Semi-Automatic Classification Plugin Research Articles

Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs

Macroalgae have been used as indicators of the health of coastal ecosystems, they function as sinks of CO2 and are essential contributors to primary production. With the increase in anthropogenic activities, it is crucial to assess the impact of such activities on these ecosystems. As traditional surveying techniques, although accurate, are time-consuming and their area coverage is limited, novel techniques are required to monitor the coverage and diversity of intertidal macroalgae. We propose a methodology using the free-source Semi-Automatic Classification Plugin from QGIS to use UAV and multispectral cameras for the spatiotemporal monitoring of intertidal macroalgae. We also compared the performance of six classifiers: Minimum Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM), for three types of macroalgae classification: general, taxonomical groups and species. As proof of concept, an intertidal rocky shore in a marine protected area (NW Spain) was studied for four months. RF and SVM achieved similar results, with both being recommended for the general (OASVM = 97.4±1.7 and OARF = 98.3±1.7) and taxonomical groups (OASVM = 91.6±1.9 and OARF = 89.2±4.5). SVM and ML were found to be more suitable for species classification (OASVM = 77.4±11.4 and OAML = 74.2±9.7). SAM and MLP provided the least performant species classifiers because of the overlap in the macroalgae spectral signatures. The plugin showed limitations when tuning the input parameters of the MLP classifier and did not let to add a validation dataset. Additionally, we present an open-access GIS web application, Alganat 2000 GIS web, to facilitate the monitoring and management of coastal areas. We conclude that the proposed methodology using the SVM or ML classifiers is an effective tool for assessing intertidal macroalgal assemblages. Its easy and rapid implementation is beneficial for researchers who are not very familiar with coding and machine learning frameworks and reduces the time and cost of fieldwork. As future work, we propose the combination of the multispectral bands with topographic and spectral indices and to research the application of deep learning models to the classification of intertidal macroalgae.

Urban Spatial Dynamics and Geo-informatics Prediction of Karachi from 1990–2050 Using Remote Sensing and CA-ANN Simulation

Rapid urbanization significantly impacts land use and land cover (LULC), leading to various socioeconomic and environmental challenges. Effective monitoring and detection of spatial discrepancies are crucial for urban planners and authorities to manage these changes. This study aims to analyze the spatial dynamics of LULC changes and predict future land use patterns. The specific objectives are to classify historical land use from 1990 to 2020, simulate future land use from 2020 to 2050, and interpret the spatial and temporal results. The study utilized remotely sensed images with the semi-automatic classification plugin (SCP) approach for land use classification from 1990 to 2020. Future land use patterns were simulated using the Modules of Land Use Change Evaluation (MOLUSCE)-based Cellular Automata-Artificial Neural Network (CA-ANN) model. The results were then interpreted to comprehend the dynamics of urban expansion. The conclusions direct a significant increase in built-up and grasslands, with a consistent decline in other land use types. From 1990 to 2020, approximately 423.75 km² and 856.97 km² of land were converted into built-up areas and grasslands, respectively. This was accompanied by a decline in rocky bare and bare soil areas, while the proportions of water bodies and mangroves remained steady. Predictions for 2020 to 2050 suggest an additional increase of 561.93 km² in built-up areas, with a progressive decline in other land use classes. The study emphasizes the critical need for spatial planning policies to address challenges arising from rapid urbanization. By analyzing historical land use changes and predicting future patterns this research offers a comprehensive view of urban growth dynamics. The novel application of these techniques provides valuable insights for urban planners to develop informed strategies for managing expansion and mitigating associated socioeconomic and environmental impacts.

Land cover change assessment in Thai Nguyen Province, Vietnam using GIS and remote sensing techniques

The expansion of agricultural production and urbanization has led to the depletion of the global forest ecosystem, resulting in land cover changes (LCCs) that pose threats to the land environment. Therefore, it is imperative to identify these LCCs as an essential step toward resolution and mitigation. This study aims to evaluate temporal LCCs in Thai Nguyen province by utilizing GIS and remote sensing techniques (RST) to analyze high-resolution satellite imagery spanning from 2001 to 2023. LCCs were identified using semi-automatic classification plugin (SCP) techniques with Landsat-8 and Sentinel-2 images in ERVI software. The accuracy of the LCC maps was validated through post-classification comparisons, yielding high precision rates ranging from 87% to 96%. The results indicate a significant reduction in forest cover area, declining by 1018 ha from 226,18 ha in 2001 to 207,22 ha in 2023. This decline primarily occurred in urban areas, highlighting the intensification of urbanization processes. Cultivated and bare land experienced a minor decrease and a substantial decrease from 18,631 ha and 105,000 ha to 16,578 ha and 86,82 ha between 2001 and 2010, followed by a slight increase and a significant increase to 5,069 ha and 24,487 ha (1.4% and 6.9%) during the 2010-2023 period. These findings underscore the escalating risk of diminishing forest cover in the study area.

Evolution of land cover from 1989 to 2023 in the southern region of the Ñeembucú department, Paraguay

This research study, conducted in the southern region of the Ñeembucú Department, Republic of Paraguay, aims to investigate the evolution of land cover from 1989 to 2023. The study area encompasses the districts of Pilar, Humaitá, Paso de Patria, General Díaz, Mayor Martínez, Isla Umbú, Desmochados, Guazu Cuá, Tacuaras, Villalbín, and Laureles. The research was initiated due to concerns regarding the degradation of wetlands, especially since this region is considered unique in the country. In recent years, the southern region of the Ñeembucú Department has undergone numerous human interventions, primarily for productive purposes. The study area, covering a total surface of 6,636.5 km², was demarcated. Subsequently, LANDSAT satellite images were obtained through the U.S. Geological Survey, selected for less than 20% cloud cover. The selected area was then delineated by cropping the images to the specified extent using QGIS 3.32 software and the Semi-Automatic Classification plugin, facilitating the semi-automatic classification of land cover. Significant deforestation, an increase in areas without land cover, urban areas, and sparse vegetation, as well as a decrease in the extent of swamps, forests, grasslands, and prairies were observed.

Geoecological analysis of the state of forest park plantations in the Dnipro city using Landsat satellite images


 
 
 The importance of the number and viability of green areas for creating a com- fortable and safe life for residents of urbanized areas is increasing with the growth of cities, which has not stopped since the first half of the 20th century. Dnipro is a city with progressive air pollution, therefore the effective use of its green areas in this aspect – the selection of tree species, regular assessment of their condition and timely response to changes in vegetative activity – becomes especially relevant. To solve the research task, a combination of on-site and remote methods of assessing the vital state of green areas of the city was chosen. First, spatial and statistical information about the city’s green areas was defined and summarized, and a cartographic and statistical base of raw data was created, potential anthropogenic factors of influence were determined, and reference areas were selected. At the second stage, a systematic survey and assessment of the vital state of the trees of selected areas was carried out for seven years: morphometric indicators of trees of certain species and assessment of the state of their morphological elements (roots, trunk, crown, leaves). According to the degree of damage, trees were classified as: healthy, weakened (damaged), severely weakened; dying and dead. At the third stage of the study, the correlation of the vital state indicator (Ln) and the normalized vegetation index (NDVI) was determined, based on images from Landsat sensors 8 and 9 of the L1 processing level, pre-processing of the images was performed using the SCP (Semi-automatic Classification Plugin) module integrated in QGIS. According to the results of on-site research, it was found that for the period 2015-2021, most parks and squares of the control group belong to the second category (weakened), three to the first (healthy) and one to the third (severely weakened). The main types of damage have been identified as well as species of trees that are most susceptible to damage. The dynamics of vegetation activity indicators reflect the general tendency to increase the vegetation cover of the city for six years. According to the results of the correlation analysis of NDVI and Ln values, a stable dependence between the index of vital state and the normalized vegetation index was revealed. The correlation between the specified indicators for each particular year is direct and varies from 0.5 to 0.9. The vital state of forest park plantations in Dnipro city is at a satisfactory level. The average Ln for the entire city is 65.6%. It was concluded that the exploitation of the gas-absorbing capacity of plants must be carried out in combination with technical measures in order to activate the processes of improving the environment.
 
 

Impact of land use and land cover dynamics on urban heat island in the Sunyani Municipality using satellite remote sensing

The rapid urbanization coupled with tremendous growth in population and built-up areas in the cities has led to issues associated with global climate change impacts such as urban heat islands. This study is concerned with the spatial impact of the dynamics of urbanisation on urban heat island (UHI) in the Sunyani Municipality using Satellite Remote Sensing techniques. Sunyani municipal is a growing area in Ghana with increasing population and urbanisation. Many people in Ghana are moving to settle in Sunyani for different purposes. In this process, vegetated areas have been converted to urban areas and this has led to a reduction in the vegetation cover and increased the urban temperature in the municipality. Therefore, there is a need for a study on how the dynamics in the land use and land cover (LULC) in the municipal are contributing to urban heat. The main aim of this research is to identify the impact of LULC dynamics on UHI using multi-temporal Landsat images from 2001 to 2021 with a 5-year epoch in the Sunyani Municipal. Landsat images acquired over Sunyani and its environs for the period from 2001 to 2021 with a 5-year epoch, were download freely from the USGS Earth Explorer website. These images were pre-processed in QGIS 3.16 using the Semi-automatic classification plugin (SCP). The pre-processed images were classified using the Random Forest (RF) algorithm. The supervised classification was done using the semi-automatic classification plugin in QGIS 3.16. This study considered two (2) classes (urban and vegetation). The thermal infrared bands of the Landsat images were used to compute the land surface temperature (LST) for each year, starting from 2001 up to 2021, at a 5-year interval. The standard deviation and mean LST were extracted and used to extract all the UHIs in the municipality. Regression analyses were used to determine the impact of LULC dynamics on LST and UHIs in the Sunyani Municipality. The analysis yielded a strong impact with an R-squared value of 75% and 77% for UHI and LST respectively. The vegetated covers yielded a strong negative correlation with UHIs while Urbanisation yielded a strong positive correlation with the intensity of UHIs. On the other hand, Vegetation reduced the intensity of UHIs in the municipality. This gives a clear understanding of how dynamics in urbanisation and vegetation are affecting UHIs in the Sunyani Municipality. A Linear model was developed for LULC dynamics, LST and UHIs in the Sunyani municipality. It was concluded that LULC dynamics have a strong impact on UHI. It is recommended that the environmental Agencies in the Municipality should ensure the protection of the vegetated areas as well as improve the green areas in the Municipality to minimize urban heat.

Administrative Transformation of the Urban Authorities and the Emergence of Un- Ecological Land Use/Cover Dynamics in Urban Authorities: Case of Dodoma National Capital City, Tanzania

This study explores how the evolutionary changes in Dodoma's urban administrative status from lowest to highest ranks in Tanzania have resulted in the emergence of Un - ecological urban land use and land cover changes, urban land degradation, and un-ecological urban villages in the locality of the newly established Dodoma National Capital City. The empirical evidence for this study has been drawn from interviewing 443 respondents in 17 wards and 56 sub-wards and through analysing Landsat satellite images of 1998, 2008, 2018 and 2022 years by semi-automatic classification plug-in (SCP) in Quantum Geographical information system (QGIS) software (version 3.12.3). The findings of this study revealed that built-up land hectares have increased by 60%, bare land hectares decreased by 23%, vegetation hectares decreased by 13%, and water hectares decreased by 1% between 1998 and 2022. If Dodoma City persists in the same way up to 2030 without green building concepts and design, New urbanism and ecological modernization theories interventions it will lose the potential of being a resilient and liveable National Capital City. Sustainable development principles are recommended to be utilized in all the major development projects to be carried out in the city advert the situation.

HABITAT STRUCTURE AND HETEROGENEITY DETERMINE A SUCCESSFUL BREEDING PROBABILITY OF THE LONG-EARED OWL (<i>ASIO OTUS</i>, STRIGIDAE, STRIGIFORMES)

In 2001 to 2020, for each Long-eared owl nest in the Crane Homeland Nature Reserve territory with an area of 48 km2, the following microhabitat parameters were determined using standard field methods: the nest type built by different bird species, the presence of a nest roof, the nesting tree species, and the nest height above the ground. In most cases, for nesting the Long-eared owl reused magpie nests (Pica pica) (77%), and half of the nests were found on willow trees (family Salicaceae) (50%). The nest height above the ground varied from 1.5 to 22 m. Vegetation on the nesting area with a radius of 500 m was determined using the “Semi-Automatic Classification Plugin” automatic vegetation classifier of QGIS software. Five habitat types were so classified: forests, shrubs, meadows, arable land, and satellite image mistakes. Village areas were allocated separately. The average shares of the largest habitat types for over 20 years on the nesting sites included meadows (51.1%) and shrubs (16%). From 2001 to 2020, a habitat change in the breeding area was observed: forest and shrub areas decreased, while the meadow, village and arable land areas increased. An analysis of the effect of habitat types on the nesting success was evaluated by means of the “gbm” and “dismo” modules in the R program using the Boosted Tree classifier algorithm. The presence of such habitat types in the nesting area as villages (24.2%), shrubs (19.3%), meadows (11.8%), and habitat heterogeneity (11.6%) most strongly affected the probability of owl successful nesting. That owls tend to use anthropogenic and agricultural territories is associated with the diversity, easy foraging and more effective protection from predators during the breeding season.

Machine Learning Approach for Detection of Water Overgrowth in Azov Sea with Sentinel-2 Data

The Azov Sea estuaries play an important role in the reproduction of semi-anadromous fish species. Spawning efficiency is closely connected with overgrowing of those species spawning grounds; thus, the objective of the water vegetation research has vital fisheries importance. Thus, the main goal of the research was to develop a machine learning algorithm for the detection of water overgrowth with Phragmites australis based on Sentinel-2 data. The research was conducted based on field botanical and vegetation investigations in 2020–2021 in Soleniy and Chumyanniy firths. Collected field and remote sensing data were processed with the semi-automatic classification plugin for QGIS. For the classification of Azov Sea estuaries, a random forest algorithm was used. The obtained results showed that in 2020 the areas occupied by reeds reached 0.37 km2, while in 2021, they increased to 0.51 km2. There was a high level of Phragmites australis growth in the Soleniy and Chumyanniy firths. The rapid growth of Phragmites australis in the period of 2020–2021, where the area covered by the reed doubled, is primarily attributed to eutrophication. This is due to the nutrient enrichment from agricultural lands located in the northern part of the research area near Novonekrasovskiy village. Additionally, changes in water flows and hydrological conditions can also contribute to the favorable growth of the reed. This can result in a high growth rate of Phragmites australis, which can reach up to 2 m per year and can propagate both through vegetative and sexual means, leading to the formation of large and dense clusters.

Land Conversion Analysis in Buleleng District, Bali: An Outlook for Sustainable Tourism Development

Buleleng District, Buleleng Regency, Bali is one of the main tourism destinations that offer various types of tourism activities. This region is also included in one of the National Tourism Strategic Areas which will intensify tourism activities and induce land conversion from non-built-up to built-up area to satisfy tourism sector demands. Departing from that background, this paper aims to analyze the land conversion that happens and study the impacts as the foundation in contextualizing sustainable tourism management to this case. For the land conversion analysis, Sentinel-2 satellite imageries capturing 2017 and 2022 images are used and the classification is done using the Semi-Automatic Classification Plugin in QGIS. As a result, satellite imageries show that there was land conversion happening with Banyuasri, Beratan, and Ligundi Villages having the highest land conversion proportion. Land conversion impacts tourists’ thermal comfort and disrupts food security as many agricultural areas were converted. As the paddy field holds a crucial role in the culture of the Buleleng Resident, it may also impact the cultural institution. Sustainable tourism development will control the land conversion and mitigate the negative impact and can be deployed together with the adat institution approach.

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI + NDSI + DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI + NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (√k), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI + NDSI + DEM parameters with the number of trees = 100, mtry = all variables (k), and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.

Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data.

The dynamic change in land use/land cover (LULC) caused by rapid urbanizationhas become a major concern in Lahore, causing a variety of socioeconomic and environmental issues relating to air quality. As a result, it is important to monitor existing LULC change detection, future projection, and the increase in atmospheric pollutants in Lahore. This research work makes use of Landsat, GIOVANNI, SRTM DEM, and vector data. The four key steps of the research approach are as follows: (i) LULC classification from 2000 to 2020 using Lansat data and semi-automatic classification plugin (SCP); (ii) simulation of future prediction using Modules of Land Use Change Evaluation (MOLUSCE) prediction model; (iii) assessment of effects of land use change on air quality using GIOVANNI-NASA product; and (iv) monitoring, change detection, and result interpretation. According to the research findings, there was an increase in metropolitan areas and a decrease in vegetation, barren land, and water bodies for both historical and future projections. The findings also indicated that from 2000 to 2020, about 27.41% of the urban area expanded, with a decline of 42.13% in vegetation, 2.3% in the barren land, and 6.51% in water bodies, respectively. Between 2020 and 2040, the urban area is predicted to increase by 23.15%, while vegetation, barren land, and water bodies are expected to decrease by 28.05%, 1.8%, and 12.31%, respectively. Also, the atmospheric pollutants have been increased including NO2 (1.60%), SO2 (1.02%), CO2 (0.71%), CO (1.56%), O3 (0.15%), and CH4 (0.20%), respectively. And it is projected that by 2040, the average annual atmospheric concentration of NO2, SO2, CO2, CO, O3, and CH4 will be increased by 28.80%, 18.36%, 12.78%, 28.08%, 2.70%, and 3.60%, respectively. In addition, it was also observed that the majority of the city's urban area expansion was found in the city's eastern and southern regions. Therefore, government should focus on natural resource conservation especially vegetation cover to reduce air pollutants concentration and the LULC effect.

Structure and heterogeneity of habitat determine diet of predators despite prey abundance: Similar response in Long-eared, Short-eared Owls and Common Kestrels

According to one of the theses of optimal foraging theory, main prey species abundance in the hunting area is the main factor determining the diet and habitat choices of birds of prey. However other factors can also be important. The habitat structure influences the predators' diets as well. In this study we examined the influence of habitat structure on diet compositions of three species of birds: Long-eared Owl (Asio otus), Short-eared Owl (A. flammeus) and Common Kestrel (Falco tinnunculus). The study was carried out from 2007 to 2019 in a 48 ​km2 area of the Crane's Homeland Reserve, Moscow Region, Russia. The habitat structures of model species' hunting territories (ratio of different types of landscape elements) were classified in module “Semi-Automatic Classification Plugin” based on the QGIS. A boosted regression tree analysis identified that the share of the main prey species in the diet is primarily determined by the landscape structure of hunting territories. The largest share of Common Vole (Microtus arvalis) in birds' diet was determined by the shrubs area (15% of hunting area), the meadow area (75%), the habitat heterogeneity (70%) and the arable land area (5%). The same predictors determined the largest share of Root Vole (Microtus oeconomus): the shrubs area 25%, the meadow area 70%, and the arable land area 3%. The annual mean abundance of prey species did not determine their importance in the diet of birds of prey. Thus, the main prey abundance in the hunting area is not a determining factor for the formation of diet composition of birds of prey.

The degradation of forest areas in Morocco: Case of Benslimane province

This article aims to shed light on the process of known degradation of the forest area of Benslimane province during the period 1990–2020 and to specify the most important human causes which contributed to it (quarries, extension of the built-up area, the impact of agricultural activities, grazing and collection of firewood), by using remote sensing techniques (spatial images for the years 1990–2000–2010–2020) to produce Land Cover maps. The following satellite images were used, Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI, with a spatial precision of 30 m, the Semi-Automatic Classification Plugin (SCP) in QGIS was used for atmospheric correction, and the Spectral Angle Mapping algorithm for the images’ classification. The rating evaluation of the Kappa coefficient shows the following ratios for the years 1990–2000–2010–2020 respectively ; 0.89–0.90–0.90–0.93. The results showed that the forest area of Benslimane province has declined by 11.4% or about 6,027.7 ha between 1990–2020 at the rate of 200 ha/year, which has been turned into matorral land or bare land. This forest also lost 35.2% of its vegetative density and has become much sparser, while the original grazing areas surrounding it have been reduced by 50.4%. Moreover, the area of quarries increased by 1,097.4%, the percentage of built-up area increased by 328.2%, and the agricultural area expanded by 32.7%. These results can be used as preliminary data for future studies and can help policymakers focus on the real drivers of forest degradation, in order to develop interventions to ensure the sustainability of natural resources.

Historical Perspectives of Land Use and Land Cover Change in the Sanga-Lake Mburo former Pastoral Rangeland Ecosystem, Uganda

Socioeconomic, political, and policy decisions by the government may influence the socioecological changes of the land use and land cover pattern for rangeland people over time. This paper examines the historical political, policy, and socioeconomic decisions that influenced land use and land cover changes in the former pastoral community in Sanga-Lake Mburo Rangeland Ecosystem in South Western Uganda. Data on historical events were documented from literature and supplemented by an opportunistic discussion with Sanga residents. Data on land use and cover change detection between 1987 and 2020 were provided by Landsat 5 TM and Landsat 8 OLI/TIRS images and from field observations. Images were processed using QGIS version 3.20.1 employing a semi-automatic classification plugin. Political decisions and government policies related to land tenure and reforms, socioeconomics, and demographic changes were noted as underlying drivers of land use and cover changes. The overall accuracies for classified maps of 1987 and 2020 were 80.36% and 89.81%, respectively. Notably, woodland cover in the protected area increased by 170.53% between 1987 and 2020, while built-up areas and farmland increased 1348.15% and 405.03%, respectively. In the same period, wetland cover in protected and unprotected decreased immensely by 46.06%. Bareland in the park decreased by 23%, while outside the park, it increased by 25.07%. This study concludes that land use and land cover change resulted from sociocultural changes, political and policy decisions on ranches, park management, and land tenure restructuring.

GEOINFORMATION MAPPING OF ANTHROPOGENICALLY TRANSFORMED LANDSCAPES OF BILA TSERKVA (UKRAINE)

Aim of the study: The main purpose of the study is the analysis and assessment of anthropogenically transformed landscapes of Bila Tserkva (Ukraine) based on a combination of remote sensing methods and GIS mapping Material and methods: Usage of geoinformatics methods for mapping anthropogenically transformed landscapes of Bila Tserkva is studied. The data was downloaded and processed using the Semi-Automatic Classification Plugin QGIS for the supervised classification of remote sensing data. Satellite images were radiometrically calibrated and atmospherically corrected, followed by a controlled classification with signature creation, visualization of spectral profiles, quality assessment and post-processing Results and conclusions: The main methods of landscape research are analyzed. The conclusion is made about the expediency of using spectrophotometry of satellite images in order to identify different types of landscapes based on satellite data. An supervised classification of satellite images different-time images was performed, as a result of which the main Bila Tserkva landscape types were identified. Those identified types are: water bodies, vegetation (grass, forest, parks) urban areas and bare soils. Spatio-temporal changes of landscapes are studied and these changes are described in quantitative indicators

Semi-automatic classification for rapid delineation of the geohazard-prone areas using Sentinel-2 satellite imagery

The study of land use land cover has become increasingly significant with the availability of remote sensing data. The main objective of this study is to delineate geohazard-prone areas using semi-automatic classification technique and Sentinel-2 satellite imagery in Bhutan. An open-source, semi-automatic classification plugin tools in QGIS software enabled efficient and rapid conduct of land cover classification. Band sets 2-8, 8A, and 11-12 are utilized and the virtual colour composites have been used for the clustering and creation of training samples or regions of interest. An iterative self-organizing data analysis technique is used for clustering and the image is classified by a minimum distance algorithm in the unsupervised classification. The Random Forest (RF) classifier is used for the supervised classification. The unsupervised classification shows an overall accuracy of 85.47% (Kappa coefficient = 0.71) and the RF classifier resulted in an accuracy of 92.62% (Kappa coefficient = 0.86). A comparison of the classification shows a higher overall accuracy of the RF classifier with an improvement of 7.15%. The study highlights 35.59% (512,100 m2) of the study area under the geohazard-prone area. The study also overlaid the major landslide polygons to roughly validate the landslide hazards.Article highlightsSemi-automatic classification technique was applied to delineate the geohazard-prone area in the heterogeneous region of Bhutan Himalaya.Unsupervised and supervised classification technique were used to perform land cover classification using the semi-automatic classification plugin (SCP).The Random Forest classifier predicted higher accuracy and the application is rapid and efficient compared to the unsupervised classification.

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping.

Крупномасштабное картографирование растительности Южно-Сахалинского грязевого вулкана и прилегающего ландшафта (о. Сахалин) по спутниковым данным

The methods of remote sensing of the Earth, due to their efficiency and information content, are widely used to research vegetation dynamics and monitor volcanic activity. The purpose of this work is to research the dynamics of the mud volcanic landscapes and vegetation cover of the Yuzhno-Sakhalinsk mud volcano, as well as its eruption, using Earth remote sensing data. The total area of the study area is 11.5 km2. The work was done in QGIS 3.16 program using Sentinel-2B satellite image, images from Google Earth program and graphic maps of the study area created by O.A. Melnikov and V.V. Ershov. An updated large-scale schematic map of the Yuzhno-Sakhalinsk mud volcano has been created, displaying all known volcanic eruption fields over the last 70 years, modern and extinct eruptive centers. A semi-automatic classification of the Sentinel-2B satellite image was carried out using the methods of supervised and unsupervised classification using the Semi-Automatic Classification Plugin module. Based on the results of two types of classification, the areas of vegetation classes of the study area were calculated and two maps of the vegetation cover of the Yuzhno-Sakhalinsk mud volcano were created on a scale of 1 : 50 000 as of 2018. The maps need to be refined, but they can already be used to analyze the dynamics of the vegetation cover of the study area. In our opinion, it is more expedient to apply unsupervised classification before conducting a field survey of the area of interest, and supervised classification after. The practical significance of satellite monitoring of the Yuzhno-Sakhalinsk mud volcano lies in the ability to quickly monitor its activity, assess the recreational load and study the impact of volcano activity on vegetation and the landscape as a whole.

Алгоритм и оценка изменения площади лесного покрова Хачмазского района Азербайджана средствами космического мониторинга

The article presents the results of long-term observations of the forest cover state in the Khachmaz region of Azerbaijan. Both the results of Earth’s remote sensing and the data of earlier observations carried out by field monitoring were used in the study. The earlier data was stored in the form of thematic maps on paper. The results of remote sensing contained multispectral satellite images obtained over several years. Image processing was performed using the QGIS program and the Semi-Automatic Classification Plugin. The processing included radiometric and atmospheric correction, as well as classification of the study area by the type of land surface coverage. Classification was performed on the basis of the spectral curves analysis in various sections of the region. The areas occupied by forests were determined based on the classification. The set of observations included data from the available archival materials – thematic maps of the Khachmaz region. A software in MATLAB was developed for processing the maps and calculating the area of forests. The program operation algorithm includes histogram analysis of the image as a whole and separately of the legend. The histogram of the legend determines the number of thematic layers of the map. They do not include layers containing unused colors, such as the colors of neighboring areas, sections of the sea, etc. Then color correction of image pixels is performed, namely, quantization of colors according to the number of specific thematic layers. Later on, morphological processing of each layer is carried out. Areas containing less than the specified number of pixels are assigned the color of the surrounding layer. The number of pixels of each layer and all layers are calculated. Finally, the obtained ratios are used to calculate the area of each layer. The data of processing of archival materials together with the results of remote sensing are summarized in a joint table. According to the table, a diagram of changes in the forest area is constructed, as well as a model in the form of a polynomial showing this dynamics. The diagram analysis revealed a tendency to a decrease in the area of forests. The forest area has decreased by 21 % for 7 years. The developed algorithm splits thematic maps into separate layers in accordance with the colors of the legend.