Landsat Satellite Research Articles

Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors

Abstract. Lakes and groundwater are two crucial components of the global terrestrial water cycle, collectively forming a vital network for Earth's water resources. However, in the Tibetan region, the spatiotemporal relationship between lakes and groundwater and their impact on the local hydrological cycle remain inadequately understood. Satellite remote sensing serves as an effective observational tool, enabling comprehensive investigation and analysis of surface lakes and groundwater in Tibet with high spatial resolution. Therefore, this study integrates Landsat and Cryosat-2 satellite data to examine the spatiotemporal patterns of surface lake extents and water levels in Tibet. Additionally, combining Gravity Recovery and Climate Experiment (GRACE) satellite observations with the Global Land Data Assimilation System (GLDAS) model data, we quantitatively analyze the spatiotemporal variations in groundwater storage and its correlation with lake water. The results indicate that: 1) Rivers and lakes in Tibet are mainly located in the central and northwest regions, displaying noticeable intra-annual variations; 2) Substantial lagged relationships exist between groundwater storage and lake water levels and areas, revealing that lakes contribute significantly to groundwater replenishment, especially in the Ngari prefecture and Lhokha prefecture. This study comprehensively utilizes multi-source remote sensing data to dynamically monitor surface and groundwater in Tibet, providing robust support for a better understanding of the interaction between groundwater and surface water.

Deep Siamese Network for annual change detection in Beijing using Landsat satellite data

The detection of spatiotemporal changes in land use/land cover (LULC-SC) plays a paramount role in the analysis of smart cities, it can describe complex urban distribution, functions, and patterns. Chinas capital city has been developing rapidly in the past two decades, however, there are only few long-term studies on an annual scale of LULC-SC. To fill this research gap, we propose a remote sensing parallel framework for the detection of LULC-SC based on the combination of the Deep Siamese Network and long time series, which focuses on the spatial semantic information at the object level. A Landsat time series from 2002 to 2022 serves as input satellite data. First, we focus on building graph constructions at the object level and then use an autonomously constructed deeper-feature graph convolutional network (DF-GCN) to mine deeper features, spatial semantic, and relationships at the object level. Finally, the Siamese Network recognizes the changes in the spatial semantic tensors of long time series of Landsat images and quickly maps LULC-SC. The results prove that the proposed spatiotemporal change detection framework is effective in LULC-SC in Beijing. Compared with other networks, the optimal accuracy of semantic mining based on DF-GCN can reach about 90%. Over the past two decades, the LULC-SC of Beijing has changed in a complex way, with urbanization occurring primarily through the replacement of farmland. Consequently, the proposed framework can generate accurate LULC change maps at high temporal frequencies, which can contribute to a better comprehension of sustainable urban development and planning.

Spatial analysis of fire sevetiry and its relationship with landscape configuration in savanna areas of Sempre Vivas National Park, Brazil

Fire is a natural disturbance in the Cerrado (Brazilian savanna) but its effects on the landscape are capable of altering its pattern of severity and frequency. Because of that, some areas of the Biome can be in dangerous in the National Park of Sempre Vivas, in Minas Gerais State. Within this context, we spatially analyzed fires in areas of Cerrado during the years 2003 and 2017 according to severity, frequency and the influence of the spatial configuration of the area. We used pre- and post-fire Landsat satellite images to calculate the dNBR index to analyze fire severity per year and used fire metrics to analyze the relationship between fire severity and the spatial configuration of the landscape. Fire-scar severity was considered low, probably due to the density and structure of the typical Cerrado vegetation and the change in annual precipitation. Thus, fires in the Cerrado can be intense and spread quickly if no proper control is implemented.

A 30 m annual cropland dataset of China from 1986 to 2021

A 30 m annual cropland dataset of China from 1986 to 2021

Agricultural land use policies and landscape dynamics: Evidence from rainforest agroecological zone

Agricultural-land use policies play a crucial role in shaping agroecological landscapes globally. The evidence suggests that some of these policies tend to have undesired feedback/trade-offs, particularly about coastal resource use and conservation planning. The present study explores how agricultural policies influence land use/land cover (LULC) transition patterns in Ghana's Rain Forest Agroecological Zone (RFAZ). Landsat satellite images for 1991, 2008, and 2022 were acquired for the LULC mapping. Change detection and Intensity analysis were used to assess the pattern of land change. Also, documentary research processes were used to review and assess agricultural land use policies. The results showed that before rubber-related policy implementation, oil palm plantation was the dominant LULC type in the 1990 s. In the 1990 s and early 2000 s, there was a conscious government drive to leverage oil palm plantation development. This led to the growth in the size of oil palm plantations within the RFAZ landscape, considering the zone’s conducive natural environment. However, between 2008 and 2022, corresponding to the period of the increased promotion of rubber cultivation through the Outgrower project in the RFAZ, rubber plantations became the predominant land use, with an area of 11,763.81 ha in 2008 and 21,611.61 ha in 2022. The results from the LULC mapping and the intensity analysis showed that the Rubber Outgrowers Plantation Project (ROPP) and Norpalm Smallholder Scheme Project (NSSP) have degraded the natural cover through rubber and oil palm cultivation. The authors conclude that landscape dynamics, particularly in rainforest agroecological zones, represent a nexus challenge transforming policy adaptation's discursive context. The study recommends that agriculture and landscape policy/projects must have/take intermediary steps to address the feedback/trade-offs from well-intended initiatives. The study further suggests that management plans and strategies in the RFAZ should be consistent with the strict protection of forest reserves and water bodies.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Analyzing Rice land changes using Remote sensing and GIS in Ben Tre province, Viet nam

Ben Tre province is located at the downstream of the Mekong river system where there are three ecosystems: saline, brackish and freshwater, which create favorable conditions for the development of the horticultural economy and the marine economy. Faced with the impact of climate change and market demand, agricultural planning has been adjusted to land use planning. In which, the rice land area was adjusted to decrease to suit the actual situation. This study applies Remote Sensing and Geographic Information Systems (GIS) to assess the change process in the rice land area of Ben Tre province over 6-time points 2000, 2005, 2010, 2015, 2020, and 2023. Through land cover classification results from Landsat and Sentinel-2 satellite image data, the study analyzes the process of converting rice land to other land types. From there, the study evaluates the effectiveness of converting agricultural land use purposes. The research results contribute to connecting the master plan for socio-economic development with land use planning in the future.

Changes in Runoff Coefficient Index as a Result of Land Use Change (Case Study: Malang City)

The result of this research is to present observations about changes in the value of the conveyance coefficient in an area or region caused by changes in land use as a basis for decision making for water resources management in urban areas, by considering field data and satellite image classification to observe changes. As well as the consequences of the development of an area as seen from population growth, economy and housing needs. Malang City continues to experience land use change as evidenced by the decrease in agricultural land area. The method used is supervised classification with the help of geographic information systems and analyzing land use change over the past 20 years through map overlay. The data used are Landsat and Terrasar-X satellite images with the land use classes used are open land, settlements, agriculture, and shrubs. Satellite images were classified to analyze the extent of land use change and to see the rate of change in the conveyance coefficient of each class over the past 20 years. The results show that the study area has a change in the value of the conveyance coefficient caused by a fairly high land use change from agricultural land to built-up land such as settlements or housing. Malang City has a tendency to increase the population significantly because it is an educational city that continues to need a place to live and allows the rotation of various economic needs. The results of the analysis showed that there were changes in the value of the conveyance coefficient from 2002 to 2022, with an upward trend. In 2002 the C value was 0.2468; in 2007 it was 0.2899; in 2012 it was 0.3457; in 2017 it was 0.3895; in 2022 it was 0.4119. With an average value of change every year is about 13.78%.

Monitoring of spatio-temporal glaciers dynamics in Bhagirathi Basin, Gharhwal Himalayas using remote sensing data

Glacier retreat represents a highly sensitive indicator of climate change and global warming. Therefore, timely mapping and monitoring of glacier dynamics is strategic for water budget forecasting and sustainable management of water resources. In this study, Landsat satellite images of 2000 and 2015 have been used to estimate area extent variations in 29 glaciers of the Bhagirathi basin, Garhwali Himalayas. ASTER DEM has been used for extraction of glacier terrain features, such as elevation, slope, area, etc. It is observed from the analysis that Bhagirathi sub-basin has a maximum glaciated area of ~ 35% and Pilang has the least with ~ 3.2%, whereas Kaldi sub-basin has no glacier. In this region, out of 29 glaciers, 25 glaciers have shown retreat, while only 4 glaciers have shown advancement resulting in a total glacier area loss of ~ 0.5%, while the retreat rate varies from ~ 0.06 m/yr to ~ 19.4 m/yr. Dokarni glacier has maximum retreat rate (~ 19.4 m/yr), whereas Dehigad has maximum advancing rate (~ 10.1 m/yr). Glaciers retreat and advance have also been analyzed based on terrain parameters and observed that northern and southern orientations have shown retreat, whereas the area change is highly correlated with glacier length. The study covers more than 65% of the total glaciated area and based on the existing literature represents one of the most exhaustive studies to cover the highest number of glaciers in all sub-basins of the Bhagirathi basin.

Dispossessed lands and land-use change in the Colombian armed conflict: Exploring a link through a regional case study

Land dispossession in the Colombian armed conflict is a critical form of victimization and a trigger of territorial configurations that shape the rurality of the country. The consequences of the illegal appropriation of land by means of usurpation or forced displacement result in a humanitarian drama and various socio-spatial and socio-economic transformations. This research analyzes the intricate dynamics of land use and cover changes in four municipalities within the Cesar department and its relationship with instances of land abandonment and dispossession over a 29-year period (1991–2020). Landsat satellite image time series of TM, ETM+ and OLI data were used to determine land use and land cover (LULC). The classification process was carried out using the Random Forest model. The dynamics of land dispossession and forced abandonment were identified by analyzing 195 land restitution court rulings. Geo-referenced properties were subsequently overlaid on the created LULC maps. As a result, a pattern indicating a direct relationship between both variables was found. Consequently, the phenomenon of land dispossession in the context of this internal armed conflict has contributed to the transition from the traditional agrarian model to the agro-industrial one.

Evaluating and predicting coastal shoreline changes in the Doi Duong - Phan Thiet beach area by remote sensing and GIS technology

Assessing the shoreline variability is paramount in understanding the erosion and coastal accretion processes. The potential impact of erosion on coastal communities, particularly in coastal regions, amplifies the vulnerability to coastal disasters. In this study, Landsat satellite imagery captured from 1990 to 2022 was utilized, in conjunction with Geographic Information System (GIS), to scrutinize alterations in the shoreline of Doi Duong Beach - Phan Thiet. The satellite images were independently processed using aspect-ratio methods and thresholding techniques to determine the prevailing condition of the coastline. The rate of shoreline change was quantified using the DSAS tool. The investigation’s findings reveal a dynamic interplay between erosion and accretion, with accretion dominance explicitly evident within the areas surrounding the Ca Ty - Phu Hai river mouth. The average annual shoreline change rate is 27.1 m/yr for the Ca Ty River mouth and 16.5 m/yr for the Phu Hai River mouth. The most significant disparity between the recorded coastline in 1990 and 2022 measures 868 m. Additionally, the projections for the coastline until 2032 indicate that accretion remains the dominant process. This research shows that combining geospatial techniques and numerical models is a reliable way to study shoreline change trends.

Land Use Change Mapping and Analysis Using Remote Sensing and GIS: A Case Study in Tam Ky City, Quang Nam Province, Vietnam

Changes in land use/land cover (LULC) play a critical role in effective natural resource management, monitoring, and development, particularly within the realm of urban planning. In the examination of Tam Ky city, Quang Nam province, Vietnam, spanning from 2000 to 2020, remote sensing and Geographic Information System (GIS) techniques were employed. The Landsat satellite data (Landsat 7 ETM+ for 2000, Landsat 5 TM for 2010, and Landsat 8 OLI for 2022) underwent analysis using the supervised classification method in ArcGIS 10.8 software to identify and categorize six primary LULC classes: water bodies, agriculture, settlements, vegetation, construction, and bare soil/rocks. The reliability of the classification was evaluated through k values, revealing high accuracy with values of 0.951, 0.953, and 0.950 for the years 2000, 2010, and 2020, respectively. Notable shifts in LULC were observed during the period from 2000 to 2020. The areas covered by vegetation and settlements expanded by 53 and 1300 ha, respectively, while water bodies, agriculture, construction, and bare soil/rocks experienced reductions of 466, 48, 413, and 425 ha, respectively. To facilitate a rapid assessment, the study also incorporated the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The trends identified in this study are consistently aligned with the results of the supervised classification. The identified changes in LULC pose a substantial environmental threat, and the study's outcomes serve as a valuable asset for future land use planning and management in the area. The method's high accuracy enhances the dependability of the results, making them crucial for well-informed decision-making and sustainable development initiatives.

Community-managed forests can secure forest regrowth and permanence in human-modified landscapes

Deforestation can impair ecosystem functions, reduce biodiversity, and accelerate climate change. One strategy proposed to decrease deforestation and promote recovery of degraded areas involves community management of forests: entrusting local communities to manage forest reserves. However, the effectiveness of this strategy remains poorly understood. Here, we assess the impact of community forest management in the Village Land Forest Reserves (VLFRs) of the Greater Gombe Ecosystem (GGE), Tanzania, using Landsat satellite images from multiple dates (2006, 2013, and 2021) to assess the extent to which 16 VLFRs promoted forest regrowth and permanence. We detected a 66 % forest cover increase in VLFRs but a 22 % decrease in identified areas not under any form of protection (unprotected area) in the village land. The Before-After-Control-Impact (BACI) statistical analysis showed that assigning an area as a VLFR was associated with a 24.3 % forest-cover gain compared to unprotected village land (β = 0.243, SE = 0.0761, P<0.001). Patrolling by local community members, following village regulations, has a positive impact (β=0.04, 95% CI: [0.02, 0.06]) on promoting forest cover persistence. Nonetheless, of land reforested between 2006 and 2013 in the VLFRs, 11 % suffered re-clearing by 2021. Ensuring further forest regeneration and persistence of these reserves thus requires additional measures.

Analysis of Land Use and Land Cover Change Detection for Indore District of Malwa Plateau Region Using Supervised Machine Learning

The main aim of this work is to find out LULC classes for the Indore region. For the last seven years, Indore has been crowned the cleanest city in India according to the latest ranking of 2023. In the Indian state of Madhya Pradesh, the city and district of Indore are situated on the southern edge of the Malwa plateau. The municipalities of the Indore district majorly cover Manpur, Depalpur, Goutampura, Mhow, Hatod, Betma, Rau and Sanwer, with an area of approximately 3900 square kilometers. In contrast, Indore covers a land area of approximately 530 square kilometres. It is in charge of the nation’s slow but steady industrialization and population growth found in the last three decades. The city was much smaller and had a far smaller population thirty years ago. However, to evaluate the changes and decide on Indore city's future planning, Indore has taken over responsibility for the city's economic activities in recent years. New appropriate patterns should be recommended based on local factors like topography characteristics. The satellite image with multiple spectrums was used for the investigation. ArcGIS is based on pixel-by-pixel supervised classification using the maximum likelihood approach of Landsat satellite images from 2003 and 2023. The ground area, agriculture, and urbanization are linked to the LULC features. The various categories of classified land use and land cover features include the area of built-up regions, water bodies, cropland, forest, and barren land taken to predict the broad changes. Remotely sensed Landsat 5 images of 2003 and 8 images of 2023 were utilized to identify changes to accomplish this goal. This work clarifies the comparison of LULC classes for the Indore region.

Integrating Tehran metropolitan air pollution into the current transport system and sprawl growth: an emphasis on urban performance and accessibility

Air pollution is one of the most challenging issues for urban environment and environmental management. The goal of this study was to determine the impact of Tehran metropolitan's performance and accessibility on urban transportation and air pollution as sprawl grows. Tehran, with a population of 15.98 million people, has many environmental issues, including air pollution. Secondary data were collected from the Tehran Air Quality Control Company as well as Landsat satellite imagery (OLI). The raw data of intra-city and suburban traffic counts for spatial analysis of movements, combined with the raw data of measuring stations, were then used as a sample in the ArcGIS software environment for three selected days in 2013, 2014, and 2016. Following geometric and radiometric correction, programming methods and a multivariate regression algorithm were applied to the images, yielding results in the form of additional stations. According to the results obtained (about 3.29 m) root-mean-square error (RMSE), the Inverse Distance Weighting (IDW) model was used in air pollution maps for better assessment. The findings suggest that Tehran is not the only source of air pollution and that TMA performance and accessibility play a significant role in the amount of air pollution. Furthermore, there is a strong correlation (more than 75%) between air pollution maps and transportation flow maps on specific days (February 23, 2013, February 26, 2015, and December 28, 2016). Finally, the analysis of this situation in three circles showed that the highest volume of traffic was done in the city of Tehran (CBD), suburban area (Suburban), and suburban area (Exurban), respectively, and the pattern of distribution and spatial accumulation of pollution has also been a function of this situation.

Impacts of Sustainable Land Management Intervention on the Soil Condition and Landscape Greenness: Evidence from Jimma Arjo District, Southwestern Ethiopia

Soil degradation has been a major environmental and agricultural challenge in Ethiopia in general and in the study area in particular. Recently, several governmental and nongovernmental organizations have made efforts to reduce the problem and improve the productivity of land through sustainable land management (SLM) practices. The main objective of this study was to investigate the impact of sustainable land management practices on soil condition and landscape greenness in the Jimma Arjo District, Southwestern Ethiopia. The impact of SLM practices on soil condition was examined by collecting twenty-eight (28) composite and core (28) soil samples from treated (14) and nontreated (14) lands. Landsat satellite images of 2012 and 2022 were used to detect changes in landscape greenness using the Normalized Difference Vegetation Index (NDVI). ArcGIS® 10.3, ERDAS® 2014, and Microsoft Excel software packages were used for analysis. The significance test was performed using a one-way ANOVA. The result showed a significant difference in soil physiochemical properties (soil texture, soil pH, soil organic carbon, soil organic matter, total nitrogen, and available phosphorous and calcium) between treated and nontreated lands (P&lt;0.01). However, the landscape greenness result shows that the lowest NDVI value in the SLM-treated kebele during 2012 was −0.15 but increased to 0.09 in 2022. Similarly, the highest value was found to be 0.41 in 2012 and the value rose to 0.53 in 2022. It is concluded that sustainable land management practices implemented in the area have resulted in an important positive effect on improving the soil condition and landscape greenness. Hence, strengthening and scaling up SLM practices and continuous maintenance are advisable for better results in land productivity and livelihood improvement.

The former industrial site of the Angarsk Metallurgical Plant (Svirsk, Russia) 10 years later: current geochemical state and interannual change analysis based on Earth remote sensing data

The former industrial site of the Angarsk Metallurgical Plant (Svirsk, Russia) 10 years later: current geochemical state and interannual change analysis based on Earth remote sensing data

MULTISOURCE AND MULTITEMPORAL LAND COVER MAPPING OF GREATER LUZON ISLAND USING GOOGLE EARTH ENGINE

Abstract. A variety of research endeavors and practical applications necessitate the use of land cover maps. These maps are valuable for tasks such as change detection, forest monitoring, urban expansion monitoring, natural resource mapping, catering to diverse user requirements. While satellite sensors offer essential data for comprehending spatial and temporal variations in land cover, relying on a single satellite system can be limiting, especially considering the potential hindrance of cloud cover in the case of optical sensors. To enhance temporal frequency, it becomes essential to utilize multiple satellite systems, albeit requiring harmonization to ensure consistent outcomes. This study presents a large-scale annual land cover mapping which utilizes harmonized Landsat-8 and Sentinel-2 satellite imagery, in conjunction with supplementary data, and a machine learning algorithm. In addition, the use of powerful computational processing platforms such as Google Earth Engine and Google Colaboratory is now a requirement to manage big geospatial data as well as to run different algorithms for processing and analysis.

Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.

Icings in the Selenga River basin

ABSTRACT In this study, we present a GIS dataset and map of icings formed in the transboundary Selenga River basin (largest tributary of the Lake Baikal). The GIS dataset of icings is compiled using Landsat and Sentinel-2 satellite images, as well as materials from the field expedition studies. It contains 29,054 individual icings with a total area of 1154.2 km2 (0.25% of the entire basin area), including at least 56 giant icings with an area ≥ 1 km2. Icings are widespread throughout the basin, but the highest icing coverage is found on the northern slopes of Hangayn ridge in the southeastern part of the basin, where most of the giant icings are located. The highest density of icings (more than 200 per km2) is found in the eastern part of the basin. Since icings can indicate climate-driven changes in streamflow and permafrost, the presented dataset will form the basis for future studies of these transformations.