High Resolution Satellite Data Research Articles

Analysis of the Influence of Clear-Sky Fluxes on the Cloud-Type Mean Cloud Radiative Effects in the Tropical Convectively Active Regions With CERES Satellite Data.

Cloud radiative effects (CREs) and cloud-type mean CREs depend upon how clear-sky fluxes are computed over a large area: those of the immediate environment of clouds or the regional mean clear-sky fluxes. Five convectively active regions in the Tropics, two over land (Africa and Amazon) and three over ocean (eastern and western Pacific and Atlantic), are selected to understand the influence of immediate environment of clouds on CREs. Fluxes derived from 19years of high-resolution CERES satellite data, categorized by cloud type, are utilized. The cloud types are classified based on the joint cloud top pressure and cloud optical depth distribution. For the entire tropical region, differences in cloud-type mean CRE with regional mean and immediate environment clear skies range from -7.8 to 10.7Wm-2 for shortwave (SW), 2.9 to 15.8Wm-2 for longwave (LW), and 6.1 to 17.9Wm-2 for net, respectively. The oceanic and Amazonia regions have negative (positive) SW (LW) CRE differences, typically 2-6Wm-2 in SW but 7-10Wm-2 in LW, whereas Africa has positive SW and LW CRE differences (typically 20-30Wm-2, up to 40-50Wm-2). The influence of immediate environment reduces the regionally averaged, that is, cloud-type mean CREs weighted by cloud fractions, SW cloud cooling, and LW cloud warming in four of the five regions except for Africa. For Africa, it increases the SW cloud cooling and greatly reduces the LW cloud warming, resulting in net cloud cooling as in other regions instead of warming. The implications of these findings for observational and modeling studies are discussed.

Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning

Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Sigtierras project topographic data. The scenarios were categorized into two groups: SC1–SC6, combining 5 m resolution data, and SC7–SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing Polylepis stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing Polylepis from non-Polylepis). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the Polylepis class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the Polylepis stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy.

Rural Settlement Dynamics in a Rapidly Urbanizing Landscape: Insights from Satellite Remote Sensing and Archaeological Field Surveys in Zanzibar, Tanzania

ABSTRACT In Africa, rapid urbanization covers archaeological sites and limits the utility of traditional archaeological field survey methods. This is not only a crisis for archaeological heritage conservation, but it also distinctly impacts anthropological understandings of African urban trajectories since much evidence for precolonial urbanism lies within areas of rapid expansion. However, high-resolution multitemporal satellite data may facilitate reconstructions of urban growth in African cities, enabling archaeological surveys to target undeveloped areas for prospection within the interstices of modern urban development. This paper describes an application of satellite remote sensing for archaeological prospection within the rapidly urbanizing hinterland of Zanzibar Stone Town, a UNESCO World Heritage site. Survey results reveal settlement trajectories around the city over the last millennium, drawing attention to the role of rural agricultural land as a factor in the emergence of precolonial urbanism and the continued significance of rural places as urbanization progressed into the Colonial era.

Rockfall Mapping and Monitoring Across the Kalymnos Sport Rock Climbing Sites, Based on Ultra-High-Resolution Remote Sensing Data and Integrated Simulations

This manuscript presents a multidisciplinary study that proposes a methodology for delineating and categorizing vulnerability at rockfall risk areas to avoid human injuries and infrastructure damage caused by rockfalls. The presented workflow includes (i) classical geological mapping, (ii) the interpretation of high-resolution satellite data for observing the spatial distribution of fallen boulders, (iii) analytical hierarchy processing of spatial information within a Geographical Information System (GIS) platform, (iv) close-range remote sensing campaigns with Unmanned Aerial Systems (UASs), and (v) integrated simulation of rockfall events. This methodology was applied to Kalymnos Island, which belongs to the Dodecanese Islands complex of the southeastern Aegean Sea in Greece. It is characterized by unique geomorphological features, including extensive vertical limestone cliffs that span the island. These cliffs make it one of the world’s most densely concentrated areas for sport climbing. The results highlighted the areas that the local authorities need to focus on and suggested measures for increasing the safety of climbers and infrastructure.

Estimation of Rice Crop Acreage in Kuttanad Region, Kerala Using Landsat 8 OLI IMAGES and GIS Techniques

Aims: The study aimed to delineate rice accurately (Oryza sativa L.) cultivation areas in the Kuttanad region, Kerala, during the Puncha season of 2023-24 using medium- to high-resolution optical satellite data, particularly Landsat 8 Operational Land Imager (OLI), to aid in preharvest prediction of agricultural production and policy making. Study Design: This study used a remote sensing-based approach for rice area estimation, focusing on supervised classification methods. Place and Duration of Study: The study was conducted in Kuttanad, Kerala, a low-lying agroecosystem, during the Puncha season of 2023-24. Methodology: The study utilised two cloud-free Landsat 8 OLI images to delineate rice-growing areas. The images were pre-processed, mosaicked, and analysed using ArcGIS software. A supervised classification approach was employed using the Maximum Likelihood Classification algorithm. The study area was classified into five categories: rice fields, other crops, low vegetation, built-up areas, and water bodies. Ground-truth data was used to validate the classification accuracy. Results: The total rice area delineated during the Puncha season was 43,550.28 hectares. The classification achieved an accuracy of 93.33%, with a kappa coefficient of 0.87, indicating high reliability. Conclusion: The accurate delineation of rice-growing areas using satellite imagery provides valuable information for assessing production levels and planning for food security. This methodology can aid in agricultural planning and contingency strategies, particularly in regions like Kuttanad, which face challenges such as flooding and soil toxicity.

Counting Cattle in Argentina with AI and Satellite Images

Abstract. From a specific need of the national institution responsible for plant and animal health in Argentina, SENASA, this collaborative work between the public sectors together with academia arises. The objective was the development of a Deep Learning algorithm for the detection of livestock, cows in particular, in very high-resolution satellite images (provided by the Argentine Space Agency (CONAE)) and its subsequent counting. The basic elements involved in the development of artificial intelligence are detailed, such as the selection and acquisition of satellite images, their very thorough preprocessing and labelling, details of the training stage and some forms of error quantification. The image database is made up of about 320 scenes (based on very high resolution (VHR) satellite data from the Pleiades and Pleiades NEO sensors, ©Airbus 2022, distributed by CONAE) of the Pampas and Patagonian regions in Argentina. Around 8,000 labels of different types of animals were generated, the most common were cows and sheep. Also labels that did not represent animals to contribute to training. Finally, some very promising preliminary results are presented, such as the average error in the count that was achieved was ± four cows. The usefulness of these tools is reflected in better management of renewable natural resources linked to animal health issues, fiscal issues and within the framework of the 2030 Agenda and Sustainable Development Goals #12, #15 and #17.

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District ofSri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available.Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

Pixel to practice: multi-scale image data for calibrating remote-sensing-based winter wheat monitoring methods

Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availability of accurate spatial information on crop growth, vigor, and health status at large scales. Challenges persist particularly in interpreting remote sensing signals within commercial crop production due to the variability in canopy appearance resulting from diverse factors. Recently, high-resolution imagery captured from unmanned aerial vehicles has shown significant potential for calibrating and validating methods for remote sensing signal interpretation. We present a comprehensive multi-scale image dataset encompassing 35,000 high-resolution aerial RGB images, ground-based imagery, and Sentinel-2 satellite data from nine on-farm wheat fields in Switzerland. We provide geo-referenced orthomosaics, digital elevation models, and shapefiles, enabling detailed analysis of field characteristics across the growing season. In combination with rich meta data such as detailed records of crop husbandry, crop phenology, and yield maps, this data set enables key challenges in remote sensing-based trait estimation and precision agriculture to be addressed.

Remote Sensing Detection of Growing Season Freeze-Induced Defoliation of Montane Quaking Aspen (Populus tremuloides) in Southern Utah, USA

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks.

Aufeis thickness and volume estimations from stereo satellite imagery and terrestrial photographs: Evidence from Central Ladakh, India

Aufeis – a phenomenon associated with permafrost and the cold arid region of Ladakh – serves as a critical water resource for local communities. In several tributaries of the Indus River, aufeis accumulation is enhanced in ice reservoirs (commonly known as “artificial glaciers”) to store winter baseflow for crop irrigation during the water-scarce period in spring. This study investigates aufeis thickness and volume across four study sites in the Trans-Himalaya of Central Ladakh: the ice reservoirs of Phuktse and Igoo and the catchments of Gya and Sasoma, where natural aufeis fields occur. Aufeis thickness and volume estimates were derived via differencing of digital elevation models calculated from very high-resolution stereo Pléiades imagery and terrestrial photographs. The study revealed ice thickness up to a maximum of 3 m and could further demonstrate the amplification effect of the walls of the ice reservoirs on aufeis accumulation in both, differenced elevation from Pléiades and terrestrial photographs. Aufeis volumes across the four study sites range from 34,106 ± 13,440 m3 in Phuktse up to 105,790 ± 28,511 m3 in Sasoma, indicating substantial amounts that need to be considered in future hydrological studies in the region. While the approach with terrestrial photographs is especially suitable for local studies, the results from very high-resolution stereo satellite data are promising for aufeis studies on large spatial scales. It represents an important contribution for the understanding of spatial aufeis patterns in the Trans-Himalaya and affected regions worldwide.

A Study on Resolving Dynamic analysis using deep learning Framework Logical Methods

Deep learning technologies have gained a great deal of interest in recent months for demonstrating material removal capabilities in a variety of separation, categorization, as well as other machine vision activities. The majority of these deep networks are built employing convolution or completely convolution structures. Our suggested Dynamic and Static gestures System guided by a hand gesture high dimensional data methodology We use a snapshot of a human's thoracic spine to determine the person's depth and the size of the space around his or her hands. In this research, we provide a novel object-based deep-learning framework for semantic segmentation of extremely high-resolution satellite data. In specifically, we leverage object-based prior record by augmenting a fully convolutional neural network's training strategy with an anisotropic diffusion data pre-processing phase and an additional loss term. The goal of this restricted framework is to ensure that similar visual data is assigned to the same semantic class. Here, we employ intermediate steps based on traditional image processing techniques to aid in the resolution of the subsequent problem of classification, detection, or segmentation. Research shows that adding pre- and post-processing steps to a deep learning pipeline can boost model performance over using only the network. Recent advances in UQ algorithms used for deep learning are analysed, and their potential for use in relevance feedback is addressed. It also emphasizes basic research difficulties and directions linked with UQ. Quantifiably, man-made classes with more precise geometry, such as buildings, benefited the most from our strategy, particularly along object borders, indicating the developed approach's enormous potential. The purpose of this paper is to offer an overview of the approaches used inside deep learning frameworks to either appropriately prepare the input (pre-processing) or enhance the outcomes of the network output (post-processing), with an emphasis on digital pathology image analysis.

Soil quality assessment and mapping in basaltic terrain of Central India for sustainable soil and crop management using integrated PCA and GIS

The cereal-based cropping system plays a vital role in ensuring food security in the Indian subcontinent. However, the productivity of these systems has seen a continuous decline due to the degradation of soil quality. This study aims to develop a Soil Quality Index (SQI) for such cropping systems. A detailed survey was conducted in the Bareli watershed of Seoni district, Madhya Pradesh, at a 1:10000 scale using high-resolution satellite data and Geographic Information System (GIS) technology. The survey identified and mapped 5 soil series: Diwartola, Diwara, Bareli-1, Bareli-2 and Bareli-3. Soil quality was evaluated based on morphological, physical and chemical properties as well as fertility parameters. Key indicators for soil quality assessment included sand, silt, clay content, bulk density, hydraulic conductivity, available water capacity and coefficient of linear extensibility (COLE). Additionally, pH, electrical conductivity, organic carbon, cation exchange capacity and nutrients like N, P, K, Fe, Mn, Cu and Zn were considered. The SQI was calculated using integrated principal component analysis, which involved selecting a minimum data set (MDS), assigning weights and scoring indicators. The results revealed that Diwartola soils had high quality (242.7 ha, 13.5 % TGA), Bareli-1 and Bareli-3 soils were of medium quality (462.8 ha, 25.7 % TGA), while Diwara and Bareli-2 soils were of low quality (966.1 ha, 53.8 % TGA). Agro-interventions such as agri-horticulture, agro-forestry, silvi-pasture, intensive cultivation and soil and water conservation measures were recommended based on the different mapping units.

Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model

Rubber is a perennial plant grown for natural rubber production, which is used in various global products. Ensuring the sustainability of rubber cultivation is crucial for smallholder farmers and economic development. Accurately predicting rubber yields is necessary to maintain price stability. Remote sensing technology is a valuable tool for collecting spatial data on a large scale. However, for smaller plots of land owned by smallholder farmers, it is necessary to process productivity estimates from high-resolution satellite data that are accurate and reliable. This study examines the impact of spatial factors on rubber yield and evaluates the technical suitability of using grouping analysis with the forest classification and regression (FCR) method. We developed a high-density variable using spatial data from rubber plots in close proximity to each other. Our approach incorporates eight environmental variables (proximity to streamlines, proximity to main river, soil drainage, slope, aspect, NDWI, NDVI, and precipitation) using an FCR model and GIS. We obtained a dataset of 1951 rubber yield locations, which we split into a training set (60%) for model development and a validation set (40%) for assessment using area under the curve (AUC) analysis. The results of the alternative FCR models indicate that Model 1 performs the best. It achieved the lowest root mean square error (RMSE) value of 19.15 kg/ha, the highest R-squared (R2) value (FCR) of 0.787, and also the highest R2 (OLS) value of 0.642. The AUC scores for Model 1, Model 2, and Model 3 were 0.792, 0.764, and 0.732, respectively. Overall, Model 4 exhibited the highest performance according to the AUC scores, while Model 3 performed the poorest with the lowest AUC score. Based on these findings, it can be concluded that Model 1 is the most effective in predicting FCR compared to the other alternative models.

The evaluation of Small River water pollution caused by tailing spill in the Northeast of China using high-resolution images

The global mining sector generates billions of tons of tailings stored in thousands of tailing ponds. The occasional spills of tailings resulting from dam failures or pipe damage can have devastating consequences, threatening nearby human populations and ecosystems, particularly those located along river corridors. Satellite remote sensing technology is a vital supplementary method to traditional field methods for monitoring and evaluating the water pollution caused by spilled tailings. The researchers have developed workflows to evaluate the effect of tailing spills on water quality using low and medium-spatial satellite imagery from satellite sensors. Due to insufficient spatial resolution, these workflows were hard to apply to monitor the water pollution caused by spilled tailing in small rivers. Using valuable on-site data from a river water pollution incident caused by spilled tailing, a workflow utilizing high-resolution satellite imagery (GF1) was developed. This workflow incorporates a machine learning algorithm (improved DeepLabV3+) to extract water masks first and a novel spectral index method to determine TSM concentrations. The improved DeepLabV3+ algorithm can obtain an accurate water mask no matter the water pixels, whether influenced by the tailing spills from GF1 imagery with IoU of 82.66%, Precision of 93.21%, Recall of 87.96%, and F1-score of 90.51%. A new spectral index combination algorithm that provides reliable TSM products for an extensive TSM magnitude range was presented to assess the level of water contamination. The strong correlation (R2 = 0.97) between in situ TSM and Mo concentrations suggests that the retrieved TSM products are suitable for assessing the water pollution caused by the spilled tailing. This workflow provides a method for monitoring and evaluating water pollution resulting from spilled tailings in small rivers. It utilizes high-resolution satellite data to observe and analyze the pollution levels.

A novel hybrid methodology integrating pixel- and object-based techniques for mapping land use and land cover from high-resolution satellite data

ABSTRACT The classification of very high-resolution satellite imagery remains a focal point in remote sensing, attracting increased attention across diverse scientific disciplines. Various classification methods, including pixel- and object-based techniques, have been proposed, and their performances and limitations have been discussed in the literature. This paper presents a hybrid method that combines the strengths of pixel- and object-based methods in image classification to minimize errors associated with the segmentation process, particularly under-segmentation errors in object-based image analysis. The core concept behind the method lies in categorizing segmented image objects as either homogeneous or heterogeneous based on their class probability. In this process, the estimated possibilities from the object-based classification model are considered, and segments are designated as homogeneous or heterogeneous using a user-defined threshold. The object-based classification model determines the class labels for homogeneous image objects, while the heterogeneous ones, containing pixels representing different land cover classes, are classified using the pixel-based model. The performance of hybrid classification models, created by varying thresholds, is analysed using high-resolution WorldView-3 and WorldView-2 imagery and compared with pixel- and object-based classification results. For the implementation of image classification methods, Canonical Correlation Forest (CCF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) were employed. The findings indicated that employing the suggested hybrid strategy with a threshold value selected within a specific range (e.g. between 60% and 80%) and employing a robust classification algorithm that provides class probabilities (e.g. CCF) results in a statistically significant improvement in overall accuracy compared to pixel and object-based methods, with gains of 5% and 4%, respectively. Visual analysis of the produced thematic maps revealed that the proposed method minimizes the salt-and-pepper effect associated with pixel-based classification while mitigating segmentation problems arising from object-based classification.

High-resolution satellite estimates of coal mine methane emissions from local to regional scales in Shanxi, China

Coal mines are significant anthropogenic sources of methane emissions, detectable and traceable from high spatial resolution satellites. Nevertheless, estimating local or regional-scale coal mine methane emission intensities based on high-resolution satellite observations remains challenging. In this study, we devise a novel interpolation algorithm based on high-resolution satellite observations (including Gaofen5-01A/02, Ziyuan-1 02D, PRISMA, GHGSat-C1 to C5, EnMAP, and EMIT) and conduct assessments of annual mean coal mine methane emissions in Shanxi Province, China, one of the world's largest coal-producing regions, spanning the period 2019 to 2023 across various scales: point-source, local, and regional. We use high-resolution satellite observations to perform interpolation-based estimations of methane emissions from three typical coal-mining areas. This approach, known as IPLTSO (Interpolation based on Satellite Observations), provides spatially explicit maps of methane emission intensities in these areas, thereby providing a novel local-scale coal mine methane emission inventory derived from high-resolution top-down observations. For regional-scale estimation and mapping, we utilize high-resolution satellite data to complement and substitute facility-level emission inventories for interpolation (IPLTSO+GCMT, Interpolation based on Satellite Observations and Global Coal Mine Tracker). We evaluate our IPLTSO and IPLTSO+GCMT estimation with emission inventories, top-down methane emission estimates from TROPOMI observations, and TROPOMI's methane concentration enhancements. The results suggest a notable right-skewed distribution of methane emission flux rates from coal mine point sources. Our IPLTSO+GCMT estimates the annual average coal mine methane emission in Shanxi Province from 2019 to 2023 at 8.9 ± 0.5 Tg/yr, marginally surpassing top-down inversion results from TROPOMI (8.5 ± 0.6 Tg/yr in 2019 and 8.6 ± 0.6 Tg/yr in 2020). Furthermore, the spatial patterns of methane emission intensity delineated by IPLTSO+GCMT and IPLTSO closely mirror those observed in TROPOMI's methane enhancements. Our comparative assessment underscores the superior performance and substantial potential of the developed interpolation algorithm based on high-resolution satellite observations for multi-scale estimation of coal mine methane emissions.

Identification precipitation threshold and resulting river discharge: an IDF-based approach in the Central Himalaya, Nepal

ABSTRACT A comprehensive understanding of the intricate relationship between precipitation threshold and extreme river discharge, especially in mountainous regions, is challenging yet crucial for effective mountain risk management. This study, conducted in the Himalayan region of Nepal, employs an innovative approach using Intensity-Duration-Frequency (IDF) curves to identify potential precipitation extremes relevant to extreme river discharges. Detailed analysis of historical hydrometeorological events and their impacts in Nepal was carried out to select a specific event for thorough examination. Additionally, the research evaluates satellite-based precipitation data and utilizes frequency analysis techniques such as Gumbel and Log Pearson Type III distributions, customized to the region’s unique conditions. Furthermore, the study evaluates Probable Maximum Precipitation (PMP) and Depth-Area-Duration (DAD) curves at various sub-basin levels. The findings underscore the significant role of 3-day consecutive precipitation over 1-day events in generating extreme river discharge within the basin. Notably, the study reveals discrepancies between PMP values derived from gauge-based and satellite-based data, emphasizing the necessity for proper bias correction of the satellite data. Despite its limitations, the research highlights the potential of high-resolution satellite data for disaggregating gauge observed daily precipitation and underscores its applicability to other Himalayan basins, offering valuable insights into precipitation-related extreme events and their implications for water resource management and disaster risk reduction.

Evaluation of ecosystem services vis-à-vis perceptions and attitudes of local communities towards Wetland conservation in Kashmir Himalaya

Valuation of ecosystem services along with the perceptions and attitudes of local communities is crucial for sustainable management of wetlands. This study assesses changes in wetland area and bathymetry, and provides insights into local community's perceptions of ecosystem services, and attitudes towards wetland conservation in Kashmir Himalaya, India. High-resolution satellite data was used to assess land use land cover (LULC) changes within the wetland. The bathymetry changes were assessed by performing depth measurements before and after dredging in 2019 and 2022, respectively. Perceptions of ecosystem services and attitudes of residents towards wetland management were examined through surveys in 182 households from the target population of 334 families. The findings reveal that Khushalsar wetland provides important provisioning (food, fodder, thatching mats, fish, vegetables), regulating (flood control, carbon sequestration, water quality improvement), supporting (habitat, biomass and oxygen production, water and nutrient cycling), and cultural services (aesthetic, education, spiritual). The economic benefits from provisioning services range from 240 USD/yr for minor vegetables to 1201–1802 USD/0.05 ha/yr for Nelumbo nucifera. The analysis of LULC changes indicates a ∼16.5% reduction in wetland area from 1980 to 2017, with a further ∼10% decrease from 2017 to 2021, resulting in an overall loss of ∼25% (27.1 ha) in wetland area from 1980 to 2021. Bathymetric assessments reveal an increase in maximum depth from 427 cm in 2019 to 547 cm in 2022 following dredging activities. Socioeconomic analysis showed the significance of Khushalsar wetland as an important source of livelihood for local communities. Survey results indicate that all the respondents have positive attitudes towards the protection and management of the Khushalsar wetland. The study demonstrates that human conservation activities have significantly contributed to positive changes in the wetland such as increase in depth and flood storage capacity. The study concludes that government protection along with active participation of local communities is indispensable for sustainable management of wetlands.

Submesoscale features of coastal upwellings in the Black Sea: Observations and modeling

Coastal upwellings significantly affect the thermohaline structure, gas exchange and the fluxes of nutrients in the upper layers. They are often characterized by complex spatial structure, which is related to impact of different factors – changes of bathymetry, impact of background currents, baroclinic instability and others. In this study, we use high-resolution NEMO numerical model-ling and satellite data to study in details several physical causes of the observed submesoscale features of the Black Sea coastal upwellings: . 1) the influence of capes on the separation of coastal upwelling from the coast and its further transformation into submesoscale eddies; 2) the influence of mesoscale eddies on the structure of coastal upwellings; 3) the role of river plumes and haline fronts in the transport of upwellings. Comparison of model and satellite data shows that the numerical calculations reasonably well reproduce the onset time, intensity and areas of upwellings. Further, we show that the discussed processes can significantly affect the intensity, evolution and propagation of coastal upwellings in the Black sea.

IMPROVING THE ASSESSMENT OF THE ECOLOGICAL STATE OF SURFACE WATERS BY SATELLITE DATA

The article presents the results of research into the ecological state of surface waters using newly developed scientific approaches based on the use of high spatial resolution satellite data. The systematization of all available indicators and the development of new methodological approaches significantly improves the existing methodology for determining the ecological state of water bodies, which is the goal of research. In the studies of water bodies within the city of Kyiv, Obolon Bay and Lake Verbne, the optimal set of such indicators as spectral indices was determined, which made it possible to comprehensively assess the state of water bodies: NDWI - for distinguishing vegetation and water bodies, GCI - to assess the extent of blue-green algae, NDTI - to determine turbidity, IO - to determine the presence of soluble iron in water, NDSI - to assess the extent of flooding. According to the NDSI, NDWI indices and a combination of the red and infrared channels of the Sentinel-2 L2A satellite, the flooding of the floodplain of the Irpin River was traced, caused by the destruction of a hydraulic structure near the village of Kozarovichi. Wave abrasion of the shores was studied on the example of the Kremenchuk Reservoir near the villages of Pronozivka and Mozoliivka using Landsat4 (1984) and Landsat8 (2016) satellite images. The study of the reshaping of the coastline near the village of Tsybli in the Kyiv region used the method of determining spatio-temporal changes of the coastline as a result of its erosion. The assessment of spatio-temporal changes of the coastline should be carried out taking into account the water levels on the dates of the measurements. The conducted studies established that all the identified evaluation indicators can be grouped as biological, hydro morphological, and physicochemical, and it can be concluded that methodical approaches to assessing the ecological state of surface waters using satellite data are based on established cause-and-effect relationships of processes affecting water objects.