SRTM DEM Research Articles

DEM BASED COMPUTATION OF TOPOGRAPHIC SURFACE ROUGHNESS TO REVEAL INCISION IN GILGIT-BALTISTAN,PAKISTAN

This research investigates interrelationship between landscape evolution and neotectonics in the Gilgit-Baltistan (GB) region based on surface roughness (SR). We analyzed the topographic surface features such as flat area, surface area and surface ratio using Shuttle RadaTopographic Mission-Digital Elevation Model (SRTM DEM) with a spatial resolution of 90 meters. The objective is to make a SR map and to identify areas influenced by neotectonics. The DEM based spatial distribution of slope gradients and slope orientations along with the aspect map were combined to portray the vertical dissection. Lower values of vertical dissection indicate relatively flat topographic basins which generally mean a region of with intermediate slope gradient and slope orientation. Areas with high vertical dissection values correspond to sharp changes in the slope gradients and slope orientations, which could be due to abrupt scrap edges (possibly neotectonic or lithological control). Higher vertical dissection values in GB are wide spread and are oriented SE- NW in Ghanche, Skardu, Hunza, Gilgit and northern Ghizer districts along Karakorum, South Pamirs and Hindu-Kush ranges. While a few higher roughness values are aligned NE- SW in Diamir and Gilgit districts along the Raikot Fault. The surface roughness map is capable of identifying deeply incised valleys, tectonic uplifts and depressions.

Environmental Performance of Regional Protected Area Network: Typological Diversity and Fragmentation of Forests

Protected areas (PAs) are among the main tools for preserving biodiversity and creating an environment for the natural course of ecological processes. The identification of forest biodiversity is especially important for large metropolitan areas. An obvious problem in assessing the efficiency of the PAs network is the lack of up-to-date cartographic materials representing the typological diversity of vegetation. The aim of the paper is to identify forest biodiversity and fragmentation in the example of the Moscow region (MR)—the largest metropolis in Eastern Europe. The typological classification was carried out at a detailed hierarchical level—33 association groups (ass.gr.) considering the diversity of the land cover. A random forest algorithm was used for cаrtographic mapping (overall accuracy 0.59). Remote sensing (RS) data included Sentinel-2A, DEM SRTM, and PALSAR radar images. Six fragmentation metrics were calculated based on the raster map of forest typological diversity. A significant correlation between the forest diversity and PAs forest patch fragmentation metrics was noted. It has been established that the PAs proportion of the territory accounts for almost 20% only within the northernmost district and noticeably decreases to the south to 1–2%. At the same time, fragmentation noticeably increases from Northeast to Southwest. The category of PAs does not affect the state of the forest cover. Additionally, there was no direct influence of the anthropogenic factor from both local sources and a large regional source, i.e., the city of Moscow. It is shown that the average area of PAs, supporting 75% of the typological diversity of regional communities, was about 1000 ha. The results of the study suggest that there is a general lack of environmental protection measures in the region. It is recommended to increase the area of PAs, primarily for less fragmented forest patches, including indigenous forest-steppe and forest types of communities.

Assessment of Land Use-land Cover Change in Irga River Catchment Using Object-based Image Classification Technique

Land use-land cover (LULC) change analysis is essential for understanding the spatial and temporal change of landscape during a known long period for sustainable management of natural resources. The main objective of this study was to assess land use-land cover change using an object-based image classification technique which is a recent image classification technique with better accuracy than traditional pixel based image classification. The study was conducted in the catchment area of the Irga River, a tributary of the Barakar River, which falls in the Giridih district of Jharkhand (India). The catchment of the study area was delineated using SRTM DEM data (30 m spatial resolution). LANDSAT images (TM and OLI-TIRS) were used to develop the land use- land cover maps of 1997, 2007, and 2017 using object-based image analysis (OBIA). The images were classified and analyzed using ArcGIS and eCognition Developer 64 software. The accuracy of the classified images for each year was assessed by preparing the error matrix and calculating the Kappa coefficient. The overall accuracies of classified images were computed to be 88%, 83% and 91% while Kappa coefficients were found to be 0.8455, 0.7706 and 0.8796 for year 1997, 2007 and 2017 respectively. Over the 20 years (1997-2017), agricultural land increased by 12.23%, settlement increased by 76.62%, wasteland decreased by 39.59%, vegetation increased by 14.83%, water-bodies increased by 26.29%, and river area decreased by 16.66%. The analysis indicated an increasing trend in agricultural land, settlement, and vegetation while decreasing trends in wasteland and river areas. However, no definite trend was observed in the extent of the water-bodies.The results indicated that waste land greatly reduced and converted into settlement and agricultural land in the catchment.

Combined analysis of PS-InSAR and hypsometry integral (HI) for comparing seismic vulnerability and assessment of various regions of Pakistan

InSAR-based deformation analysis and the geomorphic hypsometric integral (HI) technique are powerful tools for assessing the susceptibility and comparison of seismic sites to earthquakes. Therefore, this paper mainly focuses on surface deformation analysis associated with the Mw 5.0 earthquake (2019) in Mach and Quetta, Balochistan, Pakistan. Sentinel-1 IW data was used to perform PS-InSAR time series analysis. SRTM DEM of 30 m spatial resolution was utilized for the geomorphic Hypsometry Integral (HI) method. The obtained results of the Interferogram indicate the changes in velocity and vertical displacement during pre-seismic, co-seismic, and post-seismic activity. Integral values were calculated using Hypsometry curves delineating the future probability and comparison of vulnerable seismological sites in Mach, Quetta, Ghazaband, Chamman and surroundings of Balochistan region. The combined results of HI and PS-InSAR revealed that Mach and Quetta regions are in between two lines known as the mature stages. Class 1_moderate (0.35 ≤ HI ≤ 0.52); with an integral value of HIMach = 0.398 and HIQuetta = 0.435 with a modest seismic forthcoming rate in future and susceptible to both erosion/uplifting with a vertical displacement rate more than existing ± 55 mm/year. Class 2_high (HI ˃ 0.53) with the younger and more tectonically active region surrounded by Chaman fault, which possesses a future susceptible tendency towards subsidence more than an existing velocity rate ~ 8 mm/year and Ghazaband fault towards uplifting more than 5–6 mm/year. No region of the study area was found at Monadnock: class 3_Low (HI ˂ 0.35) stabilized condition, all sites are unstable and tectonically active. Therefore, obtained results through combined PS-InSAR and HI techniques can be used for the identification of most vulnerable seismic sites and can ascertain future safe metropolitan planning.

Quantifying spatio-temporal variation in aquaculture production areas in Satkhira, Bangladesh using geospatial and social survey.

Despite Bangladesh being one of the leading countries in aquaculture food production worldwide, there is a considerable lack of updated scientific information about aquaculture activities in remote sites, making it difficult to manage sustainably. This study explored the use of geospatial and field data to monitor spatio-temporal changes in aquaculture production sites in the Satkhira district from 2017-2019. We used Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) to locate aquaculture ponds based on the terrain elevation and slope. Radar backscatter information from the Sentinel-1 satellite, and different water indices derived from Sentinel-2 were used to assess the spatio-temporal extents of aquaculture areas. An image segmentation algorithm was applied to detect aquaculture ponds based on backscattering intensity, size and shape characteristics. Our results show that the highest number of aquaculture ponds were observed in January, with a size of more than 30,000 ha. Object-based image classification of Sentinel-1 data showed an overall accuracy above 80%. The key factors responsible for the variation in aquaculture were investigated using field surveys. We noticed that despite a significant number of aquaculture ponds in the study area, shrimp production and export are decreasing because of a lack of infrastructure, poor governance, and lack of awareness in the local communities. The result of this study can provide in-depth information about aquaculture areas, which is vital for policymakers and environmental administrators for successful aquaculture management in Satkhira, Bangladesh and other countries with similar issues.

Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data.

The dynamic change in land use/land cover (LULC) caused by rapid urbanizationhas become a major concern in Lahore, causing a variety of socioeconomic and environmental issues relating to air quality. As a result, it is important to monitor existing LULC change detection, future projection, and the increase in atmospheric pollutants in Lahore. This research work makes use of Landsat, GIOVANNI, SRTM DEM, and vector data. The four key steps of the research approach are as follows: (i) LULC classification from 2000 to 2020 using Lansat data and semi-automatic classification plugin (SCP); (ii) simulation of future prediction using Modules of Land Use Change Evaluation (MOLUSCE) prediction model; (iii) assessment of effects of land use change on air quality using GIOVANNI-NASA product; and (iv) monitoring, change detection, and result interpretation. According to the research findings, there was an increase in metropolitan areas and a decrease in vegetation, barren land, and water bodies for both historical and future projections. The findings also indicated that from 2000 to 2020, about 27.41% of the urban area expanded, with a decline of 42.13% in vegetation, 2.3% in the barren land, and 6.51% in water bodies, respectively. Between 2020 and 2040, the urban area is predicted to increase by 23.15%, while vegetation, barren land, and water bodies are expected to decrease by 28.05%, 1.8%, and 12.31%, respectively. Also, the atmospheric pollutants have been increased including NO2 (1.60%), SO2 (1.02%), CO2 (0.71%), CO (1.56%), O3 (0.15%), and CH4 (0.20%), respectively. And it is projected that by 2040, the average annual atmospheric concentration of NO2, SO2, CO2, CO, O3, and CH4 will be increased by 28.80%, 18.36%, 12.78%, 28.08%, 2.70%, and 3.60%, respectively. In addition, it was also observed that the majority of the city's urban area expansion was found in the city's eastern and southern regions. Therefore, government should focus on natural resource conservation especially vegetation cover to reduce air pollutants concentration and the LULC effect.

Crop-land suitability analysis using geographic information system and remote sensing

AbstractFood, water, and energy scarcity threaten India's future, and they must be addressed first. To meet the country's ever-increasing population needs, agricultural productivity must be expanded. For the crop-land suitability, we have studied an area of about 6,539 km2 in Vizianagaram district. The majority of the land is used for paddy agriculture (Kharif). The crop-land suitability has been evaluated based on the different parameters identified in that study area. “Remote sensing (RS)” and “geographic information system (GIS)” were combined for the crop-land suitability using nine parameters. The slope, elevation, rainfall, soil texture, lithology, groundwater, land use–land cover (LULC), TWI, and land surface temperature are the primary criteria used to determine the crop-land suitability in the Vizianagaram district (AP). Thematic maps were created using Landsat 8 images and SRTM DEM images from USGS Earth Explorer. Based on these maps and the influence of these parameters, we may assign weights to the parameters and then rank them, the Analytic Hierarchy Process (AHP) allowing us to identify which area is more suitable for good crop productivity and which is not. In this study, the soils are divided into four categories: low suitability, moderate suitability, high suitability, and extremely high suitability. The suitability index is found to be in the range of 0–55.2%, which indicates the lack of outstanding agricultural lands in the sudy region.

Integration of Topex Satellite Gravity and DEM SRTM Imagery for Subsurface Structure Identification at Tiris Geothermal Area, Lamongan Volcano Complex, Probolinggo, East Java

Tiris area is one area that has geothermal prospects. Tiris area is located in the Tiris sub-district, Probolinggo regency, East Java. This study aims to identify the subsurface structure of the Tiris geothermal area using Topex gravity and DEM SRTM imagery data. The Topex gravity data obtained is still in Free Air Anomali (FAA) form, so it is necessary to make Bouguer and terrain corrections to get a complete Bouguer anomaly. A residual Bouguer anomaly is needed to study shallow subsurface structures. This anomaly is obtained by separating the residual and regional anomalies from the complete Bouguer anomaly using the moving average method. The study area has residual Bouguer anomaly values ranging from -0.5 to 2.9 mGal. The low residual Bouguer anomalies are seen in the body of the Lamongan volcano (LV) and the Tiris geothermal area (TGA). The study of subsurface structures is focused on the LV and the TGA through Forward 2D modeling. The A-A' cross-section illustrates the subsurface of LV, while the B-B' cross-section illustrates the subsurface of TGA. Cross-section A-A' modeling results show that the source of the low anomaly is in the second layer D2 = 1.24 g/ at a depth of 4.0 to 0.6 km below sea level and is suspected to be magma because it has a low density. Cross-section B-B' modeling obtained a low anomaly source at the second layer D2 = 2.03 g/ at a depth of 4.0 to 2.8 km below sea level and is suspected to be intrusive rock. The lineament display on the 2D of DEM SRTM map for the 45o and 135o azimuths shows that the TGA region has a high lineament density, and the orientation of the surface geological structure in the study area is dominant in a northwest-southeast direction.

Glacier elevation change during two periods in the Lenglongling mountains derived from bistatic SAR interferometry

Bistatic SAR interferometry is a feasible technique of deriving glacier height changes. Early studies on glacier change in the Qilian Mountains showed that the glacier area declined, terminus retreated, and flow velocity slowed down. In this study, five pairs of X-band bistatic TerraSAR-X/TamDEM-X images from 2014 to 2019 and an iterative D-InSAR method respect to SRTM DEM of 2000 were employed, which improved the reliability and accuracy of glacier height changing observations. From 2000 to 2014, the height of glaciers in the Lenglonglin Mountains decreased by (–0.64 ± 0.06) m year−1. Glaciers in the Gangshika peak and its surrounding region thinned at the rate of (–0.68 ± 0.09) m year−1 from 2014 to 2019. The values observed using remote sensing was in accordance with RTK–GPS measured values. Stable precipitation and increasing temperature since 1990 could be responsible for the quick glacier mass loss.

Different characteristics of two surges in Weigeledangxiong Glacier, northeastern Tibetan Plateau

Glacier surge is a special form of glacier displacement caused by the instability of the glacial dynamic system. It is a quasi-periodic oscillation behavior, which affects the estimation of the overall change of glaciers in the region and potentially threatens the infrastructure and human life in the downstream regions. Most glaciers experience a mass loss with rising air temperatures in recent decades, but little attention has been paid to the influence of climate change on glacial surges. This study identified two surges, triggered in 1992 and 2015 in Weigeledangxiong Glacier, Ányêmaqên Mountains, northeastern Tibetan Plateau, using multi-source remote sensing data (Landsat images, Sentinel-2 images, topographic map, shuttle radar topography mission digital elevation model [SRTM DEM], and the elevation change database). The 1992 surge accelerated abruptly with the maximum velocity of 350 ± 9 m a−1, and a large volume of ice transported downward, causing a sudden advance of 392 ± 42 m from 1992 to 1994, and clear thickening of the ice tongue. The recent surge is still in the active phase, exhibiting a gentler process of slower advance speed and lower peak velocity, as well as a smaller expansion zone than the previous one. These phenomena may be associated with the reduced glacier basal resistance and energy caused by rising temperatures in recent decades. Higher temperatures may cause the discharge of subglacial water through a more developed drainage system, leading to a longer active phase duration. Similar phenomena may exist widely in the Tibetan Plateau and its surrounding areas. Meanwhile, the frontal position of Weigeledangxiong Glacier advancing in the recent surge is not expected to threaten roads near the ice tongue.

DEM Study on Hydrological Response in Makkah City, Saudi Arabia

The changes in catchments can be analyzed through the generation of DEM, which is important as input data in hydrologic modeling. This study aims to analyze the effect of anthropogenic activities on hydrological studies based on DEM comparison and GIUH hydrographs. The four DEM datasets (SRTM, ALOS, Copernicus, Sentinel-1) were compared to the topographic map of Makkah City and GPS data in order to assess the quality of the DEM elevation. The GIS Arc Hydro toolbox was used to extract morphometric and Horton–Strahler ratio characteristics to generate a GIUH hydrograph of the catchments of Wadi Nouman and Wadi Ibrahim inside Makkah City. Based on the DEM comparison, Copernicus and SRTM have the highest accuracy, with R2 = 0.9788 and 0.9765, and the lowest RMSE, 3.89 m and 4.23 m, respectively. ALOS and Sentinel-1 have the lowest R2, 0.9687 and 0.9028, and the highest RMSE, 4.27 m and 6.31 m, respectively. GIUH Copernicus DEM on Wadi Nouman has a higher qp and lower tp (0.21 1/h and 2.66 h) than SRTM (0.20 1/h and 2.75 h), respectively. On Wadi Ibrahim, the SRTM has a greater qp and lower tp than Copernicus due to the wadi having two shapes. Based on the anthropogenic effect, the stream network in the mountain area is quite similar for SRTM and Copernicus due to the dominant influence of the mountainous relief and relatively inconsequential influence of anthropogenic activities and DEM noise. In the urban area, the variation of the stream network is high due to differing DEM noise and significant anthropogenic activities such as urban redevelopment. The Copernicus DEM has the best performance of the others, with high accuracy, less RMSE, and stream flow direction following the recent condition.

Responses of sustainable development indicators to human activities and climate change in ecologically fragile areas of impoverished counties in China

AbstractBased on Landsat MSS/TM/OLI (1975–2018), MODIS NDVI (2000–2015), and DMSP/OLS (1992–2013) multitemporal remote sensing data, as well as annual average precipitation and temperature data from meteorological stations (1980–2018) and SRTM DEM data, this study extracted information on climate change, human activity, and eco‐environmental factors in the Zijin Mountain (ZJM) area of Shanxi Province, and then analyzed the spatiotemporal changes in ecological structure, landscape structure, and climate response. The results revealed that in the ZJM area some sustainable development indicators associated with human activity, ecology, climate, and landscape geography have changed. Specifically, over the 44‐year period from 1975 to 2018, the urban and rural construction land area increased by 4.29 times; in the 22‐year period from 1992 to 2013, the regional light index has increased by 26.9 times; in the past 32 years, that is, from 1987 to 2018, the average vegetation index of the entire basin increased by about 54.7%, the vegetation cover exhibited a gradual positive trend, and the land use types in the ZJM area were mainly converted from bare land to grassland or forest. Compared with 1987, the area of bare land decreased 32.5% by 2018, whereas the forest and grassland areas increased by 85.7% and 40.7%, respectively. In addition, over the 44‐year period from 1975 to 2018, the road density increased by 8.57 times. The area affected by human activities, comprised roads, urban and rural construction land, and their buffer areas, accounted for 64.87% of the total study area. As a result, the fragmentation index of landscape patches has increased by 6.81 times since 1987. We conclude that in the ZJM area, the ecosystem type has transformed from an indigenous mountainous grassland to mountainous arid agriculture, and from an isolated habitat to a fragmented habitat.

Morphometric Analysis for Sustainable Development of Watersheds Using Multisensor Satellite Data: A Case Study from Shiriya River Basin

Objectives: The goal of this research is to calculate, compare and analyze the morphometric drainage parameters of Shiriya River Basin (SRB), Kasaragod, Kerala, using DEMs acquired by different satellite sensors and topographic data (SOI toposheet) for sustainable watershed development. Methods: The Arc GIS 10.3.1 software was used to measure linear, relief, and areal aspects of morphometry. The input data used for deriving the drainage network of the SRB were SOI toposheet (1:50000 scale), SRTM DEM (90m), Cartosat, ASTER and ALOS PALSAR DEM of resolution 30 m each. The relative error percentage of basic parameters derived from DEMs concerning the toposheet is calculated. A groundwater potential map is prepared using weighted overlay analysis of thematic layers (geology, geomorphology, slope, drainage density, lineament density and depth to water level). Findings: Present study revealed that the parameters derived from SRTM DEM data are more similar to those derived from toposheet. Most of the parameters obtained from each input DEM are more or less equal, and the minor variation may be due to varied data collecting and processing methodologies used for DEM creation. SRB is characterized by 6 th order stream with a dendritic drainage pattern, which is elongated and less susceptible to soil erosion. SRB has an intermediate texture and is in a mature stage of geomorphic evolution. Inference from the ground water potential zonation map indicates that major portion of the SRB has moderate ground water potential. Application/improvement: The study would be a valuable source for identifying water recharge sites, basin modelling, and groundwater prospect mapping. Novelty:This research has attempted to compare and analyze the morphometric parameters’ results from multisensor satellite data for sustainable watershed management. An attempt has been done to understand the relation between morphometric parameters and ground water potential of SRB using Groundwater potential zonation map. Keywords: Drainage morphometry; Shiriya River basin; GIS; Karnataka; Kerala

Gravity and Remote Sensing Methods as a Solution in Identifying Geothermal Reservoirs on Volcanoes

Tiris Village, Lamongan Volcano complex is an area that has geothermal potential. The existence of geothermal potential in this study was identified using the gravity method combined with remote sensing methods. Remote sensing method is used to obtain a map of the distribution pattern of the soil surface temperature and a map of the continuity of the straightness and fault structure. The data used in the remote sensing method is secondary data in the form of Landsat-8 imagery and DEM SRTM 1 arc. Processing of the soil surface temperature map was obtained by using thermal infrared processing NDVI. The form of continuity of the straightness or fault structure is obtained by processing Landsat-8 multispectral image data in the form of 567 band composite and DEM SRTM data form of 3D topographic modeling. The data from the composite band 567 was reviewed visually using a 3D topographic model. The gravity method is the main method in this research because it is used to obtain the residual Bouguer anomaly distribution. The residual Bouguer anomaly data was obtained by using the moving average through spectrum analysis. The residual Bouguer anomaly distribution can be used for qualitative interpretation of the distribution of the earth’s gravitational acceleration at the measurement location. The quantitative interpretation of the gravity data is based on the surface density estimation graph using the Parasnis method and 3D inversion modeling, using residual Bouguer anomaly data. The results of 3D inversion modeling provide four types of subsurface rock layers based on the density distribution value, namely, the first rock layer ρ 1 = 2.52 – 2.67 g/cm3, the second rock layer ρ 2 = 2.72 – 2.77 g/cm3, the third rock layer ρ 3 = 2.80 – 2.83 g/cm3, and the fourth rock layer ρ 4 = 2.84 – 2.86 g/cm3. The third rock layer is assumed to be reservoir rock and is estimated to be at a depth of 500–800 meters below the measurement point. The fourth rock layer is assumed to be intrusive igneous rock, which penetrates dominantly in the middle of the measurement location, which is oriented southwest, south to north, which is indicated by the high anomaly continuity on the regional Bouguer anomaly map. Based on the results of 3D inversion modeling and spectrum analysis of the average depth of the regional Bouguer anomaly, it is found that the intrusive rock layer is thought to be at a depth of 350 meters below the measurement point.

Application of geographical information system-based analytical hierarchy process modeling for flood susceptibility mapping of Krishna District in Andhra Pradesh.

Flooding is one of the most catastrophic natural disasters in terms of provoking socio-economic losses. The current study is to foster a flood susceptibility map of Krishna District in Andhra Pradesh (AP) through integrating remote sensing data, geographical information system (GIS), and the analytical hierarchy process (AHP). Eleven factors, including elevation, slope, aspect, land use/land cover (LULC), drainage density, topographic wetness index, stream power index, lithology, soil, precipitation, and distance from the streams, are considered for identifying and evaluating the spatial distribution of critical flood-susceptible regions. Thematic maps of different factors were derived in GIS using remote sensing data obtained from Sentinel-2A (satellite sensor), shuttle radar topography mission digital elevation model (SRTM DEM v3), and other scientific data products. An analytical hierarchy process is a mathematical approach for decision support, primarily based on the weight and rank of different causative factors. AHP technique is implemented for flood hazard modeling and ascertaining the Flood Hazard Index (FHI) to produce a flood susceptibility map. Different thematic maps weighed with the AHP framework are combined using overlay analysis to produce the flood susceptibility map of the study region. The outcomes of the study demonstrate the potential of GIS and AHP in providing a premise to recognize the vulnerable areas that are susceptible to flood. According to the findings, the Flood Hazard Index is 42% and the study region is classified into very high, high, moderate, low, and very low susceptible, respectively. Following that, historical flood data was used to validate the accuracy of the generated flood susceptibility map. This shows that a maximum of 90% of the data points are within floodplain.

Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China

Accurate estimation of forest height is crucial for the estimation of forest aboveground biomass and monitoring of forest resources. Remote sensing technology makes it achievable to produce high-resolution forest height maps in large geographical areas. In this study, we produced a 25 m spatial resolution wall-to-wall forest height map in Baoding city, north China. We evaluated the effects of three factors on forest height estimation utilizing four types of remote sensing data (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and SRTM DEM) with the National Forest Resources Continuous Inventory (NFCI) data, three feature selection methods (stepwise regression analysis (SR), recursive feature elimination (RFE), and Boruta), and six machine learning algorithms (k-nearest neighbor (k-NN), support vector machine regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). ANOVA was adopted to quantify the effects of three factors, including data source, feature selection method, and modeling algorithm, on forest height estimation. The results showed that all three factors had a significant influence. The combination of multiple sensor data improved the estimation accuracy. Boruta’s overall performance was better than SR and RFE, and XGBoost outperformed the other five machine learning algorithms. The variables selected based on Boruta, including Sentinel-1, Sentinel-2, and topography metrics, combined with the XGBoost algorithm, provided the optimal model (R2 = 0.67, RMSE = 2.2 m). Then, we applied the best model to create the forest height map. There were several discrepancies between the generated forest height map and the existing map product, and the values with large differences between the two maps were mostly distributed in the steep areas with high slope values. Overall, we proposed a methodological framework for quantifying the importance of data source, feature selection method, and machine learning algorithm in forest height estimation, and it was proved to be effective in estimating forest height by using freely accessible multi-source data, advanced feature selection method, and machine learning algorithm.

Bayesian networks and intelligence technology applied to climate change: An application of fuzzy logic based simulation in avalanche simulation risk assessment using GIS in a Western Himalayan region

Avalanches are snowfalls that cascade down a mountainside, although rockfalls and debris flows can also occur. The study identifies the snow meteorological data for the avalanche risk forecast through various associated factors and models a tool for forecasting avalanches according to the reference database. The terrain factors such as aspect, slope, and altitude have been studied using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) at 90 m resolution. Snow meteorological observed data 2540 records were obtained for Snow and Avalanche Study Establishment (SASE) Nov 2004 to April 2009 for five years data. In the present study, nine parameters were used for Avalanche forecast in the Western Himalaya. The Bayesian classification algorithm may improve the feedback system of prediction than the existing one. Fuzzy logic decides to improve the system to predict the level of danger by studying the influence of parameters. Hazard mitigation strategies applying the Bayesian classification algorithm and Fuzzy logic were combined with Integrated GIS-based methods to determine the risk zones better to identify snow avalanches in three-dimensional terrain.

LAND SURFACE CHANGES DUE TO BUKITTINGGI CITY EARTHQUAKE USING THE 2019-2020 DInSAR METHOD

Bukittinggi city is in the sink segmentation "semangko" fault line, which causes Bukittinggi City to frequently experience earthquakes. From 2019 to 2020, there have been at least 9 earthquakes above 5 on the Richter scale. DInSAR is a radar imaging method that utilizes phase, amplitude, and wavelength in its processing to obtain topography and land surface changes. This study has three aims. This research shows that the earthquake from 2019 to 2020 uses 18 pairs of sentinel-1 image scans recorded in 2019-2020 and uses DEM SRTM 30, namely the earthquake that happened on February 2, 2019, in the sub-district of Guguak Panjang with a range of 1 – 45 mm. The smallest increase occurred in the sub-district of Guguak Panjang and Mandiangin Koto Selayan, with a length of 1 mm on November 11, 2019. The most significant reduction happened in the sub-district of Aur Birugo with -22 mm. In contrast, a less substantial reduction occurred in the sub-district of Guguak Panjang with -5 mm on October 8, 2019. The Modified Mercalli intensity scale for earthquakes in 2019-2020 uses the measure of Donovan's parameters demonstrated by numbers 2, 3, and 4. Number 2 indicates that resting people feel the earthquake's strength, especially hanging, shaking, and vibrating on the second floor. Number 3 represents the vibrations of passing trucks, windows, doors, and glassware clashing and making noises

Multitemporal Glacier Mass Balance and Area Changes in the Puruogangri Ice Field during 1975–2021 Based on Multisource Satellite Observations

Due to climate warming, the glaciers of the Tibetan Plateau have experienced rapid mass loss and the patterns of glacier changes have exhibited high spatiotemporal heterogeneity, especially in interior areas. As the largest ice field within the Tibetan Plateau, the Puruogangri Ice Field has attracted a lot of attention from the scientific community. However, relevant studies that are based on satellite data have mainly focused on a few periods from 2000–2016. Long-term and multiperiod observations remain to be conducted. To this end, we estimated the changes in the glacier area and mass balance of the Puruogangri Ice Field over five subperiods between 1975 and 2021, based on multisource remote sensing data. Specifically, we employed KH-9 and Landsat images to estimate the area change from 1975 to 2021 using the band ratio method. Subsequently, based on KH-9 DEM, SRTM DEM, Copernicus DEM and ZY-3 DEM data, we evaluated the glacier elevation changes and mass balance over five subperiods during 1975–2021. The results showed that the total glacier area decreased from 427.44 ± 12.43 km2 to 387.87 ± 11.02 km2 from 1975 to 2021, with a decrease rate of 0.86 km2 a−1. The rate of area change at a decade timescale was −0.74 km2 a−1 (2000–2012) and −1.00 km2 a−1 (2012–2021). Furthermore, the rates at a multiyear timescale were −1.23 km2 a−1, −1.83 km2 a−1 and −0.42 km2 a−1 for 2012–2015, 2015–2017 and 2017–2021, respectively. In terms of the glacier mass balance, the region-wide results at a two-decade timescale were −0.23 ± 0.02 m w.e. a−1 for 1975–2000 and −0.29 ± 0.02 m w.e. a−1 for 2000–2021, indicating a sustained and relatively stable mass loss over the past nearly five decades. After 2000, the loss rate at a decade timescale was −0.04 ± 0.01 m w.e. a−1 for 2000–2012 and −0.17 ± 0.01 m w.e. a−1 for 2012–2021, indicating an increasing loss rate over recent decades. It was further found that the mass loss rate was −0.12 ± 0.02 m w.e. a−1 for 2012–2015, −0.03 ± 0.01 m w.e. a−1 for 2015–2017 and −0.40 ± 0.03 m w.e. a−1 for 2017–2021. These results indicated that a significant portion of the glacier mass loss mainly occurred after 2017. According to our analysis of the meteorological measurements in nearby regions, the trends of precipitation and the average annual air temperature both increased. Combining these findings with the results of the glacier changes implied that the glacier changes seemed to be more sensitive to temperature increase in this region. Overall, our results improved our understanding of the status of glacier changes and their reaction to climate change in the central Tibetan Plateau.

Estimation of the Madeira floodplain dynamics from 2008 to 2018

The Madeira is one of the major tributaries of the Amazon River and is characterized by a large alluvial floodplain throughout the stream continuum. This study aims to better assess the hydrological functioning of the Madeira Basin over its alluvial floodplains at both local and global scales. We used the semi-distributed hydrological Soil and Water Assessment Tool (SWAT) model to simulate water discharge at a daily time step and water resources for each hydrological compartment. A new hydraulic module for water routing was implemented in the SWAT model considering the floodplain either as a simple reservoir or as a continuum where the water can flow along with the floodplain network. Both water surface estimated by L-band passive microwaves (SWAF data) and digital elevation model—shuttle radar topography mission (DEM–SRTM data) were used to delineate the floodplain, as inputs for the model. On the global scale, the amount of water stored in the Madeira floodplain is between 810 ± 230 km3 per year when the floodplains are delimited with SWAF and 1,300 ± 350 km3 per year with the DEM floodplain delineation between 2008 and 2018. Spatial altimetry (Jason 2-3) data were also applied to alluvial areas to validate the water height dynamic in floodplains at a local scale. Results show that more than 60% of the alluvial validation points display a correlation above 0.40 ± 0.02 regardless of the floodplain delineation. This study permits us to better characterize the spatio-temporal storage dynamics of the Madeira floodplains at both local and global scales, and it underlines the importance of a precise floodplain delineation, before computing biogeochemical fluxes and sediment yield.