Use Of Remote Sensing Data Research Articles

Using USLE, GIS and remote sensing for the soil loss assessment in the National Park of Theniet El Had, Algeria

Soil water erosion is one of the problems that affect the environment, agriculture and social life by threatening several land surfaces. The objective of this study is to use the USLE model, GIS and remote sensing (RS) to estimate the annual rate of soil loss by water erosion in the Theniet El Had National Park (THNP) which belongs to the mountainous ecosystem of Djebel El Meddad, located in the northwest of Algeria. The use of the USLE model takes into account the five factors controlling water erosion, namely: the rain erosivity (R) determined from the annual rainfall data, the soil erodibility (K) developed from soil survey data, the slope lengths (LS) generated by using DEM, the vegetation cover (C) by the use of RS data and erosion control management practices (P) by field trips. The integration of these factors made it possible to establish the quantitative map of the annual rate of soil loss varying between 0.02 and 55.10 (t/ha.year), with an average of around 6.64 (t/ha.year). Five erosion aggressiveness classes are used; very weak, weak, moderate, strong and very strong which represent a rate respectively of 23.70, 44.65, 22.72, 4.41 and 4.52 % of the study area surface. The areas with high and very high erosion rates are located in the north having a very rugged relief and low vegetation cover. This study can be used in the mountainous ecosystems and it will make it possible to set up priority intervention zones to combat the risk of water erosion.

Status and Prospects for the Use of Remote Sensing Data for the Detection of Wildfires

The article discusses the problem of fires as a serious threat to life, economy and ecosystems, highlighting the need for early detection and suppression of fires. The potential of the combination of Earth remote sensing and neural networks for rapid and accurate detection of natural fires is studied. The significance of applying artificial intelligence, the development of deep learning methods for neural network models, to analyze space images and detect early signs of fires is emphasized. The article also provides examples of successful projects and research in the field of wildfire detection. The final part of the paper emphasizes the need for further research and development of neural network training methods, expansion of training datasets and improvement of space imagery acquisition technologies for effective control and prevention of fires, in order to protect the environment and minimize damage to people.

Upgrading Maize Cultivation in Bosnia and Herzegovina from Rainfed to Irrigated Systems: Use of Remote Sensing Data and the Dual Crop Coefficient Approach to Estimate Evapotranspiration

A two-year experiment was conducted with a local maize hybrid under full (F) and deficit (D) drip irrigation and rainfed conditions (R) to estimate maize evapotranspiration in Bosnia and Herzegovina (BiH). Three approaches, namely, A&P, SIMDualKc (SD), and vegetation index (VI), to estimate the actual crop coefficient (Kc act), the actual basal crop coefficient (Kcb act), and the actual crop evapotranspiration (ETc act), were applied with the dual crop coefficient method and remote sensing (RS) data for the first time. While Kcb act from all approaches matched FAO56 tabulated values, SD showed differences in comparison to A&P of up to 0.24 in D and R conditions, especially in the initial and mid-season stages. VI demonstrated very good performance in all treatments. In F, the obtained Kc act for all approaches during the initial and end stages were higher than the tabulated values, ranging from 0.71 to 0.87 for the Kc ini act and from 0.80 to 1.06 for the Kc end act, while the mid-season period showed very good agreement with the literature. The maize crop evapotranspiration range is 769–813 mm, 480–752 mm, and 332–618 mm for F, D, and R, respectively. The results confirmed the suitability of both approaches (SD and VI) to estimate maize crop evapotranspiration under F, with the VI approach demonstrating an advantage in calculating Kcb act, Kc act, and ETc act values under water stress conditions. The higher observed yields (67.6%) under irrigation conditions emphasize the need to transition from rainfed to irrigation-dependent agriculture in BiH, even for drought-resistant crops like maize.

An Overview of the Remote Sensing and GIS Techniques Application to Detect Changes in the Surface Area of Water Bodies in Iraq

The use of Remote Sensing data in monitoring water bodies and reservoirs is a new and advanced approach that helps to study large areas covered by water in a short time and at a reasonable amount compared to traditional methods. Accordingly, the Remote Sensing technique integrated with Geographic Information Systems (GIS) has been used in several studies to monitor changes in water surface body area locally and regionally. In this study, several studies in Iraq were reviewed, using Remote Sensing techniques with the help of GIS to detect changes in the areas covered by the water bodies using satellite images captured in different periods. Natural and artificial lakes represent a large proportion of the water bodies in Iraq, including eight (8) lakes distributed in different regions of Iraq. An evaluation of the hydrological system of the studied water bodies showed that the changes in the area and size of natural and artificial lakes are affected by political, economic, and climatic conditions as the areas increase and decrease over years.

Application of remote-sensing data in geochemical studies of soils of the Yertis River basin within East Kazakhstan

This article presents the results of field research, as well as the results of the use of remote-sensing data in the geochemical study of the soil cover of the Yertis River basin. In the work, the content of the gross form of heavy metals in the soil of the river basin was investigated. The atomic absorption method determined the content of the Poor configuration of elements of elements in soils. Statistical processing of the obtained data from N. A. Plokhinsky was carried out using the Statistica program. Also, using the performed indices in the remote sensing of clay minerals, iron oxides, and carbonates, the spatial distribution and comparison of pollutants in the soil cover of the river basin were revealed. The methods and means of image processing tested in this study can be used to create maps of the distribution of pollutants. Also, the data obtained reflect the patterns of distribution of heavy metals in the soils of the basin and can be used to optimize landscapes and improve the organization of ecological and geochemical monitoring.

Use of Remote Sensing Data to Assess the Effects of the Floodplain Vegetation Burning in the Lower Dniester National Park (Odesa Region)

The aim of the study was to determine the burnt areas, response of the floodplain ecosystems to burning, and to determine the difference between them and the control sites using the remote sensing data. The impact of burning was assessed using the spectral indices: Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (GCI), Specific Leaf Area Vegetation Index (SLAVI), Normalized Difference Moisture Index (NDMI) and Moisture Stress Index (MSI). It was established that during the winter/spring period of 2020, fires in the territory of the Lower Dniester NNP covered almost 4453 hectares (33.1% of the floodplain ecosystems area). Simulation showed that the difference between the burned and control areas gradually decreased over the vegetation period. Maximal difference was observed at the beginning of the growing season (the quality of group selection was 43.2%), which indicates forming of relatively individual environmental conditions for the floodplain vegetation development. In spring, the difference was equal to 20.3%, due to development of <i>Phragmites australis</i>, the edifier of the plant complexes of wetland ecosystems, whose stems have developed a leaf blade. The summer period was characterized by further decrease in the group selection quality (19.9%), owing to equalization of environmental conditions for vegetation development. The autumn period was characterized by dry conditions and end of the growing season, which also contributes to the almost complete disappearance of differences between the plots (14.5%). Based on the analysis of similarity (ANOSIM), it was found that degree of difference between the burned plots was small, owing to local conditions and burning intensity, and amount of dry organic matter in certain areas. It was found that gradual decrease in differences during the vegetation season took place only at winter (February) burning. The influence of spring (March, April) burning resulted in significant variability of the similarity degree over the vegetation period.

Use of Remote Sensing Data for Environmental Monitoring of Desertification

Use of Remote Sensing Data for Environmental Monitoring of Desertification

Use of Remotely Sensed Data for the Evaluation of Inter-Row Cover Intensity in Vineyards

Information on vegetation cover and soil management is used in hydrological and soil erosion modeling, but in most cases, reference values are used solely based on land use classification without considering the actual spatial and temporal variation adopted at the field scale. This work focused on the adoption of satellite optical data from the Copernicus Sentinel-2 (S2) mission to evaluate both spatial and temporal variations of vineyard ground cover. First, on a wider scale, fields were mapped by photointerpretation, and a cluster analysis was carried out. Results suggest that vineyards can be classified according to different inter-row soil management, with the best results obtained using NDVI and NDWI. A pilot area in the municipality of Carpeneto, in the wine-growing area of Alto Monferrato, was also analyzed due to the availability of reference data on inter-row vegetation cover from experimental plots. Those are set on sloping areas and present different inter-row soil managements (conventional tillage—CT, and permanent grass cover—GC). Time series of different vegetation indices (VIs) have been obtained, and both S2 native bands and the derived VIs were evaluated to assess their capability of describing the vineyard’s inter-row coverage growth trends at plot level for the agrarian year 2017–2018. Results suggest that a seasonality effect may be involved in the choice of the most suitable band or index that better describes soil coverage development at a given moment of the year. Further studies on open-source remotely sensed (RS) data could provide specific inputs for applications in erosion risk management and crop modeling.

The Use of Remote Sensing Data for Investigation of Environmental Consequences of Russia-Ukraine War

Abstract The usage of remote sensing data for tracking or monitoring war conflicts is a reality nowadays. The Russian invasion in Ukraine seriously impacted on the environment of the attacked country in all areas: air, soil, water, flora and fauna. The war has created a massive increase in air pollution in some regions of Ukraine, and might have effect in neighbouring countries. The Russian attack has caused many fires at places such as oil depots, industrial complexes, equipment and ammunition stockpiles, the forests, and residential areas. In addition to this, many bridges, hydraulic and other structures located over and near the water bodies were destroyed. These events were analysed in this paper by using publicly available remote sensing data: NOAA-2, Suomi NPP, Aqua and Terra satellites, Sentinel and Landsat satellites, as well as from other sources open to public. We assume that the use of remote sensing data is an excellent tool to monitor effects of military conflicts on environment. The conducted research with proposed method can serve as a good scientific practice that should be implemented for monitoring of the harm to nature caused by wars.

The Use of Remote Sensing Data for Probabilistic Assessment of an Impact on a Linear Structure

The Use of Remote Sensing Data for Probabilistic Assessment of an Impact on a Linear Structure

Assessment of Natural Plant Communities through the Use of Remote Sensing Data of the Stavropol Territory Steppes

Introduction. The relevance of the study of steppe phytocenoses is caused by the unsatisfactory state of natural grass stands, namely a low level of biodiversity and a high degree of degradation. The aim of the work is to determine the features of the connection of the Earth remote sensing data with the state and degree of degradation of natural grass stands in unstable moistening zone and arid zone of the Stavropol Territory. The Earth remote sensing data with certain temporal and spatial resolutions make it possible to carry out almost continuous monitoring of the state of natural grass stands. Materials and Methods. The study of steppe phytocenoses was carried out in 2016–2020 on the ground at discount areas (100 m2) according to the requirements of methods generally accepted in phytocenology. Vegetation condition was assessed using the Earth remote sensing data based on the values of the Normalized Difference Vegetation Index. According to the satellite data, Normalized Difference Vegetation Index cartograms were constructed for each point of the study. Results. The proportion of polygons with a high degree of degradation is 18.8% of research objects located in the zone of unstable moistening and the proportion of polygons with an average degree of degradation is 37.5%, while in the arid zone 70.6 and 23.5%, respectively. In the zone of unstable moistening, the highest coefficients of rank correlation between the degradation degree and the area occupied by herbaceous vegetation with a certain value of the vegetation index are observed in the case if Normalized Difference Vegetation Index is in the range of 0.0–0.4, and in the arid zone 0.0–0.3 (at 0.01 significance level). Discussion and Conclusion. When using the Earth remote sensing data to assess the degree of degradation of steppe ecosystems of the Stavropol Territory, it is necessary to use regression models specific to various soil and climatic conditions.

ESTIMATION OF WATER BALANCE OF BARSACHHU WATERSHED IN CHUKHA BHUTAN USING REMOTE SENSING AND GIS

The water balance of a watershed is critical for the estimation of the water budget to meet the present and future domestic and economic demand and supply of water. Water balance parameters like precipitation, temperature, evapotranspiration soil moisture content deficit and surplus determine the water budget of a watershed which is further influenced by factors like land use, soil types, seasonal variation and other environmental factors. This paper intends to estimate the water balance of the Barsachhu watershed using Thornthwaite and Mather (TM) model. The use of Remote Sensing data and GIS as a tool were used in deriving the required data in executing the model. The study indicates that the monthly soil moisture content (deficit or surplus) is substantially influenced by the change in monthly rainfall, temperature and land use land cover pattern. April to October remain water surplus months while November to March remains a period of deficit. The soil moisture content has a different response to different land use in the Barsachhu watershed. The maximum annual moisture deficit and surplus of the basin are 126.27 mm and 2430.79 mm respectively and is observed in built-up areas. The runoff coefficient of the Barsachhu watershed computed from the runoff simulation of the TM model is 0.63.

A spatial-spectral adaptive thin-cloud removal method based on slow feature analysis

ABSTRACT Clouds cover corrupts the spatial and spectral information of optical remote sensing (RS) images, which seriously affects the use of RS data. To solve the problem of missing information, a spatial-spectral adaptive method based on slow feature analysis (SFA) is proposed to restore cloudy scenes in this letter. SFA converts the sequence signal into slowly varying signal signatures and the clouds will be located in the first component. We propose spatial and spectral adaptive correction methods to reduce interference from highlighted pixels and jointly constrain the cloud coefficients in each band according to reflectance and gradient. The effectiveness of our method is verified on Landsat-8 OLI simulated and real cloudy datas, the restored results are visually rich in texture detail and moderately corrected. The average PSNR of the four real scenes is 42.9738 dB and coefficient of determination (R 2) is 0.8203, and many indicators have proved that our method is better than the existing methods.

The Use of Remote Sensing Data to Estimate Land Area with Forest Vegetation Cover in the Context of Selected Forest Definitions

(1) Background: Like many other countries, Poland is obliged to report forest area to the Climate Convention (UNFCCC Kyoto Protocol) and the Food and Agriculture Organization of the United Nations (FAO/UN). Differences between national and international forest definitions lead to differences between actual and reported forest area. Remote sensing is a useful tool for estimating forest area for reporting purposes. One of the most important parts of the estimation is the choice of a basal area to calculate the percentage of vegetation cover. (2) Methods: Height, crown projection area, and minimum complex area were used to classify the area with forest vegetation. Percentage canopy cover was determined using three different methods based on segmentation polygons, triangular grid and canopy height model pixels. The accuracy of the above methods was verified by manual vectorization performed on a selected set of test plots in the Milicz study area according to the international definitions. The differences were examined using three statistical metrics. (3) Conclusions: This paper compares for the first time methods for determining the area for which canopy cover is calculated (using data from (ALS) and discusses the differences between them in the context of accuracy (the correspondence between the results and the reference data) and the complexity of the process (time and effort required to perform the analysis). This is important in the context of reporting, estimating carbon stocks and biodiversity to mitigate the effects of climate change. Method 2 proved to be the most accurate method, Method 1 was found to be the worst option. Accuracy was better in the case of the Kyoto Protocol definition.

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments.

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia

Periodic assessment of forest aboveground biomass (AGB) is essential to regulate the impacts of the changing climate. However, AGB estimation using field-based sample survey (FBSS) has limited precision due to cost and accessibility constraints. Fortunately, remote sensing technologies assist to improve AGB estimation precisions. Thus, this study assessed the role of remotely sensed (RS) data in improving the precision of AGB estimation in an Afromontane forest in south-central Ethiopia. The research objectives were to identify RS variables that are useful for estimating AGB and evaluate the extent of improvement in the precision of the remote sensing-assisted AGB estimates beyond the precision of a pure FBSS. Reference AGB data for model calibration and estimation were collected from 111 systematically distributed circular sample plots (SPs) of 1000 m2 area. Independent variables were derived from Landsat-8, Sentinel-2 and PlanetScope images acquired in January 2019. The area-weighted mean and standard deviation of the spectral reflectance, spectral index and texture (only for PlanetScope) variables were extracted for each SP. A maximum of two independent variables from each image type was fitted to a generalized linear model for AGB estimation using model-assisted estimators. The results of this study revealed that the Landsat-8 model with the predictor variable of shortwave infrared band reflectance and the PlanetScope model with the predictor variable of green band reflectance had estimation efficiency of 1.40 and 1.37, respectively. Similarly, the Sentinel-2 model, which had predictor variables of shortwave infrared reflectance and standard deviation of green leaf index, improved AGB estimation with the relative efficiency of 1.68. Utilizing freely available Sentinel-2 data seems to enhance the AGB estimation efficiency and reduce cost and extensive fieldwork in inaccessible areas.

The Use of Remotely Sensed Data and Polish NFI Plots for Prediction of Growing Stock Volume Using Different Predictive Methods

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons.

The Use of Remote Sensing Data to Improve the Efficiency of Pastoral Sheep

in the study of agricultural land, the priority is the use of remote monitoring techniques using unmanned aerial vehicles, technological portable devices. Obtaining materials by remote methods will expand the possibilities of introducing grazing livestock, increase the yield and thereby improve the efficiency of production indicators. The article considers the issues of assessing the nutritional value of pasture fodder, in particular, the amino acid composition in combination with the vegetation index in the conditions of pasture livestock raising in the Stavropol Territory. Productive qualities of replacement young animals directly depend on the quality of feed. Monitoring the amino acid composition of feeds allows you to optimize the technology of growing sheep, taking into account their productivity direction. The combination of remote methods for assessing the vegetation index and the in-depth composition of the nutritional value of feeds makes it possible to optimize the production schedule for the use of pasture plots by various sex and age groups of animals and increase the average daily gain in live weight of young fattening groups by 8-10%. It has been shown that the introduction of top-dressing with roughage in especially dry periods contributes to preserve of animal growth energy when changing grazing areas. Remote assessment of pasture vegetation was carried out using a special camera to calculate the NDVI (Normalized Difference Vegetation Index), which was installed on an unmanned aerial vehicle. At the same time, the dynamics of the NDVI using a portable manual nitrogen sensor was studied in the same pasture plots. The obtained results of monitoring of the vegetative index on pasture plots were compared with the results of chemical analysis of plant feed. Information on the availability of gross amount of plant biomass in the fields was used for priority selection of a specific site for grazing animals and optimizing the use of agricultural territories. It was proven that the parameters of the NDVI for the pasture keeping of young sheep should not be lower than 0.4. The development and implementation of innovative methods of aerospace monitoring makes it possible to increase the efficiency of land use and increase the efficiency of grazing.