Multi-temporal Images Research Articles

ASSESSMENT OF IMPERVIOUS SURFACE CHANGES FROM MULTI-TEMPORAL LANDSAT DATA AND MACHINE LEARNING TECHNIQUES. A CASE STUDY IN HO CHI MINH CITY

Impervious surface is an artificial surface that prevents water from seeping into the ground. Impervious surface is not only an indicator of the level of urbanization but also a key indicator of the quality of the urban environment. This article presents the results of an impervious surface classification in the Ho Chi Minh City area from multi-temporal Landsat image data. Three Landsat image scenes from 2010 - 2020, including Landsat 5 images taken on February 11, 2010, Landsat 8 images taken on February 9, 2015 and February 23, 2020 are used to classify land cover/land use, including impervious surface. Three machine learning algorithms (Random Forest, Support Vector Machine, Classification and Regression Tree) and maximum likelihood method are tested to select the algorithm with the highest accuracy. The results indicate that the Random Forest algorithm achieves the highest accuracy in classifying impervious surfaces, with an overall accuracy exceeding 93% and a Kappa coefficient of 0.915. The results received in the study also show an increase in impervious surface area in Ho Chi Minh City in the period 2010 - 2020. This is important information, helping managers in monitoring and planning urban areas.

Quantitative Assessment of Channel Migration in the Ghaghara River, Ganga Plain, India

ABSTRACT The Ghaghara River is one of the longest tributary of the Ganga River. The Ghaghara, a snow fed river is well known for channel shifting/migration. It was analyzed for lateral migration using multi-temporal satellite images and SOI toposheets. It shows 4.2 to 6.1 km lateral migration in last 45 years (1975–2020) between Bahraich and Deoria in the state of Uttar Pradesh. The lateral migration on the left side of the river (NNE) is 6144, 4838, 6475 and 4755 m, whereas on the right side (SSW) is 7021, 4555, 4718 and 4257 m during 1975–1990, 1975–2000, 1975–2010 and 1975–2020, respectively. The rate of migration (m/year) on left side (NNE) is 410, 194, 185 and 106, whereas on the right side (SSW) is 468, 182, 135 and 95 during 1975–1990, 1975–2000, 1975–2010 and 1975–2020, respectively. The major factors such as heavy precipitation, reactivation of chute channels, deposition of sediments, breaching of embankments and sand mining etc. might be responsible for the lateral migration of the river. Human settlements should be constructed away from the migration-prone area and regular monitoring and management system needs to be in place to mitigate the impacts of such natural events.

Submerged Surface Area and Water Storage Volume of Hamrin Dam Lake Using Contour Lines

The predictions and warnings from drying up lakes and rivers are increasing nowadays, especially in Iraq, due to climatic changes and water flow reduction from neighboring countries. Consequently, this paper deals with monitoring the changes in submerged surface areas and Hamrin dam lake storage volume from December 2011 to the end of 2022. Hamrin Lake supplies Diyala province, Iraq, with irrigation water. Multitemporal satellite images were acquired from google earth pro and Earth Observation (EO) browsers. Triangular Irregular Network (TIN) layers generated contour lines through the ArcGIS environment. Volumes were calculated using the end area method. The results show that the water storage volume reached 1.8 billion cubic meters (bcm) in December 2016 and decreased to 12 million cubic meters in December 2021. Hamrin Lake lost 99% and 98% of its capacity relative to the normal water storage volume in 2021 and 2022, respectively. At normal storage, the maximum water volume of Hamrin Lake is about 3 bcm. The scarcity of irrigation water from Hamrin Lake causes decreasing agricultural areas, desertification, and people migrating to other regions.

Reinforced deep learning approach for analyzing spaceborne-derived crop phenology

Vegetation Index (VI) curves capture the crop-specific phenological events from multi-temporal Earth observation images. Although advanced machine learning classifiers are the existing state-of-the-art, most of them assume the samples to be independent and identically distributed, which is not true for VI curve classification. This research proposed the consideration of spatial non-stationarity for VI curve-based crop classification. The proposed approach does not assume the samples to be independent and identically distributed. Different state-of-the-art approaches attempted to solve this problem by introducing architectural variations and considering the neighborhood patches. However, the inherent nature of the problem demanded an online approach. In the current research, this problem is formulated in a reinforcement learning framework where the generalizability of the predictor is improved using a feedback system. Different approaches for feedback were investigated in this study. The predictor was composed of a graph convolutional framework where the VI curves were transformed into a graph representation, incorporating the spatial, spectral, and temporal context. Graph convolutional operations were then used to learn the embedded representations and assign labels to the unlabeled points based on the labeled ones in the proximity. Further, a semi-supervised strategy was proposed where some initial feedback was employed to improve the generalizability of the model. Additionally, neighborhood-based feedback was also considered. The experiments using the VI curves collected over three farms in Israel covering wheat, potato, and barley crops illustrated the need for accounting the spatial non-stationarity in VI curve classification. The proposed dynamic feedback system and the graph-based strategy to consider spatial heterogeneity significantly improved the accuracy of the proposed framework. Additionally, the proposed reinforcement learning strategy ensures the dynamic refinement of model weights to facilitate the real-time applications even when the target data varies significantly. Although the proposed approach is generic, the end-to-end framework, as adopted in this research, is suitable for aerial or agricultural datasets. Hence, further fine tuning and remodeling may be required for non-agriculture or non-spatial datasets. However, the dynamic weight update may be adopted for any applications irrespective of the datasets.

Image Fusion of Multi-Temporal Images of Eyes Feature using Hybrid Approach of PCA and SWT

A wide variety of information is represented by an image feature, which may be applied in various applications such as mage fusion, video processing, medical diagnosis, traffic safety monitoring, visual surveillance, feature matching, image segmentation, pattern matching, person identification, sentiment analysis, human computer interaction and many more fields. This paper focused on multi-temporal feature extraction of eyes feature from human face images captured in two different time slots to reduce the semantic gap from images and to improve the image quality by image fusion using PCA, SWT and hybrid approach of PCA and SWT algorithm. In this paper, comparative study of multi-temporal image fusion using Principle Component Analysis (PCA), Stationary Wavelet Transform (SWT) algorithms and hybrid approach of PCA and SWT are employed and its experimental results are evaluated with its performance analysis. Image fusion performance is compared based on eight quantitative quality measures as SSIM, MSE, NAE, CC , SC , AD, SD and MI.The outcomes of comparison show that employing the hybrid approach of PCA+SWT transform can improve image fusion performance. The applicability of this work approach may have several uses when the utilization of human facial features is feasible.

Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images

Building change detection monitors building changes by comparing and analyzing multi-temporal images acquired from the same area and plays an important role in land resource planning, smart city construction and natural disaster assessment. Different from change detection in conventional scenes, buildings in the building change detection task usually appear in a densely distributed state, which is easy to be occluded; at the same time, building change detection is easily interfered with by shadows generated by light and similar-colored features around the buildings, which makes the edges of the changed region challenging to be distinguished. Aiming at the above problems, this paper utilizes edge information to guide the neural network to learn edge features related to changes and suppress edge features unrelated to changes, so as to accurately extract building change information. First, an edge-extracted module is designed, which combines deep and shallow features to supplement the lack of feature information at different resolutions and to extract the edge structure of the changed features; second, an edge-guided module is designed to fuse the edge features with different levels of features and to guide the neural network to focus on the confusing building edge regions by increasing the edge weights to improve the network’s ability to detect the edges that have changed. The proposed building change detection algorithm has been validated on two publicly available data (WHU and LEVIR-CD building change detection datasets). The experimental results show that the proposed model achieves 91.14% and 89.76% in F1 scores, respectively, demonstrating superior performance compared to some recent learning change detection methods.

The Legacy of Sycamore Gap: The Potential of Photogrammetric AI for Reverse Engineering Lost Heritage with Crowdsourced Data

Abstract. The orientation of crowdsourced and multi-temporal image datasets presents a challenging task for traditional photogrammetry. Indeed, traditional image matching approaches often struggle to find accurate and reliable tie points in images that appear significantly different from one another. In this paper, in order to preserve the memory of the Sycamore Gap tree, a symbol of Hadrian's Wall that was felled in an act of vandalism in September 2023, deep-learning-based features trained specifically on challenging image datasets were employed to overcome limitations of traditional matching approaches. We demonstrate how unordered crowdsourced images and UAV videos can be oriented and used for 3D reconstruction purposes, together with a recently acquired terrestrial laser scanner point cloud for scaling and referencing. This allows the memory of the Sycamore Gap tree to live on and exhibits the potential of photogrammetric AI (Artificial Intelligence) for reverse engineering lost heritage.

Application of multi-temporal satellite imagery for urban tree species identification

Accurate tree inventories are critical for urban forest management but challenging to obtain, as many urban trees are on private property (backyards, etc.) and are excluded from public inventories. Here, we examined the feasibility of tree species identification in a large heterogenous urban area (>850 km2) by using multi-temporal PlanetScope images (3.2 m resolution, multi-spectral) and inventory data from more than 20,000 ground observations within the urban forest of the Greater Chicago area. Our approach achieved an overall classification accuracy of 0.60 and 0.71 for 18 species and ten genera, respectively, but varied from moderate to high for certain species (0.59–0.92) and genera (0.61–0.91). In particular, we identified key host tree species (Fraxinus americana, F. pennsylvanica, and Acer saccharinum) for two damaging invasive insects, emerald ash borer (EAB, Agrilus planipennis) and Asian longhorn beetle (ALB, Anoplophora glabripennis), with over 0.80 accuracies. In addition, we demonstrated that including images from the autumn months (September–November), either for a single-season model or a combined multiple-season model, improved the identification accuracy of temperate deciduous trees. Further, the high classification accuracy of support vector machine (SVM) over random forest (RF) and neural network (NN) approaches suggests that future work might benefit from comparing multiple classification methods to select the approach that maximizes species classification accuracy. Our study demonstrated the potential for applying multi-temporal high-resolution images in urban tree classification, which can be used for urban forest management at a large spatial scale.

Tracking dynamics characteristics of tidal flats using landsat time series and Google Earth Engine cloud platform

Tidal flats serve as vital spatial resources within coastal wetlands and play crucial roles in biological habitat preservation, environmental conservation, and human development. However, accurately delineating their extent from instantaneous remote sensing images poses challenges, owing to the dynamic interplay between seawater and land. Therefore, this study utilized the consistency of remote sensing satellites and developed a multi-feature decision approach based on the long-term remote sensing datasets and the Google Earth Engine (GEE) cloud platform for determining the spatial extent, classifying types, and mapping the spatial-temporal distribution of the tidal flats. Initially, a multi-temporal image stack was compiled from the filtered remote sensing images. Subsequently, the modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) were calculated to establish the maximum and minimum water surfaces using the extreme value composite algorithm (EVCA). The Otsu algorithm and spatial overlay analysis method were then utilized to accurately delineate the spatial extent of the tidal flats. Additionally, the Normalized Difference Vegetation Index (NDVI) was utilized to accurately identify tidal flat types. Post-processing involves elevation data and mathematical morphological methods to mitigate the impact of permanent water bodies. Finally, the accuracy of the extracted spatial extent and types of tidal flats was evaluated. The efficacy and practicality of the proposed method were validated using the Zhoushan Archipelago and Landsat time series in China. The results revealed the following: (1) the acquired tidal flat information exhibited evident boundaries and accurate types, achieving an overall accuracy (OA) of 94.80 % and a Kappa coefficient of 0.89; (2) the area of tidal flats in the Zhoushan Archipelago increased from 2,182.19 ha in 1985 to 3,761.21 ha in 2020, with an average annual increase of 45.12 ha and an average annual increase rate of 1.57 %; and (3) over 35 periods, the area transformed from other land types to tidal flats amounted to 2,895.74 ha, while the area converted from tidal flats to other land types totaled 1,651.90 ha. The three islands with the most rapid growth in tidal flat area were Liuheng Island, Dachangtu Island, and Daishan Island, with changes of 675.68 ha, 380.47 ha, and 362.63 ha, respectively. This method can offer an automated, swift, and accurate approach for tidal flat extraction, demonstrating high precision and practical effectiveness, thereby providing robust technical support for coastal resource surveys.

Spatio-Temporal Detection of Land Use/Land Cover Changes in Kokrajhar District of Assam

The use of multi-temporal satellite images in digital change detection algorithms aids in the comprehension of landscape dynamics. The present study illustrates the spatio temporal dynamics of land use/land cover of Kokrajhar district of Assam, India. Landsat Satellite imageries of four different time periods. i.e., Landsat Thematic Mapper (TM) of 1991, 2001, 2011 and 2021 were acquired from Google Earth Explorer site and quantify the changes of Kokrajhar district from 1991 to 2021 over a period of 30 years. Supervised classification methodology has been employed using maximum likelihood technique in ArcMap 10.8 Software. The images of the study area were categorised into four different classes namely vegetation, agriculture, built up and water body. The results indicate that during the last three decades, built up have been increased by 3.8% (658.75 km2) while agriculture, vegetation and water body have been decreased by 0.74 (708.9 km2) %, 0.56(1494.46 km2) % and 2.46 (273.5 km2) % respectively.

Using AI to Empower Norwegian Agriculture: Attention-Based Multiple-Instance Learning Implementation

Agricultural development is one of the most essential needs worldwide. In Norway, the primary foundation of grain production is based on geological and biological features. Existing research is limited to regional-scale yield predictions using artificial intelligence (AI) models, which provide a holistic overview of crop growth. In this paper, the authors propose detecting several field-scale crop types and use this analysis to predict yield production early in the growing season. In this study, the authors utilise a multi-temporal satellite image, meteorological, geographical, and grain production data corpus. The authors extract relevant vegetation indices from satellite images. Furthermore, the authors use field-area-specific features to build a field-based crop type classification model. The proposed model, consisting of a time-distributed network and a gated recurrent unit, can efficiently classify crop types with an accuracy of 70%. In addition, the authors justified that the attention-based multiple-instance learning models could learn semi-labelled agricultural data, and thus, allow realistic early in-season predictions for farmers.

Cross-Modal Segmentation Network for Winter Wheat Mapping in Complex Terrain Using Remote-Sensing Multi-Temporal Images and DEM Data

Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain.

Research on Monitoring and Application of Ecological Restoration Engineering in Open Pit Backfilling Mines Based on Satellite Remote Sensing Data

Abstract. The restoration and management of mining ecological environment is an important component of China's ecological civilization construction. The implementation of restoration projects is to avoid the serious impact caused by the continuous deterioration of mining ecological structure, and is a key way to maintain regional ecological security and energy and mineral security. Therefore, efficient and rapid monitoring and evaluation of the effectiveness of mining ecological restoration is an essential and important link. With the rapid development of remote sensing technology, remote sensing monitoring has become an important means to objectively, quickly, and accurately obtaining changes in the mining environment. It can provide timely and long-term monitoring of mining and backfilling conditions in mining areas. This article uses the multi temporal stereo images of the Resource 3 satellite over four years to monitor the mining and backfilling situation of the Zhungeer open-pit coal mine area in Inner Mongolia. By reconstructing the Digital Surface Model (DSM) of the open-pit mining area, the changes and thresholds of the multi temporal DSM data are statistically analyzed to extract the mining and backfilling areas, and the earthwork volume is calculated. The results showed that from 2013 to 2016, the main large-scale mining faces in the study area of Zhungeer open-pit mining area had a total mining operation of about 563.5 million cubic meters, and a total backfilling operation of about 604.29 million cubic meters; During the period from 2016 to 2018, the main large-scale mining faces in the region had a total of approximately 721.71 million cubic meters of mining operations and a total of approximately 805.42 million cubic meters of backfilling operations. The overall operational intensity increased from 2016 to 2018. The research results show that based on satellite image data, it is convenient and efficient to obtain DSM and corresponding changes of multiple time periods in mining areas. Monitoring results can provide important support for regional environmental governance and protection, as well as safety production in mining areas.

Research and application of land illegal behavior monitoring based on video image recognition

Abstract. Our country had increasingly high requirements for land management, especially for arable land, which was the most precious resource. Strict policies for protecting arable land are being implemented. The enforcement of satellite imagery in mega cities had certain limitations in terms of timeliness and accuracy. To meet the requirements of refined management, a high-altitude camera was constructed to form a near ground monitoring network covering the cultivated land area. Multi temporal image groups were obtained through video frame extraction. A monitoring sample library for typical behaviors of natural resources was established. Land objects closely related to illegal activities such as construction machinery and bulldozing areas were identified based on deep learning algorithms. By using the technology of bidirectional conversion between video spatial location and geographic coordinates, a fusion of spatial information video monitoring patterns was formed. Taking the 20000 acre permanent basic farmland area as an example, the recognition rate of illegal land related behaviors was about 81%. By combining approval information and conducting spatial analysis on the identified patterns, real-time warning information could be pushed. This application can effectively improve the timeliness of supervision and enhance the level of supervision.

Global structure graph mapping for multimodal change detection

ABSTRACT Multimodal change detection (MCD) combines multiple remote sensing data sources to realize surface change monitoring, which is essential for disaster evaluation and environmental monitoring. However, due to the ‘incomparable’ features in multimodal data, traditional change detection methods for unimodal (homogenous) data no longer apply. To address this issue, this paper proposes a novel MCD method with global structure graph mapping (GSGM) which extracts the ‘comparable’ structural features between multimodal datasets and constructs a global structure graph (GSG) to express the structure information for each of the multi-temporal images, which are then cross-mapped to the other data domain. The change intensity (CI) is determined by measuring the change of GSGs after mapping and the differences between GSGs and mapped GSGs. The forward and backward CI maps (CIMs) are then fused with the latent low-rank representation method (LLRR), and the change map (CM) is obtained by threshold segmentation. Experiments on five multimodal and four unimodal datasets demonstrate the effectiveness and robustness of the proposed method (source code is made available at https://github.com/rshante0426/GSGM).

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Pre- and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural Network

ABSTRACT This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1, Sentinel-2, spectral indices, and terrain features and GEDI canopy height (rh99) as target variables. Our research consisted of three primary objectives: (1) an evaluation of GEDI rh99 canopy height in relation to an nDSM derived from airborne LiDAR, (2) the development of two CNN models for pre- and post-fire scenarios, and (3) utilizing the trained CNN models to generate pre- and post-fire canopy height maps and canopy height change maps across the continuous landscape. GEDI rh99 accuracy analysis revealed R 2 = 0.85, RMSE = 7.25 m and nRMSE = 0.23 for pre-fire GEDI and R 2 = 0.78, RMSE = 10.1 m and nRMSE = 0.3 for post-fire GEDI. The pre-fire CNN model achieved R 2 = 0.75, RMSE = 7.29 m and nRMSE = 0.22, and post-fire CNN model achieved R 2 = 0.62, RMSE = 11.67 m, and nRMSE = 0.34 when validated against nDSM. Bias analysis was conducted, revealing that GEDI rh99 underestimated nDSM across all canopy height ranges, and slope has no significant impact on GEDI accuracy. Notably, post-fire GEDI rh99 exhibited significant underestimation in high to extreme fire severity. The CNN models’ prediction displayed overestimation in short forests < 20 m and increasing underestimation in taller forests > 20 m and underestimation in high and extreme fire severity. Subsequent investigation indicated that canopy layer thinning in high fire severity conditions resulted in weak GEDI waveform signals and consequent bias. Additionally, the GEDI rh99 bias was propagated to the CNN models, together with insensitivity of spectral band values to canopy height in dense and burnt forests potentially contributed to models’ bias. Finally, we demonstrated CNN models’ ability to monitor forest recovery by generating canopy height maps spanning East Gippsland from 2019 to 2023.

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources.

Detailed Landslide Traces Database of Hancheng County, China, Based on High-Resolution Satellite Images Available on the Google Earth Platform

Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The occurrence of landslide disasters often causes a large number of casualties along with economic losses in the local area, seriously restricting local economic development. Although risk assessment and deformation mechanism analysis for single landslides have been performed for landslide disasters in the Hancheng area, this area lacks a landslide traces database. A complete landslide database comprises the basic data required for the study of landslide disasters and is an important requirement for subsequent landslide-related research. Therefore, this study used multi-temporal high-resolution optical images and human-computer interaction visual interpretation methods of the Google Earth platform to construct a landslide traces database in Hancheng County. The results showed that at least 6785 landslides had occurred in the study area. The total area of the landslides was about 95.38 km2, accounting for 5.88% of the study area. The average landslide area was 1406.04 m2, the largest landslide area was 377,841 m2, and the smallest landslide area was 202.96 m2. The results of this study provides an important basis for understanding the spatial distribution of landslides in Hancheng County, the evaluation of landslide susceptibility, and local disaster prevention and mitigation work.

Fluvio-geomorphic change of the Padma-Meghna river course using the NDWI and MNDWI techniques

ABSTRACT This study intends to investigate the fluvial-geomorphic change and erosion-accretion processes of two significant rivers. The 2149 km2 research area’s overall erosion and accretion were depicted using multi-temporal Landsat images and Normalized Difference Water Index, Modified Normalized Difference Water Index methods to measure and investigate change. However, focus has been placed on four unique sections that are prone to erosion, and the bank migration of the section has been measured in order to depict the actual state of erosion. The Padma-Meghna River eroded approximately 11.0, 77.0, and 13.0 km2 between 1973–1978, 1978–1989, and 1989–1994. However, 78.0 and 48.0 km2 of accretion occurred in the years between 1994 and 1999 and 1999 and 2004. Moreover, between 2004 and 2021, the river’s erosional activity was remarkably dynamic compared to deposition. Besides, 64 km2 and 4 km2 of land accretion occurred during 2008–2014 and 2014–2019, while 77 km2 and 68 km2 of land eroded during 2004–2008 and 2019–2021. The overall rates of erosion and accretion are 5.12% and 4.04%, respectively. Statistically, both banks of the Padma River eroded about 2 km from 1973 to 2021. Padma-Meghna confluences are more unstable and susceptible to change. The Meghna’s left bank moved 4.5 km east between Goshairhat and Haimchar in 1989, and in 2008, the river shifted 4.8 km west from the river of 1994. In 1989, it moved 5 km east from a previous location between Goshairhat and Haiderganj, and in 2008, it moved 6.3 km west from the 1989 river. The bank migrated 12 km eastward in 2014 from where it was in 2008 through the bar development. The Mehendiganj Upazila’s eastern side lost more than 7.4 km between 1973 and 2021, and the current major channel is simply 7.4 km west of the 1973 river. The findings of the dynamic changes might be useful to planners, developers, researchers, and government agencies as they develop and put into practice new development initiatives and river basin management.