Satellite Information Research Articles

Shine a (night)light: Decentralization and economic development in Burkina Faso

Decentralization, championed by international institutions, has been one of the most prominent public sector reforms of the last decades, particularly in sub-Saharan Africa. To date, few studies propose a quasi-experimental evaluation of its capacity to contribute to local development. We exploit the phase-in of decentralization at the commune level in Burkina Faso. We use satellite information on night-time light density as a proxy for local development levels, which has the advantage of being measured and comparable over time and space. The communes that were decentralized first can be compared to the others after the reform relative to the pre-reform situation. The difference-in-difference approach includes commune fixed effects and inverse propensity score reweighting to account for time-varying differences across communes. We find a positive impact of decentralization on the night-light intensity trends of the early-decentralized communes. This is supported by alternative measures (remote sensing of built-up settlements and a welfare index), which shows the possibly broader scope of decentralization gains. We show that decentralization did not lift all boats: only the communes with the ability to generate own-source revenues benefited from effective decentralization.

ВЛИЯНИЕ ПРИЛИВА НА ВОДООБМЕН ЧЕРЕЗ ПРОЛИВ ЛАПЕРУЗА

Based on the analysis of oceanographic survey materials on the standard section S1 and satellite information, important features of the influence of tidal currents on the formation of hydrological conditions in the La Perouse Strait and adjacent waters have been identified. It was revealed that in the warm season, when the seasonal flow is directed from the Sea of Japan to the Sea of Okhotsk (Soya Warm Current), at the high tide in the northern part of S1 section the cold water with low salinity is observed in the surface layer (the temperature may differ from the southern stations by 3 times, and the salinity by 2 psu). At low tide in the northern part of S1 the water is also cold, but with high salinity, which indicates the inflow of water from the West Sakhalin current into the Sea of Okhotsk. The temperature contrasts between the northern and southern parts of the Strait are also confirmed by satellite observations. During the cold season in the northern part of the strait, there is a flow of colder and less salty Okhotsk Sea water into the southeastern part of the Tatar Strait. It is wider and more developed in depth compared to the summer season. At low tide, when the tidal current is directed to the east, this flow narrows noticeably.

Leveraging crop yield forecasts using satellite information for early warning in Senegal

Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20 ​% decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (R-squared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.

Оценка возможности использования спутниковой информации для мониторинга засух на территории южных регионов России

A brief description of the automated operational system for 10-day monitoring of droughts developed and operating at the Drought Monitoring Center of the All-Russian Research Institute of Agricultural Meteorology is provided. The system uses meteorological information from the network of weather stations. To obtain more detailed information on the spatial distribution of droughts, it is proposed to use satellite information (VIRS scanner of the SUOMI NPP satellite) along with ground-based data. For this purpose, it is proposed to calibrate satellite information (VCI index) based on ground information (HTC index) using test sites located near weather stations. Calculations of the aridity assessment for the territory of the considered federal districts using the proposed method are presented. Satisfactory convergence with the data obtained from ground information is observed. It is planned to refine the proposed procedure to operational application.

Decoupling anthropogenic, climatic, and tectonic factors in the hydrology of playa-lakes in a semi-arid, tectonically active region (betic orogen, southern Spain)

Here, we explore the dynamic interplay of climate, tectonic forces and human impacts, that have shaped the current hydrology of the 27 main endorheic continental playa-lakes in southern Spain, most of them protected as Natural Reserves. This research offers a thorough examination of the hydro-geomorphic, geochemical, and tectonic characteristics that influenced the development of these wetlands. We introduce and apply several hydro-morphological indices that effectively characterize the hydrological functioning of these wetlands. Importantly, the examination of the geological setting and analysis of recent tectonics has been found to be effective in reconstructing the evolution of some of the major playa-lakes, including Fuente de Piedra playa-lake and El Gosque playa-lake. Lastly, the analysis of satellite information using the online tool water occurrence change intensity (WOCI) of Surface Water Explorer in the studied playa-lakes was found to be inconsistent in the long-term analysis. Enhanced attention is required to refine the WOCI tool, thereby ensuring a more precise depiction of wetland dynamics in the Mediterranean. Fine-tuning will better represent complex behaviors, thus improving its utility for monitoring and managing wetlands in the region.

Oceanographic conditions of spring migration of European anchovy as a factor of formation of its spawning stock in the Sea of Azov

The aim of the work is to determine the influence of sea water circulation and temperature on the formation of the spawning stock of anchovy in the Sea of Azov.Materials and methods: Fishery and biological data on the spawning stock of the Azov anchovy were collected in surveys and bioresources monitoring carried out by the Azov-Black Sea branch of the VNIRO («AzNIIRKH») in 2000–2021. Satellite sea level data were used in the analysis of sea water circulation. These data, as well as satellite information on sea surface temperature for March-June 2000–2021 for the north-eastern part of the Black Sea and the southern part of the Azov Sea were taken from the website of the Copernicus Marine Service information system. Graphical and statistical methods of analyzing multi-year changes in the data were used in the work. The novelty of the performed work lies in the study of oceanographic conditions of spring migration of anchovy to the Sea of Azov as factors determining the size of its spawning stock in this sea.Result: intensive growth of the spawning stock of anchovy in the Sea of Azov was observed in 2008, 2010 and 2011. This was facilitated by: predominance of north-western geostrophic currents in the north-eastern part of the Black Sea in April 2008–2011, weakening of the Azov currents in the Kerch Strait in April 2007–2010 and predominance of the Black Sea currents in May 2004–2012, Decrease in the meridional gradient of water temperature in the Kerch Strait in April 2007–2010.Practical significance: the possibility of using oceanographic information for April-May to predict the size of the spawning and commercial stock of anchovy in the Sea of Azov.

Oil pollution near the ports of Aktau, Kuryk and Ersai (the Caspian Sea): Satellite and in-situ data

To study oil pollution on the surface of the Caspian Sea in the area of the ports of Aktau, Kuryk and Ersai, the archives of radar data obtained from the synthetic aperture radars of the Sentinel-1 satellite constellation belonging to the European Space Agency for the period 2013 - 2022 were selected. For this purpose, a specialized satellite information service “See the Caspian Sea” (STCS) (http://caspian.geosmis.ru) created in 2023 jointly by Space Research Institute and Shirshov Institute of Oceanology of Russian Academy of Sciences was used. This system is specifically designed for the Caspian Sea and is created on the basis of the existing “See the Sea” (STS) service, which is part of the “IKI-Monitoring” Center for Collective Use established in Space Research Institute. For a given polygon in the region of the ports of Aktau, Kuryk and Ersai, in the STCS system, time series archives consisting of more than 3,700 radar images were examined. Each image was manually analyzed to identify dark areas of reduced radar signal that could be identified as oil contamination. A total of 15 oil slicks were discovered, up to 89 km in length and up to 8 km2 in area. A satellite image of each oil slick is presented in the article, as well as its main characteristics. In addition, as part of the project “Satellite monitoring of the water areas of the ports of Aktau and Kuryk”, sea water samples were taken in the area of the ports of Aktau and Kuryk, which showed the content of oil products in the range of 0.014-0.1 mg/dm3. Thus, the resulting concentrations turned out to be less than the maximum permissible concentration of 0.1 mg per liter.

Wave energy assessment and wave converter applicability at the Pacific coast of Central America

Nowadays, numerous governments have instituted diverse regulatory frameworks aimed at fostering the assimilation of sustainable energy sources characterized by reduced environmental footprints. Solar, wind, geothermal, and ocean energies were subject to extensive scrutiny, owing to their ecological merits. However, these sources exhibit pronounced temporal fluctuations. Notably, ocean dynamics offer vast energy reservoirs, with oceanic waves containing significant amounts of energy. In the Central American Pacific context, the exploration of wave energy resources is currently underway. Accurate numerical wave models are required for applied studies such as those focused on the estimation of exploitable wave power; and even more so in Central American region of the Pacific Ocean where existing numerical models simulations have so far relied on coarse resolution and limited validation field data. This work presents a high-resolution unstructured wave hindcast over the Central American Pacific region, implemented using the third-generation spectral wave model WAVEWATCH III over the period between 1979 and 2021. The results of the significant wave height have been bias-corrected on the basis of satellite information spanning 2005 to 2015, and further validation was performed using wave buoy and acoustic Doppler current profiler (ADCP) records located in the nearshore region of the Central America Pacific coast. After correction and validation of the wave hindcast, we employed the dataset for the evaluation and assessment of wave energy and its possible exploitation using different wave energy converters (WECs). This evaluation addressed the need to diverse the energy portfolio within the exclusive economic zones of Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, Panama, Colombia, and Ecuador in a sustainable manner. Moreover, a comprehensive analysis was carried out on the advantages of harnessing wave energy, juxtaposed with the imperative of regulatory frameworks and the current dearth of economic and environmental guidelines requisite for development within the region.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Planting area and production decreased for winter-triticeae crops but increased for rapeseed in Ukraine with climatic impacts dominating

On-time mapping dynamics of crop area, yield, and production is important for global food security. Such information, however, is often not available. Here, we used satellite information, a spectral-phenology integration approach for mapping crop area, and a machine learning model for predicting yield in the war-stricken Ukraine. We found that in Ukraine crop area and production declined in 2022 relative to 2017–2021 and 2021 for winter-triticeae crops, which was invaded before the cropping season in February of that year. At the same time, crop area and production for rapeseed increased in Ukraine, with yields consistently lower by 6.5% relative to 2021. The low precipitation and the Russian-Ukrainian conflict-related factors contributed to such yield variations by -1.3% and -0.9% for winter-triticeae crops and -4.2% and -0.5% for rapeseed in 2022. We demonstrate a robust framework for monitoring country-wide crop production dynamics in near real-time, serving as an early-food-security-warning system.

Nomadic responses to rainfall: Nighttime light evidence from wadis in Djibouti

This study examined the association between human activities and precipitation in dry areas, focusing on nomadic people in Djibouti. Nomadic communities often lack access to essential social services, and water resource management is crucial for their survival and social welfare. Understanding how they respond to variations in water availability could contribute to effective policy implementation. Using satellite-derived information, this study found that nighttime light brightness increased in response to precipitation around wadis, with potential water spring locations, and decreased with distance from a wadi. Specifically, we observed no effect when the distance was more than 5 km. Furthermore, no effect of precipitation was found in urban areas, while a positive effect was found in remote areas. These findings suggest that the activity of nomadic people increases around wadis in response to increased water resource availability, indicating that water supply is important for settlement projects because it can reduce water-seeking costs and improve social welfare. This study provides the first empirical evidence using satellite data and statistical analysis to show that water availability is critical for nomadic communities which can inform better resource management and policy decisions in African countries.

Hydrological simulation and evaluation of drought conditions in the ungauged watershed Parishan lake Iran, using the SWAT model

ABSTRACT ke Parishan has experienced significant water scarcity, leading to its complete disappearance. This study utilized the SWAT model to assess the impact of changes in vegetation, precipitation, temperature, and evaporation on runoff and the lake's surface. Two strategies were employed: analyzing runoff variations based on land use and land cover (LULC) changes and evaluating the effects of precipitation, temperature, and humidity on runoff during the simulation period. A key challenge was the lack of runoff statistics, which was addressed by using data from donor watershed stations and Copernicus satellite information to improving simulation processes and calibration. The findings reveal that environmental changes, particularly land use, have increased evaporation and temperature fluctuations which results in reducing runoff. While Copernicus satellite data proved useful, runoff statistics from the neighboring Chamchit station provided more accurate simulations. The study finding suggests that the LULC strategy is an effective management approach, as in this case Parishan Lake's historical conditions with less urbanization and agriculture correlated with higher runoff and lake levels. Finally, meteorological and hydrometric drought and its impact on lake area changes were measured to reach a more comprehensive conclusion.

Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response.

The 2021 Tropical Storm Dianmu in Thailand: Disaster Responses and Roles of Information Technology

At the end of September 2021, Tropical Storm Dianmu caused catastrophic floods in Thailand, placing thousands of lives, properties, infrastructure, and other things, in danger. These circumstances provide an opportunity to learn how to respond to tropical storm situations in the future. Our investigation reveals that Thailand has access to a wealth of data, including geospatial, radar, satellite, and sensor information. Thus, these data could be utilized for an urgent alert system and communication channel to mitigate and reduce the potential for large-scale destruction by tropical storms and floods in the future.

Satellite Information Technologies in a Time of Crisis and Military Operations: Current State, Challenges, Future Prospects

Satellite Information Technologies in a Time of Crisis and Military Operations: Current State, Challenges, Future Prospects

Scenarios for the Altamira cave CO2 concentration from 1950 to 2100

A data-driven approach insensitive to the initial conditions was developed to extract governing equations for the concentration of CO2 in the Altamira cave (Spain) and its two main drivers: the outside temperature and the soil moisture. This model was then reformulated in order to use satellite observations and meteorological predictions, as a forcing. The concentration of CO2 inside the cave was then investigated from 1950 to 2100 under various scenarios. It is found that extreme levels of CO2 were reached during the period 1950–1972 due to the massive affluence of visitors. It is demonstrated that it is possible to monitor the CO2 in the cave in real time using satellite information as an external forcing. For the future, it is shown that the maximum values of CO2 will exceed the levels reached during the 1980s and the 1990s when the CO2 introduced by the touristic visits, although intentionally reduced, still enhanced considerably the micro corrosion of walls and pigments.

Temporally transferable crop mapping with temporal encoding and deep learning augmentations

Detailed maps on the spatial and temporal distribution of crops are key for a better understanding of agricultural practices and for food security management. Multi-temporal remote sensing data and deep learning (DL) have been extensively studied for deriving accurate crop maps. However, strategies to solve the problem of transferring crop classification models over time, e.g., training the model with data for a recent year and mapping back to the past, have not been fully explored. This is due to the lack of a generalized method for aggregating optical data with regard to the irregularity in annual clear sky observations and the scarcity of multi-annual crop reference data to support a more generalized DL model. In this study, we tackled these challenges by introducing a method namely Temporal Encoding (TE) to capture the irregular phenological information. Subsequently, we adapted and integrated two methods, i.e., Random Observations Selection (ROS) and Random Day Shifting (RDS) to simulate the variability of temporal sparsity as well as the shifts of crop phenology over different years. We tested this approach with a 1-dimensional Convolutional Neural Network (1D-CNN) and a Transformer Network models. Our results for both classifiers showed that models trained with crop reference data from 2018 and a dense time series of Landsat 7/8 and Sentinel-2 A/B data can be transferred with little decreases in accuracy to map 12 consecutive years from 2010 to 2021. The Transformer Network was slightly more accurate, while the 1D-CNN was much three times faster. Furthermore, the proposed models could achieve similar performances in the same years with and without fully available satellite information. The TE with ROS and RDS appears well suited for improving temporal transferability to support long term historic crop mapping.

Combining LiDAR and Spaceborne Multispectral Data for Mapping Successional Forest Stages in Subtropical Forests

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs.

Assessment of the state of agricultural crops and forecast their yields using ground and satellite information

Assessment of the state of agricultural crops and forecast their yields using ground and satellite information

Towards accurate spatial prediction of Glossina pallidipes relative densities at country-scale in Kenya

Vector-borne diseases, like those transmitted by tsetse flies, pose a significant global public health threat. Reducing vector populations is a promising strategy for disease control, especially in the case of tsetse-transmitted African trypanosomiasis. However, the cost-effective implementation of large-scale vector surveillance and control measures face challenges due to the lack of spatially explicit and reliable maps identifying vector hotspots. In this study, we assessed the accuracy of predicting Glossina pallidipes relative densities across Kenya by linking constrained in-situ tsetse catch data from 660 traps across three Kenyan regions with readily available gridded satellite information (human population, land cover, soil properties, elevation, precipitation, and land surface temperature) using a classical random forest algorithm. To enhance predictive performance, we employed two feature elimination techniques specifically designed for machine learning algorithms, i.e., Recursive Feature Elimination (RFE) and Variable Selection Using Random Forests (VSURF). For each set of retained variables, we trained a Random Forest model using a spatial cross-validation technique. Our findings showed that tsetse fly relative densities decreased with mean annual precipitation, and soil moisture, and conversely increased with higher tree cover. Based on the cross-validated R2, 41% of the spatial variability in relative densities of tsetse flies could be explained. For spatial extrapolation, only the set of predictors retained by VSURF closely matched known tsetse fly distributions in Kenya. This more accurate performance of VSURF may be attributed to its approach of assessing variables for both importance and their contribution to reducing prediction error. Our study demonstrates the potential of using a random forest method to upscale tsetse relative abundance predictions to the national level. However, the reliability of the current extrapolated map remains uncertain. We recommend: 1) increasing tsetse fly sampling efforts, particularly in the data-limited northern and eastern regions of Kenya, and 2) developing a more precise and accurate land cover map with classes that directly associate with known habitat characteristics of the target tsetse species.