SRTM DEM Research Articles

Combining LiDAR, SAR, and DEM Data for Estimating Understory Terrain Using Machine Learning-Based Methods

Currently, precise estimation of understory terrain faces numerous technical obstacles and challenges that are difficult to overcome. To address this problem, this paper combines LiDAR, SAR, and DEM data to estimate understory terrain. The high multivariable-precision spaceborne LiDAR ICESat-2 data, validated by the NEON, are divided into training and validation sets. The training dataset is used as a dependent variable, the SRTM DEM and Sentinel-1 SAR data are regarded as independent variables, a total of 13 feature parameters with high contributions are extracted to construct a Multiple Linear Regression model (MLR), BAGGING model, Random Forest model (RF), and Long Short-Term Memory model (LSTM). The results indicate that the RF model exhibits the highest accuracy among the four models, with R2 = 0.999, RMSE = 0.701 m, and MAE = 0.249 m. Then, based on the RF model, the understory terrain at the regional scale is generated, and an accuracy assessment is performed using the validation dataset, yielding R2 = 0.999, RMSE = 0.847 m, and MAE = 0.517 m. Furthermore, this paper quantitatively analyzes the effects of slope, vegetation coverage, and canopy height on the estimation accuracy of understory terrain. The results show that as slope, and canopy height increase, the estimation accuracy of the RF model for understory terrain gradually decreases. The accuracy of the understory terrain estimated by the RF model is relatively stable and not easily affected by slope, vegetation coverage, and canopy height. The research on the estimation of understory terrain holds significant practical implications for forest resource management, ecological conservation, and biodiversity protection, as well as natural disaster prevention.

Landslides: A Review from the Southern Western Ghats of India

ABSTRACT Landslides are the most unpredictable catastrophic events in mountainous and hilly regions. South Western Ghats one among of the regions in India that have experienced recurring landslides due to increase in pre-and post-monsoon rains. Landslides often cause damage to buildings, roads, natural vegetation, even result in the loss of human lives in South Western Ghats with indirect impact on the socioeconomic status of the country. It is crucial to depict the current trend technologies and their performance in landslide studies, offering valuable insights into mitigation strategies. This article highlights on prevalent techniques for mapping landslide zones, along with major conditional factors and assess the performance of these techniques in landslide susceptibility mapping of the South Western Ghats. Landslide studies of the South Western Ghats reveal 20% of the research work with the Landsat 8 OLI for mapping landslides, 13.3% of the studies using the higher spatial resolutions 2.5m and 10m of Cartosat DEM, the rest of the studies were based on the spatial resolution of 12.5m and 30m from ALOS PALSAR, ASTER and SRTM DEM. About 26% landslide studies have employed quantitative approaches primarily the frequency ratio of bivariate. Subsequently machine learning techniques such as random forest and support vector machine have gained prominence, accounting for 33% landslide studies. The literature depicts precipitation as a significant triggering factor and slope as the major conditional factor in the occurrence of landslides within the South Western Ghats. Prioritized densely populated areas in landslide studies have increased attention to smaller geographical areas and forest regions in the South Western Ghats to protect both inhabitants and ecosystems.

A geospatial approach to delineate lineaments on the granite gneissic terrain of Mpwapwa district, Dodoma, central Tanzania

The study sought to utilise Geographic Information Systems (GIS), Remote Sensing, and a blend of manual and automatic techniques to delineate and analyse the spatial distribution and orientation of lineaments within the terrain of Mpwapwa District. The study employed various techniques, including the Optimum Index Factor (OIF), Band Combination, Principal Component Analysis (PCA), and Directional Filtering on the Landsat 8 imagery, and Hill Shading technique was used on Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). These techniques were employed in the processing steps for both manual and automatic lineament extraction, utilizing various software tools. Lineament control and validation were executed using digitized geological maps, topographical maps, and Google Earth images.The results revealed lineaments with a total length of 2060.6 km, non-uniformly distributed and predominantly present in hard rock lithologies, such as migmatitic granite, migmatitic biotite gneiss, biotite gneiss, granitoid gneiss, and porphyroblastic gneiss. The findings further indicate that NE-SW and NW-SE are the dominant lineament trends. Additionally, the study revealed that areas with medium, high, and very high lineament density and intersection are primarily located in the southwest and northeast parts of the Mpwapwa District. Meanwhile, regions with low lineament density are spread through the entire district.In this study, GIS and remote sensing emerged as valuable tools for identifying and mapping lineaments within a challenging hydrogeological setting. Findings from this study will serve as a foundational framework for subsequent geological and hydrogeological investigations in the area of study.

Glacial geomorphology of Newfoundland, Canada

ABSTRACT We present a synthesis of glacial geomorphology comprising Newfoundland and the surrounding continental shelf. During the last glaciation, this region was encompassed by the Newfoundland Ice Cap that coalesced with the Laurentide Ice Sheet and extended to the continental shelf edge. The subglacial geomorphic signal of terrestrial and marine portions of this ice sheet has been mapped using aerial photography, satellite imagery, SRTM DEMs, multibeam bathymetric data and onshore field mapping over the last 50 years. Here, we build on this, digitizing ∼146,380 features identified in published literature. Using newly available digital elevation data and multibeam bathymetric data, we identify ∼38,550 new features, such as undocumented ice thrust, crevasse and esker ridges and meltwater channels. This updated geomorphological map provides a record of paleo landforms over Newfoundland, enabling future efforts to reconstruct the dynamics, timing and erosion history of ice advance and retreat during the last glacial period..

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data and several topographical features. While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). The primary objectives of this study encompass two key aspects. Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. Secondly, the research systematically assesses the effectiveness of different algorithms to identify the most precise method for establishing any potential relationship between field-measured AGB and predictor variables. Our study revealed that the Random Forest (RF) model was the most efficient method utilizing Landsat-9 OLI and SRTM DEM (L3) predictors, achieving remarkable accuracy. This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. The XGBoost model is the subsequent choice with RMAE, RRMSE, and R2 values of 15.54%, 23.85%, and 0.77, respectively. The study further highlights the significance of specific spectral bands, notably B4 and B5 from Landsat 9 OLI data, in capturing spatial AGB distribution patterns. Integration of vegetation-based indices, including TNDVI, NDVI, RVI, and GNDVI, further refines AGB mapping precision. Elevation, slope, and the Topographic Wetness Index (TWI) are crucial proxies for representing biophysical and biological mechanisms impacting AGB. Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. This comprehensive approach offers a valuable avenue for informed decision-making in forest management, carbon assessment, and ecological monitoring initiatives.

Satellite-based rainwater harvesting sites assessment for Dera Ghazi Khan, Punjab, Pakistan.

Water scarcity in arid regions poses significant livelihood challenges and necessitates proactive measures such as rainwater harvesting (RWH) systems. This study focuses on identifying RWH sites in Dera Ghazi Khan (DG Khan) district, which recently experienced severe water shortages. Given the difficulty of large-scale ground surveys, satellite remote sensing data and Geographic Information System (GIS) techniques were utilized. The Analytic Hierarchy Process (AHP) approach was employed for site selection, considering various criteria, including land use/land cover, precipitation, geological features, slope, and drainage. Landsat 8 OLI imagery, GPM satellite precipitation data, soil maps, and SRTM DEM were key inputs. Integrating these data layers in GIS facilitated the production of an RWH potential map for the region. The study identified 9 RWH check dams, 12 farm ponds, and 17 percolation tanks as suitable for mitigating water scarcity, particularly for irrigation and livestock consumption during dry periods. The research region was classified into four RWH zones based on suitability, with 9% deemed Very Good, 33% Good, 53% Poor, and 5% Very Poor for RWH projects. The generated suitability map is a valuable tool for hydrologists, decision-makers, and stakeholders in identifying RWH potential in arid regions, thereby ensuring water reliability, efficiency, and socio-economic considerations.

Exploring and modelling the hydro-morphological landscape of a Himalayan basin: a geospatial study of Nandakini Basin in Uttarakhand, India

This study comprehensively examines the hydro-morphological, topo-hydrological, and physiographic features of the Nandakini catchment in the north-western Himalayas. In the absence of extensive hydrological data, employing morphometric parameters proves to be a crucial and efficient approach for delineating geological structures and assessing hydrodynamic activity in the river basin. Utilizing SRTM DEM, SOI toposheets, and GIS, the study identified twenty watersheds within the catchment, estimating its total area at 540.98 km2. The analysis revealed a 6th-order catchment with a primarily sub-dendritic to dendritic drainage pattern, susceptible to flooding and gully erosion from slow surface runoff. The elongated shape and compactness coefficient indicated delayed peak runoff. Coarse drainage texture and high relief ratio suggested increased vulnerability to erosion. Physiographic indices indicated a later youth stage of basin development, while topo-hydrological indices highlighted significant topographic and spatial variability in water availability, emphasizing erosion potential. This study provides valuable insights for developing sustainable catchment management plans and informing decision-making in water resources management for the Nandakini catchment.

Integrating multispectral remote sensing and geological investigation for gold prospecting in the Borongo-Mborguene gold field, eastern Cameroon

Multispectral remote sensing data has great promise for mineral prospecting via regional-scale mapping of alteration zones and geological structures, once the method is calibrated using in-situ geological observations. The Borongo-Mborguene area, located in eastern Cameroon at the transition from sub-tropical to arid climates, hosts several alluvial gold deposits. In this investigation, maps developed by processing of Landsat-8 OLI images, ASTER satellite images, and SRTM DEM data were compared to results of a comprehensive field survey to locate zones of hydrothermal alteration and regional structural lineaments, and to verify the association of these features with gold mineralization. Specific Landsat-8 and ASTER band ratios, as well as principal component analysis of the multispectral images, were selected to isolate spectral signals that characterize particular groups of alteration minerals, such as clays, hydrated carbonates, hydrated sulphates, iron oxides, and iron hydroxides. Lineaments and their preferred orientations were extracted by filtering of the digital elevation data. A fuzzy logic approach was subsequently applied to the ensemble of resulting maps to highlight five areas of concentrated hydrothermal alteration and structural complexity as potential gold prospects (Mborguene, Boutila, Nandoungue, Borongo and Ouaden villages). Petrographic study of samples collected from the remotely-identified alteration zones revealed a hydrothermal ore assemblage of gold, galena, pyrite and hematite in mineralized quartz veins. Structural control of the alteration zones in the Borongo-Mborguene gold field was confirmed by field mapping along a shear zone. Hence, our petrographic and field studies indicate that the multispectral remote sensing data and data processing methods developed herein are appropriate for mineral exploration in remote sub-tropical areas of Africa. More generally, the computationally efficient and simple procedure developed here should yield accurate preliminary maps of the spatial distribution of hydrothermal halos and related mineral deposits in challenging mixed-type vegetated terrain around the globe.

Feature Selection and Regression Models for Multisource Data-Based Soil Salinity Prediction: A Case Study of Minqin Oasis in Arid China

(1) Monitoring salinized soil in saline–alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R2 = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions.

Urban development and the loss of natural streams leads to increased flooding

Developing countries with hard pressed economies and a rapidly growing population have a myriad of issues to face. Karachi is one of the largest cities of Pakistan and is rapidly growing. The city has been hit by floods in the year 2016, 2018, 2019 and 2020, recently. Since the early 1990s Karachi has seen a rapid spike in development and in the process many natural streams have been built over. This has led reduced space for rainwater runoff and has created problems for urban flood managers. To ascertain this, topographic records of 1940s and 1980s and Landsat imagery of 2000, 2008, 2015 and 2020 were used to map historical urban extent. Sentinel—1, Synthetic Aperture Radar (SAR) imagery was used to extract flooded areas during 2015, 2017, 2019 and 2020 floods. In the year 2020, 64.3 km2 of area was flooded which was the highest. There has been a growth of 286% in urban area observed between 1980 and 2000. The city was built with several natural flood water carrying streams, north and west of it. It was found that that out of 3600 km of these streams, 870 km of streams have either been blocked or removed and a 100% in core urban area. DEM of difference (DOD) prepared by subtracting 1945 DEM from SRTM DEM indicated that most of embankments along these streams were in the areas that show erosion in DOD surface, thus explaining the expansion of built areas along historical stream channels. Forcing floods to enter these built areas.

Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

Spatial Vulnerability Investigation by Morphotectonic Analysis of the Mandakini River Basin near Kedarnath, Uttarakhand, India using SRTM DEM in a GIS Platform and GPS Data

Spatial Vulnerability Investigation by Morphotectonic Analysis of the Mandakini River Basin near Kedarnath, Uttarakhand, India using SRTM DEM in a GIS Platform and GPS Data

Geomorphic effect of landslide dam on the Jinsha River

As an extreme surface process, landslide dam affects the long-term change of river landform, especially the change of channel and hillslope. The Jinsha River is located on the eastern edge of the Qinghai-Tibet Plateau, the structural geology is active, developed more landslide geological disaster. Based on the 30 m SRTM DEM, we automatically extracted the river longitudinal profile, steepness index, river knickpoint and excess topography using TopoToolbox. Through interpretation of remote sensing imagery and field work, we identified 660 landslide dams, more than 90% are located in the excess topography with a threshold hillslope of 30. Our analysis reveals that there are 481 river knickpoints were extracted with heights above 30 m, Among them, 70 river knickpoints have good spatial correlation with the landslide dam, landslide dam has an impact can form knickpoints of up to 478m. In the densely distributed river channel of the landslide dam, the steepness index with the relatively high values. Therefore, landslide dams may have a significant inf1uence on the channel and hillslope, we should pay attention to the role of landslide dam in the evolution of river landscape.

Hydrogeochemical analysis of groundwater quality during the pre- monsoon season of Manipur, India

ABSTRACT The water quality index (WQI) and irrigational index for groundwater were studied in the northeastern section of the Manipur Valley in northeast India. Water samples were collected in 2022 during the pre-monsoon season. To compute the water quality index for drinking water, the basic chemical parameters of total hardness, pH, electrical conductivity, total dissolved solids, Calcium, Sodium, Iron, Magnesium, Manganese, Chlorine, and Hydrogen Carbonate were used. The assessment of bacteriological quality is also done, which is crucial for overall water quality evaluation, alongside physical and chemical analyze. For determining irrigation suitability, irrigational indices such as sodium absorption ratio, sodium percentage, and magnesium hazard were calculated. WQI, %Na+, Sodium Adsorption Ratio (SAR), magnesium hazard, permeability Index and total hardness indicate that most water samples are harmless for irrigation and drinking purpose. They have affirmative relationships indicating that these characteristics are interdependent. Approximately 25% of the Piedmont zone groundwater is found to be unfit for agricultural and drinking usage. The encrustation of gypsum, halite, and evaporation into the Disang shares accelerate the dissolution of ions in Piedmont water, resulting in quality degradation. According to Gibbs plots, Durov Scatter Plot, and Hill Piper Trilinear Diagram of water dominated the rock-weathering process, while hydrochemical facies progressed from the beginning to the intermediate stage of Chadha’s graphical representation illustrates the progression of hydrochemical processes in surface water. Therefore, proper integrated water resource management and development are required for effective water resource utilization, particularly around the Piedmont zone. In recent years, the quality of groundwater has caused a significant deal of alarm, and an increasing number of studies relating to it have been published. The geological, structural, and geomorphological features in the intermontane Imphal Valley in Manipur, India, were identified using SRTM DEM data by QGIS Software. This area has simple geology and structural elements, making it an appropriate site to evaluate the use of remote sensing and GIS tools in geological studies. To prevent a water crisis, groundwater resources must be managed sustainably. The current study also concentrated on a bibliometric examination of groundwater management and access to gauge the state of the field. A bibliometric examination of these papers may offer insight into current research and trends. We are the first to join Groundwater Quality Assessment with bibliometric analysis. This prediction, if taken into consideration strategically during the planning of preventive measures for groundwater quality can help to analyze future evaluation to a great extent.

The vegetation–topography heterogeneity coupling in the Loess Plateau, China

As a result of adaptation to the environment, the great environmental spatial heterogeneity leads to the high spatial heterogeneity of vegetation status. This coupling may be more apparent in water-limited drylands, where topography is the main determinant of small-scale variation in water availability and energy. Metrics describing this coupling may contribute to the detection of the extension of vegetation reshaped by human intervention and other driven forces. In this study, the heterogeneity index of coupling (HIC) was developed to indicate the coupling between spatial heterogeneity of vegetation status (Hv) and the spatial heterogeneity of topography (HT) in the Loess Plateau in northern China. The 16-day composed MODIS normalized vegetation index (NDVI) with a resolution of 250 m and SRTM DEM were employed to quantify the heterogeneity of vegetation status and the topographical heterogeneity. The results show that HIC varies among geomorphic zones, land cover types, and land cover change categories. Among all land cover types, HIC of sandy areas was the largest, followed by the HIC of the forest, shrub, farmland, and grassland. Among geomorphic zones, the highest HIC value appeared in plains with dense residential areas, followed by sandy land that is frequently reshaped by wind, rocky mountainous areas, hilly and gully loess plateaus, and loess tableland. It was revealed that the alternation of vegetation by human activities and natural disturbances shaped greater HIC. Results of this study approved the effectiveness of the HIC in reflecting the coupling of the vegetation status with topography at regional scale.

A more effective approach for species-level classifications using multi-source remote sensing data: Validation and application to an arid and semi-arid grassland

The distribution of grassland communities significantly influences biodiversity conservation and resource management. Remote sensing datasets, such as Sentinel land cover images with high spatial and temporal resolution, have been commonly employed to simulate the distribution and community structure of plant species in grasslands. Nevertheless, the precision of grassland and community classification requires further enhancement for more effective grassland management. In response, our study presents a dynamic workflow for simulating the distribution of grassland plant species under different hydroclimatic conditions. Leveraging a random forest classifier with multi-source remote sensing features, we mapped the distribution of nine plant communities and six types of land cover in the upstream grasslands of the Xilingol River Basin, Inner Mongolia from 2017 to 2022. Utilizing the Google Earth Engine platform, our findings reveal a remarkable consistency in the overall spatiotemporal accuracy and classification precision of grassland communities across different years, even under varying hydroclimatic conditions. When critical features were carefully selected, the integration of Sentinel-1 (C-band synthetic aperture radar), Sentinel-2 (surface reflectance), SRTM DEM 30 m (Digital Elevation Model data), and MOD16A2.006 (evapotranspiration/latent heat flux product) data yielded a high overall accuracy ranging from 87.0 % to 89.5 % over multiple years. Our study not only determined the spatial patterns of various grassland plant communities but also explored the mechanism of their responses to different hydroclimatic conditions. The proposed approach holds practical implications for fine-scale grassland classification, providing valuable support for landscape classification and monitoring efforts. Furthermore, the insights derived from our findings offer valuable guidance for land managers and policymakers, aiding them in making informed decisions related to grassland conservation and sustainable land use planning.

KARAKTERISTIK GEOMORFOLOGI DAERAH PASIRIPIS DAN SEKITARNYA, KECAMATAN SURADE, GEOPARK CILETUH PALABUHANRATU

The research was conducted in Pasiripis area, Surade sub-district, Sukabumi district, West Java province, which is part of the Ciletuh Palabuhanratu Geopark with the aim of identifying the characteristics of developing geomorphological Units, and classifying the distribution of lithological constituents in each unit. Identification of geomorphological characteristics is based on remote sensing analysis of DEM SRTM Data which is then processed in Arcgis Software, Global Mapper and field observations are made in mapping geomorphology and lithology distribution according to scientific standards to match the data from remote sensing analysis. The research findings are in the form of distribution maps based on morphographic aspects, flow pattern maps, landform maps, slopes and geomorphological components in geomorphological maps. The study area is divided into lowlands and low hills with a developed drainage pattern that is dendritic, sub-dendritic, and parallel. In the morphometric aspect, the slopes are flat or almost flat, very gentle, gently sloping, and slightly steep. The morphogenetic aspect in Pasiripis area shows the existence of endogenous processes, such as tectonism and volcanism activities, as well as exogenous processes, such as weathering, erosion, and dissolution. The lithology of the study area consists of limestone, sandstone, and tuff. Based on the geomorphological reconstruction aspect, the characteristics of Pasiripis area in Surade sub-district can be classified into five geomorphological unit, namely flat denudational lowland, gentle denudational lowland, gentle maritime lowland, gentle denudational lowland hills, and slightly steep denudational lowland hills.

Horan Valley Basin Geomorphological Aspects Assessment by Integrating Hypsometric Analysis with Remotely Sensed Morphometric Characteristics

The extraction, study, and accurate interpretation of the morphology database of a basin are the basic blocks for building a valid geomorphological understanding of this basin. In this work, a new approach is presented which is to use three different GIS based methods to extract databases with specific geographical information and then use the concept of information intersection to make a realistic geomorphological perspective for the study area. In the first method, data integration of remote sensing images from Google Map and SRTM DEM images were used to identify Horan basin borders. In the second method, the principle of data integration was represented by extracting the quantitative values of the morphometric characteristics that were affected by the geomorphological condition of the studied basin, such as the shape factor, circulation factor, and relief ratio, then eliciting an optimal conception of the geomorphological condition of the basin from the meanings and connotations of these combined transactions. The third method used the same principle by taking the optimal inferences from the integration of the interpretation of the values of the Hypsometry integration coefficients for each area in the basin separately with the integration value of the drawing curve for the relative heights of the basin areas with their relative areas. It was found, from the values of the coefficients, that the areas (A, B, C, D, and F) were still in the early stages of youth. Whereas the E region was in the maturity stage and the G region was in the monadnock stage of the geomorphological cycle. As for the integral value of the curve, it indicated 48. 559 % erosion from the surface of the basin only, and that its boundaries were subject to change and widening.

An Evaluation of the Impact of Urban Growth on Runoff in Abeokuta, Southwestern Nigeria

Abeokuta is one of the urban areas in Nigeria with high cases of runoff fatalities in recent times, indicating the need for a proper understanding of prominent runoff-generating mechanisms, as well as the causative factors. Consequently, this paper, which is focused on flood-prone settlements, is aimed at providing information on the impact of change in urban land use on runoff in the study area. The specific objectives were to determine the change in average runoff in terms of the Soil Conservation Service Curve Number (SCN-CN), and to assess the impact of urban growth on runoff in the area. The SCN-CN was derived from the 30m spatial Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). At the same time, land cover change was estimated using Landsat TM+ from 2000 and Landsat 8 OLI from 2018. Data were analysed using the Hydrologic Engineering Centre’s Hydrologic Modelling System (HEC-HMS) and ArcGIS (version 10.1). The results showed a 14% increase (from 39% in 2000 to 53% in 2018) in urban areas of selected catchments; and a relative increase in average CN from 76.9 units in 2000 to 79.9 units in 2018, suggesting an increase in runoff potential relative to the increase in urban/impermeable space in the catchment. Annual discharge depth increased from 891.84mm to 956.9mm, while peak discharge increased from 161.9m3 /s to 196.2m3 /s. Runoff in the study area tends to exhibit spatial variability that is similar to the pattern exhibited by built-up areas across the study area, suggesting that the development of built-up areas can explain runoff exacerbation in part of the area. The use of SCN-CN and satellite images makes the approach reproducible, and the mixed methods of geographic information system and hydrological model revealed the spatial variability typically hidden in stand-alone hydrological models. The paper recommends further studies with the use of less coarse datasets, as well as the implementation of policies that focus on sustainable urban growth in the region, and other cities.

2000–2020年喀喇昆仑山洪扎河流域冰川高程变化数据集

Under the influence of climate warming, glaciers in High Mountain Asia (HMA) have undergone significant recession in recent decades. However, stable or even positive glacier mass budgets have been found in the Karakoram Mountains in the past decades, which is termed as “Karakoram anomaly”. The Hunza Basin, located in the western Karakoram, is a region with concentrated glacier development along the Sino-Pakistan Economic Corridor. The glacier mass balance in Hunza Basin plays an important role in water resource utilization and infrastructure construction. In this study, we used the SRTM DEM (2000) and ZY3-02 stereo images (2020) to obtain the glacier height changes in the Hunza Basin by employing the method of Geodetic (GeoTIFF format with a resolution of 30 m). This study covers an area of around 7600 km2, within the spatial extent of 36°N–37°N, 74°E–76°E. The statistical analysis of the residual movement in the non-glacial region indicates an overall accuracy of about 1.1 m. The findings suggest that glaciers experienced a moderate thinning during the early 21st century, with significant spatial variations. Factors such as debris cover, crevasses, supraglacial lake, ice cliff and glacier surge have distinct influences on glacier height changes. This dataset can be used as basic data for the research on glacier mass balance in the Karakoram, as well as water resources utilization, glacier disaster warning and climate change in the Sino-Pakistan Economic Corridor.