SRTM Research Articles

Landslide hazard zonation (LHZ) mapping of Doon Valley using multi-criteria analysis method based on remote sensing and GIS techniques

The occurrence of landslides is a costly and cataclysmic natural hazard that mainly occurs in hilly areas due to factors like earthquakes, cloud burst, extreme rainfall, human pressure, etc. leading to loss of biodiversity, property, and life. Effective and comprehensive landslide risk management is crucial to address landslide susceptibility. Utilizing remote sensing and Geographic Information System (GIS) techniques, this work focuses on the landslide hazard zonation (LHZ) mapping. The study is specifically conducted in the Doon Valley. These advanced technologies help in identifying and categorizing the areas prone to landslides. The Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data at 30 m resolution and Sentinel-2B data at 10 m resolution were used to perform the remote sensing and GIS operations in ArcGIS Software. Thematic layers such as Land Use/Land Cover (LULC), Slope, Geology, Soil Type, Elevation, Drainage Density, Vegetation, and Aspect were produced utilizing remote sensing and GIS data. The weighted overlay, a multi-criteria analysis method, was applied to assign attribute values to each thematic layer based on their importance, which was then combined to calculate the landslide hazard zone. The findings of this study demonstrate that landslides are more likely to occur near and above the Main Boundary Thrust/Fault (MBT). The high to very high hazard zone covers 16.64% of the total area, making landslides occurring more frequently. Additionally, the study found that the Doon Valley rivers’ upper segments are more susceptible to landslides than their lower segments. By integrating Remote Sensing and GIS techniques, it is possible to obtain extensive knowledge of regions prone to landslides. This information will be helpful for decision-makers and planners to reduce the impact of landslides in the near future.

Application of decision support system/remote sensing/GIS techniques in groundwater recharge assessment

ABSTRACT Managing water resources and storing water through the identification of groundwater recharge zones (GWRZs) are critical to water security in Egypt. Decision-support systems (DSSs), remote sensing, and GIS techniques have yielded significant data for water resources modeling. The geologic, geomorphic, climatic, and hydrologic features of the Toshka area, Western Lake Nasser, Egypt, have been generated by data from Shuttle Radar Topography Mission, Climatic Research Unit gridded Time Series, Sentinel 2 time series, and Landsat-8 OLI. Fifteen GIS thematic maps have been ranked and normalized using five DSS techniques: Analytical Hierarchy Process (AHP), Fuzzy Analytical Hierarchy process (FAHP), Frequency Ratio (FR), Shannon Entropy (SE), and Multi-Influencing Factor (MIF). To ensure the computational usefulness of these models, GWRZs have been extracted and compared. The outcomes showed that 83, 87.5, 99.1, 99.1, and 87.5% of the existing wells are in high to extreme GWRZs for AHP, FAHP, FR, SE, and MIF, respectively. The receiver operating characteristic curve (ROC) was used to assess the effectiveness of these models. It was found that the SE model had the highest predictive performance rates, as its ROC accuracy value was 91.1%, while the FR, AHP, FAHP, and MIF approaches had values of 91, 84.4, 81.9, and 89.9%, respectively.

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.

Semi-automated mangrove mapping at National-Scale using Sentinel-2, Sentinel-1, and SRTM data with Google Earth Engine: A case study in Thailand

Mangroves are a crucial part of the coastal ecosystem; thus, precise and up-to-date monitoring is essential to guide regional policies and inform conservation strategies. This study investigates the capabilities of semi-automated remote sensing approaches within a Google Earth Engine framework for national-scale mangrove mapping in Thailand. Remote sensing data from 2018—10,000 data points acquired from Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM)—was used to train several machine learning models. The Gradient Tree Boost (GTB) proved to be the most reliable, with the least variation in validity (the lowest IQR) and the highest average Overall Accuracy of 96.75 ± 0.63 % compared to the others—96.64 ± 0.72 % for Random Forest (RF); 96.12 ± 0.80 %for Classification and Regression Trees (CART); and 95.43 ± 0.74 % for Support Vector Machines (SVM). Thus, the GTB was instrumental in mapping mangrove distribution with 10-m spatial resolution across Thailand from 2016 to 2022, the period in which the mangrove areas increased by 11 %, reflecting successful conservation efforts over the past decade. The developed framework establishes the foundation for semi-automated mangrove mapping that can be developed for other geographical contexts.

Land Cover Dynamics and Environmental Implications in Türkiye’s Riparian Zones: A Comprehensive Seven-Year Study

This study examines the Riparian Buffer Zones (RBZs) in Türkiye, analyzing land cover changes from 2017 to 2023 to assess their effectiveness in ecological conservation and water resource management. The research employs Esri-Sentinel land cover data, Shuttle Radar Topography Mission digital elevation models, and RiverATLAS data to identify and delineate RBZs, classify land cover types, and evaluate trends and changes over seven years. The findings indicate that 44.8% of RBZs are covered by ‘Rangelands’, 32.2% by ‘Crops’, and 16.1% by ‘Trees’ classes. The study notes a considerable increase in built areas and agricultural fields, which may indicate ongoing urban expansion and its potential impact on the effectiveness of RBZs. Despite regulatory efforts, the substantial presence of agriculture within RBZs indicates a need for more robust environmental protection policies. The results indicate a pressing need for a policy reassessment to enhance forest cover within RBZs and reduce agricultural encroachment. This would improve water quality and facilitate the achievement of sustainability goals numbered 2, 3, 6, 11, 14 and 15. The findings thus call for a strategic enhancement of RBZ management to better protect water resources against the adverse effects of agricultural and urban activities.

Assessing the level of river basin evolution, erosion susceptibility and its correlation with morphometric characteristics using Geoinformatics techniques

The application of Geoinformatics have become fastest and improved advanced tools to analyse and interpret the topography and hydrological characteristics. This study aimed to utilize the advantage of high resolution (30 m) remote sensing data of Shuttle Radar Topography Mission (SRTM) and the geoinformatic tools of Quantum Geographical Information System (QGIS) for extraction of detailed and accurate drainage morphometry and elevation information in preparation of hypsometric curves. Making use of QGIS, the SRTM data and topographic maps are georeferenced and pre-processed algorithm tools of stream burning and fill-DEM are applied for DEM preparation and extraction of accurate drainage network and its basin areas. From the DEM map, the details of elevation and area of basins are extracted for preparation of hypsometric curves. Hypsometric curves are analyzed to interpret and understand the heterogeneity in the levels of basin evolution/development and erosion at 4th order sub-basin level. The concave hypsometric curves of main basin and sub-basins indicate that total basin as well as sub-basins reached the old/monadnock stage due to the dominance of fluvial process. Based on the hypsometric area/integral (Ea) and Concavity of hypsometric curve values (Eh), five classes/stages of basin development and erosion levels are identified within the old/monadnock stage. The Ea values range from 0.10 to 0.35 and Eh range from 0.25 to 0.77 in this study area. Initial old/monadnock stage of basins have Ea values > 0.28 and Eh values < 0.4 indicating low levels of erosion among the sub-basins. Low hypsometric area/integral value (Ea) indicates the higher level of landmass removal by erosion and basin development. The oldest stage basins are identified with Ea values range from 0.10 to 0.15 and Eh values range from 0.61 to 0.77 indicating higher level of landmass removal/erosion. The lithological variation plays a significant role on hypsometry of the basins. The high hypsometric integral (Ea) values (i.e., 0.28 to 0.35) of 1,2 and 3 sub-basins indicating the early/ initial level of old stage in its’ basin development, since the resistant basalt formation is sandwiched between sandstones prevented deep erosion and depicting less dissected topography. The hypsometric integral has positive correlation with form factor and negative correlation with drainage morphometric characteristics. It indicates that the level of basin evolution increases as the form factor decreases but number of streams, length of streams, mean bifurcation ratio, relief, relief ratio and ruggedness ratio increases. This study is highly useful to understand the relation among morphometric characteristics, basin development and erosion levels through the analysis of hypsometric curves making use of Geoinformatic techniques.

Evaluación del modelo de elevación digital de la misión topográfica de radar en transbordador (SRTM) en la macrocuenca del Papaloapan, Mexico, usando LiDAR como referencia

The Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) is evaluated using a LiDAR DEM from INEGI as benchmark in the Papaloapan Basin (~58 000 km2) in Mexico. Three representative regions are selected: 1) a hilly region with strong slopes and elevations over 3 000 m; 2) a transitional region with relatively smoother slopes and elevations around 1 000 m, and 3) a floodplain with flat terrain and elevations below 100 m. The straight comparison of both datasets shows very similar elevation values at the hilly and transitional regions. However, in the floodplain, the relationship has a parabolic shape, and errors are relatively higher, in terms of the elevation range. This is probably due to systematic errors in SRTM being very close to the actual low elevations. Maps of errors suggest strong association with water bodies and the aspect. For example, in the transitional region, most negative errors are found on slopes facing east, while positive errors are found on slopes facing west. Three-dimensional histograms of errors vs. topographic features (elevation, slope, and aspect) are estimated. The histograms suggest a systematic error, which means SRTM could be improved with a simple calibration at least in these cases. Evaluations of public DEMs from different sources in Mexico are considered necessary for the identification of their strengths and weaknesses. We believe these evaluations might provide the grounds for the creation of improved MEDs in the future either by either a simple calibration or through composite MEDs from multiple sources.

A dataset of the distribution and characteristics of debris flows along the National Highway 318 in the Sino-Nepalese transport corridor (2022)

Due to the unique geographical and geological conditions in the Sino-Nepal transportation corridor, there is a lack of reliable and comprehensive data on debris flow distribution data in the region. This dataset is compiled based on early identification of debris flows and field surveys, integrating Digital Elevation Models (DEM) and geological maps. It provides a catalog and detailed feature information of debris flows within the corridor along the National Highway 318 between China and Nepal from 1990 to 2022. Precipitation and temperature parameters are derived from 30 years (1980–2009) of interpolated data from national meteorological stations. Fault distribution information is sourced from the 1:50,000 digital geological maps published by the China Geological Survey. Terrain information is obtained from NASA's Shuttle Radar Topography Mission (SRTM) data, which provides a global DEM at a resolution of 12.5 m. Within the geographical coordinates of 28.0°–29.4°N and 85.1°–88.9°E, a total of 255 debris flow records have been extracted, covering an area of 1660.73 km². We primarily employed hydrological analysis methods in ArcGIS for the extraction of debris flow basins. Considering the limitations in DEM accuracy, we made necessary manual corrections with reference to Google Earth imagery. The dataset comprises three files: a vector file depicting the main distribution range of debris flows along the National Highway 318 in the Sino-Nepalese transport corridor; a vector file containing the catalog of debris flows; and a document detailing the parameters and characteristics of the debris flows.The dataset has undergone on-site field surveys and confirmation to ensure its reliability. Therefore, it can serve as a foundational dataset applicable to debris flow studies in the region.

Geographical Information System, Remote Sensing and Multi Influencing Factors Techniques for Delineation of Groundwater Potential Zones in District Charsadda, Khyber Pakhtunkhwa, Pakistan

The effective harvesting of groundwater and its sustainable management is possible with the proper identification of zones on its potential basis. For this purpose, geographical information system (GIS) and Remote Sensing (RS) data approaches were combined and used for the investigation, preservation, and evaluation of groundwater supply. This study aimed to assess groundwater delineation and availability-based potential of groundwater area in Charsadda District, KPK, Pakistan, by applying the approach of RS, GIS, and multi-influencing factors (MIF). For this purpose, digital elevation model (DEM), shuttle radar topographic mission (SRTM), and sentinel 2 satellite images were employed to produce numerous thematic layers, i.e., land use land cover (LULC), drainage density, lineament density, geology, slope soil, and rainfall as MIF in this study. After assigning a fixed score and weight to each thematic layer (MIF techniques), then an analyzed spatial layer was combined with a weighted overlay using ArcGIS software (ArcMap 10.5) and finally potential area for groundwater was defined. The obtained potential area of groundwater was categorized spatially as, very high, high, moderate, and low zones which depict most of the area is covered with moderate (547.66 km2) to high (306.7 Km2), as well as, high highest groundwater potential in 7 km2. The results of this study and the approach will be applicable and insightful for regional and extensive levels of developmental planning and harvesting of groundwater resources.

Morphometric analysis of Mogamureru river basin at the YSR Kadapa District, Andhra Pradesh, India using GIS and remote sensing

Water availability and scarcity are impacted by geomorphological changes that occur within a catchment. As a result, determining the influence of geomorphological processes on the catchment’s hydrology requires a quantitative study of the catchment geometry. Approaches based on remote sensing (RS) and geographic information systems (GIS) have grown in popularity in recent years because they assist strategists and decision-makers in making accurate and effective choices and plans. For this research, the Mogamureru River basin was chosen. The study shows that GIS and RS data can be used to analyse and approximate the period and erosional operations’ risk in a Mogamureru river basin for better design and maintenance. The quickest and most economical method of displaying the hydrologic and physiographic features of the Mogamureru river basin is to assess the morphometric parameters. All of the following parameters such as: linear (7) areal (5), relief (5), and drainage texture (5) characteristics were obtained with hypsometric curve for the Mogamureru river basin utilizing the Shuttle Radar Topography Mission (SRTM) images. According to the linear parameter values, the majority of streams (82 %) belong to the first order, Statistical analysis shows that there is a good relationship between stream order and stream length, as well as stream order and stream number. The circularity ratio (0.55) depicts the young, elongated topography. The basin as a whole is in the mature stage of formation, with good to moderate potential, according to the hypsometric index (0.46). Due to land deterioration, Pulivendula and Vemula have had the most soil loss. This study concludes that morphometric analysis based on GIS & remote sensing techniques is a competent tool for hydrological studies. The research findings will help policymakers for integrated river basin management, agricultural development, and water management. In addition, researchers of morph hydrological, geological and climatological research will be beneficiary.

MORPHOGENIC ECOREGIONS OF EAST CARPATHIANS BIOSPHERE RESERVE

The East Carpathians Biosphere Reserve is located at the junction of Poland, Slovakia, and Ukraine; and therefore, lacks harmonized and detailed ecological regionalization encompassing all national parts. Thus, within the biosphere reserve and the vicinity, we delineated and classified morphogenic micro- and mesoecoregions as regional ecosystems, which spatially coincide with morphostructures of the 3rd and 2nd ranks respectively. The microecoregions were manually delineated using altitude and slope geodata derived from the Shuttle Radar Topography Mission Digital Elevation Model as a primary input, and national geological, geomorphological, and geoecological regionalizations as collateral inputs. Then, microecoregions were grouped into mesoecoregions and attributed with zonal statistics on mean altitude and mean relative elevation within a 1000-m circular neighborhood – both metrics were derived from the digital elevation model. Finally, microecoregions were divided into orographic classes according to the mean altitude and mean relative elevation metrics using agglomerative cluster analysis. We delineated 21 microecoregions and grouped them into five mesoecoregions. Overall accuracy of the resulting geodataset corresponds to a map of a 1:100,000 scale. Each microecoregion belongs to a certain structural-lithological zone (nappe), although the boundaries were modified by exogeneous processes. These are all flysch mountains, except one volcanic massif. Grouping of microecoregions into larger units – mesoecoregions – did not always follow hierarchy of geotectonic units. Cluster analysis on elevation metrics allowed to distinguish five orographic classes of microecoregions: 1) very low mountains, 2) low mountains, 3) dissected low mountains, 4) elevated low mountains, and 5) medium mountains. The regionalization and the classification reveal that mesoecoregions contain rather different microecoregions in terms of geological structure and orography. The latter also demonstrate rather significant internal heterogeneity. This study will be followed by a bioclimatic characterization and classification of microecoregions and subsequent descriptions of the potential natural and actual landcovers. Key words: East Carpathians Biosphere Reserve; ecoregions; morphostructures; orographic classes.

On-land manifestation of along-strike transitioning of the Red Sea from continental rifting to sea floor spreading

This study uses Landsat Thematic Mapper (TM), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and aeromagnetic data to examine possible reactivation of the Ediacaran, NE-trending Ad Damm Fault Zone (AFZ) and the Fatima Fault Zone (FFZ) in the western Arabian Shield. Reactivation is by latest Paleogene to Neogene and Quaternary structures of the Red Sea. The AFZ is the tectonic boundary between the Tonian Jeddah terrane in the northwest and the Asir terrane to the southeast of the Arabian Shield. The AFZ and FFZ coincide with possible onshore continuation of a transform fault that separates the sea floor spreading segment of the Red Sea in the south from its transitional segment to the north. It has been suggested that the AFZ is a candidate for the formation of a future onshore transform fault that would result in an eastward jump of the Red Sea sea floor spreading center onto the Arabian Shield. The future spreading center will follow the latest Neogene to Quaternary volcanic rocks (Harrat) that are exposed on the eastern side of the Red Sea. The Landsat TM image and the SRTM DEM show that the AFZ dissects the Red Sea topographic escarpment, and both the AFZ and FFZ control paleo-drainage system (wadis). The aeromagnetic data show that there is a ∼100 km wide zone between the AFZ and FFZ dominated by NE-trending short wavelength magnetic anomalies, possibly related to regional tectonic fabric. Additionally, the aeromagnetic data show that both fault zones dissect NW-trending short wavelength magnetic anomalies, interpreted as latest Paleogene to earliest Neogene dike swarms associated with the opening of the Red Sea. This dissection also includes possible Red Sea related narrow marginal grabens and horsts that were formed onshore and offshore parallel to the Red Sea shoreline. Red Sea-related structure might have reactivated the AFZ and FFZ.

Integrating remote sensing classification techniques for land use mapping in semi-arid regions: a case study of the Tamlouka basin, Algeria.

Worldwide, the majority of countries are actively devising strategies to address the challenges associated with unregulated and unmanageable development, the decline in environmental quality and the depletion of valuable agricultural land. This has led to a growing emphasis on understanding land use and land cover. In order to determine a better land use policy, legislators and planners need to know the current distribution of agricultural and urban lands, as well as information about changes in their proportions. Our approach combines data centred on main four themes-geology, slope gradient, hydrographic network and land use-in order to exploit classifier complementarities in our targeted agricultural study area of Tamlouka Basin, Algeria. Landsat 8 OLI-TIRs multispectral imagery and Shuttle Radar Topography Mission (SRTM-1arc v3) were used experimentally for classification and Digital Elevation Model (DEM) analysis. The classification's accuracy is confirmed by comparing the results of the decision tree classification with the validation samples. Results of the combination of several maps of classifications from the different methods show that the Tamlouka alluvial plain, having an area of 19,300 ha and an average slope gradient of less than 2°, drains the elevated reliefs that surround it via hydrographic network. The plain occupies 37% of the total basin area, with over of 60% being used for crop cultivation, regardless of fallow land areas in agricultural rotation at that time. The slope has been identified as a crucial factor determining land use patterns in the study area. This result can be used in prospective watershed management.

Prioritization of sub-watersheds based on quantitative morphometric analysis of the Narmada River, India, using SRTM-DEM and GIS techniques

Investigating the contribution of basin to the cycle of hydrological and the area's size, shape, and creation through quantitative analysis of these characteristics of the local scenery. Additionally, estimates of the denudation rate and hypsometric analysis were made in order to comprehend the sub-basin's quantitative geomorphological properties. The link between the morphometric parameters shows that the local geological and geomorphological features significantly influence the drainage system. On a less elevated surface with a moderate slope, stream geometry displays a pattern as semi-dendritic in greater stream order flow. On mountainous terrain surfaces, main-order streams display a dendritic drainage pattern combined with a coarse texture of drainage. The center zone of the Narmada River basin's sub-watersheds area (9461 km2) underwent morphometric investigation utilizing geographical information systems (GIS) with remote sensing methods. It highlights the usefulness of using Shuttle Radar Topographic Mission – Digital Elevation Model (SRTM-DEM) and satellite images to enhance basin management to evaluate and comprehend many geo-hydrological aspects, like topographic &amp; drainage analyses. ArcGIS hydrological modeling has been used to identify and study basins utilizing SRTM-DEM having 10 m resolution. For the micro-level research of its physiographic characteristics and flow structural control along with runoff, using morphometric parameters like relief, aerial, and linear is also beneficial. This may assist in predicting floods, their extent, and their severity.

Application of accuracy improvement algorithms for extraction of topographic information and drainage network from DEM using GIS

The extraction of drainage network and watershed information is prerequisite for the study of watershed characteristics like morphometric analysis, which provides a basis for hydrological planning and modeling. The advanced tools of algorithms, Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) data and Geographical Information System (GIS) software are used to extract drainage networks and their watershed boundaries. These tools are complicated to use or produce more errors in the extraction of elevation and drainage networks when applied to flat areas. For removal of errors and to improve the accuracy in preparation of DEM and extraction of drainage network, Burada Kalava River Basin, Andhra Pradesh, India has been taken for application of accuracy improvement algorithms. An automatic generation of drainage network and watershed using digital elevation model results in positional errors due to variations in slope and topography. This study aimed to generate a catchment area and stream network that closely represent the natural stream network and the streams’ real positions. The step-by-step methodology using GRASS-interfaced Quantum GIS algorithms are given for pre-processing of DEM data to improve the positional accuracy before automatic extraction of the stream network and catchment area to resemble the real situation of the watershed. Secondly, efforts are made to analyze the DEM during automatic generation of the stream network and catchment area by assigning various area threshold values, including the application of pour point coordinates in improving the stream network and watershed characteristics. The results are verified and validated with the field information in order to improve the accuracy levels of DEM quality in generation of drainage network and catchment area.

Vertical Accuracy of Google Earth Data

Digital Elevation Models (DEMs) are an important data source used in many engineering and Geographic Information System (GIS) applications. This paper illustrates a strategy for creating a DEM by utilizing elevation data from Google Earth and evaluating the vertical positional accuracy of the generated DEM adopting a well-defined methodology. To ensure the accuracy of the elevation data obtained from Google Earth, a thorough evaluation was done in three diverse small districts of the northern shoreline in Egypt. The evaluation process involved determining the ground coordinates of reference points utilizing two surveying techniques: total station and Real-Time Kinematic (RTK) Global Positioning System (GPS) surveys. These coordinates were compared with the ones predicated by the DEM generated by putting into service Google Earth's elevation data. Furthermore, the vertical accuracy was assessed using Shuttle Radar Topographic Mission (SRTM) data of Google Earth collected at two different periods in 2015 and 2023. The vertical accuracy of the Google Earth data is detailed utilizing Mean Error (ME), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE). According to the results, Google Earth's elevation data accuracy remains consistent from 2015 to 2023, and refining SRTM data does not improve the vertical accuracy. The vertical accuracy of the total station survey surpasses the one of the RTK GPS survey, and the elevation accuracy of the RTK GPS survey decreases with increasing height difference. In addition, the vertical accuracy of DEMs was found to be sufficient for some engineering applications but not accurate enough for precise engineering studies. The accuracy achieved in small height difference terrain can be utilized to produce large-scale cadastral maps, city plans, or land use maps. Finally, the elevation data offered by Google Earth can be utilized for preliminary studies at a low cost. However, to ensure the accuracy of these data, it is recommended that users compare them with reference data before implementation.

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

AbstractAccurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

Rock glacier distribution across the Himalaya

In High Mountain Asia, human-induced climate warming threatens the cryosphere. Expected long-term reductions in future runoff from glacial catchments raises concerns regarding the sustainability of these natural ‘water towers’ and the implications of reduced water availability for regional human and ecological systems. Ice-debris landforms (I-DL), containing ice whether moving or not include rock glaciers and ice-cored moraines, and are likely to be climatically more resilient than debris-covered and debris-free glaciers. Recent work has shown that rock glaciers contain globally valuable water supplies yet over High Mountain Asia information regarding their number, spatial distribution, morphometric characteristics and water content are scarce. Here, we present the first systematic estimate of the current extent and distribution of rock glaciers for a subset of High Mountain Asia (the Himalaya). A sample of 2070 intact and relict rock glaciers were digitized on Google Earth imagery from the Western, Central and Eastern Himalaya regions and then quantitative and qualitative characteristics were analysed regionally based on topographic data from the NASA Shuttle Radar Topography Mission (SRTM) Version 3.0 and then aggregated across the Himalaya using an “upscaling” method. The majority of the digitized landforms (∼65%) were categorised as intact rock glaciers (i.e., ice-debris Landforms, or I-DLs, containing ice) and the remainder as relict rock glaciers (i.e., discrete debris accumulations or DDAs, not containing ice). They range in elevation from 3225 to 5766 m a.s.l., with the lowest in the Central Himalaya. Sampled relict and intact rock glaciers are primarily situated on northern quadrants. Over the entire Himalaya, we identified ∼25,000 landforms, with a total estimated areal coverage of 3747 km2. The area upscaling method was validated in the Manaslu region of Nepal using high-resolution Planet data (5 m) and freely available, fine spatial resolution optical satellite data accessed through Google Earth Pro and ESRI basemaps. In absence of complete rock glacier inventories over the Himalaya, our approach proves useful to investigate the nature, distribution and infer potential future behaviour of these landforms across the Himalaya in a changing climate.

Exploring the Differences in Tree Species Classification between Typical Forest Regions in Northern and Southern China

Focusing on the trend of continuously seeking high-precision tree species classification results in small areas from the perspectives of sensors and classification algorithms. This study aimed to explore the effects of data sources, classifiers, and seasons on classification accuracy in regions with significant environmental variation, examining patterns of tree species classification to enhance the transferability of classification. Considering two typical forest distribution regions in the north and south of China, this study utilized the revisitation cycle and open-source advantages of Sentinel-2 and Landsat-8. Leveraging the Google Earth Engine (GEE) platform, this study captured spectral features, vegetation indices, and texture features for single seasonal and seasonal combination images. With the assistance of Sentinel-1A and SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), backscattering coefficient features and topographical features were extracted and input with features captured from Sentinel-2 and Landsat-8 into three types of classifiers: random forest (RF), support vector machine (SVM), and gradient tree boosting (GTB) for major tree species classification. In this research, we discovered that the best classification for single season in the northern study area was spring, whereas, for the southern study area, it was winter. Seasonal combination images effectively improved the classification accuracy of single seasonal images, with Sentinel-2 imagery displaying better classification performance compared to Landsat-8, and the optimal classifier differing between the north and the south. The inclusion of topographical or backscattering coefficient features in the four-season combination imagery contributed to improvements in classification accuracy, with topographical features significantly enhancing the classification performance in the topographically varied southern study area. The evaluation of feature importance indicated that elevation was the most critical feature for classification, while spectral features and vegetation indices were also significant. In the southern study area with large topographical discrepancies, subdividing into different terrain units led to improved tree species classification accuracy in medium-altitude, gentle slope areas. These findings provide insights into the regularity of enhancing tree species classification accuracy in environmentally diverse areas through the use of multi-source remote sensing data and multi-seasonal imagery. Consequently, the results offer a reference for the identification of tree species across large areas and the creation of spatial distribution maps.

#2489 The prevalence of chronic kidney disease in different altitude areas: results from the China multi-ethnic cohort study

Abstract Background and Aims Chronic Kidney Disease (CKD) poses a significant public health challenge with substantial implications for human well-being. In China, there are significant geographical differences in the prevalence of CKD, with the highest prevalence in the southwest region, which may be related to differences in geographical environment and lifestyle. This study aimed to investigate variances in the prevalence of CKD across different altitude areas in southwest China. Method In this cross-sectional study, we used data from the China Multi-Ethnic Cohort (CMEC). CMEC was established in five provinces of Southwest China and recruited a total of 99,556 participants between 2018 and 2019. Altitude information for each participant was extracted from the Shuttle Radar Topography Mission (SRTM) (version 4) data based on their residential address. To facilitate analysis, we categorized them into groups of low altitude (0-1499 m) and high altitude (≥1500 m). CKD was defined as an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2, using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Crude and age-standardized prevalence of CKD was calculated by using R. Results A total of 92,163 participants were included in the study, and 39.7% were men. Among the current cohort, 30,904 were high-altitude residents, and 61,259 were from low altitude. Among the participants, 1636 cases of CKD were identified, corresponding to a prevalence of 1.78%. We found, the prevalence of CKD in high altitude group was higher than that in low altitude group, and the former is 2.53%, the latter is 1.40%. The age-standardized prevalence were 1.35% and 2.73% for the low and high altitude groups, respectively, and 1.38% and 2.57% for the sex-standardized groups. Conclusion In southwestern China, the prevalence of CKD was higher in the high altitude group than the low altitude group. And after adjusting for age and gender, the prevalence of CKD remained lower in the low-altitude group, and the gap slightly increased.