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EN
Soil erosion is both the cause and effect of land degradation. Land use/land cover conversion that changes the inherent landscape structure of watersheds leads to soil loss increase. Pantabangan-Carranglan Watershed (PCW) as a major source of irrigation, electricity, biodiversity, livelihood, and other ecosystem services, thus, it is imperative to spatially and temporally estimate the soil erosion within its boundary to assist and guide decision-makers in planning conservation and management of the watershed. Using the Revised Universal Soil Loss Equation (RUSLE) model, remotely sensed data, soil analysis, and geographical information system, the soil erosion rate in PCW was estimated. Results showed that there is increasing soil erosion in PCW over time. In 2010 soil erosion rate was estimated to be 134 tons·ha-1·yr-1 which increased to 141 tons·ha-1·yr-1 and 154 tons·ha-1·yr-1 in 2015 and 2020, respectively. Considering the average soil erosion rate and land cover types in PCW, annual crop and open/barren land cover types have the highest average soil erosion rate through time with moderate and catastrophic erosion levels, respectively.
EN
Land cover/land use is one of the main factors influencing the development of soil erosion. It has been included in the calculation and modelling of erosion and sediment transport in many studies. In the current research NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index) are used for quantifying the cover management factor (C-factor). They are calculated on the base of Sentinel 2 multispectral images. Taking into account the vegetation phenology two time points were analyzed: end of May - June – active vegetation and September (beginning of October) – late vegetation. The changes in the values of the indices were considered for 2018, 2021 and 2022. The study area is the watershed of the river Sarayardere, located in the southern part of Bulgaria. This is a hilly to low-mountain area, prone to erosion due to rare vegetation, high slope gradients and a relatively long dry period followed by intensive rainfall. The calculated values of the C-factor are indicators for higher susceptibility to erosion in September than it is in June. The spatial distribution of the C-factor shows different patterns. The results, received on the base of the image of September 2021, show increasing the areas with C-factor < 0.1 and these ones > 0.5, in comparison with the results of September 2018. C-factor values calculated on the image of October 2022 indicate the highest susceptibility to erosion. Using NDRE instead NDVI results in slightly higher values of the C-factor. The advantage of the NDRE index is that it provides information on the content of chlorophyll in the vegetation during the end of the vegetation period and allows a more accurate assessment of the state of the separate plants, regarding the determination of diseased or damaged plants. In addition to the vegetation indices, an expert evaluation of the state of vegetation was done. The results of the current study show that the watershed of the river Sarayardere is in a relatively good condition regarding the development of erosion processes. The attention should be directed to the possible increase of erosion on deforested slopes and the availability of loose materials, in case of intense rainfall.
EN
Present study investigated the effect of land-use variations on the excess flow for a Nandigama, Andhra Pradesh, India by using HEC-HMS model. The model was calibrated and validated using observed rainfall and runoff data. The R2 and NSE values were both greater than 0.65 after calibration, indicating a reasonable fit of the model. An analysis was conducted to understand how the land-use changes in a basin have affected the runoff. The analysis revealed that the stream flow increased due to variations in land use, and a reduction in the timing of peak flow at the outlet was observed. Additionally, the study analysed the trend of maximum rainfall time series and found that the months of June, July, and August show a decreasing trend in maximum rainfall over the study period, while other months show an increasing trend. The results of the analysis can be used to implement informed policies and management practices aimed at mitigating the negative impact of land-use changes and climate changes in Nandigama.
EN
This study utilized remote sensing data to monitor the relationship between land cover and drought exposure in Nusa Tenggara Timur (NTT) Province. NTT is a province in Indonesia, located in the Nusa Tenggara archipelago, characterized by low to medium rainfall, which contributes to frequent drought events. In 2018 and 2019, the province was impacted by El Niño, resulting in approximately 865,900 and 1,154,714 affected and displaced individuals, respectively. Due to the limited availability of time-series data, observations from the Landsat-8 OLI/ TIRS mission, spanning from 2018 to 2023, were utilized. The normalized difference vegetation index (NDVI) was employed to assess land conditions, while the vegetation health index (VHI), calculated from the Temperature Condition Index (TCI) and vegetation condition index (VCI), was used to estimate drought severity. To validate the dry season period in the study area, ERA5 climate reanalysis data from 1990 to 2020 was used. This study provides new insights into drought monitoring in NTT Province, Indonesia, by analyzing temporal variations in vegetation. The results indicated that seasonal dynamics, climatic variability, seasonal farming practices, and land fires are major contributors to severe drought conditions in NTT. Notably, this research highlighted a finding absent from previous studies: seasonal farming and land fires are the primary drivers of elevated drought levels in the province. The study is significant, as it elucidated the impacts of drought on development, agriculture, and water resources. Through remote sensing data, it revealed spatial drought distribution patterns during the study period in NTT. This research could provide information about land-use and environmental planning in tropical regions.
EN
The study aimed to determine how changes in land cover and surface water are being made using stratified objectoriented analysis based on the interpretation of remote sensing images. It is the first step toward managing the region’s annual land-use inventories projects. The study used Sentinel-2 images from 2019 through 2021 to delineate the changing urban land cover in the Ninh Kieu District, Can Tho City, Viet Nam. The study used QGIS software to interpret the images and eCognition software to classify the objects based on the NDBI, NDVI, and NDWI indices. The interpretation results were checked for the accuracy, and the land cover was changed over the years. The results show that urban land cover changes with the increase of urban land and the decrease of vegetation land used for urban land, while water surface area inwards decreased from 2019 to 2020 but increased in 2021. Maps of the current state of the urban land covers in the study area were delineated. The interpretation results contribute to the preliminary method by using satellite images for the annual land use inventory project in the region, even though some difficulties still exist and need to be modified.
PL
Wpływ sposobu użytkowania w obrębie obszarów otaczających miasta jest szczególnie silny w przypadku małych ośrodków, gdyż tereny leśne i rolnicze graniczą tam częstokroć bezpośrednio ze strefami mieszkalnymi. Coraz liczniej pojawiają się publikacje, które dokumentują oddziaływanie tych dwóch sposobów użytkowania, wskazując m.in. na znaczenie uwarunkowań przestrzennych, które determinują wielkość wpływu otoczenia na jakość życia mieszkańców miast. Celem pracy było oszacowanie udziału sposobów użytkowania w otoczeniu 738 małych miast Polski (<20 tys. mieszkańców), z uwzględnieniem potencjału rekreacyjnego lasów i ewentualnych zagrożeń wyni¬kających z bliskiego sąsiedztwa z intensywnie użytkowanymi gruntami rolnymi. Podstawą uzyskanych wyników była baza Corine Land Cover z 2018 roku, z której pozyskano informację o usytuowaniu zabudowy miejskiej, lasach oraz terenach rolnych (grunty orne i sady). Uzyskane wyniki wskazują, że w przypadku 35,6% małych miast Polski teren zabudowany nie sąsiadował z lasami, a 7,3% miast nie posiadało lasów w promieniu 1,5 km. Natomiast tylko w 5,6% miastach teren zabudowany nie graniczył z jakimkolwiek terenem rolnym, a w 4,9% teren ten otoczony był w ponad 80% polami. Należy sądzić, że w grupie miast otoczonych polami, przy planowaniu struktury przestrzennej, szczególną uwagę należałoby poświęcić projektowaniu nowych terenów zielonych i rewaloryzacji już istniejących.
EN
The impact of land use within the areas surrounding cities is particularly strong in the case of small towns, as forest and agri¬cultural areas often directly border residential areas. There are more and more publications that document the impact of these two forms of land use, highlighting, among other things, the importance of spatial conditions that determine the impact of the environment on the quality of life of city inhabitants. The aim of the study was to estimate the share of different land use methods in the vicinity of 738 small Polish towns (<20,000 inhabitants), taking into account the recreational potential of forests and possible threats resulting from close proximity to intensively used agricultural land. The study was based on the Corine Land Cover database from 2018. Information on the location of urban development, forests and agricultural land (arable land and orchards) was obtained from this database. The results indicate that in the case of 35.6% of small Polish towns, built-up areas were not adjacent to forests, and 7.3% of towns did not have forests within a radius of 1.5 km. On the other hand, only in 5.6% of cities did no built-up areas border agricultural areas, and in 4.9% of cities more than 80% of built-up areas were surrounded by fields. In cities surrounded by fields, special attention should be paid to the design of new green areas and the redevolopment of existing ones when planning the spatial structure.
EN
Land surface temperature (LST) estimation is a crucial topic for many applications related to climate, land cover, and hydrology. In this research, LST estimation and monitoring of the main part of Al-Anbar Governorate in Iraq is presented using Landsat imagery from five years (2005, 2010, 2015, 2016 and 2020). Images of the years 2005 and 2010 were captured by Landsat 5 (TM) and the others were captured by Landsat 8 (OLI/TIRS). The Single Channel Algorithm was applied to retrieve the LST from Landsat 5 and Landsat 8 images. Moreover, the land use/land cover (LULC) maps were developed for the five years using the maximum likelihood classifier. The difference in the LST and normalized difference vegetation index (NDVI) values over this period was observed due to the changes in LULC. Finally, a regression analysis was conducted to model the relationship between the LST and NDVI. The results showed that the highest LST of the study area was recorded in 2016 (min = 21.1°C, max = 53.2°C and mean = 40.8°C). This was attributed to the fact that many people were displaced and had left their agricultural fields. Therefore, thousands of hectares of land which had previously been green land became desertified. This conclusion was supported by comparing the agricultural land areas registered throughout the presented years. The polynomial regression analysis of LST and NDVI revealed a better coefficient of determination (R2) than the linear regression analysis with an average R2 of 0.423.
EN
The remote sensing technique is crucial for creating maps showing land use and land cover from a procedure known as image classification. For the process of image classification to be successful, many aspects must be taken into consideration; one of these factors is the availability of high-quality Landsat images. This study aims to classify and map the studied area’s land use and cover using remote sensing and geographic information system techniques. This study is divided into two parts: part one focuses on classifying land use and land cover, while part two evaluates how accurate the classification is. Several classification methods are compared for their efficacy in this study. Some image classification methods have shown promising results when used to remote sensing data. An efficient classifier is necessary for extracting data from remote-sensing images. The maximum likelihood classification was the most effective classifier in our study. In this study, the Maximum Likelihood classification accuracy has achieved an overall accuracy of 91% and an overall kappa accuracy of 86.83%. This study provides essential data for planners and decision-makers to design sustainable environments.
EN
Quantifying and understanding global land use change and its spatial and temporal dynamics is critical to supporting international policy debates. The main area of transformation of spatial structures nowadays are suburban areas of the largest cities. Constant land development and urbanization, including such forms as urban sprawl, influence significant changes in land use. The aim of this study was to analyse a land use change pattern in a selected rural area which is under pressure of spatial development of a regional city. Data used for a land use change detection was based opensource Urban Atlas dataset for 2006, 2012, and 2018, enriched by recent update from 2021 orthophoto map. Spatial analyses presenting statistics of land use change were conducted in QGIS. Besides analysis of land use change, the paper discusses observed spatial patterns also taking into account changing social, environmental and economic conditions and spatial policies influencing land cover complexity. Understanding these dynamics would help better spatial management of real estates for more sustainable land development.
EN
Economic growth and development are connected with the increase in consumption. One of the side effects of progress is waste production. Sustainable development would also include proper management of waste, focusing on their recycling. However, the direct costs of recycling sometimes exceed the costs of waste storage. Therefore, waste storage in landfills is still widespread. Improper waste storage or deliberate actions can lead to waste fires. In the work, the statistics of landfill fires from the years 2012 to 2021 were analyzed. The work includes statistics of the parameters of fires reported in the reports of Polish State Fire Services. Additionally, the usage of the resources and materials for firefighting and their trends were discussed. It was shown that resources required for extinguishing waste fires were increasing in this period. The statistics are accompanied by spatiotemporal analyses of the location of fires based on Corine Land Cover which showed that approximately half of the fires are on arable land and non-continuous urban fabric while fires at dumpsites are relatively rare. The important concern is also that around 10% of very big waste fires are in forests. All these analyses lead to the assessment of some environmental impacts which are caused by waste fires.
EN
The aim of the study was to diagnose the main trends of changes in land cover in selected communes of Polish metropolitan areas. Detailed studies were conducted in deliberately selected housing estates located in the core of metropolitan area (at least one housing estate) and communes located directly at the border of cities and located on the outskirts of metropolitan areas. The examined communes also differed in the quality of natural conditions of agricultural production. The study used LANDSAT 5 TM and RapidEye satellite images from three limited-time registrations (1996/1999, 2011, 2016/2017). On the basis of remote sensing data, changes in land use were specified by presenting them in a graphic form as compilation of numerical maps. The analyses were performed on processed images (colour compositions), which were subjected to supervised classification using the maximum-likelihood technique. The quality control of supervised classification showed accuracy of 89.3% for LANDSAT 5 TM scene analyses and 91.8% for RapidEye images. Kappa coefficient for the discussed classification was: 0.84 (LANDSAT TM) and 0.89 (Rapid Eye). The results obtained for individual metropolitan areas allow to identify the directions of changes (Land Use Change Cover) taking place in them, with consideration to specificity of each of them.
EN
The article deals with the research on the quantitative classification of land use, which directly affects the amount of land use data collected in the real estate cadastre. For the purpose of this article, the cadastral systems of seven European countries – Austria, Bulgaria, Estonia, Spain, Lithuania, Germany and Poland – have been examined, taking into account how detailed is the classification in agricultural and forest areas. The research covered the provisions of legal acts applicable in the researched seven European countries and made available in national languages by the government bodies. The article asks the following three questions: 1) whether the researched countries adopted the same approach to isolating classes of items related to agricultural and forest areas; 2) whether the researched countries feature the same number of classes of items recorded at various levels of detail; 3) what is the percentage of the distinguished item classes of land uses in the agricultural and forest areas in relation to all of the distinguished item classes at all the levels of detail. The conducted research can be used as a material supporting works consisting of the modernization of the functioning of land registration in the real estate cadastre in Poland.
EN
Urban land-cover change is increasing dramatically in most emerging countries. In Iraq and in the capital city (Baghdad). Active socioeconomic progress and political stability have pushed the urban border into the countryside at the cost of natural ecosystems at ever- growing rates. Widely used classifier of Maximum Likelihood was used for classification of 2003 and 2021 Landsat images. This classifier achieved 83.20% and 99.58% overall accuracies for 2003 and 2021 scenes, respectively. This study found that the urban area decreases by 16.4% and the agriculture area decrease by 5.4% over the period. On the other hand, barren land has been expanded up to more than 7% as well as increasing in water land that should probably due to flooding (almost 15% more than 2003). To reduce the undesirable effects of land-cover changes over urban ecosystems in Baghdad and in the municipality in specific, it is suggested that Baghdad develops an urban development policy. The emphasis of policy must be the maintenance an acceptable balance among urban infrastructure development, ecological sustainability and agricultural production.
EN
In order to analyze the impact of land use and land cover change on land surface temperature (LST), remote sensing is the most appropriate tool. Land use/cover change has been confirmed to have a significant impact on climate through various aspects that modulate LST and precipitation. However, there are no studies which illustrate this link in the Fez-Meknes region using satellite observations. Thus, the aim of this study was to monitor LST as a function of the land use change in the Saïss plain. In the study, 12 Landsat images of the year 2019 (one image per month) were used to represent the variation of LST during the year, and 2 images per year in 1988, 1999 and 2009 to study the interannual variation in LST. The mapping results showed that the land use/cover in the region has undergone a significant evolution; an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. On the basis of statistical analyses, LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of land use type. The relationship between LST and NDVI shows a negative correlation (LST decreases when NDVI increases). This explains the increase in LST in rangelands and arable land, while it decreases in irrigated crops and arboriculture.
EN
Unmanned Aerial Vehicles (UAVs), commonly known as drones are increasingly being used for three dimensional (3D) mapping of the environment. This study utilised UAV technology to produce a revised 3D map of the University of Lagos as well as land cover change detection analysis. A DJI Phantom 4 UAV was used to collect digital images at a flying height of 90 m, and 75% fore and 65% side overlaps. Ground control points (GCPs) for orthophoto rectification were coordinated with a Trimble R8 Global Navigation Satellite System. Pix4D Mapper was used to produce a digital terrain model and an orthophoto at a ground sampling distance of 4.36 cm. The change detection analysis, using the 2015 base map as reference, revealed a significant change in the land cover such as an increase of 16,306.7 m2 in buildings between 2015 and 2019. The root mean square error analysis performed using 7 GCPs showed a horizontal and vertical accuracy of 0.183 m and 0.157 m respectively. This suggests a high level of accuracy, which is adequate for 3D mapping and change detection analysis at a sustainable cost.
EN
Timely and accurate detection of land use/land cover (LULC) change is important for the macro and micro level sustainable development of any region. For this purpose, geospatial techniques are the best tool for change analysis as they supply timely, cheaper, precise and up to date information. This paper examines the spatial temporal change trend in LULC in the case of Central Haryana. Landsat 2, 3, 5, 7 and 8 images for the years 1975–2020 for pre and post monsoon periods were analyzed for the study. Radiometric correction was performed to derive better information. ArcGIS 10.2 and ENVI 5.3 are used for thematic layout and thematic change preparation. An unsupervised classification using ERDAS IMAGINE 2015 has also been done to classify study area in eight classes. The year 1975 is considered as the base year for change detection analysis. Results showed an increasing trend for the land use classes of built up, water body, and agricultural land without waterlogging in the pre and post monsoon periods between 1975 and 2020. Remaining land use classes of agriculture with waterlogging, open waterlogged area, vegetation and fallow land/sand dunes decreased during the same period. Increased human activities have changed the LULC in the region and have had a great impact on its sustainable regional development.
EN
The real estate cadastre is the primary source of information on land use. It re cords information related to the division of land into types based on the actual way of land use or development. The distinguished types of land use depend on many geographical factors, as well as historical and economic conditions. The study presents a comparison of the detail of land use classification registered in the real estate cadastre in areas functionally related to the urban areas of 9 European countries: Austria, Bulgaria, the Czech Republic, Estonia, Spain, Lithuania, Luxembourg, Germany and Poland. The research concerned the determination of the degree to which the classification of land use in urbanized areas is detailed, whether the studied European countries are characterized by the same number of distinguished classes of ob jects at different levels of detail, and what percentage are the distinguished classes of land use objects in urbanized areas in relation to all of the distinguished classes of objects land use at different levels of detail of classification. The study used legal acts regulating land use issues which have been made available in national languages by government institutions.
EN
The present study aimed to analyse changes in the land cover of Vilnius city and its surrounding areas and propose a scenario for their future changes using an Artificial Neural Network. The land cover dynamics modelling was based on a multilayer perceptron neural network. Landscape metrics at a class and landscape level were evaluated to determine the amount of changes in the land uses. As the results showed, the Built-up area class increased, while the forest (Semi forest and Dense forest) classes decreased during the period from 1999 to 2019. The predicted scenario showed a considerable increase of about 60 % in the Built-up area until 2039. The vegetation plant areas consist about 47 % of all the area in 2019, but it will be 36 % in 2039, if this trend (urban expansion) continues in the further. The findings further indicated the major urban expansion in the vegetation areas. However, Built-up area would expand over Semi forest land and Dense forest land, with a large part of them changed into built- up areas.
PL
Celem badań jest ocena możliwości realizacji klasyfikacji nadzorowanej z wykorzystaniem obrazów (komponentów) uzyskiwanych w wyniku przetworzenia oryginalnych obrazów Sentinel-2A za pomocą metody głównych składowych (PCA). Klasyfikację wykonano w ośmiu wariantach, z wykorzystaniem algorytmów najmniejszej odległości (MD, Minimum Distance) oraz największego prawdopodobieństwa (ML, Maximum Likelihood), przy czym zastosowano oryginalne kanały 2, 3, 4, 8 Sentinel-2A oraz różną liczbę komponentów. Wyniki klasyfikacji oceniono poprzez porównanie z danymi o pokryciu terenu według Ewidencji Gruntów i Budynków (EGiB). Przeprowadzenie klasyfikacji na ograniczonej do dwóch liczbie komponentów uzyskanych w procedurze PCA tylko nieznacznie zmieniło wyniki w porównaniu do klasyfikacji na oryginalnych, nieprzetworzonych kanałach Sentinel-2A. Najbardziej zbliżone do danych EGiB rezultaty uzyskano stosując klasyfikację ML kanałów oryginalnych, nieprzetworzonych lub używając wszystkich komponentów PCA. Podjęta próba porównania pokrycia terenu ustalonego za pomocą klasyfikacji obrazów satelitarnych z klasami pokrycia, które zostały wyodrębnione z mapy EGiB wykazała, że przetworzenie mapy z postaci wektorowej na rastrową wpływa istotnie na uzyskiwane wyniki.
EN
The aim of the research is to assess the feasibility of supervised classification using images (components) obtained through processing the original Sentinel-2A images by means of the principal component method (PCA). The classification was performed in eight variants, using the algorithms of the minimum distance (MD) and the maximum likelihood (ML), with the original channels 2, 3, 4, 8 of Sentinel-2A and a various number of components. The results of the classification were assessed by comparing them to the land coverage data of Land and Buildings Register (Ewidencja Gruntów i Budynków – EGiB). Performing the classification on a number of PCA components limited to two only slightly altered the results compared to the classification on the original, raw Sentinel-2A channels. The results most similar to the EGiB data were obtained using the ML classification of the original channels, i.e. raw channels or using all PCA components. The attempt to compare the land coverage established by the classification of satellite images to the coverage classes that were extracted from the EGiB map revealed that processing the map from vector to raster form significantly influences the obtained results.
EN
Human disturbance and nutrient runoff lead to water pollution, particularly in downstream waters and reservoirs. We hypothesized that increased human activity in summer would affect the trophic state of downstream reservoirs, affecting the interannual species composition of rotifers. We used long-term data for the Unmun Reservoir in South Korea (2009–2015), which is increasingly affected by human activity. The interannual variation of nitrogen and phosphorus levels was higher in summer and autumn, resulting in eutrophication. This led to a change in species composition of rotifers. Anuraeopsis fissa, Brachionus calyciflorus and Trichocerca gracilis were abundant in the most eutrophic state, while high densities of Ascomorpha ovalis and Ploesoma hudsoni were observed when nutrient concentrations were lower. The trophic state changes in the Unmun Reservoir were largely attributed to summer human activity in tributary streams. Our study location is typical of the stream network in South Korea and we assume that similar trophic state changes in reservoirs will be common. Changes in the density and species diversity of rotifers due to eutrophication indicate the need for active management and conservation, including the restriction of human activity around streams.
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