Optical Satellite Imagery Research Articles

Fractures in glaciers—Crack tips and their stress fields by observation and modeling

AbstractHigh‐resolution optical camera systems are opening new opportunities to study fractures in ice. Here, we present data obtained from the Modular Aerial Camera System camera system operated onboard of Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) polar aircraft in northeast Greenland in 2022. In addition, we are using optical and radar satellite imagery. The study area is the 79°N Glacier (Nioghalvfjerdsbræ, 79NG), an outlet glacier of the Northeast Greenland Ice Stream. We found that crack tips are exhibiting additional isolated cracks ahead of the main crack. Subsequent crack propagation is starting from those isolated cracks, leading to an advance of the crack, with bridges between crack faces. The bridges provide information of the episodic crack propagation. Fractures have typically a length scale of kilometers and the distance of crack faces is in the order of meters to tenths of meters. Fracture modes will be inferred from stress fields computed by an inverse modeling approach using the Ice Sheet and Sea Level System Model. To this end, a surface velocity field derived from satellite remote sensing is used for the optimal control method that constrains model parameters, for example, basal friction coefficient or rheology.

Unveiling the past: Utilizing satellite imagery archives to study archaeological landscapes in the northeastern Nile Delta, Egypt

This paper investigates the utilization of satellite imagery archives in the northeastern Nile Delta to examine and explore the evolution of archaeological landscapes. The study incorporates optical satellite imagery from multiple sensors, including Landsat, ASTER, and CORONA, in addition to historical topographic maps, and digital elevation data. The methodology involves employing Support Vector Machine (SVM) algorithm, spatial analysis, and analyzing vegetation indices. The findings reveal significant landscape changes during the period from the prehistoric to the Byzantine era, influenced by natural factors such as sea level changes and land subsidence. Moreover, the landscape changed after the construction of the Aswan High Dam, influenced by human activities such as agriculture and urbanization. The study was successful in detecting the remains of the Tanitic branch, an ancient branch of the Nile River. Additionally, the analysis of vegetation indices showed crop anomalies, which may be indicative of buried structures. However, further investigations, including ground surveys and excavations, are needed for validation. The research contributes to the understanding of the region's historical and cultural significance, highlights the impact of human activities on archaeological sites, and underscores the importance of preserving cultural heritage in the face of environmental changes and urban development.

The High-Low Arctic boundary: How is it determined and where is it located?

Geobotanical subdivision of landcover is a baseline for many studies. The High-Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High-Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up-to-date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High-Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50-100 km further north compared to all the previously presented ones. Long-term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High-Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High-Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High-Low Arctic boundary in the circumpolar extent.

Super-resolution deep neural networks for water classification from free multispectral satellite imagery

Recent years have seen rapid progress in the adoption of fully convolutional neural networks (FCN) to classify optical satellite imagery, made possible by a combination of new FCN architectures, next-generation GPUs, and publicly available satellite imagery from, e.g., the Landsat and Sentinel missions. These satellites offer repeat global coverage at intervals of only a few days at a spatial resolution of ≥10 m, which is sufficient for some but not all applications of interest. A smaller body of literature considers similar tools to classify commercial satellite imagery that offer 1 – 2 orders of magnitude higher spatial resolutions but with limited spatial and temporal coverage. In this work, we develop a super-resolution FCN to achieve the best of both worlds: land cover classification at commercial-level spatial resolutions but with the spatiotemporal coverage of public satellite imagery. To do so, we label 1 – 2 m resolution commercial imagery and use this as training data for super-resolution FCN. As a specific application, we focus on the segmentation of rivers, with the goal of tracking changes in reach-averaged river widths, depths, and discharges over time. We present detailed performance analyses and demonstrate that, surprisingly, we achieve ≳ 90% classification accuracy at meter-scale resolutions from free Sentinel-2 imagery. We extensively validate our model through in situ USGS gage data and ground-truth GPS tracing of river shorelines. By making our super-resolution FCN codes and training weights publicly available, we hope that these tools will be of use to the broader hydrology community and beyond, as the models can be re-trained for other segmentation tasks.

FARM: A fully automated rice mapping framework combining Sentinel-1 SAR and Sentinel-2 multi-temporal imagery

Rice farming exemplifies intensive agriculture, demanding significant inputs to achieve optimal yields. Thus, accurate and precise mapping of rice cultivation is vital for effective agricultural management and food security. However, such studies have been limited by the challenges of obtaining optical cloud-free data and dealing with radar data's speckle noise. Identifying crops using a single data source poses many difficulties. Additionally, acquiring sufficient representative training samples that accurately reflect diverse phenological patterns is challenging for large-scale monitoring and rice cultivation classification. To address these challenges, this study proposed a fully automated rice-mapping framework (FARM) that combines the strengths of time-series synthetic aperture radar (SAR) and optical satellite imagery for large-scale rice mapping without manual sample collection. First, an object-based, fully automatic training sample generation strategy is introduced. The phenology constraint rule, based on time-series SAR satellite images and specific rice-flooding features, is used to extract rice sample objects. Second, the extracted rice sample objects, adhering to phenological rules, serve as training samples for paddy rice extraction by integrating multiple random forest (RF) classifiers, referred to as the multi-RF method, where each RF is individually built using images acquired during each phenological phase of the growing season. Furthermore, the study explored the availability of the method in early-season rice identification by transferring the training samples acquired by the FARM to a new year. The proposed FARM approach was then validated under different cropping conditions at three study sites in China and two sites in other countries. The results showed that the FARM framework proved to be more effective than other methods at all five study sites, achieving average overall accuracies (OA) ranging from 89.67%−97.00%. In addition, when transferring the training samples from 2021 to other years (2020/2022), the OAs of site A, site B and site C were high during the heading period, with accuracies of 97.57%, 84.28% and 89.27%, respectively. These results demonstrated that, first, the FARM framework exhibits high efficiency and accuracy in different study areas without the need for extensive fieldwork to collect training samples. Second, the method has good performance in the early-season rice mapping of the new year and can be used to perform timely and accurate rice identification and monitoring tasks. The method shows great potential in obtaining large-area automatic rice mapping results in a timely and accurate manner. The latest release of FARM can be viewed at https://feature-selected.users.earthengine.app/view/farm, and the code is available on GitHub at the link https://github.com/gactyxc/FARM.git.

Accumulation and Cross-Shelf Transport of Coastal Waters by Submesoscale Cyclones in the Black Sea

High- and medium-resolution satellite optical imagery show that submesoscale cyclonic eddies (SCEs) trap coastal waters and induce their rapid cross-shelf transport. Due to the presence of a rigid boundary, the convergence is observed in the coastal part of SCEs. It causes accumulation of suspended matter, which spins inward in a spiral motion toward the SCE core. Small SCEs with a radius of 1–10 km transport waters with local anomalies in the concentration of chlorophyll, total suspended matter and temperature to a distance of up to 150 km and are observed for more than 10 days. Lagrangian calculations based on realistic NEMO numerical model are used to estimate the fate of the coastal waters in such SCEs. The eddy entrains the largest number of particles during its separation from the coast when its vorticity reaches the maximum. Then, the SCE weakens, which is accompanied by the flattening of initially risen isopycnals and deepening of the trapped coastal waters. The described mechanism shows that coastal SCEs may cause intense short-period cross-shelf transport of the biological and chemical characteristics, and is another process affecting the functioning of the marine ecosystems.

SATELLITE-DERIVED SANDY SHORELINE CHANGE (1984-2020) AND PRIMARY DRIVERS IN SW FRANCE

Sandy shoreline erosion has become one of the biggest threats to coastal zones globally, both in developed and less developed regions of the world, which calls for improved understanding of past and future shoreline evolution and its drivers. This is true for long-term shoreline change trends but also for interannual shoreline response linked to largescale climate patterns of atmospheric variability, which so far has been mostly explored locally based in situ monitoring program (e.g. Dodet et al., 2019). Free-of-charge publicly available optical satellite imagery can now be used to provide short-term to multi-decadal shoreline data from the local to the global scale using different techniques (e.g., Bishop-Taylor et al., 2019), with errors typically under 10 m at microtidal beaches (Vos et al., 2019). However, on high-energy and/or meso to macrotidal low-gradient beaches satellite-derived shoreline (SDS) errors dramatically worsen (Castelle et al., 2021). In this contribution we explore if an adapted space-averaging of such uncorrected (noisy) SDS dataset can allow addressing the time- and space variability of shoreline change and their primary drivers including large-scale climate patterns of atmospheric variability.

Evolution of the dynamics of Airdrop Glacier, western Axel Heiberg Island, over a seven-decade-long advance

Various remotely sensed data, including historical aerial photographs, declassified intelligence satellite photographs, optical satellite imagery, and synthetic aperture radar data, were used to undertake the first comprehensive reconstruction of the dynamics of Airdrop Glacier on Axel Heiberg Island, Nunavut. Observations show a continuous terminus advance totalling ∼6 km since 1950 and notably less variability in its surface velocities in comparison to adjacent Iceberg Glacier. This advance is concurrent with relatively high flow rates over its entire surface, resulting in significant thickening near the terminus and thinning at higher elevations. Velocities have more than halved from the mid-2000s to 2021, but without any definitive evidence of previous flow instabilities, we cannot confirm whether Airdrop's behaviour is cyclic in nature and therefore characteristic of a surge. Instead, Airdrop Glacier could be experiencing a delayed response to the positive mass balance conditions of the Little Ice Age, which could also explain the advance of other nearby glaciers. Its recent slowdown could then be indicative of a gradual adjustment to recent climatic conditions. This study highlights the need for comprehensive studies of glacier dynamics in the Canadian Arctic to improve our understanding of the factors triggering dynamic instabilities and causing the observed variety of behaviours.

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

AbstractOver half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.

Satellite Imagery for Rapid Detection of Liquefaction Surface Manifestations: The Case Study of Türkiye–Syria 2023 Earthquakes

The 6 February 2023 earthquake doublet (Mw 7.7 and Mw 7.6) that occurred on the East Anatolian Fault Zone (EAFZ) triggered a significant amount of soil liquefaction phenomena in SE Türkiye and NW Syria. The great areal extent of the affected area and the necessity of rapid response led to the adoption and improvement of a workflow for mapping liquefaction phenomena based on remote sensing data. Using satellite imagery, we identified 1850 sites with liquefaction manifestation and lateral spreading deformation. We acquired a thorough map of earthquake-triggered liquefaction based on visual mapping with optical satellite imagery (high and very high-resolution) and the aid of radar satellite imagery and interferometry. The majority of sites are found along meandering sections of river valleys, coastal plains, drained lakes, swamps, and lacustrine basins along the East Anatolian Fault, highlighting once again the influence of geomorphology/surficial geology on the distribution of liquefaction phenomena. A total of 95% of the liquefaction occurrences were mapped within 25 km from the surface trace of the fault, confirming the distance from fault rupture as a more effective tool for predicting the distribution of liquefaction than epicentral distance. Thus, taking into consideration the rapid documentation of these phenomena without the limitations in terms of time, cost, and accessibility of the field investigation techniques, this desktop-based approach can result in a rapid and comprehensive map of liquefaction from a strong earthquake, and can also be used as a future guide for subsequent field investigations for liquefaction hazard mapping.

Summer sea ice floe perimeter density in the Arctic: high-resolution optical satellite imagery and model evaluation

Abstract. Size distribution of sea ice floes is an important component for sea ice thermodynamic and dynamic processes, particularly in the marginal ice zone. Recently processes related to the floe size distribution (FSD) have been incorporated into sea ice models, but the sparsity of existing observations limits the evaluation of FSD models, thus hindering model improvements. In this study, perimeter density has been applied to characterise the floe size distribution for evaluating three FSD models – the Waves-in-Ice module and Power law Floe Size Distribution (WIPoFSD) model and two branches of a fully prognostic floe size-thickness distribution model: CPOM-FSD and FSDv2-WAVE. These models are evaluated against a new FSD dataset derived from high-resolution satellite imagery in the Arctic. The evaluation shows an overall overestimation of floe perimeter density by the models against the observations. Comparison of the floe perimeter density distribution with the observations shows that the models exhibit a much larger proportion for small floes (radius <10–30 m) but a much smaller proportion for large floes (radius >30–50 m). Observations and the WIPoFSD model both show a negative correlation between sea ice concentration and the floe perimeter density, but the two prognostic models (CPOM-FSD and FSDv2-WAVE) show the opposite pattern. These differences between models and the observations may be attributed to limitations in the observations (e.g. the image resolution is not sufficient to detect small floes) or limitations in the model parameterisations, including the use of a global power-law exponent in the WIPoFSD model as well as too weak a floe welding and enhanced wave fracture in the prognostic models.

Remote sensing of glacier change (1965–2021) and identification of surge-type glaciers on Severnaya Zemlya, Russian High Arctic

Abstract Glaciers in the Russian High Arctic have undergone accelerated mass loss due to atmospheric and oceanic warming in the Barents–Kara Sea region. Most studies have concentrated on the western Barents–Kara sector, despite evidence of accelerating mass loss as far east as Severnaya Zemlya. However, long-term trends in glacier change on Severnaya Zemlya are largely unknown and this record may be complicated by surge-type glaciers. Here, we present a long-term assessment of glacier change (1965–2021) on Severnaya Zemlya and a new inventory of surge-type glaciers using declassified spy-satellite photography (KH-7/9 Hexagon) and optical satellite imagery (ASTER, Sentinel-2A, Landsat-4/5 TM and 8 OLI). Glacier area reduced from 17 053 km2 in 1965 to 16 275 in 2021 (−5%; mean: −18%, max: −100%), with areal shrinkage most pronounced at land-terminating glaciers on southern Severnaya Zemlya, where there is a recent (post-2010s) increase in summer atmospheric temperatures. We find that surging may be more widespread than previously thought, with three glaciers classified confirmed as surge-type, eight as likely to have surged and nine as possible, comprising 11% of Severnaya Zemlya's 190 glaciers (37% by area). Under continued warming, we anticipate accelerated retreat and increased likelihood of surging as basal thermal regimes shift.

Refining National Forest Cover Data Based on Fusion Optical Satellite Imageries in Indonesia

Precision mapping towards tropical forest cover data is critical to address the global climate crisis, such as land-based carbon measurement and potential conservation areas identification. In the recent decade, accessibility to open public datasets on forestry is rapidly increased. However, the availability of finer-resolution of forest cover data is still very limited. As a developing country with numerous rainforests, Indonesia suffered multifaceted threats, particularly deforestation. Thus, precise forest cover data can be useful to fulfill Indonesia’s nationally determined contribution to climate change. In this study, we mapped the national forest cover data for Indonesia using a new object-based image classification approach based on combined Planet-NICFI and Sentinel-2 optical imageries. Our findings had relatively high accuracy compared with the other studies, with the F score ranging from 0.67 to 0.99 and can capture the fragmented forest in fine resolution (i.e., ∼5 m). In addition, we found that Planet-NICFI bands had a higher contribution in predicting forest cover than Sentinel-2 imageries. Utilizing forest cover data for further analyses should be performed to help the achievement of national and global agenda, e.g., related to the FOLU net sink in 2030 and the Global Biodiversity Framework.

Prediction of Unpaved Road Conditions Using High-Resolution Optical Satellite Imagery and Machine Learning

Rural roads play a crucial role in fostering economic and social development in Africa. Local Road Authorities (LRAs) struggle to collect road condition data using conventional means due to logistical and resource issues. Poor road conditions and restricted mobility have severe economic consequences for the transport of goods and services. Lack of maintenance can increase costs three-fold. In this work, a novel framework is proposed in which earth observations using high-resolution optical satellite imagery are applied to measure the condition of unpaved roads, providing a vital input to maintenance planning and prioritisation. A trial was conducted using this method on 83 roads in Tanzania totalling 131.7 km. The experimental results demonstrate that, by analysing variations in pixel intensity of the road surface, the condition can be estimated with an accuracy of 71.9% when compared to ground truth information. Machine Learning techniques are applied to the same network to test the performance of the system in predicting road conditions. A blended classifier approach achieves an accuracy of 88%. The proposed framework enables LRAs to define the information they receive based on their specific priorities, offering a rapid, objective, consistent and potentially cost-effective system that overcomes the current challenges faced by LRAs.

Windthrow damage detection in Nordic forests by 3D reconstruction of very high-resolution stereo optical satellite imagery

ABSTRACT We tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair. The post-storm ground reference data consisted of field and unmanned aerial vehicle (UAV) observations of windthrow combined with no-damage areas collected by visual interpretation of the available very high resolution (VHR) satellite imagery. We trained and tested a thresholding model using canopy height change as the sole predictor. We undertook a two-step accuracy assessment by (1) running k-fold cross-validation on the ground reference dataset and examining the effect of the potential imperfections in the ground reference data, and (2) conducting rigorous accuracy assessment of the classified map of the study area using an extended set of VHR imagery. The thresholding model produced accurate windthrow maps in dense, productive forest stands with a sensitivity of 96%, specificity of 71%, and Matthews correlation coefficient (MCC) over 0.7. However, in sparse and high elevation stands, the classification accuracy was poor. Despite certain collection challenges during the winter months in the Nordic region, we consider VHR stereo satellite imagery to be a viable source of forest canopy height information and sufficiently accurate to map windthrow disturbance in forest stands of high to moderate density.

A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter

The onset of melt ponds on landfast first-year and multiyear sea ice is detected using dual co-polarized Ku-band spaceborne scatterometer data. Twenty-nine locations in the Canadian Arctic Archipelago are used to develop and test the methodology. The daily ScatSat-1 sigma-nought backscatter composite product with a spatial resolution of 2.216 km is used. Vertical (VV) and horizontal (HH) polarizations of these data are used to derive the co-polarized ratio (VV/HH). An increase in VV/HH is associated with an increase in melt pond fraction on the sea ice. This occurs because there is a larger increase in resonant Bragg scattering for VV than for HH when melt pond surfaces exhibit wind-generated capillary waves. VV backscatter magnitude is evaluated in time series using a peak-finding methodology to initiate detection of melt ponds with VV/HH. The peak-finding is based on geophysical controls on backscatter from snow-covered sea ice during the melt season. Daily time-series data from ScatSat-1 are used in conjunction with ERA-5 reanalysis data (2-m air temperature and wind) and a semi-empirical snowmelt model. Air temperature data are used to identify snow melt onset and to force the snowmelt model. Wind data are used to evaluate if the wind speed is sufficient for capillary wave formation. The NSCAT-4DS geophysical model function is used to model Ku-band backscatter from water, which is then used in a sea ice mixture model to simulate the VV/HH magnitude for a range of pond fractions and wind speeds. The potential for detecting pond onset is evaluated in relation to the noise inherent in the ScatSat-1 VV/HH sea ice measurements, which is found to be approximately 0.16 dB. When the melt pond fraction is sufficiently large to cause VV/HH to exceed the noise, pond onset is detected. Cloud-free Sentinel-2 optical satellite imagery is used for validation; specifically, the near-infrared albedo. We find an overall mean bias of −0.5 days, and an absolute deviation of 1.4 days, when comparing our method to a near-infrared method for detecting pond onset. The mean biases (absolute deviations) for first-year and multiyear sea ice are −0.3 (1.5) and −0.8 (1.2) days, respectively. Results show that the temporal resolution, spatial resolution, and efficacy of this method can provide robust sea ice melt pond onset timing for landfast sea ice regions of the Arctic.

Flash flood detection and susceptibility mapping in the Monsoon period by integration of optical and radar satellite imagery using an improvement of a sequential ensemble algorithm

Rainfall monsoons and the resulting flooding have always been cataclysmic disasters that have heightened global concerns in light of climate change. Flood susceptibility modeling is an indirect method for reducing flood disaster losses. This study aimed to improve flood susceptibility modeling by developing a sequential ensemble (extreme gradient boosting (XGBoost)) model utilizing three swarm-based algorithms (bacterial foraging optimization (BFO), cuckoo search (CS), and artificial bee colony (ABC) algorithms). Initially, an integration of optical (Landsat-8) and radar (Sentinel-1) satellite images were used to monitor the flooded areas during the July 2022 monsoon in the Kazerun region, Iran. A total of 1358 flood occurrence points were considered from the monitored flood areas; 70% (952 points) and 30% (406 points) were used for modeling and evaluating the models, respectively. Based on flood points and thirteen spatial criteria influencing floods, four flood models ((XGBoost, XGBoost-ABC, XGBoost-BFO, and XGBoost-CS)) were used to generate a flood susceptibility map (FSM). According to the results, the XGBoost-CS (area under the curve (AUC) = 0.96), XGBoost-BFO (AUC = 0.953), XGBoost-ABC (AUC = 0.941), and XGBoost (AUC = 0.939) models have greater accuracy in flood susceptibility modeling, respectively. The results indicated that the models coupled with the three metaheuristic algorithms (XGBoost-ABC, XGBoost-BFO, and XGBoost-CS) exhibited higher flood susceptibility modeling accuracy than the standalone XGBoost model.

Incorporation of neighborhood information improves performance of SDB models

Mapping of water depth in nearshore waters is important for safe navigation and other human uses of coastal areas. Optical satellite imagery offers a cost-efficient source of data for this purpose. Several methods have been developed for satellite-derived bathymetry (SDB), but the details of their implementation, as well as their relative performance and its dependence on the data and environmental context, is unclear. We compared five SDB models, calibrated and tested against airborne lidar data from southern Corsica. We used a blocked strategy to split data into calibration, validation, and test data, because randomly splitting the data resulted in spatial autocorrelation between these data sets and underestimation of model prediction errors. In general, the non-parametric random forest and neural network models outperformed the parametric multi-band and band-ratio models. The convolutional neural network (CNN) model performed the best, especially in relatively deep (10–20 m) areas, and it could be near-optimally tuned with only ∼1000 data points. However, developing this CNN model was an iterative trial-and-error process that took several months. Using the random forest model, we demonstrated that using information from more than two bands in the visible and near-infrared spectrum contributed to improving model performance, as did incorporation of information from the local neighborhood. We suggest that the use of more than two bands, and the inclusion of spatial contextual information in addition to the values of the individual pixel, should become standard practice in SDB.

Identifying Potential Landslides in Steep Mountainous Areas Based on Improved Seasonal Interferometry Stacking-InSAR

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area.