 7 Data with Fuzzy Logic Algorithm
    Nurwita Mustika Sari, Tito Latif Indra, Dony Kushardono
    International Journal on Advanced Science Engineering and Information Technology, 2022
  • Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data
    Fajar Yulianto, Dony Kushardono, Syarif Budhiman, Gatot Nugroho, Galdita Aruba Chulafak, et al.
    Scientific World Journal, 2022