Quantitatively detecting ground surface changes of slope failure caused by heavy rain using ALOS-2/PALSAR-2 data: a case study in Japan.

Many SAR images have been utilized for geologic disasters investigations with the continuous launch of new Synthetic Aperture Radar (SAR) satellites such as ALOS-2/PALSAR-2. However, to proactively respond to transient slope failures caused by heavy rainfall, rapid extraction of areas of surface change accompanying slope failures is required. This study proposes two methods for quantitatively extracting slope failure areas using L-band SAR observations with slope units (SUs) as the evaluation units. The first method is based on the threshold method, which automates the selection of thresholds for various disaster-affected conditions, such as land use and topography. The second method is a machine-learning-based density ratio estimation method, which uses multi-temporal periodic observation data and pre- and post-disaster data to detect outliers through feature selection optimization. In the observation direction with the shortest satellite observation period, the F1 score (The F1 score is the harmonic mean of the precision and recall) of the threshold method for accuracy evaluation is 61.91%, and the F1 score of the density ratio method is 65.87%. Both methods can reduce the problem of low extraction accuracy caused by the effect of speckle noise. When slope failure occurs, both methods can extract the area of surface change within hours of a disaster. The method proposed in this study displays good applicability in supporting emergency rescue and the prevention of secondary disasters.

Sensitive and specific quantification of antisense oligonucleotides using probe alteration-linked self-assembly reaction technology.

Quantitative bioanalysis is essential when establishing pharmacokinetic properties during the drug development process. To overcome challenges of sensitivity, specificity and process complexity associated with the conventional analysis of antisense oligonucleotides (ASOs), a new approach to nonenzymatic hybridization assays using probe alteration-linked self-assembly reaction (PALSAR) technology as a signal amplifier was evaluated. PALSAR quantification of ASOs in mouse tissue and plasma was able to achieve a high sensitivity ranging from 1.5 to 6 pg/ml, intra-/interday accuracies in the range of 86.8-119.1% and 88.1-113.1%, respectively, and precision of ≤17.2%. Furthermore, crossreactivity of 3'n-1, a metabolite with a single base difference, was <1%. Our approach provides an auspicious method for distinguishing metabolites and detecting ASOs with high sensitivity and specificity.

An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model.

Soil salinity has been a major factor affecting agricultural production in the Keriya Oasis. It has a destructive effect on soil fertility and could destroy the soil structure of local land. Therefore, the timely monitoring of salt-affected areas is crucial to prevent land degradation and sustainable soil management. In this study, a typical salinized area in the Keriya Oasis was selected as a study area. Using Landsat 8 OLI optical data and ALOS PALSAR-2 SAR data, the optical remote sensing indexes NDVI, SAVI, NDSI, SI, were combined with the optimal radar polarized target decomposition feature component (VanZyl_vol_g) on the basis of feature space theory in order to construct an optical-radar two-dimensional feature space. The optical-radar salinity detection index (ORSDI) model was constructed to inverse the distribution of soil salinity in Keriya Oasis. The prediction ability of the ORSDI model was validated by a test on 40 measured salinity values. The test results show that the ORSDI model is highly correlated with soil surface salinity. The index ORSDI3 (R2 = 0.656) shows the highest correlation, and it is followed by indexes ORSDI1 (R2 = 0.642), ORSDI4 (R2 = 0.628), and ORSDI2 (R2 = 0.631). The results demonstrated the potential of the ORSDI model in the inversion of soil salinization in arid and semi-arid areas.

Feasibility of using PALSAR technology as a signal amplifier for antibody bridging assay.

The immunogenicity testing of oligonucleotide drugs using an antibody bridging assay has been scarcely investigated. We developed a highly sensitive antibody bridging assay model and assessed it using probe alteration link self-assembly reactions (PALSAR) technology as a signal amplifier. Methods: The concentration of each probe was optimized, and the bridging assay model was compared with and without signal amplification. Cut-point and analytical sensitivity were determined, and accuracy, precision and drug tolerance were evaluated. Results: The PALSAR bridging assay achieved a net signal 21-36 times higher than that obtained with the conventional method. The analytical sensitivity achieved was 48.8 ng/ml, with adequate accuracy, precision and drug tolerance. Conclusion: PALSAR technology is feasible for developing an antibody bridging assay using oligonucleotides as capture and detection probes.

Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis.

Peatlands in Indonesia are subject to subsidence in recent years, resulting in significant soil organic carbon loss. Their degradation is responsible for several environmental issues; however, understanding the causes of peatland subsidence is of prime concern for implementing mitigation measures. Here, we employed time-series Small BAseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) using ALOS PALSAR-2 images to assess the relationship between subsidence rates and land use/land cover (LULC) change (including drainage periods) derived from decadal Landsat data (1972-2019). Overall, the study area subsided with a mean rate of -2.646 ± 1.839 cm/year in 2018-2019. The subsidence rates slowed over time, with significant subsidence decreases in peatlands after being drained for 9 years. We found that the long-time persistence of vegetated areas leads to subsidence deceleration. The relatively lower subsidence rates are in areas that changed to rubber/mixed plantations. Further, the potential of subsidence prediction was assessed using Random Forest (RF) regression based on LULC change, distance from peat edge, and elevation. With an R2 of 0.532 (RMSE = 0.594 cm/year), this machine learning method potentially enlarges the spatial coverage of InSAR method for the higher frequency SAR data (such as Sentinel-1) that mainly have limited coverage due to decorrelation in vegetated areas. According to feature importance in the RF model, the contribution of LULC change (including drainage period) to the subsidence model is comparable with distance from peat edge and elevation. Other uncertainties are from unexplained factors related to drainage and peat condition, which need to be accounted for as well. This work shows the significance of decadal LULC change analysis to supplement InSAR measurement in tropical peatland subsidence monitoring programs.

Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses.

Iran, as a semi-arid and arid country, has a water challenge in the recent decades and underground water extraction has been increased because of improper developments in the agricultural sector. Thus, detection and measurement of ground subsidence in major plains is of great importance for hazard mitigation purposes. In this study, we carried out a time series small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) analysis of 15 L-band PALSAR-2 images acquired from ascending orbits of the ALOS-2 satellite between 2015 and 2020 to investigate long-term ground displacements in East Azerbaijan Province, Iran. We found that two major parts of the study area (Tabriz and Shabestar plains) are subsiding, where the mean and maximum vertical subsidence rates are -10 and -98 mm/year, respectively. The results revealed that the visible subsidence patterns in the study area are associated with either anthropogenic activities (e.g., underground water usage) or presence of compressible soils along the Tabriz-Shabestar and Tabriz-Azarshahr railways. This implies that infrastructure such as railways and roads is vulnerable if progressive ground subsidence takes over the whole area. The SBAS results deduced from L-band PALSAR-2 data were validated with field observations and compared with C-band Sentinel-1 results for the same period. The C-band Sentinel-1 results showed good agreement with the L-band PALSAR-2 dataset, in which the mean and maximum vertical subsidence rates are -13 and -120 mm/year, respectively. For better visualization of the results, the SBAS InSAR velocity map was down-sampled and principal component analysis (PCA) was performed on ~3600 randomly selected time series of the study area, and the results are presented by two principal components (PC1 and PC2).

Evaluation of the Trend of Deformation around the Kanto Region Estimated Using the Time Series of PALSAR-2 Data.

In the Kanto region of Japan, a large quantity of natural gas is dissolved in brine. The large-scale production of gas and iodine in the region has caused large-scale land subsidence in the past. Therefore, continuous and accurate monitoring for subsidence using satellite remote sensing is essential to prevent extreme subsidence and ensure the safety of residences. This study focused on the small baseline subset (SBAS) method to assess ground deformation trends around the Kanto region. Data for the SBAS method was acquired by the Advanced Land Observing Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 from 2015 to 2019. A comparison of our results with reference levelling data shows that the SBAS method underestimates displacement. We corrected our results using linear regression and determined the maximum displacement around the Kujyukuri area to be approximately 20 mm/year; the mean displacement rate for 2015-2019 was -7.9 ± 2.9 mm/year. These values exceed those obtained using past PALSAR observations owing to the horizontal displacement after the Great East Japan Earthquake of 2011. Moreover, fewer points were acquired, and the root mean-squared error of each time-series displacement value was larger in our results. Further analysis is needed to address these bias errors.

Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia.

This study develops a modelling framework by utilizing multi-sensor imagery for classifying different forest and land use types in the Phnom Kulen National Park (PKNP) in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with three different machine learning (ML) regression techniques (Support Vector Machines (SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band & ALOS backscatter data, and (c) Landsat spectral band, ALOS backscatter data, & LiDAR CHM data. The Landsat-ALOS combination produced more accurate classification results (95% overall accuracy with SVM) compared to Landsat-only bands for all ML models. Inclusion of LiDAR CHM (which is a proxy for vertical canopy heights) improved the overall accuracy to 98%. The research establishes that majority of PKNP is dominated by cashew plantations and the nearly intact forests are concentrated in the more inaccessible parts of the park. The findings demonstrate how different RS datasets can be used in conjunction with different ML models to map forests that had undergone varying levels of degradation and plantations.

Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model.

The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm.

Massive tree mortality from flood pulse disturbances in Amazonian floodplain forests: The collateral effects of hydropower production.

Large dams built for hydroelectric power generation alter the hydrology of rivers, attenuating the flood pulse downstream of the dam and impacting riparian and floodplain ecosystems. The present work mapped black-water floodplain forests (igapó) downstream of the Balbina Reservoir, which was created between 1983 and 1987 by damming the Uatumã River in the Central Amazon basin. We apply remote sensing methods to detect tree mortality resulting from hydrological changes, based on analysis of 56 ALOS/PALSAR synthetic aperture radar images acquired at different flood levels between 2006 and 2011. Our application of object-based image analysis (OBIA) methods and the random forests supervised classification algorithm yielded an overall accuracy of 87.2%. A total of 9800 km2 of igapó forests were mapped along the entire river downstream of the dam, but forest mortality was only observed below the first 49 km downstream, after the Morena rapids, along an 80-km river stretch. In total, 12% of the floodplain forest died within this stretch. We also detected that 29% of the remaining living igapó forest may be presently undergoing mortality. Furthermore, this large loss does not include the entirety of lost igapó forests downstream of the dam; areas which are now above current maximum flooding heights are no longer floodable and do not show on our mapping but will likely transition over time to upland forest species composition and dynamics, also characteristic of igapó loss. Our results show that floodplain forests are extremely sensitive to long-term downstream hydrological changes and disturbances resulting from the disruption of the natural flood pulse. Brazilian hydropower regulations should require that Amazon dam operations ensure the simulation of the natural flood-pulse, despite losses in energy production, to preserve the integrity of floodplain forest ecosystems and to mitigate impacts for the riverine populations.

Evaluating the ability of community-protected forests in Cambodia to prevent deforestation and degradation using temporal remote sensing data.

Community forests are known to play an important role in preserving forests in Cambodia, a country that has seen rapid deforestation in recent decades. The detailed evaluation of the ability of community-protected forests to retain forest cover and prevent degradation in Cambodia will help to guide future conservation management. In this study, a combination of remotely sensing data was used to compare the temporal variation in forest structure for six different community forests located in the Phnom Kulen National Park (PKNP) in Cambodia and to assess how these dynamics vary between community-protected forests and a wider study area. Medium-resolution Landsat, ALOS PALSAR data, and high-resolution LiDAR data were used to study the spatial distribution of forest degradation patterns and their impacts on above-ground biomass (AGB) changes. Analysis of the remotely sensing data acquired at different spatial resolutions revealed that between 2012 and 2015, the community forests had higher forest cover persistence and lower rates of forest cover loss compared to the entire study area. Furthermore, they faced lower encroachment from cashew plantations compared to the wider landscape. Four of the six community forests showed a recovery in canopy gap fractions and subsequently, an increase in the AGB stock. The levels of degradation decreased in forests that had an increase in AGB values. However, all community forests experienced an increase in understory damage as a result of selective tree removal, and the community forests with the sharpest increase in understory damage experienced AGB losses. This is the first time multitemporal high-resolution LiDAR data have been used to analyze the impact of human-induced forest degradation on forest structure and AGB. The findings of this work indicate that while community-protected forests can improve conservation outcomes to some extent, more interventions are needed to curb the illegal selective logging of valuable timber trees.

Self-assembly formed by a short DNA probe pair: Application for highly sensitive mRNA species detection without reverse transcription.

We describe a novel technology for detecting nucleic acids: Probe Alteration Link Self-Assembly Reactions (PALSAR). PALSAR comprises DNA self-assembly of pairs of short DNA probes formed by alternate hybridization of three complementary regions in a pair of honeycomb probes (HCPs). Self-assembly occurs at designated salt concentrations and reaction temperatures and requires no enzymes. We prepared pairs of HCPs to detect mRNAs encoded by the GAPDH gene ß-actin (BA) gene, CD3D gene, CD4 gene, major vault protein (MV) gene and the signalling lymphocytic activation molecule-associated protein (SAP) gene, and succeeded in quantitatively detecting these mRNAs. PALSAR could detect mRNA directly without synthesizing cDNA. Moreover, multiple mRNAs could be detected simultaneously in a single reaction tube and there was a good correlation between the results obtained PALSAR and those by real-time PCR.

Time-series maps of aboveground carbon stocks in the forests of central Sumatra.

BACKGROUND: Efforts to reduce emissions from deforestation and forest degradation in tropical Asia require accurate high-resolution mapping of forest carbon stocks and predictions of their likely future variation. Here we combine radar and LiDAR with field measurements to create a high-resolution aboveground forest carbon stock (AFCS) map and use spatial modeling to present probable future AFCS changes for the Riau province of central Sumatra. RESULTS: Our map provides spatially explicit estimates of the AFCS with an accuracy of ±23.5 Mg C ha-1. According to this map, the natural forests in the province currently store 265 million Mg C, with a density of 72 Mg C ha-1, as aboveground biomass. Using a spatially explicit modeling technique we derived time-series AFCS maps up to the year 2030 under three forest policy scenarios: business as usual, conservation, and concession. The spatial patterns of AFCS and their trends under different scenarios vary on a local scale, and some areas are highlighted that are at eminent risk of carbon emission. Based on the business as usual scenario, the current AFCS could decrease by 75 %, which may lead to the release of 747 million Mg CO2. The other two scenarios, conservation and concession, suggest the risk reductions by 11 and 59 %, respectively. CONCLUSION: The time-series AFCS maps provide spatially explicit scenarios of changes in AFCS. These data may aid in planning Reducing Emissions from Deforestation and forest Degradation in developing countries projects in the study area, and stimulate the development of AFCS maps for other regions of tropical Asia.