Operational deforestation detection and forest early warning is one hot topic in synthetic aperture radar (SAR) Earth observation (EO) today. Especially in tropical regions where persistent cloud coverage is incapacitating optical sensors during large parts of the year, active microwave is regarded as the most promising technology to combat illegal and unsustainable deforestation activities. The key aspect for a true EWS is the timely detections of fresh and ongoing deforestation activities in a matter of days. allowing authorities to act on a given warning in meaningful fashion, e.g., inspecting detected sites, detain deforesters and pretend the remaining forest from further destruction. The development of JAXA’s next-generation algorithm (NGA) for deforestation detection builds upon the important breakthroughs in long-term pantropical forest monitoring made by the ALOS-2 mission. A field survey conducted in the Brazilian Amazon verified the unparalleled performance of the NGA to reliably detect ongoing deforestation.

Forest monitoring is a prime objective for orbital synthetic aperture radar (SAR) missions. Especially in tropical regions where persistent cloud coverage incapacitates optical satellite observations for large parts of the year, radar remote sensing provides a unique tool for consistent all-year round data acquisition. In contrast to high-frequencies SARs at C- and X-band, L-band radar constitutes a powerful tool to obtain direct information from the full forest strata ranging from the top canopy all the way down to the forest ground. However, despite important advantages, SAR faces various challenges when it comes to potential error sources. As radar backscattering from distributed natural forest targets is profoundly complicated in nature, ill-defined signal fluctuations often limited the reliability of models and applications. To overcome these challenges by improved understanding of the spatial-temporal fluctuations in forest backscatter, coherent, multitemporal and polarimetric observations covering all kinds of environment conditions are required. Nearing 9 years in orbit since its launch in 2014, ALOS-2 has acquired the world’s first long-term pantropical L-band SAR archive fulfilling exactly these requirements.

Japanese local governments are increasingly utilizing forest cloud systems, and we have developed practical logging detection methodology using ALOS-2/PALSAR-2 data to improve the efficiency of forest management based on these systems. In the study in Ibaraki Prefecture, we established a method to improve both user's and producer's accuracies, and quantitatively compared the characteristics of logging detection by SAR and optical sensors. Currently, the logging information is being used in the Ibaraki Prefecture's forest cloud system.

Nearing 8 years in orbit, ALOS-2/PALSAR-2 has acquired the world's first long-term pantropical L-band SAR forest monitoring data archive. The unprecedented dual-polarimetric time-series data allow to revolutionize our knowledge of the complex spatial-temporal processes governing the backscattering behaviors in tropical forests from local to global scale. The paper discusses ALOS-2's tremendous contributions to fundamental SAR forest research, forest cover mapping, biomass estimation, and deforestation monitoring, from better forest/non-forest maps and improved forest type classifications to all-new detailed flooded forest maps and groundbreaking insights on the rainfall impacts on L-band SAR imaging shedding light on the often overlooked relation between precipitation and forest backscatter. Based on these pioneering achievements a novel AI-supported false alarm suppression method was developed for the JICA-JAXA forest early warning system in the tropics (JJ-FAST) enabling the first operational fully automatic near real-time deforestation detection service with overall accuracies above 80% in the Amazon basin.

Forest observation is one prime objective for current and future L-band SAR missions. Despite the tremendous achievements made in the last 3 decades, the reliability of derived products, e.g., forest cover and biomass maps, is still largely hampered by ill-defined backscatter fluctuations. To better understand the rain-induced noise-like variations in tropical forest backscatter timeseries, we demonstrate big data deep learning applications exploiting the unprecedented long-term data sets provided by ALOS-2/PALSAR-2 from 2016–2021. Robust fully automatic deforestation detection is achieved in an operational forest monitoring system by using a CNN-based false alarm suppression method. Using 400 TB of ScanSAR images together with 50 billion auxiliary rainfall data an automatic rainfall prediction and correction procedure for L-band SAR is proposed based on LSTM artificial neural network. First results show the approach's potential to reliably reduce rain related disturbances in L-band forest timeseries in an unsupervised fashion.

JICA-JAXA Forest Early Warning System in the Tropics (JJ-FAST) monitors tropical forest in 77 countries by using PALSAR-2/ScanSAR time-series data. The deforestation detection algorithm used in JJ-FAST shows overall user's accuracies of 71.1%, but the user's accuracies was 53.3 % for Latin America. Four convolutional neural network models (CNN) have been developed and tested to improve deforestation detection accuracies in Peru and Brazil. By applying the suggested CNN models, averaged user's accuracies are improved from 45 % to 77 - 85 % in dry season, and from 34 % to 48 - 57 % in rainy season. Averaged relative F1 are improved from 0.6 to 0.67 - 0.75 in dry season, and from 0.49 to 0.52 - 0.63 for rainy season. It is shown that adding CNN operation improves the deforestation detection accuracies. Intermediate CNN outputs were visualized to gain insights about what features of the image does each layer learn for improving the accuracies.

Domain-specific architectures are being studied to improve computer performance beyond the end of Moore's Law. Here, we propose a new computing architecture, the CMOS annealing machine, which provides a fast means of solving combinatorial optimization problems. Our architecture is based on in-memory computing architecture through utilizing the locality of interactions in the Ising model. The prototype presented in 2019 has two processors on a business-card-sized board and solves problems 55 times faster than conventional computers.

One of the prime objectives for orbital civilian SARs is forest observation. Though tremendous effort has been devoted to improving applications like biomass estimation and deforestation detection, the reliability has been impeded by insufficient knowledge about spatial-temporal variabilities of forest backscatter. In the tropics, these backscatter fluctuations are largely controlled by the rainfall regime. This paper investigates the impact of rainfall on L-band forest observation using 300 TB pan-tropical ALOS-2 ScanSAR timeseries with 1.5 billion TRMM based GSMaP precipitation datasets. The results show that the correlation between forest gamma naught and antecedent rainfall can exceed R=0.9 in many areas outside the rainforest. In the rainforest strong negative correlation below R=-0.8 can occur in the severely flooded forest. We assess the impact of rainfall on deforestation detection accuracies and discuss a new classification scheme for tropical forests using statistics of seasonal backscattering behaviors together with response to precipitation.

JICA and JAXA have operated a tropical forest early warning system (JJ-FAST) since 2016 and ALOS-2 L-band SAR system (PALSAR-2/ScanSAR) is used for forest monitoring and deforestation detection. There are many factors to cause commission errors in SAR image and ground moisture is one of them. To reduce such commission errors, seasonal change analysis is effective. In this study, a time-series analysis method using Savitzky-Golay (SG) filtering was developed. The method of speckle noise filtering by frost and 2-step thresholding and time-series analysis using SG was good method. The accuracy of forest change detection was improved from 19% of the threshold method to 80%.

JJ-FAST is the SAR-satellite based early deforestation monitoring system, and deforestation information obtained by using PALSAR-2/ScanSAR data are released on the JJ-FAST web site, 3 to 4 days after the observation. ScanSAR/50m spatial resolution data is currently used for the deforestation detection, and the minimum detection size is 3ha. In this paper, ScanSAR/25m spatial resolution data is used for the deforestation detection, and the detection accuracies are compared with the one obtained with the 50m spatial resolution data for four test sites. The minimum deforestation detection size is set to be 1ha in this time. The results show that the 25m spatial resolution data always shows higher detection accuracies. The results obtained in Cambodia test site show that user's accuracies are 21.4 % higher than the case of 50m resolution, and number of correctly detected polygon is 2.3 times more than the one for the 50m resolution case. It is indicated that the 25m spatial resolution ScanSAR data improves the detection accuracies with minimum detection size of 1 ha.

To validate terrestrial ecological products of the Global Change Observation Mission-Climate (GCOM-C) satellite, a large-scale ecological observation project "JAXA Super Sites 500" was initiated. The project's purpose is to obtain the representative leaf area index (LAI), above-ground biomass (AGB), and fraction of absorbed PAR (fAPAR) in the satellite footprint scale. This study aimed to determine the appropriate observation methods in the satellite scale for the target land-cover types such as a deciduous broadleaved forest, evergreen/deciduous needle-leaved forests, and a grassland. As a result of the comparative observations, a combination method using litter-traps and LAI- 2200 was adopted as the LAI observation in a forest, a tree census method was adopted as the AGB observation in a forest, and a clipping method was adopted as the AGB and LAI observations in a grassland. LAI, AGB, and fAPAR were observed and were used to validate the GCOM-C terrestrial ecological products.

Carbon emissions from peatland significantly affect climate change. We adopted SAR interferometry technique in order to estimate the amount of carbon emissions from peatland receiving development pressure in Borneo. As a result, ground subsidence of 15.0 cm yr-1on average and 31.0 cm yr-1at maximum were observed. The carbon emissions were estimated at 39.7 tC ha-1yr-1. We will compare the results with in-situ subsidence data and CO2flux data in the future,

In order to reduce speckle noise effects in forest cover change analysis, two types of SAR processing approach have been proposed so far. One is a type of object-based segmentation approaches and another is pixel-based processing approaches using speckle noise filtering such as Lee and Frost filter. This paper proposes a methodology of the pixel-based algorithm for forest change detection using PALSAR-2 ScanSAR data. Frost filter is applied to reduce speckle noise and normalization filter is implemented to reduce ground moisture effects. In case of Peru and Paraguay, the user's accuracy of developed algorithm was almost same as those of JJ-FAST version-2 while the producer's accuracy was 3 times better than JJ-FAST latest version (version-2).

JJ-FAST is the first SAR-based global and early deforestation monitoring system, and started the operation from November 2016. In this paper, improved deforestation detection algorithm were suggested for using JJ-FAST system. One of the improvement is to increase maximum number of data used from 15 to 20 to suppress error detection occurred in mountain area and swamp forest. The other improvement is to adopt 0.5 dB lower threshold level to increase number of deforestation detection. Suggested algorithm were evaluated by using optical satellite data (GLAD), obtained in one of the active deforestation spots in Peru, Brazil, Cambodia, and Mozambique. User's accuracies increases by 6.7%, and number of correctly detected deforestation polygon make it 1.3 times as large.

JJ-FAST is an operational forest early warning system using PALSAR-2/ScanSAR mode data. Its deforestation detection algorithm has been updated since 2016 and the current is version 1 (as of January, 2018) and the algorithm version 2 is preparing to implement in March 2018. Overall user's accuracy of version-1 products was estimated from several field experiments, and achieved as 83.3%. There are omission errors in the version 1 products because of its simple HV change detection approach.

Remote sensing has established as key technology for monitoring of environmental degradation such as forest clearing. One of the state-of-the-art microwave EO systems for forest monitoring is Japan's L-band ALOS-2/PALSAR-2 which provides outstanding means for observing tropical forests due its cloud and canopy penetration capability. However, the complexity of the physical backscattering properties of forests and the associated spatial and temporal variabilities, render straightforward change detection methods based on simple thresholding rather inaccurate with high false alarm rates. In this paper, we develop a framework to alleviate problems caused by forest backscatter variability. We define three essential elements, namely “structures of density”, “speed of change”, and “expansion patterns” which are obtained by differential computing between two repeat-pass PALSAR-2 images. To improve both the detection and assessing of deforestation, a “deforestation behavior pattern” is sought through temporal machine learning mechanism of the three proposed elements. Our results indicate that the use of “structure of density” can introduce a more robust performance for detecting deforestation. Meanwhile, “speed of change” and “expansion pattern” are capable to provide additional information with respect to the drivers of deforestation and the land-use change.

A preliminary version of the semi-automatic deforestation detection algorithm was developed for the JJ-FAST system, which monitors deforestation and forest changes in tropical regions for approximately every 1.5 months and covers 77 countries. PALSAR-2/ScanSAR HH, HV, HH and HV ratio were used to detect various deforestation stages. Multitemporal data were used to suppress the effect of seasonality and rainfall. Moreover, the deforestation accuracies for the active deforestation spots in Brazil and Peru were estimated. The user's accuracies achieved as 80.5% in the dry season, but these decreased to 11.1% in the rainy season.

ALOS-2 ScanSAR data was used for examining the effect of precipitation and soil moisture variations on L-band forest backscatter in tropical regions. Multitemporal data spanning from observation cycle 39 to 62 acquired over tropical forests in South America and Africa has been used. Precipitation data provided through JAXA's global rainfall watch was used to examine the temporal change of L-band σ0for forests as well as deforestation areas. The best choice for temporal integration of rainfall prior to the image acquisitions was investigated. The results show that σ0HHvalues show an average increase smaller 1 dB, whereas σ0HVvalues can increase by more than 2 dB in dense forests after heavy rain. Our findings also indicate that dense forest and open/moderately dense forests can show quite different rainfall effects. The latter may even exhibit a decrease in both σ0HHand σ0HVafter heavy rain due to standing water. Implementations for the ongoing development of an advanced tropical forest monitoring system are discussed.

Time series PALSAR-2/ScanSAR data and Landsat data were used for examining the differences in detection timing of deforestation. Optical sensor-based (Landsat) deforestation information taken about every 16 days and SAR data taken about every 1.5 months were used, and the temporal change of L-band γ0was examined for the deforestation areas. The γ0HHvalue increased by 1.2 dB on average for areas undergoing the early stages of deforestation, where fallen trees were left on the ground. The detection timing was almost same as that using the optical sensor. On the other hand, the γ0HVvalue decreased by 1.2 dB on average for areas undergoing the late stages of deforestation, where fallen trees were removed. The detection timing using γ0HVwas about a few month after the detection using γ0HH, or optical sensor. It is concluded that γ0HHis useful for early-stage deforestation detection, and γ0HVis useful for late-stage deforestation detection.

The global leaf area index (LAI) and above-ground biomass (AGB) are essential parameters in ecology and hydrology. Therefore, dataseis derived from satellite remote sensing are needed. To assess data accuracy, in-situ datasets of LAI and AGB measured at a large scale (that of satellite footprints) are desired. However, such data are rarely available. Therefore, we initiated the project "JAXA Super Sites 500" to obtain in-situ LAI and AGB data at the 500 m × 500 m scale. We established four observation sites in Japan and carried out pilot studies at two sites in 2014. This study aimed to examine an observation method at this scale and to evaluate the data quality. At the Fujihokuroku site, the mean LAI was 1.89 (LAI-2000), 2.85 (fish-eye camera), and 2.14 (tree census within a partial area). The mean AGB was 117 t/ha (Bitterlich's method) and 126 t/ha (tree census within a partial area).

Wireless ad-hoc network technology is an important technology for achieving pervasive computing. Very few studies up until now have focused on applying and evaluating ad-hoc network technology for an actual system. Therefore, we constructed a tourist information delivery system in an actual tourist area by using this ad-hoc network technology and conducted a field trial. Our system can automatically deliver tourist guide information when someone who has a FDA approaches a specific location. We measured ad-hoc network performance in the real field environment. We also handed out a simple questionnaire, and about 90% of the respondents had a positive impression of our tourist information delivery system. However, many respondents were concerned about the network quality and security. To put the ad-hoc network technology to practical use, we need to further study from the viewpoint of users' needs as well as technical aspects

Two trajectory schemes far called rigid has been proposed. flexible are proposed by using a vir- In this paper, some trajectory schemes for the flex- tual rigid concept. A Re- ible are proposed by using the virtual rigid ma- solved Acceleration Control (RAC) fcr flexible ma- nipulator concept. A seudo Resolved Acceleration Control nipulators is presented. To suppress vibrating mo- (pseudo RAC) for flexible manipulators is presented. To tion, a composite of pseudo RAC and reduced- suppress vibrating motion a iicomposite of pseudo RAC and reduced-order modal control is developed. The order modai control is developed. The validity of the proposed schemes is explaiined through the singular perturbation method. The robust sta- bility of the pseudo RAC is proved. A sufficient condition for the asymptotic stability of the corn- posite is provided. The effectiveness of the proposed schemes is demonstrated through two case studies, i.e., one link flexible manipulator with a hardware demonstration and numerical ex- amples of a free-flying space robot with a flexible manipulator.