Advanced Land Observing Satellite Research Articles

Decision tree-based classification in coastal area integrating polarimetric SAR and optical data

PurposeAs one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.Design/methodology/approachFour polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.FindingsThe experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.Originality/valueThis research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

Mapping forest in the southern Great Plains with ALOS-2 PALSAR-2 and Landsat 7/8 data

Southern Great Plains (SGP) of the United States, comprising the states Kansas, Oklahoma, and Texas, spans diverse climatic regions. In recent decades, woody plant continues to expand and form forest (above 2 m in height) across the SGP. However, our knowledge of the forest amount and distribution in this region is very limited. This study aims to map forest, especially evergreen forest (above 2 m in height) in the SGP for the time period of 2015–2017. Annual mosaic data of HH and HV polarization backscattering (25 m) from Phased Arrayed L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard Advanced Land Observing Satellite-2 (ALOS-2), along with their difference (HH-HV) and ratio (HH/HV) were utilized. With the four bands (HH, HV, difference, ratio) of 2017, decision rules of forest were developed based on 30 randomly selected forest plots (as of 2017) across the study area. With the decision rules, a PALSAR-2 based forest map was created for each year from 2015 to 2017. Then an annual maximum normalized difference vegetation index (NDVI) threshold of 0.5, derived from Landsat 8 data of 2017 and the 30 forest plots, was used to filter potential commission error of rough surface and building in each PALSAR-2 based forest map. After that, a forest map circa 2016 was generated, in which each forest pixel was identified as such at least twice during 2015 and 2017. Lastly, a threshold of seasonal NDVI change (0.3) was derived to extract evergreen forest out of the forest map circa 2016. Accuracy assessment for the result forest map suggests a user’s accuracy of 99.2% and a producer’s accuracy of 88.7% for forest. Accuracy assessment for the evergreen forest map suggests a user’s accuracy of 97.3% and a producer’s accuracy of 90.5% for evergreen forest. The result forest map, especially the evergreen forest map, paves the way for follow-up studies on forest resource and woody plant encroachment in the SGP.

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.

Evaluation of different DEMs for gully erosion susceptibility mapping using in-situ field measurement and validation

The spatial variability in any kind of geomorphic studies based on terrain attributes are the most important issues. This terrain attributes and their respective characteristics play a significant role in the formation and expansion of ephemeral gullies. Therefore, nowadays, the accuracy of terrain based geomorphic studies has been mostly dependent on the resolution and quality of the DEM (digital elevation model) data. As the rate of erosional power of water flow is dependent on the terrain characteristics, therefore the extraction of several terrain features from DEM data is necessary in the study of gully erosion. This case study investigates the scale-dependence of DEM-derived terrain factors in gully erosion susceptibility (GES). This work on Garhbeta-I C.D. Block has focused on the comparison among the predicted GES maps through five types of DEM i.e. Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Cartosat-1, ALOS World 3D-30 m (AW3D30) and Advanced Land Observation satellite (ALOS) coupled with the machine learning modelling approach of artificial neural network (ANN), convolution neural network (CNN) and deep learning neural network (DLNN) algorithm. A total of sixteen conditioning factors were chosen for GES assessment based on the topographical, hydro-climatological conditions and multi-collinearity analysis. Here, importance variables are measured by mean decrease accuracy (MDA) method of random forest (RF) algorithm and the result is shown that elevation is the most important factor for gully occurrences. Validation result of receiver operating characteristics-area under curve (ROC-AUC) has been indicates that DLNN model in ALOS DEM (AUC = 0.958) gives the most optimal accuracy in GES assessment. The output maps can assist in identifying gully-prone risk areas, and several suitable with sustainable managements should be taken for conservation accordingly.

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.

Satellite and UAV-based remote sensing for assessing the flooding risk from Tibetan lake expansion and optimizing the village relocation site

Climate change in recent decades led to the remarkable expansions for most lakes in endorheic basins of the Tibetan Plateau (TP). Enlarged lake inundation areas may pose adverse effects and potential threats on the local human living environment, especially for high-risk villages adjacent to rapidly expanding lakes. Taking a rapidly expanding lake, Angzi Co in the central TP as a study case, we investigated the flooding risk of lake growth on the local living environment and proposed an optimized solution of village relocation selection on the basis of satellite and unmanned aerial vehicle (UAV) remote sensing. The detection of spatiotemporal variations of Angzi Co using optical and altimetric satellite observations revealed a significant area and water level increase by 81.28 km2 and 5.78 m, respectively, from 2000 to 2020. We also assessed the vertical accuracy of multi-source digital elevation model (DEM) products using Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) altimetry data and further examined the flooding risk and potential influences of lake expansion on adjacent settlements (Guozha Village). Results indicated that UAV-DEM achieves excellent advantages in depicting details of lake shoreline variations and simulating potential submergence regions, followed by Advanced Land Observing Satellite World 3D DEM (AW3D DEM). Moreover, assuming that Angzi Co maintains the water level at a growth rate of 0.29 m/a (the average change rate during 2000–2020), the village will be submerged in approximate 10 years based on our assessment. Furthermore, we designed an optimal relocation site southwest of Guozha Village and approximately 3 km away based on the GIS-MVDA method and field investigations. An initial remote sensing-based approach for assessing the flooding risk from dramatic lake expansions in the TP and optimizing the village relocation site was proposed in this study to provide an essential scientific reference for formulating risk mitigation solutions under future climate change scenarios.

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.

Seasonal deformation monitoring of the northern Yellow River Delta based on InSAR technology.

Due to overexploitation of natural resources in the northern Yellow River Delta and the resulting land subsidence, interferometric synthetic aperture radar (InSAR) technology is utilized to analyze the deformation trend. Based on Advanced Land Observation Satellite PHASE Array Type L-band Synthetic Aperture Radar and Sentinel-1 data, this paper obtains the annual subsidence rate in two time periods (2007-2010 and 2017-2020) and verifies the correctness by comparing the ascending and descending orbit results. Subsequently, the 11 deformation interference pairs of the three-month interval are extracted to analyze the time series displacement of deformation areas. The results show that there are three large-scale subsidence areas with maximum annual subsidence of 250 mm, all of which are located in the oil or brine exploitation areas, and each deformation area displays a larger linear rate from January to May and then displays different nonlinear deformation from June to December.

UPDATES OF ‘AW3D30’ ALOS GLOBAL DIGITAL SURFACE MODEL IN ANTARCTICA WITH OTHER OPEN ACCESS DATASETS

Abstract. In 2016, the first processing of the semi-global digital surface models (DSMs) utilizing all the archives of stereo imageries derived from the Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) was successfully completed. The dataset was freely released to the public in 30 m grid spacing as the ‘ALOS World 3D - 30m (AW3D30)’, which was generated from its original version processed in 5 m or 2.5 m grid spacing. The dataset has been updated since then to improve the absolute/relative height accuracies with additional calibrations. However, the most significant update that should be applied for improving the data usability is the filling of void areas, which correspond to approx. 10% of semiglobal coverage, mostly due to cloud covers. In 2020, we completed the filling process by using other open-access digital elevation models (DEMs) such as Shuttle Radar Topography Mission (SRTM) DEM, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), ArcticDEM, etc., except for Antarctica. In this paper, we report on the filling process of the remaining voids in Antarctica by using other open-access DEMs such as Reference Elevation Model of Antarctica (REMA) DSM, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X, TDX) 90m DEM, and ASTER GDEM to complete the void-free semi-global AW3D30 datasets.

Assessing the effects of georeferencing error in a vertical comparison study of GDEMs on a local scale

ABSTRACT Global digital elevation models (GDEMs) are one of the most important sources of elevation data. Like other spatial datasets, GDEMs are inevitably affected by various types of errors in both vertical and horizontal dimensions. One of the main sources of horizontal errors is georeferencing error which in steep areas intensifies the vertical error. Since horizontal and vertical errors are interwoven with one another, any error introduced in horizontal dimension influences its vertical counterpart. In this paper, for the first time, we intend to answer this question: to what extent the impact of georeferencing error is really important in vertical accuracy of GDEMs and their vertical comparison. Hence, the main purposes of this study are evaluation of georeferencing effects on (i) the vertical accuracy of GDEMs and (ii) vertical comparison between GDEMs. In this regard, Advanced Spaceborne Thermal Emission and Reflection (ASTER), Shuttle Radar Topography Mission (SRTM), and Advanced Land Observing Satellite (ALOS) World 3D-30 m (AW3D30) GDEMs over three different study areas (BumeHen, TazehAbad and Sainte-Maxime regions) were used and assessed. In order to assess the effects of georeferencing error on GDEMs accuracy, studies were conducted in two general categories, namely Local to Global (LG) and Global to Global (GG). In the first category, these effects were studied on vertical and horizontal differences of ASTER, AW3D30, and SRTM GDEMs with their corresponding reference DEMs (REFDEMs). In the second category, these effects were assessed on vertical and horizontal differences of ASTER and SRTM GDEMs with AW3D30 GDEM. To this end, prior to making GDEMs georeferencing consistent, they were vertically assessed. For the sake of consistency of GDEMs georeferencing, a registration process was performed and then, the registered GDEMs were vertically and horizontally assessed. After the registration process, in the first category, 4.4%, 13%, and 3.3% and in the second category 2.3%, 5%, and 1.2% improvement occurred on average root mean square error (RMSE) over all GDEMs for BumeHen, TazehAbad and Sainte-Maxime regions, respectively. These results refer to the fact that GDEM registration always increases their vertical accuracy, which is in fact originated from the improvement in GDEM georeferencing consistency. Experimental results also indicate that the improvement rate of vertical errors after the DEM registration process is directly related to terrain slope angle. From another perspective, we illustrated that in general the amounts of achieved improvements in GDEMs vertical accuracy are much smaller than their nominal accuracies. Finally, our results show that the effect of horizontal error on vertical comparison between GDEMs is unavoidable, but this amount of error can be ignored because the ratio of improvements occurred in the GDEMs with respect to their nominal accuracies is very low.

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.

Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan

Pakistan is a flood-prone country and almost every year, it is hit by floods of varying magnitudes. This study was conducted to generate a flash flood map using analytical hierarchy process (AHP) and frequency ratio (FR) models in the ArcGIS 10.6 environment. Eight flash-flood-causing physical parameters were considered for this study. Five parameters were based on the digital elevation model (DEM), Advanced Land Observation Satellite (ALOS), and Sentinel-2 satellite, including distance from the river and drainage density slope, elevation, and land cover, respectively. Two other parameters were geology and soil, consisting of different rock and soil formations, respectively, where both layers were classified based on their resistance against water percolation. One parameter was rainfall. Rainfall observation data obtained from five meteorological stations exist close to the Chitral District, Pakistan. According to its significant importance in the occurrence of a flash flood, each criterion was allotted an estimated weight with the help of AHP and FR. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of the drainage density was given the highest value. This gave the output in terms of five flood risk zones: very high risk, high risk, moderate risk, low risk, and very low risk. According to the results, 1168 km2, that is, 8% of the total area, showed a very high risk of flood occurrence. Reshun, Mastuj, Booni, Colony, and some other villages were identified as high-risk zones of the study area, which have been drastically damaged many times by flash floods. This study is pioneering in its field and provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists.

Comparison of Surface Subsidence Measured by Airborne and Satellite InSAR Over Permafrost Areas Near Yellowknife Canada.

In addition to spaceborne Interferometric Synthetic Aperture Radar (InSAR), airborne data such as those obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) have also been utilized to measure surface subsidence in permafrost areas in recent years. Motivated by the integration of multiplatform InSAR data, we generated two UAVSAR interferograms and one Advanced Land Observing Satellite (ALOS)‐2 L‐band interferogram over a permafrost area near Yellowknife, Canada, then compared the surface subsidence in the thaw seasons of 2017. The correlation coefficient and the root mean square error (RMSE) of subsidence difference are calculated to compare the airborne and spaceborne InSAR measurements. The results demonstrate that the two UAVSAR measurements are self‐consistent, with the correlation coefficient between independent airborne measurements ∼0.7. While the RMSE of the difference between surface subsidence measured by UAVSAR and ALOS2 is ∼2.0 cm, and the correlation coefficients are less than 0.41, that is, a noticeable deviation exists between the UAVSAR and ALOS2 results possibly due to different spatial resolution and the calibration processing of airborne and spaceborne InSAR data. In addition, both UAVSAR and ALOS2 interferograms show larger surface subsidence within taiga needleleaf forest regions than in regions of other biome types (including needleleaf forest, shrubland, and grassland). The results demonstrate that a scheme for the elimination of systematic differences needs to be developed before merging multisource InSAR results. This intercomparison will provide valuable insights for narrowing the gap between radar‐based measurements and planning the integration of airborne and satellite InSAR measurements in permafrost environments.

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.

Hydrological modeling of Ar Rub Al Khali, Arabian Peninsula: a modified remote sensing approach based on the weight of hydrological evidence

Hydrological modelling of Ar Rub Al Kahli over a very regional scale using the weight of evidence based on pixel calculation and several pieces of evidences is timely consuming. This study aims to modify an approach, which calls a weight of hydrological evidence (WoHE) approach. The approach is based on calculating the logarithmic ratio of the total areas of distribution good (event) and distribution bad (non-event) of features such as faults, palaeochannels and zones of flow accumulation. The approach starts by mapping fault zones, palaeochannels, drainage basins and flow accumulation zones from the Advanced Land Observing Satellite (ALOS) DEM. The proposed approach is an effective approach to improve the hydrological modeling and summarize large datasets over a very regional scale obtained using machine learning, which is used widely in the literature. The results exhibited that the basins of Dhofar has the highest values for the WoHE of 0.0432.

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. The unique terrain characteristics of a particular landscape are derived from DEM, which are responsible for initiation and development of ephemeral gullies. As the topographic features of an area significantly influences on the erosive power of the water flow, it is an important task the extraction of terrain features from DEM to properly research gully erosion. Alongside, topography is highly correlated with other geo-environmental factors i.e. geology, climate, soil types, vegetation density and floristic composition, runoff generation, which ultimately influences on gully occurrences. Therefore, terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility (GES) mapping. In this study, remote sensing-Geographic information system (GIS) techniques coupled with machine learning (ML) methods has been used for GES mapping in the parts of Semnan province, Iran. Current research focuses on the comparison of predicted GES result by using three types of DEM i.e. Advanced Land Observation satellite (ALOS), ALOS World 3D-30 m (AW3D30) and Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) in different resolutions. For further progress of our research work, here we have used thirteen suitable geo-environmental gully erosion conditioning factors (GECFs) based on the multi-collinearity analysis. ML methods of conditional inference forests (Cforest), Cubist model and Elastic net model have been chosen for modelling GES accordingly. Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods (Cforest = 21.4, Cubist = 19.65 and Elastic net = 17.08), followed by lithology and slope. Validation of the model’s result was performed through area under curve (AUC) and other statistical indices. The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs (AUC value of Cforest in ALOS DEM is 0.994, AW3D30 DEM is 0.989 and ASTER DEM is 0.982) used in this study, followed by elastic net and cubist model. The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.

The evaluation of the potential of global data products for snow hydrological modelling in ungauged high-alpine catchments

Abstract. For many ungauged mountain regions, global datasets of different meteorological and land surface parameters are the only data sources available. However, their applicability in modelling high-alpine regions has been insufficiently investigated so far. Therefore, we tested a suite of globally available datasets by applying the physically based Cold Regions Hydrological Model (CRHM) for a 10-year (September 2000–August 2010) period in the gauged high-alpine Research Catchment Zugspitze (RCZ), which is 12 km2 and located in the European Alps. Besides meteorological data, snow depth is measured at two stations. We ran CRHM with a reference run with in situ-measured meteorological data and a 2.5 m high-resolution digital elevation model (DEM) for the parameterization of the surface characteristics. Regarding different meteorological setups, we used 10 different globally available datasets (including versions of ERA, GLDAS, CFSR, CHIRPS) and additionally one transferred dataset from a similar station in the vicinity. Regarding the different DEMs, we used ALOS (Advanced Land Observing Satellite) and SRTM (Shuttle Radar Topography Mission) (both 30 m) as well as GTOPO30 (1 km). The following two main goals were investigated: (a) the reliability of simulations of snow depth, specific snow hydrological parameters and runoff with global meteorological products and (b) the influence of different global DEMs on snow hydrological simulations in such a topographically complex terrain. The range between all setups in mean decadal temperature is high at 3.5 ∘C and for the mean decadal precipitation sum at 1510 mm, which subsequently leads to large offsets in the snow hydrological results. Only three meteorological setups, the reference, the transferred in situ dataset and the CHIRPS dataset, substituting precipitation only, showed agreeable results when comparing modelled to measured snow depth. Nevertheless, those setups showed obvious differences in the catchment's runoff regime and in snow depth, snow cover, ablation period, the date, and quantity of maximum snow water equivalent in the entire catchment and in specific parts. All other globally available meteorological datasets performed worse. In contrast, all globally available DEM setups reproduced snow depth, the snow hydrological parameters and runoff quite well. Differences occurred mainly due to differences in radiation model input due to different spatial realizations. Even though SRTM and ALOS have the same spatial resolution, they showed considerable differences due to their different product origins. Despite the fact that the very coarse GTOPO30 DEM performed relatively well on the catchment mean, we advise against using this product in such heterogeneous high-alpine terrain since small-scale topographic characteristics cannot be captured. While global meteorological data are not suitable for sound snow hydrological modelling in the RCZ, the choice of the DEM with resolutions in the decametre level is less critical. Nevertheless, global meteorological data can be a valuable source to substitute single missing variables. For the future, however, we expect an increasing role of global data in modelling ungauged high-alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.

A Geo-spatial approach for quantifying rooftop photovoltaic energy potential in Karnal smart city, Haryana - A case study

Solar energy is one of the best sustainable forms of renewable energy. India has a cumulative installed capacity of 9.23 GW of grid-connected solar power and set an ambitious target of attaining 100 GW of solar capacity by 2022, including 40 GW of grid-connected rooftop solar installations well. The present study demonstrates the Geospatial technology to estimate the potential of solar photovoltaic on the rooftops of Karnal city. The satellite data of Sentinel -2 and World View-II data was interpreted so as to extract the building footprints. Digital Elevation Model (DEM) data derived from ALOS (Advanced Land Observing Satellite) PALSAR (2019) data was used to calculate the Global Horizontal Irradiance (GHI). It was calculated that the average annual GHI varied between 0.79-5.9 kWh/m2/day. The study revealed maximum GHI (462 kWh/m2) was recorded during the monsoon season. It was estimated that the seasonal energy generation capacity in urban area was minimum (268.4MWh) in the winter season, while the maximum (2632.4MWh) energy generation capacity was observed during the monsoon season. In the case of the industrial area, the minimum seasonal energy generation capacity was found to be 23.9 MWh in winter while the maximum of 234.8 MWh during the monsoon season. If solar panels installed on the proposed rooftops, an estimated energy potential of 5.9 GWh would be generated in the study area.

Assessment of the Seismic Hazards of the Marikina Valley Fault from 2019 Mw 6.1 Castillejos Earthquake and Historical Events

Abstract The 2019 Mw 6.1 Castillejos earthquake occurred in the Zambales range of the central Luzon Island in Philippines. No active fault was reported around the seismogenic zone according to previous investigations. This earthquake draws attention for assessment in seismic risk along the Marikina Valley fault system (MVFS) near the Manila dense metropolitan population. The Coulomb failure stress (CFS) change on the MVFS is estimated by the coseismic faulting model derived from the inversion of coseismic deformation field observed from the Differential Interferometric Synthetic Aperture Radar using both the Advanced Land Observing Satellite-2 and Sentinel-1 Synthetic Aperture Radar (SAR) images. The predicted CFS change is less than 0.5 kPa that implies insignificant Coulomb stress accumulation on the MVFS after the Mw 6.1 Castillejos event. However, the recorded 14 moderate and strong earthquakes in and around the Luzon islands caused significant CFS drop on the MVFS. This might delay the occurrence of the earthquake for 0.2–50 yr on the MVFS.

Quality assessment of open sourced digital elevation models in southeast coast of India

Digital elevation model (DEM) provides elevation values of the terrain. At present several open source DEMs are available and its vertical accuracy varies from place to place. The present study assessed the vertical accuracy of 1 arc-sec data of Advanced Land Observing Satellite digital surface model (AW3D30) Version-3, Shuttle Radar Topographic Mission (SRTM) DEM (V3), Carto DEM (V3R1) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (V2) using Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic Global Positioning System (RTK GPS) data in Southeast coast of India. Root mean square error (RMSE) was calculated for DEMs, showed the elevation accuracy as 2.48 m (AW3D30), 3.02 m (SRTM DEM), 3.88 m (Carto DEM) and 8.02 m (ASTER GDEM) in the Tamil Nadu state while it was 2.29 m (AW3D30), 3.10 m (SRTM DEM), 3.93 m (Carto DEM) and 8.98 m (ASTER GDEM) in the 10 km buffer zone of coastal region and the results are satisfactory than the official product’s specifications. Landform analysis was carried out at selected RTK GPS surveyed sites where the elevation of different coastal landforms is validated with DEMs and it was found the AW3D30 showed least RMSE than others. This paper suggests that AW3D30 is best for representing terrain elevation values followed by SRTM and Carto DEMs which are apt choices for application studies.