L-band Synthetic Aperture Radar Research Articles

Multispectral and Radar Data for the Setting of Gold Mineralization in the South Eastern Desert, Egypt

Satellite-based multi-sensor data coupled with field and microscopic investigations are used to unravel the setting and controls of gold mineralization in the Wadi Beitan–Wadi Rahaba area in the South Eastern Desert of Egypt. The satellite-based multispectral and Synthetic Aperture Radar (SAR) data promoted a vibrant litho-tectonic understanding and abetted in assessing the regional structural control of the scattered gold occurrences in the study area. The herein detailed approach includes band rationing, principal component and independent component analyses, directional filtering, and automated and semi-automated lineament extraction techniques to Landsat 8- Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Phased Array L-band Synthetic Aperture Radar (PALSAR), and Sentinel-1B data. Results of optical and SAR data processed as grayscale raster images of band ratios, Relative Absorption Band Depth (RBD), and (mafic–carbonate–hydrous) mineralogical indices are used to extract the representative pixels (regions of interest). The extracted pixels are then converted to vector shape files and are finally imported into the ArcMap environment. Similarly, manually and automatically extracted lineaments are merged with the band ratios and mineralogical indices vector layers. The data fusion approach used herein reveals no particular spatial association between gold occurrences and certain lithological units, but shows a preferential distribution of gold–quartz veins in zones of chlorite–epidote alteration overlapping with high-density intersections of lineaments. Structural features including en-echelon arrays of quartz veins and intense recrystallization and sub-grain development textures are consistent with vein formation and gold deposition syn-kinematic with the host shear zones. The mineralized, central-shear quartz veins, and the associated strong stretching lineation affirm vein formation amid stress build-up and stress relaxation of an enduring oblique convergence (assigned as Najd-related sinistral transpression; ~640–610 Ma). As the main outcome of this research, we present a priority map with zones defined as high potential targets for undiscovered gold resources.

Advanced Applications of Synthetic Aperture Radar (SAR) Remote Sensing for Detecting Pre- and Syn-eruption Signatures at Mount Sinabung, North Sumatra, Indonesia

DOI:10.17014/ijog.6.2.123-140Mount Sinabung was re-activated at August 28th, 2010 after a long repose interval. The early stage of a phreatic eruption was then followed by magmatic eruptions at September 15th, 2013 for years until now. To understand the ground surface changes accompanying the eruption periods, comprehensive analyses of surface and subsurface data are necessary, especially the condition in pre- and syn-eruption periods. This study is raised to identify ground surface and topographical changes before, intra, and after the eruption periods by analyzing the temporal signature of surface roughness, moisture, and deformation derived from Synthetic Aperture Radar (SAR) data. The time series of SAR backscattering intensity were analyzed prior to and after the early eruption periods to know the lateral ground surface changes including estimated lava dome roughness and surface moisture. Meanwhile, the atmospherically corrected Differential Interferometric SAR (D-InSAR) method was also applied to know the vertical topographical changes prior to the eruptions. The atmospheric correction based on modified Referenced Linear Correlation (mRLC) was applied to each D-InSAR pair to exclude the atmospheric phase delay from the deformation signal. The changes of surface moistures on syn-eruptions were estimated by calculating dielectric constant from SAR polarimetric mode following Dubois model. Twenty-one Phased Array type L-band SAR (PALSAR) data on board Advanced Land Observing Satellite (ALOS) and nine Sentinel-1A SAR data were used in this study with the acquisition date between February 2006 and February 2017. For D-InSAR purposes, the ALOS PALSAR data were paired to generate twenty interferograms. Based on the D-InSAR deformation, three times inflation-deflation periods were observed prior to the early eruption at August 28th 2010. The first and second inflation-deflation periods at the end of 2008 and middle 2009 presented migration of magma batches and dike generations in the deep reservoir. The third inflation-deflation periods in the middle of 2010 served as a precursor signal presenting magma feeding to the shallow reservoir. The summit was inflated about 1.4 cm and followed by the eruptions. The deflation of about 2.3 cm indicated the release pressure and temperature in the shallow reservoir after the early eruption at August 28th, 2010. The last inflation-deflation period was also confirmed by the increase of the lava dome roughness size from 5,121 m2 on July to 6,584 m2 on August. The summit then inflated again about 1.1 cm after the first eruption and followed by unrest periods presented by lava dome growth and destruction at September 15th, 2013. The volcanic products including lava and pyroclastics strongly affected the moisture of surface layer. The volcanic products were observed to reduce the surface moisture within syn-eruption periods. The hot materials are presumed responsible for the evaporation of the surface moisture as well.

Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery

Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.

On Extreme Winds at L-Band with the SMAP Synthetic Aperture Radar

In this letter, we discuss some observations of the Soil Moisture Active Passive (SMAP) mission’s high-resolution synthetic aperture radar (SAR) for extreme winds and tropical cyclones. We find that the L-band cross-polarized backscatter is far more sensitive to wind speed at extreme winds than the co-polarized backscatter and it is essential to observations of extreme winds with L-band SAR. We introduce a cyclone wind speed retrieval algorithm and apply it to the limited SMAP SAR dataset of cyclones. We show that the SMAP SAR instrument is capable of measuring extreme winds up to the category 5 (70 m/s) wind speed regime providing unique capabilities as compared to traditional scatterometers with C and Ku-band radars.

An Object-Based Assessment of Multi-Wavelength SAR, Optical Imagery and Topographical Datasets for Operational Wetland Mapping in Boreal Yukon, Canada

The authors evaluated multiple remotely sensed datasets for their contributions to operational wetland mapping in a subarctic, boreal cordillera study site in Yukon, Canada. They assessed Sentinel-2 optical imagery, Sentinel-1 C-band and ALOS PALSAR L-band synthetic aperture radar (SAR) imagery, and topographical data from the territorial digital elevation model (DEM) using an object-based image analysis (OBIA) approach. Three machine-learning algorithms were tested, namely random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN), using various data combinations (11 model scenarios). RF produced the most accurate results when incorporating all optical, SAR and DEM data (86.5%, kappa 0.84), with open water (100% producer accuracy, PA), marsh (75% PA) and swamps (85.7% PA) being detected most accurately. When assessed in isolation, Sentinel-2 optical data consistently generated more accurate classifications than either SAR platform or DEM data. RF variable importance metrics provided further explanation to these results, indicating the 8 most powerful variables to be optical. Variable reduction tests also produced comparable accuracies, indicating that an optimal RF model can be built based on predictive power rankings. The results can be used to inform resource managers on the efficacy of current datasets and their applications to wetland mapping in northern, subarctic environments.

Classifying Inundation in a Tropical Wetlands Complex with GNSS-R

The use of global navigation satellite system reflectometry (GNSS-R) measurements for classification of inundated wetlands is presented. With the launch of NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, space-borne GNSS-R measurements have become available over ocean and land. CYGNSS covers latitudes between ±38°, providing measurements over tropical ecosystems and benefiting new studies of wetland inundation dynamics. The GNSS-R signal over inundated wetlands is driven mainly by coherent scattering associated with the presence of surface water, producing strong forward scattering and a distinctive bistatic scattering signature. This paper presents a methodology used to classify inundation in tropical wetlands using observables derived from GNSS-R measurements and ancillary data. The methodology employs a multiple decision tree randomized (MDTR) algorithm for classification and wetland inundation maps derived from the phased-array L-band synthetic aperture radar (PALSAR-2) as reference for training and validation. The development of an innovative GNSS-R wetland classification methodology is aimed to advance mapping of global wetland distribution and dynamics, which is critical for improved estimates of natural methane production. The results obtained in this manuscript demonstrate the ability of GNSS-R signals to detect inundation under dense vegetation over the Pacaya-Samiria Natural Reserve, a tropical wetland complex located in the Peruvian Amazon. Classification results report an accuracy of 69% for regions of inundated vegetation, 87% for open water regions, and 99% for non-inundated areas. Misclassification of inundated vegetation, primarily as non-inundated area, is likely related to the combination of two factors: partial inundation within the GNSS-R scattering area, and signal attenuation from dense overstory vegetation, resulting in a low signal.

Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR–RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR–RF integrated model showed that the prediction accuracy of FR–RF–PALSAR (0.917) was higher than FR–RF–ASTER (0.865) and FR–RF–SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures.

Surface displacements of Aso volcano after the 2016 Kumamoto earthquake based on SAR interferometry: implications for dynamic triggering of earthquake–volcano interactions

SUMMARYThe 2016 Kumamoto earthquake involved a series of events culminating in an Mw 7.0 main shock on 2016 April 16; the main-shock fault terminated in the caldera of Aso volcano. In this study, we estimated surface displacements after the 2016 Kumamoto earthquake using synthetic aperture radar interferometry analysis of 16 Phased Array Type L-band Synthetic Aperture Radar-2 images acquired from 2016 April 18 to 2017 June 12 and compared them with four images acquired before the earthquake. Ground subsidence of about 8 cm was observed within about a 3 km radius in the northwestern part of Aso caldera. Because this displacement was not seen in data acquired before the 2016 Kumamoto earthquake, we attribute this displacement to the 2016 Kumamoto earthquake. Furthermore, to estimate the source depth of the surface displacement, we applied the Markov chain Monte Carlo method to a spherical source model and obtained a source depth of about 4.8 km. This depth and position are nearly in agreement with the top of a low-resistivity area previously inferred from magnetotelluric data; this area is thought to represent a deep hydrothermal reservoir. We concluded that this displacement is due to the migration of magma or aqueous fluids.

Inundation Extent Mapping by Synthetic Aperture Radar: A Review

Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data.

Forest Growing Stock Volume Estimation in Subtropical Mountain Areas Using PALSAR-2 L-Band PolSAR Data

Forest growing stock volume (GSV) extraction using synthetic aperture radar (SAR) images has been widely used in climate change research. However, the relationships between forest GSV and polarimetric SAR (PolSAR) data in the mountain region of central China remain unknown. Moreover, it is challenging to estimate GSV due to the complex topography of the region. In this paper, we estimated the forest GSV from advanced land observing satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar (PALSAR-2) full polarimetric SAR data based on ground truth data collected in Youxian County, Central China in 2016. An integrated three-stage (polarization orientation angle, POA; effective scattering area, ESA; and angular variation effect, AVE) correction method was used to reduce the negative impact of topography on the backscatter coefficient. In the AVE correction stage, a strategy for fine terrain correction was attempted to obtain the optimum correction parameters for different polarization channels. The elements on the diagonal of covariance matrix were used to develop forest GSV prediction models through five single-variable models and a multi-variable model. The results showed that the integrated three-stage terrain correction reduced the negative influence of topography and improved the sensitivity between the forest GSV and backscatter coefficients. In the three stages, the POA compensation was limited in its ability to reduce the impact of complex terrain, the ESA correction was more effective in low-local incidence angles area than high-local incidence angles, and the effect of the AVE correction was opposite to the ESA correction. The data acquired on 14 July 2016 was most suitable for GSV estimation in this study area due to its correlation with GSV, which was the strongest at HH, HV, and VV polarizations. The correlation coefficient values were 0.489, 0.643, and 0.473, respectively, which were improved by 0.363, 0.373, and 0.366 in comparison to before terrain correction. In the five single-variable models, the fitting performance of the Water-Cloud analysis model was the best, and the correlation coefficient R2 value was 0.612. The constructed multi-variable model produced a better inversion result, with a root mean square error (RMSE) of 70.965 m3/ha, which was improved by 22.08% in comparison to the single-variable models. Finally, the space distribution map of forest GSV was established using the multi-variable model. The range of estimated forest GSV was 0 to 450 m3/ha, and the mean value was 135.759 m3/ha. The study expands the application potential of PolSAR data in complex topographic areas; thus, it is helpful and valuable for the estimation of large-scale forest parameters.

Tree diversity assessment and above ground forests biomass estimation using SAR remote sensing: A case study of higher altitude vegetation of North-East Himalayas, India

Forest biomass is one of the significant components for assessing the productivity and sustainability of the forest's ecosystem. In the present study, the mapping and estimation of above ground biomass (AGB) was performed using field observations employing allometric equations and C band Sentinel 1A and L band Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing satellite images for the parts of north eastern Himalayan forests, Arunachal Pradesh. Total 57 transects of 500 m × 10 m size was laid in West Kameng and Tawang districts (altitude range: 1047–4161 m) of Arunachal Pradesh, in which a total of 12203 individuals of 67 tree species (Diameter at Breast Height (DBH) range: 3.18–235.45 cm) was recorded. The study exhibited higher tree density (428 individuals' ha−1) and basal cover (7658.63 m2ha-1) in the studied forests. The highest above ground biomass was contributed by Castanopsis indica (103.82 t ha−1) followed by Pinus roxburghii (46.24 t ha−1) and Quercus semicarpifolia (23.14 t ha−1). Further, AGB was estimated using the Horizontal-Horizontal (HH), Horizontal-Vertical (HV) polarizations acquired by the ALOS PALSAR 50 m mosaic (2008) dataset and Vertical-Vertical (VV), Vertical-Horizontal (VH) polarizations acquired by the Sentinel 1A dataset. The relationship between field based AGB and radar backscatter for selected sample plots was established using pair wise correlation. While comparing the two satellite-based SAR images, it is observed that high correlation between radar backscatter and field based AGB was observed in ALOS PALSAR as compared to Sentinel 1A SAR. This is due to higher sensitive to backscatter values and higher penetration level of L band ALOS PALSAR as compared to C band Sentinel 1A SAR. The correlation study indicates high correlation of HV polarizations in ALOS PALSAR (HV polarization R2 = 0.63; HH polarization R2 = 0.58) as compared to Sentinel 1A (VV polarization R2 = 0.054 and VH polarization R2 = 0.044). The most accurate and reliable method to estimate AGB is the field based method, but it is time consuming and tedious. Whereas, AGB estimation by L band SAR remote sensing is time effective and can be used for larger area in comparatively less time. The present study exhibited poor species diversity in higher altitudes northeast Himalayan forests as species diversity decreases with increase in altitudes. On the other hand, the higher AGB in the studied forests may be due to favorable environmental conditions including less temperature and more nutrient availability as well as less biotic interferences in terms of anthropogenic disturbances, herbivory and grazing. The study signifies the potential use of L and C band backscatter information in the reliable estimation of spatial AGB in sub-tropical to alpine forests of Arunachal Pradesh.

L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia

This article discusses an experiment on the estimation of rubber tree biomass using L-band Synthetic Aperture Radar (SAR), to support recent efforts to include trees outside forest in global biomass and carbon accounting. We noted that date of acquisition is important, but certainly the selection of allometric equation serving as the reference data was paramount. Similarly, choosing a proper form of fully polarimetric data was instrumental, although this requires validation in different environmental settings. As expected, modern data mining approaches consistently delivered high accuracy. Extreme learning machine yielded the best estimate in terms of R2 (0.98) and RMSE (1.88 Mg/ha); nonetheless, it also delivered a slight negative estimation. In this case, we found that a variant of random forest produced an outcome without any negative estimation. This research suggests that estimated biomass or carbon information from rubber plantations would be an invaluable candidate for the improvement of global biomass data.

Estimation of Above Ground Biomass Using Support Vector Machines and ALOS/PALSAR data

L-band Synthetic aperture radar (SAR) data has been extensively used for forest aboveground biomass (AGB) estimation due to its higher saturation level. However, SAR backscatter is highly influenced by the topography characteristics along with the bio-geophysical properties of vegetation and underneath soil characteristics. This has limited the accuracy of directly relating the SAR backscatter with above ground biomass in highly undulated terrain. In this study, it has been observed that terrain degree of slope and aspect plays a vital role in influencing the SAR backscatter in addition with AGB. Because of this, the degree of slope and aspect along with SAR backscatter in HH (transmit and receive polarizations are horizontal) and HV (transmit horizontal and receive vertical) polarizations have been considered as inputs for Support Vector Machine (SVM) to improve the biomass retrieval accuracy. Our results demonstrate that the accuracy of AGB estimation over hilly terrain can be significantly improved by considering topographical characteristics in addition to L-band backscatter.

SUMMARY OF NATURAL DISASTER RESPONSES BY THE ADVANCED LAND OBSERVING SATELLITE-2 (ALOS-2)

Abstract. The Advanced Land Observing Satellite-2 (ALOS-2) was launched on May 24, 2014, and it is operating very well in space more than 4.5 years. The designed mission life is five years as nominal operational phase and the target is over seven years since launch the satellite. The mission objectives of ALOS-2 are 1) disaster monitoring, 2) national land and infrastructure information, 3) cultivated area monitoring, and 4) global forest monitoring. To achieve the objectives, ALOS-2 carries on the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2), which is an active microwave radar using the 1.2 GHz frequency band and observes in day and night times even in bad weather conditions as successor PALSAR instrument onboard ALOS satellite operated from 2006 to 2011. PALSAR-2 instrument has several enhanced features from PALSAR e.g. finer spatial resolution, spotlight observing mode, dual-polarisation ScanSAR. This paper summarises an introduction of typical data analysis results for monitoring natural disasters by ALOS-2 during the operational phase. As the response natural disasters, more than 400 times of the emergency observations have been conducted to identify damages caused by volcanic activities, earthquakes, flooding etc. happened in Japan and the World.

Inference of the impact of wildfire on permafrost and active layer thickness in a discontinuous permafrost region using the remotely sensed active layer thickness (ReSALT) algorithm

The Yukon–Kuskokwim (YK) Delta is a region of discontinuous permafrost in the subarctic of southwestern Alaska. Many wildfires have occurred in the YK Delta between 1971–2015, impacting vegetation cover, surface soil moisture, and the active layer. Herein, we demonstrate that the remotely sensed active layer thickness (ReSALT) algorithm can resolve the post-fire active layer dynamics of tundra permafrost. We generated a stack of Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar interferograms over a study region in the YK Delta spanning 2007–2010. We applied ReSALT to this stack of interferograms to measure seasonal subsidence associated with the freezing and thawing of the active layer and subsidence trends associated with wildfire. We isolated two wildfire-induced subsidence signatures, associated with the active layer and the permafrost layer. We demonstrate that InSAR is sensitive to increases in active layer thickness following wildfire, which recovers to pre-fire values after approximately 25 years. Simultaneously, we show that fire gradually thins the permafrost layer by 4 m, which recovers to pre-fire thickness after 70 years.

Source Parameter Estimation of the 2009 Ms6.0 Yao’an Earthquake, Southern China, Using InSAR Observations

On 9 July 2009, an Ms6.0 earthquake occurred in mountainous area of Yao’an in Yunnan province of Southern China. Although the magnitude of the earthquake was moderate, it attracted the attention of many Earth scientists because of its threat to the safety of the population and its harm to the local economy. However, the source parameters remain poorly understood due to the sparse distribution of seismic and GNSS (Global Navigation Satellite System) stations in this mountainous region. Therefore, in this study, the two L-band ALOS (Advanced Land Observing Satellite-1) PALSAR (Phased Array type L-band Synthetic Aperture Radar) images from an ascending track is used to investigate the coseismic deformation field, and further determine the location, fault geometry and slip distribution of the earthquake. The results show that the Yao’an earthquake was a strike-slip event with a down-dip slip component. The slip mainly occurred at depths of 3–8 km, with a maximum slip of approximately 70 cm at a depth of 6 km, which is shallower than the reported focal depth of ~10 km. An analysis of the seismic activity and tectonics of the Yao’an area reveals that the 9 July 2009 Yao’an earthquake was the result of regional stress accumulation, which eventually led to the rupture of the northwestern most part of the Maweijing fault.

Mapping oil palm plantation expansion in Malaysia over the past decade (2007–2016) using ALOS-1/2 PALSAR-1/2 data

ABSTRACTMalaysia is one of the major oil palm producing regions in the world, with rapid growth over recent decades. While this expansion has boosted socio-economic development, it has also caused severe ecological problems: deforestation and the use of fire to clear secondary forest have damaged the balance of regional ecosystems and the carbon cycle. In this study, oil palm mapping in Malaysia over the past decade (2007–2016) was produced annually, using Advanced Land Observing Satellite (ALOS)-1 Phased Array Type L-band Synthetic Aperture Radar (PALSAR)-1 (2007–2010) images and ALOS-2 PALSAR-2 (2015 and 2016) images at 100 m resolution with maximum likelihood classifier-based supervised classification. Two independent verification systems (testing samples and Malaysian Palm Oil Board statistics) were used to evaluate the mapping accuracy. The average overall accuracy for the six years was 94.62% and the accuracy of oil palm mapping was greater than 83%. The results show the dynamics of oil palm distribution from 2007 to 2016. Two approaches (i.e. pixel counting (PC) and error matrix-based model-assisted (EM)) were used to estimate the oil palm area from the maps. In total, over the past decade, the Malaysian oil palm area increased by 69.25% from 4.00 million ha (Mha) in 2007 to 6.77 in 2016 with PC method (EM method increased by 85.76% ± 15.36%, from 3.73 ± 0.19 Mha in 2007 to 6.90 ± 0.22 Mha), with an annual average increase of 6.93% (EM: 8.58% ± 1.54). This exceeds the MPOB estimates of growth from 5.00 Mha in 2011 to 5.74 Mha in 2016, an annual average increase of 2.47%. For the oil palm plantations mapping in the whole Malaysia, the Random Forest (RF) algorithm outperformed Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC) in terms of mapping accuracy and computation cost.

Estimating prescribed fire impacts and post-fire tree survival in eucalyptus forests of Western Australia with L-band SAR data

Prescribed burning is a technique commonly applied to reduce fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity and rate of spread. Such conditions deviate from those encountered during the fire season. As a result, prescribed burns mostly affect surface fuels and understorey vegetation, an outcome markedly different when compared to wildfires.Information on prescribed burning outcomes is important for evaluating the extent to which land management objectives have been achieved. This study focused on the ability of Radar Burn Ratio (RBR), a Synthetic Aperture Radar (SAR)-based multi-temporal index, to appraise the outcome of prescribed burns in eucalypt forests of Western Australia. L-band datasets acquired by the Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) sensor were used to relate changes in radar backscatter coefficient with the effects of prescribed burning. Two modelling approaches based on post- to pre-fire ratios were used to evaluate prescribed burn impacts. Post-fire tree survival in areas affected by high fire impacts was inferred by assessing the recovery rates of the backscatter coefficient for the first year after the burn.The impact of prescribed burns was estimated with relatively high overall accuracy (79%) using cross-polarized backscatter (HV) under dry conditions. The HH polarization and the Normalized Difference Backscatter Intensity (NDBI) showed certain ability to detect burned areas under wet conditions. The results suggest that L-band backscatter coefficient may contribute to evaluating the success of prescribed burns in Western Australian eucalypt forests through its sensitivity to changes in vegetation structure. In addition, tree survival (as opposed to mortality) and recovery towards the pre-fire state might be inferred by analysing short-term post-fire trends of the backscatter coefficient in areas affected by high fire impacts.

Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016.

Landslide movement monitoring with ALOS-2 SAR data

Landslide is a hazard that threaten the people who lives in the mountain area, it comes active especially rainy seasons and causes a large number of casualties every year. The movement of the slope is an indicator of activity of the landslide, it is helpful to capture the precursor of the activity, the monitoring of the movement of the slope is very important. However it is a difficult problem due to complex topography, cloudy and rainy weather for optical sensors, In this paper we will show the capability of up-to-date Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) data in monitoring the movement of the landslide which located in south China, which can capture the fast and slow movement with different spatial and temporal baseline combination, the results shows that the L-band SAR data has its advantage in monitoring the movement of the landslides especially in the low latitude, cloudy and rainy area.