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Object-oriented classification of Landsat ETM+ satellite image

Stanisław Lewiński
Journal of Water and Land Development | 2006 | No 10 | 91-106
Keywords object-oriented classification land use Landsat ETM+
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Abstract

This paper presents results of object-oriented classification of Landsat ETM+ satellite im-age conducted using eCognition software. The classified image was acquired on 7 May 2000. In this particular study, an area of 423 km2 within the borders of Legionowo Community near Warsaw is considered.

Prior to classification, segmentation of the Landsat ETM+ image is performed using panchro-matic channel, fused multispectral and panchromatic data. The applied methods of classification en-abled the identification of 18 land cover and land use classes. After the classification, generalization and raster to vector conversion, verification and accuracy assessment are performed by means of vis-ual interpretation. Overall accuracy of the classification reached 94.6%. The verification and classifi-cation results are combined to form the final database.

This is followed by comparing the object-oriented with traditional pixel-based classification. The latter is performed using the so-called hybrid classification based on both supervised and unsuper-vised classification approaches. The traditional pixel-based approach identified only 8 classes. Com-parison of the pixel-based classification with the database obtained using the object-oriented ap-proach revealed that the former reached 72% and 61% accuracy, according to the applied method.

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Authors and Affiliations

Stanisław Lewiński

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Wojciech Drzewiecki
Geodesy and Cartography | 2016 | vol. 65 | No 2 | 193-218 | DOI: 10.1515/geocart-2016-0016
Keywords machine learning model ensembles sub-pixel classification impervious areas Landsat
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Abstract

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
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Authors and Affiliations

Wojciech Drzewiecki

Earth Observation and Geospatial techniques for Soil Salinity and Land Capability Assessment over Sundarban Bay of Bengal Coast, India

Sumanta Das Malini Roy Choudhury Subhasish Das M. Nagarajan
Geodesy and Cartography | 2016 | vol. 65 | No 2 | 163-192 | DOI: 10.1515/geocart-2016-0012
Keywords Landsat GIS and Remote Sensing kriging soil properties land capability
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Abstract

To guarantee food security and job creation of small scale farmers to commercial farmers, unproductive farms in the South 24 PGS, West Bengal need land reform program to be restructured and evaluated for agricultural productivity. This study established a potential role of remote sensing and GIS for identification and mapping of salinity zone and spatial planning of agricultural land over the Basanti and Gosaba Islands(808.314sq. km) of South 24 PGS. District of West Bengal. The primary data i.e. soil pH, Electrical Conductivity (EC) and Sodium Absorption ratio (SAR) were obtained from soil samples of various GCP (Ground Control Points) locations collected at 50 mts. intervals by handheld GPS from 0–100 cm depths. The secondary information is acquired from the remotely sensed satellite data (LANDSAT ETM+) in different time scale and digital elevation model. The collected field samples were tested in the laboratory and were validated with Remote Sensing based digital indices analysisover the temporal satellite data to assess the potential changes due to over salinization.Soil physical properties such as texture, structure, depth and drainage condition is stored as attributes in a geographical soil database and linked with the soil map units. The thematic maps are integrated with climatic and terrain conditions of the area to produce land capability maps for paddy. Finally, The weighted overlay analysis was performed to assign theweights according to the importance of parameters taken into account for salineareaidentification and mapping to segregate higher, moderate, lower salinity zonesover the study area.
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Authors and Affiliations

Sumanta Das
Malini Roy Choudhury
Subhasish Das
M. Nagarajan

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

Wojciech Drzewiecki
Geodesy and Cartography | 2017 | vol. 66 | No 2 | DOI: 10.1515/geocart-2017-0012
Keywords impervious areas sub-pixel classification machine learning model ensembles Landsat
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Abstract

We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R 2 . These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for five percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifier. For the threshold of relevant change set as ten percent all approaches performed satisfactory.
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Authors and Affiliations

Wojciech Drzewiecki

A study of the climate and human impact on the future survival of the Al-Sannya marsh in Iraq

Amal Jabbar Hatem Ali Adnan N. Al-Jasim Hameed Majeed Abduljabbar
Journal of Water and Land Development | 2021 | No 51 | 168-173 | DOI: 10.24425/jwld.2021.139027
Keywords climate change human impact Landsat 5 Landsat 8 maximum likelihood classification south of Iraq
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Abstract

The marshes are the most abundant water sources and ecological rich communities. They have a significant impact on the ecological and economic well-being of the communities surrounding them. However, climatic changes directly impact these bodies of water, especially those marshes which depend on rainwater and flooding for their survival. The Al-Sannya marsh is used as the example of marshes in Southern Iraq for this study between 1987–2017. The research takes place throughout the winter season due to the revival of marshes in southern Iraq at this time of year. The years 1987, 1990, 1995, 2000, 2007, 2014, 2017 are the focus of this study. Satellite imagery from the Landsat 5 (TM) and Landsat 8 (OLI) and the meteorological parameters affecting the marsh were acquired from NASA. The calculation of the areas of water bodies after classification using satellite imagery is done using the maximum likelihood method and comparing it with meteorological parameters. These results showed that these marshes are facing extinction due to the general change of climate and the interference of humans in utilising the drylands of the marsh for agricultural purposes. The vegetation area can be seen to have decreased from 51.15 km2 in 2000 to 8.77 km2 in 2017.
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Authors and Affiliations

Amal Jabbar Hatem
1
Ali Adnan N. Al-Jasim
1
ORCID: ORCID
Hameed Majeed Abduljabbar
1
  1. University of Baghdad, College of Education for Pure Science (Ibn-Al-Haitham), Department of Physics, Baghdad, Iraq

Application of satellite remote sensing methods in mineral prospecting in Kosovo, area of Selac

Michał Lupa Katarzyna Adamek Andrzej Leśniak Jaroslav Pršek
Gospodarka Surowcami Mineralnymi - Mineral Resources Management | 2020 | vol. 36 | No 1 | 5-22 | DOI: 10.24425/gsm.2020.132554
Keywords remote sensing GIS geology mineral mapping Landsat 8
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Abstract

Traditional methods of mineral exploration are mainly based on very expensive drilling and seismic methods. The proposed approach assumes the preliminary recognition of prospecting areas using satellite remote sensing methods. Maps of mineral groups created using Landsat 8 images can narrow the search area, thereby reducing the costs of geological exploration during mineral prospecting. This study focuses on the identification of mineralized zones located in the southeastern part of Europe (Kosovo, area of Selac) where hydrothermal mineralization and alterations can be found. The article describes all the stages of research, from collecting in-situ rock samples, obtaining spectral characteristics with laboratory measurements, preprocessing and analysis of satellite images, to the validation of results through field reconnaissance in detail. The authors introduce a curve-index fitting technique to determine the degree of similarity of a rock sample to a given pixel of satellite imagery. A comparison of the reflectance of rock samples against surface reflectance obtained from satellite images allows the places where the related type of rock can be found to be determined. Finally, the results were compared with geological and mineral maps to confirm the effectiveness of the method. It was shown that the free multispectral data obtained by the Landsat 8 satellite, even with a resolution of 30 meters, can be considered as a valuable source of information that helps narrow down the exploration areas.

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Authors and Affiliations

Michał Lupa
Katarzyna Adamek
Andrzej Leśniak
Jaroslav Pršek

Methodology for determining deforestation areas in Lviv region using remote sensing data

Borys Chetverikov Ihor Trevoho Lubov Babiy Mariia Malanchuk
Advances in Geodesy and Geoinformation | 2022 | vol. 71 | No 1 | e21 | DOI: 10.24425/gac.2022.141174
Keywords Landsat-8 remote sensing space images satellite monitoring system logged area
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Abstract

The object of the study is the processing of space images on the territory of the Carpathian territory in the Lviv region, obtained from the Landsat-8 satellite. The work aims to determine the area of deforestation in the Carpathian territory of the Lviv region from different time-space images obtained from the Landsat-8 satellite. Methods of cartography, photogrammetry, aerospace remote sensing of the Earth and GIS technology were used in the experimental research. The work was performed in Erdas Imagine software using the unsupervised image classification module and the DeltaCue difference detection module. The results of the work are classified as three images of Landsat-8 on the territory of the Carpathian territory in the Lviv region. The areas of forest cover for each of them for the period of 2016-2018 have been determined. During the three years, the area of forests has decreased by 14 hectares. Our proposed workflow includes six stages: analysis of input data, band composition of space images on the research territory, implementation of unsupervised classification in Erdas Imagine software and selection of forest class and determination of implementing this workflow, the vector layers of the forest cover of the Carpathians in the Lviv region for 2016, 2017, 2018 were obtained, and on their basis, the corresponding areas were calculated and compared.
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Authors and Affiliations

Borys Chetverikov
1
ORCID: ORCID
Ihor Trevoho
1
ORCID: ORCID
Lubov Babiy
1 2
ORCID: ORCID
Mariia Malanchuk
1
ORCID: ORCID
  1. Lviv Polytechnic National University, Lviv, Ukraine
  2. Kryvyi Rih National University, Kryvyi Rih, Ukraine

Evaluating four remote sensing based models to estimate latent heat flux in semi-arid climate for heterogeneous surface coverage of western Algeria

Tewfik A. Oualid Abderrahmane Hamimed Abdelkader Khaldi
Journal of Water and Land Development | 2022 | No 55 | 259-275 | DOI: 10.24425/jwld.2022.142329
Keywords Algeria energy balance evapotranspiration Landsat METRIC SPARSE TIM TSEB
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Abstract

Optimal estimation of water balance components at the local and regional scales is essential for many applications such as integrated water resources management, hydrogeological modelling and irrigation scheduling. Evapotranspiration is a very important component of the hydrological cycle at the soil surface, particularly in arid and semi-arid lands. Mapping evapotranspiration at high resolution with internalised calibration (METRIC), trapezoid interpolation model (TIM), two-source energy balance (TSEB), and soil-plant-atmosphere and remote sensing evapotranspiration (SPARSE) models were applied using Landsat 8 images for four dates during 2014–2015 and meteorological data. Surface energy maps were then generated. Latent heat flux estimated by four models was then compared and evaluated with those measured by applying the method of Bowen ratio for the various days. In warm periods with high water stress differences and with important surface temperature differences, METRIC proves to be the most robust with the root-mean-square error ( RMSE) less than 40 W∙m –2. However, during the periods with no significant surface temperature and soil humidity differences, SPARSE model is superior with the RMSE of 35 W∙m –2. The results of TIM are close to METRIC, since both models are sensitive to the difference in surface temperature. However, SPARSE remains reliable with the RMSE of 55 W∙m –2 unlike TSEB, which has a large deviation from the other models. On the other hand, during the days when the temperature difference is small, SPARSE and TSEB are superior, with a clear advantage of SPARSE serial version, where temperature differences are less important.
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Authors and Affiliations

Tewfik A. Oualid
1
ORCID: ORCID
Abderrahmane Hamimed
1
ORCID: ORCID
Abdelkader Khaldi
1
ORCID: ORCID
  1. University Mustapha Stambouli of Mascara, Laboratory of Biological Systems and Geomatics, P.O. Box 305, Route de Mamounia, 29000, Mascara, Algeria

Extraction of urban construction development with using Landsat satellite images and geoinformation systems

Aybek M. Arifjanov Shamshodbek B. Akmalov Luqmon N. Samiev
Journal of Water and Land Development | 2021 | No 48 | 65-69 | DOI: 10.24425/jwld.2021.136147
Keywords ArcGIS Geographic Information System (GIS) Landsat satellite image remote sensing (RS) urban
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Abstract

In recent times there have been many changes on Earth, which have appeared after anthropogenic impact. Finding solu-tions to problems in the environment requires studying the problems quickly, make proper conclusions and creating safe and useful measures. Humanity has always had an effect on the environment. There can be many changes on the Earth be-cause of direct and indirect effects of humans on nature. Determining these changes at the right time and organizing meas-urements of them requires the creation of quick analysing methods. This development has improved specialists’ interest for remote sensing (RS) imagery. Moreover, in accordance with analysis of literature sources, agriculture, irrigation and ecolo-gy have the most demand for RS imagery. This article is about using geographic information system (GIS) and RS technol-ogies in cadastre and urban construction branches. This article covers a newly created automated method for the calculation of artificial surface area based on satellite images. Accuracy of the analysis is verified according to the field experiments. Accuracy of analysis is 95%. According to the analysis from 1972 to 2019 artificial area enlargement is 13.44%. This method is very simple and easy to use. Using this data, the analysis method can decrease economical costs for field measures. Using this method and these tools in branches also allows for greater efficiency in time and resources.
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Bibliography

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Authors and Affiliations

Aybek M. Arifjanov
1
ORCID: ORCID
Shamshodbek B. Akmalov
1
ORCID: ORCID
Luqmon N. Samiev
1
ORCID: ORCID
  1. Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, 39 Kari Niyazov Str. Tashkent 100000, Uzbekistan

GIS and geodata contribution to the cartographic modelling of blue-green infrastructure in urbanised areas

Szymon Czyża Anna M. Kowalczyk
Journal of Water and Land Development | 2023 | No 59 | 183-194 | DOI: 10.24425/jwld.2023.147243
Keywords blue-green infrastructure drainage systems GIS Landsat 8 images map reference databases retention spatial analysis
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Abstract

The development of cities and peri-urban areas is exerting an increasingly strong impact on the natural environment and, at the same time, on the living conditions and health of people. Problems and challenges that need to be addressed include increasing air pollution in these areas, formation of a surface urban heat island (SUHI), water management disruptions (water scarcity or excess), and the destruction of natural habitats. One of the solutions that contributes to climate change mitigation is the introduction of blue-green infrastructure into the city space and urbanised areas. The research objective was to identify spatial features (geodata) that determine the optimum location of selected blue-green infrastructure (BGI) components, acquire them, and then use the Geographical Information System (GIS) to determine their optimum locations. As the first step, cartographic models were developed which indicated areas that enable the development of selected blue-green infrastructure components in the Olsztyn city area, Warmińsko-Mazurskie Province, Poland. The models were juxtaposed with other two models developed by the authors, i.e. a surface urban heat island model and a demographic model that showed the age structure of the city’s population. Consequently, maps with potential locations for the blue-green infrastructure were developed, while taking into account reference data from the National Land Surveying and Cartographic Resource and Landsat 8 images.
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Authors and Affiliations

Szymon Czyża
1
ORCID: ORCID
Anna M. Kowalczyk
2
ORCID: ORCID
  1. University of Warmia and Mazury in Olsztyn, Faculty of Geoengineering, Institute of Geodesy and Civil Engineering, Department of Geoinformation and Cartography, Olsztyn, Poland
  2. University of Warmia and Mazury in Olsztyn, Faculty of Geoengineering, Institute of Geodesy and Civil Engineering, Department of Geodesy, St. Heweliusza 12, Olsztyn, Poland

Spatiotemporal mapping of inundation area at Lake Limboto in Gorontalo, Indonesia, using cloud computing technology

Rakhmat Jaya Lahay Syahrizal Koem
Journal of Water and Land Development | 2022 | No 52 | 27-33 | DOI: 10.24425/jwld.2021.139940
Keywords Google Earth Engine Lake Limboto lake shrinkage Landsat remote sensing water index
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Abstract

Monitoring activities on the dynamics of water shrinkage at Lake Limboto are essential to the lake’s ecosystem’s recovery. A remote sensing technology functions to monitor the dynamics of lake inundation area; this allows one to produce a comprehensive set of spatial and temporal data. Such complex satellite dataset demands extra time, greater storage resources, and greater computing capacity. The Google Earth Engine platform emerges as the alternative to tackle such problems. The present study aims to explore the capability of Google Earth Engine in formulating spatial and temporal maps of the inundation area at Lake Limboto. A total of 345 scenes of Landsat image on the study area (available during the period of 1989–2019) were involved in generating a quick inundation area map of the lake. The whole processes (pre-processing, processing, analysing, and evaluating) were automatized by using the Google Earth Engine interface. The evaluation of mapping result accuracy indicated that the average score of F1-score and Intersection over Union (IoU) was at 0.88 and 0.91, respectively. Moreover, the mapping results of the lake’s inundation area from 1989 to 2019 showed that the inundation area tended to decrease significantly in size over time. During the period, the lake’s area also shrank from 3023.8 ha in 1989 to 1275.0 ha in 2019. All in all, the spatiotemporal information about the changes in lake area may be treated as a reference for decision-making processes of lake management in the future.
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Authors and Affiliations

Rakhmat Jaya Lahay
1
ORCID: ORCID
Syahrizal Koem
1
ORCID: ORCID
  1. Universitas Negeri Gorontalo, Department of Earth Science and Technology, B.J Habibie Street, Bone Bolango, 96183, Gorontalo, Indonesia

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