Shuttle Radar Topography Mission Data Research Articles

Vertical Accuracy Assessment of the ASTER, SRTM, GLO-30, and ATLAS in a Forested Environment

Understory topography serves as a crucial data source, playing an instrumental role in numerous forest ecosystem applications. However, the use of synthetic aperture radar interferometry and optical stereo for the acquisition of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), SRTM (Shuttle Radar Topography Mission), and GLO-30 (Copernicus Digital Elevation Model) DEM presents unique challenges, particularly in forested environments. These challenges are primarily due to limitations in penetration capability and the effects of foreshortening. ICESat-2/ATLAS, with its higher spatial sampling rate and strong penetrability, presents a new opportunity for estimating forest height parameters and understory terrain. We assessed the vertical accuracy of ASTER, SRTM, GLO-30, and ATLAS in the forest study areas of the United States compared to the reference dataset DTM provided by G-LiHT and we will further discuss the influence of different ground altitudes, forest types, slopes, and aspects on vertical accuracy. The study reveals that in a forested environment, ICESat-2 ATL03 exhibits the highest accuracy at the footprint scale, with a correlation coefficient (R2) close to 1 and Root Mean Square Error (RMSE) = 1.96 m. SRTM exhibits the highest accuracy at the regional scale, with an R2 close to 0.99, RMSE = 11.09 m. A significant decrease in accuracy was observed with increasing slope, especially for slopes above 15°. With a sudden increase in altitude, such as in mountainous situations, the accuracy of vertical estimation will significantly decrease. Aspect and forest cover indeed influence the accuracy of the four DEM products, but this influence lacks a clear pattern. Our results show that ICESat-2 and SRTM data might show sufficient and stable vertical accuracy in a forested environment.

Gamification of Indonesia Geospatial Data as An Advanced and Sustainable Digital Geotourism Media

Easier internet access makes space and time boundaries in the real world no longer significant challenges in the virtual world. This means that tourists do not always have to physically visit the places they want. Digital tourism has become a new realm that is positively growing in the current era. Unfortunately, even though this is in great demand in various parts of the world, Indonesia is still not fully exploiting (in positive terms) this potential. Geospatial data and technology can be the primary key in solving these problems and opening broader development potential although data utilization and optimization of existing geospatial technology still need improvement. The various existing innovations are still relatively conventional and do not attract youngsters’ attention. An advanced but fun project is, therefore, needed to bridge geospatial experts, creative innovators, and the youth to develop and accelerate digital tourism in Indonesia. Gamification is a promising solution to this problem. This research uses the 4D method (define, design, develop, disseminate). The gamification resulting from the 4D method is used by researchers as an instrument to compare with conventional tourism media to determine public interest and promotional reach for traveling to Indonesia. The primary data used in this project is the Indonesia Shuttle Radar Topography Mission (SRTM) data. The first part of the results discusses the gamification model that the researchers propose. This model uses Minecraft as a basis for gamification in implementing the exploitable open-world concept. The second part of the results discusses the validation and potential of the gamification that the researchers created through the triangulation of survey data, interviews, and literature.

Estimation of Forest Height Using Google Earth Engine Machine Learning Combined with Single-Baseline TerraSAR-X/TanDEM-X and LiDAR

Forest height plays a crucial role in various fields, such as forest ecology, resource management, natural disaster management, and environmental protection. In order to obtain accurate and efficient measurements of forest height over large areas, in this study, Terra Synthetic Aperture Radar-X and the TerraSAR-X Add-on for Digital Elevation Measurement (TerraSAR-X/TanDEM-X), Sentinel-2A, and Shuttle Radar Topography Mission (SRTM) data were used, and various feature combinations were established in conjunction with measurements from Light Detection and Ranging (LiDAR). Classification and regression tree (CART), gradient-boosting decision tree (GBDT), random forest (RF), and support vector machine (SVM) algorithms were employed to estimate forest height in the study area. Independent validation on the basis of LiDAR forest height samples showed the following results: (1) Regarding feature combinations, the combination of coherence and decorrelation of volume scattering provided by TerraSAR-X/TanDEM-X data outperformed the combination of backscatter coefficient and local incidence angle, as well as the combination of coherence, decorrelation of volume scattering, backscatter coefficient, and local incidence angle. The best results (R2 = 0.67, RMSE = 2.89 m) were achieved with the combination of coherence and decorrelation of volume scattering using the GBDT and RF algorithms. (2) In terms of machine learning methods, the GBDT algorithm proved suitable for estimating forest height. The most effective approach for forest height mapping involved combining the GBDT algorithm with coherence, decorrelation of volume scattering, and a small amount of LiDAR forest height data, used as training data.

Morphometric Determination and Digital Geological Mapping by RS and GIS Techniques in Aseer–Jazan Contact, Southwest Saudi Arabia

The hydrological characteristics of the watershed in the southern Aseer and northern Jazan regions of Saudi Arabia (SA) were identified by integrated remote sensing (RS) and geographic information system (GIS) techniques using Shuttle Radar Topography Mission (SRTM) and Landsat data. For this purpose, the Wadi Ishran, Wadi Baysh, Wadi Itwad, Wadi Tabab, and Wadi Bayd drainage basins were extracted. Wadi Ishran is the largest, and Wadi Tabab is the smallest. Stream order and bifurcation ratio show that the Itwad and Bayd basins are permeable and of high aquifer potentiality. The multisupervised classification found seven rock units that were spread out in different ways across the basins. The areas with the highest vegetation were in the southeast, the centre, and the northwest. The bands’ ratios show more iron-rich sediments in the northeast and southwest. This paper’s outcomes serve as the basis for planning and managing groundwater resources. It finds potential groundwater zones, determines the risk of flooding, and chooses places where harvesting can be undertaken.

STUDY OF DRAINAGE SYSTEM AND ITS HYDROLOGICAL IMPLICATIONS USING GEO-SPATIAL TECHNIQUES: A MORPHOMETRIC ANALYSIS IN MUTHUGAD WATERSHED OF GRAHWAL HIMALYA, UTTARAKHAND

Measurements of the shape or geometry of any natural form, including those of plants, animals, and relief features, are referred to as morphometry. Morphometry is the accurate measurement of landforms. One of the crucial requirements for effective management and planning of water resources within the watershed is an understanding of the behavior of the surface drainage network. In order to comprehend and interpret the dynamics of the watersheds drainage system and to understand the drainage network features, morphometric study of a watershed is a vital stage in watershed development and management. Watershed characterization includes morphometry as a key component. Aspects that are dimensional, aerial, and relief are typically present for the conservation of surface and ground water, watershed development and management strategies are crucial. Applying mathematical equations and statical measures to data from topographic maps and satellite images, morphometric attributes reflect a measurement of the earths surface and are processed in accordance with the principles of quantitative analysis. A study of drainage morphometry and its impact on the hydrology of the Mathugad watershed, Grahwal Himalaya, Uttarakhand (India) has been made. Total basin covers 77.12 sq km area. Shuttle Radar Topographic Mission (SRTM) data and on 1:50000 scale and Survey of India Toposheet as reference were utilized to create slope maps, aspect grids, and digital elevation models (DEMs) for the thorough investigation. Geographic information systems (GIS) were employed to assess the linear, areal, and relief aspects of the morphometric parameters. Numerous morphometric features of the Mathugad watershed have been intended by applying GIS techniques and using SRTM data. The research shows that the rainfall has a moderate to large impact on how stream segments develop in the basin area thrusting and faultings controlling influence is primarily responsible for the basins elongated structure. Relief ratio indicates that the discharge capability of these watersheds is very high and the groundwater potential is meager. Dendritic drainage pattern in the area shows that the area consists of homogeneous rock material which is structurally undisturbed. Form factor and circulatory ratio statics indicates basin shape is elongated the results clearly indicate relations among various morphometric attributes of the basin and help to understand their role in sculpturing the surface of the region. These studies are very useful for planning rainwater harvesting and watershed management.

Monitoring and analysis of ground subsidence in Shanghai based on PS-InSAR and SBAS-InSAR technologies

Shanghai is susceptible to land subsidence due to its unique geological environment and frequent human activities. Traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. Interferometric Synthetic Aperture Radar (InSAR) technology is a widely used method for monitoring ground subsidence due to its low cost, high efficiency, and ability to cover large areas. To monitor the surface sink condition of Shanghai over the past 2 years, monitoring data were obtained through the technical processing of 24 images from Sentinel-1A data covering Shanghai from 2019 to 2020 using the Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS-InSAR) technique. The ground subsidence (GS) results were extracted via PS and SBAS interferometry processing, while Shuttle Radar Topography Mission data were used to correct the residual phase. According to PS and SBAS methods, the maximum ground subsidence in the study area reached 99.8 mm and 47.2 mm, respectively. The subsidence rate and the accumulated amount of subsidence derived from the monitoring results revealed the urban area in Shanghai to be principally characterized by uneven GS, with multiple settlement funnels being found to be distributed across the main urban area. Moreover, when compared with the historical subsidence data, geological data, and urban construction distribution data, the individual settlement funnels were observed to correspond to those data concerning the historical surface settlement funnel in Shanghai. By randomly selecting GS time-series data regarding three feature points, it was determined that the morphological variables of the GS remained largely consistent at all time points and that their change trends exhibited a high degree of consistency, which verified the reliability of the PS-InSAR and SBAS-InSAR monitoring method. The results can provide data support for decision making in terms of geological disaster prevention and control in Shanghai.

Lineament Density and Implications for the Distribution of Ground Fissures After 2021 MW 7.3 Flores Sea Earthquake on Kalaotoa Island, Indonesia

A 7.3 MW earthquake occurred at 11:20 am on December 14, 2021, in the Flores Sea, and the main shake was centered ±100 km north of Maumere, Indonesia, with a depth of 14.3 km, antecedent a landslide. This research consists of two stages, namely: quantitative data collection in the form of tectonic lineament density measurements using Shuttle Radar Topography Mission (SRTM) data which is extracted manually using GIS-based applications, and qualitative data in the form of field observations which include strike-dip measurements of rocks, lithological data, morphological conditions, ground fissures, and the distribution of damage caused after 2021 earthquake. This study focused on analyzing the value of lineament density, its correlation to the history of seismicity and surface lithological conditions, and the impact after the 2021 earthquake damage. The lineaments of the southern area are dominated by NE – SW orientation along with various lithological conditions and with lineament density values very low – very high. A crack width from 0.5 to 112 cm, and a vertical offset occurs with a depth of up to 270 cm. The western area is dominated by lineament with an orientation NE – SW with a crack width from 8 to 18 cm, and there is a vertical offset with a depth of up to 24 cm. The distribution of ground fissures in the Garaupa Raya area is categorized as low. The orientation of the northern area lineament is relatively NW – SE directional and the lineament density value is categorized as low. Horizontal displacement with an orientation of NW – SE is found at the port of Kalaotoa Island, Kawawo village with measured crack width of ±17 cm, an observable horizontal offset from 15 to 24 cm, and a vertical offset of ±12 cm with a trend of movement towards the south.

Remote Sensing-Based Yield Estimation of Winter Wheat Using Vegetation and Soil Indices in Jalilabad, Azerbaijan

Concerns about the expanding human population’s adequate supply of food draw attention to the field of Food Security. Future-focused analysis and processing of agricultural data not only improve planning capabilities in this field but also enables the required precautions to be taken beforehand. However, given the breadth and number of these regions, field research would be an expensive and time-consuming endeavour. With the advent of remote sensing and optical sensors, it is now possible to acquire diverse data remotely, quickly, and inexpensively. This study investigated the limitations and capabilities of remote sensing data application in the field of planning Food Security. As a result, Sentinel 2 and Shuttle Radar Topography Mission (SRTM) data were used to estimate winter wheat yields with a high degree of accuracy (98.03%) using the Mamatkulov technique and the MEDALUS model, which was both free and widely available. This method can make it possible to make predictions about the productivity of newly created crop fields or for which we do not have information about the productivity of previous years, without the need to wait for building regression models or any field studies. Considering the outcome, wide-range and larger analyses on this topic can be carried through.

Mapping and analysis of structural lineaments using SRTM radar data and Landsat-8 OLI image: an example from the Telouet–Tighza area, Marrakech High Atlas, Morocco

This study provides the first evaluation of the potential of both the Landsat-8 Operational Land Imager (OLI) sensor images and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data for automated lineament extraction in the south side of the Marrakech High Atlas (Telouet–Tighza area). After image corrections, enhancement methods such as principal component analysis, band composite (BC), and directional filtering were adopted to create new images that provided high visibility of linear structures. The new Landsat BC image used in this study was selected based on the calculation of the optimum index factor and correlation index. In addition to the Landsat image, the SRTM DEM was used to detect structural lineaments in the area by generating shaded relief images. Multisource data, such as band ratio image, geological maps, and fieldwork, were used to eliminate the nongeological lineaments extracted. The results indicate that an automated method was applied successfully for lineament mapping in this area, by detailing the main tectonic faults. Moreover, new lineaments are identified and are validated by fieldwork. Structural lineaments extracted show compatibility in their direction, length, distribution, and density with the tectonic evolution of the study area. A total of 2945 lineaments were extracted with major ENE–WSW and predominant E–W directions. The new structural map shows more structural information compared with the geological map of this area and exemplifies the performance of Landsat-8 OLI bands and SRTM data in this kind of study.

Artificial neural networks for assessing forest fire susceptibility in Türkiye

Wildfires often threaten natural and economic resources and human lives. Wildfire susceptibility assessments have become essential for efficient disaster management and increasing resilience. In this study, we assessed the forest fire susceptibility in Istanbul Province and Thrace Region, Türkiye using a well-known machine learning technique, Artificial Neural Networks (ANN). Benefiting from freely available Earth Observation datasets such as Sentinel-2 images, Tree Cover Density from European Union (EU) European Environment Agency (EEA) Copernicus Land Monitoring Service, Shuttle Radar Topography Mission (SRTM) data, etc., and a forest inventory with ignition locations recorded over a period of eight years, we utilized a total of 16 independent and one dependent variables. The variables can be categorized as anthropogenic, topographic, vegetation, and hydrological factors. A ratio of 1:2 was preferred for the fire/non-fire location samples. The results show that the ANN exhibited high prediction performance with Area Under the Receiver Operating Characteristic Curve (AUC) value and F-1 score of 0.94 and 0.80, respectively. Based on feature importance analyses, we found that a human-related factor, proximity to forest roads, was the most predictive input variable. The ANN model trained with openly available data (i.e., without forest database) also yielded a high F-1 score, but produced maps with fewer details. Our results confirm that data-driven machine learning methods are promising for regional forest fire susceptibility assessments and can be extended further for other regions by deriving similar parameters from freely available Earth Observation datasets.

Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery

Europe’s mountain forests, which are naturally valuable areas due to their high biodiversity and well-preserved natural characteristics, are experiencing major alterations, so an important component of monitoring is obtaining up-to-date information concerning species composition, extent, and location. An important aspect of mapping tree stands is the selection of remote sensing data that vary in temporal, spectral, and spatial resolution, as well as in open and commercial access. For the Tatra Mountains area, which is a unique alpine ecosystem in central Europe, we classified 13 woody species by iterative machine learning methods using random forest (RF) and support vector machine (SVM) algorithms of more than 1000 polygons collected in the field. For this task, we used free Sentinel-2 multitemporal satellite data (10 m pixel size, 12 spectral bands, and 21 acquisition dates), commercial PlanetScope data (3 m pixel size, 8 spectral bands, and 3 acquisitions dates), and airborne HySpex hyperspectral data (2 m pixel size, 430 spectral bands, and a single acquisition) with fusion of the data of topographic derivatives based on Shuttle Radar Topography Mission (SRTM) and airborne laser scanning (ALS) data. The iterative classification method achieved the highest F1-score with HySpex (0.95 RF; 0.92 SVM) imagery, but the multitemporal Sentinel-2 data cube, which consisted of 21 scenes, offered comparable results (0.93 RF; 0.89 SVM). The three images of the high-resolution PlanetScope produced slightly less accurate results (0.89 RF; 0.87 SVM).

PEMANFAATAN DEM-SRTM DAN DEM-NAS SEBAGAI DASAR SIMULASI HUJAN-DEBIT DI DAS KALIWADAS

The accuracy of flow prediction in a watershed for water resource development is highly dependent on various factors such as the transformation model used, the availability and accuracy of hydrological data and watershed characteristics such as topography, drainage networks, land cover and soil texture. Watershed characteristics are considered as the most dominant determinants in various hydrological analyzes, especially with regard to the data used to compile the watershed model, in this case the Digital Elevatian Model (DEM) data. This study aims to develop a watershed model based on National DEM (DEMNAS) data to estimate daily discharge using the HEC-HMS model and design discharge using the Nakayasu Synthetic Unit Hydrograph (SUH) method. The research was conducted in the Kaliwadas watershed, which is located in Kandang Serang District, Pekalongan Regency, Central Java Province. Kaliwadas River is one of the sub-watersheds with a drainage area of approximately 165.78 km2 which has a good enough potential to supply hydro-based electricity. The performance of the DAS model formed with DEMNAS data is compared with the performance of the watershed model formed with the DEM Shuttle Radar Topography Mission (SRTM) data, where these two DEM data have a resolution of 8 meters and 90 meters, respectively. Performance testing is based on the Root Mean Square Error (RMSE) number of the two predictive flow from the HEC-HMS Model to the observed discharge. The results showed that the estimated discharge of the HEC-HMS Model using the DEMNAS Watershed Model showed slightly better performance than using the SRTM DAS Model, with a RMSE difference of 0.27.

Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Landscape

Land use and land cover (LULC) mapping can be of great help in changing land use decisions, but accurate mapping of LULC categories is challenging, especially in semi-arid areas with extensive farming systems and seasonal vegetation phenology. Machine learning algorithms are now widely used for LULC mapping because they provide analytical capabilities for LULC classification. However, the use of machine learning algorithms to improve classification performance is still being explored. The objective of this study is to investigate how to improve the performance of LULC models to reduce prediction errors. To address this question, the study applied a Random Forest (RF) based feature selection approach using Sentinel-1, -2, and Shuttle Radar Topographic Mission (SRTM) data. Results from RF show that the Sentinel-2 data only achieved an out-of-bag overall accuracy of 84.2%, while the Sentinel-1 and SRTM data achieved 83% and 76.44%, respectively. Classification accuracy improved to 89.1% when Sentinel-2, Sentinel-1 backscatter, and SRTM data were combined. This represents a 4.9% improvement in overall accuracy compared to Sentinel-2 alone and a 6.1% and 12.66% improvement compared to Sentinel-1 and SRTM data, respectively. Further independent validation, based on equally sized stratified random samples, consistently found a 5.3% difference between the Sentinel-2 and the combined datasets. This study demonstrates the importance of the synergy between optical, radar, and elevation data in improving the accuracy of LULC maps. In principle, the LULC maps produced in this study could help decision-makers in a wide range of spatial planning applications.

Immiscible two-phase model for air blasts created during natural avalanches

An immiscible two-phase model based on the incompressible Navier-Stokes (N-S) equations is used to simulate the air blast generated by an avalanche. For simplicity, the avalanche is treated as an assembly of monodisperse spherical grains and described as a continuous media. The constitutive law of local µ(I) rheology is introduced to model the moving granular material. The motion of the avalanche and the induced air blast fits into a unified framework that combines the N-S−type governing equations with a µ(I)-rheology−based kinematic viscosity and a constant viscosity. The avalanche-air interface is treated using the volume-of-fluid method. A numerical program was developed on the open-source platform OpenFOAM specifically for this model to simulate the entire evolutionary process of the avalanche as well as the air blast generated. The model was validated by comparing the results of numerical simulations with those from inclined-plane laboratory experiments. With terrain input from the Shuttle Radar Topography Mission data, the model was further applied to simulate the air blast generated in two natural avalanches, namely, the Baige and Wenjia valley avalanches fo China, which occurred in 2008 and 2018, respectively. The simulation results were found to be consistent with field observations following a statistical analysis of the properties of the air blast including flow speed and area of impact of the above-mentioned natural events.

Fault geometry and slip rates from the Nullarbor and Roe Plains of south‐central Australia: Insights into the spatial and temporal characteristics of intraplate seismicity

AbstractAnalysis of TanDEM‐X and Shuttle Radar Topography Mission (SRTM) data reveals geomorphic evidence for 292 fault‐propagation fold scarps across the Miocene Nullarbor and Pliocene Roe Plains in south‐central Australia. Vertical displacements (VD) are determined using topographic profiling of a subset (n = 48) of the fold traces. Fault dips (mean = 44 +16/−14° at 1σ) are estimated from seismic reflection data; the mean dip is assigned to faults with unknown dip and combined with VD to estimate net displacements (ND) and average net displacements (AD) for each fault. AD exceeds single‐event displacements estimated from fault‐length scaling regressions, indicating the identified faults have hosted multiple earthquakes. Combining AD with (i) faulted surface ages (Nullarbor ~10–5 Ma, Roe ~2.5 Ma), (ii) ages of faulted erosional–depositional features (e.g. relic Late Miocene dune fields and Pliocene paleochannels), and (iii) onset of the neotectonic regime in Australia at ~10 Ma yields average slip rates from <0.1 m Myr−1to >17 m Myr−1(mean = 1.1 m Myr−1). Summation of displacements across faults yields crustal horizontal shortening rates lower than geodetically detectable resolution (≤0.01 mm yr−1) since the Late Miocene. The ca. 10 Myr‐long record of neotectonic faulting on the Nullarbor Plain provides important insights into earthquake spatial–temporal behaviours in a slowly deforming intraplate continental region.

The Luangwa Rift Active Fault Database and fault reactivation along the southwestern branch of the East African Rift

Abstract. Seismic hazard assessment in slow straining regions is challenging because earthquake catalogues only record events from approximately the last 100 years, whereas earthquake recurrence times on individual faults can exceed 1000 years. Systematic mapping of active faults allows fault sources to be used within probabilistic seismic hazard assessment, which overcomes the problems of short-term earthquake records. We use Shuttle Radar Topography Mission (SRTM) data to analyse surface deformation in the Luangwa Rift in Zambia and develop the Luangwa Rift Active Fault Database (LRAFD). The LRAFD is an open-source geospatial database containing active fault traces and their attributes and is freely available at https://doi.org/10.5281/zenodo.6513691. We identified 18 faults that display evidence for Quaternary activity, and empirical relationships suggest that these faults could cause earthquakes up to Mw 8.1, which would exceed the magnitude of historically recorded events in southern Africa. On the four most prominent faults, the median height of Quaternary fault scarps varies between 12.9 ± 0.4 and 19.2 ± 0.9 m, which suggests they were formed by multiple earthquakes. Deformation is focused on the edges of the Luangwa Rift: the most prominent Quaternary fault scarps occur along the 207 km long Chipola and 142 km long Molaza faults, which are the rift border faults and the longest faults in the region. We associate the scarp on the Molaza Fault with possible surface ruptures from two 20th century earthquakes. Thus, the LRAFD reveals new insights into active faulting in southern Africa and presents a framework for evaluating future seismic hazard.

Characterizing Global Patterns of Mangrove Canopy Height and Aboveground Biomass Derived from SRTM Data

Numerous studies have been done using remotely sensed data to produce global mangrove height and biomass maps; however, little is known about the worldwide pattern of mangroves in the Northern and Southern Hemispheres that corresponds to their height and biomass. The objective of this study was to investigate whether there is a specific pattern that can be seen between northern and southern mangroves according to height and biomass. Based on an empirical model, we processed Shuttle Radar Topographic Mission (SRTM) elevation data in combination with 450 field data points to produce a global mangrove height map and its corresponding aboveground biomass (AGB) per hectare at 30 m spatial resolution. We also refined the global mangrove area maps and provided a set of equations to determine the maximum mangrove height at any given latitude. Results showed that 10,639,916 ha of mangroves existed globally in the year 2000, with a total AGB of 1.696 Gt. Even though the areal coverage of mangroves was higher in the Northern Hemisphere, the total mangrove AGB was higher in the Southern Hemisphere. The majority of mangroves in both hemispheres were found to be between 6 and 8 m tall, although height distribution differed in each hemisphere. The global mangrove height equation for northern and southern mangroves produced from this study can be used by relevant stakeholders as an important reference for developing an appropriate management plan for the sustainability of the global mangrove ecosystem.

Regional “Bare-Earth” Digital Terrain Model for Costa Rica Based on NASADEM Corrected for Vegetation Bias

A large percentage of the Costa Rican territory is covered with high evergreen forests. In order to compute a 1″ Bare-Earth Digital Terrain Model (DTM) for Costa Rica CRDTM2020, stochastic Vegetation Bias (VB) was reduced from the 1″ NASADEM, Digital Elevation Model (DEM) based on the Shuttle Radar Topography Mission (SRTM) data. Several global models such as: canopy heights from the Global Forest Canopy Height 2019 model, canopy heights for the year 2000 from the Forest Canopy Height Map, and canopy density from the Global Forest Change model 2000 to 2019, were used to represent the vegetation in the year of SRTM data collection. Four analytical VB models based on canopy heights and canopy density were evaluated and validated using bare-earth observations and canopy heights from the Laser Vegetation Imaging Sensor (LVIS) surveys from 1998, 2005, and 2019 and a levelling dataset. The results show that differences between CRDTM2020 and bare-earth elevations from LVIS2019 in terms of the mean, median, standard deviation, and median absolute difference (0.9, 0.8, 7.9 and 3.7 m, respectively) are smaller than for any other of the nine evaluated global DEMs.

Application of Multi-Channel Convolutional Neural Network to Improve DEM Data in Urban Cities

A digital elevation model (DEM) represents the topographic surface of the Earth and is an indispensable source of data in many applications, such as flood modeling, infrastructure design and land management. DEM data at high spatial resolution and high accuracy of elevation data are not only costly and time-consuming to acquire but also often confidential. In this paper, we explore a cost-effective approach to derive good quality DEM data by applying a multi-channel convolutional neural network (CNN) to enhance free resources of available DEM data. Shuttle Radar Topography Mission (SRTM) data, multi-spectral imaging Sentinel-2, as well as Google satellite imagery were used as inputs to the CNN model. The CNN model was first trained using high-quality reference DEM data in a dense urban city—Nice, France—then validated on another site in Nice and finally tested in the Orchard Road area (Singapore), which is also an equally dense urban area in Singapore. The CNN model not only shows an impressive reduction in the root mean square error (RMSE) of 50% at validation site in Nice and 30% at the test site in Singapore, but also results in much clearer profiles of the land surface than input SRTM data. A comparison between CNN performance and that of an earlier conducted study using artificial neural networks (ANN) was conducted as well. The comparison within this limited study shows that CNN yields a more accurate DEM.

The Hypsometric Integral Based on Digital Elevation Model for the Area West of Lvliang Mountains in Loess Plateau, Shanxi, China

A hypsometric integral (HI) is a topographical index with important geomorphological meaning. It can both describe the development state of the watershed and reflect the strength of activity in the geological structure. Based on this index, the integral curve method was used to analyze the HI characteristics of watersheds in the region west of the Lvliang Mountains on the Loess Plateau of Shanxi Province, China, in combination with 30-m resolution Shuttle Radar Topography Mission data. In order to accurately characterize erosion development within the watershed, the HI characteristics of each topographic feature object in the watershed were comprehensively analyzed. The results of HI scale effects show that HI was not strongly dependent on the spatial resolution of Digital Elevation Model (DEM) data, but had some scale dependence on the stable area of the watershed. The spatial distribution of the HI shows that the HI values of the watersheds in the southern loess residual tableland were the largest, and the degree of erosion was the weakest; the HI values of the watersheds in the north-central part of the loess hilly-gully region were relatively small, and the degree of erosion was stronger. This result is consistent with the results of the spatial distribution of sediment yield in literatures. The reasons for the spatial distribution of the HI were analyzed in terms of geological formations and climatic environments. The results show that for the study area, tectonic activity, formation lithology, rainfall, and agrotype were not the main factors affecting the HI values. The structure of the paleotopography and erosion cutting due to the uplift of the Lvliang Mountains and later water erosion controlled the spatial distribution state of HI. Thus, for the study area, the HI value cannot reflect the strength of tectonic activity. The combination of topographic feature object method and the HI index further quantified the erosion development state of the watershed.