Operational deforestation detection for forest early warning systems (EWS) is a hot topic in Earth observation today. Due to the persistent cloud cover in tropical regions, active microwave is regarded as one promising technology for EWS. Despite significant progress in the last decade, a reliable, genuine deforestation EWS is still lacking, because the development of powerful algorithms demand a near perfect understanding of the forest backscatter nature. Building upon 9-years of ALOS-2 long-term pantropical forest observations with various breakthrough findings, we introduce the next-generation algorithm for L-band SAR deforestation detection which realize the first real EWS in the tropics with unmatched accuracy and speed.
Monitoring of land vegetation is one of the prime objectives for Earth Observation satellite missions. Due to the penetration capabilities of low-frequency radar signals through vegetation canopies, L-band SAR is widely considered as a valuable tool for advanced vegetation monitoring. Based on the physical backscattering properties of the above-ground vegetation strata, polarimetric SAR (PolSAR) imaging can provide detailed information on crucial plant parameters such as amounts of biomass, growth heights, water contents, crop types, etc. 9 years in orbit, ALOS-2 has pioneered as a veritable long-term L-band SAR land observation mission. ALOS-2/PALSAR-2 has acquired unprecedented L-band time-series data with seamless coverage of the entire vegetated land area. Based on the achievements of its predecessor, we discuss the potential of ALOS-4/PALSAR-3 for further breakthroughs in both agriculture and forest monitoring from meter scale to continental scale. Particularly, the cutting-edge capability to observe 200-km swaths in high-resolution Stripmap (SM) mode, achieved by Digital Beam Forming (DBF), will allow overcoming the limitations of ALOS-2’s widely used 50-m resolution ScanSAR modes. Better spatial resolution and image quality paired with higher revisit frequency is expected to improve the reliability of numerous applications ranging from land cover classifications to biomass and yield estimations.
Increasing human and economic losses from urban disasters demand synthetic aperture radar (SAR), which has allweather, day-and-night observation capability. The Japan Aerospace Exploration Agency (JAXA) has been operating the Advanced Land Observing Satellite-2 (ALOS-2), carrying PALSAR-2, an L-band SAR, for monitoring disasters and environmental changes. We developed the first fully automated algorithms for detecting flood extents and earthquakeinduced building damage from ALOS-2 data. The algorithms rapidly process ALOS-2 and ancillary data (flood simulation, hazard map, and other geographical information) and provide damage information less than an hour after the input data are entered. The validation results showed that the accuracy of the estimated flood extent was 60 to 94%, depending on the observation conditions, especially the incidence angle of ALOS-2 observation. The accuracy of the damaged building detection was 72% for buildings with a footprint larger than 200 m2 in the area. We implemented these algorithms in an operational disaster response system. We launched a one-stop operation that automatically processes ALOS-2 data after emergency observation and provides the damage maps rapidly via email for disaster response workers.
The tremendous potential of ALOS-4/PALSAR-3 for further advancements in forest monitoring from local to global scales are discussed on the basis of the groundbreaking achievements made by its predecessor ALOS-2/PALSAR-2, the first long-term L-band SAR forest observation mission in history. The unprecedented seamless and frequent dual-polarization observation of the entire tropical forest belt between 2016 and 2022 has revolutionized the idea of global forest monitoring. In the upcoming ALOS-4 era, wide-area imaging with greatly improved spatial resolution and image quality at shorter revisit times will further boost the reliability for all kinds of forest remote sensing applications including forest classification, biomass estimation and deforestation detection.
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