Visual Image Interpretation Research Articles

Geospatial Analysis of LULC in the Srinagar Region of Jammu and Kashmir using Sentinel-2 Data

Geospatial analysis based LULC mapping finds great utility in Himalyan region in order to carry proper assessment, evaluation and sustainable management of the existing resources. With the aid of remote sensing (RS) and geographic information system (GIS), the current study on LULC mapping was conducted in district Srinagar of Jammu and Kashmir for the year 2022. European Space Agency (ESA) based Sentinel-2 satellite was used as a data source. By using the technique of visual image interpretation, the study area was demarcated into 11 LULC classes viz, agriculture, forest, forest scrub, built-up, TOF, grassland, wasteland, waterbody, wetland, snow and horticulture. The findings of the study revealed that from the delineated LULC classes highest percentage (19.61%) of area was covered by forest and least (2.17%) was covered by wetland. Moreover, the values obtained for overall classification accuracy and kappa statistics were 89.09% and 0.88 respectively. Due to increasing population and urbanisation some of the important LULC classes like wetland, agriculture etc have been encroached thereby disturbing the overall ecological balance. Therefore, findings of the current study might be useful in planning and demarcating the area where we can develop green infrastructure which might be fruitful in achieving the sustainable management goals.

Urban expansion and agricultural land loss: a GIS-Based analysis and policy implications in Hawassa city, Ethiopia

This study investigated the historical and future trends of urban expansion and its subsequent impact on agricultural land-use in Hawassa city, Ethiopia. A time-series of remote-sensing imageries from Landsat Thematic Mapper for the years 1984, 1990, 2000, and 2010 and Operational Land Imager for 2021 were used to extract the LULC information from the study area. Seven major land-cover classes’ waterbody, built-up, agricultural land, wetland, grassland, woody vegetation, and agroforestry were identified with visual image interpretation along with supervised image classification techniques using the maximum-likelihood algorithm for the study years. The urban and agricultural lands were then extracted from the original LULC data to quantify the extent, rates, and number of area conversion between the two. The Land Change Modeler module of TerrSet software was used to predict the spatial extents of built-up and agricultural lands in 2030 and 2050. The results showed that there have been significant changes between the LULC types in Hawassa city within the past 37 years, from which built-up and agricultural land have shown the most prevalent changes. It showed that built-up land has increased from 584.73 ha in 1,984–3,939.03 ha in 2021, representing a 573.65% increase at an annual growth rate of 15.50%. However, agricultural land decreased from 8,324.64 ha to 3,595.68 ha in the respective years, with a 56.81% decrease at a rate of −1.54% each year. A total of 3,148.74 ha (37.82%) of agricultural land was converted into built-up land within the past 37 years (85.10 ha per year, a rate of 1.02%. The built-up land is projected to increase to 5,009.85 ha and 6,794.73 ha from 2021 to 2030 and 2050, with annual growth rates of 3.02% and 2.50%, respectively. In the same years, agricultural land will decrease to 2,849.58 ha and 2033.46 ha by 2.31% and 1.50% annually, respectively, from which 64.76 ha (1.80%) and 48.41 ha (1.35%) will be converted into built-up land, respectively. Future planning and development in the city should consider the rapid increase in built-up land toward agricultural land areas and develop appropriate adaptation mechanisms for the local community, which is highly dependent on agriculture.

Patterns of natural vegetation removal in the Caatinga biome in Prodes Project scope

Abstract. The native vegetation removal (NVR) of Caatinga Brazilian biome has been mapped by Prodes Project coordinated by National Institute for Spatial Research (INPE) between 2000 to 2023 year. Prodes is considered an official government product for achieving Brazilian and international targets for the conservation of natural vegetation. Therefore, Prodes is committed to achieving the highest possible accuracy associated with the history of mapping already carried out, by visual interpretation of satellite images and the use of past mapping masks techniques. Mapping NVR in Caatinga biome is a major challenge compared to biomes where the tree canopy with native vegetation prevails. Caatinga is one of the least protected of Brazilian biomes and is in one of the most pressured semi-arid regions on the planet. This work aims to present the NVR in Caatinga biome from Prodes data, in terms of its trajectory and main geographical patterns, concerning the municipalities, and areas that are the focus of monitoring, conservation or restoration. According to Prodes results, by 2023, the cumulative NVR in the entire Caatinga reached 43,05%; in 2000, this figure was 29,54%. The major NVR occurred in the easternmost areas of Caatinga and regions of Serrinha and Feira de Santana in Bahia State. Also, there are areas with considerable NVR further inside the biome, such as, Irecê and Guanambi region in Bahia, west of Pernambuco and Paraíba States and South and North of Ceará State. Areas prioritized for conservation actions showed high NVR values, indicating the urgent need for implementation of such actions. The fact that Desertification Cores showed low NVR values is important, and these regions should be closely monitored to prevent uncontrolled NVR rates. The two regions with high anthropic pressure or speculation showed contrasting behaviors; while the Araripe Gypsum Pole exhibited higher NVR levels compared to the control area, the São Francisco River integration project (PISF) so far does not appear significantly different from the rest of the biome. Overall, this study clearly demonstrates the potential of monitoring the Caatinga through Prodes in understanding and integrating the various factors influencing changes in the biome's native vegetation cover. Future approaches should move beyond descriptive studies to increasingly contribute to the territorial management of this important Brazilian region.

Spatiotemporal trends and drivers of forest cover change in Metekel Zone forest areas, Northwest Ethiopia.

The spatiotemporal dynamics of forest cover are essential for understanding the patterns and processes of forest change over time and space. This research focused on the spatiotemporal trends and drivers of forest cover change in the Metekel Zone of Northwest Ethiopia. Landsat 5, Landsat 7, and Landsat 8 imagery, covering the period from 1986 to 2019, were used for land use/cover classification. Land use/cover classification was performed using random forest (RF) and support vector machine (SVM) algorithms in the Google Earth Engine (GEE) platform, with training samples obtained through visual image interpretation. Spectral indices, such as the normalized difference vegetation index, soil-adjusted vegetation index, leaf area index, and normalized difference water index, were analyzed to examine forest cover dynamics over time. In addition, key informant interviews (KIIs) and focus group discussions (FGDs) were conducted. Findings revealed that forest cover decreased significantly from 51.37% in 1986 to 17.20% in 2019, driven largely by human activities such as agricultural expansion, increased demand for firewood, and urban expansion. Findings from spectral indices further corroborated the finding that forest cover in the study region (mainly in the southwestern part) substantially decreased from 1986 to 2019. Concerning forest depletion, the lack of local community awareness has become a key challenge. This problem is attributed to communities prioritizing immediate needs such as fuel and land for agriculture over long-term forest conservation. To combat ongoing deforestation, the Metekel Zone Administration, in collaboration with the land administration office and other stakeholders, revisited and strengthened existing forest policies and control systems. It is also suggested that community awareness, chiefly among youth, should be enhanced through the strategic expansion of formal and nonformal educational initiatives, which empower the youth as agents of change and promote the dissemination of knowledge throughout the community.

DCV2I$\text{DCV}^2\text{I}$: Leveraging deep vision models to support geographers' visual interpretation in dune segmentation

AbstractVisual interpretation is extremely important in human geography as the primary technique for geographers to use photograph data in identifying, classifying, and quantifying geographic and topological objects or regions. However, it is also time‐consuming and requires overwhelming manual effort from professional geographers. This paper describes our interdisciplinary team's efforts in integrating computer vision models with geographers' visual image interpretation process to reduce their workload in interpreting images. Focusing on the dune segmentation task, we proposed an approach called featuring a deep dune segmentation model to identify dunes and label their ranges in an automated way. By developing a tool to connect our model with ArcGIS—one of the most popular workbenches for visual interpretation, geographers can further refine the automatically generated dune segmentation on images without learning any CV or deep learning techniques. Our approach thus realized a noninvasive change to geographers' visual interpretation routines, reducing their manual efforts while incurring minimal interruptions to their work routines and tools they are familiar with. Deployment with a leading Chinese geography research institution demonstrated the potential of in supporting geographers in researching and solving drylands desertification.

The Early Perfusion Image Is Useful to Support the Visual Interpretation of Brain Amyloid-PET With 18F-Flutemetamol in Borderline Cases.

Visual interpretation of brain amyloid-β (Aβ) PET can be difficult in individuals with borderline Aβ burden. Coregistration with individual MRI is recommended in these cases, which, however, is not always available. This study evaluated coregistration with the early perfusion frames acquired immediately after tracer injection to support the visual interpretation of the late Aβ-frames in PET with 18F-flutemetamol (FMM). Fifty dual-time-window FMM-PET scans of cognitively normal subjects with 0 to 60 Centiloids were included retrospectively (70.1 ± 6.9 years, 56% female, MMSE score 28.9 ± 1.3, 42% APOE ɛ4 carrier). Regional Aβ load was scored with respect to a 6-point Likert scale by 3 independent raters in the 10 regions of interest recommended for FMM reading using 3 different settings: Aβ image only, Aβ image coregistered with MRI, and Aβ image coregistered with the perfusion image. The impact of setting, within- and between-readers variability, region of interest, and Aβ-status was tested by repeated-measure analysis of variance of the Likert score. The Centiloid scale ranged between 2 and 52 (interquartile range, 7-19). Support of visual scoring by the perfusion image resulted in the best discrimination between Aβ-positive and Aβ-negative cases, mainly by improved certainty of excluding Aβ plaques in Aβ-negative cases (P = 0.030). It also resulted in significantly higher between-rater agreement. The setting effect was most pronounced in the frontal lobe and in the posterior cingulate cortex/precuneus area (P = 0.005). The early perfusion image is a suitable alternative to T1-weighted MRI to support the visual interpretation of the late Aβ image in FMM-PET.

A new approach to dust source mapping using visual interpretation and object-oriented segmentation of satellite imagery

The emission of dust particles, mainly from arid and semi-arid lands, as a result of climate change and human activities, is known to be a global issue. Identifying dust emission sources is the first key step in dealing with the hazardous consequences of this rising phenomenon. This study is an attempt to address one of the major challenges in mapping dust emission sources. Accordingly, an innovative approach based on visual interpretation of multi-temporal MODIS-Terra/Aqua imagery and object-oriented image segmentation techniques has been developed and implemented in the study areas of the Tigris and Euphrates basin and eastern Iran. This approach takes advantage of land surface characteristics (i.e., dust drivers), including geomorphology, soil, land use/cover, and land surface radiation, to attribute dust emission hotspots to their corresponding areas using multi-source remote sensing data. The results show that the multi-resolution segmentation algorithm with optimized parameters can identify homogeneous segments corresponding to dust emission sources in the study areas with an average spatial agreement of ∼92% compared to the reference areas. Our findings emphasize the robustness and generalizability of the proposed approach, and its capabilities can be used in a complementary way with visual interpretation of satellite images to map dust sources with high spatial accuracy.

Deep Learning-Enabled Quantification of 99mTc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis.

Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with 99mTc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard for interpretation but is inherently subjective. Manual quantitation of SPECT myocardial 99mTc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of 99mTc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps. Methods: Patients who underwent SPECT/CT 99mTc-pyrophosphate imaging for suspected ATTR CA were included. Diagnosis of ATTR CA was determined using standard criteria. Cardiac chambers and myocardium were segmented from CT attenuation maps using a foundational deep learning model and then applied to attenuation-corrected SPECT images to quantify radiotracer activity. We evaluated the diagnostic accuracy of target-to-background ratio (TBR), cardiac pyrophosphate activity (CPA), and volume of involvement (VOI) using the area under the receiver operating characteristic curve (AUC). We then evaluated associations with the composite outcome of cardiovascular death or heart failure hospitalization. Results: In total, 299 patients were included (median age, 76 y), with ATTR CA diagnosed in 83 (27.8%) patients. CPA (AUC, 0.989; 95% CI, 0.974-1.00) and VOI (AUC, 0.988; 95% CI, 0.973-1.00) had the highest prediction performance for ATTR CA. The next highest AUC was for TBR (AUC, 0.979; 95% CI, 0.964-0.995). The AUC for CPA was significantly higher than that for heart-to-contralateral ratio (AUC, 0.975; 95% CI, 0.952-0.998; P = 0.046). Twenty-three patients with ATTR CA experienced cardiovascular death or heart failure hospitalization. All methods for establishing TBR, CPA, and VOI were associated with an increased risk of events after adjustment for age, with hazard ratios ranging from 1.41 to 1.84 per SD increase. Conclusion: Deep learning segmentation of coregistered CT attenuation maps is not affected by the pattern of radiotracer uptake and allows for fully automatic quantification of hot-spot SPECT imaging such as 99mTc-pyrophosphate. This approach can be used to accurately identify patients with ATTR CA and may play a role in risk prediction.

Visual Strategies in (Visual Arts) Education: A Critical Perspective on Reading and Making Images

This study takes its point of departure from a nationwide project called Visual Storytelling and the Art of Reading Images carried out in Sweden between 2019–2020. This national project, implemented by Läsrörelsen (The Reading Movement Association), aimed to develop the professionalism of educators and visual art teachers by offering an in-service training day to enhance their competence in visual storytelling and image interpretation. The aim of our study, which uses material from the project, is to contribute knowledge regarding the role of visual culture and visual strategies in school, particularly in visual arts education. The role of visual culture in education is to develop a critical perspective on images and cultural ways of seeing and making images. This approach involves focusing on the global and digital visual culture of young people’s daily lives and working on societal issues. Through teaching and learning how to analyse and discuss contemporary, complex images with others, as well as expressing oneself and communicating through image making, the use of visual strategies can bring pupil’s different opinions, thoughts, and feelings to the fore.

Short post-injection seizure duration is associated with reduced power of ictal brain perfusion SPECT to lateralize the seizure onset zone.

The aim of this study was to assess the impact of the post-injection electrical seizure duration on the identification of the seizure onset zone (SOZ) in ictal brain perfusion SPECT in presurgical evaluation of drug-resistant epilepsy. 176 ictal SPECT performed with 99mTc-HMPAO (n = 140) or -ECD (n = 36) were included retrospectively. Visual interpretation of the SPECT images (together with individual MRI and statistical hyperperfusion maps) with respect to lateralization (right, left, none) and localization (temporal, frontal, parietal, occipital) of the SOZ was performed by 3 independent readers. Between-readers agreement was characterized by Fleiss' κ. An ictal SPECT was considered "lateralizing" if all readers agreed on right or left hemisphere. It was considered "localizing" if it was lateralizing and all readers agreed on the same lobe within the same hemisphere. The impact of injection latency and post-injection seizure duration on the proportion of lateralizing/localizing SPECT was tested by ANOVA with dichotomized (by the median) injection latency and post-injection seizure duration as between-subjects factors. Median [interquartile range] (full range) of injection latency and post-injection seizure duration were 30 [24, 40] (3-120) s and 50 [27, 70] (-20-660) s, respectively. Fleiss' κ for lateralization of the SOZ was largest for the combination of early (< 30s) injection and long (> 50s) post-injection seizure duration (κ = 0.894, all other combinations κ = 0.659-0.734). Regarding Fleiss' κ for localization of the SOZ in the 141 (80.1%) lateralizing SPECT, it was largest for early injection and short post-injection seizure duration (κ = 0.575, all other combinations κ = 0.329-0.368). The proportion of lateralizing SPECT was lower with short compared to long post-injection seizure duration (estimated marginal means 74.3% versus 86.3%, p = 0.047). The effect was mainly driven by cases with very short post-injection seizure duration ≤ 10s (53.8% lateralizing). Injection latency in the considered range had no significant impact on the proportion of lateralizing SPECT (p = 0.390). The proportion of localizing SPECT among the lateralizing cases did not depend on injection latency or post-injection seizure duration (p ≥ 0.603). Short post-injection seizure duration is associated with a lower proportion of lateralizing cases in ictal brain perfusion SPECT.

Evolution of the Polygon of the Protected Natural Area “Parque Nacional Sistema Arrecifal Veracruzano” Due to the Expansion of the Port of Veracruz, Mexico

The Port of Veracruz, Mexico was artificially built at the beginning of the 21st century, dredging the sea floor and removing reefs to allow the entry of large ships, which led to a growth in the traffic of goods and people through it, making it the largest commercial port in all of Mexico. In 1992 the Government of Mexico decreed the area of coastal reefs near the Port of Veracruz as a protected natural area with the name of the Parque Nacional Sistema Arrecifal Veracruzano (PSNSAV). In 2013, with the demand of international trade, the decision was made to expand the port, generating job growth in the area and an increase in the flow of containers and automobiles, also have a greater capacity to transport cereals, minerals and fuels. In contrast to the disagreements of the society due to the loss of protected space, the Parks polygon had to be modified. This work try to demonstrated that the construction of the port expansion began previous the presentation to the environmental authorities of the Environmental Impact Statement and the change in the protected limits with the disappearance of the Punta Gorda reefs and the dredging of Vergara Bay for the arrival of deep draft vessels. Through a systematic mapping of the scientific literature combined with satellite mapping and visual interpretation of images, and documentary analysis, we determined the damage to the reef system, which has protected species according to Mexican national regulations.

Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level.

In the context of surgical interventions for lung adenocarcinoma (LADC), precise determination of the extent of LADC infiltration plays a pivotal role in shaping the surgeon's strategic approach to the procedure. The prevailing diagnostic standard involves the expeditious intraoperative pathological diagnosis of areas infiltrated by LADC. Nevertheless, current methodologies rely on the visual interpretation of tissue images by proficient pathologists, introducing an error margin of up to 15.6%. In this study, we investigated the utilization of Micro-Raman technique on isolated specimens of human LADC with the objective of formulating and validating a workflow for the pathological diagnosis of LADC featuring diverse degrees of infiltration. Our strategy encompasses a thorough pathological characterization of LADC, spanning different tissue types and levels of infiltration. Through the integration of Raman spectroscopy with advanced deep learning models for simultaneous diagnosis, this approach offers a swift, precise, and clinically relevant means of analysis. The diagnostic performance of the convolutional neural network (CNN) model, coupled with the microscopic Raman technique, was found to be exceptional and consistent, surpassing the traditional support vector machine (SVM) model. The CNN model exhibited an area under the curve (AUC) value of 96.1% for effectively distinguishing normal tissue from LADC and an impressive 99.0% for discerning varying degrees of infiltration in LADCs. To comprehensively assess its clinical utility, Raman datasets from patients with intraoperative rapid pathologic diagnostic errors were utilized as test subjects and input into the established CNN model. The results underscored the substantial corrective capacity of the Micro-Raman technique, revealing a misdiagnosis correction rate exceeding 96% in all cases. Ultimately, our discoveries highlight the Micro-Raman technique's potential to augment the intraoperative diagnostic precision of LADC with varying levels of infiltration. And compared to the traditional SVM model, the CNN model has better generalization ability in diagnosing different infiltration levels. This method furnishes surgeons with an objective groundwork for making well-informed decisions concerning subsequent surgical plans.

DCV2I: A Practical Approach for Supporting Geographers’ Visual Interpretation in Dune Segmentation with Deep Vision Models

Visual interpretation is extremely important in human geography as the primary technique for geographers to use photograph data in identifying, classifying, and quantifying geographic and topological objects or regions. However, it is also time-consuming and requires overwhelming manual effort from professional geographers. This paper describes our interdisciplinary team's efforts in integrating computer vision models with geographers' visual image interpretation process to reduce their workload in interpreting images. Focusing on the dune segmentation task, we proposed an approach featuring a deep dune segmentation model to identify dunes and label their ranges in an automated way. By developing a tool to connect our model with ArcGIS, one of the most popular workbenches for visual interpretation, geographers can further refine the automatically-generated dune segmentation on images without learning any CV or deep learning techniques. Our approach thus realized a non-invasive change to geographers' visual interpretation routines, reducing their manual efforts while incurring minimal interruptions to their work routines and tools they are familiar with. Deployment with a leading Chinese geography research institution demonstrated the potential of our approach in supporting geographers in researching and solving drylands desertification.

Visual interpretation of clinical images among dental students using eye-tracking technology.

Tele-consultations are increasingly used for screening and diagnosis. Only a few studies have assessed dental students' visual attention to clinical images. To (i) determine dental students' gaze behavior, visual fixations, and diagnostic competence while viewing clinical images, and (ii) explore potential opportunities to strengthen the teaching-learning approaches. Tobii Pro Nano-device captured the eye-tracking data for 65 dental undergraduate students in this cross-sectional study. The predetermined areas of interest (AOI) for all five clinical photographs were uploaded onto Tobii software. All participants used a think-aloud protocol with no restrictions to view time. A total of 325 clinical pictures were analyzed, and the average view time was 189.25±76.90 s. Most participants started at the center of the image (three frontal photos), spent a significant share of their view time on prominent findings, did not follow a systematic pattern, and exhibited diagnostic incompetence. Also, most participants followed a "Z" viewing pattern (oscillating movement from left to right) for the remaining two pictures. Subjects frequently fixated on the prominent AOI, however, failed to make the correct diagnosis. Their view patterns revealed no sequential viewing. Therefore, emphasizing knowledge about common dental abnormalities and focusing on full coverage of clinical pictures can improve dental students' diagnostic competence and view patterns.

Combining visual interpretation and image segmentation to derive canopy cover index from high resolution satellite imagery in functionally diverse coniferous forests

Forest canopy cover is one of the most significant structural parameters of the forest stand that can be estimated using of aerial and satellite remote sensing. Even though sub-pixel analysis can be used to estimate the index on low-resolution imagery, high-resolution imagery provides more accurate details on forest canopy variability for ecological and forestry applications. However, the high variability of the images demands a more advanced approach to canopy cover measurement than the visual interpretation of single images or stereo pairs. These traditional methods are inefficient and limited in providing a comprehensive and accurate canopy cover assessment. An improvement of the method could involve classifying high spatial resolution images, separating and extracting the areas corresponding to canopy gaps, and generating canopy cover maps. This study offers valuable insights and reveals key differences between three methods for estimating canopy cover: ground measurements, visual photo interpretation and automatic extraction from classified images using pixel and object-based methods. The texture analysis approach was used for separating the “shadow” objects corresponding to gaps in the canopy from the shades cast on the lower trees. The sample plot-based visual interpretation of the images revealed comparable results between ground and satellite image canopy cover values (correlation coefficient of 0.74 for all plots), with lower correlations (r = 0.39) for uneven-aged stands. The results encourage the use of the texture analysis method, with satisfying accuracy (forest canopy cover differences of maximum 0.06 between ground, photo interpreted and extracted datasets). The method could be further integrated with complementary data like LIDAR or hyperspectral images.

Research on Individual Tree Canopy Segmentation of Camellia oleifera Based on a UAV-LiDAR System

In consideration of the limited accuracy of individual tree canopy segmentation algorithms due to the diverse canopy structure and complex environments in mountainous and hilly areas, this study optimized the segmentation parameters of three algorithms for individual tree canopy segmentation of Camellia oleifera in such environments by analyzing their respective parameters. Utilizing an Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system, we obtained Canopy Height Models (CHM) of Camellia oleifera canopies based on Digital Elevation Models (DEM) and Digital Surface Models (DSM). Subsequently, we investigated the effects of CHM segmentation, point cloud clustering segmentation, and layer stacking fitting segmentation on Camellia oleifera canopies across different research areas. Additionally, combining ground survey data from forest lands with visual interpretation of UAV orthophoto images, we evaluated the performance of these three segmentation algorithms in terms of the F-score as an evaluation indicator for individual tree canopy segmentation accuracy. Combined with the Cloth Simulation Filter (CSF) filtering algorithm after removing the ground point cloud, our findings indicate that among different camellia densities and terrain environments, the point cloud clustering segmentation algorithm achieved the highest segmentation accuracy at 93%, followed by CHM segmentation at 88% and the layer stacking fitting segmentation method at 84%. By analyzing the data from UAV-LiDAR technology involving various land and Camellia oleifera planting types, we verified the applicability of these three segmentation algorithms for extracting camellia canopies. In conclusion, this study holds significant importance for accurately delineating camellia canopies within mountainous hilly environments while providing valuable insights for further research in related fields.

Long-Term SAR Data Analysis for Subsidence Monitoring and Correlation Study at Beijing Capital Airport

Land subsidence, resulting from natural or human activities, is a global environmental geological disaster. The Interferometric Synthetic Aperture Radar (InSAR) time-series analysis technique offers high spatial and continuous temporal resolution, providing data and a foundation for investigating regional land subsidence and its evolution mechanism. Beijing Capital International Airport (BCIA) has experienced uneven land subsidence since 1935, together with severe fissures significantly affecting its normal operations. In this study, the time-series InSAR method was successfully applied to monitor the gradual increase in uneven local subsidence and ground fissures activity at BCIA from June 2003 to March 2023. Initially, ENVISAT-ASAR, Cosmo-SkyMed, and Sentinel-1 data were processed by time-series InSAR techniques to generate deformation rate maps and time series for the airport area. Subsequently, a comparison was made between the displacement time series from InSAR and ground leveling measurements to assess the accuracy of InSAR-derived measurements. Through a comprehensive analysis of the distribution characteristics of land subsidence at the airport, a long-standing ground fault was located within the airport was identified. A preliminary discussion on the development status of this ground fissure was carried out based on the visual interpretation of optical images. Lastly, the inducing factors and evolutionary conditions of land subsidence were discussed. This case demonstrates the applicability of InSAR technology in identifying and monitoring geological processes such as land subsidence and ground fissure activities. It provides a scientific approach to exploring and studying the causes and formation mechanisms of land subsidence and ground fissures in the Beijing Capital Airport area.

Multiscale analysis of land use and land cover changes in Sri Lanka by remote sensing: the impacts of post-war infrastructure development in the last 20 Years (2002-2022)

On a worldwide scale, land use and land cover changes (LULCC) is a major driver of global change and biodiversity erosion. This study aims to monitor at the scale of Sri Lanka, LULCC and vegetation dynamics, to identify the main changes and their drivers. It places emphasis on multiscale geospatial monitoring with satellite data but also mobilises the interdisciplinary knowledge of a research programme. First, national scale data allows the identification of major changes with the MODIS NDVI dataset using Mann-Kendall tests and time profile analysis. A second local scale was mobilised with a systematic diachronic visual interpretation of high-resolution images on Google Earth. The third step, a macro-regional scale focus on the South and East coasts, used LANDSAT imagery (Stacked K-means) verified by field studies (botanical and vegetation analysis, and interviews). About 92.5% of the island of Sri Lanka is stable or presents no significant trends in the vegetation cover. 5% show a significant positive (greening) trend between 2000 and 2020 around the Puttalam lagoon, west of the Samanalawewa Reservoir, in the Udawalawe National Park, east and north of Ella, and the Nuwaragala Forest Reserve. Only about 2.5% (165,000 ha) of the Island shows a negative significant trend mainly in the periphery of Colombo and Trincomalee. The first driver is a set of national planning decisions in terms of infrastructure development (including urban growth, housing programmes and agricultural fields, and the management of protected areas). The second driver comes under rural dynamics with increasing tree density in rural landscapes over the Uva Province. Infrastructure development initiated by the state, creates both underlying conditions for other activities and negative impacts on coastal ecosystems, such as degradation of wetlands (including protected areas).

Gray Level Co-occurrence Matrix textural analysis for temporal mapping of sea ice in Sentinel-1A SAR images.

Sea ice is a critical component of the cryosphere and plays a role in the heat and moisture exchange processes between the ocean and atmosphere, thus regulating the global climate. With climate change, detailed monitoring of changes occurring in sea ice is necessary. Therefore, an analysis was conducted to evaluate the potential of using the Gray Level Co-occurrence Matrix (GLCM) texture analysis combined with the backscattering coefficient (σ°) of HH polarization in Sentinel-1A Synthetic Aperture Radar (SAR) images, interferometric imaging mode, for mapping sea ice in time series. Data processing was performed using cloud computing on the Google Earth Engine platform with routines written in JavaScript. To train the Random Forest (RF) classifier, samples of regions with open water and sea ice were obtained through visual interpretation of false-color SAR images from Sentinel-1B in the extra-wide swath imaging mode. The analysis demonstrated that training samples used in the RF classifier from a specific date can be applied to images from other dates within the freezing period, achieving accuracies ≥ 90% when using 64-bit grayscale quantization in GLCM combined with σ° data. However, when using only σ° data in the RF classifier, accuracies ≥ 93% were observed.

Rapid Mapping of Landslides Induced by Heavy Rainfall in the Emilia-Romagna (Italy) Region in May 2023

Heavy rainfall is a major factor for landslide triggering. Here, we present an inventory of 47,523 landslides triggered by two precipitation episodes that occurred in May 2023 in the Emilia-Romagna and conterminous regions (Italy). The landslides are manually mapped from a visual interpretation of satellite images and are mainly triggered by the second rainfall episode (16–17 May 2023); the inventory is entirely original, and the mapping is supplemented with field surveys at a few selected locations. The main goal of this paper is to present the dataset and to investigate the landslide distribution with respect to triggering (precipitation) and predisposing (land use, lithology, slope and distance from roads) factors using a statistical approach. The landslides occurred more frequently on steeper slopes and for the land use categories of “bare rocks and badlands” and woodlands. A weaker positive correlation is found for the lithological classes: silty and flysch-like units are more prone to host slope movements. The inventory presented here provides a comprehensive picture of the slope movements triggered in the study area and represents one of the most numerous rainfall-induced landslide inventories on a global scale. We claim that the inventory can support the validation of automatic products and that our results on triggering and predisposing factors can be used for modeling landslide susceptibility and more broadly for hazard purposes.