Depok, June 25, 2025. Indonesia, home to extraordinary global biodiversity, faces significant challenges due to land conversion and forest fires. However, Prof. Dr. Rokhmatuloh, S.Si., M.Eng., is optimistic about the positive trend in forest recovery thanks to policies such as social forestry and the forest management program. Deforestation and forest Degradation (REDD+), which demonstrates the commitment of the government and communities.
He emphasized the importance of Indonesia’s multifunctional forests, not only as climate regulators and biodiversity hotspots, but also as abundant economic resources. The potential for timber and non-timber forest products is enormous, ranging from leading export commodities to high-value products for indigenous communities. However, this potential can only be optimally utilized if forests are managed sustainably. This is where remote sensing plays a vital role, supporting evidence-based forest management (evidence-based forest management).
In the era of industry 4.0, the integration of remote sensing with artificial intelligence (AI), machine learning (Machine Learning – ML), dan cloud computing is key. These advances enable fast, automated, and accurate data analysis, and facilitate the use of drones for flexible and cost-effective high-resolution imagery acquisition. Overall, remote sensing has become a critical foundation for modern forestry management, supporting data-driven policymaking and increasing transparency in natural resource management.
Furthermore, the use of big data and AI has revolutionized remote sensing data analysis in forestry. Large and complex data volumes from optical satellites, radar, drones, and LiDAR can be efficiently processed with advanced computing systems. The advantages of AI in automated analysis improve the accuracy of real-time forest mapping and monitoring. cloud computing such as Google Earth Engine (GEE), which provides access to thousands of petabytes of satellite data, enabling efficient analysis across time and regions, supporting the scientific community and policymakers.
Furthermore, the combination of big data and ML not only accelerates data processing but also enables predictive modeling. This significantly supports projection-based forestry planning, such as estimating carbon stocks, tree growth, and wildlife habitat. Deep Learning (DL) in the processing of remote sensing data in the forestry sector, especially with algorithms Convolutional Neural Network (CNN), is capable of land cover classification, forest change detection, and vegetation species mapping with high accuracy.
Prof. Rokhmatuloh also highlighted the further applications of DL, including automatic deforestation detection using data time-series and the use of U-Net to map forest boundaries and identify vegetation regeneration post-fire. Platforms like GEE are making it increasingly easier to integrate big data from remote sensing with DL models, enabling large-scale analysis to support conservation and sustainable forest management in Indonesia.
In his inaugural speech at the UI Depok Conference Hall on Wednesday (June 25), Prof. Rokhmatuloh emphasized that the benefits of remote sensing in Indonesia’s forestry are not only as tools for monitoring and reporting, but also as an important instrument in law enforcement, forestry planning, and climate change mitigation. The utilization of big data and deep learning has opened a new paradigm in sustainable forest management, providing fast, adaptive, and high-precision analytical systems. This supports policy making that… real-time and predictive, which is very important for sustainable forest management efforts in Indonesia. Before being inaugurated as the 33rd Professor at UI in 2025, Prof. Rokhmatuloh completed his undergraduate education in the Department of Geography, Faculty of Mathematics and Natural Sciences (FMIPA) at UI in 1996. He then completed his Master’s and Doctorate programs at Chiba University, Japan, in 2004 and 2007, respectively. Present at his inauguration ceremony were the Head of the Geospatial Information Agency Prof. Dr. rer.nat. Muh Aris Marfai, S.Si., M.Sc.; Vice Dean for Education, Teaching, and Student Affairs, Faculty of Geography, UGM Dr. Sigit Heru Murti B.S. S.Si. M.Si.; Director of Human Resources of the Geospatial Information Agency Dr. rer. nat Sumaryono, M.Sc.; CEO of Golden Energy Mines (Sinarmas Group) Dr. Ir. Hartana, S. H, M. H, M. M.; and Permanent Lecturer at UNHAN, Obsgyn Specialist at RSPAD Gatot Soebroto Mayjen TNI Dr. dr. Sutan Finekri Arifin A, Sp.OG., Subsp.K.FM., M.A.R.S., M.H.
