Phased Array Type L-band Synthetic Aperture Radar Research Articles

Equatorial Ionospheric Scintillation Measurement in Advanced Land Observing Satellite (ALOS) Phased Array-Type L-Band Synthetic Aperture Radar (PALSAR) Observations

Amplitude stripes imposed by ionospheric scintillation have been frequently observed in many of the equatorial nighttime acquisitions of the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR). This type of ionospheric artifact impedes PALSAR interferometric and polarimetric applications, and its formation cause, morphology, and negative influence have been deeply investigated. However, this artifact can provide an alternative opportunity in a positive way for probing and measuring ionosphere scintillation. In this paper, a methodology for measuring ionospheric scintillation parameters from PALSAR images with amplitude stripes is proposed. Firstly, sublook processing is beneficial for recovering the scattered stripes from a single-look complex image; the amplitude stripe pattern is extracted via band-rejection filtering in the frequency domain of the sublook image. Secondly, the amplitude spectrum density function (SDF) is estimated from the amplitude stripe pattern. Thirdly, a fitting scheme for measuring the scintillation strength and spectrum index is conducted between the estimated and theoretical long-wavelength SDFs. In addition, another key parameter, the scintillation index, can be directly measured from the amplitude stripe pattern or indirectly derived from the scintillation strength and spectrum index. The proposed methodology is fully demonstrated on two groups of PALSAR acquisitions in the presence of amplitude stripes. Self-validation is conducted by comparing the measured and derived scintillation index and by comparing the measurements of range lines and azimuth lines. Cross-validation is performed by comparing the PALSAR measurements with in situ Global Position System (GPS) measurements. The processing results demonstrate a powerful capability to robustly measure ionospheric scintillation parameters from space with high spatial resolution.

Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data

Soil salinization is one of the major problems affecting arid regions, restricting the sustainable development of agriculture and ecological protection in the Keriya Oasis in Xinjiang, China. This study aims to capture the distribution of soil salinity with polarimetric parameters and various classification methods based on the Advanced Land Observing Satellite-2(ALOS-2) with the Phased Array Type L-Band Synthetic Aperture Radar-2 (PALSAR-2) and Landsat-8 OLI (OLI) images of the Keriya Oasis. Eleven polarization target decomposition methods were employed to extract the polarimetric scattering features. Furthermore, the features with the highest signal-to-noise ratio value were used and combined with the OLI optimal components to form a comprehensive dataset named OLI + PALSAR2. Next, two machine learning algorithms, Support Vector Machine (SVM) and Random Forest, were applied to classify the surface characteristics. The results showed that better outcomes were achieved with the SVM classifier for OLI + PALSAR2 data, with the overall accuracy, Kappa coefficient, and F1 scores being 91.57%, 0.89, and 0.94, respectively. The results indicate the potential of using PALSAR-2 data coupled with the classification in machine learning to monitor different degrees of soil salinity in the Keriya Oasis.

Using interferometric synthetic aperture rader (InSAR) analysis to detect ground deformation related to irreversibly changing ground ice, Mongolia

AbstractPermafrost in Mongolia shows highly heterogeneous features in space and the contents of ground ice are dependent on the local geographies such as topography, ground wetness, and vegetation cover. Recent permafrost degradation would cause thawing and disappearance of ground ice, destabilizing and subsiding the ground surface. This study aimed to detect topographic deformation related to irreversibly changing ground ice over the permafrost terrain of Mongolia. To end this we used interferometric synthetic aperture RADAR (InSAR) technique, which is capable to measure vertical ground deformation on a centimeter scale. Advanced land observing satellite‐based phased array type L‐band synthetic aperture radar (PALSAR) images (June to September 2007 to 2010) and PALSAR‐2 images (June to September 2014 to 2017) quantified near‐year‐round displacement of the ground surface. The overall deformation was in a range of −3 to 3 cm (subsidence and uplift, respectively) between the image interval and relatively high subsidence occurred during warm years. The areas experiencing consecutive uplift and subsidence correspond to ground underlain by ice‐rich soils, indicating the dominant roles of thawing and growing ground ice for local deformation over permafrost terrain. We discuss the relation of observed ground deformation trends with regional climate and local geohydrology that influence ground ice formation.

Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia

This study examines the sediment retention in Wadi Baysh using the Revised Universal Soil Loss Equation (RUSLE) and TerrSet models, accompanied by integrated remote sensing and Geographic Information System (GIS) techniques. The contribution of this study is mainly associated with the application of TerrSet integrated with high resolution datasets to precisely estimate sediments load, which provide useful information to operate dams and improve the operational efficiency of dams. The Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data are utilized to delineate the basin and have been used as an input to the TerrSet model. The rainfall erosivity (R factor) was calculated using the Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) in the research area during 2015–2020. The soil erodibility (K factor) and LULC categorization are calculated using the digital soil map of the world (DSMW) and Sentinel-2 datasets, respectively. The R factor calculated for Wadi Baysh ranges between 91.35 and 115.95 MJ mm/ha/h/year, while the estimated K factor ranges from 0.139 to 0.151 t ha h/ha M. The Support Vector Machine (SVM) method categorized LULC of the study area into four major classes including barren land (81% of the total area), built-up area (11%), vegetation (8%), and water bodies (1%). Results from the sediment retention module (TerrSet) indicated that each year, 57.91 million tons of soil loss occurred in the basin. The data show that soil loss is greater in the northeast and south, whereas it is typical in the middle of Wadi Baysh. It is concluded from the current analyses that the dam lake of Wadi Baysh, located downstream, will be filled soon in the coming few years if sediment loads are carried to the lake at the same rate. Surface dam operators can obtain a full understanding of sedimentation and take proactive measures to reduce its influence on dam operations by leveraging TerrSet’s sophisticated capabilities.

Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane‐dominated African savannah dry forest

AbstractThis study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L‐band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north‐western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite‐derived metrics, a two‐pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands.

A COMPARATIVE ANALYSIS OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING RANDOM FOREST MODEL

Abstract. Providing an accurate above-ground biomass (AGB) map is of paramount importance for carbon stock and climate change monitoring. The main objective of this study is to compare the performance of pixel-based and object-based approaches for AGB estimation of temperate forests in north-eastern of New York State. Second, the capabilities of optical, SAR, and optical + SAR data were investigated. To achieve the goals, the random forest (RF) regression algorithm was used to model and predict the AGB values. Optical (i.e. Landsat 5TM, Landsat 8 OLI, and Sentinel-2), synthetic aperture radar (SAR) (Sentinel-1 and global phased array type L-band SAR (PALSAR/PALSAR-2)), and their integration have been used to estimate the AGB. It is worth mentioning that the airborne light detection and ranging (LiDAR) AGB raster has been used as a reference data for training/testing purposes. The results demonstrate that the OBIA approach enhanced the RMSE of AGB estimation about 5.32 Mg/ha, 8.9 Mg/ha, and 5.29 Mg/ha for optical, SAR, and optical + SAR data, respectively. Moreover, optical + SAR data with the RMSE of 42.63 Mg/ha and R2 of 0.72 for pixel-based and RMSE of 37.31 Mg/ha and R2 of 0.77 for object-based approach provided the best results.

Optimizing Image Scale and Resolution to Obtain Lineament Related Structures using ALOS PALSAR Dual Orbit Data

Geological structures identification using conventional optical remote sensing encountered some problems due to atmospheric and surface conditions. Therefore, we exploited the Synthetic Aperture Radar (SAR) in Ascending and Descending orbits to characterize lineaments related to geological structures. We have analyzed two images from the Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS). The adjustment of their resolution in the multilooking process was applied to obtain the best understanding between detected lineament and reference fault. The cross-polarized data of ALOS PALSAR level SLC were multilooked with variation of resolution from 15 to 120 m. Obtaining the optimum detected lineament-related faults, which applied the Yamaguchi criterion to extract the lineament visually in the multilooked images. The Kamojang-Guntur Geothermal Fields in West Java were selected as a study area because there is a challenge to confirm geological structure in the area with high alteration and high weathering conditions. According to the proposed criterion, we have obtained those images with resolution of 75 to 90 from the analysis images can represent surface geological structure conditions, and the correlation with existing published geological map. The field verifications have been confirmed that the geological structure indication from images has been proved with the existence of slickenside and morphological conditions such as drainage of rivers, ridges, and valleys. Based on lineaments, stress regimes, and field verifications, it was concluded that Yamaguchi criterion analysis with image resolution 75 to 90 m can describe the presence of regional structures and interpreted structures.

Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions

Remote sensing and GIS approaches have provided valuable information on modeling water resources, particularly in arid regions. The Sahara of North Africa, which is one of the driest regions on Earth, experienced several pluvial conditions in the past that could have stored significant amounts of groundwater. Thus, harvesting the stored water by revealing the groundwater prospective zones (GWPZs) is highly important to water security and the management of water resources which are necessary for sustainable development in such regions. The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR), Tropical Rainfall Measuring Mission (TRMM), and Landsat-8 OLI data have all successfully revealed the geologic, geomorphic, climatic, and hydrologic features of Wadi El-Tarfa east of Egypt’s Nile River. The fusion of eleven predictive GIS maps including lithology, radar intensity, lineament density, altitude, slope, depressions, curvature, topographic wetness index (TWI), drainage density, runoff, and rainfall data, after being ranked and normalized through the GIS-based analytic hierarchy process (AHP) and weighted overlay methods, allowed the GWPZs to be demarcated. The resulting GWPZs map was divided into five classes: very high, high, moderate, low, and very low potentiality, which cover about 10.32, 24.98, 30.47, 24.02, and 10.20% of the entire basin area, respectively. Landsat-8 and its derived NDVI that was acquired on 15 March 2014, after the storm of 8–9 March 2014, along with existing well locations validated the GWPZs map. The overall results showed that an integrated approach of multi-criteria through a GIS-based AHP has the capability of modeling groundwater resources in arid regions. Additionally, probing areas of GWPZs is helpful to planners and decision-makers dealing with the development of arid regions.

EVALUATING PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF OPTICAL, SAR, AND AN EXTREME GRADIENT BOOSTING MODEL

Abstract. The above-ground biomass (AGB) estimation monitoring provides a powerful tool for the assessment of carbon emission and sequestration. Using remote sensing technique is an environmentally friendly way of biomass estimation. Thus, this paper investigated optical (i.e. Landsat 8 OLI and Sentinel-2), synthetic aperture radar (SAR) (global phased array type L-band SAR (PALSAR/PALSAR-2) and Sentinel-1), and their integration for AGB estimation of the Pack demonstration forest in Warrensburg, NY. Importantly, a LiDAR AGB raster of the study area was used as reference data for training/testing purposes. Then, an extreme gradient boosting (Xgboost) machine learning model was used to predict biomass values. The major goal of this study was to compare the performance of pixel-based and object-based image analysis (OBIA) for the AGB estimation. Results indicated that the object-based approach improved the RMSE of AGB prediction about 6.28 Mg/ha for optical + SAR, 6.17 Mg/ha for SAR, and 5.6 Mg/ha for optical data in comparison to the pixel-based approach. Moreover, the combination of optical and SAR data increased the prediction accuracy regardless of feature extraction approach.

Inter-eruptive spatiotemporal variation of caldera contraction on Miyakejima volcano revealed by PALSAR and PALSAR-2 time-series data

Monitoring caldera deformation helps improve the understanding of pressure conditions within a volcanic conduit beneath the caldera. Miyakejima volcano in Japan experienced a caldera collapse associated with an eruption in 2000, which included dike intrusions. Although a previous InSAR study suggested deceleration of caldera subsidence in 2009, few observations on the caldera deformation have been reported. We applied SAR time-series analysis to Phased Array-type L-band Synthetic Aperture Radar (PALSAR) and PALSAR-2 data to investigate spatiotemporal variations of ground deformations at Miyakejima volcano during 2006–2011 and 2014–2019. The spatial characteristics of the caldera contraction transformed from a concentric pattern into a north-south asymmetric pattern in mid-2009, implying migration of a deformation peak from the central aspect to the southern edge of the caldera floor. These peaks of the caldera contraction are located at the initial position of the caldera collapse on the central caldera floor, and at an active fumarole on its southern edge. The asymmetric pattern of the caldera contraction continued during 2014–2019 based on PALSAR-2 data. Using the results of numerical modeling and earthquake distribution beneath the caldera, we interpreted that the continuous asymmetric caldera contraction was driven by brittle failures induced by continuous hydrothermal alterations. Although a previous study proposed deceleration of caldera subsidence in mid-2009, we believe that their findings revealed only a snapshot of the spatial variation of the caldera contraction detected in this study.

Deforestation detection using scattering power decomposition and optimal averaging of volume scattering power in tropical rainforest regions

Synthetic aperture radar (SAR) has been a powerful tool for deforestation detection in tropical rainforests. Polarimetric SAR (POLSAR) data are acquired in a quad-polarization mode, and L-band POLSAR data in particular are one of the few SAR data types that preserve the dielectric properties and structures of the scatterer. POLSAR data consist of 2 × 2 scattering matrices and consequently offer superior target recognition compared with dual-polarization data. In this study, we applied scattering power decomposition suitable for detecting deforestation in near real-time to POLSAR data obtained from the Earth observation sensor Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). The reflection symmetry condition is known to apply to natural distributed objects (i.e., the cross-correlation between co- and cross-polarization data is zero). Inspired by this, we theoretically and experimentally examined the volume scattering power component to distinguish natural forests from the surrounding area. Two important results were verified for natural forests: as the window size of the ensemble average increases, (i) the coherency matrix approaches a simple theoretical form and (ii) the volume scattering power becomes dominant among the scattering power components. Based on these results, we constructed an algorithm that applies scattering power decomposition for detecting deforestation. We produced reference data using high-resolution optical images and evaluated the performance of the derived deforestation map in the Amazon natural forest when employing various window sizes for the ensemble average. At an optimal window size of 15 × 15 pixels, the deforestation detection performance reached a user's accuracy of 94.9% ± 1.5%, a producer's accuracy of 72.3% ± 1.2%, and a kappa coefficient of 0.816 ± 0.0039. Sparse trees left after logging increased the volume scattering power and reduced the producer's accuracy. The proposed algorithm can contribute to deforestation detection with slightly lower accuracy than that of the annual map provided by Global Forest Change. Further, the proposed algorithm is robust to the seasonal variations in tropical rainforests and temporal variations in the deforestation process. Consequently, the proposed algorithm employing the six-component scattering power decomposition method can be utilized in near real-time without considering applicable areas in tropical rainforests. Subsequently, we applied our algorithm to dual-polarization data, which are acquired by PALSAR-2 much more frequently than POLSAR data. The false detection rate did not increase when using the dual-polarization data; however, the omission error increased considerably compared with that when using POLSAR data owing to the low total power obtained from the dual-polarization data.

Fusion of optical, radar and waveform LiDAR observations for land cover classification

Land cover is an integral component for characterizing anthropogenic activity and promoting sustainable land use. Mapping distribution and coverage of land cover at broad spatiotemporal scales largely relies on classification of remotely sensed data. Although recently multi-source data fusion has been playing an increasingly active role in land cover classification, our intensive review of current studies shows that the integration of optical, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) observations has not been thoroughly evaluated. In this research, we bridged this gap by i) summarizing related fusion studies and assessing their reported accuracy improvements, and ii) conducting our own case study where for the first time fusion of optical, radar and waveform LiDAR observations and the associated improvements in classification accuracy are assessed using data collected by spaceborne or appropriately simulated platforms in the LiDAR case. Multitemporal Landsat-5/Thematic Mapper (TM) and Advanced Land Observing Satellite-1/ Phased Array type L-band SAR (ALOS-1/PALSAR) imagery acquired in the Central New York (CNY) region close to the collection of airborne waveform LVIS (Land, Vegetation, and Ice Sensor) data were examined. Classification was conducted using a random forest algorithm and different feature sets in terms of sensor and seasonality as input variables. Results indicate that the combined spectral, scattering and vertical structural information provided the maximum discriminative capability among different land cover types, giving rise to the highest overall accuracy of 83% (2–19% and 9–35% superior to the two-sensor and single-sensor scenarios with overall accuracies of 64–81% and 48–74%, respectively). Greater improvement was achieved when combining multitemporal Landsat images with LVIS-derived canopy height metrics as opposed to PALSAR features, suggesting that LVIS contributed more useful thematic information complementary to spectral data and beneficial to the classification task, especially for vegetation classes. With the Global Ecosystem Dynamics Investigation (GEDI), a recently launched LiDAR instrument of similar properties to the LVIS sensor now operating onboard the International Space Station (ISS), it is our hope that this research will act as a literature summary and offer guidelines for further applications of multi-date and multi-type remotely sensed data fusion for improved land cover classification.

Automatic detection of geological lineaments in central Turkey based on test image analysis using satellite data

The Hamurcu and Başdere study areas, part of the Central Anatolian Volcanic Provence (CAVP), are associated with metalliferous ore deposits. The assumption that hydrothermal alterations might be related to faults and fractures makes mapping the lineaments around the study areas vital. Accordingly, geological lineaments in the vicinity of the studied areas in Central Anatolia (Turkey) were delineated using Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR) data. We adopted a new methodology for determining the optimal values of six parameters used in a fully automated extraction of lineaments. The lineaments identified in the region including the study areas strike mainly the NW-SE direction in the PALSAR data. Moreover, hydrothermal alterations around the towns of Hamurcu and Başdere are directly related to the NW-SE oriented faults, indicating that the lineations in the region might be carefully scrutinized in the exploration of mineralization zones. As a result, it was demonstrated in this study that the most appropriate parameter values specified for automatic lineament extraction successfully map geological structures around both study areas and can confidently be employed in different geographical locations.

Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data

Satellite forest inventories are the only feasible way to map Canada’s vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) plots to the entire forest land base using wall-to-wall features typically derived from Landsat data. However, the benefits of integrating L-band synthetic aperture radar (SAR) data, strongly correlated to forest biomass, have not been assessed for Canadian northern boreal forests. Here we describe an optimized multivariate k-NN implementation of a 151,700 km2 area in southern NWT that included ca. 2007 Landsat and dual-polarized Phased Array type L-band SAR (PALSAR) data on board the Advanced Land Observing Satellite (ALOS). Five forest attributes were mapped at 30 m cells: stand height, crown closure, stand/total volume and aboveground biomass (AGB). We assessed accuracy gains compared to Landsat-based maps. To circumvent the scarcity of FI plots, we used 3600 footprints from the Geoscience Laser Altimeter System (GLAS) as surrogate FI plots, where forest attributes were estimated using Light Detection and Ranging (LiDAR) metrics as predictors. After optimization, k-NN predicted forest attribute values for each pixel as the average of the 4 nearest (k = 4) surrogate FI plots within the Euclidian space of 9 best features (selected among 6 PALSAR, 10 Landsat, and 6 environmental features). Accuracy comparisons were based on 31 National Forest Inventory ground plots and over 1 million airborne LiDAR plots. Maps that included PALSAR HV backscatter resulted in forest attribute predictions with higher goodness of fit (adj. R2), lower percent mean error (ME%), and percent root mean square error (RMSE%), and lower underestimation for larger attribute values. Predictions were most accurate for conifer stand height (RMSE% = 32.1%, adj. R2 = 0.58) and AGB (RMSE% = 47.8%, adj. R2 = 0.74), which is much more abundant in the area than mixedwood or broadleaf. Our study demonstrates that optimizing k-NN parameters and feature space, including PALSAR, Landsat, and environmental variables, is a viable approach for inventory mapping of the northern boreal forest regions of Canada.

Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms

Forest aboveground biomass (AGB) is of great significance since it represents large carbon storage and may reduce global climate change. However, there are still considerable uncertainties in forest AGB estimates, especially in rugged regions, due to the lack of effective algorithms to remove the effects of topography and the lack of comprehensive comparisons of methods used for estimation. Here, we systematically compare the performance of three sources of remote sensing data used in forest AGB estimation, along with three machine-learning algorithms using extensive field measurements (N = 1058) made in the Khingan Mountains of north-eastern China in 2008. The datasets used were obtained from the LiDAR-based Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation satellite (ICESat/GLAS), the optical-based Moderate Resolution Imaging Spectroradiometer (MODIS), and the SAR-based Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR). We show that terrain correction is effective for this mountainous study region and that the combination of terrain-corrected GLAS and PALSAR features with Random Forest regression produces the best results at the plot scale. Including further MODIS-based features added little power for prediction. Based upon the parsimonious data source combination, we created a map of AGB circa 2008 and its uncertainty, which yields a coefficient of determination (R2) of 0.82 and a root mean squared error of 16.84 Mg ha−1 when validated with field data. Forest AGB values in our study area were within the range 79.81 ± 16.00 Mg ha−1, ~25% larger than a previous, SAR-based, analysis. Our result provides a historic benchmark for regional carbon budget estimation.

Mapping Apple Orchards in the municipality of São Joaquim (Santa Catarina, Brazil) using Sentinel-2 data

Abstract This study carried out a mapping procedure focusing on apple orchards considering the planted area, spatial location, altitude range, slope interval, and presence of anti-hail nets in the city of São Joaquim (Southern Santa Catarina Plateau, Brazil). Spectral images from the Sentinel-2 orbital platform acquired in August 2018 and an enhanced digital elevation model from the Shuttle Radar Topography Mission (SRTM) were used. In a GIS application configured with the SIRGAS 2000,4 reference system and UTM cartographic projection, Sentinel-2 constellation images and digital elevation models from the SRTM mission and more recently refined with sensor data Phased Array type L-band Synthetic Aperture Radar (PALSAR) were added. All images were resampled to a spatial resolution of 10m. The results were validated based on high spatial resolution images available from Google Earth. The results show that São Joaquim has a planted area of 7,974.80 ha, and only 12% use an anti-hail coverage system. The majority of the orchards range from one to five ha and belong to small producers. More than 50% of the orchards are between 1,200 and 1,400 m in altitude, with 45% of orchards located in areas with slopes between 8 to 20%. Interestingly, most of the orchards are concentrated in a radius of up to 20km from the urban center of São Joaquim, where industries and cooperatives are located for packaging, processing, and logistics. This study demonstrated that orbital data from Sentinel-2 can effectively quantify the distribution of apple orchards, being a viable and effective alternative for collecting information for agricultural monitoring. In this way, it enables efficient planning of apple production, such as technical assistance, marketing with producers, and production flow.

Mitigation of Ionospheric Noise in Azimuth Offset Based on the Split-Spectrum Method

The pixel-offset method has been utilized as a powerful tool to measure large ground movements. However, L-band spaceborne synthetic aperture radar (SAR) data are often affected by the ionosphere, which produces serious noises in the azimuth component of the pixel offset field, called as azimuth streaks. Here, we propose a new method to mitigate azimuth streaks based on physical modeling. Azimuth streaks cannot be removed by simply combining the known relationship between ionospheric azimuth offset and the ionospheric phase delay with the phase delay obtained by the split-spectrum method. Thus, taking into account that image matching (coregistration) affects the measurement of azimuth offsets, we formulate a theoretical correction formula of azimuth streaks by subtracting the coregistration-induced effects approximated by a polynomial function from ionospheric azimuth offsets modeled using the split-spectrum method. Applying the method to two pairs of Advanced Land Observing Satellite-2 (ALOS-2)/Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) stripmap images which are severely affected by the ionosphere, we demonstrate effective mitigation of azimuth streaks. In the application to the case that no significant ground movement is detected by Global Navigation Satellite System (GNSS), while the standard deviation of azimuth pixel offset before the correction is 87.2 cm, the value after the correction is 29.2 cm, which is comparable to the theoretical measurement accuracy of the azimuth pixel offset. In the application to the 2016 Kumamoto earthquake, we substantially reduce the azimuth streaks and successfully extract the ground movements from the azimuth offset fields within an accuracy of about 20 cm. The result suggests the proposed method enables more accurate and operational estimation of the 3-D ground displacement.

Observing Posteruptive Deflation of Hydrothermal System Using InSAR Time Series Analysis: An Application of ALOS‐2/PALSAR‐2 Data on the 2015 Phreatic Eruption of Hakone Volcano, Japan

AbstractFrom June 29 to July 1, 2015, a phreatic eruption occurred in Owakudani, the largest fumarole area in Hakone volcano, Japan. In this study, an interferometric synthetic aperture radar (InSAR) time series analysis of the Advanced Land Observing Satellite‐2 (ALOS‐2)/Phased Array type L‐band Synthetic Aperture Radar‐2 (PALSAR‐2) data was performed to measure deformation after the eruption. The results show that the central cones of the volcano have subsided since the eruption and its deflation source is located beneath the previously estimated bell‐shaped conductor, which is considered as a sealing layer confining a pressurized hydrothermal reservoir. Therefore, the InSAR results demonstrate the deflation of the hydrothermal system beneath the volcano. One possible cause of this deflation is compaction due to a decrease in pore pressure caused by rupture and fluid migration during and after the eruption.

Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan–Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan–Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi–Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014–2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi–Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000–5000 m, the slope range of 10–40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi–Ya’an section of the Sichuan–Tibet Railway.

A COMPARISON OF RANDOM FOREST AND LIGHT GRADIENT BOOSTING MACHINE FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF LANDSAT, ALOS PALSAR, AND AIRBORNE LIDAR DATA

Abstract. Sustainable forest management is a critical topic which contributes to ecological, economical, and socio-cultural aspect of the environment. Providing accurate AGB maps is of paramount importance for sustainable forest management, carbon accounting, and climate change monitoring. The main goal of this study was to leverage the potential of two machine learning algorithms for predicting AGB using optical and synthetic aperture radar (SAR) datasets. To achieve this goal random forest (RF) and light gradient boosting machine (LightGBM) models were deployed to predict AGB values in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. Both models were trained and evaluated based on airborne light detection and ranging (LiDAR) data, Landsat imagery, advanced land observing satellite (ALOS) phased array type L-band Synthetic Aperture Radar (PALSAR), and their combination. The integration of airborne LiDAR, optic, and SAR datasets provided the best results in terms of root mean square error (RMSE) and mean bias error (MBE). The RF model outperformed the LightGBM in all scenarios (LiDAR, Landsat 5, ALOS PALSAR, and their combination). The RF model was able to predict AGB values with the RMSE of 51.90 Mg/ha and MBE of −0.189 Mg/ha for the combination of LiDAR, optic, and SAR data, while LightGBM estimated the AGB values with the RMSE of 52.78 Mg/ha and MBE of −0.253 Mg/ha. LightGBM is more sensitive to noise and there are lots of hyperparameters that need to be tuned which highly affect its performance.