scholarly journals Study of morphometry and land coverage to help urban planning in Barbosa and Barbosinha Brooks watershed, Lins - SP

2021 ◽  
Vol 17 (6) ◽  
Author(s):  
Isadora Vitali Lobo ◽  
César Gustavo da Rocha Lima ◽  
José Augusto Di Lollo
Keyword(s):  
Land Use ◽  
Surface Runoff ◽  
Supervised Classification ◽  
Satellite Images ◽  
Hydrological Cycle ◽  
Urban Expansion ◽  
Morphometric Parameters ◽  
Landsat 8 ◽  
Extreme Hydrological Events ◽  
Impervious Areas

Urbanization in hydrographic basins promotes changes in the hydrological cycle through the impermealized areas. Not only surface runoff is increased, reducing infiltration, but also the susceptibility to extreme hydrological events. The objective of this study is to analyze the natural susceptibility of Córregos Barbosa e Barbosinha (Barbosa and Barbosinha Brooks) watershed to flooding. Morphometric parameters of land use and occupation were analyzed, in order to subsidize management and planning in an area of urban expansion. The analysis of land use and occupation was based on the supervised classification method of Landsat-5 Thematic Mapper (TM) sensor and Landsat-8 Operational Land Imager sensor (OLI) satellite images dated 1990, 2006 and 2020. Morphometric indices were calculated using the SPRING 5.4.3 software and a SRTM image with a spatial resolution of 30 meters. The results indicate that the natural susceptibility to flooding of the study area is medium to high, and can be intensified by the dynamics of land use and occupation and increasing impervious areas in the basin. Over the period of study, the growth of impervious areas was 133% relative to 1990.

scholarly journals Land use change impact on flood reduction capacity of lake sentani, jayapura

2018 ◽  
Vol 7 (3.29) ◽  
pp. 115
Author(s):  
Elroy Koyari ◽  
Runi Asmaranto
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Human Activities ◽  
Surface Runoff ◽  
Hydrological Cycle ◽  
Land Use Changes ◽  
Natural Phenomenon ◽  
Flood Discharge ◽  
Change Impact ◽  
Impervious Areas

Flood is a natural phenomenon that occurs in certain places due to natural causes and human activities. However, the imbalance in hydrological cycle will cause the flood to do damage, both materially and non-materially. Therefore, it is important to control the occurrence and magnitude. Human activities that can cause such imbalance, one of them, is land use change. Many areas of pervious area are shifting into impervious areas, which will increase the amount of surface runoff generated. This research will cover about how land use changes over the year can influence the surface runoff generated in a certain area. This research is conducted in Sentani watershed, Jayapura, Papua, Indonesia. Calculation with the aid of ArcMap 10.1 and WinTR-20 results in around 6% changes in flood discharge in the outlet for land use in year 2007, 2010, 2012, and 2016. The reservoir capacity in reducing flood discharge is also increasing over the years.   

scholarly journals Impact of Land Use Change Due to Urbanisation on Surface Runoff Using GIS-Based SCS–CN Method: A Case Study of Xiamen City, China

Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 839
Author(s):  
Sabita Shrestha ◽  
Shenghui Cui ◽  
Lilai Xu ◽  
Lihong Wang ◽  
Bikram Manandhar ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Development ◽  
Surface Runoff ◽  
Hydrological Cycle ◽  
Urban Expansion ◽  
Eastern China ◽  
Spatial And Temporal Variation ◽  
The Impact ◽  
Scs Cn

Rapid urban development results in visible changes in land use due to increase in impervious surfaces from human construction and decrease in pervious areas. Urbanisation influences the hydrological cycle of an area, resulting in less infiltration, higher flood peak, and surface runoff. This study analysed the impact of land use change due to urbanisation on surface runoff, using the geographic information system (GIS)-based soil conservation service curve number (SCS–CN) method, during the period of rapid urban development from 1980 to 2015 in Xiamen, located in south-eastern China. Land use change was analysed from the data obtained by classifying Landsat images from 1980, 1990, 2005, and 2015. Results indicated that farmland decreased the most by 14.01%, while built-up areas increased the most by 15.7%, from 1980 to 2015. Surface runoff was simulated using the GIS-based SCS–CN method for the rainfall return periods of 5, 10, 20, and 50 years. The spatial and temporal variation of runoff was obtained for each land use period. Results indicate that the increase in surface runoff was highest in the period of 1990–2005, with an increase of 10.63%. The effect of urbanisation can be realised from the amount of runoff, contributed by built-up land use type in the study area, that increased from 14.2% to 27.9% with the rise of urban expansion from 1980 to 2015. The relationship between land use and surface runoff showed that the rapid increase in constructed land has significantly influenced the surface runoff of the area. Therefore, the introduction of nature-based solutions such as green infrastructure could be a potential solution for runoff mitigation and reducing urban flood risks in the context of increasing urbanization.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Remote Sensing and GIS based Land Use Land Cover Analysis in Chandel District, Manipur, India

2021 ◽  
Vol 889 (1) ◽  
pp. 012046
Author(s):  
Ashangbam Inaoba Singh ◽  
Kanwarpreet Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Geographical Area ◽  
Temporal Trends ◽  
Water Bodies ◽  
Landsat 8 ◽  
Rapid Urbanization ◽  
Barren Area ◽  
Landsat 7

Abstract Rapid urbanization has dramatically altered land use and land cover (LULC). The focus of this research is on the examination of the last two decades. The research was conducted in the Chandel district of Manipur, India. The LULC of Chandel (encompassing a 3313 km2 geographical area) was mapped using remotely sensed images from LANDSAT4-5, LANDSAT 7 ETM+, and LANDSAT 8 (OLI) to focus on spatial and temporal trends between years 2000 and 2021. The LULC maps with six major classifications viz., Thickly Vegetated Area (TVA), Sparsely Vegetated Area (SVA), Agriculture Area (AA), Population Area (PA), Water Bodies (WB), and Barren Area (BA) of the were generated using supervised classification approach. For the image classification procedure, interactive supervised classification is adopted to calculate the area percentage. The results interpreted that the TVA covers approximately 65% of the total mapped area in year 2002, which has been decreased up to 60% in 2007, 56% in 2011, 55 % in 2017, and 52% in 2021. The populated area also increases significantly in these two decades. The change and increase in the PA has been observed from year 2000 (8%) to 2021 (11%). Water Bodies remain same throughout the study period. Deforestation occurs as a result of the rapid rise of the population and the extension of the territory.

scholarly journals Comparação entre diferentes algoritmos de classificação supervisionada em imagens Landsat-8 no mapeamento temático da Bacia Hidrográfica do rio Caldas, Goiás

2019 ◽  
Vol 12 (3) ◽  
pp. 961
Author(s):  
Leovigildo Aparecido Costa Santos ◽  
Paulo Eliardo Morais de Lima
Keyword(s):  
Land Use ◽  
Maximum Likelihood ◽  
River Basin ◽  
Supervised Classification ◽  
Likelihood Method ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Entire Area ◽  
Area Of Interest

Diferentes métodos são empregados para a classificação digital de imagens, porém, podem apresentar desempenhos diferentes, sendo importante testá-los para verificar suas eficácias no mapeamento de uso e cobertura da terra com intuito de se selecionar o classificador que apresente os melhores resultados e maior veracidade em relação à verdade de campo. O objetivo deste estudo foi avaliar e comparar os desempenhos de quatro algoritmos de classificação supervisionada para o mapeamento do uso e cobertura da terra da bacia hidrográfica do Rio Caldas – GO, utilizando imagens Landsat-8. Para tanto, foram utilizadas as cenas de órbita/ponto 222/71 e 222/72, com datas de passagem em 24/10/2017 e 22/10/2017, mosaicadas para formar uma única imagem de dimensões que abrangesse toda a área de interesse. A composição RGB utilizada foi das bandas 6, 5 e 4 (R=6, G=5, B=4). Para a realização do processamento digital da imagem foi empregado o software ENVI versão 5.0 e à elaboração de mapas temáticos o QGIS 2.18. Os algoritmos testados foram: Paralelepípedo, Distância de Mahalanobis, Distância Mínima e Máxima-verossimilhança. Como parâmetros de comparação foram utilizados os coeficientes de Kappa, acurácias global e matrizes de confusão. Os melhores resultados para a classificação de uso e cobertura foram obtidos pelo método da Máxima-verossimilhança (MaxVer), os piores pelo método do Paralelepípedo, os outros classificadores apresentaram resultados intermediários entre o melhor e o pior. Com os resultados obtidos pela classificação por MaxVer, constatou-se que atualmente a maior parte do solo da bacia é ocupada pelas classes Pastagem (63,14%) e Vegetação nativa (22,07%). Comparison between different supervised classification algorithms in Landsat-8 images in the thematic mapping of the caldas river basin, GoiásA B S T R A C TDifferent methods are used for a digital classification of images, however, they can present different performances, being important to test them to verify their efficiencies in the mapping of land use and coverage in order to select the classifier that presents the best results and greater truthfulness In relation to the truth of the field. The objective of this study was to evaluate and compare the performance of four supervised classification algorithms for the mapping of the land use and land cover of the Caldas river basin - GO, using Landsat-8 images. To do so, they were like the orbit / dot scenes 222/71 and 222/72, with passing date on 10/24/2017 and 10/22/2017, mosaicked to form a single image of dimensions covering an entire area of interest . An RGB composition used for bands 6, 5 and 4 (R = 6, G = 5, B = 4). For the realization of digital image processing and the use of ENVI version 5.0 software and the development of thematic maps, QGIS 2.18. The algorithms tested were: Parallelepiped, Mahalanobis Distance, Minimum Distance and Maximum Likelihood. As the comparison parameter is used by Kappa coefficients, global accuracy and matrices of confusion. The best results for a classification of use and coverage are obtained by the Maximum-likelihood method (MaxVer), the most common methods, the other classifiers presented the intermediates between the best and the worst. With the results obtained by classification by MaxVer, it was verified that at the moment it is part of the soil of the basin is occupied by classes Pasture (63.14%) and native vegetation (22.07%).Keywords: Use and coverage; remote sensing; geoprocessing; Landsat.

scholarly journals PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 91-93 ◽  
Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
H. Akcin
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Landsat 8 Oli ◽  
Sentinel 2

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.

scholarly journals Surface Runoff Management Model of Bandung Regency Through Remote Sensing Analysis

2021 ◽  
Vol 21 (2) ◽  
pp. 123-134
Author(s):  
Dede Sugandi ◽  
Riki Ridwana ◽  
Arif Ismail ◽  
Jalu Rafli Ismail ◽  
Rafi'i Diva Sephana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Experimental Method ◽  
Surface Runoff ◽  
Landsat Imagery ◽  
Management Model ◽  
Landsat 8 ◽  
Well Model ◽  
Rainfall Volume ◽  
House Building

Flood is caused by surface runoff, therefore controlling the surface runoff is necessary especially on built areas. The aim of this research is to analyze the width, calculating the volume of surface runoff and analyze the model of infiltration wells on built areas in Bandung Regency. The methods implemented in this research is experimental method. This method was carried while analyzing rainfall on built areas samples, which is house building. The land use was analyzed through Landsat 8 imagery in the year of 2019. Rainfall volume was calculated by equation V = h x l. Meanwhile the volume of infiltration well was calculated by equation V = h x k. The result of 2019 Landsat imagery analysis shows that 19.01% of total watersheds in Bandung Regency or as much as 1382.13 km2 is the built areas. The highest rainfall in total of 0.02431 m occurred in October on the area of 197.67 m2 and became a surface runoff of 377,534 m3. In a house building, as built area example, as wide as 90 m3, the amount of 2.19 m3 rainwater needed to be infiltrated. Infiltration well model is a management model on each built areas, so that rainwater on built areas would not be turned into a surface runoff.

scholarly journals Land Use Land Cover Analysis using Geospatial Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2561-2566
Author(s):  
Babita Singh
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Anthropogenic Activities ◽  
Google Earth ◽  
Landscape Configuration ◽  
Landsat 8 ◽  
Visual Interpretation ◽  
Open Forest

Abstract: Remote sensing and Geographic information system (GIS) techniques can be used for the changing pattern of landscape. The study was conducted in Dehradun, Haridwar and Pauri Garhwal Districts of Uttarakhand State, India. In order to understand dynamics of landscape and to examine changes in the land use/cover due to anthropogenic activities, two satellite images (Landsat 5 and Landsat 8) for 1998 and 2020 were used. Google Earth Engine was used to perform supervised classification. Spectral indices (NDVI, MNDWI, SAVI, NDBI) were calculated in order to identify land cover classes. Both 1998 and 2020 satellite images were classified broadly into six classes namely agriculture, built-up, dense forest, open forest, scrub and waterbody. Using high resolution google earth satellite images and visual interpretation, overall accuracy assessment was performed. For land cover/use change analysis, these images were imported to GIS platform. Landscape configuration was observed by calculating various landscape metrices Images. It was observed that scrub land area had increased from 11 % to 14 % but a decrease in agriculture by 4.65 %. The increased value of NP, PD, PLAND, LPI and decrease in AI landscape indices shows that land fragmentation had increased since 1998. The most fragmented classes were scrub (PD - 3.32 to 5.18) and open forest (PD - 3.57 to 5.07). Decrease in AI for open forest, agriculture, built-up indicated that more fragmented patches of these classes were present. The result confirmed increase in the fragmentation of landscape from 1998 onwards. Keywords: GIS, LULC, landscape metrics, Remote Sensing

scholarly journals Supervised Classification of Satellite Images to Analyze Multi-Temporal Land Use and Coverage : A Case Study for the Town of MARABA, State of PARA, Brazil

2015 ◽  
Author(s):  
Priscila Siqueira Aranha ◽  
Flavia Pessoa Monteiro ◽  
Paulo Andre Ignacio Pontes ◽  
Jorge Antonio Moraes de Souza ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
...  
Keyword(s):  
Land Use ◽  
Supervised Classification ◽  
Satellite Images ◽  
Multi Temporal ◽  
The Town

scholarly journals SPATIOTEMPORAL LAND USE CHANGE ANALYSIS AND FUTURE URBAN GROWTH SIMULATION USING REMOTE SENSING: A CASE STUDY OF ANTALYA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 657-662
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Growth ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Urban Expansion ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Land Use Pattern ◽  
Use Pattern

Abstract. The objectives of this study are: to create land-use maps by 5-year interval from 1995 to 2015, to analyse the land use change and urban development, and to estimate future land-use pattern and urban growth for the years: 2030, 2045 and 2060. Antalya, which is the 5th biggest city of Turkey, was selected as study area. In this study, there are basically three stages: (i) preprocessing and preparing additional bands, (ii) spatiotemporal land use detection using image classification and (iii) land use simulation using urban growth models. Firstly, atmospheric correction was applied to the Landsat 5 TM and Landsat 8 OLI images and land-cover indices, ASTER Global Digital Elevation Model (GDEM), and Nighttime data were prepared to use them as additional bands during the classification process. Secondly, Landsat images were classified using Random Forest (RF) machine-learning algorithm. Thirdly, urban simulations were performed for the years 2005, 2010, and 2015 and land-use pattern and urban growth was estimated for the years 2030, 2045 and 2060. The RF classification accuracies range from 84.44% to 92.82%. The urban areas increased from 49.56 km2 to 96.25 km2 from 1995 to 2015. The simulation accuracies were computed above 80%. According to the 2030, 2045 and 2060 simulation results, the urban areas were computed as 133.61 km2, 148.27 km2 and 156.85 km2, respectively. As a result, it was seen that the urban area of Antalya has almost doubled between the years 1995–2015 and the urban expansion is expected to continue increasing up to 1960.

Sign in / Sign up

Export Citation Format

Share Document