scholarly journals Análise da ocupação no entorno da Barragem Bico da Pedra, no Município de Janaúba (MG) / Analysis of occupation in the surroundings of Bico da Pedra dam, municipality of Janaúba (MG)

2015 ◽  
Vol 25 (44) ◽  
pp. 221-236
Author(s):  
Marcos Esdras Leite ◽  
Lucimar Sales Dias ◽  
André Medeiros Rocha
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Historical Data ◽  
Satellite Image ◽  
Landsat 8 ◽  
Water Tank ◽  
Landsat 8 Oli ◽  
Area Reduction ◽  
The People ◽  
The Subject

O presente trabalho consiste no diagnóstico do uso e ocupação do solo no entorno da Barragem Bico da Pedra, no município de Janaúba-MG, por meio de imagens de satélites do satélite Landsat 8 - sensor OLI, além de incursões em campo e levantamento bibliográfico sobre o tema estudado. As análises e avaliações realizadas indicam a presença do número de edificações no entorno da barragem do Bico da Pedra, construída em 1979, sob responsabilidade da Companhia de Desenvolvimento dos Vales do São Francisco e Parnaíba - CODEVASF. O reservatório foi construído com a finalidade de abastecimento humano da população dos municípios de Janaúba e Nova Porteirinha-MG, abastecimento do Perímetro de Irrigação do Gorutuba e perenização do rio Gorutuba, que outrora se encontrava em regime intermitente devido às condições ambientais da região em estudo. A partir de levantamento de dados históricos, foi possível detectar um aumento nos números de edificações motivado por diversos fatores, como a ausência de fiscalização e a prática do turismo. As consequências oriundas dessa ocupação podem gerar impactos ambientais negativos, tais como, desmatamento da Área de Preservação Permanente - APP, redução da biodiversidade, erosão do solo e poluição do corpo hídrico. Vale ressaltar que muitos desses impactos já são observados no entorno da Barragem Bico da Pedra. Portanto, este estudo, a partir do uso das Geotecnologias, traz informações sobre a forma e o processo de construção de imóveis residenciais no entorno da barragem do Bico da Pedra, bem como das consequências dessa ocupação. Palavras chaves: Ocupação, barragem, Degradação, Imagem de satélite, Sensoriamento Remoto      AbstractThe present work consists on the diagnosis of the land use on the neighborhood of Bico da Pedra dam, in the municipality of Janaúba-MG, through images of satellite Landsat 8 - OLI sensor and incursions in the field and survey literature on the subject studied. The analysis and evaluations done indicates the increasement on the number of buildings close to Bico da Pedra dam, constructed in 1979, by responsibility of Development Company of the Valleys São Francisco e Parnaíba (CODEVASF). The water tank was constructed to fill up the people that live in Janaúba and Nova Porteirinha, provide water to Perimeter of Irrigation of Gorutuba e evergreening of Gorutuba river that once before was in intermittent regime caused by environmentals conditions of the region. From survey of historical data it was possible to detect an increase in the numbers of buildings caused by several factors, such as lack of supervision and the practice of tourism. The consequences of that occupation can cause negatives environmental impacts: deforestation of Permanent Preservation Area, reduction on biodiversity, land erosion and hydrous body pollution. It is important to emphasize that a lot of these impacts are already observed next to the Bico da Pedra dam, as well as the consequences of this occupation. Keywords: Occupation, watertank, degradation, Satellite Image, Remote Sensing 

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

Penentuan Luas Area Sawah Kabupaten Sleman Pada Citra Landsat 8 Operational Land Imager (OLI)

Respati ◽  
2018 ◽  
Vol 13 (3) ◽  
Author(s):  
Sulidar Fitri ◽  
Novi Nurjanah
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Support Vector Machine ◽  
Rice Field ◽  
Image Data ◽  
Rice Fields ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Learning Machine

INTISARITeknologi penginderaan jauh sangat baik dijadikan data pembuatan peta penggunaan lahan, karena kebutuhan pemetaan semakin tinggi terutama untuk mendeteksi perubahan penggunaan lahan terutama untuk penentuan luas area khususnya sawah di kabupaten Sleman. Untuk mendapatkan informasi luasan area sawah dari interpretasi citra landsat-8 OLI (Operational Land Imager) diperlukan metode khusus, terutama untuk pengolahan data citra penginderaan jauh secara digital. Salah satu metode pengolahan citra penginderaan jauh adalah metode Support Vector Machine (SVM). Metode SVM merupakan metode learning machine (Pembelajaran mesin) yang dapat mengklasifikasikan pola serta mengenali pola dari inputan atau contoh data yang diberikan dan juga termasuk ke dalam supervised learning. Hasil area sawah yang didapati dari citra Landsat 8 OLI dengan pengolahan metode SVM didapati berada di 18 kecamatan dala Kabupaten Sleman. Luasan tertinggi ada di kecamatan Ngaglik dengan 19,78 KM2 dan terendah di kecamatan Turi seluas 2,14 KM2. Nilai keseluruhan akurasi yang didapat untuk kelas lahan sawah dan area non sawah adalah adalah 53%.Kata kunci— Landsat-8 OLI, SVM, Data Citra, Geospasial, Luas Area Sawah ABSTRACTRemote sensing technology is very well used as a data for making land use maps, because mapping needs are increasingly high especially for detecting land use changes, especially for determining the area, especially rice fields in Sleman district. To get information about the area of the rice fields from the interpretation of Landsat-8 OLI (Operational Land Imager), special methods are needed, especially for processing remote sensing image data digitally. One method of processing remote sensing images is the Support Vector Machine (SVM) method. The SVM method is a learning machine method that can classify patterns and recognize patterns from input or sample data provided and also includes supervised learning. The results of the rice field that were found from the Landsat 8 OLI image by processing the SVM method were found in 18 sub-districts in Sleman Regency. The highest area is in Ngaglik sub-district with 19.78 KM2 and the lowest in Turi sub-district is 2.14 KM2. The overall value of the accuracy obtained for the class of rice field and non-rice field is 53%.Kata kunci—  Landsat-8 OLI, SVM, Image Data, Geospatial, Area of Rice Fields

scholarly journals Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollutants ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Regional Studies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

Determination of Drought Threshold for Agricultural Land-use using Satellite Image Analysis Techniques

2020 ◽  
Author(s):  
Jieun Kim ◽  
Jaehyung Yu ◽  
Sang Kee Seo ◽  
Jin-Hee Baek ◽  
Byung Chil Jeon
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Near Infrared ◽  
Agricultural Land ◽  
Irrigation Management ◽  
Satellite Image ◽  
Agricultural Drought ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Eastern Area

<p>The climate change causes major problems in natural disasters such as storms and droughts and has significant impacts on agricultural activities. Especially, global warming changed crops cultivated causing changes in agricultural land-use, and droughts along with land-use change accompanied serious problems in irrigation management. Moreover, it is very problematic to detect drought impacted areas with field survey and it burdens irrigation management. In South Korea, drought in 2012 occurred in western area while 2015 drought occurred in eastern area. The drought cycle in Korea is irregular but the drought frequency has shown an increasing pattern. Remote sensing approaches has been used as a solution to detect drought areas in agricultural land-use and many approaches has been introduced for drought monitoring. This study introduces remote sensing approaches to detect agricultural drought by calculation of local threshold associated with agricultural land-use. We used Landsat-8 satellite images for drought and non-drought years, and Vegetation Health Index(VHI) was calculated using red, near-infrared, and thermal-infrared bands. The comparative analysis of VHI values for the same agricultural land-use between drought year and non-drought year derived the threshold values for each type of land-use. The results showed very effective detection of drought impacted areas showing distinctive differences in VHI value distributions between drought and non-drought years.</p>

scholarly journals APPLICATION OF REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM (GIS) TO ANALYSE THE IMPACT OF LAND USE ON THE WETLANDS IN PORT HARCOURT AND OBIO/ AKPOR L.G.A. RIVERS STATE, NIGERIA

2020 ◽  
Vol 8 (5) ◽  
Author(s):  
J. U. Richard ◽  
Ibochi Andrew Abah
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Satellite Image ◽  
Gis And Remote Sensing ◽  
Percentage Decrease ◽  
Detection Analysis ◽  
The People ◽  
Port Harcourt ◽  
Landsat Satellite ◽  
The Impact

The study area is in verge the state of wetlands extinction due to over utilization and lack of regulations that will ensure effective management of its resources. The study areas: Port Harcourt and Obio/ Akpor L.G. As are undergoing rapid urban and industrial expansion due to available natural resources and these activities have degraded the wetlands which hither-to represent the source of living for the people. This study investigated the impact of land use/ land cover on wetlands in Port Harcourt and Obio/ Akpor L.G. As using GIS and remote sensing approach. The study was conducted using Landsat satellite image of 2000, 2007, and 2013 to analysed change in land use/ cover as a result of the aforesaid activities. The supervised classification method in ENVI 5.0 software was adopted and change detection analysis was computed in order to examine change in land use that has impacted on wetlands habitat. The change results show percentage decrease in water body by -0.39% from 2000 to 2007 and -0.70% from 2000 to 2013, built-up areas also increased by 11.95% from 2000 to 2007 and 28.47% from 2000 to 2013. Similarly, wetlands decreased by -13.52% from 2000 to 2013. Therefore, it was concluded that urbanization in built-up, industrial, and dredging activities are the main causes of wetlands loss in the area with most wetlands being converted to built-up areas and dredged sites. The study justified the ability of remote sensing and GIS software for modelling wetlands change over these periods.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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.

More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses

2021 ◽  
pp. 030913332199181
Author(s):  
Andreas Christian Braun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Geographic Information Systems ◽  
Image Classification ◽  
Satellite Image ◽  
Physical Geography ◽  
Data Availability ◽  
Scientific Results ◽  
Normative Considerations

Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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