Phased Array Type L-band Synthetic Aperture Radar Research Articles

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.

Examining the Impact of Different DEM Sources and Geomorphology on Flash Flood Analysis in Hyper-Arid Deserts

Digital elevation models (DEMs) are the cornerstone for hydrological and geomorphological modeling. Herein, two Nile-tributary catchments (Wadi Al Rishrash and Wadi Atfeh) in Egypt are selected to examine the contribution of different DEMs to the accuracy of hydrological and geomorphological analyses in the hyper-arid Sahara. DEMs sources include: Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) (12.5 m resolution), ALOS World 3D with 30 m resolution (AW3D30), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER DEM with 30 m resolution) and the Shuttle Radar Topography Mission (SRTM with 30 and 90 m resolution), in addition to topographic map-derived DEM (90 m resolution). Using a hypothetical uniformly-distributed 10 mm rainfall event, the estimated parameters, including: flow duration, time to peak and peak discharge rates, are almost similar for the different DEMs and thus technical aspects related to sources and resolutions of the datasets impose insignificant control on quantitative flash-flood analyses. Conversely, variations in geological and geomorphological characteristics of the catchments show more significant control on the hydrograph magnitudes as indicated by the different parameters of the two catchments. These findings indicate that understanding the geological and hydrological evolution of the catchment is essential for integrated management strategies of floods especially in the Saharan–Arabian deserts and in similar conditions of hyper-aridity and scarce in situ data worldwide.

Lognormal Random Field models to identify temporal Land cover changes using full polarimetric L-Band SAR imagery

Multiplicative Autoregressive Random Field (MAR) based texture models have been identified as one of the most appropriate models for SAR intensity images to capture the stochastic spatial interaction among neighboring pixels. But very few studies have tested their viability particularly in disaster applications. In this paper, we analyse the MAR texture models for their advantageous in land cover change detection compared to the changes resulting from logarithm of SAR image intensity and speckle filtered SAR imagery. The paper shows that lognormal random fields with multiplicative spatial interactions in the form of MAR models can be an effective alternative to suppress speckle noise and model SAR image intensity in time series data analysis. The pre and post disaster observational data of the Tohoku earthquake, in the east coast of Japan, acquired by the Advanced Land Observation Satellite (ALOS)/phased array type L-band synthetic aperture radar (PALSAR) were synthesized using MAR model based texture measures. Two of the main texture descriptors of the MAR model were considered primarily in this study. Those are the neighborhood weighting and the noise variance parameters. A 2nd order neighborhood configuration was used to estimate them. We present a variogram based analysis, structural similarity index measure (SSIM), and the mean ratio detector (MRD) as three different approaches to analyse the changes in land cover using radar texture. The change detection results of the MRD were further tested using area error proportion (AEP), root mean square error (RMSE) and correlation coefficient (CC), keeping normalized ratio, principle component analysis (PCA) and adaptive Lee filtered polarimetric intensity based change as the references.

Multi-sensor datasets-based optimal integration of spectral, textural, and morphological characteristics of rocks for lithological classification using machine learning models

We present an optimal integration of multi-sensor datasets, including Advanced Spaceborne Thermal and Reflection Radiometer (ASTER), Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and digital elevation model for lithological classification using Machine Learning Models (MLMs). Different input features such as spectral, spectral and transformed spectral, spectral and morphological, spectral and textural, and optimum hybrid features were derived and evaluated to accurately classify different rock types found in the Chhatarpur district (Madhya Pradesh), India using the Support Vector Machine (SVM) and Random Forest (RF). The SVM achieves better classification accuracy and shows less sensitivity to the number of samples used in model development. The optimum hybrid features outperform other input features with an overall accuracy and κ coefficient of 77.78% and 0.74, which is around 15% higher as obtained using ASTER spectral data alone. Thus, the proposed multi-sensor optimal integration approach is recommended for successful lithological classification using MLMs.

Mapping potential signs of gas emissions in ice of Lake Neyto, Yamal, Russia, using synthetic aperture radar and multispectral remote sensing data

Abstract. Regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of lake ice of Lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake’s sediments. However, to assess this connection, only analyses of data from boreholes in the vicinity of Lake Neyto and visual comparisons to medium-resolution optical imagery have been provided due to a lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with very high resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel spacing) and holes in lake ice from the VHR data (0.5 m pixel spacing) and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes (71 %) in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier, and similarities to SAR imagery acquired more than a month before are evident, supporting the hypothesis that anomalies may be related to gas emissions. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by lake ice subsidence and consequent flooding through the holes over the ice top leading to wetting and/or slushing of the snow around the holes, which might also explain outcomes of polarimetric analyses of auxiliary L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) data. C-band SAR data are considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies.

Mapping structural attributes of tropical dry forests by combining Synthetic Aperture Radar and high‐resolution satellite imagery data

AbstractAimOptical satellite imagery has been used for mapping the spatial distribution of vegetation structure attributes; however, obtaining accurate estimates with optical imagery can be difficult in tropical forests due to their dense canopy and multi‐layered vegetation. Synthetic aperture radar imagery can be more suitable in this case, as the radar signal can penetrate the forest canopy and interact with stems, providing a better estimation of the vegetation structure. This study compared the accuracy of forest species richness, tree diameter, height, and basal area estimates obtained using Sentinel‐2 and Advanced Land Observing Satellite ‐1 (ALOS) Phased Array type L‐band Synthetic Aperture Radar (PALSAR) data, either combined or separately.LocationThe Yucatan Peninsula, Mexico.MethodsField data were collected in three 3600‐km2‐window areas with three different types of tropical dry forest. Three random forest regression models were fitted: one using explanatory variables derived from Sentinel‐2 data, a second using predictor variables derived from ALOS PALSAR, and the third using a combination of explanatory variables from both sensors. A variance partitioning analysis was carried out to examine the percent variability of each vegetation attribute that was explained by the models combining the explanatory variables of the two sensors (ALOS PALSAR and Sentinel‐2).ResultsVegetation attribute estimation errors ranged from 13% to 38.5% when using ALOS PALSAR variables and from 11% to 33% when using Sentinel‐2 variables. Combining variables from both sensors provided more accurate estimates of vegetation attributes. A 5% reduction of the estimated error, and an increase from 0.50 to 0.63 of the percentage of variation explained by the models (R2) were achieved.ConclusionsOur results suggest that both ALOS PALSAR and Sentinel‐2 data provide accurate estimates of vegetation structure and species richness in tropical dry forests. However, combining explanatory variables from the two sensors improved the estimation accuracy of vegetation attributes.

The use of remotely sensed data to reveal geologic, structural, and hydrologic features and predict potential areas of water resources in arid regions

Egypt’s arid deserts resemble the Sahara. They lack water resources because of climatic conditions, although the high topographic areas receive rainfall during sporadic storms. In this article, integrating radar Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR), Landsat-8, Sentinel-2, and Shuttle Radar Topography Mission (SRTM) data reveal the geological, geomorphic, structural, and hydrological features in Wadi Batur (WB), Egypt. This defunct tributary of the Nile basin, in the eastern Egyptian desert, covers approximately 6600 km2. Analysis of the remotely sensed data shows that the northwest–southeast structures control the structures discussed in this study, which are later captured by the east–west trend. A fused ALOS/PALSAR and Landsat-8 image reveals two water outlets for the WB: one extends between the Precambrian and Phanerozoic rocks and drains in a northwestern direction to Wadi Matula in the north, and the second cuts the sedimentary rock and drains to the west, at Idfu. Ten sub-basins were extracted by using SRTM data and arranged by their infiltration properties. Eight evidential maps were combined after assigning a weight to each, and a Geographic Information Systems (GIS) technique was used to predict the groundwater recharge potential zones (GPZs). The results show that the predicted GPZs in the WB fall into six categories, ranging from excellent to bare/absent. Importantly, about 10% of the surface areas in the WB basin are characterized by an excellent recharging potential and represent the most promising zones for future groundwater extraction. Combining various datasets derived from satellite sensors is a substantial tool to detect geological features and explore groundwater resources in arid environments.

Integration of remote-sensing data for mapping lithological and structural features in the Esh El-Mallaha area, west Gulf of Suez, Egypt

Esh El-Mallaha west of the Gulf of Suez is a potential area for hydrocarbon resources. In this article, Landsat-8 Operational Land Imager (OLI), Shuttle Radar Topography Mission (SRTM), and Advanced Land Observing Satellite (ALOS) Phased Array Type L‐band Synthetic Aperture Radar (PALSAR) data were utilized to identify the geological and structural features of this area which represents a part of the North Eastern Desert (NED), Egypt. The use of band ratios (BR) and principal component analysis (PCA) allowed characterizing the different varieties of rock units. These approaches allowed discriminating the various rock units e.g., metavolcanics, granitoids including the two phases of the older granites and younger granites along with the third phase which represented by elongated ridges cut the rocks and clearly allowed tracing the contacts of different lithological units of the Neoproterozoic basement and Phanerozoic sedimentary rocks. Applying the fused Landsat-8 OLI and ALOS/PALSAR data improved the structural elements that difficult to reveal by optical images. Combination of Landsat-8 OLI, ALOS/PALSAR, and SRTM data effectively display the structural and lithological contacts. The lineament extraction and interpretation indicated that the area is clearly shaped by the NW-SE graben and controlled by the Gulf of Suez tectonic trend. Such structures allowed the accumulation of thick sediments in the faulted basin that represents the promise area for oil, gas, and water resources. Lineament analysis revealed that the major tectonic trends controlling the area are NW-SE and NE-SW directions with less effect of N-S and E-W trends. The NW-SE sinistral and NE-SW dextral lateral movements are recognized. Overall results revealed that the integration of optical and radar remotely sensed data is an effective tool in deciphering geological and structural features in the arid regions.

Mapping Groundwater Potential Zones Using a Knowledge-Driven Approach and GIS Analysis

Despite the Sahara being one of the most arid regions on Earth, it has experienced rainfall conditions in the past and could hold plentiful groundwater resources. Thus, groundwater is one of the most precious water resources in this region, which suffers from water shortage due to the limited rainfall caused by climatic conditions. This article will assess the knowledge-driven techniques employed to develop a model to integrate the multicriteria derived from geologic, geomorphic, structural, seismic, hydrologic, and remotely sensed data. This model was tested on the defunct Kom Ombo area of Egypt’s Nile river basin in the eastern Sahara, which covers ~28,200 km2, to reveal the promising areas of groundwater resources. To optimize the output map, we updated the model by adding the automated depression resulting from a fill-difference approach and seismic activity layers combined with other evidential maps, including slope, topography, geology, drainage density, lineament density, soil characteristics, rainfall, and morphometric characteristics, after assigning a weight for each using a Geographic Information System (GIS)-based knowledge-driven approach. The paleochannels and soil characteristics were visualized using Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) data. Several hydromorphic characteristics, sinks/depressions, and sub-basin characteristics were extracted using Shuttle Radar Topography Mission (SRTM) data. The results revealed that the assessed groundwater potential zones (GPZs) can be arranged into five distinctive groups, depending on their probability for groundwater, namely very low (6.56%), low (22.62%), moderate (30.75%), high (29.71%), and very high (10.34%). The downstream areas and Wadi Garara have very high recharge and storage potential. Interferometry Synthetic Aperture Radar (InSAR) coherence change detection (CCD) derived from Sentinel-1 data revealed a consistency between areas with high InSAR CCD (low change) that received a plausible amount of surface water and those with very low InSAR CCD values close to 0 (high change). Landsat data validated the areas that received runoff and are of high potentiality. The twenty-nine groundwater well locations overlaid on the GPZs, to assess the predicted model, indicated that about 86.17% of the wells were matched with very good to moderate potential zones.

Performance evaluation of multi-parametric synthetic aperture radar data for geological lineament extraction

ABSTRACT The demarcation of linear geomorphological features due to fracture systems, underlying or exposed faults, drainage lines etc. from satellite images has been one of the most widely used applications in geological studies. Synthetic Aperture Radar (SAR) data has been recognized as an effective tool for lineament extraction due to its unique sensitivity to subtle topography, soil moisture and texture. However, the typical SAR sensor parameters such as frequency, polarization and look direction plays a vital role in enhancing the performance for a specific application. Therefore, the study aims to analyse the multi-frequency (C- and L-band), multi-polarization (like and cross) and multi-look direction SAR data for geological lineaments extraction. In this paper, Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 datasets acquired over a part of the Indo-Burma ranges of Manipur region (northeastern part of India) are considered for analysis. The lineaments extracted from SAR datasets are compared with reference to lineaments provided by the Geological Survey of India (GSI) through their portal. The study observes that the like-polarization C-band SAR data provide optimal results for this application. It is also observed that the integration of lineaments extracted from multi-look SAR data can significantly improve the performance than that derived from single-look data. Overall, the study also focuses on the spatial distribution of lineaments with respect to the local structural regime for a better understanding of the regional tectonic setup.

Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden

Much attention is paid to the estimation of forest biomass-related variables (stem volume and above-ground biomass) with synthetic aperture radar (SAR) backscatter images because of the increasing number of sensors in space providing global and repeated coverage and the sensitivity of the backscattered intensity to forest properties. One of the most popular models used to estimate a biomass-related variable from SAR backscatter observations is the Water Cloud Model (WCM) because of its simplicity allowing for a straightforward retrieval. Nonetheless, a common feature of these estimates is the tendency to over- or underestimate specific ranges due to simplifying assumptions in the model. In this study, the WCM has been revisited by exploring pathways for a physically-based, Light Detection and Ranging (LiDAR)-aided, model parameterization at larger scale with the overall aim to reduce systematic retrieval errors associated with empirical assumptions in the model. The study was undertaken in Sweden where repeated observations of backscatter by the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) were available. The integration was prototyped in Sweden thanks to detailed allometries relating forest variables in the WCM. These were derived from spatially dense estimates of canopy density and vegetation height from observations by the Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) and measurements of height and stem volume from the Swedish National Forest Inventory (NFI). The SAR backscatter predicted by the revisited WCM was in strong agreement with the observations. When evaluated against stem volumes estimated from the NFI data, the SAR-based stem volumes presented strong dispersion at the pixel level. Average stem volume at the level of five or more pixels, i.e., for an area larger than 0.3 ha, were instead unbiased and similar to the average values obtained from the NFI data (relative root mean square error: 21.4%, estimation bias: 0.9 m3/ha and coefficient of determination: 0.67). This study demonstrates that the integration of allometries in the WCM effectively reduces estimation errors. The method here prototyped in Sweden qualifies to provide large-scale estimates of biomass-related variables using multiple observations of L-band backscatter with potential application worldwide.

PSInSAR-Based Surface Deformation Mapping of Angkor Wat Cultural Heritage Site

Satellite data are making revolutionary changes in the field of archaeology, and nowadays they have been widely used by the scientific community for regular mapping and monitoring of cultural heritage sites. The development of synthetic aperture radar (SAR) sensors helps in monitoring the heritage site with higher accuracy. This study uses the Persistent Scatter Interferometric SAR (PSInSAR), which is an advanced technique of differential SAR interferometry to find the surface deformation of the Angkor Wat temple and its premises. Seven interferometric data from the Phased Array type L-band Synthetic Aperture Radar (PALSAR) of Advanced Land Observing Satellite-1 (ALOS-1) for the period 2006–2009 and ten interferometric data from the ALOS-2 PALSAR-2 for the period 2014–2018 have been used in this study to identify the deformation pattern. It is found from this study that the Angkor Wat temple and its outer walls are stable.

A METHOD FOR QUALITATIVELY MAPPING THE FLOOD PHYSICAL VULNERABILITY OF RESIDENTIAL AREAS

Mapping flood physical vulnerability is spatially limited because it requires input data such as building structures and materials, which are unavailable on large spatial scales. In this study, we propose a new method for qualitatively evaluating the flood vulnerability of residential areas in the context of the exposure and resilience to flood hazard on large spatial scales. This method utilizes the possible correlations between the structural physical vulnerability and residential types obtained from the statistical classifications of multispectral satellite images. Because multispectral classification is well-established as an inexpensive technique for automatically classifying land cover types over wide areas, our method is feasible and efficient for mapping the physical vulnerability of residential areas. As a case study, we present an application of the proposed approach to the Thach Ha district, Ha Tinh province, Vietnam, using the Japanese type 2 Advanced Visible and Near Infrared Radiometer (AVNIR-2) images and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images captured by the Advanced Earth Observing Satellite (ADEOS).

Estimation of snow water equivalent from L-band radar interferometry: simulation and experiment

ABSTRACT The Snow Water Equivalent (SWE) is the main feature of a snowpack that determines its impact on the natural environment. Because microwaves penetrate through the snowpack, microwave remote sensing enables the measurement of depth and snow water equivalent. A promising tool for determining SWE is synthetic-aperture radar, which provides a high spatial resolution (from metres to tens of metres). This paper presents a theoretical and experimental study of snowpack sensing using radar interferometry. An approximate model of interferometric sensing based on the small perturbation method is proposed. As opposed to the well-known interferometric model, the proposed one takes into account microwave backscattering from the snow surface. We estimated the contribution of scattering from the snow surface to amplitude and interferometric phase values. Using Advanced Land Observing Satellite – 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar – 2 (PALSAR-2), SWE was first determined by means of a radar interferometry method at a test site near Lake Baikal, Russia. The experiment consisted of comparing the phases of radar signals received when there was no snowpack (in September) and those in its presence (in February). We used a reference scatterer in the form of a trihedral corner reflector to calibrate the phase measurements. The SWE values were restored from the linear dependence between SWE and the interferometric phase. A comparison between the calculated and experimental data shows their good agreement.

Assessment of oil palm yield and biophysical suitability in Indonesia and Malaysia

ABSTRACT The most crucial technical challenge facing the Malaysian and Indonesian oil palm industry is that the actual yield in the form of Fresh Fruit Bunch (FFB; unit in tonne per hectare (t ha−1)) are well below of potential levels and have stagnated over last two decades. Closing this wide yield gap would have a positive impact on the revenue as it increases productivity per hectare and it eventually leads to less pressure on opening new land and mitigates environmental costs of production. With respect to the indispensable need for closing this gap for future prosperity of this industry and sustainable production of palm oil, this study assessed oil palm yield, considering the potential growth of oil palm dependent on the site qualities and actual yield. Firstly, we mapped oil palm plantations combining yearly Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and ALOS-2 mosaics of L-band backscatter, Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance (MOD13Q1), and the MODIS Vegetation Continuous Field canopy cover product (MOD44B); where 10.3 and 6.68 million ha (Mha) of oil palm plantations were mapped, respectively, in Indonesia and Malaysia in 2017. Secondly, the age after planting was estimated at detected plantations using time series of MODIS canopy cover with correlation coefficient (r) of 0.68 and Root Mean Square Error (RMSE) of 4.7 years. Thirdly, the biophysical suitability of detected plantations was evaluated considering the spatial-temporal variation of different biophysical criteria. Combining information from second and third steps, we estimated the potential yield at 250 m spatial resolution. The average potential yield in Malaysia ranges between 13.8 t ha−1 and 19.3 t ha−1 in 2017, where in Indonesia it ranges between 17.8 t ha−1 and 21.7 t ha−1 in the same year. The actual yield in next step, has been quantified by HH-HV attribute of ALOS PALSAR and ALOS-2 mosaics, where the average actual yield in Malaysia ranges between 14.48 t ha−1 and 20.63 t ha−1 and in Indonesia it ranges between 8.49 t ha−1 and15.40 t ha−1 in 2017. Finally, comparing estimated potential and actual yields, we evaluated oil palm industries’ performances where distinct differences were found between two countries. In most of the Malaysian states quantified actual yields were above or at the level of estimated potential yields, whereas in all Indonesian provinces quantified actual yields were well below the potential level. Considering the favourability of environment, among all provinces/states, Sabah, and Sarawak states in Malaysia and Aceh and North Kalimantan provinces in Indonesia distinctly differ due to their poor performances from rest of provinces/states. The information on different yields provided in this study are indispensable needs for efficient and accountable policies as it enables governors to directly target specific objectives such as subsidies on fertilizers, productive cultivars, and new technologies for the plantations suffering from low yield. Also, this study provides benchmarks for each province/state for scopes of actual yield improvements for long-term planning.

Quantitative Assessment and Driving Force Analysis of Mangrove Forest Changes in China from 1985 to 2018 by Integrating Optical and Radar Imagery

Mangrove ecosystems are valuable, yet vulnerable, and therefore they have been an important subject of protection and restoration in China. Reliable information on long-term China mangrove dynamics is lacking but vital to analyze the driving forces and evaluate the efforts of mangrove conversation. This study aims to quantify the conversions among mangroves and other land covers with high accuracy. The updated mangrove base map for 2018 was produced by integrating Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) yearly mosaics and Landsat imagery with an overall accuracy of 95.23 ± 6.02%. Then, a novel approach combining map-to-image and image-to-image methods was proposed to detect the changed pixels in mangrove forests from 1985 to 2018. The mangrove base map was adopted to mask the images from other years. To determine the changed pixels, the differencing values in the masked area between two images were calculated and compared with the corresponding thresholds. Based on the changed pixels, the possible driving forces were analyzed and associated with socioeconomic development. The resultant mangrove dynamics demonstrated that mangrove forests in China experienced a tendency of loss first and recovery later during the past 30 years. Most mangrove gains came from aquaculture and mudflat, whilst losses were due to the built-up construction and aquaculture reclamation. These conversions indicated that mangrove deforestations were mainly due to human-induced destruction, while the recoveries were strongly associated with conservation and restoration actions.

Evaluating the Effects of Digital Elevation Models in Landslide Susceptibility Mapping in Rangamati District, Bangladesh

Digital elevation models (DEMs) are the most obvious data sources in landslide susceptibility assessment. Many landslide casual factors are often generated from DEMs. Most studies on landslide susceptibility assessments rely on freely available DEMs. However, very little is known about the performance of different DEMs with varying spatial resolutions on the accurate assessment of landslide susceptibility. This study compared the performance of four different DEMs including 30 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), 30–90 m Shuttle Radar Topographic Mission (SRTM), 12.5 m Advanced Land Observation Satellite (ALOS) Phased Array Type L band Synthetic Aperture Radar (PALSAR), and 25 m Survey of Bangladesh (SOB) DEM in landslide susceptibility assessment in the Rangamati district in Bangladesh. This study used three different landslide susceptibility assessment techniques: modified frequency ratio (bivariate model), logistic regression (multivariate model), and random forest (machine-learning model). This study explored two scenarios of landslide susceptibility assessment: using only DEM-derived causal factors and using both DEM-derived factors as well as other common factors. The success and prediction rate curves indicate that the SRTM DEM provides the highest accuracies for the bivariate model in both scenarios. Results also reveal that the ALOS PALSAR DEM shows the best performance in landslide susceptibility mapping using the logistics regression and the random forest models. A relatively finer resolution DEM, the SOB DEM, shows the lowest accuracies compared to other DEMs for all models and scenarios. It can also be noted that the performance of all DEMs except the SOB DEM is close (72%–84%) considering the success and prediction accuracies. Therefore, anyone of the three global DEMs: ASTER, SRTM, and ALOS PALSAR can be used for landslide susceptibility mapping in the study area.

Topographically and hydrologically signatures express subsurface geological structures in an arid region: a modified integrated approach using remote sensing and GIS

This study aims to investigate the ability of remote sensing sensors to map subsurface fault zones with the support of hydrological information collected from boreholes. We first extract linear features crosscut and drain the study area and its adjoining mountainous areas from the Phased Array type L-band Synthetic Aperture Radar (PALSAR), the Landsat 8 images and a set of slope and shaded relief maps derived from the PALSAR DEM using visual interpretation and D8 algorithm. To delineate displacements of subsurface faults were then, interpreted from interpolated maps of groundwater level and aquifer thickness. The subsurface faults are well evident in the form palaeochannels, abruption change in flow direction and the change in the groundwater table and aquifer thickness. The extracted surface linear features were compared against those that exist in the geological map. The result showed that the subsurface geological structures can be indicated from remote sensing and hydrological data.

Penetration Depth Inversion in Hyperarid Desert From L-Band InSAR Data Based on a Coherence Scattering Model

The potential of interferometric synthetic aperture radar (InSAR) for subsurface height estimation has long been recognized; however, this method is greatly limited by the data sources and the various errors encountered in a highly dynamic environment such as a desert. In this letter, a coherence scattering model based on the volume coherence and imaging geometry of the InSAR acquisitions is proposed to retrieve the penetration depth of the synthetic aperture radar (SAR) signal in a hyperarid desert area. The proposed method includes two main parts: 1) the dielectric constant of the study area is first derived by employing an empirical model with the L-band SAR data, and then, the results are used to calibrate the vertical effective wavenumber after the refraction process and 2) together with the extracted volume coherence from the SAR data, the scattering model is employed to retrieve the penetration depth. The application scope of the vertical effective wavenumber in the volume and temporal decorrelation effect of the model is also discussed in this letter. The method was tested with the Advanced Land Observing Satellite-1 (ALOS-1) Phased Array-type L-Band Synthetic Aperture Radar (PALSAR) data from a desert area in southeast Libya. The results show that the average penetration depth of the L-band SAR in the study area is 2.98 m, and the standard deviation is 1.06 m.