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.

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.

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.

Damage detection due to Typhoon Hagibis was attempted on an agricultural field in Miyagi Prefecture, Japan, using Synthetic Aperture Rada (SAR) data. Sentinel-l data obtained immediately following the heavy rainfall was analyzed. Additionally, the possibility for damage detection by ALOS-2 PALSAR-2 data obtained four days after the rainfall was examined. Image composition of Sentinel-l data obtained directly after the rainfall and the flooding time during paddy rice transplanting is effective in detecting heavily flooded areas caused by dike breach. Flooding because of heavy rain causes a decrease in the backscattering coefficient in both the inside of the field and footpath between the fields. Pixel spacing of PALSAR-2 strip map mode data was 2.5 m and showed the disturbed pattern of the paddy fields caused by the receding floodwater.

GEDI Satellite laser mission will take 3D data globally from International Space Station. The sensor has an improved navigation system to position laser shooting areas accurately. However, the ground validation is still difficult due to the misalignment caused by poor reception of GPS signals under the thick forest. This study introduces a new way to get more accurate ground positioning of trees in tropical forest using 3D data matching technique between airborne and terrestrial data. Our study site is in tropical forest at Robson Creek, Australia. RMSE of XY directions was 0.5 m in and Z direction was 0.6 m, which was impossible to achieve efficiently from the traditional positioning by GPS under dense canopy. The 3D data taken by terrestrial laser is not only useful for capturing structure data for the validation of the waveform analysis of satellite laser but also helps to position accurate ground coordinates in tropical forest through this technique.

Insufficient knowledge about spatial-temporal forest heterogeneities in the tropics is a major impediment to better estimation of carbon storage and prevention of deforestation by remote sensing. While the differences between the dense evergreen rainforest and the open (semi-) deciduous dry forest is obviously large, variations caused by seasonal changes can easily introduce fluctuations in the same order of magnitude within the same forest class. In this study, we present a comprehensive analysis of the variability of tropical forests based on homogeneous big data analysis on multitemporal ALOS-2 dual-polarized ScanSAR data. The first, easy to understand global forest variability maps provide unseen insights into forest structures for the entire tropical belt. Based on these results, we discuss the development of a new global classification scheme for tropical forests. Preliminary results demonstrate how the use of various statistical parameters obtained from the long-term systematic L-band SAR forest monitoring data, including the average γ0, its temporal standard deviation and the γ0range, can improve the classical global-scale forest classifications. In addition to the basic separation into the three main forest types i) rainforest, ii) moist forest and iii) dry forest, the results provide a detailed mapping of the seasonally flooded forest areas.

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.

In the dry tropical forest and semi-arid savanna information about the soil moisture dynamics and distribution are vital to better understand and mitigate damages caused by drought. To estimate the soil moisture over large areas from 50-m resolution ALOS-2 ScanSAR data, we adopt dual-polarization L-band algorithms originally developed for high resolution ALOS data. Based on the partial-polarimetric eigenvector decomposition, the scattering entropy and alpha angles are employed to correct for the above ground vegetation effects. The large incidence angle variation along the 350-km ScanSAR swath requires the use of an incidence angle correction factor. However, due to the relative poor data quality of the ScanSAR products suffering from scalloping and near-range/far-range striping along the subswathes, we observe lower accuracies than Stripmap mode. While in the dry season soil moisture retrieval in the open forest is well possible, in the rainy season the entropy exceeds the validity range due to the larger amounts of canopy biomass resulting in unreliable soil moisture estimates. Savanna vegetation allows a biomass-corrected soil moisture estimation throughout the whole year. Due to the lack of distributed in-situ data, we use coarse scale SMOS and SMAP soil moisture data to validate the accuracy of dual-polarization ScanSAR soil moisture estimates. Comparisons between the kilometer-scale radiometer derived soil moisture estimates and 50-m ALOS-2 results show a good agreement in the dry season with RMSEs smaller 5 Vol.-%, while in the rainy season the RMSEs can be as high as 15 Vol.-%.

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.

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.

Forest disaster monitoring requires frequent data observation of remotely sensed data. It is getting more common to use open satellite image archives to monitor land use change from multi-temporal data. In this study, multi-temporal DSM created by UAV-SfM is used to identify forest gap. ALOS 2 full polarization data is compared with the finer scale height difference detected by multi-temporal UAV based DSMs. Advantage of UAV based observation is to identify forest gap area efficiently in wide area and collect more samples to compare wide coverage of the satellite radar backscatter to understand backscatter characteristics. As a result, HH and HV polarization has detection power of forest gap size.

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 polarimetric characteristics of early- and late-stage deforestation areas were examined using PALSAR-2. An early-stage deforestation site, where the fallen trees had been left on the ground, showed an increase in σsurface0of 2.9 dB, and decrease in σvolume0of 1.4 dB. This induced the 0.9 dB increase in σHH0. A late-stage deforestation site, where the fallen tree had been removed, showed an increase in σsurface0of 2.3 dB, and a decrease in σvolume0of 6.4 dB. This induced the 6.1 dB decrease in σHV0. Polarimetric analysis clarified the scattering mechanism for the early- and late-stage deforestation areas.

We analyzed airborne L-band Synthetic aperture radar data from the Pi-SAR-L2 platform to detect agricultural fields in semi-mountainous areas of Japan. Full-polarimetric data were acquired during the rice growing season after transplanting. We examined the usefulness of single look complex and ortho-rectified data products by attempting polarimetric decompositions for the single look complex data and examining ortho-rectified images to evaluate image distortions and match these to map coordinates. The three component decomposition images show obvious differences between paddy rice and other fields; the former generally show low backscattering while other crop fields show a larger surface scattering component. Agricultural parcels with an area more than 3,000 m2are clearly recognized on the ortho-rectified image, though narrow agricultural fields in valleys were missed due to the effects of foreshortening.

One of the main missions of the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2” ) is the disaster monitoring. Japan Aerospace Exploration Agency (JAXA) has operated the emergency observation more than hundred times in 2015. Not only the most important event in 2015, the Mw 7.8 Gorkha earthquake on April 25, the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard ALOS-2 observed various floods, volcano eruptions and earthquakes. In this paper, we present some emergency observation results which were impossible to be performed by the previous ALOS. That is, automatically burst aligned ScanSAR to ScanSAR interferometry and, left / right looking for increasing acquisition opportunity.

In this paper, we report a radio frequency interference (RFI) detection method which is exclusively sensitive to the temporally pulsed wide-band signal. We use local autocorrelation in the range-time azimuth-frequency domain instead of typical range-frequency azimuth-time domain. Traditional RFI detectors assume that RFI have time-varying wide-band (TVWB) and / or time-stationary narrow-band (TSNB) features. The proposed method assumes that there is another type of RFI, namely, intermittently transmitted wide-band RFI. This kind of RFI superimposes on the SLC image however, its short pulse duration hinders us to detect it in range-frequency azimuth-time domain. Contrarily, in range-time azimuth-frequency domain, this kind of RFI can be detected easily. Here, we present a basic methodology and experimental results.

Decreasing the carbon emission from the deforestation and forest degradation is one of the issues that the humans need to tag for sustaining the Earth. Monitoring the forest from the space is the recent achievements that the space agencies contribute. Measuring the forest cover change is being conducted by using the high resolution optical and/or SAR images. ALOS-2 has been on-orbit carrying the L-band SAR since May 24, 2014. After more than one year data acquisition and related processing, the first version of the 25-m resolution global PALSAR-2 mosaic dataset from the 2014-2015 acquisitions, forest-non-forest map, and the forest cover change from 2007 to 2015 were generated.

An experiment to examine the detectability of volcanic ash cover and its thickness by using L-band synthetic aperture radar (SAR) was carried out. Test sites with 0, 10, 20, and 40 cm ash layers were developed. PALSAR-2 and Pi-SAR-L2 observations were carried out several times with and without the ash cover. The PALSAR-2 backscatter coefficients in HH polarization, σHH0, for soil with ash layers are on average 1.7 to 3.5 dB lower than that for soil without ash layers. On the other hand, the 0 cm site shows almost the same value throughout the experiment. The ash layer was also measured by ground based radar operated in C-band. The reflectivity-frequency plot obtained from the soil with 2 to 6 cm ash layers appears as a sine-like curve, indicating the reflection from two layers (soil plus ash). It indicates that no linear correlation is expected between σ0and thickness of ash layer. Correlations between the ash thickness and representative parameters obtained from L-band SAR observation are examined. It indicates that volume scattering component shows the highest correlation with R2of 0.7143.

High-resolution optical satellite images of the reconstructed agricultural fields in Sendai Plain damaged by tsunami caused by the 2011 Great East Japan Earthquake were analyzed to observe variability in crop growth. The main crop of the target area is paddy rice obtained through transplantation, and dry and wet direct seeding. Soybean is the second main crop, with some other vegetables being planted. Normalized difference vegetation index (NDVI) was computed from the images captured in early July in 2014, and early July, late July, and middle August in 2015. Growth variability in the same type of crops was recognized. The differences in the growth statuses resulting from the differences in crop types and cultivation methods appeared on the NDVI images. In a comparison of the three images acquired in 2015, the early July image showed the most obvious variability in crop growth.

Field tree measurement to collect ground truth data is labor intensive. To make it efficient to collect the field data in a large scale, Unmanned Aerial Vehicle is introduced in this study. An automated photogrammetric technique called Structure from Motion (SfM) is used to create 3D data out of photos collected by UAV. Tree structure created by SfM is compared with the 3D data obtained by several Terrestrial Laser Scanners to see the difference in vertical forest structure. Canopy structure is mainly captured by UAV-SfM and there is more point density around tree canopy than 3D data obtained by airborne laser. The point density of UAV around tree canopy is equivalent to that of TLS data, but tree trunk underneath the canopy cannot be reconstructed well, because the trunk is obscured by tree canopy and it is not visible on photos.

ALOS-Next/Tandem-L is a proposal for a highly innovative L-band SAR satellite mission for the global observation of dynamic processes on the Earth's surface with hitherto unparalleled quality and resolution. It is based on a collaboration between DLR and JAXA which started with a pre-phase A study in 2013 and is currently undergoing a phase A study. Thanks to the novel imaging techniques and the vast recording capacity with up to 8 Tbytes/day, it will provide vital information for solving pressing scientific questions in the biosphere, geosphere, cryosphere, and hydrosphere. By this, the new L-band SAR mission will make an essential contribution for a better understanding of the Earth system and its dynamics. ALOS-Next/Tandem-L will, moreover, open new opportunities for risk analysis, disaster management and environmental monitoring by employing especially designed acquisition modes and techniques in combination with a reconfigurable tandem satellite configuration and an L-band SAR instrument with advanced digital beamforming techniques.

In the field of Synthetic Aperture Radar (SAR) interferometry, we generally expect that there is only unique dominant scatterer in one pixel. To make a SAR interferogram, we have to observe a place twice. The dominant scatterer in every pixel must be identical in those observations. However, in an actual situation, there are multiple scatterers in one pixel physically, and the dominant one may be changed in the two observations. Slight difference of incident angle may cause the change of the dominant scatterer and the propagation path. The change can generate singular points (SPs) in the inter-ferogram which prevent us from accurate phase unwrapping. Here, we present experimental results in an anechoic chamber which show the mechanism of the singular point generation.

Correlations between L-band SAR polarimetric parameters obtained with airborne SAR (Pi-SAR-L2) and metrics obtained with airborne LiDAR were examined to identify the relationship between the full polarimetric parameters and forest parameters obtained for a natural forest in Indonesia. Values of a0HVand a0VVshow good correlations with canopy height and 90thpercentile metrics of LiDAR, with R2values of 0.85-0.89. These correlations show higher R2values than the value of 0.33 for a0HVvs. above-ground biomass. The results indicate that a0shows a robust correlation with tree height weighted to taller trees. Polarimetric parameters estimated from a single tree also support the results. Entropy and anisotropy obtained from a single tree shows almost the same value, and a is about 45°, while the tree height ranges 2 m to 6 m. This indicates that these parameters are preferable for land cover classification.

Full polarimetric space- and air-borne L-band SAR data acquired on the same day were analyzed. The target areas were agricultural fields, including paddy in the maturation stage, that were severely damaged by a tsunami three years earlier. Lodging of paddy was found in some fields. Three-and four-component decomposition and eigenvalue-eigenvector decomposition analysis were performed. On paddy fields, the dominant component was double-bounce scattering. Paddy fields including lodged paddy showed larger volume scattering ratio than paddy fields without lodged paddy in Pi-SAR-L2 data. On soybean, weed, and bare-soil fields, surface scattering was dominant in a four-component decomposition. Paddy field and other fields were separated by a threshold of alpha angle in the eigenvalue-eigenvector decomposition. Subtle differences were found although PALSAR-2 and Pi-SAR-L2 data behaved almost similarly. L-band SAR is suitable for rice field extraction during this season.

The temporal variations (diurnal and annual) in arboreal (εTree) and bare soil (εSoil) dielectric constants and their correlation with precipitation were examined for several trees in Japan. A significant (1 σ (standard deviation) and 2 σ) εTreeincrease is observed after rainfall at 89.8% and 90.5% probability. However, rainfall does not always induce significant εTreeincreases. Rainfall of more than 5 mm/day can induce 1 σ εTreeTree increase at a 59.6% probability. In order to examine whether the increase in εTree affects the L-band σ0variation in a forest, the four-year temporal variation of the L-band backscattering coefficient (σ0) was estimated from observations by the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar. Observed maximum absolute deviations from the mean over the forest area were 1.0 and 1.2 dB for σHH0and σHV0, respectively, and 4.0 and 3.0 dB over open land. σ0and rainfall correlations show that εTreeand σForest0are proportional to precipitation integrated over seven or eight days; εSoiland σOpen land0are proportional to precipitation integrated over three days. This finding indicates that εTreevariations influence σForest areas0. A stronger correlation between σHV0and precipitation is observed in several sites with low σHV0, where less biomass is expected, and several sites with high σHV0, where more biomass is expected. A weaker correlation between σHV0and precipitation is observed for several sites with high σHV0. These differences may be explained by the different contributions of double bounce scattering and potential transpiration, which is a measure of the ability of the atmosphere to remove water from the surface through the processes of transpiration. The two other results were as follows: 1) The functional relation between aboveground biomass and σ0showed dependence on precipitation data, this being an effect connected with seasonal changes of the εTree. This experiment reinforces the fact that the dry season is preferable for retrieval of woody biomass from inversion of the functional dependence of SAR backscatter and for avoiding the influence of rainfall. 2) The complex dielectric constant for a tree trunk, which is measured between 0.2 and 6 GHz, indicates that free water is dominant in the measured tree.

Various remote sensing technologies are utilized to monitor tropical forest for REDD project. In developing countries, the accuracy of field measurement does not satisfy the quality required for the validation of satellite remotely sensed data. Therefore, we introduce the most portable laser sensor, SICK LMS511, to measure trees in the field of tropical forest and the semi-automatic process to derive tree parameters from the 3D point cloud is established to provide accurate forest inventory data. From the accuracy assessment of the portable laser system provided 3.61 cm of root mean square error (RMSE) of stem diameter and 0.50 m of RMSE of tree height measurement. The portable terrestrial laser sensor can be the standard technique to create forest inventory data for the field validation of satellite remote sensing.

PALSAR full polarimetry data were used to detect thaw/freeze conditions on the ground to try to determine the cause of a spring flood on a northern river in Russia. Full polarimetric data was used to identify the forest area, and the radar backscattering coefficient, σ0HV, was examined. The σ0HV over a frozen forest area was -19.9 dB, while that of a thawing forest area was -11.9 dB, and freeze/thaw events over the forest area were well represented by this parameter. Using this characteristic, together with optical sensor (AVNIR-2) data and microwave radiometer (AMSR-E) data, freeze/thaw conditions were examined for an area including Markha, where a large ice jam flood occurred in 2007, and compared it to 2009, when no flooding occurred. The results indicate that the thawing event proceeded more rapidly between March 29 and April 10, 2007 for the forest, and this rapid thawing may be one of the possible causes for the large ice jam flood in Markha in 2007.

The spatiotemporal distribution of subsidence at Palangkaraya, Central Kalimantan, Indonesia, was studied by a time-series analysis of PALSAR data obtained over 26 passes of ALOS over the study area. This area consists mainly of peatland and tends to subside because of climate change, i.e., global warming. Applying a moving weighted window to the stacking of a DinSAR dataset, each of which was calibrated using stable points in the image, we quantitatively captured subsidence phenomena and found that the area has subsided at an average rate of 2.02 cm/yr .

This paper describes the performance, calibration method, and observation scenarios for the ALOS-2/PALSAR-2 polarimetry mode. It also describes simulation studies conducted using data from Pi-SAR-L2, JAXA's L-band airborne SAR, for calibration and application studies.

Aboveground (AG)-biomass was estimated from a field biomass collection and LiDAR observations for a natural forest in Indonesia. The derived AG-biomass data were plotted against full polarimetric parameters calculated from Polarimetric and Interferometric Airborne Synthetic Aperture Radar L2 (Pi-SAR-L2) and PALSAR data. The α°-AG-biomass curve shows saturation by around 100 tons/ha, while for entropy, correlation is observed up to 200 tons/ha. The largest coefficient of determination (R2= 0.2348) was observed for the range with AG-biomass of more than 100 tons/ha for the relation between AG-biomass and entropy. The α°HV-biomass plot derived from PALSAR data shows smaller variance in the dry season than in other seasons, indicating that dry season data is preferable for a more accurate estimate of AG-biomass.

High resolution active remote sensing data are getting more accessible. ALOS PALSAR radar data can provide 10 to 30 m resolution data. With those resolutions, land use changes can be detected and monitored well. The land use map of multi-temporal data is made by radar images, but seasonal change caused by deciduousness should be separately identified from the human activities. In this study, airborne lidar is used as ground truth data to estimate canopy volume and the estimated volume is compared with the change on the backscattering coefficients of multi-temporal radar images. The process used in this study is useful to monitor the small change detection with high resolution radar data given by ALOS 2 satellite launch near the future.

Japan is one of the main forest countries in the developed countries. For management and administration of the forest, amount of cost and work is needed to be observed vast woodland with regularity. Since the earth observation satellites can repeatedly observe wider area at one time is significant, they are suitable for observing the boundless natural environment. It is important for forest management and forest industry to measure forest area and height of trees. In this study, tree height was calculated using digital surface model (DSM) generated by the image taken by the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS), which was launched by the Japan Aerospace Exploration Agency (JAXA) on January 24, 2006. This paper reports procedure of calculating trees height using the DSM generated by the PRISM and its validation.

The 2011 off the Pacific coast of Tohoku earthquake was observed using phased array type L-band synthetic aperture radar (PALSAR) and polarimetric and interferometric airborne synthetic aperture radar (PiSAR) full-polarimetric data. Representative polarimetric parameters were calculated from full-polarimetric data for urban areas, where most of the buildings were destroyed by the subsequent tsunami, in order to identify the radar scattering mechanism in these areas. These parameters were compared with the ones observed before the disaster. The full-polarimetric data analysis shows that the affected areas were represented by surface scattering with high entropy, indicating that a complex scattering mechanism with nonreflection symmetry is involved. The coherence between HH and VV and that between RR and LL are the most important factors in distinguishing the disaster areas from the data. Alpha angle and anisotropy are also important factors in this respect; however, anisotropy derived from PiSAR data does not show the difference between areas with collapsed and still-standing buildings. This may be because the azimuth slope angle for the target urban area is different before and after the disaster for both PALSAR and PiSAR data. Owing to the double-bounce scattering from azimuthally rotated targets in the urban areas, the power estimated from the four-component decomposition model is distributed within a wide range not only for double-bounce scattering but also for volume and surface scatterings. Additionally, the model does not show a systematic change between before and after the disaster, and σ0for four polarizations with 30-m resolution does not show a systematic difference.

Simultaneous field data collections and Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (PALSAR) full polarimetry observations were performed in Ulaanbaatar (Mongolia) and Alaska (USA). Permafrost is present at the Alaska test sites. Backscattering copolarization ( σco-pol0) values derived from the PALSAR data were compared with those calculated using the integrated equation method (IEM) model, a popular theoretical model describing surface scattering. PALSAR data taken in Ulaanbaatar matched the IEM model results to within a few decibels, whereas data taken in Alaska were 5 to 7 dB lower than those calculated using the IEM model. On the other hand, the σcross-pol0(σVH0) components estimated from the Oh model were well matched to the PALSAR data in both Ulaanbaatar and Alaska. Moisture levels of the sphagnum moss layer in Alaska were estimated to be about 10% while moisture levels of the underlying organic and mineral layers were 25% to 79%; the moisture values of the organic and mineral layers were factored into the IEM and Oh models. When surface moisture levels of 10% were assumed for Alaska ground conditions, the σco-pol0values calculated using the IEM model and those derived from the PALSAR data were well matched. From these observations, we conclude that the sphagnum moss layer, which is a seasonally unfrozen layer that occurs above permafrost, plays an important role in radar backscattering processes in permafrost regions and is a main contributor to the σco-pol0backscattering component; the underlying organic and mineral layers contribute mainly to the σcross-pol0backscattering component. A two-layer model was applied to the data from a test site in Alaska; the model described the co- and cross-polarization backscatter (σ0) derived from PALSAR data with off-nadir angles of 21.5° and 34.3°.

ALOS PALSAR uses L band which is related with volume scattering. The volume scattering is the reflection comes from the surface and the inside of canopy. Forest management activities can be monitored using PALSAR L-band for Reducing Emissions from Deforestation and Forest Degradation (REDD). But it is still unknown how the biomass changed by the thinning (forest management) influences the backscattering coefficients. In this study, the change of stem volume (biomass) was computed and mapped using multi-temporal airborne lidar data in wide area. The stem volume using field measured tree parameters were interpolated and mapped by the airborne lidar data and the mapped biomass was used as the ground truth for radar image to see the polarization change caused by the thinning. The polarization axis of HV/HH backscattering coefficients is shifted in 9.8 degrees. This axis change is a useful indicator for the end member analysis using HV/HH polarization.

We propose an algorithm to estimate surface roughness and moisture level of active layer of permafrost over permafrost area. This algorithm is based on the Oh's semi-empirical model, and PALSAR data observed both in winter and summer seasons with vh polarization. PALSAR vh polarization data observed in winter is used to estimate surface roughness of permafrost. Then, the estimated surface roughness and PALSAR vh polarization data observed in summer is used to estimate the moisture level of the active layer of the permafrost. The moisture levels estimated from PALSAR data moderately matched with those of validation data taken in the field, while the surface roughness value shows some difference. The possible cause of this difference is that the surface roughness derived from the field data collection represents the roughness of the top of the sphagnum moss layer covered on the active layer of the permafrost, while the one estimated from PALSAR represents the roughness of the underlying active layer of the permafrost.

Full polarimetric L-band ALOS PALSAR and C-band RADARSAT-2 datasets observing paddy rice field, JAPAN were analyzed by polarimetric decomposition method. From the analysis results by three component decomposition method, remarkable seasonal change is observed by double bounce scattering component ratio on ALOS PALSAR data. RADARSAT-2 data show more than 50% of volume scattering component and it increases with rice growth. Double bounce scattering component reached to 30 40% on PALSAR data, as of August, while it was less than 20% on RADARSAT-2 data. The difference of penetration depth on vegetation area between L-band and C-band may be cause of the difference of the scattering mechanism.

Genetic Programming (GP) is well-known as an evolutionary method for automatic programming. GP can optimize tree-structural programs. Cartesian GP (CGP) is one of the extensions of GP, which generates the graph structural programs. By using the graph structure, the solutions can be represented by more compact programs. Therefore, CGP is widely applied to the various problems. As a different approach from the evolution, there is the Ant Colony Optimization (ACO), which is an optimization method for combinatorial optimization problems based on the cooperative behavior of ants. By using pheromone communication, the promising solution space can be searched intensively. In this paper, we propose a new automatic programming method, which combines CGP and ACO. In this method, ants generate programs by moving in the node-network used in CGP. We call this method, Cartesian Ant Programming (CAP). We examined the effectiveness of CAP by comparing with CGP on the search performance in a symbolic regression and a classification problem.

We performed three field experiments with PALSAR/polarimetry observations in Ulaanbaatar/Mongolia, Arctic National Wildlife Region (ANWR/ Alaska)/US, and Sendai/Japan, to evaluate soil moisture models. The PALSAR polarimetry observations with off-nadir angle of 21.5deg were done in May 4, 2007, Jul. 29, 2007, and Apr. 14, 2007 in each place. On the other hand, the field experiments were done for May 2-7, 2007, Jul. 28-Aug 4, 2007, and Apr. 14, 2007. The surface parameters derived from field measurements show large dynamic ranges, which are from 4% to 80% for soil moisture value, from 0.16 to 2.22 for surface roughness, and from 1.97 to 8.09 for correlation length. We applied SPM model, X-bragg model, and Oh model in this time. While some discrepancies are appeared, the SPM and Oh models show agreements with absolute/relative sigma0value in some degree. The X-bragg model shows moderate agreement in anisotropy-ks relation.

We performed a Ground Based SAR (GB-SAR) and PiSAR simultaneous experiment over a flood test site. In this site, one prefab was constructed in a baseball field of a Nihon University in Chiba, Japan. The PiSAR observations were done for three days with different soil moisture condition of ~25%, ~50%, and 100%. Some GB-SAR experiments have been also done in two of three days and the scattering process are examined. Preliminary experiment with GB-SAR showed that L-band sigma0from a double scattering of ground-wall is increased for a few dB, when a reflectivity of the ground is assumed to be 1. These increase are also confirmed in the test site for both PiSAR and GB-SAR. Furthermore, high resolution GB-SAR images show double and single bounce scattering from a metal bar behind the wooden board of the prefab. We conclude that L-band SAR has a enough ability to detect a flooding area in urban site.

JERS-1/SAR images taken in 1990’s and ALOS/PALSAR images taken in 2000’s are used and examined the deforestation status during ∼14 years. Many trees had been fallen by a typhoon hit in 2004 in a test site, Tomakomai. There is also a plantation area and the site show active change during the term. The forest stands could be roughly classified for four types, forest stands, vacant, where almost all trees were carried out, and two types of transitional stands from a forest to a vacant. Many deforested areas are easily detected by using the difference of backscattering coefficients between two images, if fallen trees have been carried out from the stands. The change from the normal forest to the vacant stands causes 3.1dB decrease in the σ0HH. On the other hands, transitional stands show almost same backscattering as the normal forest stands, although. Threecomponent scattering model shows surface scattering component accounts for 50% over the vacant stands, while volume scattering component accounts for ∼60% over the forest stands. But the model doesn’t show the clear difference between transitional forest site and normal forest. The temporal changes of the forest during 14 years are also examined for the plantation area. One stand show gradual increase of σ0and the values seem to be saturated around 17.4 tons/ha (∼5m in average height).

The Advanced Land Observing Satellite (ALOS) is Japan's new-generation Earth Observation satellite, launched in January 2006 by the Japan Aerospace Exploration Agency. ALOS carries two optical instruments (Panchromatic Remote-sensing Instrument for Stereo Mapping and Advanced Visible and Near-Infrared Radiometer type 2) and, to maintain Japan's commitment to spaceborne L-band Synthetic Aperture Radar (SAR), the Phased Array L-band SAR (PALSAR). The successor to the SAR onboard the Japanese Earth Resources Satellite (1992-1998), the PALSAR instrument provides enhanced sensor characteristics, including full polarimetry, variable off-nadir viewing, and ScanSAR operations, as well as significantly improved radiometric and geometric performance. As important as the technical improvements and the reason PALSAR here is referred to as a pathfinder mission for global environmental monitoring is the systematic data-acquisition strategy which has been implemented for ALOS. With a priority second only to emergency observations, the PALSAR observation strategy has been designed to provide consistent, wall-to-wall observations at fine resolution of all land areas on the Earth on a repetitive basis, in a manner which has earlier been conceived only for coarse- and medium-resolution instruments.

This paper discusses an ambiguity resolution of polarimetric calibration parameter. Since ALOS was successfully launched on January 24, 2006, the utilization of polarimetric data observed by PALSAR is expected for various remote sensing applications. Thus, polarimetric calibration becomes important issue. However, there is a possibility that calibration algorithm has a 180deg sign ambiguity with respect to cross-polarized components of a calibrated scattering matrix. The reason is that two polarimetric calibration parameter sets are derived. On the other hand, we found that a 180deg phase shift of cross-polarized component occurred due to the change of an angle between a direction parallel to a building wall and a radar line of sight in azimuth plane. Thus, we examined that this scattering property was applied for selecting a correct polarimetric calibration parameter set. The experimental result shows that urban structures can be used for polarimetric calibration.

This paper describes the initial results of the PALSAR characterization using the data acquired in the commissioning phase for the five modes of FBS, FBD, DSN, SCANSAR and POL, and using the active radar calibrator deployed at the calibration site in Japan. This paper also describes the radiometric and geometric performances of the PALSAR images shortly. This paper will end up with the parameters which should be evaluated and determined within the calibration phase.

The advanced land observing satellite (ALOS, the nickname is "Daichi") was successfully launched on January 24th, 2006. This paper introduces the initial results of calibration and validation for two optical sensors of ALOS i.e., the panchromatic remote-sensing instrument for stereo mapping (PRISM) and the advanced visible and near infrared radiometer type-2 (AVNIR-2). PRISM consists of three panchromatic radiometers, and is used to derive a digital surface model (DSM) with high spatial resolution, which is also an objective of the ALOS mission. The geometric calibration is important in generating a highly accurate DSM by stereo pair image of PRISM. The radiometric calibration is also important for PRISM as well as AVNIR-2. The relative radiometric calibration will be done using acquired images over homogeneous targets such as ocean, deserts, ice and snow areas and the nighttime observation. The absolute radiometric calibration will be carried out by the cross calibration using with calibrated satellite images i.e., MODIS, ASTER etc. In this paper, results of the first images acquisition and initial analysis for calibration and validation are described.

Relation between coherence, forest biomass, and L-band sigma0were examined by using the L-band data taken by Japanese Polarimetric and Interferometric Airborne SAR (PiSAR). Several PiSAR observations were performed over a well-managed national forest, in which four kinds of conifers were dominantly planted. A coherence map over the forest area was made from two flight data, whose observation interval was about 30 minutes. The coherence in this site are ranging from 0.5 to 0.8. High coherence values of 0.7 ~ 0.8 were appeared at the young stands. On the other hands, some stands with high biomass also show high coherence value. Since the coherence values are weakly correlated with sigma0HHand sigma0HVat the stands with high biomass, the high coherence value and the high sigma0value are considered to be the same origin. The regression curve between the coherence and a stem volume derived in this analysis shows higher coherence value than the other two JERS-1 (L-band) 44-day pair data and one ERS 1/2 C-band 1-day pair data. This may be due to the shorter repeat-cycle of the PiSAR pair data and difference of the scattering elements.

The purpose of this letter is to present the results on the study of searching effective parameters that describe the relation between high-resolution synthetic aperture radar (SAR) images and forest parameters. The study is based on the non-Gaussian texture analysis of the polarimetric airborne Pi-SAR data over coniferous forests in Hokkaido, Japan. The radar cross section (RCS) in terms of a forest biomass is first analyzed. It is found that the$hboxL$-band RCS increases steadily with the biomass and saturates at approximately 40 tons/ha. These results are similar to the previous studies. The probability density function of the image amplitude is then investigated, and among Rayleigh, log-normal, Weibull, and$K$-distributions, the$K$-distribution is found to fit best to the$hboxL$-band data of all polarizations, although the Weibull distribution fits equally well. Further, the correlation between the tree biomass and the order parameter of the$K$-distribution in the cross-polarization images is found to be very high, and the order parameter increases consistently with the biomass to approximately 100 tons/ha, which is well beyond the saturation limit of the$hboxL$-band RCS. Thus, the order parameter of the$K$-distribution can be a promising new parameter to estimate the forest biomass from high-resolution polarimetric SAR data in a much wider range than the conventional RCS method.

Biophysical parameters and L-band polarimetry synthetic aperture radar observation data were taken for 59 test sites in Tomakomai national forest, which is located in the northern part of Japan. Correlations between the derived sigmaHH0, sigmaHV0, and sigmaVV0and the biophysical parameters are investigated and yield the following results. 1) The above-ground biomass-sigma0curves saturate above 50 tons/ha for sigmaVV0, 100 tons/ha for sigmaHH0, and over 100 tons/ha for sigmaHV0when all forest species are included in the curves. 2) The sigmaHH0-above-ground biomass curve for one forest species indicates a higher saturation level than that for the other forest species. Dependence on the forest species was absent for VV polarization and low for HV polarization. 3) A simple three-component scattering model indicates that volume scattering accounts for 80%-90% when the above-ground biomass exceeds 50 tons/ha. The surface-scattering components are up to ~20% for young stands, and the volume-scattering components are down to 70%. The origin of the dependency among the forest species was examined for the sigmaHH0-above-ground biomass. It is concluded that a possible cause of the dependency is the different characteristics of the stands rather than forest species

This paper describes the updated status of the PALSAR CAL/VAL and scientific activity of 2005, which is being conducted as a part of the JAXA's ALOS science program. The updated information includes 1) Detailed schedule of the initial calibration phase, 2) Distribution of the calibration targets, 3) Calibration strategy (antenna pattern, polarimetric calibration, absolute calibration, image quality evaluation, noise equivalent sigma-naught), 4) PALSAR data format update, and 5) Yaw steering mode and observation scenario for polarimetry.

The Advanced Land Observing Satellite (ALOS) is scheduled for launch by the Japan Aerospace Exploration Agency (JAXA) in 2005, and it carries three remote sensing instruments: an L-band polarimetric Synthetic Aperture Radar (PALSAR), an along-track 2.5 metre panchromatic resolution stereo mapper (PRISM) and a 10-metre multi-spectral scanner (AVNIR-2). The successor of the JERS-1 satellite (1992-1998), ALOS not only provides enhanced sensor performance, but also feature an entirely new acquisition concept. Abandoning traditional, local-focused instrument operations, JAXA is implementing a comprehensive acquisition strategy, in which geographical region, sensor mode, acquisition timing and repetition frequency, are fixed in advance, to achieve spatially and temporally consistent, global coverage on a repetitive basis, at the same time as reducing programming and user conflicts.

This paper describes generation of the digital elevation model (DEM) using the L band repeat pass airborne polarimetric Interferometric SAR (Pi-SAR) system, which is jointly developed by Japan Aerospace Exploration Agency (JAXA) and National Institute for Communication Technology (NICT). An image pair acquired over the Tottori calibration site in 2000 had a baseline of less than hundred meters and was used for this interferometric data analysis. Although the quantitative evaluation has not been conducted, the result shows that the detailed feature of the topography in the target area was well obtained.

Proposes a new method to calibrate L band polarimetric SAR using the forest data. Model based covariance matrix containing the distortion matrices and the scattering components from the forest were solved using the Pi-SAR data over the Japan uniform forest of Tomakomai, Hokkaido. The result showed that the distortion matrices depend on the function of the incidence angle and the operation timings.

This paper introduces the updated plans for sensor calibration and product validation of the two optical sensors i.e., the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2), which are to fly on the Advanced Land Observing Satellite (ALOS). PRISM consists of three panchromatic radiometers, and is used to derive Digital Elevation Models (DEMs) with very high spatial resolution, which is also one of the objectives of the ALOS mission. The geometric calibration is important in generating a highly accurate DEM by using PRISM's triplet images. Highly accurate ground control points (GCPs) are necessary to calibrate the geometric accuracy and validate the generated DEM. The geometric calibration of AVNIR-2 can be done using the calibrated PRISM product. In this paper, we describe the current plans for calibrating and validating PRISM and AVNIR-2. In particular, preparation status of GCPs with evaluation items for geometric calibration, including expected problem effects regarding geometric accuracy, orbit and observation simulations to determine pointing accuracy, and preliminary investigation of nighttime GCP using current available satellite images

We examined relationship between forest parameters and /spl sigma//sup 0/ derived by airborne multi-polarization SAR (PiSAR). PiSAR observations and field measurements were simultaneously performed on November 2002 and August 2003. One data set was taken from stands with pure pine (Picea glehnii Masters) forest. More than 70% of the trees in biomass are Picea glehnii Masters in the data set with same stand age. The other data set was taken from the stands where mixture of species is proceeding in some degree. The former data set shows more tight correlations compared to the other and correlation coefficient reaches 0.98. We conclude that (1) the mixture of the species and stand age are two of the major reasons of data scattering between forest parameters and /spl sigma//sup 0/, and (2) the above ground biomass saturation level is about /spl sim/150 tons/ha for L-band /spl sigma//sub HH//sup 0/ and /spl sigma//sub HV//sup 0/ and /spl sim/70 tons/ha for /spl sigma//sub VV//sup 0/ in this akaezomatsu site, using a biomass expansion factor of 1.36 to convert from the trunk biomass to the above-ground biomass.

The purpose of this study is to develop a method to estimate the quantity of buildings with different seismic vulnerability by using the digital numbers obtained IKONOS. In developing countries, it is difficult to get the building inventory data for seismic risk assessment. In this paper, IKONOS image was chosen for analysis because of its resolution with 1 m and the availability of such data for the major cities in earthquake prone developing countries. IKONOS image of Metro Manila in Philippines was separated into grid cells with 25 m. The pattern of frequency distribution of the digital number for each of four bands subjected to cluster analysis to yield a set of land cover grids with different natural and built environments. We could successfully identify the following different land covers: river, grass, trees, grounds and road, in addition to three kinds of buildings; small, medium, and large. As to the building size of each land cover classification, building inventory stored in GIS data set offered by NAMRIA, Philippines was used to identify the thresholds for different building size.

We used multi-sensor, multi-frequency and multi-polarization SAR data for biophysical parameter retrieval in plantation forests of Northern Japan. The statistical relationships with different biophysical parameters are quite robust for certain frequencies-polarization combination. A combination of different frequencies and polarizations facilitate the retrieval of these parameters with R 2 of 0.95 and rms error of 15.19 tons ha -1 .

The Kyoto & Carbon Initiative is an international collaboration initialised by NASDA in 2000 [1], set out to support dataand information needs raised by certain multinational environmental conventions and by global carbon cycle science, through provision of data products and high level information derived from ALOS, JERS-1 and ADEOS-II data. The conventions primarily in focus are the UNFCCC Kyoto Protocol and the Ramsar Convention on Wetlands, to which fineresolution, multi-scale information about the status and changes in forests and wetlands will be derived. There is apparent synergy with terrestrial carbon cycle science information needs, where improved spatial information about carbon pools, sources and sinks at local, regional to global scales are of high priority. Support to the UN Millennium Development goal on water access, as well as to the UN Convention to Combat Desertification, are