Remote Sensing Satellite Data Research Articles

Geo environmental green growth towards sustainable development in semi-arid regions using physicochemical and geospatial approaches.

The process of determining whether a specific portion of land is suitable for a specific purpose is known as land suitability analysis (LSA). In order to promote sustainable development in semi-arid regions, the objective of this study is to analyse, evaluate, and identify the land for green growth based on topography, climate, and soil characteristics. Twelve thematic maps are prepared by using remote sensing satellite data. The Landsat 8 OLI/TIRS is used for the preparation of the thematic maps like land use land cover (LULC), normalized difference vegetation index (NDVI), top soil grain size index (TGSI), and geomorphology (GM), and DEM data is used for the preparation slope, and drainage density (DD). The collateral data is used to prepare geology and soil thematic maps. From the field work, we have collected soil samples for the compulsory physicochemical parameters such as soil EC and soil N-P-K which were taken into consideration and prepared thematic maps. The analytical hierarchy process (AHP) was used to generate the LSA of the research region, by assigning the appropriate weights to each criterion and sub-criterion for the thematic maps. Geographic information systems (GIS) and the multicriteria decision-making (MCDM) approach were used in the study's methodology. The LSA of the study area has been categories in to four types, i.e., highly suitable, moderately suitable, marginally suitable, and not suitable. The results revealed that 421.31 sq.km (40.09%) is not suitable for agriculture green growth in the study region, whereas 89.58 sq.km (8.52%) is moderately suitable, 267.66 sq.km (25.47%) is marginally suitable, and 266.54 sq.km (25.36%) is highly suitable. Accuracy assessment has validated the LSA map's accuracy (AA). The AA of LSA is 84.22%, which demonstrates a strong connection with the actual data. The research's results could be helpful in locating productive agricultural areas in various parts of the world. The decision-making AHP tool paired with GIS provides a novel method.

The Application Effect of Remote Sensing Technology in Hydrogeological Investigation under Big Data Environment

The hydrogeological investigation is a work carried out by comprehensive utilization of various exploration methods to identify hydrogeological conditions in the target area, and develop and utilize groundwater resources. There are great differences in hydrogeological conditions in different regions. Hence, it is necessary to take exploration technology according to local conditions to master hydrogeological information as much as possible. Among them, the remote sensing (RS) technology can reflect the ground surveying and mapping results with high efficiency and precision through the analysis of satellite or aerial photographs, which is a commonly used method in the current hydrogeological investigation. According to satellite RS data, this work evaluates the distribution of groundwater levels in the study area and explores the geological and hydrogeological conditions of the groundwater system in the affected area. Firstly, the human-computer interactive interpretation method is used to analyze the topography and geomorphology conditions. Secondly, the spectral characteristic curve analysis method is used to extract the spectral characteristics of regional stratum lithology, and analyze and determine the lithology composition and structure of the aquifer. Thirdly, the single-band and multiband models of soil moisture RS estimation of groundwater level are implemented. Finally, the measured data are employed to verify and analyze the estimated value of the model. The results are in line with the actual value, and good results have been achieved.

Flash flooding hazard assessment, modeling, and management in the coastal zone of Ras Ghareb City, Gulf of Suez, Egypt

The assessment of flash flood hazards in the coastal zone of Ras Ghareb City (RGC), Red Sea, Egypt, was accomplished through the use of GIS tools and the spatial multi-criteria approach. The presented work aims to assess, integrate, and generate potential flash flood hazard maps. The analytic hierarchy process was utilized to calculate weights of hazard and vulnerability of flash flood controlling factors and their parameters such as topographical, geological, and hydrological factors. In addition, the risk degree of each specific basin and its sub-basins is estimated by combining standardized parameter values. Despite its location in an arid region, the basin may receive a large amount of rainwater, which can cause flash floods at the basin's outlet. Recently, flash flooding took place in the coastal region of RGC in October 2016 and September 2020, resulting in the loss of many human lives and catastrophic effects on local infrastructure and surrounding environments. According to reports from Hurghada, Red Sea Governorate, the RGC experienced exceptional flooding that exceeded 120 million cubic meters as a result of 51 mm of rainfall. Based on the remote sensing satellite data such as Aster Digital Elevation Model (GDEM) and the GIS tools, the basin and sub-basin drainage patterns were delineated and compared with reference topographical map sheets of a scale of 1: 50,000. The sub-basins have been isolated and morphometrically studied to determine vulnerability to flash flooding. In addition, the flash flood threat model was developed using geoprocessing tools provided by ArcGIS software to incorporate all contributing factors spatially. The flash flood risk in the Ras Ghareb (RG) basins and their sub-basins has been identified and classified into three classes (high, medium, and low hazard degree). High and moderate flood-risk basins require comprehensive studies to introduce measures to protect certain areas from flood danger. Finally, the GIS and the spatial multi-criteria analyses were effective ways to carry out a flash flood management system in order to support the decision-makers by recommendations to conserve and mitigate the possible flash flood hazards in the study areas.

Spatial information on the rate of subsidence in North Coastal Area of Java and the estimation of inundation in 2031

North Coastal Java Java is a fast-growing area because many big cities such as Jakarta, Cirebon, Pekalongan, Semarang, and Surabaya exist. This area is experiencing several environmental problems, the biggest of which is the problem of the rate of land subsidence. This problem is due to the geological structure. The Java Coast is located on soft soil, and some groundwater utilization activities are less controlled. This study aimed at (1) Obtaining information on the rate of land subsidence in 5 major cities on the northern coast of Java (Jakarta, Cirebon, Pekalongan, Semarang, and Surabaya) using remote sensing satellite data; (2) Analysing the accuracy of land subsidence using field survey data; (3) Estimating the potential of the region to be below 0 masl in 2031; and (4) Analysing the causes of land subsidence. The method used was Difference Interferometry Synthetic Aperture Radar (DInSAR) to determine the rate of land subsidence and simulate the rate of land subsidence in the future. The results showed that the rate of land subsidence in Jakarta (8/year), Cirebon (2 cm/year), Pekalongan (11.2 cm/year), Semarang (6 cm/year), and Surabaya (4.3 cm/year). The results of the research show that the error rate of the surface subsidence rate with validation results in the City of Jakarta is ± 0.01 m. with the value of Nash Sutcliffe Efficiency (NSE) =. 0.787 with a good category. Simulation results in 2031 from 3 big cities show that the area under 0 masl will increase by 2710 ha (Jakarta), 3565 ha (Pekalongan), and 846 ha (Semarang) from 2021. It seems that Pekalongan needs special attention to deal with the rate of land subsidence compared to other cities.

Reconstruction of Monthly Surface Nutrient Concentrations in the Yellow and Bohai Seas from 2003–2019 Using Machine Learning

Monitoring the spatiotemporal variability of nutrient concentrations in shelf seas is important for understanding marine primary productivity and ecological problems. However, long time-series and high spatial-resolution nutrient concentration data are difficult to obtain using only on ship-based measurements. In this study, we developed a machine-learning approach to reconstruct monthly sea-surface dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved silicate (DSi) concentrations in the Yellow and Bohai seas from 2003–2019. A large amount of in situ measured data were first used to train the machine-learning model and derive a reliable model with input of environmental data (including sea-surface temperature, salinity, chlorophyll-a, and Kd490) and output of DIN, DIP, and DSi concentrations. Then, longitudinal (2003–2019) monthly satellite remote-sensing environmental data were input into the model to reconstruct the surface nutrient concentrations. The results showed that the nutrient concentrations in nearshore (water depth < 40 m) and offshore (water depth > 40 m) waters had opposite seasonal variabilities; the highest (lowest) in summer in nearshore (offshore) waters and the lowest (highest) in winter in nearshore (offshore) waters. However, the DIN:DIP and DIN:DSi in most regions were consistently higher in spring and summer than in autumn and winter, and generally exceeded the Redfield ratio. From 2003–2019, DIN showed an increasing trend in nearshore waters (average 0.14 μmol/L/y), while DSi showed a slight increasing trend in the Changjiang River Estuary (0.06 μmol/L/y) but a decreasing trend in the Yellow River Estuary (–0.03 μmol/L/y), and DIP exhibited no significant trend. Furthermore, surface nutrient concentrations were sensitive to changes in sea-surface temperature and salinity, with distinct responses between nearshore and offshore waters. We believe that our novel machine learning method can be applied to other shelf seas based on sufficient observational data to reconstruct a long time-series and high spatial resolution sea-surface nutrient concentrations.

Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland

Abstract. The Carnegie–Ames–Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scales. However, the CASA is still driven by multisource data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data can conveniently provide real-time regional information and may replace ground observation data to drive the CASA model. We attempted to improve the CASA model in this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) RS products, the GlobeLand30 RS product, and the digital elevation model data derived from radar RS. We applied it to simulate the NPP of alpine grasslands in the Qinghai Lake basin, which is located in the northeastern Qinghai–Tibetan Plateau, China. The accuracy of the RS-data-driven CASA, with a mean absolute percent error (MAPE) of 22.14 % and root mean square error (RMSE) of 26.36 g C m−2 per month, was higher than that of the multisource-data-driven CASA, with a MAPE of 44.80 % and RMSE of 57.43 g C m−2 per month. The NPP simulated by the RS-data-driven CASA in July 2020 shows an average value of 108.01 ± 26.31 g C m−2 per month, which is similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapid simulation of grassland NPP to satisfy the requirements of accounting carbon stocks and other applications.

A deep learning multi-layer perceptron and remote sensing approach for soil health based crop yield estimation

• Soil health parameter estimation using simple regression techniques. • Soil parameter-based wheat crop yield estimation. • Cost-effective study using freely available satellite data. • Different algorithms compared. In recent years, Deep Learning Multi-Layer Perceptron (DLMLP) neural networks have shown remarkable success in addressing crop yield forecast related problems. The methodologies used so far for crop yield forecast with remotely sensed data were focused upon vegetation indices generated from optical data. The prediction of crop yield in an accurate manner by developing robust machine learning models based on soil health parameters is crucial since it helps keep a track of soil health as well as its impact on overall yield. This study aims to utilize remotely sensed Microwave satellite data from Sentinel-1 and optical data from Sentinel-2, and field data to estimate three important soil health parameters- Soil Moisture, Soil Salinity, and Soil Organic Carbon (SOC). The study has been carried out in the Rupnagar district of Punjab in India. The estimated soil health parameters, SAR backscatter, and optical remote sensing satellite data parameters were utilized to estimate wheat crop yield. The soil health based DLMLP model performed best in crop yield estimation and gave R 2 values of 0.723 and 0.684 in the training and testing phases, respectively, and Mean Absolute Error (MAE) of 0.98 and Root Mean Square Error (RMSE) value of 1.24 for the 2019–20 season. The DLMLP test R 2 was 42.2% more than the Ordinary Least Squares Regressor (OLS), while the MAE and RMSE were 37.97% and 38.61% less than the OLS regressor for wheat crop yield estimation. The soil health-based DLMLP model gave satisfactory yield estimation accuracy in the absence of validation of soil health parameter values for the preceding years-2015–16 till 2018–19 wheat seasons. This study's novel feature is that it estimates soil health parameters for the early stages of wheat crop growth when soil lies mostly exposed and utilises them for crop yield prediction.

High-Coverage Reconstruction of XCO2 Using Multisource Satellite Remote Sensing Data in Beijing-Tianjin-Hebei Region.

The extreme climate caused by global warming has had a great impact on the earth’s ecology. As the main greenhouse gas, atmospheric CO2 concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO2 concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO2 and various factors affecting the spatial distribution of CO2, this study used multisource satellite-based data and a random forest model to reconstruct the daily CO2 column concentration (XCO2) with full spatial coverage in the Beijing–Tianjin–Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R2) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO2 concentrations from 2015 to 2019 in the Beijing–Tianjin–Hebei region was conducted using the established model. The study of the spatial distribution of XCO2 concentration in the Beijing–Tianjin–Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO2 map has the potential to monitor regional carbon emissions and evaluate emission reduction.

Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data.

The low-carbon city has become an important global urban development-oriented goal. One important aspect of urban space is low-carbon urban planning, which has a vital role in urban carbon emissions. Which types of urban form and function allocations are conducive to reducing carbon emissions is therefore a key issue. In this study, the Futian and Luohu Districts of Shenzhen, Guangdong Province, China, are taken as an example to investigate this issue. Firstly, a “head/tail” breaks method based on the third fractal theory is adopted to obtain the minimum evaluation parcel of urban space. Then, the Landscape Shape Index (LSI), Fragmentation Index (C), Shannon’s Diversity Index (SHDI), and Density of Public Facilities (Den) are used to evaluate the form and function allocation of each parcel. In addition, the CO2 concentration distribution in this study area is acquired from remote sensing satellite data. Finally, the relationships between urban form, function allocation, and CO2 concentration are obtained. The results show that the lower the urban form index or the higher the urban function index, the less the CO2 concentration. To verify this conclusion, three experiments are designed and carried out. In experiment A, the CO2 concentration of the tested area is reduced by 14.31% by decreasing the LSI and C by 6.1% and 9.4%, respectively. In experiment B, the CO2 concentration is reduced by 15.15% by increasing the SHDI and Den by 16.3% and 12.1%, respectively. In experiment C, the CO2 concentration is reduced by 27.72% when the urban form and function are adjusted in the same was as in experiments A and B.

Study on the Spatial Interaction between Urban Economic and Ecological Environment—A Case Study of Wuhan City

In order to study the interactive relationship between urban economic and ecological environment, taking Wuhan as an example, Landsat and MODIS remote sensing satellite data and social and economic data were fused with multisource data, and multidimensional indicators were selected to construct the comprehensive evaluation index system of urban economic and ecological environment. The weights were determined by combining subjective and objective methods. Then, the decoupling elasticity coefficient method and spatial autocorrelation model were used to evaluate the dynamic relationship and spatial relationship between economic development and ecological environment in Wuhan from 2014 to 2020. The results showed that there was an interaction between the urban economic and the ecological environment in Wuhan. The ecological level index had a spatial effect, the adjustment of industrial structure had a positive effect on the improvement of the ecological level, and the improvement of the ecological level was also helpful to promote economic development. The typical districts of Huangpi District, Xinzhou District, Jiangxia District, Hannan District, Caidian District, and Hongshan District had superior location and ecological advantages, as well as high development potential. Lastly, on the basis of the empirical analysis results, policy suggestions are made from four aspects: regional differentiated construction, green development, energy consumption, and wetland construction.

MONITORING VEGETATION DENSITY USING SPECTRAL VEGETATION INDICES: A CASE STUDY OF MALAM JABBA REGION, DISTRICT SWAT, PAKISTAN

Abstract. The limited forest resources with a higher deforestation rate per annum, Pakistan ranks the second highest in Asia. FAO reported that the annual forest cover change rate during 1990–2000 was −1.8% and increased to −2.2 % between 2000–2010. Most of Pakistan's total forest resources, dominantly natural forest, are situated in the Northern regions. Stepping into the corridor of the 21st century, the Spatio-temporal analysis has been evolved using Satellite Remote Sensing data aided with Geographic Information System) GIS) platforms. The study is carried out over the mountainous vegetation land of Malam Jabba, district Swat, Khyber Pakhtunkhwa, Pakistan. Due to varying topography and the region being part of the agro-forestry zone, drastic changes were observed in vegetation and built-up areas. The vegetation cover has been identified and classified based on elevation throughout the area. This study has provided essential insights into vegetation cover change over a period of four decades. Vegetation cover is classified into high to very high, medium, and low to very low. The Landsat and the SRTM DEM satellite imageries were exported to the ERDAS software for pre-and post-processing, and for further analysis ArcGIS 10.5 was used, where the vegetation density change for each period was computed from the pixels by using vegetation indices like VCI, NDVI, and SAVI. The results show a significant decline from 1980 to 2010 in vegetation density in the Northwestern direction; however, an increasing trend can be seen in 2020 due to awareness and the Government’s Billion Tree Tsunami initiative. Such studies can significantly benefit researchers and decision-makers interested in satellite remote sensing for forest and other vegetation cover monitoring and management at a regional scale.

Analysis of Space-Borne GPS Data Quality and Evaluation of Precise Orbit Determination for COSMIC-2 Mission Based on Reduced Dynamic Method

COSMIC-2 is a remote sensing satellite mission that mainly provides scientific data for weather forecasting, ionosphere, and climate research. High precise orbit is the basis for the application of remote sensing satellite data. In order to realize the precise orbit determination (POD) of COSMIC-2, we have assessed the quality of space-borne GPS observation in detail, including the utilization of GPS observations, cycle slip ratio (o/slps), multipath error, single-noise ratio (SNR) and ionospheric delay rate (IOD) of the data, realized the POD of COSMIC-2 with the reduced dynamic (RD) method, and evaluated the accuracy of the solved orbit by means of the carrier-phase residual, overlapping orbit comparison and the reference orbit comparison. The data quality assessments show that the data is less affected by the multipath effect, the utilization of the data is low, cycle slips occur frequently, and the carrier-phase data is often interrupted. The POD results indicate that the root mean square (RMS) values of the carrier-phase residuals of six COSMIC-2 satellites are between 6.0 mm and 7.5 mm, The mean RMS values of the overlapping orbit are better than 0.92 cm, 1.33 cm and 1.03 cm in the radial (R), tangential (T) and normal (N) directions respectively, and the mean RMS values of the six satellites in the 3D direction are between 1.38 cm and 1.75 cm. The mean RMS values in R, T and N directions orbit determination accuracy of the reference orbit comparison are better than 5.61 cm, 6.59 cm and 2.29 cm respectively, and the mean RMS values of the six satellites in the 3D direction are between 7.35 cm and 8.79 cm.

Analysis of Meandering River Morphodynamics Using Satellite Remote Sensing Data—An Application in the Lower Deduru Oya (River), Sri Lanka

River meandering and anabranching have become major problems in many large rivers that carry significant amounts of sediment worldwide. The morphodynamics of these rivers are complex due to the temporal variation of flows. However, the availability of remote sensing data and geographic information systems (GISs) provides the opportunity to analyze the morphological changes in river systems both quantitatively and qualitatively. The present study investigated the temporal changes in the river morphology of the Deduru Oya (river) in Sri Lanka, which is a meandering river. The study covered a period of 32 years (1989 to 2021), using Landsat satellite data and the QGIS platform. Cloud-free Landsat 5 and Landsat 8 satellite images were extracted and processed to extract the river mask. The centerline of the river was generated using the extracted river mask, with the support of semi-automated digitizing software (WebPlotDigitizer). Freely available QGIS was used to investigate the temporal variation of river migration. The results of the study demonstrated that, over the past three decades, both the bend curvatures and the river migration rates of the meandering bends have generally increased with time. In addition, it was found that a higher number of meandering bends could be observed in the lower (most downstream) and the middle parts of the selected river segment. The current analysis indicates that the Deduru Oya has undergone considerable changes in its curvature and migration rates.

Assessment of Changes in Key Ecosystem Factors and Water Conservation with Remote Sensing in the Zoige

As the largest alpine peat swamp wetland distribution area in the world, the Zoige has important ecological functions, including water conservation and biodiversity maintenance. In the past 20 years, the regional ecological protection and restoration measures continuously strengthened under the leadership of the local government have led to gradual improvements in the ecological environment of the region. In this study, multisource satellite remote-sensing image data were used to carry out quantitative monitoring and assessment of the main ecological elements (vegetation and water), as well as the regional leading ecosystem service function in the Zoige. Combined with local ecological protection management policies and measures, we analyzed the characteristics and effectiveness of ecological protection. We compared the ecosystem change trends of the Zoige reserve and the county, from 2001 to 2020, and found that the fractional vegetation cover (FVC) of Zoige county has increased at a rate of 0.25%/year. The growth rate was highest between 2015 and 2020, and the growth rate of FVC in the Zoige Wetland National Nature Reserve is approximately 1.89-fold that of the whole county. The water area also shows similar variation characteristics. On the whole, the water conservation capacity of the Zoige showed a significant increase from 2001 to 2020. We used high-resolution satellite remote-sensing images to capture the details of land use changes brought about by local ecological protection policies and measures, and together with macroecological indicators, we reflected on the effectiveness of regional ecological protection measures. We observed that the ecological effects of nature reserves are more direct and rapid, and the amount of water conservation within the nature reserve is about 1 × 104 m3/km2 higher than that of the surrounding grasslands. Satellite remote-sensing images can not only capture the multiscale change information of ecological indicators, such as vegetation and water, in a timely manner, but can also help us to identify the effectiveness of conservation measures by distinguishing and analyzing the causes of these changes.

Satellite Observation of the Long-Term Dynamics of Particulate Organic Carbon in the East China Sea Based on a Hybrid Algorithm

The distribution pattern and flux variation of POC in the continental shelf seas are essential for understanding the carbon cycle in marginal seas. The hydrodynamic environment and complicated estuarine processes in the East China Sea result in challenging estimates and substantial spatio-temporal variability in terms of POC concentrations. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study to effectively and dynamically monitor the surface POC concentration in the East China Sea in a long-term series for the first time using MODIS/Aqua remote sensing satellite data from 2003 to 2020. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study. The MODIS/Aqua remote sensing satellite data from 2003 to 2020 were employed for the first time to dynamically monitor the surface POC concentrations in the East China Sea for a long time series. The results demonstrated that the performance (R2 = 0.84, RMSE = 156.14 mg/m3, MAPE = 43.30%, bias = −64.79 mg/m3) exhibited by this hybrid retrieval algorithm confirms the usability of inversion studies of surface POC in the East China Sea. Different drivers such as river discharge, phytoplankton, wind, and the sea surface current field jointly influence the spatial and temporal distribution of POC concentrations in the East China Sea. This paper also verifies that the hybrid algorithm can be applied to retrieval tasks for POC in different seas with similar optical properties to the waters of the East China Sea. In conclusion, the long-term series East China Sea POC data record, which was established based on MODIS/Aqua, provides supplementary information for in-situ sampling, which will aid the long-term monitoring of POC fluxes in shelf seas. At the same time, it has also improved our understanding of the transport and spatio-temporal variability of POC in the East China Sea, enhancing our comprehension of the impact of POC on environmental changes and carbon cycling in marginal seas.

Detection and classification of land cover change using ISOCLUST classifier and Land Change Modeler

The agricultural sector is a mainstay sector in the Indonesian economy. The government can detect and classify land cover change phenomena using remote sensing technology to present agricultural improvement solutions. Remote sensing is an effective tool for monitoring change that occurs continuously and in a large area. This technology can record the earth’s surface from a height of hundreds of kilometers in outer space. This paper uses the ISOCLUST classifier (by producing 6 classes) and Land Change Modeler on Landsat image data in the study area located in Malang Regency, East Java Province. ISOCLUST is an iterative supervised/unsupervised hybrid classifier. Land Change Modeler is an innovative land planning and decision support software that simplifies change analysis, resource management, and habitat assessment. The results of the study using ISOCLUST showed that the overall Kappa was in medium category, 0.4018 and 0.5711. The agriculture area increased to 14.94%, the open forest area increased to 6.46% between 2015 and 2020, while sparse vegetation decreased by 19.36%. The process of detection and classification of land cover using the ISOCLUST classifier and Land Change Modeler shows that the use of remote sensing satellite data is still a consideration in government policy recommendations.

Drought Risk Evaluation in Iran by Using Geospatial Technologies

A drought risk map has been developed at the national scale by using remote-sensing satellite data over Iran by combining output layers resulting from three main components of a risk-evaluation procedure including Hazard Quantification (HQ), Vulnerability Assessment (VA) and Identification of Elements at Risk (IER) in a GIS environment. In this respect, Drought Severity (DS) was calculated by using the monthly Normalized Difference Vegetation Index (NDVI) (over 31 years from 1986–2016). Iran landcover classification and a slope map, population density maps, and irrigated farm percentages at the provincial scale were utilized within the drought risk evaluation (DRE) process. The final risk map reveals that the northwest of the country, with a climate similar to the central European weather conditions, is exposed to the maximum drought risk. In contrast, the areas with an arid climate, mainly located in the middle of Iran, exhibits minimum risk against drought. Based on the risk map, the southern part of the Caspian Sea shows very low drought risk due to the moderate and subtropical climate in this region. The outputs of this research will provide advice and warnings to help decision makers reduce drought risk consequences after prioritizing risk areas at the administrative scale.

Assessment of wildfire hazard on the territory of Kazakhstan using remote sensing data

Kazakhstan ranks 9th in terms of land area in the world, however, most of the territory is in the arid zone, which causes a high exposure to the wildfire risk. According to the Ministry of Emergency Situations, more than 14 thousand fires annually affect forest, steppe and rural areas, which is almost 78% of all fires in the country. This work is aimed at assessing the territory of Kazakhstan in terms of the degree of fire danger using methods and means of remote sensing, which is relevant today. The purpose of this work is to create a fire hazard forecast map that allows you to determine the degree of fire hazard of territories based on remote sensing satellite data. The works of foreign researchers who have direct experience in research and assessment of the degree of fire danger, based on the use of satellite information, are considered. To determine the likelihood of a fire risk, we studied the natural conditions and their impact on the possible spread of a fire, and evaluated 11 types of initial data in the Karaganda region. Based on the experience of the authors, the input data are arranged into the following groups: fuel, topography, climate, and fire statistics. Economic efficiency, and the significance of the results of this work, can serve to reduce socio-economic losses. Similarly, it is possible to significantly speed up the notification of local fire services, pre-evaluate the possible threat of various fires and reduce the time to eliminate them.

BUILDING FOOTPRINT EXTRACTION FROM SPACE-BORNE IMAGERY USING DEEP NEURAL NETWORKS

Abstract. One of the important and high-level detailing contained within basemaps is the ‘building feature’. Though pre-trained Deep Learning (DL) models are available for Building Feature Extraction (BFE), they are not efficient in predicting the buildings in other locations. This study explores the need and the major issue of implementing DL models for BFE from Very High Resolution Remote Sensing (VHRS) satellite data for any given area. Though advanced DL models are invented, in order to implement them, huge amount of potential training data is demanded for feed in. the building typologies are highly subjected to the context of study area including soil characteristics, culture/lifestyle/economy, architectural style and the building byelaws. The study believes that availability of enough training data of contextual buildings as one of the concern for effective model training. The study aims to extract the buildings present in the study area from Pleiades 1A (2019) RGB VHRS data using simple Mask R-CNN instance segmentation model which is training on the native contextual buildings. Here, an automated method of generating the location-specific training data for a given area is followed using Google Maps API (2021). The generated training data when trained on a deep learning architecture and predicted by the input data yielded promising results. The prediction accuracy of about 98.41% specificity, 96.20% predictive accuracy and 0.89 F1 score are achieved. The methods adopted assist the planning/governing bodies to accelerate the qualitative urban map preparation.

Counter-rotating eddy pair in the Luzon Strait

Abstract. Based on satellite remote-sensing observation data and Hybrid Coordinate Ocean Model (HYCOM) re-analysis data, we studied the counter-rotating eddy pair in the Luzon Strait (LS). Statistical analysis reveals that when an anti-cyclonic mesoscale eddy (AE) (cyclonic mesoscale eddy [CE]) in the Northwest Pacific (NWP) gradually approaches the east side of the LS, a CE (an AE) gradually forms on the west side of the LS, and it is defined as the AE (CE) mode of the counter-rotating eddy pair in the LS. The counter-rotating eddy pair exhibits obvious seasonal variation: the AE mode mainly occurs in the summer half of the year, while the CE mode mainly occurs in the winter half of the year. The mean durations of the AE and CE modes are both approximately 70 d. Based on the vorticity budget equation and energy analysis, the dynamic mechanism of counter-rotating eddy-pair occurrence is determined to be as follows: the AE (CE) on the east side of the LS causes a positive (negative) vorticity anomaly through horizontal velocity shear on the west side of the LS, and the positive (negative) vorticity anomaly is transported westward by the zonal advection of the vorticity, finally leading to the formation of the CE (AE) on the west side of the LS.