High Resolution Satellite Images Research Articles

Canopy composition drives variability in urban growing season length more than the heat island effect

The elevated heat of urban areas compared to their surroundings makes humid temperate cities a useful preview of future climate effects on natural forest phenology. The utility of this proxy rests on the expectation that trees in urban areas alter their phenology in response to warmer site conditions in spring and fall. However, it is possible that apparent lengthening of the growing season is instead governed by human-driven tree species selection and plant functional type (PFT; trees, shrubs, turfgrass) heterogeneity typical of managed landscapes. Without the use of highly spatially and temporally resolved remote sensing data, the roles of tree taxonomy and local site characteristics (e.g., impervious cover) in controlling phenology remain confounded. To understand the drivers of earlier start of season (SOS) and later end of season (EOS) among urban trees, we estimated individual tree phenology using >130 high-resolution satellite images per year (2018–2020) for ~10,000 species-labeled trees in Washington, DC. We found that species identity alone accounted for 4× more variability in the timing of SOS and EOS compared with a tree's planting location characteristics. Additionally, the urban mix of PFTs may be more responsible for apparent advances in SOS (by between 1.8 ± 1.3 and 3.5 ± 1.3 days) than heat per se. The results of this study caution against associating longer growing seasons in cities—observed in moderate to coarse resolution remote sensing imagery—to within-species phenological plasticity and demonstrate the power of high-resolution satellite data for tracking tree phenology in biodiverse environments.

Evaluation of River Water Quality Index Using Remote Sensing and Artificial Intelligence Models

To restrict the entry of polluting components into water bodies, particularly rivers, it is critical to undertake timely monitoring and make rapid choices. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. In this study, the water quality index (WQI) for the Hudson River has been estimated using Landsat 8 OLI-TIRS images and four Artificial Intelligence (AI) models, such as M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Programming (GEP), and Evolutionary Polynomial Regression (EPR). In this way, 13 water quality parameters (WQPs) (i.e., Turbidity, Sulfate, Sodium, Potassium, Hardness, Fluoride, Dissolved Oxygen, Chloride, Arsenic, Alkalinity, pH, Nitrate, and Magnesium) were measured between 14 March 2021 and 16 June 2021 at a site near Poughkeepsie, New York. First, Multiple Linear Regression (MLR) models were created between these WQPs parameters and the spectral indices of Landsat 8 OLI-TIRS images, and then, the most correlated spectral indices were selected as input variables of AI models. With reference to the measured values of WQPs, the WQI was determined according to the Canadian Council of Ministers of the Environment (CCME) guidelines. After that, AI models were developed through the training and testing stages, and then estimated values of WQI were compared to the actual values. The results of the AI models’ performance showed that the MARS model had the best performance among the other AI models for monitoring WQI. The results demonstrated the high effectiveness and power of estimating WQI utilizing a combination of satellite images and artificial intelligence models.

Monitoring vessel deadweight tonnage for maritime transportation surveillance using high resolution satellite image

With the continuous development of shipping industry, vessel traffic faced the challenges including concealment of vessel tonnage, insufficient vessel lock passing capacity, the forged small certificates of large vessels, etc. This paper presents a novel method of monitoring vessel deadweight tonnage (DWT) using high-resolution satellite images to solve the impact of the above problems on maritime transportation. The length and width of the vessels were obtained by Rotation Invariant Task-Aware Spatial Disentanglement (RITSD) algorithm, and the vessel DWT can be obtained efficiently by using the regression formula of vessel DWT and shape. The results indicate that, compared with the cumbersome and time-consuming traditional methods, the non-contact method proposed in this paper reduces the calculation amount and improves the efficiency under the premise of using fewer calculation factors. For shipping management and port development, apply this method to estimate the DWT or goods throughput of vessels entering and leaving the port is helpful to formulate a reasonable port operation plan and channel layout. It can also help the maritime safety agencies with the supervision and monitoring of the maritime transportation conditions.

Multi-Scale and Context-Aware Framework for Flood Segmentation in Post-Disaster High Resolution Aerial Images

Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate resources to the affected areas. To efficiently respond to the natural disaster, it is very crucial to swiftly obtain accurate information, which is hard to obtain during a post-flood crisis. Generally, high resolution satellite images are predominantly used to obtain post-disaster information. Recently, deep learning models have achieved superior performance in extracting high-level semantic information from satellite images. However, due to the loss of multi-scale and global contextual features, existing deep learning models still face challenges in extracting complete and uninterrupted results. In this work, we proposed a novel deep learning semantic segmentation model that reduces the loss of multi-scale features and enhances global context awareness. Generally, the proposed framework consists of three modules, encoder, decoder and bridge, combined in a popular U-shaped scheme. The encoder and decoder modules of the framework introduce Res-inception units to obtain reliable multi-scale features and employ a bridge module (between the encoder and decoder) to capture global context. To demonstrate the effectiveness of the proposed framework, we perform an evaluation using a publicly available challenging dataset, FloodNet. Furthermore, we compare the performance of the proposed framework with other reference methods. We compare the proposed framework with recent reference models. Quantitative and qualitative results show that the proposed framework outperforms other reference models by an obvious margin.

EVALUATING THE POTENTIAL OF 8 BAND PLANETSCOPE DATASET FOR CROP CLASSIFICATION USING RANDOM FOREST AND GRADIENT TREE BOOSTING BY GOOGLE EARTH ENGINE

Abstract. One of the challenging task in agriculture is mapping of crops using satellite images as spectral properties of the crops looks similar to each other, and there are many complexities which are there in the field such as small land holdings, heterogeneous and often distinct field patterns. In past, mapping with high resolution satellite images was not possible due to the non-availability of data and even they were costlier. However, with the free availability of PlanetScope dataset having 8 spectral bands, with daily revisit cycle and resolution of 3m, it is now possible to carry out mapping and monitoring of agriculture crops. The objective of the study is to classify major crops of rabi (December-April) season using single date PlanetScope imagery in the Haridwar district, Uttarakhand, India. Classification of crops has been carried out using an ensemble based machine learning algorithms within Google earth engine. Off late, Google Earth Engine (GEE), a cloud based platform has attracted the attention of remote sensing analyst since it expediates the classification yielding good results of high quality. In this study, Random Forest (RF) and Gradient Tree Boosting algorithm (GTB) have been used after performing hyper-parameter tuning and results shows that overall accuracy obtained by RF and GTB are 88% and 86.5% respectively. According to the results, both classifier performed well but RF achieved 1.5% high accuracy over GTB. Analysis of the results show that highest accuracy was attained by agriculture class (wheat) while other crops class exhibited lower accuracy. In this study, RF was found to be more competent as compared to GTB in classifying the crops and PlanetScope 8 band dataset has also proved its potential in classifying crops in heterogeneous fields.

Dynamics of Mountain Urbanisation: Evidence from the Trans-Himalayan Town of Kargil, Ladakh, India

Small and medium-sized towns in the high mountain regions of South Asia are characterised by rapid and mostly unplanned urbanisation processes, regularly resulting in an increased risk of urban agglomerations being exposed to natural hazards. After the administrative capital of Leh, Kargil town is the second-largest urban centre in the Union Territory (UT) of Ladakh. This article discusses the development and dynamics of the Trans-Himalayan town in terms of its historical development, expansion and population; land use and land cover (LULC) change; and the specific role of tourism. Based on a multi-temporal approach using high-resolution satellite images, statistical data and field surveys, the urban landscape dynamics of Kargil town are analysed. The total population of this town increased almost tenfold from 1681 in 1961 to 16,338 in 2011, which resulted in an increase in the urban population from 3.7% to 11.6%, while the population of the entire Kargil district only tripled from 45,064 to 140,802 over the same period. Migration from rural villages to Kargil town has been a major cause for the construction and growth of new residential colonies and settlements. The built-up area increased more than ninefold from 0.25 km2 (1.3%) to 2.30 km2 (11.7%) between 1965 and 2020. During the same period, irrigated land and hygrophilous vegetation increased considerably from 4.51 km2 (23.0%) to 8.56 km2 (43.6%) due to the construction of new water channels. Similarly, the barren area decreased significantly from 14.88 km2 (75.8%) to 8.78 km2 (44.7%) between 1965 and 2020. Moreover, the massive increase in tourist arrivals has led to the construction of more hotels, guesthouses and travel agencies. These key factors play a significant role in the emergence, growth and development of this high-mountain town.

On the Robustness and Generalization Ability of Building Footprint Extraction on the Example of SegNet and Mask R-CNN

Building footprint (BFP) extraction focuses on the precise pixel-wise segmentation of buildings from aerial photographs such as satellite images. BFP extraction is an essential task in remote sensing and represents the foundation for many higher-level analysis tasks, such as disaster management, monitoring of city development, etc. Building footprint extraction is challenging because buildings can have different sizes, shapes, and appearances both in the same region and in different regions of the world. In addition, effects, such as occlusions, shadows, and bad lighting, have to also be considered and compensated. A rich body of work for BFP extraction has been presented in the literature, and promising research results have been reported on benchmarking datasets. Despite the comprehensive work performed, it is still unclear how robust and generalizable state-of-the-art methods are to different regions, cities, settlement structures, and densities. The purpose of this study is to close this gap by investigating questions on the practical applicability of BFP extraction. In particular, we evaluate the robustness and generalizability of state-of-the-art methods as well as their transfer learning capabilities. Therefore, we investigate in detail two of the most popular deep learning architectures for BFP extraction (i.e., SegNet, an encoder–decoder-based architecture and Mask R-CNN, an object detection architecture) and evaluate them with respect to different aspects on a proprietary high-resolution satellite image dataset as well as on publicly available datasets. Results show that both networks generalize well to new data, new cities, and across cities from different continents. They both benefit from increased training data, especially when this data is from the same distribution (data source) or of comparable resolution. Transfer learning from a data source with different recording parameters is not always beneficial.

Potential landslides risk assessment based on remote sensing technique-a cause study from Dorset, southern England, United Kingdom

This paper presents a method of potential landslide risk assessment from the Dorset Southern England, United Kingdom. It concentrated on potential geological hazards and vulnerability to build a quantitative landslide risk framework in Dorset. In this study, the landslides risk assessment is centered on three components: identification of risks, calculation of risks, and evaluation of risks. Therefore, different natural environmental situations were collected using a large set of optical high-resolution satellite images and remote sensing to determine the probability of landslide occurrence and provide the quantitative model by combining the remote sensing technique. The result shows Dorset area risk level is high. The possibility of landslides mainly comes from natural conditions and human actions. The calculation of the possibility of potential landslides is based on the Bayes’ theorem. Moreover, the limitation of the probability potential risk calculation is insensitivity. The challenge is related to the temporal resolution of precipitation information.

Spatial variability in melting on Himalayan debris-covered glaciers from 2000 to 2013

Glaciers covered by debris are widespread in the Himalayas. Debris layer makes the melting process of glaciers complicated, and the correlation between the spatial melt differences of glacier and its surface features is still not clear. The aim of this study was to identify possible causes of thickness changes for Himalayan debris-covered glaciers. Firstly, three high resolution Digital Elevation Model (DEM) datasets including SRTM DEM, AW3D and TanDEM-X DEM were used to estimate glacier thickness changes from 2000 to 2013. By checking and removing geometric displacements, systematic deviations and elevation-related bias, as well as correction of radar wave penetration depth in the clean glacier areas, reliable thickness change information in this complex glaciated region were provided. Then debris thicknesses on the glaciers were estimated. The surface lowering caused by ice cliffs or supraglacial ponds significantly reduces debris thickness. Finally, some surface features including debris thicknesses, spatial patterns of supraglacial ponds and ice cliffs extracted from high-resolution satellite images in different times, were comprehensively used to evaluate the main drivers of glacier melting differences. Results demonstrate that spatial variability of glacier melting in Himalayas is caused by the combined effects of debris thicknesses, ice cliffs and supraglacial lakes, with different dominant factors for different regions. The thickened glacier region, which mostly appears above 5300 m a.s.l., with minimal debris cover, displayed large patches of thickening and partial serious thinning in the debris-free areas. Although the thick debris layer (> 0.20 m) would slow down the glacier melting at the low-elevations, it leads to the heavy glacier thinning for the regions located 5000–5300 m a.s.l., where there are dense ice cliffs. In some areas with >1 m of average debris thickness, glaciers have thinned slowly because of erosion from the ice cliffs or large supraglacial ponds. Area and number of cliffs have proven to be key factors in controlling regional ablation for debris-covered glaciers, and supraglacial lakes serve as assistance.

Linking PM10 and PM2.5 Pollution Concentration through Tree Coverage in Urban Areas

AbstractParticulate matter, PM10 and PM2.5, represents common air pollutants in cities and constitute a considerable threat to public health impacting daily activity of people living in city. In large cities, the main sources of PM10 and PM2.5 are diesel engine exhaust, brake dust, and particulate matter from vehicle tires. These particles can be deposited, filtered, and considerably reduced if there is a vegetative surface in the neighborhoods, thus eliminating a part of these particles and reducing their harmful footprint. This study evaluates the effect of tree coverage in urban areas on PM10 and PM2.5 removal considering air quality monitoring stations. Estimation of tree coverage is made by using high spatial and temporal resolution satellite images from Planet constellations. An empirical relationship between these two variables, with an acceptable correlation (R2 = 0.478 and R2 = 0.589 for PM10 and PM2.5, respectively), is obtained. A higher abundance of green space is associated with significantly lower PM10 and PM2.5 values. Preliminary results suggest that the amount of tree coverage do cause some degree of air quality improvement and can be used to inform national clean air strategies aimed at reducing pollutant emissions.

Public remotely sensed data raise concerns about history of failed Jagersfontein dam

A mine waste deposit known as a tailings dam recently failed in the town of Jagersfontein, South Africa. The failure occurred amidst global concern about the safety record of these structures. Herein we use public remotely sensed data to gain insights into the construction history of the dam. The data suggest a construction sequence that is inconsistent with sound tailings management practices: asymmetric deposition, erosion gullies, large ponds and absence of beaches. These observations highlight the criticality of adhering to good construction practices and the potential of public data to monitor such adherence. Additionally, we present commercially available very high resolution satellite images to illustrate some of the immediate consequences of the failure.

Monitoring Glacier Lake Outburst Flood (GLOF) of Lake Merzbacher Using Dense Chinese High-Resolution Satellite Images

Establishing an effective real-time monitoring and early warning system for glacier lake outburst floods (GLOFs) requires a full understanding of their occurrence mechanism. However, the harsh conditions and hard-to-reach locations of these glacial lakes limit detailed fieldwork, making satellite imagery a critical tool for monitoring. Lake Mercbacher, an ice-dammed lake in the central Tian Shan mountain range, poses a significant threat downstream due to its relatively high frequency of outbursts. In this study, we first monitored the daily changes in the lake area before the 2022 Lake Mercbacher outburst. Additionally, based on historical satellite images from 2014 to 2021, we calculated the maximum lake area (MLA) and its changes before the outburst. Furthermore, we extracted the proportion of floating ice and water area during the period. The results show that the lake area of Lake Mercbacher would first increase at a relatively low speed (0.01 km2/day) for about one month, followed by a relatively high-speed increase (0.04 km2/day) until reaching the maximum, which would last for about twenty days. Then, the lake area would decrease slowly until the outburst, which would last five days and is significant for early warning. Moreover, the floating ice and water proportion provides more information about the outburst signals. In 2022, we found that the floating ice area increased rapidly during the early warning stage, especially one day before the outburst, accounting for about 50% of the total lake area. Historical evidence indicates that the MLA shows a decreasing trend, and combining it with the outburst date and climate data, we found that the outburst date shows an obvious advance trend (6 days per decade) since 1902, caused by climate warming. Earlier melting results in an earlier outburst. This study provides essential references for monitoring Lake Mercbacher GLOFs and building an effective early warning system.

Assessing the influence of transect interval in monitoring and analysis of shoreline change

Shoreline analysis helps to understand the coastal dynamism for decision-making in coastal management. As there are still doubts in transect-based analysis, this study attempts to understand the influence of transect intervals in shoreline analysis. Shorelines were delineated on high-resolution satellite images in Google Earth Pro for twelve beaches in Sri Lanka under different spatial and temporal scales. Shoreline change statistics were calculated using Digital Shoreline Analysis System in the ArcGIS 10.5.1 software under 50 transect interval scenarios, and influence of the transect interval for shoreline change statistics were interpreted using standard statistical methods. Transect interval error was calculated with respect to the 1m scenario as this has the best beach representation. Results revealed that there is no any significant difference (p > 0.05) of shoreline change statistics between 1 and 50m scenarios in each beach. Furthermore, it was found that the error was extremely low up to 10m scenario and then after it was subject to fluctuate in an unpredictable manner (R2 < 0.5). Overall, the study concludes that the influence of the transect interval is negligible and 10-m transect interval is ideal in shoreline analysis for the highest efficacy in small sandy beaches.

Tractable large-scale deep reinforcement learning

Reinforcement learning (RL) has emerged as one of the most promising and powerful techniques in deep learning. The training of intelligent agents requires a myriad of training examples which imposes a substantial computational cost. Consequently, RL is seldom applied to real-world problems and historically has been limited to computer vision tasks, similar to supervised learning. This work proposes an RL framework for complex, partially observable, large-scale environments. We introduce novel techniques for tractable training on commodity GPUs, and significantly reduce computational costs. Furthermore, we present a self-supervised loss that improves the learning stability in applications with a long-time horizon, shortening the training time. We demonstrate the effectiveness of the proposed solution on the application of road extraction from high-resolution satellite images. We present experiments on satellite images of fifteen cities that demonstrate comparable performance to state-of-the-art methods. To the best of our knowledge, this is the first time RL has been applied for extracting road networks. The code is publicly available at https://github.com/nsarang/road-extraction-rl.

GIS-based Valuation of Ecosystem Services in the Lower Delta Plain of West Bengal with Special Reference to Indian Sundarbans

The Indian Sundarbans partly hosts the world’s largest mangrove forest and support the livelihood of almost five million people. The Holocene delta of Indian Sundarbans was formed by the river Ganges. The decay of the distributaries of the river Ganges in deltaic West Bengal began with the eastward avulsion of the river Ganges. The rivers of Sundarbans now stand disconnected from their sources and therefore do not get a significant amount of freshwater from upstreams. The people of Sundarbans depend on the services of the Sundarbans ecosystem which is directly linked to the upstream freshwater flow, rainfall and groundwater. The scarcity of freshwater during dry seasons, high salinity of surface water and soil, siltation and drainage congestion are major constraints on livelihood security in this area. The biodiversity of Sundarbans is also threatened by water stress. To ensure the sustainable conservation of the rich biodiversity and well-being of the people of Sundarbans, improving ecosystem services in the region is extremely essential. From this perspective, the present study aims to identify major flow-dependent ecosystem services in the region and estimate the value of those ecosystem services using geoinformatics. The land use land cover (LULC) maps have been prepared for the years 1990, 2000, 2010 and 2020 from Landsat images for the valuation of major ecosystem services. Accuracy assessment has been performed using high-resolution satellite images and field data. Change detection analysis has been done to understand the changing land use pattern in the study area. The LULC-based valuation of the ecosystem services has been carried out for the years 1990, 2000, 2010 and 2020. The area under each LULC class has been multiplied by the global Ecosystem Service Value (ESV) of that class to calculate the total ESV of the specific LULC class. The study reveals that the ESVs are the highest for freshwater bodies including rivers, and estuaries followed by forests. With the decreasing upstream freshwater flow and deteriorating mangrove health, the services of flow-dependent ecosystems have changed drastically.

RSCDNet: A Robust Deep Learning Architecture for Change Detection From Bi-Temporal High Resolution Remote Sensing Images

Accurate change detection from high-resolution satellite and aerial images is of great significance in remote sensing for precise comprehension of Land cover (LC) variations. The current methods compromise with the spatial context; hence, they fail to detect and delineate small change areas and are unable to capture the difference between features of the bi-temporal images. This paper proposes Remote Sensing Change Detection Network (RSCDNet) - a robust end-to-end deep learning architecture for pixel-wise change detection from bi-temporal high-resolution remote-sensing (HRRS) images. The proposed RSCDNet model is based on an encoder-decoder framework integrated with the Modified Self-Attention (MSA) andthe Gated Linear Atrous Spatial Pyramid Pooling (GL-ASPP) blocks; both efficient mechanisms to regulate the field-of-view while finding the most suitable trade-off between accurate localization and context assimilation. The paper documents the design and development of the proposed RSCDNet model and compares its qualitative and quantitative results with state-of-the-art HRRS change detection architectures. The above mentioned novelties in the proposed architecture resulted in an F1-score of 98%, 98%, 88%, and 75% on the four publicly available HRRS datasets namely, Staza-Tisadob, Onera, CD-LEVIR, and WHU. In addition to the improvement in the performance metrics, the strategic connections in the proposed GL-ASPP and MSA units significantly reduce the prediction time per image (PTPI) and provide robustness against perturbations. Experimental results yield that the proposed RSCDNet model outperforms the most recent change detection benchmark models on all four HRRS datasets.

Lateral growth of Safeen anticline as deduced from drainage analysis, Iraqi Kurdistan region

Safeen anticline is one of the largest anticlines in the Iraqi Kurdistan Region (IKR), with NW – SE trend. The anticline shows clear lateral growth as deduced from different anomalous drainage patterns and forms, besides different geomorphological forms, which are indicative of lateral growth. Among those forms are water gaps, wind gaps, abandoned alluvial fans, and different shapes of valleys. Two hanging synclines exist on both sides of the anticline forming unique geomorphological and structural forms in IKR. The anticline is developed in the High Folded Zone (HFZ) of Iraq. It is part of the Zagros Fold–Thrust Belt (ZFTB), with the exposed rocks of the Qamchuqa Formation (Early Cretaceous), which consists mainly of limestone and dolostone, which are the oldest rocks in the core of the anticline. Two main streams named Shaqlawa and Kori dissect the anticline from the northern and southern sides. We have explained their trends and abnormal courses. We have used geological maps and high-resolution satellite images to interpret geomorphological forms and to confirm the lateral growth of the anticline. A field investigation was carried out to check and confirm the interpreted data; accordingly, we identified the anticline's lateral growth based on the interpreted geomorphological and structural forms.

Water Hyacinth's Extent and Its Implication on Water Quality in Lake Victoria, Uganda.

Water hyacinth (Eichhornia crassipes) degrades and obstructs the integrity of freshwater ecosystems. However, little attention has been paid to monitoring water hyacinth's spatial extent, its determinants, and its effects on water quality in Lake Victoria, Uganda. The specific objectives of this paper are to (i) assess the spatial extent and distribution of water hyacinth; (ii) examine the determinants of water hyacinth distribution, and (iii) assess its impact on water quality. High-resolution satellite images (2016-2019) were obtained and used to monitor the spatial extent of the water hyacinth, a household survey was conducted to examine the determinants of the water hyacinth's extent and patterns while water samples were drawn and analysed for physicochemical properties. Results show that the coverage and distribution of water hyacinth varied over space and time. Water hyacinth coverage primarily increased with a decrease in water surface area. The perceived factors that triggered the water hyacinth spread included the morphology of the Bay, effluent discharge, strong winds, speed of water current, water-level changes, ferry navigation, and construction activities at the shore. Water parameters significantly impacted by hyacinth were pH, TP, BOD, COD, DO, turbidity, and transparency. This study recommends the strict development and implementation of integrated weed control measures, catchment management plans, and point and nonpoint pollution source control.

Freely accessible inventory and spatial distribution of large-scale landslides in Xianyang City, Shaanxi Province, China

In this study, we used high-resolution optical satellite images on the Google Earth platform to map large-scale landslides in Xianyang City, Shaanxi Province, China. After mapping, a comprehensive and detailed large-scale landslide inventory that contains 2924 large-scale landslides was obtained. We analyzed the spatial distribution of landslides with seven influencing factors, including elevation, slope angle, aspect, curvature, lithology, distance to a river, and distance to the fault. Landslide Number, Landslide Area, Landslide Number Density (LND), and Landslide Area Percentage (LAP) were selected as indexes for the spatial distribution analysis. The results show that the number and area of landslides in the elevation range of 1000–1200 ​m is the highest. The highest number of landslides was observed in the slope angle of 25°–30°. North-facing slopes are prone to sliding. The area and number of landslides are the largest when the slope curvature ranges from −1.28 to 0. The LND and LAP reach their maxima when the slope curvature is less than −2.56. Areas covered by the Tertiary stratum with weakened fine-grained sandstone and siltstone show the highest LND and LAP values. Regarding distance to a river, the LAP peaks in the range of 300–600 ​m, whereas the LND peaks in an area larger than 2100 ​m. The values of LND and LNP rise as the distance from the faults increases, except for the locations 30 ​km away from active faults. This phenomenon is because active faults in this area pass through the plain areas, while landslides mostly occur in mountainous areas. The cataloging of landslide development in Xianyang City provides a significant scientific foundation for future research on landslides. In addition, the spatial distribution results are useful for landslide hazard prevention decisions and provide valuable references in this area.

Geolocation Accuracy Validation of High-Resolution SAR Satellite Images Based on the Xianning Validation Field

The geolocation accuracy of Synthetic Aperture Radar (SAR) images is crucial for their application in various industries. Five high-resolution SAR satellites, namely ALOS, TerraSAR-X, Cosmo-SkyMed, RadarSat-2, and Chinese YG-3, provide a vast amount of image data for research purposes, although their geometric accuracies differ despite similar resolutions. To evaluate and compare the geometric accuracy of these satellites under the same ground control reference, a validation field was established in Xianning, China. The rational function model (RFM) was used to analyze the geometric performance of the five satellites based on the Xianning validation field. The study showed that each image could achieve sub-pixel positioning accuracy in range and azimuth direction when four ground control points (GCPs) were placed in the corners, resulting in a root mean square error (RMSE) of 1.5 pixels. The study also highlighted the effectiveness of an automated GCP-matching approach to mitigate manual identification of points in SAR images, and results demonstrate that the five SAR satellite images can all achieve sub-pixel positioning accuracy in range and azimuth direction when four GCPs are used. Overall, the verification results provide a reference for SAR satellite systems’ designs, calibrations, and various remote sensing activities.