High Resolution Satellite Images Research Articles

Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach

Abstract. Floe size distribution (FSD) has become a parameter of great interest in observations of sea ice because of its importance in affecting climate change, marine ecosystems, and human activities in the polar ocean. A most effective way to monitor FSD in the ice-covered regions is to apply image processing techniques to airborne and satellite remote sensing data, where the segmentation of individual ice floes is a challenge in obtaining FSD from remotely sensed images. In this study, we adopt a deep learning (DL) semantic segmentation network to fast and adaptive implement the task of ice floe instance segmentation. In order to alleviate the costly and time-consuming data annotation problem of model training, classical image processing technique is applied to automatically label ice floes in local-scale marginal ice zone (MIZ). Several state-of-the-art (SoA) semantic segmentation models are then trained on the labelled MIZ dataset and further applied to additional large-scale optical Sentinel-2 images to evaluate their performance in floe instance segmentation and to determine the best model. A post-processing algorithm is also proposed in our work to refine the segmentation. Our approach has been applied to both airborne and high-resolution optical (HRO) satellite images to derive acceptable FSDs at local and global scales.

Multiscale Feature Extraction by Using Convolutional Neural Network: Extraction of Objects from Multiresolution Images of Urban Areas

The emergence of deep learning-based classification methods has led to considerable advancements and remarkable performance in image recognition. This study introduces the Multiscale Feature Convolutional Neural Network (MSFCNN) for the extraction of complex urban land cover data, with a specific emphasis on buildings and roads. MSFCNN is employed to extract multiscale features from three distinct image types—Unmanned Aerial Vehicle (UAV) images, high-resolution satellite images (HR), and low-resolution satellite images (LR)—all collected within the Fengshan District of Kaohsiung, Taiwan. The model in this study demonstrated remarkable accuracy in classifying two key land cover categories. Its success in extracting multiscale features from different image resolutions. In the case of UAV images, MSFCNN achieved an accuracy rate of 91.67%, with a Producer’s Accuracy (PA) of 93.33% and a User’s Accuracy (UA) of 90.0%. Similarly, the model exhibited strong performance with HR images, yielding accuracy, PA, and UA values of 92.5%, 93.33%, and 91.67%, respectively. These results closely align with those obtained for LR imagery, which achieved respective accuracy rates of 93.33%, 95.0%, and 91.67%. Overall, the MSFCNN excels in the classification of both UAV and satellite images, showcasing its versatility and robustness across various data sources. The model is well suited for the task of updating cartographic data related to urban buildings and roads.

Retracted: Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion

Retracted: Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion

Prediction of Stem Water Potential in Olive Orchards Using High-Resolution Planet Satellite Images and Machine Learning Techniques

Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem water potential (Ψstem). In this study, we carried out field measurements of Ψstem in an irrigated olive orchard in southern Italy during the 2021 and 2022 seasons. Water status data were acquired at midday from 24 olive trees between June and October in both years. Reflectance data collected at the time of Ψstem measurements were utilized to calculate vegetation indices (VIs). Employing machine learning techniques, various prediction models were developed by considering VIs and spectral bands as predictors. Before the analyses, both datasets were randomly split into training and testing datasets. Our findings reveal that the random forest model outperformed other models, providing a more accurate prediction of olive water status (R2 = 0.78). This is the first study in the literature integrating remote sensing and machine learning techniques for the prediction of olive water status in order to improve olive orchard irrigation management, offering a practical solution for estimating Ψstem avoiding time-consuming and resource-intensive fieldwork.

Deep learning-based burned forest areas mapping via Sentinel-2 imagery: a comparative study.

In order to evaluate the effects of forest fires on the dynamics of the function and structure of ecosystems, it is necessary to determine burned forest areas with high accuracy, effectively, economically, and practically using satellite images. Extraction of burned forest areas utilizing high-resolution satellite images and image classification algorithms and assessing the successfulness of varied classification algorithms has become a prominent research field. This study aims to indicate on the capability of the deep learning-based Stacked Autoencoders method for the burned forest areas mapping from Sentinel-2 satellite images. The Stacked Autoencoders, used in this study as an unsupervised learning method, were compared qualitatively and quantitatively with frequently used supervised learning algorithms (k-Nearest Neighbors (k-NN), Subspaced k-NN, Support Vector Machines, Random Forest, Bagged Decision Tree, Naive Bayes, Linear Discriminant Analysis) on two distinct burnt forest zones. By selecting burned forest zones with contrasting structural characteristics from one another, an objective assessment was achieved. Manually digitized burned areas from Sentinel-2 satellite images were utilized for accuracy assessment. For comparison, different classification performance and quality metrics (Overall Accuracy, Mean Squared Error, Correlation Coefficient, Structural Similarity Index Measure, Peak Signal-to-Noise Ratio, Universal Image Quality Index, and KAPPA metrics) were used. In addition, whether the Stacked Autoencoders method produces consistent results was examined through boxplots. In terms of both quantitative and qualitative analysis, the Stacked Autoencoders method showed the highest accuracy values.

A comprehensive study on ecological insights of Ulva lactuca seaweed bloom in a lagoon along the southeast coast of India

Ulva lactuca, commonly known as sea lettuce is a green macroalgae (chlorophyta) and its uncontrolled proliferation forms a bloom, which causes ecological imbalances and adversely impacts brackishwater ecosystems. Algal bloom can harm the environment and aquatic organisms, but it can also be beneficial if harvested and utilized for industrial purposes. A massive bloom of green macroalgae was observed in the Muttukadu lagoon, located along the southeast coast of India in the Bay of Bengal. A comprehensive study was taken up to assess the spatial distribution, proximate, nutrients, minerals, heavy metals, and factors inducing the bloom. For this study, high-resolution satellite images (Sentinel 2) were used. The spatial distribution of seaweed was mapped using Floating Algae Index (FAI), Modified Floating Algae Index (MFAI), Normalized Difference Red Edge (NDRE) Index and Seaweed Index (SI) models. Results were validated using the MAVIC 3 pro drone and field survey. The SI model was found to be more accurate compared to other models. The bloom exhibited notable temporal dynamics, with peak coverage observed in the last week of May 2023 with a spread area of about 100.05 ha, the bloom intensity was found to be high till the last week of June 2023, after which it gradually declined. The proximate composition (%) of Ulva lactuca showed the moisture content is about 80.00 ± 2.98, crude protein 24.17 ± 1.80, crude lipid 0.81 ± 0.05, Total Ash 37.45 ± 1.44, Fiber (8.92 ± 0.41), and Acid Insoluble Ash (16.18 ± 1.16). The mineral concentration was in the order Ca > Na > Mg > K > P. Iron (Fe) was observed to be high compared to other micronutrients, with a concentration of 1164.40 mg/kg. Rich nutrients and low heavy metal concentration significantly can boost the selling cost of Ulva lactuca providing livelihood support for the coastal fisherfolk.

Enhanced glacial lake activity threatens numerous communities and infrastructure in the Third Pole

Glacial lake outburst floods (GLOFs) are among the most severe cryospheric hazards in the Third Pole, encompassing the Tibetan Plateau and surrounding Himalayas, Hindu Kush, and Tianshan Mountains. Recent studies on glacial lake changes and GLOF characteristics and risks in this region have shown scattered and insufficiently detailed features. Here, we conduct an appraisal of the GLOF risks by combining high-resolution satellite images, case-by-case high-precision GLOF modeling, and detailed downstream exposure data. The glacial lake changes from 2018 to 2022 in the region were primarily driven by the accelerated expansion of proglacial lakes. The GLOF frequency has exhibited a significant increasing trend since 1980, with intensified activity in Southeastern Tibet and the China-Nepal border area over the past decade. Approximately 6,353 km2 of land could be at risk from potential GLOFs, posing threats to 55,808 buildings, 105 hydropower projects, 194 km2 of farmland, 5,005 km of roads, and 4,038 bridges. This study directly responds to the need for local disaster prevention and mitigation strategies, highlighting the urgent requirement of reducing GLOF threats in the Third Pole and the importance of regional cooperation.

Analysis of Illumination Conditions in the Lunar South Polar Region Using Multi-Temporal High-Resolution Orbital Images

The illumination conditions of the lunar south pole region are complex due to the rugged terrain and very low solar elevation angles, posing significant challenges to the safety of lunar landing and rover explorations. High-spatial and temporal-resolution analyses of the illumination conditions in the south pole region are essential to support mission planning and surface operations. This paper proposes a method for illumination condition analysis in the lunar pole region using multi-temporal high-resolution orbital images with a pre-selected landing area of Chang’E-7 as the study area. Firstly, a database of historical multi-temporal high-resolution (0.69–1.97 m/pixel) orbital images, with associated image acquisition time, solar elevation angle, and azimuth angle, is established after preprocessing and registration. Secondly, images with the nearest solar elevation and azimuth at the planned time for mission operations are retrieved from the database for subsequent illumination condition analysis and exploration support. The differences in the actual solar positions at the mission moments from that of the nearest sun position image are calculated and their impact on illumination conditions is evaluated. Experimental results of the study area demonstrate that the constructed image database and the proposed illumination analysis method using multi-temporal images, with the assistance of DEM in a small number of cases, can effectively support the mission planning and operations for the Chang’E-7 mission in the near future.

Mapping Small-Scale Irrigation Areas Using Expert Decision Rules and the Random Forest Classifier in Northern Ethiopia

The mapping of small-scale irrigation areas is essential for food security and water resource management studies. The identification of small-scale irrigation areas is a challenge, but it can be overcome using expert knowledge and satellite-derived high-spatial-resolution multispectral information in conjunction with monthly normalized difference vegetation index (NDVI) time series, and additional terrain information. This paper presents a novel approach to characterize small-scale irrigation schemes that combine expert knowledge, multi-temporal NDVI time series, multispectral high-resolution satellite images, and the random forest classifier in the Zamra catchment, North Ethiopia. A fundamental element of the approach is mapping small-scale irrigation areas using expert decision rules to incorporate the available water resources. We apply expert decision rules to monthly NDVI composites from September 2020 to August 2021 along with the digital elevation model (DEM) data on the slope, drainage order, and distance maps to derive the sample set. The samples were based on the thresholds obtained by expert knowledge from field surveys. These data, along with the four spectral bands of a cloud-free Planet satellite image composite, 12 NDVI monthly composites, slope, drainage order, and distance map were used as input into a random forest classifier which was trained to classify pixels as either irrigated or non-irrigated. The results show that the analysis allows the mapping of small-scale irrigation areas with high accuracy. The classification accuracy for identifying irrigated areas showed a user accuracy ranging from 81% to 87%, along with a producer accuracy ranging from 64% to 79%. Furthermore, the classification accuracy and the kappa coefficient for the classified irrigation schemes were 80% and 0.70, respectively. As a result, these findings highlight a substantial level of agreement between the classification results and the reference data. The use of different expert knowledge-based decision rules, as a method, can be applied to extract small-scale and larger irrigation areas with similar agro-ecological characteristics.

A SHALLOW NEURAL NETWORK MODEL FOR URBAN LAND COVER CLASSIFICATION USING VHR SATELLITE IMAGE FEATURES

Abstract. Recently, image classification techniques using neural networks have received considerable attention in sustainable urban development, since their applications have an extreme effect on building distribution, infrastructural networks, and water resource management. In this research, a back-propagation shallow neural network model is presented for very high resolution satellite image classification in urban environments. Workflow procedures consider selecting and collecting data, preparing required study areas, extracting distinctive features, and applying the classification process. Visual interpretation is performed to identify observed land cover classes and detect distinctive features in the urban environment. Pre-processing techniques are implemented to present the used images in a more suited form for the classification techniques. A shallow neural network model (supported by MathWorks MATLAB environment) is successfully applied and results are evaluated. The proposed model is tested for classifying both WorldView-2 and WorldView-3 multispectral images with different spatial and spectral characteristics to check the model’s applicability to various kinds of satellite imagery and different study areas. Model outcomes are compared to two well-known classification methods; the Nearest Neighbour object-based method and the Maximum Likelihood pixel-based classifier, to validate and check the model stability. The overall accuracy achieved by the proposed model is 86.25% and 83.25%, while the nearest neighbour approach has obtained 84.50% and 82.75%, and the maximum likelihood classifier has accomplished 82.50% and 80.25% for study area 1 and study area 2 respectively. Obtained results indicate that the developed shallow neural network model achieves a promising accuracy for urban land cover classification in comparison with the standard techniques.

Building damage assessment using improved combined adjustment and automatic building corners extraction method

ABSTRACTAccurately estimating the collapse height of buildings during earthquakes holds great significance. Acquiring precise height information of buildings using dual-phase high-resolution stereo satellite images (HRSSIs) requires high-precision ground control points, which are difficult to obtain in earthquake-stricken areas. Spaceborne laser data has been validated to significantly improve the vertical accuracy of HRSSIs. Therefore, this paper first proposes the application of a combined stereo adjustment model in building collapse estimation and thoroughly verifies its feasibility. Firstly, the laser altimetry points (LAPs) acquired by GF-7 and ICESat-1 serve as elevation control points (ECPs), and the tie points from HRSSIs were used as constraint to conduct combined adjustment to improve the consistency of stereoscopic positioning accuracy between HRSSIs before and after the earthquake. Subsequently, a footprints-to-conners, coarse-to-fine, automatic building corners extraction method was performed. Avoiding the time-consuming generation of large-scale DSM while also improving the accuracy of building collapse assessment. Finally, according to the height change of building corners calculated from HRSSIs, building damage assessment was completed. According to the above methods, independent experiments were carried out respectively for the Oaxaca earthquake in Mexico in 2020 and the Kahramanmaras earthquake in Türkiye in 2023. GF-7 HRSSIs was used to verify the accuracy and stability of the proposed method for 3D assessment of building damage. The results indicate that the RMSE of elevation positioning accuracy has been greatly reduced in both regions, ranging from 10.29 m to 0.95 m and from 6.67 m to 0.99 m, respectively. This accuracy guarantees the feasibility of detecting the building floor-level collapse. And 2541 building corner points were extracted from two disaster areas, with correct corner points accounting for 85.18%. A total of 520 buildings were correctly extracted corner points, with an accuracy rate of 94.12% for assessing the degree of damage.

Lahar Inundation Estimation of Mount Semeru Relating to the 4th December 2021 Volcano Eruption

The Semeru volcano – located at the boundary region of Lumajang and Malang districts of East Java province, Indonesia – which is categorized as one of the active volcanoes in Indonesia was enormously erupted the volcanic materials – the hot lahar and the volcanic ash - on 4th December 2021. The scale of the eruption event caused some disaster to several villages along the path of the lahar and volcanic ash in south to southeast direction from the Mount Semeru lahar dome. An inundation simulation of the lahar is done as a contribution to mitigating the disaster that may potentially occur in the future. The LAHARZ algorithm was used for these estimation calculations. The Digital Terrain Model (DTM) and Digital Surface Model (DSM) were used as the base data for estimating and simulating the lahar inundation scenario. The use of two types of elevation model data in the simulation was done for study the inundation characteristics in the presence of forest vegetation. Three scenarios of lahar volume were used for simulating the inundation over elevation models. Results of the estimation then be compared to the actual lahar inundation that was delineated from the high-resolution satellite images. Lahar flow simulations using DTM show that more flows were formed compared to using DSM. The DTM simulation results showed a flow that more closely resembled the existing flow map compared to the DSM simulation results.

GAN-assisted Road Segmentation from Satellite Imagery

Geo-information extraction from satellite imagery has become crucial to carry out large-scale ground surveys in a short amount of time. With the increasing number of commercial satellites launched into orbit in recent years, high-resolution RGB color remote sensing imagery has attracted a lot of attention. However, because of the high cost of image acquisition and even more complicated annotation procedures, there are limited high-resolution satellite datasets available. Compared to close-range imagery datasets, existing satellite datasets have a much lower number of images and cover only a few scenarios (cities, background environments, etc. ). They may not be sufficient for training robust learning models that fit all environmental conditions or be representative enough for training regional models that optimize for local scenarios. Instead of collecting and annotating more data, using synthetic images could be another solution to boost the performance of a model. This study proposes a GAN-assisted training scheme for road segmentation from high-resolution RGB color satellite images, which includes three critical components: a) synthetic training sample generation, b) synthetic training sample selection, and c) assisted training strategy. Apart from the GeoPalette and cSinGAN image generators introduced in our prior work, this paper in detail explains how to generate new training pairs using OpenStreetMap (OSM) and introduces a new set of evaluation metrics for selecting synthetic training pairs from a pool of generated samples. We conduct extensive quantitative and qualitative experiments to compare different image generators and training strategies. Our experiments on the downstream road segmentation task show that 1) our proposed metrics are more aligned with the trained model performance compared to commonly used GAN evaluation metrics such as the Fréchet inception distance (FID); and 2) by using synthetic data with the best training strategy, the model performance, mean Intersection over Union (mean IoU), is improved from 60.92% to 64.44%, when 1,000 real training pairs are available for learning, which reaches a similar level of performance as a model that is standard-trained with 4,000 real images (64.59%), i.e. , enabling a 4-fold reduction in real dataset size.

Examining the informal urban growth trends in a Port city

Rapid urban developmental growth is a heated debate worldwide due to environmental challenges. This research has examined the spatiotemporal trend of informal built-up growth in Karachi city. Using a geo-information system, the past twenty years (2000–2020) trends of informal built-up growth are examined. For attaining the research objectives, geo-referenced high-resolution maps and satellite images are used for accuracy based spatial data. Karachi is divided into five different land use and land cover (LULC): formal built-up, informal built-up, vacant, water bodies, and green spaces. Spatial data of informal built-up growth change of five different years, 2000, 2005, 2010, 2015, and 2020 are generated through acquired maps digitization using ArcMap. Subsequently, the gains and transfers of Karachi's informal built-up growth based on five years 2000–2005, 2005–2010, 2010–2015, and 2015–2020 are analyzed using the Land Change Modeler (LCM) in IDRISI software. Also, land use land cover changes (LULCC) are predicted for the next 40 years (2020–2060) using the integrated Cellular Automata Markov (CA-Markov) simulation model in IDRISI. The results revealed that Karachi's built-up is expanding rapidly. Land conversion into the informal built-up area is alarming, as it has changed from 144.31 km2 to 217.19 km2 with 72.88 km2 in the past twenty years (2000–2020) and has occupied green and agricultural land. Most informal built-up areas have transitioned from vacant (71.01 km2) land use land cover (LULC). The informal built-up area could expand from 217.19 km2 to 317.63 km2, with about 100.44 km2 up to 2060. The planned and unplanned development will be towards the city's East (E) direction and will convert and ruin agriculture and vacant land. The present study provides suggestions to urban planners, administrative authorities, and policymakers to control informal growth and achieve sustainable development goals in developing countries.

Crosswalk Detection from Satellite Imagery for Pedestrian Network Completion

Pedestrian network creation and completion for various tasks in transportation planning engineering generally requires laborious manual labeling of sidewalks and crosswalks. In this study, we employed the deep learning-based object detection model YOLO v5 for detecting crosswalks using Google Maps satellite imagery data of four North American cities. We also tested for two additional scenarios involving two image-processing techniques to exploit the special properties of crosswalks. Observations showed that the original images performed better than with binary thresholding and nearly the same even when noisy regions outside the right-of-way were removed. We proposed an algorithm to assign the detected crosswalks to intersection legs. We demonstrated the effectiveness of this technique for Washington, D.C. and Los Angeles, CA, showing classification accuracy rates of 71% and 89%, respectively. We also showed the influence of increasing distance threshold, a tolerance radius of prediction accuracy, in degrading the classification performance. This method, when combined with existing methods for sidewalk detection from aerial and street view images, could be reasonably used to help complete urban pedestrian networks in cities where high-resolution satellite images are available.

Susceptibility Mapping of Unhealthy Trees in Jiuzhaigou Valley Biosphere Reserve

Jiuzhaigou Valley is recognized as both a world natural heritage site and a biosphere reserve. Conducting research on vegetation health within its scope can provide a demonstration role for sustainable development research. In this study, we proposed a technology integration approach that combined remote sensing intelligent identification and quantitative retrieval, and achieved vegetation health monitoring and susceptibility mapping of unhealthy trees. Leveraging WorldView-2 high-resolution satellite images, unhealthy trees were elaborately identified through the object-oriented classification method employing spectral and texture features, with F1 Score exceeding 75%. By applying fuzzy operations on indices related to leaf pigment and canopy architecture, we ultimately generated susceptibility maps of unhealthy trees on Sentinel-2 satellite images, with Area Under the Curve (AUC) exceeding 0.85. Our findings underscore that the vegetation health in Jiuzhaigou Valley is predominantly influenced by human activities and geological hazards. The forests of Jiuzhaigou Valley exhibit a certain degree of resilience to geological disasters, while human activities have been continuously exerting adverse effects on forest health in recent years, necessitating heightened attention. The methodology proposed in this study for mapping unhealthy trees susceptibility presents a cost-effective solution that can be readily applied for vegetation health monitoring and early warning in analogous biosphere reserves.

An Overview of Deep Learning Techniques for Road Detection in High-Resolution Satellite Images

One of the main and most difficult problems in computer vision research has been removing road networks from high-resolution remote sensing photos. Numerous applications, such as urban planning, traffic management, disaster response, and environmental monitoring, depend on the accurate identification of road networks. The field of artificial intelligence has witnessed tremendous advancements and advances in road extraction technology, especially in the area of deep learning. This research provides an extensive analysis of deep learning techniques for road extraction from remote sensing imagery, emphasizing how these technologies improve process accuracy and efficiency. Deep learning techniques are divided into fully supervised, semi-supervised, and unsupervised learning approaches, each having more focused subcategories, depending on the kind of annotated data that is used. The tenets, advantages, and disadvantages of these approaches are contrasted and examined. The review also provides an overview of the metrics applied to assess road extraction algorithms and the high-resolution remote sensing picture datasets used for these kinds of activities. In conclusion, we go over the main obstacles and potential applications of computational intelligence methods to enhance the precision, automation, and intelligence of road network extraction.

Siamese Unet Network for Waterline Detection and Barrier Shape Change Analysis from Long-Term and Large Numbers of Satellite Imagery

Barrier islands are vital dynamic landforms that not only host ecological resources but often protect coastal ecosystems from storm damage. The Waisanding Barrier (WSDB) in Taiwan has suffered from continuous beach erosion in recent decades. In this study, we developed a SiamUnet network compared to three basic DeepUnet networks with different image sizes to effectively detect barrier waterlines from 207 high-resolution satellite images. The evolution of the barrier waterline shape is obtained to present two special morphologic changes at the southern end and the evolution of the entire waterline. The time periods of separation of the southern end from the main WSDB are determined and discussed. We also show that the southern L-shaped end has occurred recently from the end of 2017 until 2021. The length of the L-shaped end gradually decreases during the summer, but gradually increases during the winter. The L-shaped end obviously has a seasonal and jagged change. The attenuation rate of the land area is analyzed as −0.344 km2/year. We also explore two factors that affect the analysis results, which are the number of valid images selected and the deviation threshold from the mean sea level.

Multi-Temporal and Multiscale Satellite Remote Sensing Imagery Analysis for Detecting Pasture Area Changes after Grazing Cessation Due to the Fukushima Daiichi Nuclear Disaster

Despite advancements in remote sensing applications for grassland management, studies following the 2011 Fukushima Daiichi nuclear disaster have often been constrained by limited satellite imagery with insufficient focus on pasture changes. Utilizing different resolutions of optical satellite data is essential for monitoring spatiotemporal changes in grasslands. High resolutions provide detailed spatial information, whereas medium-resolution satellites offer an increased frequency and wider availability over time. This study had two objectives. First, we investigated the temporal changes in a mountainous pasture in Japan from 2007 to 2022 using high-resolution data from QuickBird, WorldView-2, and SPOT-6/7, along with readily available medium-resolution data from Sentinel-2 and Landsat-5/7/8. Second, we assessed the efficacy of different satellite image resolutions in capturing these changes. Grazing ceased in the target area after the 2011 Fukushima Daiichi nuclear accident owing to radiation. We categorized the images as grasses, broadleaf trees, and conifers. The results showed a 36% decline using high-resolution satellite image analysis and 35% using Landsat image analysis in the unused pasture area since grazing suspension in 2011, transitioning primarily to broadleaf trees, and relative stabilization by 2018. Tree encroachment was prominent at the eastern site, which has a lower elevation and steeper slope facing north, east, and south. WorldView-2 consistently outperformed Landsat-8 in accuracy. Landsat-8’s classification variation impedes its ability to capture subtle distinctions, particularly in zones with overlapping or neighboring land covers. However, Landsat effectively detected area reductions, similar to high-resolution satellites. Combining high- and medium-resolution satellite data leverages their respective strengths, compensates for their individual limitations, and provides a holistic perspective for analysis and decision-making.

Geospatial Tool-Based Geomorphological Mapping of The Lower Kulsi Basin, India

Detailed geomorphological map of a region provides necessary information on landforms to understand the variations of surface and subsurface processes. Geomorphological maps prepared based on a combined geospatial and field-observation approach are preliminary data for precise, prompt, and efficient watershed-level planning. The Kulsi is a significant left-bank tributary of the Brahmaputra. It has potential for agricultural, land, and water resources, but the region needs to catch up due to frequent climatic-geomorphic hazards. Therefore, this article aims to prepare an object-oriented detailed geomorphological map using geospatial tools. High-resolution satellite images and a digital elevation model were used to generate the detailed geomorphological map of the studied area. The resultant map is verified with extensive fieldwork. The investigated basin is characterized by structural and denudation hills, anthropogenetic escarpment; pediment plain, older and young alluvial plain; active and older flood plain; islands and sandbar deposits, and highly sinuous river and natural and artificial surface waterbodies. The research can contribute to local governments' and communities' land and water resource development plans. Keywords: geomorphology, morphometry, satellite image, Geographic Information System