High Spatial Resolution Data Research Articles

Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8

INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data. 
 OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images. 
 METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation.
 RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result. 
 CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.

Usefulness of techniques to measure and model crop growth and yield at different spatial scales

ContextWithin-field yield variability affects crop production and management decisions. To understand and manage this variability, different techniques have been deployed to measure and monitor the crops (and soils) at various spatial scales, including manual measurements, harvester-mounted yield monitors, proximal and remote sensing and crop simulation modelling. The value of this increasing data availability to enhance process understanding and on-ground management is unclear. ObjectiveThis study aimed to investigate the value of the increasingly available spatial data from different sources to understand important soil-plant processes amenable to improvement in both simulation modelling and for better management decisions for dryland cropping. MethodsWe collected three types of measurement data (manual sampling, sensed data from satellite and drone, and yield maps) over a 10 ha field and conducted simulations using the process-based soil-plant model APSIM at different spatial scales (varied from 1 m2 up to 10 ha). We assessed the agreement between ground measurements and yield maps, analysed the potential to use remotely sensed vegetation indices to estimate yield, and the scale at which process-based modelling could be reliable. ResultsWheat yield extracted from yield map at 1 m2 spatial resolution only explained 30% of the variation in yield measured from 1 m2 manual sampling, with better agreement when data was aggregated to 1 ha strip-scale (R2 = 0.66, NRMSE = 9.1%). Remotely sensed vegetation indices (VI) were better correlated with the yield map when aggregating images to coarse spatial resolution (> 50 m × 50 m), while high-resolution drone VI increased the correlation at finer scales. However, the relationship and the timing of the highest correlation differed between years. APSIM simulated point-based yield measured from manual samples with NRMSE of 19.4%, but it was difficult to capture spatial variation in yield due largely to uncertainties in input data. However, APSIM simulations captured the average crop growth dynamics and yield well at 1 ha strip- and 10 ha whole field scales. ConclusionsThe results highlight the need for caution when using yield maps and remote sensing data to quantify spatial variability and inform spatially explicit management decisions at a fine resolution (e.g., 1 m2). In our case, remote sensing data and yield maps only became consistent and process-based modelling became skilful at scales larger than a 1 ha strip. ImplicationsDespite an increasing amount of high-resolution spatial data, the usefulness at fine resolution needs further investigation, particularly under heterogeneous field conditions. Such data need to be analysed in conjunction with the landscape, soil and climate data to understand the drivers of spatial variability and inform process understanding and modelling. This further implies potential problems in developing spatial management practices at finer scales using such data.

Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom.

Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to ) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to to 10s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity. The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization. An in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.

Assessing the Impact of Preprocessing and Conversion Technologies on the Sustainable Aviation Fuel Supply from Forest Residues in the Southeast U.S.A.

Forest residues have been suggested as potential feedstock for sustainable aviation fuel (SAF) production through different pathways. However, the bulky density and scatter distribution require efficient size-reduction processes to enhance the feedstock supply. Thus, this study analyzes the impact of alternative preprocessing and conversion technologies on the SAF supply chains from logging residues (the predominant share of forest residues) using a two-stage mixed-integer linear programming model. The model determines the optimal location of biomass preprocessing depots, conversion facilities, and airports receiving SAF by minimizing the net present value of the total supply chain cost over 10 years. Using high-resolution spatial data in the Southeast U.S.A. as a case study, the results show that the technology and scale of the facilities heavily influenced the SAF’s breakeven cost and maximum supply quantity. The most economically efficient system that adopts a rotary shear milling system and pyrolysis conversion process could generate up to 650 million gallons of SAF from logging residues in the region, with nearly 62% of the SAF being produced from hardwood residues. Also, the most efficient system has an estimated breakeven cost of US$5.51 per gallon of SAF.

Spatial Correlations between Nitrogen Budgets and Surface Water and Groundwater Quality in Watersheds with Varied Land Covers

Anthropogenic nitrogen (N) inputs can have detrimental environmental effects, necessitating a comprehensive understanding of the nitrogen budget (NB) and its spatial correlation with the water quality. This study, utilizing a 2016 dataset, scrutinized 850 subwatersheds with diverse land covers across the Republic of Korea (ROK). Employing Geographically Weighted Regression (GWR), it examined the spatial correlations between the NBs and the quality of the groundwater and river water at the watershed scale. Robust correlations (R2 = 0.87) were observed between the groundwater quality and NBs, surpassing those of the surface water (R2 = 0.48). Sensitivity analyses highlighted the importance of high-resolution spatial data in capturing nuances within complex land covers. The integration of such data led to increases in the spatial correlations between the groundwater and river water quality of approximately 0.6–0.9 and 0.3–0.5, respectively. Notably, when the agricultural land cover exceeded 10%, significant enhancements in the spatial correlations were observed, emphasizing the pivotal role of agriculture in nutrient and water quality. At a 10% cropland ratio, the spatial correlations between the watershed-scale NBs and river/groundwater quality increased by approximately 76% and 501%, respectively. This study provides novel insights into the spatial relationships among NBs, water quality, and land use, highlighting the significance of high-resolution data and the impact of agricultural practices on watershed management. These findings contribute valuable information for developing strategies to mitigate nitrogen pollution.

Demonstrating the relevance of spatial-functional statistical analysis in marine ecological studies: The case of environmental variations in micronektonic layers

In this study, we conducted an analysis of a multifrequency acoustics dataset acquired from scientific echosounders in the West African water. Our objective was to explore the spatial arrangement of marine organism aggregations. We investigated various attributes of these intricate biological entities, such as thickness, relative density, and depth, in relation to their surroundings. These environmental conditions were represented at a fine scale using a towed multiparameter system. This study is closely intertwined with two key domains: Fisheries acoustics techniques and functional data analysis. Fisheries acoustics techniques facilitate the collection of high-resolution spatial and temporal data concerning marine organisms at various depths and spatial scales, all without causing any disturbance. On the other hand, spatial-functional data analysis is a statistical approach for examining data characterised by functional attributes distributed across a spatial domain. This analysis encompasses dimension reduction techniques, as well as supervised and unsupervised methods, which take into consideration spatial dependencies within extensive datasets.We began by applying multivariate statistical techniques and subsequently employed Functional Data Analysis (FDA). In the modeling section, we introduced the spatial dimension with the spatial coordinates as covariates in the General Additive Model (GAM) and Functional Generalized Spectral Additive Model (FGSAM) models, aiming to underscore its relevance in those contexts. In an exploratory phase, Multivariate Functional Principal Component Analysis provided detailed insights into the variations of parameters at different depths, a capability not offered by traditional Principal Component Analysis. When it came to regression tasks, we explored the interactions between descriptors of Sound Scattering Layers and key environmental variables, both with and without considering spatial dimensions. Our findings revealed significant distinctions between northern and southern Sound Scattering Layers, as well as between coastal and high-sea regions. The use of the spatial locations enhanced the performance of GAM and FGSAM, particularly in the case of salinity, reflecting the influence of water mixing and seawater temperature. The multifaceted effects of environmental variations on Sound Scattering Layers underscore the importance of spatial-functional statistical analysis in ecological studies involving complex, spatially functional objects. Beyond the scope of this specific case study, the application of functional data analysis shows promise for a wide array of ecological studies dealing with extensive spatial datasets.

LiDAR topo‐bathymetry for riverbed elevation assessment: A review of approaches and performance for hydrodynamic modelling of flood plains

AbstractTopo‐bathymetric LiDAR (TBL) can provide a continuous digital elevation model (DEM) for terrestrial and submerged portions of rivers. This very high horizontal spatial resolution and high vertical accuracy data can be promising for flood plain mapping using hydrodynamic models. Despite the increasing number of papers regarding the use of TBL in fluvial environments, its usefulness for flood mapping remains to be demonstrated. This review of real‐world experiments focusses on three research questions related to the relevance of TBL in hydrodynamic modelling for flood mapping at local and regional scales: (i) Is the accuracy of TBL sufficient? (ii) What environmental and technical conditions can optimise the quality of acquisition? (iii) Is it possible to predict which rivers would be good candidates for TBL acquisition? With a root mean square error (RMSE) of 0.16 m, results from real‐world experiments confirm that TBL provides the required vertical accuracy for hydrodynamic modelling. Our review highlighted that environmental conditions, such as turbidity, overhanging vegetation or riverbed morphology, may prove to be limiting factors in the signal's capacity to reach the riverbed. A few avenues have been identified for considering whether TBL acquisition would be appropriate for a specific river. Thresholds should be determined using geometric or morphological criteria, such as rivers with steep slopes, steep riverbanks, and rivers too narrow or with complex morphologies, to avoid compromising the quality or the extent of the coverage. Based on this review, it appears that TBL acquisition conditions for hydrodynamic modelling for flood mapping should optimise the signal's ability to reach the riverbed. However, further research is needed to determine the percentage of coverage required for the use of TBL as a source of bathymetry in a hydrodynamic model, and whether specific river sections must be covered to ensure model performance for flood mapping.

ASSESSMENT OF PYROGENIC IMPACT ON FOREST AREAS IN THE TVER REGION

We consider the potential and limitations of widely accepted remote sensing algorithms for detecting forest areas damaged by fires, which allow the monitoring systems to automatically form data about the fire areas and the areas where death of tree stands is subsequently recorded. The resulting size of the detected areas has a measurement error, which is typically determined on the basis of a onetime survey for a large territory and one forest fire season. Depending on the geographical features of the territory, forest fires have specific spatiotemporal and qualitative characteristics, and are accompanied by specific heterogeneous damage to forests, which affect the accuracy of remote detection of a burnt area or firedamaged forest. Hence the use of the unified largearea error estimate for locallevel surveys could lead to inaccurate results. The analysis of space images of forest fund lands in the Tver region for the period 2007-2022 demonstrated the need to establish regional values of the measurement error for fireimpacted areas. By comparing the medium spatial resolution data with the data of high spatial resolution we identify a regional bias, which is significant given the relatively small size of the detected areas. The study demonstrates the expediency of establishing regional error values for measurements of pyrogenic impact on forest areas. By implementing the suggested changes we could improve the accuracy of remotely-sensed estimates of fireimpacted areas and the amount of associated damage to forests.

ASSESSMENT OF SPATIAL DATA OBTAINED BY MEANS OF THE USE OF UNMANNED AERIAL VEHICLE (UAV)

At present, in industry or research, engineering projects demand continuous spatial data from the Earth's surface. Depending on the required level of detail, collecting such data through traditional methods like topographic surveying can be prohibitively expensive. Moreover, these methods often involve extended timeframes and challenges in accessing difficult terrains, such as steep slopes. The use of an unmanned aerial vehicle (UAV), commonly referred to as a drone, equipped with sensors like photographic cameras, offers a means of consistently acquiring very high spatial resolution data from the Earth's surface. The incorporation of Ground Control Points (GCPs) ensures the data 's spatial accuracy. Photogrammetric process like structure from motion (SfM), generate various spatial products, including orthophotos and point clouds. This research centers on the application of a fixed-wing UAV to survey a 120 ha area to generate spatial data. The study demonstrates the high precision of the derived spatial data, validated against ground true data, using a flight height of 100 m over the above ground with an 80% overlap. The results include the generation of an orthophoto, a Digital Terrain Model (DTM), and a Digital Surface Model (DSM), with both terrain models derived from the point cloud.

Use of an Autonomous Surface Vehicle to Collect High Spatial Resolution Water Quality Data at Lake Wateree, SC

Freshwater resources including lakes and reservoirs are increasingly threatened by harmful cyanobacterial blooms (HCBs). The scarcity of high spatial and temporal resolution data presents challenges for monitoring, predicting, and managing these blooms. Autonomous surface vehicles (ASVs) equipped with water quality sensors represent a powerful tool to obtain high-resolution spatial data at Lake Wateree (LW), South Carolina (SC). LW is a hydroelectric reservoir commonly covered with extensive blooms of the benthic cyanobacteria Microseira (Lyngbya) wollei and Phormium sp., with the thickest mats in shallow coves. The main objective of this study was to determine the best speed and duration of loiter (i.e., pauses) required to collect accurate quantitative data on a mobile platform. We present a low-cost motorized kayak (USD) designed to run autonomously equipped with a YSI EXO2 sonde measuring depth, temperature, conductivity, dissolved oxygen, pH, turbidity, and phycocyanin. The sonde was positioned horizontally on a rigid mount at 0.5 m below the surface to efficiently reduce the effect of turbulence. The data were compared to another YSI EXO2 sonde installed on the same ASV design, maintained stationary midway along the moving ASV’s path to assess the data accuracy obtained at different speeds and loiter periods. No statistically significant differences were observed for measurements collected on the stationary and moving ASVs for all water-quality sensors at a speed of up to 2.7 m/s (6 mph). Differences observed between the moving and stationary sondes for phycocyanin and turbidity sensors were within the reported factory accuracy at speeds up to 1.8 m/s (4 mph) and outside the expected factory accuracy at higher speed (2.7 m/s), showing the effects of motion and mixing on the collected data. Dissolved oxygen was outside of the reported factory accuracy for all tests. It is recommended to loiter periodically when moving at a faster speed to obtain more accurate data, as the differences between the sondes were alleviated during the loiter period. Overall, our ASV design has the potential to be employed to obtain robust spatial data at LW when deployed at optimal operating conditions.

Importance of high-resolution spatial data for the detection of winter wildlife responses to edges

Several wildlife species are thought to avoid edges of large habitat gaps, such as clear-cuts, but detailed evidence is rarely available for edges of smaller gaps. We compared the responses of nine wintering mammal species to forest edges in southern Quebec, Canada, using high-resolution spatial data from light detection and ranging (LiDAR) and low-resolution photo-interpretation. We defined edges of open areas as roads, lakes, rivers, or forest open areas. We geolocated mammal snow tracks along systematic transect lines between 2009 and 2018. We compared distances of snow tracks and reference points along transects to the nearest edge with linear models. LiDAR data revealed five species avoiding forest open area edges, whereas no avoidance was shown using photo-interpretation data. Weasels ( Mustela sp.) were the only species showing a positive association with forest open area edges using photo-interpreted data. No significant response was detected for river or lake edges. Four species were positively associated with road edges. We conclude that avoidance of small forest open area edges is widespread in our study area, but it can only be detected with high-resolution spatial data. Our results imply that edge effect can operate at a fine scale and using appropriate spatial resolution is crucial to detect such effects.

UAV and satellite remote sensing for inland water quality assessments: a literature review.

High spatial and temporal resolution data is crucial to comprehend the dynamics of water quality fully, support informed decision-making, and allow efficient management and protection of water resources. Traditional in situ water quality measurement techniques are both time-consuming and labor-intensive, resulting in databases with limited spatial and temporal frequency. To address these challenges, satellite-driven water quality assessment has emerged as an efficient and effective solution, offering comprehensive data on larger-scale water bodies. Numerous studies have utilized multispectral and hyperspectral remote sensing data from various sensors to assess water quality, yielding promising results. However, the recent popularity of unmanned aerial vehicle (UAV) remote sensing can be attributed to its high spatial and temporal resolution, flexibility, ability to capture data at different times of day, and relatively low cost compared to traditional platforms. This study presents a comprehensive review of the current state of the art in monitoring water quality in small inland water bodies using satellite and UAV remote sensing data. It encompasses an overview of atmospheric correction algorithms and the assessment of different water quality parameters. Furthermore, the review addresses the challenges associated with monitoring water quality in these bodies of water and emphasizes the potential of UAVs to overcome these challenges by providing accurate and reliable data.

Rendering lineament induced stream alignment in Upper Krishna Basin, India: a geospatial approach

Lineaments play an important role in drainage development, stream alignment and groundwater recharge. These are traced as surface or subsurface feature. A linear feature that is associated with dislocation and deformation is known as lineament. The present study was aimed to identify the influence of lineaments extracted from satellite images in the Upper Krishna River basin in India. Objective set for the present study is to characterize and analyse the spatial organization of lineaments from ASF-DEM and sentinel-2 satellite data which may help to determine the role of lineaments in groundwater flow and to identify groundwater potential zones. High resolution spatial data allows determining the linear features for considerable distance and play an important role to identify mineral potential areas (Mohammadpour et al. in Geotectonics 54:366–382, 2020). Comprehensive understanding of regional lineament maps is important in terms of their prospectivity for mineral exploration (Richards in SEG Newsl 42:1–20, 2000). Remote sensing datasets are considered the best option when using image enhancement techniques for extracting lineaments. Lineament studies are useful for groundwater and mineral exploration and also in the field of engineering geology (Anbazhagan in Bhu-Jal News 8:8–12, 1994; Anbazhagan et al. in New approaches to characterizing groundwater flow, 2001. http://dspace.library.iitb.ac.in/xmlui/handle/10054/16176, http://hdl.handle.net/100/2781) more generally in the geomorphological studies like forms of surface features and structural geological fields also in physical setting and control over the rocky terrain. The availability of high-resolution satellite data and image processing techniques have rendered it further convenient to map lineaments. In the present study, lineaments were extracted from Sentinel-2 images with a resolution of 10 m, using various image processing techniques. A total of 1314 lineaments were extracted from the study area with a total length of 3983.44 km. The analysis of the extracted lineaments revealed that the lineament density was higher in the upper reaches of the basin, where the undulating hilly region which is located in Western Ghats. This finding implied that these regions have a high structural deformation and a higher groundwater infiltration potential. Moreover, 15% of the total stream length was observed to be influenced by the lineaments. The maximum influence of lineaments was observed in the source region. The lineament extraction results of the present study would assist in understanding the geomorphology of this region and the structural control on the streams and groundwater potential zones, particularly as a contribution to water resource management in this region.

Simultaneous observations of a breakout current sheet and a flare current sheet in a coronal jet event

ABSTRACT Previous studies have revealed that solar coronal jets triggered by the eruption of minifilaments (MFs) conform to the famous magnetic-breakout mechanism. In such a scenario, a breakout current sheet (BCS) and a flare current sheet (FCS) should be observed during the jets. With high spatial and temporal resolution data from the SDO, the NVST, the RHESSI, the Wind, and the GOES, we present observational evidence of a BCS and a FCS formation during coronal jets driven by a MF eruption occurring in the active region NOAA 11726 on 2013 April 21. Magnetic field extrapolation shows that the MF was enclosed by a fan-spine magnetic structure. The MF was activated by flux cancellation under it, and then slowly rose. A BCS formed when the magnetic fields wrapping the MF squeezed to antidirectional external open fields. Simultaneously, one thin bright jet and two bidirectional jet-like structures were observed. As the MF erupted as a blowout jet, a FCS was formed when the two distended legs inside the MF field came together. One end of the FCS connected the post-flare loops. The BCS’s peak temperature was calculated to be 2.5 MK. The FCS’s length, width, and peak temperature were calculated to be 4.35–4.93, 1.31–1.45, and 2.5 MK, respectively. The magnetic reconnection rate associated with the FCS was estimated to be from 0.266 to 0.333. This event is also related to a type III radio burst, indicating its influence on interplanetary space. These observations support the scenario of the breakout model as the trigger mechanism of coronal jets, and flux cancellation was the driver of this event.

An individual-based model to predict the spatial distribution of northern pike, European perch and pikeperch from habitat use in a reservoir subject to water level fluctuations

Reservoirs are subject to artificial water level fluctuations leading to a degradation of the littoral habitats. This often results in reduced productivity and biological diversity, particularly for fish. The development of predictive tools of fish population dynamics linked to habitat characteristics can help to optimize management actions such as habitat restoration. We developed a spatially explicit individual-based model, the 3Plake model, which predicts the spatial distribution of three fish species, northern pike, European perch and pikeperch, over seasons and diel cycles according to their habitat use. The model was calibrated with an accurate description of habitat and high spatial and temporal resolution data of fish movements in a French reservoir. The model calibration is only based on a species-specific coefficient of habitat selectivity. It was chosen to (i) match the observed and simulated traveled hourly and daily distances and (ii) minimize a metric quantifying the distance between observed and simulated density maps. The 3Plake model correctly simulated the means of traveled distances and the species distributions along the year on the study reservoir. The minimal input required (HSI maps and maximum distance range) allows its easy implementation on other reservoirs and fish species. It could be used to assess the impact of habitat restoration or water level management scenarios on fish distribution.

Downscaling air temperatures for high-resolution niche modeling in a valley of the Amazon lowland forests: A case study on the microclima R package.

The forests of the Amazon basin are threatened by climate and land use changes. Due to the transition towards a drier climate, moisture-dependent organisms such as canopy epiphytes are particularly affected. Even if the topography in the Amazon lowland is moderate, mesoscale nocturnal katabatic flows result from cold air production related to radiative cooling. From a certain level of mass the cold air starts to flow downslope towards the valley centers leading to temperature inversions. The resulting cooling in the valleys drives localized fog formation in the valleys at night. This correlates with high epiphyte abundance and diversity in the valleys, which is much less pronounced upslope. The underlying temperature dynamics are, however, not sufficiently included in coarse-resolution reanalysis models such as ERA5-Land. Since high resolution climate data are needed e.g. for proper niche modeling of locally distributed species such as canopy epiphytes, downscaling models such as microclima have been developed and include micro- and mesoscale effects. However, it is unclear how well the elevation-related diurnal course of air temperature can be simulated. Here, we test functions for downscaling coarse-resolution temperature data to high spatial resolution data implemented in the R-package microclima for the South American tropical lowland forests. To do so we compared microclima-downscaled ERA5-Land air temperature data with meteorological station data. We found that the microclima functions only properly detect 73 temperature inversions out of 412 nocturnal cold air drainage (CAD) events during the dry season study period and only 18 out of 400 during the wet season with default settings. By modifying default values such as the emissivity threshold and time frames of possible CAD condition detection, we found 345 of 412 CAD events during the dry season and 177 out of 400 during the wet season. Despite problems with the distinction between CAD and non-CAD events the microclima algorithms show difficulties in correctly modeling the diurnal course of the temperature data and the amplitudes of elevational temperature gradients. For future studies focusing on temperature downscaling approaches, the modules implemented in the microclima package have to be adjusted for their usage in tropical lowland forest studies and beyond.

Real-time flood inundation monitoring in Capital of India using Google Earth Engine and Sentinel database

This study focuses on researching flood inundation and vulnerable areas in Delhi NCT using Remote Sensing (RS) and GIS techniques during flood from July 8 to July 15, 2023, with the entire analysis conducted through satellite and cloud-based processing methods, specifically employing the Google Earth Engine (GEE). Leveraging high-temporal satellites that gather data enables the identification of flooded zones in real-time, during floods, and in the aftermath. Analyzing data collected at different stages of a flood provides valuable insights for pinpointing affected areas. Water naturally flows from high to low elevation areas, and based on elevation data, lowest elevation regions, particularly along the Yamuna riverbank under Delhi NCT, are highly susceptible to flooding, are considered flood-prone areas and flood water inundated. A thorough comprehension and flood-prone area mapping, along with a map illustrating highly inundated zones. After obtaining flood inundation maps and overlaying them with Land Use and Land Cover (LULC) classified maps, the study identified specific areas that experienced flood inundation. The analysis generated a flood zone map, indicating that the flooded area encompasses approximately 110 km², within a total study area of 1488.4 km². The affected areas have elevations ranging from 200 to 210 meters, whereas the maximum elevation in the study area is approximately 326 meters. The GEE platform is employed for processing, utilizing a Supervised classification algorithm for LULC mapping, and an Inverse Distance Weight method for mapping temperature and rainfall. This study utilized the GEE platform to create pre- and post-flood maps based on Sentinel 1 satellite datasets. Generated DEM, and employed it to create various surface estimation maps, including a stream order map. The GIS is employed to enhance the efficiency of monitoring and managing flood disasters, with the high temporal and spatial resolution data playing a pivotal role in flood monitoring.

Some features of thermohaline processes in the coastal zone of Arctic seas

The thermohaline processes in the Arctic coastal zone of seas in Russia are analyzed on the example of some parts of the Barents and Kara Seas: specifically, the Medynsko-Varandey site and the near-mouth coast of the Ob and Yenisey. For comparison with the thermohaline characteristics of international waters, the authors used observation data obtained by Admiral Vladimirsky oceanographic research ship in the summer — autumn of 2014 and Professor Multanovsky in the summer — autumn of 2019 (“Transarctica 2019”). The study aims to obtain typical and extreme distributions of hydrological characteristics in order to estimate heat and salt fluxes in the bottom layers. The characteristic substance fluxes can be used in the calculations of fluxes in bottom sediments, which have a significant effect on permafrost thawing. Such sedimentary rocks constitute a new source of minerals in the Arctic zone. In order to calculate heat and salt fluxes, it is necessary to obtain high spatial resolution data. The available data may be insufficient, thus requiring integrated oceanological observations in the coastal zone.

Growth of the wildland-urban interface and its spatial determinants in the Polish Carpathians

The Wildland-Urban Interface (WUI) is the area where natural vegetation is close to housing and area of concern due to various negative consequences for humans and the environment including fire ignitions, landscape fragmentation and human-wildlife interactions. The WUI is a global phenomenon, and widespread in many countries but long-term WUI dynamics and the main factors causing WUI growth are unknown. Our goal was to assess WUI changes in the Polish Carpathians since the mid-19th century, based on high-resolution spatial data for 1860s, 1970s and 2013. We found that WUI covered already 30% of the study area in the 1860s but grew to cover nearly half by 2013, especially at lower elevations. Detailed analysis of WUI determinants confirmed the areas closer to regional administrative centres or located on steep slopes were more WUI-prone. Tourist trail density also fostered WUI occurrence. We conclude that in Central Europe, with a long history of human settlements and agricultural activities, WUI has been a persistent landscape feature for centuries, but increased in area in recent decades due to widespread abandonment of agricultural land combined with development of new residential areas.

Methodology of operational monitoring of crop status based on the internet of things technologies

Digital technologies are being actively introduced into Russian agriculture at different levels of information analysis (from the plot to the field, farm, region and country as a whole). In crop production at the field level, one of the most important values is the introduction of systems for accurate, rapid and automated monitoring of crop condition, the success of which largely predetermines the effectiveness of precision farming systems. The aim of the research is to develop a methodology for using Internet of Things technologies for non-contact monitoring of crops and related meteorological and soil-hydrological parameters. A wireless network is used as the basis for monitoring, which includes sensor nodes equipped with sensors for meteorological parameters, soil moisture and cameras equipped with a fish-eye lens. Sensor nodes equipped with sensors and cameras are placed in the field according to a specially designed scheme, individualized for each field. Development of the scheme of sensor placement on the field is based on the analysis of long-term archives of satellite data of high spatial resolution and refined soil maps of large scale. Information from sensors is wirelessly transmitted to the network coordinator (or base station) and then to the remote server in the database, and there it is automatically analyzed and interpolated for the whole field. Based on the analysis, recommendations for correction of agrotechnology of crop cultivation are formed. Elements of the methodology were tested on a number of test fields and showed high efficiency. Implementation of the proposed approaches can serve as an alternative to the use of remote sensing data for crop monitoring in offline precision farming systems.